University of Southern California Dissertations and Theses

Extracellular matrix regulation of mitochondrial function in engineered cardiac myocytes

(USC Thesis Other)

Extracellular matrix regulation of mitochondrial function in engineered cardiac myocytes

PDF

Download

Open document

Flip pages

Download a page range

Download transcript

Copy asset link

Request this asset

Transcript (if available)

Content UNIVERSITY OF SOUTHERN CALIFORNIA

VITERBI SCHOOL OF ENGINEERING

DEPARTMENT OF BIOMEDICAL ENGINEERING

EXTRACELLULAR MATRIX REGULATION OF
MITOCHONDRIAL FUNCTION IN ENGINEERED CARDIAC
MYOCYTES

by
DAVI MARCO LYRA LEITE

A Dissertation Presented to the
Faculty of the USC Graduate School
University of Southern California
In Partial Fulfillment of the
Requirements for the Degree
DOCTOR OF PHILOSOPHY
(BIOMEDICAL ENGINEERING)

August 2019

Copyright 2019 Davi Marco Lyra Leite

i

Dedication
To my grandpa who decided to leave the little town in Northeastern Brazil, when life would
tell him to stay. Your move to Minas and Rio made this possible.
To my father who decided to stay, when his family left Brasília, and embraced every
opportunity that came in his way. Your perseverance and dedication were always inspiring to me.
And to my mom who tied these two strands together. Without your unwavering support, I
would have not reached this far.

ii

Abstract
The heart is the most metabolically demanding organ in our body due to the energy required for
cardiac myocytes to continuously contract from birth to death. Mitochondria are the organelles
that provide the energy needed to maintain this contractile activity in the form of ATP. From
development to adulthood, and from healthy to diseased states, mitochondrial function, substrate
preference, and structure changes in cardiac myocytes. Simultaneously, myocardial tissue
remodels, with progressive increases in extracellular matrix (ECM) rigidity and alterations in
tissue alignment and cardiac myocyte elongation. Based on these observations, we hypothesize
that changes in mitochondrial structure and function are dictated by these physical changes in the
tissue. To address this, our goal is to engineer new platforms to identify the effects of multiple
parameters within the extracellular microenvironment on mitochondrial structure and function in
cardiac myocytes. First, we engineered cell culture microplates with layers of synthetic and natural
biomaterials to delineate the effects of ECM elasticity and protein composition on mitochondrial
function in engineered NRVM tissues. Our data indicate that gelatin hydrogels enhance several
metrics associated with baseline and maximum mitochondrial function in cardiac myocytes,
irrespective of substrate rigidity. Second, we created microcontact printed polydimethylsiloxane
(PDMS) discs of varying elasticities that were combined with an extracellular flux analysis assay
to determine the effects of ECM elasticity and tissue alignment on mitochondrial function in
engineered neonatal rat ventricular myocytes (NRVMs). Our data indicate that parameters
associated with baseline metabolism are predominantly regulated by ECM elasticity, whereas the
ability of tissues to adapt to metabolic stress is regulated by both ECM elasticity and tissue
alignment. Collectively, our results provide new understanding of the microenvironmental factors
iii

that regulate mitochondrial function and structure in cardiac myocytes, validating the hypothesis
that microenvironment regulates mitochondrial function in cardiac myocytes.
iv

Acknowledgements
I am deeply grateful to Professor Megan McCain who, little over five years ago, took an
electrical engineer and as her student and helped shape him into a tissue engineer. Without your
advice, your help with experiments – design and execution – and literature, your trust in me with
different responsibilities, and, from time to time, your steering me in the right direction, I would
not be here today. Your door was always open when I needed, and no matter how busy you were,
you always asked us to come and talk. Thank you!
I am also deeply grateful to Dr. Allen Andres. When I came to you in 2016, to discuss a
crazy idea about engineering Seahorse plates, you were receptive and encouraging. You helped
me test different hypothesis, taught me biology, a little bit of chemistry, and was a constant
supporter of my scientific endeavors. Also, discussing alternative careers at Carl’s Jr was always
a source of amusement! These three years working with you were excellent, and I could not be
more grateful.
Dr. Roberta Gottlieb, you still overwhelm me with your knowledge and dedication every
time we meet. I could not appreciate more your insights and ideas, even though I did not have time
to explore all of them – but lots of them will be projects that I will hopefully have my own students
pursuing in the future.
I would also like to thank Dr. Keyue Shen, who in addition to be a member of my qualifying
committee, helped me with different ideas over the past four years. We discussed different
components of my project both at the original tea time, and at our “McShen” lab meetings and
your contributions were deeply appreciated.
Dr. Michael Khoo, even though I study cardiac systems on a completely different level
from your research, you took the time and had the patience to evaluate my work both as a member
v

of my qualifying committee and my dissertation one. I still remember when you asked me where
my models fit in the P-V diagram for the heart, and I hope now I have fully understood what you
wanted me to think that day back in 2017. Thank you for your time and patience.
Dr. Ellen Lien, until I was exposed to your laboratory’s research, I saw zebrafish (peixe
paulistinha in my native Portuguese) only as an aquarium “ornament”. You and Dr. Michael
Harrison taught me how powerful research tools they are – even though Joycelyn took over the
project – and now I cannot look at them without thinking about heart regeneration. I am also deeply
thankful for you participating in both my qualifying and dissertation committees.
Dr. Stacey Finley, you are one of the nicest professors I know. Your enthusiasm in class
was contagious and I learned a lot with you about how to combine simple experimental data to
create complex models of cell behavior. I also learned from presenting at your class and watching
all the comments that you would give us, and that was really helpful. In addition, you took the time
to be on my qualifying committee without second guessing, and I am extremely grateful for that.
Dr. George Tolomiczenko, there are so many things you have done for me that I am
thankful for… For starters, HTE has provided me with my favorite classes during my PhD. My
life – and career goals – would not be the same without ICM or the clinical block. In this, I have
also to thank Drs. Helga van Herle, Vivian Mo, and Terry Sanger – one of the smartest persons I
have ever met. George, you also helped me when I was switching from my EE PhD to a BME
PhD, and on my AHA fellowship applications. I will never forget that! I hope HTE 2.0 will become
an even better version of the program and that your future students find it as a worthy experience
as I did.
Lastly, on the professor side, I would like to thank Dr. Krishna Nayak who gave me my
first opportunity at USC. I moved to LA to work on medical imaging, but after two years realized
vi

that was not what I wanted to do for the rest of my life. But I am forever grateful for the shot you
gave me in 2012, for the help finding my first internship in 2013, and for the lab that you have
here which gave me good friends without whom life would not be the same.
I would also like to thank Diane Demetras from the EE Department. When I decided to
switch from Electrical Engineering to BME, you helped me with all the hoops. This moving
seemed easy because I had you by my side.
Mischal Diasanta was an angel that helped me with all the BME bureaucracy. Without you,
I could not have navigated everything as easily as I did, nor have found a new home without lots
of worries. Also, you were always there to help with silly questions, to get us free food, and even
a free registration to BMES in 2016. It was delightful spending hours with you trying to recruit
new BME students and I hope that you are having a great time in the Bay Area.
I would like to thank the team from the GAPP office for all the opportunities and support.
Cami, Jessica, Ray, Camila, Billy, Mary: you trusted me to present USC (and my love for this
place) to new students and their families, and, even though it might be a silly thing, the chance of
giving the tours always made my days happier. It also gave me a little chance to pay it back to
USC for all the opportunities it gave me. I will definitely miss it…
To my MREL friends, thanks for all the support on my first years in LA. Yi, Brian,
Yinghua, Hung, Eamon, Wayne, Yoon, Chris: it was great discussing science with you guys! It
was also amazing learning more about programming, mathematical models, image reconstruction,
and different signal processing strategies. Hung, it was also a pleasure volunteering for your scans
– I never expected MRI sessions could be so relaxing (sorry for taking naps when I was supposed
to be getting ready to hold my breath).
vii

Dr. Terrence Jao, thanks for all the debriefing sessions after ICM and all the study tips for
the clinical block. You helped and inspired me in different ways, and I hope I lots of patients will
benefit from your knowledge. Also, I still do not get competitive Pokémon, but my Stoutlands are
very strong…
Vanessa, I always cherished for our froyo escapades and your culinary endeavors. EEB
414 would not be the same without you even though you took the window seat that I wanted…
Your enthusiasm for teaching was contagious and every student will be lucky to have you as a
mentor in the future.
Ahsan, I cannot start to say how thankful I am to have met you, my friend. Learning how
to eat spicy food was a challenge, but you have no idea how I enjoyed every random dinner that
we had at your place, and the three months you were my roommate in Chicago. You helped me
understand so much more about living away from family, and having your company made things
easier. Whenever I needed a helping hand in my first two years, you were there, and we were also
able to enjoy lots of different things with Vanessa, Talha, Hassan, and so many more people.
Thanks for being almost a big brother from time to time, and for all the friendship.
Tika, my lab sister… We started this journey together 5 years ago, and we are finishing at
the same time – OK, you finished earlier. I could not ask for someone better to be at my side when
we were starting in a new lab, learning a ton of different things, and navigating the process of
quals, defense, job applications. You were always there when I needed someone to talk, and vent
about frustrations with experiments or life in general. It was also fun to tie the molds – I hope you
acknowledged me in the paper for that… And now we closed this chapter together. In November,
Homecoming weekend will have a different taste for both of us.
viii

Nacho, I know I would be the victim 0 on your “hunger games’ list” (games start, you’d
try to get rid of me), but no hard feelings. Your faith and dedication to people were always inspiring
and I really enjoyed our random philosophical conversations. Your future students will be lucky
to have someone who loves teaching as much as you do, and who is enthusiastic on providing them
with tools to succeed. I hope you also learned a little bit from my “harsh standards” on grading,
and how to think about science on a different perspective.
Andrew, lab nights would not have been as fun without you. I might have disturbed you
too much on some of them, but having someone to talk to in the office after midnight was not
always a thing… You impress me with your breadth of knowledge and willingness to learn about
random stuff – and making true to your electrical engineering degree (mine is pretty much long
forgotten). It is impossible to learn everything about the world (Hayek), but if one could, you
would be a serious candidate for that. Keep always your good spirit and tender view of people.
Jeff, you are the golden boy of the lab and I am sure Megan could not have asked for a
better person to bridge our generation and the new students… We have all learned at least a little
from you and this helped our projects move faster or more smoothly. In addition, I have definitely
enjoyed your company during lunches, watching games, and discussing random things about
sports. Life in lab would not be the same without our annual March Madness bracket, without
Super Smash nights, and so many other things. Thanks for being such a good friend, for helping
me whenever I needed either in lab, with rides to the airport, or moving. You are a model of student
and friend for me. These past three years would never be the same without you!
JY, you are for sure one of the most dedicated people that I know. Even though I joke about
not seeing you in lab sometimes, it still amazes me how you can make time for all the work at
GSBME, VGSA, GSG, your startup projects (I believe they are completely on hold now, right?),
ix

and to be such a great friend, sister, daughter, niece, cousin. If I had so many things in my plate as
you do, I am sure I would be collapsing… Also, thanks for all the food that you provided us.
Without it, some of my late nights in lab would not have been as fun (and Taco Bell would have
taken a bigger slice of my budget that it already did).
Patrick, merci pour le mentorat et les conseils. J'ai beaucoup appris sur la biologie avec toi,
ce qui me motive à approfondir encore plus de sujets. Tu m'as également aidé tout au long du
processus de recherche d'emploi et examiné mes différents documents de candidature. Je t’en serai
toujours reconnaissant. Et maintenant, dans ta deuxième année à LA, je crois que tu compris enfin
pourquoi je n'aime pas la chaleur... J'espère que tu seras vraiment réussi votre carrière dans
l'industrie et que dans quelques années, on qui sait, notre BME département peut être nommer le
Département Vigneault.
Jezell, thanks for taking the summer to work with me on a crazy project of developing tiny
silicone disks for cell culture. We had no idea it would work as well as it did, and those two months
of SHINE became one of the cornerstones of my PhD. Your passion for learning, your attention
to detail, and your dedication were inspiring. And, when I grow up, I want to read as many books
as you do, while playing the violin and speaking German.
Sarah and Preethi, thanks for the help with different projects and for trusting in me to teach
you a little about biology and engineering. Megan R, Natalie, and Divya, thanks for the help with
different projects and for the delicious cookies that you made for us in lab. LLSE baking culture
is strong and envied in every other place.
Luann and Dan, you little genius kids impressed me every time. Your dedication to the
smallest projects, and eagerness to help other undergrads become better engineers was always
inspiring. If I were them, I could not ask for better role models of students and of someone to help
x

me navigate the USC undergraduate experience. I am looking forward for all the good things you
will do in the future!
Hydari and Yuta, thanks for your patience with some of my questions, and for making me
feel I could help on a variety of different areas. Even though I do not understand lots about cancer
models, at least I could prove to myself that my knowledge in microscopy and in different assays
was worthwhile. You also gave me a variety of happy moments, especially when we were staying
longer in lab and needed to take a break. Thanks for all the kind words during these years, and for
companionship when things were not moving as well as they could have been.
Günce, you were the first student that I can say I directly help USC recruit, and I could not
be happier with how lucky we were to get you! You are kind, caring, have an enormous heart and
was always willing to help me. For the moments you took just to get coffee or have lunch with me,
and help me clear my mind, I will be forever grateful. Çok teşekkürler!
Zümra, you have no idea how some small things like inviting me for hikes or for the
Christmas and New Year’s dinners at your place made my life happier. I know I am not always
the best on replying messages promptly (indeed I am terrible at that), but having you by my side
since I was your TA in BME 410 was excellent. You and Scott are such great people, I can even
start to describe. Thanks for all the happy moments.
Gene, you trusted me to help and later take over BGT and for this trust I will always be
grateful. I could not get as many speakers as I wished, but I did my best to keep your legacy alive.
Clay, you were always such a caring and motivating friend during these years. I cannot imagine a
person that has a bigger heart than you and can motivate those around to be better versions of
themselves. Jon, Noah, Debs, Dr. Chris Poon, Adam, John, Quique, Julio, Alex, Ali, Jess, thanks
for all the conversations, tips, or just silly moments that we shared. Small happy things, over the
xi

course of years sometimes are overlooked but are the cornerstones to make a better life and having
you by my side definitely helped with that!
Now, it is time to acknowledge and thank all my Brazilian friends and family, so,
everything from here on will be in Portuguese.
João Luiz, sem você eu não teria chegado tão longe quanto cheguei até aqui. Em 2010 você
me aceitou como aluno de pesquisa em um momento que eu estava bem desgostoso com a UnB e
pensava em me mudar pra São Paulo. No ano seguinte, você me deu a chance de tentar um código
maluco no meu projeto de iniciação científica e tudo funcionou belamente. No final daquele ano,
seguindo seus passos, eu apliquei para a USC e fui aceito em fevereiro de 2012. Até hoje lembro
quando eu fui começar a defesa do meu projeto final em que você falou que o que estava em jogo
era se eu viria para LA ou não. E em 2013 eu te recebia na minha casa por aqui. Obrigado por toda
ajuda, todo o empenho e toda orientação. Tenho orgulho de ter sido seu aluno e te ter como um
grande exemplo de orientador, professor e colega.
Pedro e Gedeon, vocês estiveram comigo em dois momentos diferentes da vida em LA.
Pedro foi essencial na mudança do Brasil pra cá e saber que teria alguém pra conversar e enfrentar
os mesmos desafios do doutorado foi essencial. Muito obrigado pela companhia nos almoços no
Taco Bell, pela noite de pizza com a tentativa frustrada de assistir ao Super Bowl (porque o jogo
ficou sem luz), e por todo o resto. Gedeon, sem você eu não teria conseguido enfrentar algumas
barras nesse último ano por aqui. Fora ter me ajudado com coisas simples como a doação dos
móveis, você me deu um amparo muito grande quando eu tive um breakdown recentemente – acho
que não faz ideia do quão importante foi aquela conversa via WhatsApp. E, por mais que você
tenha gostado mais do pão-de-queijo do que da bacalhoada que eu fiz, eu te perdoo. Muito obrigado
por tudo, meu amigo!
xii

Eu gostaria também de agradecer imensamente à minha família. São sete anos morando
longe de vocês, sem conseguir estar perto em momentos importantes – sejam eles fáceis ou difíceis.
Muita coisa mudou nesse tempo e eu não consegui acompanhar como gostaria, mas sem o apoio
de vocês, tudo esse processo teria sido impossível.
Tia Ana, obrigado por todo o carinho e por todas as mensagens de apoio. Sei que fui terrível
em respondê-las sempre atrasado, mas saiba que ficava feliz sempre que recebia alguma lembrança
sua. Não esqueço da sua alegria quando contava a história daquele projetinho de pessoa que se
apaixonou por um boneco do Mickey quando criança... Ele passou os últimos sete anos vivendo
na terra da Disney e, esse ano, teve a sorte de ir no parque contigo. Muito obrigado por tudo.
TP, quando eu saí do Brasil você era jornalista e eu engenheiro eletricista. Sete anos depois,
você está nos bastidores ajudando pessoas e empresas a navegarem o tumulto político do Brasil e
eu virei um biólogo... Muita coisa mudou nesse tempo, mas eu ainda carrego com muito carinho
as nossas intermináveis conversas sobre política e música – as bandas que você me apresentou
depois das aulinhas de evangelização no Irmão Jorge me acompanharam nesses anos e foram parte
essencial da trilha sonora desse doutorado. Tomara que a Luíza e o mini-Biel herdem seu gosto
musical – mesmo sem o primo por perto pra influenciar do jeito que você fez comigo...
Tio Nivardo, obrigado por todas as visitas ao longo desses anos e por todo o apoio que
você tem me dado. Em diversos momentos, só a certeza de saber que um “pedacinho” de casa
estaria aqui já dava uma propulsão em confiança e motivação. Você sempre foi um dos meus
grandes incentivadores, seja nas pequenas coisas como tentar melhorar o meu tempo de corrida
para as provas de orientação, seja nas grandes como completar o doutorado. Muito obrigado
também por todo o apoio que você deu com os meus pais nos momentos que eu não pude estar ao
lado deles nesses anos longe de casa.
xiii

Vó Tereza e Vô Jayme, umas das pequenas constantes que eu tive durante os meus
primeiros anos de doutorado foram as nossas conversas aos domingos. Eu sei que nos últimos dois
anos eu vacilei muito nesse aspecto, mas sem elas no começo o processo de mudança não teria
sido tão tranquilo. Vocês me deram encorajamento contínuo e carinho, mesmo que à distância, e
serviram de apoio para meus pais quando a gente precisou. Muito obrigado por tudo!
Mô, quando eu mais precisei de um ombro amigo em 2012, você fugiu do frio de Boston
para passar a semana de Ação de Graças comigo e acho que eu nunca vou conseguir te agradecer
o suficiente por esse apoio. Você é a irmã mais nova que eu não tive e fico sempre muito feliz e
admirado com as suas conquistas. Seu apoio e do Vi foram essenciais nesses anos por aqui e
quando eu precisava fortalecer o meu foco nos dias em São Paulo ano passado, vocês foram um
porto seguro. Esses momentos me fizeram ver o que é importante e como a gente deve priorizar as
coisas. Muito obrigado por tudo!
Biel e Dubois, se nem sempre a gente concorda em muita coisa, pelo menos a gente
escolheu se aturar e ajudar pelos últimos 15 anos... Vocês são os irmãos mais velhos que eu não
tive, que me puxam a orelha quando eu faço por merecer, mas também me dão o braço quando eu
preciso de ajuda para me levantar. Sem as suas piadas e seus comentários de apoio, eu não teria
chegado até aqui. Vocês estiveram comigo nos momentos em que eu mais precisei e se não fosse
sua amizade, talvez tivesse entrado em algumas situações bem difíceis e colapsado nos últimos
anos. Mesmo de longe eu fico muito feliz com as suas conquistas e sei que o sentimento é
recíproco. Que a gente possa continuar a fortalecer nossa irmandade durante os anos e alçar voos
sempre mais altos juntos.
Tiago, meu irmãozinho, sua dedicação pelas criancinhas me encanta e motiva. Apesar de
termos personalidades bem diferentes em vários aspectos, você me entende como ninguém e soube
xiv

me ajudar demais nesses anos de distância da família. Eu não conseguiria ter alçado esse voo se
não tivesse a certeza de que papai e mamãe estariam bem cuidados em suas mãos. Também de
longe pude ver e admirar o bom médico que você se tornou, dedicado como poucos aos pequenos
e sempre com motivação de trazer um sorriso para o rostinho deles. Continue assim, maninho, e
obrigado por tudo.
Pai e Mãe, sem vocês eu não estaria aqui (literalmente). Ciência foi minha paixão desde
pequeno, quando eu assisti um desenho do Homem-Aranha e não tirei da cabeça a ideia de fazer
pesquisa. E vocês sempre se esforçaram para que eu pudesse ter a tranquilidade de seguir esse
caminho. Eu sei que essa carreira que eu escolhi nunca foi o seu sonho (de nenhum dos dois) para
mim e que ter saído do Brasil não seria o plano – apesar de vocês terem-no abraçado quando eu
comecei a falar da ideia ainda na adolescência. Esses anos de distância e ausência não foram fáceis
para nós, e eu peço desculpas por não ter estado ao seu lado em momentos que vocês precisaram...
Vocês estiveram comigo antes da química, da biologia, da física, e sem o seu carinho e apoio essa
jornada não teria sido tão fantástica. Eu agradeço por todas as palavras de carinho e de apoio que
vocês me dedicaram, e por todas as vibrações boas que mandavam quando sabiam (ou sentiam)
que eu não estava bem. Se hoje eu quero trabalhar para fazer da vida das pessoas algo melhor, isso
se deve ao incansável esforço que vocês sempre tiveram em ajudar aqueles que precisavam – seja
com um ombro amigo, seja com uma passagem de ônibus, ou um lanche no Giraffas. Vocês são
meu exemplo de pessoas, de pais e de companheiros, e espero que a gente consiga celebrar ainda
muita coisa junto. Obrigado por tudo.
E, por fim, eu gostaria de agradecer a Deus, inteligência suprema e causa primeira de todas
as coisas, pela bênção do conhecimento e pela oportunidade de trabalhar para torna-lo útil para a
xv

vida das pessoas. Obrigado por ter colocado em meu caminho pessoas tão fantásticas sem as quais
eu não seria um milionésimo do que sou hoje. Por favor continue a me inspirar nessa jornada.

xvi

“We hold from God the gift which includes all others. This gift is life -- physical, intellectual,
and moral life. But life cannot maintain itself alone. The Creator of life has entrusted us with the
responsibility of preserving, developing, and perfecting it. In order that we may accomplish this,
He has provided us with a collection of marvelous faculties. And He has put us in the midst of a
variety of natural resources. By the application of our faculties to these natural resources we
convert them into products, and use them. This process is necessary in order that life may run its
appointed course.” (Claude Frédéric Bastiat)

xvii

Table of Contents
Dedication ....................................................................................................................................... i
Abstract .......................................................................................................................................... ii
Acknowledgements ...................................................................................................................... iv
Table of Contents ...................................................................................................................... xvii
List of Figures .............................................................................................................................. xx
List of Tables ............................................................................................................................ xxvi
Chapter 1: Introduction ............................................................................................................... 1
1.1. Physiological Development of Myocardial Tissue .................................................. 2
1.1.1. Cardiac Contraction and Myofibrillogenesis ..................................................... 2
1.1.2. Extracellular Matrix Remodeling ...................................................................... 5
1.1.3. Maturation of the Mitochondrial Network ........................................................ 7
1.2. Pathological Remodeling of Myocardial Tissue .................................................... 11
1.2.1. Remodeling of Myocardial Tissue and Function During Disease ................... 11
1.2.2. Remodeling of Myocardial Metabolism During Disease ................................ 12
1.3. Existing Approaches to Quantify Metabolic Processes in Cardiac Myocytes ....... 14
1.3.1. In vivo and ex vivo Models ............................................................................. 14
1.3.2. In vitro Models ................................................................................................ 16
1.3.3. Human Relevant Models ................................................................................. 18
1.4. New in vitro Technologies: Microphysiological Systems ..................................... 19
1.4.1. Biomaterials and Substrate Engineering .......................................................... 19
1.4.2. Quantitative Structural and Functional Assays ............................................... 21
1.5. Outlook ................................................................................................................... 23
1.6. Outline .................................................................................................................... 24
Chapter 2: Regulation of Mitochondrial Function in Engineered Cardiac Myocyte Tissues
by ECM Elasticity and Protein Composition ........................................................................... 26
2.1. Introduction ............................................................................................................ 26
2.2. Methods .................................................................................................................. 29
2.2.1. PDMS and Gelatin Hydrogel Substrate Preparation ....................................... 29
2.2.2. Bulk Compressive Elastic Modulus Measurements ........................................ 31
2.2.3. Neonatal Rat Ventricular Myocyte Harvest and Culture ................................. 31
xviii

2.2.4. Immunostaining, Microscopy, and Image Analysis ........................................ 32
2.2.5. Pierce LDH Cytotoxicity Assay ...................................................................... 33
2.2.6. Extracellular Flux Analysis ............................................................................. 34
2.2.7. Measurements of Protein Concentration ......................................................... 34
2.2.8. mtDNA:nucDNA Quantification ..................................................................... 35
2.2.9. Statistical Analysis .......................................................................................... 35
2.3. Results .................................................................................................................... 36
2.3.1. Characterization and Preparation of Biomaterials ........................................... 36
2.3.2. Substrate Effects on Cardiac Myocyte Adhesion, Viability, and Morphology 37
2.3.3. Substrate Regulation of Mitochondrial Function ............................................ 41
2.4. Discussion .............................................................................................................. 49
2.5. Conclusions ............................................................................................................ 59
Chapter 3: Mitochondrial Function in Engineered Cardiac Tissues is Regulated by
Extracellular Matrix Elasticity and Tissue Alignment ............................................................ 61
3.1. Introduction ............................................................................................................ 61
3.2. Materials and Methods ........................................................................................... 63
3.2.1. Fabrication of Mechanically Tunable PDMS .................................................. 63
3.2.2. Bulk Compressive Elastic Modulus Measurements ........................................ 64
3.2.3. Master Wafer and PDMS Stamp Fabrication .................................................. 64
3.2.4. Fabrication of Micropatterned PDMS Discs ................................................... 65
3.2.5. Neonatal Rat Ventricular Myocyte Harvest and Culture ................................. 66
3.2.6. Immunostaining ............................................................................................... 67
3.2.7. Microscopy and Image Analysis ..................................................................... 67
3.2.8. Mitochondrial Respirometry ............................................................................ 68
3.2.9. Measurements of Protein Concentration ......................................................... 68
3.2.10. Statistical Analysis ........................................................................................ 69
3.3. Results .................................................................................................................... 69
3.3.1. Tuning Tissue Alignment and ECM Elasticity within XF24 Cell Culture
Microplates ............................................................................................................................ 69
3.3.2. Engineering Isotropic and Aligned Cardiac Tissues within XF24 Cell Culture
Microplates ............................................................................................................................ 72
3.3.3. Co-Regulation of Mitochondrial Function by ECM Elasticity and Tissue
Alignment .............................................................................................................................. 75
3.4. Discussion .............................................................................................................. 79
Chapter 4: Concluding Remarks and Future Work ............................................................... 86
4.1. Biomaterial class, ECM protein composition and rigidity regulate metabolic
demands in isotropic cardiac tissues ......................................................................................... 88
4.2. Tissue alignment and mitochondrial function ........................................................ 89
xix

4.3. Limitations and future directions ........................................................................... 90
4.4. Final conclusions .................................................................................................... 93
Chapter 5: References: ............................................................................................................... 95
Appendix I: Supplementary Figures for Enhanced Mitochondrial Function in Cardiac
Myocytes on Matrix-Derived Hydrogels ................................................................................. 123
Appendix II: Mitochondrial Function in Engineered Cardiac Tissues is Regulated by
Extracellular Matrix Elasticity and Tissue Alignment .......................................................... 125

xx

List of Figures
Figure 1-1: Schematic of the heart wall identifying the different types of tissue that compose
the organ. Myocardial tissue consists primarily of highly aligned cardiac myocytes, which is
essential for optimizing cardiac contractility. ................................................................................. 2
Figure 1-2: A: Schematic of the heart. B: Ventricular myocardium consists of elongated cardiac
myocytes and supporting cells, such as fibroblasts. C: The sarcomere is the contractile unit of the
cardiac myocytes and is composed of Z discs (red), the M-bands (brown), myosin filaments (blue),
actin filaments (globular green), and titin (green). Mitochondria are located adjacent to sarcomeres
to efficiently provide ATP to be used by the sarcomeres for contraction. Z-discs are attached to
focal adhesions, which connect extracellularly to the ECM via integrins. D: Myosin walking on
the actin fibers occurs with ADP/ATP hydrolyzation (adapted from [3]). ..................................... 3
Figure 1-3: Changes in myocardial tissue organization and cardiac myocyte morphology
during development. A: Schematic of myocardial tissue, indicating the trabecular structure
present in the developing left ventricle (left), and highly organized ventricle in the adult tissue
(right). Image adapted from [16]. Immunostained cryosections of mice hearts for different time
points in development: ED14.5 (B), PD2 (C), and 6 months old (D). Cadherin: green, Sarcomeres:
red, Nuclei: blue. Adapted from [9]. ............................................................................................... 5
Figure 1-4: ECM Remodeling During Cardiac Development. A: Changes in protein
composition of the rat heart extracellular matrix during development. Percentage corresponding to
the 15 most abundant proteins as detected by mass spectrometry. Source: [18]. B: Stiffness of rat
cardiac tissue for different age points. Source: [20] ....................................................................... 6
Figure 1-5: Mitochondrial metabolism and substrate preference. (A) Different pathway of
energy substrate metabolism. (B) Percentage of ATP production corresponding to each pathway
per age group. Source: [2]. .............................................................................................................. 7
Figure 1-6: A: Schematic of heart muscle fibers indicating the intercalated discs that contain gap
junctions and desmosomes. It is possible to see the localization of the mitochondria, in orange,
next to the sarcomeres. Adapted from [52]. B: Schematic of mitochondrial fusion and fission, also
indicating the process of mitophagy (mitochondrial turnover). Adapted from [53]. C: Transmission
xxi

electron micrograph of mouse myocardium indicating the mitochondrial distribution in cardiac
myocytes. In adult cells, the organelles usually present a round-like shape and have their
localization dictated by myofibrils, being interleaved with sarcomeres. Subsarcolemmal
mitochondria (white arrows) are located underneath the sarcolemma (black arrow);
intermyofibrillar mitochondria are indicated with grey arrows. Source: [54]. D: Schematic of
oxidative phosphorylation in the mitochondria indicating the citric acid cycle that occurs in the
mitochondrial matrix; identifying the inner mitochondrial membrane complexes used in the
electron transport chain, and the ATP synthase, also located in the inner mitochondrial membrane.
Adapted from: [55]. ...................................................................................................................... 10
Figure 1-7: Types of cardiac hypertrophy, their causes and consequences in tissue and
cellular morphology levels. Source: [1]. ..................................................................................... 11
Figure 1-8: Examples of different types of microphysiological systems. A: PDMS-based
muscular thin films combined with human induced pluripotent stem cells used to study contraction
of cardiac myocytes. Source: [102]. B: Micromyocardium system developed as a higher
throughput system for studying contraction of myocytes using TFM. Source: [107]. C:
Micropatterning of single cell islands in polyacrylamide hydrogels to study myofibril and
mitochondrial organization, in addition to perform TFM. Source: [15]. ...................................... 23
Figure 2-1: Characterization and Fabrication of Biomaterial Substrates. A: XF24 cell culture
microplates were coated with either 10 µL of PDMS (top row) or gelatin hydrogel (bottom row)
solution (i). After overnight curing, the plates were treated with UVO (ii). PDMS-coated wells
were coated with 100 µL of ECM protein solution (iii, top row). Both types of wells were rinsed
with sterile PBS (iv, top row, iii, bottom row) and seeded with neonatal rat ventricular myocytes
(v, top row, iv, bottom row). B: Average elastic moduli for the four different formulations of
gelatin hydrogels, before and after incubation in culture-like conditions. Values are means ± SE;
n = 4 for all conditions. * p<0.05. Refer to Table II-1 for details related to statistical analysis. . 37
Figure 2-2: Cardiac tissues cultured on PDMS substrates within XF24 Cell Culture
Microplates. Composite images of neonatal rat ventricular myocyte tissues cultured on indicated
(A) gelatin hydrogels or (B) PDMS. Blue: nuclei; white: α-actinin; scale bars: 25 µm. .............. 38
xxii

Figure 2-3: Cell adhesion and viability. (A) Nuclei per 0.1 mm2 and (B) total protein content per
well in cell culture microplates. n indicated below each box. Cytotoxicity on day 4 (C) and day 5
(D) after seeding. n = 8 for all conditions. Letters above each box indicate a statistical difference
(p<0.05) with the condition represented by the letter on the x-axis. For example, “a” indicates
p<0.05 compared to gelatin hydrogel, 17 kPa. Refer to Tables II-2 to II-4 for details related to
statistical analysis. ......................................................................................................................... 39
Figure 2-4: Cell geometry. (A) Composite images of single cardiac myocytes seeded on
coverslips. Blue: nuclei; white: α-actinin; scale bars: 10 µm. Myocyte area (B), height (C), and
volume (D). n indicated below the boxes on (C). Refer to Table II-5 for details related to statistical
analysis. ......................................................................................................................................... 41
Figure 2-5: OCR measurements in engineered cardiac tissues. Average experimental OCR
measurements for tissues on gelatin hydrogels (A), tissues on PDMS-fibronectin (B), and tissues
on PDMS-gelatin (C). Measurements at baseline and after addition of oligomycin, FCCP, and
antimycin and rotenone. Data are presented as mean ± s.e.m., n = 16 for all conditions. ............ 42
Figure 2-6: Baseline oxidative and glycolytic activity. Average OCR (A) and ECAR (B)
associated with basal respiration. n = 16 for all conditions. Letters above each box indicate a
statistical difference (p<0.05) with the condition represented by the letter on the x-axis. For
example, “a” indicates p<0.05 compared to gelatin hydrogel, 17 kPa. (C) Energy map at baseline
for cardiac tissues culture on the different substrates, colors correspond to the bars in (A) and (B).
Values are presented as mean ± s.e.m., n = 16 for all. For details related to statistical analysis, refer
to Tables II-6 and II-7. .................................................................................................................. 43
Figure 2-7: Baseline metabolic functions. Average OCR associated with (A) ATP production,
(B) proton leak, and (C) non-mitochondrial respiration. n = 16 for all conditions. Letters above
each box indicate a statistical difference (p<0.05) with the condition represented by the letter on
the x-axis. For example, “a” indicates p<0.05 compared to gelatin hydrogel, 17 kPa. For details
related to statistical analysis, refer to Tables II-8 to II-10. ........................................................... 45
Figure 2-8: Metabolic stress responses. Average OCR associated with (A) maximum respiration
and (B) spare respiratory capacity. n = 16 for all conditions. Letters above each box indicate a
statistical difference (p<0.05) with the condition represented by the same letter on the x-axis. For
xxiii

example, “a” indicates p<0.05 compared to gelatin hydrogel, 17 kPa. For details related to
statistical analysis, refer to Tables II-11 to II-12. ......................................................................... 46
Figure 2-9: Bioenergetic Health Index and quantity of mitochondria. (A) Average Bioenergetic
Health Index for all conditions. n = 16 for all conditions. (B) Mitochondrial DNA to nuclear DNA
copy number ratio. n indicated below each box. Letters above each box indicate a statistical
difference (p<0.05) with the condition represented by the same letter on the x-axis. For example,
“a” indicates p<0.05 compared to gelatin hydrogel, 17 kPa. For details related to statistical
analysis, refer to Tables II-13 and II-14. ....................................................................................... 48
Figure 3-1: Engineering cardiac tissues within XF24 Cell Culture Microplates. (A) Glass
coverslips were spin-coated with layers of PNIPAm and PDMS (i), laser-engraved into discs (ii),
and microcontact printed (iii-v) with fibronectin. Micropatterned discs were then detached from
the coverslip, transferred into a XF24 Cell Culture Microplate (vi), and seeded with neonatal rat
ventricular myocytes (vii). (B) Laser-engraved PDMS discs on a square glass coverslip (22 mm ×
22 mm) obtained after step (ii) in Panel A. Scale bar: 5 mm. (C) Immunostained 15x2 fibronectin
on a micropatterned PDMS disc obtained after step (v) in Panel A. Scale bar: 50µm. (D) Isotropic
cardiac tissue on PDMS disc patterned with uniform fibronectin and (E) aligned cardiac tissue on
PDMS disc patterned with 15x2 fibronectin. Scale bars: 50µm. .................................................. 71
Figure 3-2: Elastic moduli of PDMS formulations. (A-B) Stress-strain curves for the three
PDMS formulations (low: pure Sylgard 527; moderate: 1:20 Sylgard 184:Sylgard 527; and high:
pure Sylgard 184), with different y-axis scales. (C) Average elastic moduli for the three different
formulations of PDMS. Data are presented as mean ± s.e.m., n=4 for low, n=5 for moderate, n=7
for high. *p<0.001. ....................................................................................................................... 72
Figure 3-3: Cardiac tissues engineered within XF24 Cell Culture Microplates. Composite
images of neonatal rat ventricular myocyte tissues cultured on the indicated conditions. (D) and
(H) are zoomed-in images of the white boxes in (C) and (G), respectively. Blue: nuclei; green:
actin, red: α-actinin; scale bars: 100µm. ....................................................................................... 73
Figure 3-4: Quantification of tissue architecture. (A) Actin alignment (calculated as the
orientational order parameter) (n=9 for all conditions), (B) cells per 0.1mm
2
(n=9 for all
conditions), and (C) total protein content per well measured using BCA assay (n= 16 for all
xxiv

conditions). Data are presented as mean ± s.e.m.. ╪ p<0.05 compared to isotropic tissues, same
elasticity. ....................................................................................................................................... 74
Figure 3-5: OCR measurements in engineered cardiac tissues. Average experimental OCR
measurements for isotropic (left) and aligned (right) tissues at baseline and after addition of
oligomycin, FCCP, and antimycin and rotenone. Data are presented as mean ± s.e.m., n=16 for all
conditions. ..................................................................................................................................... 75
Figure 3-6: Metabolic function in engineered cardiac tissues. Average OCR associated with (A)
basal respiration, (B) ATP production, (C) proton leak, (D) maximum respiration, (E) spare
respiratory capacity, and (F) non-mitochondrial respiration. Data are presented as mean ± s.e.m.,
n=16 for all conditions. * p<0.05 compared to tissues on low PDMS, same architecture. ┼ p<0.05
compared to tissues on moderate PDMS, same architecture. ╪ p<0.05 compared to isotropic
tissues, same elasticity. ................................................................................................................. 77
Figure 3-7: Bioenergetic Health Index in engineered cardiac tissues. Average Bioenergetic
Health Index for all conditions. Data are presented as mean ± s.e.m., n=16 for all conditions. *
p<0.05 compared to tissues on low PDMS, same architecture. ╪ p<0.05 compared to isotropic
tissues, same elasticity. ................................................................................................................. 78
Figure 4-1: Engineering Cardiac Microphysiological Systems to Define the Functional
Impacts of Pathological Extracellular Matrix Remodeling. (A) Distinct features of native
healthy and diseased/fibrotic myocardium, such as myocyte shape, tissue alignment, ECM rigidity,
and cell demographics, are used as design templates for engineering cardiac microphysiological
systems. (B) Features of native healthy and diseased/fibrotic myocardium are replicated by
combining appropriate biomaterials and microfabrication techniques. (C) Engineered cardiac cells
and tissues are interrogated with functional assays to quantify contractility, electrophysiology, and
metabolism as a function of their microenvironment. Collectively, these approaches implemented
as cardiac microphysiological systems can identify the functional impact of ECM remodeling to
streamline mechanistic studies and therapeutic development. Images in (C) adapted from
References [14], [213], [214], and [215]. Figure and caption from [212]. ................................... 87
xxv

Figure 4-2: Adult somatic cells from patients can be reprogrammed into induced pluripotent stem
cells (iPSCs) and differentiated in vitro into different cell types for applications in disease
modelling and drug screening. Adapted from [219]. .................................................................... 91
Figure I-1: Cytotoxicity for different substrates. Average cytotoxicity values for tissues after 3
days in culture. Letters above each box indicate a statistical difference (p<0.05) with the condition
represented by the same letter on the x-axis. For example, “a” indicates p<0.05 compared to gelatin
hydrogel, 17 kPa. For details related to statistical analysis, refer to Table II-4. ......................... 123
Figure I-2: ECAR measurements in cardiac myocytes. Average experimental ECAR
measurements for tissues on gelatin hydrogels (A), PDMS-fibronectin (B), and PDMS-gelatin (C)
at baseline and after addition of oligomycin, FCCP, and antimycin and rotenone during the
mitochondrial stress test. Data are presented as mean ± s.e.m., n = 16 for all conditions. ......... 124

xxvi

List of Tables
Table 2-1: Two-way ANOVA analysis of mitochondrial respirometry data for tissues on
gelatin hydrogels. Data for all conditions was normally distributed, as determined by the
Kolmogorov-Smirnov test. p-values for each comparison are indicated, with * indicating p<0.05.
n = 16 for all conditions. ............................................................................................................... 51
Table 2-2: Two-way ANOVA analysis of mitochondrial respirometry data for tissues on
PDMS substrates. Data for all conditions was normally distributed, as determined by the
Kolmogorov-Smirnov test. p-values for each comparison are indicated, with * indicating p<0.05.
n = 16 for all conditions. ............................................................................................................... 51
Table 3-1: Two-way ANOVA analysis for mitochondrial respirometry data. Data for all
conditions was normally distributed, as determined by the Lilliefors test. P-values for each
comparison are indicated. *p<0.05. .............................................................................................. 79
Table II-1: Statistical analysis for compressive elastic moduli of hydrogel cylinders. Data were
normally distributed, as determined by the Kolmogorov-Smirnov test. For comparisons of samples
of different composition, Before and After PBS incubation, an un-paired t-test was performed. For
comparisons of samples of same composition Before and After PBS incubation, a paired t-test was
performed. ................................................................................................................................... 125
Table II-2: Statistical analysis for cell number per 0.1mm
2
. All data was normally distributed,
as determined by the Kolmogorov-Smirnov test. Multiple comparisons were performed using
Tukey’s test, with the p-values indicated in the table below. ..................................................... 126
Table II-3: Statistical analysis for total protein content per well. All data was normally
distributed, as determined by the Kolmogorov-Smirnov test. The p-value for the one-way ANOVA
test was 3.4 x 10e-4. Multiple comparisons were performed using Tukey’s test, with the p-values
indicated in the table below. Comparisons not listed were not statistically significant. ............ 126
Table II-4: Statistical analysis for Cytotoxicity. All data was normally distributed, as determined
by the Kolmogorov-Smirnov test. Multiple comparisons were performed using Tukey’s test, with
xxvii

the p-values indicated in the table below. Comparisons not listed were not statistically significant.
..................................................................................................................................................... 127
Table II-5: Statistical analysis for cell height and cell volume. All data was normally distributed,
as determined by the Kolmogorov-Smirnov test. Multiple comparisons were performed using
Tukey’s test, with the p-values indicated in the table below. ..................................................... 128
Table II-6: Statistical analysis for Basal Respiration OCR. All data was normally distributed,
as determined by the Kolmogorov-Smirnov test. Multiple comparisons were performed using
Tukey’s test, with the p-values indicated in the table below. Comparisons not listed were not
statistically significant. ............................................................................................................... 128
Table II-7: Statistical analysis for Baseline ECAR. All data was normally distributed, as
determined by the Kolmogorov-Smirnov test. Multiple comparisons were performed using
Tukey’s test, with the p-values indicated in the table below. Comparisons not listed were not
statistically significant. ............................................................................................................... 129
Table II-8: Statistical analysis for ATP Production OCR. All data was normally distributed, as
determined by the Kolmogorov-Smirnov test. Multiple comparisons were performed using
Tukey’s test, with the p-values indicated in the table below. Comparisons not listed were not
statistically significant. ............................................................................................................... 130
Table II-9: Statistical analysis for Proton Leak OCR. All data was normally distributed, as
determined by the Kolmogorov-Smirnov test. Multiple comparisons were performed using
Tukey’s test, with the p-values indicated in the table below. Comparisons not listed were not
statistically significant. ............................................................................................................... 131
Table II-10: Statistical analysis for Non-Mitochondrial Respiration. All data was normally
distributed, as determined by the Kolmogorov-Smirnov test. Multiple comparisons were
performed using Tukey’s test, with the p-values indicated in the table below. Comparisons not
listed were not statistically significant. ....................................................................................... 131
Table II-11: Statistical analysis for Maximum Respiration OCR. All data was normally
distributed, as determined by the Kolmogorov-Smirnov test. Multiple comparisons were
xxviii

performed using Tukey’s test, with the p-values indicated in the table below. Comparisons not
listed were not statistically significant. ....................................................................................... 132
Table II-12: Statistical analysis for Spare Respiratory Capacity. All data was normally
distributed, as determined by the Kolmogorov-Smirnov test. Multiple comparisons were
performed using Tukey’s test, with the p-values indicated in the table below. Comparisons not
listed were not statistically significant. ....................................................................................... 133
Table II-13: Statistical analysis for Bioenergetic Health Index. All data was normally
distributed, as determined by the Kolmogorov-Smirnov test. Multiple comparisons were
performed using Tukey’s test, with the p-values indicated in the table below. Comparisons not
listed were not statistically significant. ....................................................................................... 133
Table II-14: Statistical analysis for mtDNA:nucDNA. All data was normally distributed, as
determined by the Kolmogorov-Smirnov test. Multiple comparisons were performed using
Tukey’s test, with the p-values indicated in the table below. Comparisons not listed were not
statistically significant. ............................................................................................................... 134

1

Chapter 1: Introduction
Cardiovascular diseases are the leading cause of death in the United States, claiming one
of every four lives [4] and costing an estimated $555 billion per year in direct treatments and
opportunity costs [5]. While the genetic blueprint of the patient is known to contribute to the
evolution of heart disease, the major risk factors for cardiovascular diseases are environmental
factors and lifestyle choices that typically lead to remodeling of multiple chemical and physical
components of myocardial tissue and to decreased cardiac output [6]. Existing studies have
demonstrated that physical factors associated with both cardiac development and disease, such as
extracellular matrix remodeling, have a direct impact on the contractile and electrical properties of
cardiac myocytes, the muscular cells in the myocardium. However, an important component that
has been relatively overlooked is the metabolic response of cardiac myocytes to changes in their
physical microenvironment. One reason for these shortcomings is that existing methodologies
struggle to controllably isolate individual microenvironmental factors and test their effects on
cardiac myocyte metabolism. These limitations have stifled the development of new therapeutic
strategies targeted to mitochondria in cardiac myocytes, which have potential to impact many
patients because mitochondrial dysfunction is a common underlying factor in many forms of heart
disease. In this chapter, we will review the structure and function of myocardial tissue in the
context of development and disease, with a focus on mitochondria in cardiac myocytes. We will
then discuss the present methods for investigating myocardial tissues and their intrinsic limitations.
Lastly, we will describe innovative new approaches to engineer cardiac tissues, which we
hypothesize can be applied to identify changes in metabolic parameters caused by remodeling of
the microenvironment in cardiac myocytes.
2

1.1. Physiological Development of Myocardial Tissue
Ventricular tissue is divided into four categories (see Figure 1-1). The pericardium is a
fibrous sac that surrounds the heart and the roots of the great vessels. The inner most layer of the
pericardium is called epicardium and functions as a protective layer to the underlying muscle
tissue. The endocardium is primarily composed of endothelial cells and lines the ventricular
chambers. Lastly, the myocardium is the muscular tissue of the ventricles, responsible for
contraction and pumping blood throughout
the body. Myocardial tissue is composed of
diverse cell types, including cardiac
myocytes, cardiac fibroblasts, and
pacemaker cells [7], embedded in a well-
defined mesh of proteins, macromolecules,
ions, and nutrients, collectively referred to
as the extracellular matrix (ECM). In this
section, we will detail the important
structural and functional cellular and
extracellular components of myocardial
tissue, including how they emerge during
cardiac development.
1.1.1. Cardiac Contraction and Myofibrillogenesis
Cardiac myocytes are the striated muscle cells in myocardial tissue that rhythmically and
synchronously contract to pump blood [8]. Cardiac myocytes are packed with contractile actin-
myosin units, called sarcomeres, that are arranged in series into fibers, called myofibrils. Myofibril
Figure 1-1: Schematic of the heart wall identifying the
different types of tissue that compose the organ. Myocardial
tissue consists primarily of highly aligned cardiac myocytes,
which is essential for optimizing cardiac contractility.
3

contraction is driven by action
potentials initiated by
pacemaker cells in the sinoatrial
node, located in the right atrium
[7]. Action potentials then travel
through atrial tissue and a
conduction system composed of
the atrioventricular node, the
bundle of His, the bundle
brunches, and the Purkinje
fibers until reaching myocardial
tissue, where they propagate
between cardiac myocytes via
gap junctions [9, 10]. Each
action potential induces
membrane depolarization,
which causes voltage-sensitive sodium channels to open and allow inflow of sodium ions. Due to
membrane depolarization, voltage sensitive L-type calcium channels open and allow extracellular
calcium ions into the cells. This process induces the opening of calcium channels in the
sarcoplasmic reticulum (SR), increasing cytoplasmic concentration of calcium ions. The increased
concentration of calcium ions triggers the contraction of cardiac myocytes by enabling myosin and
actin binding: as calcium binds to troponin, active sites on actin fibers become exposed to myosin
heads allowing the formation of a cross-bridge between the fibers [11]. The contraction occurs
Figure 1-2: A: Schematic of the heart. B: Ventricular myocardium consists
of elongated cardiac myocytes and supporting cells, such as fibroblasts. C:
The sarcomere is the contractile unit of the cardiac myocytes and is
composed of Z discs (red), the M-bands (brown), myosin filaments (blue),
actin filaments (globular green), and titin (green). Mitochondria are located
adjacent to sarcomeres to efficiently provide ATP to be used by the
sarcomeres for contraction. Z-discs are attached to focal adhesions, which
connect extracellularly to the ECM via integrins. D: Myosin walking on the
actin fibers occurs with ADP/ATP hydrolyzation (adapted from [3]).
4

when myosin heads “walk” on actin filaments (Figure 1-2), in a process dependent on ATP
hydrolyzation. Within each myocyte, myofibrils are uniaxially aligned, and within each layer of
myocardial tissue, myocytes are also uniaxially aligned. This multi-scale alignment is essential for
optimizing the contractile performance of the heart, as it ensures that all sarcomeres are
cooperating to pump blood.
Myofibrils progressively mature as cardiac myocytes develop, leading to significant
changes in overall cytoarchitecture (see Figure 1-3). Embryonic cardiac myocytes have polygonal
shapes and small aspect ratios (1.7:1 to 3:1). Nuclei occupy a significant portion of the cells and
myofibrils are sparse, concentrated in the periphery of the cell, and have little directional
orientation (Figure 1-3B). As the tissue matures, cells undergo a physiological unidirectional
hypertrophy. Cardiac myocytes become more rectangularly shaped and increase their aspect ratio
(5:1), see Figure 1-3C-D [9]. Concurrently, the number of myofibrils increases, as they are added
to the cytoskeleton in the direction of hypertrophic growth. Additionally, myofibril distribution
becomes well-defined and periodic: they gradually become more perpendicular to the cellular
longitudinal axis, with little angular deviation from each other [9, 12]. This process of myofibril
alignment follows the emergent global alignment of myocardial tissue, leading to improved
contractile performance in the developing organism [13-15]. Thus, alterations in myocyte shape,
myofibril maturity, and alignment of myocardial tissue are all essential to the proper functioning
of the heart. Each of these processes occur in parallel during the development of the organ from
its embryonic to adult stage.
5

Figure 1-3: Changes in myocardial tissue organization and cardiac myocyte morphology during development.
A: Schematic of myocardial tissue, indicating the trabecular structure present in the developing left ventricle (left),
and highly organized ventricle in the adult tissue (right). Image adapted from [16]. Immunostained cryosections of
mice hearts for different time points in development: ED14.5 (B), PD2 (C), and 6 months old (D). Cadherin: green,
Sarcomeres: red, Nuclei: blue. Adapted from [9].
1.1.2. Extracellular Matrix Remodeling
As mentioned above, the ECM is a well-defined mesh of proteins, macromolecules, ions,
and nutrients. It performs two important functions in myocardial tissue: mechanical support to
cardiac cells, and biochemical and mechanical signaling responsible for regulating cellular
behavior [8]. Consequently, the ECM has an important role in myocardial development and
maturation [17, 18]. As seen in Figure 1-4A, during development, ECM protein composition
changes significantly. In fetal and neonatal hearts, the matrix is primarily composed of
glycoproteins such as fibronectin, associated with a network of collagen fibers that provide
structural support to the cells [18]. As the tissue matures, other cells present in the myocardium,
especially cardiac fibroblasts, become activated, change their matrix protein secretion, and co-
regulate the function of the tissue [16]. These chemical and mechanical modifications are sensed
by cardiac myocytes via integrins, which are transmembrane receptors that attached both to the
6

cellular cytoskeleton and to protein ligands in the ECM [8] (see Figure 1-2). In addition to
anchoring the cytoskeleton to the ECM, integrins activate many different signaling pathways,
especially those related to focal adhesions [19].

Figure 1-4: ECM Remodeling During Cardiac Development. A: Changes in protein composition of the rat heart
extracellular matrix during development. Percentage corresponding to the 15 most abundant proteins as detected by
mass spectrometry. Source: [18]. B: Stiffness of rat cardiac tissue for different age points. Source: [20]
Changes in ECM protein composition occur simultaneously with significant alterations in
matrix elasticity (see in Figure 1-4B) [20-22]. Fetal ECM can have an elastic modulus as low as
1-2kPa in its early stages [21], which increases to approximately 10kPa in late fetal development
[20, 23]. After birth, the elasticity of the ECM doubles to about 20kPa [20-23], see Figure 1-4B.
This process is accompanied by increased organization of ECM fibers, which will provide
alignment cues to the cells [24, 25], contributing to a highly-organized tissue (see Figure 1-3B-C).
Hence, partially based on mechanical cues from the ECM, cardiac myocytes will form concentric
rings that will rhythmically contract to pump blood to the body.
7

1.1.3. Maturation of the Mitochondrial Network
As cardiac myocytes rhythmically
contract from birth to death, they have
relatively high metabolic demands
compared to most other tissues, consuming
approximately 30 kg of ATP per day [26].
Due to this heavy energetic demand, the
heart is among the most mitochondria-rich
organs in the body [27], with 30-40% of the
intracellular volume of cardiac myocytes
occupied by mitochondria [28, 29].
Mitochondria are unique organelles, in that
they have their own DNA and machinery for
producing proteins, especially the inner
membrane proteins used in the electron transport chain. However, most mitochondrial proteins are
still produced through nuclear transcription and cytoplasmic translation [30], including important
proteins used in the fusion and fission processes that will described later. Hence, there is a
continuous and important interplay between mitochondria and the nucleus.
Mitochondria produce ATP in a process called oxidative phosphorylation that involves
consecutive reactions in the cytosol, the mitochondrial matrix and the mitochondrial inner
membrane (see Figure 1-5A). In short, Acetyl CoA, a by-product of fatty acid or glucose oxidation
that occurs in the cytoplasm, is transported to the mitochondrial matrix and undergoes the
tricarboxylic acid cycle (TCA). Products of the TCA, specifically NADH and Succinate, pass
Figure 1-5: Mitochondrial metabolism and substrate
preference. (A) Different pathway of energy substrate
metabolism. (B) Percentage of ATP production
corresponding to each pathway per age group. Source: [2].
8

through an electron transport chain at the mitochondrial inner membrane, in which they create an
electrochemical proton gradient that is used to maintain the activity of ATP synthase – also located
in the mitochondrial inner membrane –, responsible for phosphorylating ADP into ATP. In
addition to their important role in the ATP production, mitochondria regulate calcium homeostasis
[31, 32], equilibrating the pH and the voltage inside the cells [31], and they are the initiators of
programmed cell death process called apoptosis, via the cytochrome c pathway [33, 34].
Although the oxidative phosphorylation process is a vital part of metabolism and essential
to homeostasis maintenance, it results in important by-products called reactive oxygen species [35-
38], especially superoxide and hydrogen peroxide [38]. These molecules are associated with
degradation of inner cellular structures, and in excess can cause genetic mutations or lead to
cellular apoptosis [39]. Hence, when functioning properly, mitochondria play an essential role in
cellular homeostasis. However, dysregulated organelles can damage cells and tissues, being
responsible for different types of disease [38, 40].
An important hallmark of cardiac maturation after birth is the switch in metabolic substrate
preference from glucose to fatty acids (see Figure 1-5). This process happens naturally as the
available amount of oxygen increases, and cardiac myocyte metabolism becomes preferentially
oxidative [2, 41-43]. As fatty acids are more efficient in terms of ATP produced per mole of
substrate, and oxygen is not a limiting factor, the cells can use a substrate that is not as efficient in
terms of oxygen consumption [2, 44]. However, cardiac myocytes remain capable of switching
back to developmental metabolic profile if oxygen becomes limited, such as in cases of ischemia,
during and post-myocardial infarction, or during high intensity exercise.
To understand mitochondrial function, it is imperative to study mitochondrial structure, as
it can provide us with important elements correlated with metabolic dynamics. Individual
9

mitochondria are usually bean or round shaped and distributed across the cell. An important
property of this organelle is its dynamics: ability to fuse and fragment due to cellular signaling and
localization. Using an array of proteins (especially Opa1, Mfn1 and Mfn2), individual
mitochondrion can fuse and form an elongated mitochondrial network [30, 41, 43, 45]. Conversely,
large mitochondrion can fragment and give birth to daughter mitochondria, with the use of the
protein Drp1 [30, 46]. This combination of fusion and fragmentation (fission) allows for
mitochondrial reorganization inside the cells, in order to make ATP provision more efficient. Cells
can also increase their mitochondrial mass and population in a process called mitochondrial
biogenesis, that uses mitochondrial fission [30, 47, 48]. The fragmentation mechanism can also be
used to remove damaged areas of a mitochondrion: they will become depolarized daughter
mitochondria and will be eliminated via mitophagy, a selective degradation of mitochondria via
autophagy [30, 43, 48].
In cardiac myocytes, the most important mitochondrial populations are perinuclear
mitochondria (a collection of mitochondria that are localized closed to the nucleus) [49],
intermyofibrillar mitochondria (a collection of mitochondria localized between the myofibrils),
and subsarcolemmal mitochondria (a group located directly beneath the sarcolemma) [50]. These
mitochondrial networks evolve as the cardiac tissue develops, moving from an interconnected
structure [42, 43], to fragmented set of organelles that are interleaved with sarcomeres (Figure 1-6)
– a structure unique among fully differentiated tissues [30, 43, 51]. This interleaved distribution
of the mitochondria and myofibrils allows for a more efficient delivery of ATP to the contractile
units, as the distance between organelles and fibers is reduced.
10

Figure 1-6: A: Schematic of heart muscle fibers indicating the intercalated discs that contain gap junctions and
desmosomes. It is possible to see the localization of the mitochondria, in orange, next to the sarcomeres. Adapted from
[52]. B: Schematic of mitochondrial fusion and fission, also indicating the process of mitophagy (mitochondrial
turnover). Adapted from [53]. C: Transmission electron micrograph of mouse myocardium indicating the
mitochondrial distribution in cardiac myocytes. In adult cells, the organelles usually present a round-like shape and
have their localization dictated by myofibrils, being interleaved with sarcomeres. Subsarcolemmal mitochondria
(white arrows) are located underneath the sarcolemma (black arrow); intermyofibrillar mitochondria are indicated
with grey arrows. Source: [54]. D: Schematic of oxidative phosphorylation in the mitochondria indicating the citric
acid cycle that occurs in the mitochondrial matrix; identifying the inner mitochondrial membrane complexes used in
the electron transport chain, and the ATP synthase, also located in the inner mitochondrial membrane. Adapted from:
[55].
In summary, the maturation of the mitochondrial network and the remodeling of its
function are essential for the development and function of cardiac myocytes. Mitochondrial
remodeling occurs simultaneously with ECM remodeling, cardiac myocyte reorganization and
alignment, and myofibrillogenesis. Therefore, understanding how these different components of
myocardial tissue develop and interact with each other is essential to obtain a clear picture of
myocardial tissue maturation and development.
11

1.2. Pathological Remodeling of Myocardial Tissue
Similar to the physiological remodeling that happens in healthy tissue, cardiac tissue
undergoes significant modifications in disease. For example, in heart failure, the matrix stiffens
and tissue architecture becomes disorganized [15, 56], a process correlated with a metabolic
preference change [57-59]. In this section, we will detail the structural, functional, and metabolic
remodeling of myocardial tissue that occurs during disease.
1.2.1. Remodeling of Myocardial Tissue and Function During Disease
Cardiovascular diseases are the leading cause of death in the United States, claiming
approximately one in every four lives [4]. They comprise a plethora of different conditions caused
by both genetic and environmental factors [6, 60]. Among the different cardiovascular diseases,
two of the most prevalent are myocardial infarction and chronic hypertension. Both of these
conditions are associated
with cardiac tissue
remodeling, including
pathological hypertrophy.
Hypertrophy is an increase
in the size of the heart, either
via cardiac dilation (in the
case of myocardial
infarction), or concentric hypertrophy (chronic hypertension) [1], see Figure 1-7.
A myocardial infarction occurs when a blockage in one of the coronary arteries that feed
the heart deprives the myocardial tissue of oxygenation. Cardiac myocytes undergo necrosis and,
because they are non-regenerative, are replaced by cardiac fibroblasts. These cells secrete an
Figure 1-7: Types of cardiac hypertrophy, their causes and consequences in
tissue and cellular morphology levels. Source: [1].
12

excess of ECM proteins, leading to thickening and scarring of the myocardial tissue in a process
called fibrosis [15, 61]. Fibrosis and scarring of the tissue results in increased tissue stiffness [8,
15], disorganization of the cardiac myocytes [62], and pathological hypertrophy of surviving
cardiac myocytes [1]. Specifically, sarcomeres are added in series to lengthen the cell, resulting in
an increased aspect ratio (10-11:1) [1, 63-66] and in a dilated ventricle [67], see Figure 1-7 bottom
left drawing. This combination of factors leads to declines in cardiac output and often culminates
in heart failure.
A second disease associated with myocardial tissue remodeling is chronic hypertension,
which results from a complex interaction between genetic and environmental factors [68].
Differently from myocardial infarctions, chronic hypertension results in moderate fibrosis and
increased ECM stiffness, with little to no cardiac myocyte death. However, due to the systolic
overload caused by chronic hypertension [69], the ventricle undergoes concentric hypertrophy in
an attempt to compensate for the increased load. In this process, sarcomeres are added both in
series and in parallel to the existing ones in cardiac myocytes, resulting in decreased cell aspect
ratio (2:1) [1, 15, 70], see Figure 1-7 bottom row, center drawing. This condition can also progress
to heart failure.
These are only two examples of relatively prevalent diseases that are associated with
simultaneous remodeling of cardiac myocytes and physical features within their
microenvironment. Therefore, it is essential to understand which factors dominate these processes
in order to identify better avenues for therapeutic intervention.
1.2.2. Remodeling of Myocardial Metabolism During Disease
Combined with remodeling of cell shape, cardiac myocytes also undergo metabolic
remodeling in the case of disease, especially in response to ischemia/reperfusion [71, 72], heart
13

attack, and heart failure [44, 57-59]. In pathological hypertrophy in response to pressure overload,
as in hypertension, metabolism in cardiac myocytes reverts to a neonatal phenotype, extremely
reliant on glycolysis and glucose oxidative phosphorylation, instead of beta-oxidation of fatty acids
[2, 41, 44, 73]. The change of energy substrate is associated with reduced contractile performance,
which is likely because cells can obtain less ATP per mole of glucose than per mole of fatty acids
[2, 44, 73]. Additionally, this phenotype reversal is associated with changes in mitochondrial
biogenesis and mitophagy [40, 41, 48, 71], such as increased mitochondrial fragmentation due to
disruptions in the mitochondrial fusion pathway, which is also responsible for mitochondrial
quality control [40]. This process impairs long-term viability of the cells, as mitochondrial
machinery will not be replenished as needed, and will carry on defects that might lead to increased
oxidative stress [40, 71, 74].
After a myocardial infarction, metabolism is significantly perturbed due to the deprivation
of both oxygen and nutrients [40, 57, 58]. In response to sustained ischemia, extensive
mitochondrial fission is observed [40]. Additionally, fatty acid uptake and utilization is decreased
[57-59, 75], combined with an initial increase of glucose utilization that might return to normal
levels [57, 75]. Similar to the case of pathological hypertrophy due to pressure-overload, in cardiac
dilation, the proper turnover of the mitochondrial network is disturbed, especially the pathways
dependent on Mfn2 [40], resulting in decreased mitophagy, cell death, and contractile dysfunction.
In summary, metabolic remodeling in cardiac myocytes is associated with many disease
processes, which are also associated with changes in myocyte shape and ECM properties.
However, how pathological remodeling of the microenvironment underlies metabolic changes in
disease is currently not understood, primarily due to a lack of model systems for controllably
investigating these phenomena.
14

1.3. Existing Approaches to Quantify Metabolic Processes in Cardiac Myocytes
Even with the present knowledge of mitochondrial biology, little is known about how
extracellular factors, especially those related to mechanotransduction and the ECM, regulate
mitochondrial structure and function in cardiac myocytes. This lack of understanding makes it
difficult to identify possible therapeutic targets for conditions such as heart failure, or to identify
trade-offs that should be considered when choosing a treatment strategy. One reason for these
shortcomings is that current models for mitochondrial research, and biology in general, have many
limitations related to identifying how single physical factors regulate mitochondrial function and
structure [76]. Here, we will describe existing models for investigating biological phenomena, and
their intrinsic limitations.
1.3.1. In vivo and ex vivo Models
In vivo animal models allow researchers to study physiological development and disease
on a systems level due to their similarities to humans in genetic outlook, function, and/or
development. These models can be as simple as a nematode, like C. elegans, or complex as non-
human primates, like chimpanzees. Using animal models, one can assess some functional and
morphological changes in the whole body, organ, or tissue [41, 77, 78]. Usually, these analyses
focus on biochemical parameters, such as protein and gene expression [43, 78], and are performed
with a combination of treatment and control conditions.
Ex vivo models utilize tissues and/or organs harvested from the body (typically of animals)
and enable researchers to analyze physiological and disease developments on tissue and/or organ
level instead of a whole organism level [79]. Using this type of system, it is possible to perform
some functional and biochemical assays that would be more specific than those performed in
complete in vivo systems [80, 81]. An important characteristic of ex vivo models is that they
15

provide greater access for imaging, enabling more experiments to be performed compared to in
vivo systems [82]. For example, using Langendorff perfused hearts, where the hearts are extracted
from animals and perfused outside the body with buffer solution, it is possible to optically map
whole hearts and measure conduction velocity, a type of assay impossible to conduct in vivo [81,
83]. Hence, even though ex vivo models have reduced complexity compared to in vivo models, as
they curtail the organ-to-organ interaction, they can still provide important data about intact organ-
specific responses to a treatment [84].
Even though in vivo and ex vivo models can provide important and useful data about
biological processes, they do not enable the isolation of single contributing factors to the observed
results. For example: if we induce a heart attack on a mouse and monitor the impact on the
metabolism of the cardiac tissue, what is the dominant factor that dictates how the mitochondria
are performing? Is it the change in ECM composition from a fibronectin/laminin-based matrix into
a collagen-heavy mesh? Is it the increasing rigidity that coincides with this change in protein
composition? Is it the change in tissue architecture that occurs due to fibrosis that dominates the
remodeling of mitochondrial function? Or are paracrine signals from fibroblasts, neutrophils, and
macrophages inducing the change from fatty acid processing into glucose metabolism? Animal
models can indicate that all these phenomena happen and that they are likely all contributing to
some of the results that we see, but we cannot identify which factor is the primary contributor.
Additionally, it is important to note that the use of in vivo or ex vivo methodologies constrains the
throughput of a biological study, as they are relatively expensive and time consuming.
Furthermore, animal models always have questionable translatability to humans.
16

1.3.2. In vitro Models
Based on the limitations of in vivo and ex vivo models, model systems that are lower cost,
higher throughput, and more controllable are needed, which is achieved with in vitro systems. In
vitro models consist of cell lines or primary tissue cultures usually grown on plastic dishes or glass
coverslips. For example, in the case of cardiac research, the primary in vitro model is neonatal rat
ventricular myocytes, a primary tissue sample that is harvested from 1-2 day(s) old pups and
purified in lab for cell culture. The intrinsic advantages of this modality are throughput and control
of experiments, as cell cultures enable the simultaneous tests of different treatments and controls
with moderate cost [85]. Additionally, because in vitro models allow a greater control of
conditions, the mechanisms behind biological findings can be more easily established [45, 86, 87],
especially when combined with technologies such as genetic engineering.
In vitro models usually provide information associated with biochemical parameters, such
as total ATP production, “omics" outputs (genomics, proteomics, metabolomics), and treatment
toxicity. Additionally, some adaptations in experimental techniques allow for the study of
functional metrics within cells and organelles. In the case of mitochondrial function, oxygen
consumption rates can be measured using techniques, such as the Clark electrode [28], an electrode
that measures ambient oxygen concentration in a liquid using a catalytic platinum surface. A more
recent technique is based on extracellular flux analysis, an assay that simultaneously measures
changes in both oxygen concentration and pH to quantify oxygen consumption rates and
extracellular acidification rate for cultured cells or isolated mitochondria [88, 89]. In brief, for
extracellular flux analysis experiments, cells or tissues are cultured in a specific type of microplate.
The microplates are specifically designed to couple with a reading cartridge that contains two
fluorophore-based sensors and four injection ports for different drug treatments per well. In a
17

standard mitochondrial stress test, three out of the four ports will be loaded with the following
drugs: Oligomycin (a mitochondrial depolarizer), FCCP (a mitochondrial hyperpolarizer), and a
mixture of Antimycin and Rotenone (inhibitors of the electron transport chain). The plates and
cartridges are loaded into an Extracellular Flux Analyzer that will maintain the temperature, load
the drugs, and read the changes in fluorophores emissions using fiber optics. This is an
advantageous method as it reduces human variability during the experiment, is medium- to high-
throughput, and has a relatively fast preparation protocol. Additionally, it is possible to measure
the rates of production of reactive oxygen species [28], especially those obtained using
extracellular flux analysis, and profile the metabolic rates of different substrates [90, 91] when
combining metabolism studies with
13
C and
2
H tracing.
The major limitation of conventional in vitro models is the lack of important physiological
conditions that regulate cellular behavior. For example, conventional cell culture techniques do
not consider the role of mechanical forces as an important regulator of cellular behavior, as cells
are cultured on substrates with elastic moduli far greater than native physiological tissues. They
also do not control for tissue architecture when designing experiments and interpreting data. This
could lead to biases in the results, as tissue alignment is an essential architectural feature of healthy
striated muscle [92], and its absence possibly replicates disease phenotypes. Additionally, not all
the studies consider the role of cell-cell interactions when assessing morphological and functional
changes, as they are performed with a single cell type or cellular population that might not replicate
the one found in physiological conditions. Therefore, conventional in vitro models are not robust
or sophisticated enough to study many of the complex aspects associated with the regulation of
mitochondrial function and structure by the extracellular microenvironment.
18

1.3.3. Human Relevant Models
To obtain human-relevant data, one approach is to analyze either tissue samples obtained
from biopsies [71, 93] or ex vivo tissues and organs [94-96]. These types of samples preserve native
tissue morphology, physiology, and genetic background. However, there is limited availability of
samples, there are ethical considerations on how to obtain them, and human biopsies and ex vivo
organs and tissues are usually prohibitively expensive to be used as a general platform for research
or drug development. Additionally, primary tissues and organs used in human relevant ex vivo
studies often come from subjects that are not healthy [96], reducing the quality of the data when
assessing disease versus control groups.
Another approach is humanized animal models, which have increased complexity, but
reduced human-relevancy [97, 98]. These are small animal models with different types of human
cells and tissues engrafted into them, which function similarly as they would in a human [99].
They are easier and cheaper to obtain than different human samples. Additionally, they allow the
study of physiological responses in a more systemic fashion, enabling whole organ or tissue
functional analysis. However, because animal models still present non-human tissues, are much
smaller than humans, and have different life spans, the results that one can obtain are still limited.
Lastly, with the lowest level of complexity and relevancy, one can use cell lines isolated
from humans like HeLa or MCF-7 [100], work with cells differentiated from human induced
pluripotent stem cells [101], or obtain primary cells from human samples – usually from biopsies,
blood or bone marrow [102-104]. However, it is almost impossible to obtain healthy, fully
differentiated and mature samples of some human cell types, such as neurons and cardiac
myocytes. This restrains possible research endeavors in neuroscience and cardiology, limiting the
power of the experiments that can be performed. Additionally, as we reduce the complexity of the
19

system, we reduce the conclusive power of its results. Nevertheless, these types of models allow
high-throughput testing in a human-relevant system, are cheap to use and maintain, and enable the
isolation of individual contributing factors to the results – even though we might have limited
control over cell and tissue architecture.
1.4. New in vitro Technologies: Microphysiological Systems
In order to study tissues and cells in vitro with more physiological relevance, new in vitro
technologies, known as microphysiological systems (MPS), have been developed to at least
partially address the concerns with current methodologies. These platforms aim at bridging the
gap between in vitro and in vivo systems, allowing more control over experimental conditions by
engineering the microenvironment of the cells. This approach is advantageous because it still
maintains higher throughput and enables the isolation of individual mechanisms present in
development and disease. The idea behind MPS is to combine engineering techniques to fabricate
physiologically-relevant platforms that can enable well-designed biological studies. In this section,
we will detail some existing methodologies to engineer tissues with controllable cellular and
extracellular properties and perform robust functional studies in vitro.
1.4.1. Biomaterials and Substrate Engineering
With biomaterials, researchers can finely design and control the properties of cell culture
substrates. For example, to study the impact of mechanical cues provided by the extracellular
environment, one can fabricate constructs that mimic the elasticity of native and diseased tissue
using natural biomaterials, such as gelatin [105] or agarose hydrogels [62], or synthetic
biomaterials, such as polyacrylamide hydrogels [15, 106] or polydimethylsiloxane (PDMS) [107].
With synthetic biomaterials, both the elasticity and protein composition of the ECM can be
independently tuned in order to decouple chemical and mechanical properties [106], although
20

many synthetic biomaterials have long-term toxicity. On the other hand, with natural biomaterials,
the platform is generally non-toxic, naturally adhesive for cell and tissue culture, and allows some
tuning of elastic modulus, which collectively can improve the stability of constructs for longer
term cell culture [62, 92, 105]. However, there are limitations in changing protein composition and
decoupling the chemical and mechanical properties of the ECM.
To control tissue architecture, several microfabrication techniques have been developed.
For example, with photolithography, one can fabricate master wafers with different sets of µm-
scale features that can be used to dictate cell and tissue alignment. Wafers are used as molds to
produce PDMS stamps that can be used to transfer ECM proteins in the desired pattern to culture
substrates, a process called microcontact printing. PDMS stamps can also be used to mold
biomaterial constructs to achieve desired tissue organizations. In the case of cardiac tissue
engineering, it is possible to create tissues of controlled alignment [13, 62, 105, 106, 108, 109] that
mimic physiological and disease conditions. Additionally, if the goal is to study the response on a
cellular level, not on tissues as a whole, one can use a similar microfabrication approach to control
cell aspect ratio on different types of substrates [14, 15, 110-112]. Figure 1-8 illustrates examples
of different types of substrate preparation using PDMS as backbone and fibronectin as an ECM
protein (Figure 1-8A), polyacrylamide hydrogels with biotinylated fibronectin or laminin as the
ECM protein (Figure 1-8B), and a combination of polyacrylamide hydrogels with Matrigel to
pattern single cell islands (Figure 1-8C-D). With the use of different types of biomaterials and
substrate engineering approaches, we can engineer platforms that recapitulate mechanical,
chemical, and architectural features that cells and tissues encounter in the body, creating a more
physiologically-relevant scaffold for biological studies.
21

1.4.2. Quantitative Structural and Functional Assays
Combined with platform engineering, new types of structural and functional assays have
also been developed that are more quantitative than previous approaches. For example, muscular
thin films (MTFs) have been developed as a platform for quantifying contractility. In brief, a
biomaterial is thinly coated onto glass coverslips and laser-engraved into cantilevers as a structural
support for muscle tissues (see Figure 1-8A). The biomaterial can be microcontact printed PDMS
[13, 101], alginate hydrogels [62], or micro-molded gelatin hydrogels [105]. Muscle cells are
seeded onto the substrate and cultured to form confluent, aligned tissues. On the day of the
experiment, the constructs are moved to a stereomicroscope coupled with a heating chamber and
a high speed camera [13]. The tissue-biomaterial constructs are peeled away from their supporting
coverslips, and the cells are induced to contract either via electrical stimulation [13, 101, 105] or
with drug treatments [113]. The bending of the cantilevers due to cellular contraction is recorded
and post-processed to calculate the contractile stress generated by the tissues for each culture
condition (Figure 1-8A). MTFs are a very versatile approach for measuring contractility of muscle
cells, as they allow for variations in substrate elasticity, protein composition, tissue architecture,
type and strength of stimulus, and drug treatments. Additionally, this method provides a
framework to design constructs [114-116] that could be coupled with other platforms for
performing different types of functional assays.
Another type of functional assay for muscle cells is called traction force microscopy
(TFM). In this case, polyacrylamide hydrogels doped with fluorescent beads are fabricated and
used as substrates. Using microcontact printing, the ECM protein is added to the hydrogels to
dictate the arrangement of cells either in a microtissue (Figure 1-8B) [106], in isolated single cells
islands (Figure 1-8C) [14, 15], or in cell pairs [117]. Cardiac myocytes are then seeded onto the
22

substrates and cultured. On the day of the experiments, the constructs are moved to a fluorescent
microscope coupled with an incubation chamber and an electrical pacing system. Cells are paced
and the movement of the beads due to cell/tissue contraction is recorded using a high-speed
camera. Videos are then analyzed to calculate different contractile measurements, such as systolic
displacement, systolic force, systolic work, traction stress, and average time to peak contraction.
Similar to MTF studies, TFM enables the quantification of cell/tissue contractility as a function of
ECM elasticity and composition or different drug treatments. In addition to their functional
capabilities, this polyacrylamide based platform can be used to study structural characteristics of
cells, especially those pertaining to cytoskeleton organization [14, 15] (see Figure 1-8C-D),
mitochondrial localization [14], and even cell-cell interactions [117]. A similar approach to study
single cell cytoskeleton organization was developed with the use of PDMS [12, 112], and can be
expanded to the study of mitochondrial structure, specifically fragmentation [118].
In summary, recent developments in substrate engineering for cell culture and well-
designed functional and structural assays enable more relevant morphological and functional
studies using cells in culture.
23

Figure 1-8: Examples of different types of microphysiological systems. A: PDMS-based muscular thin films
combined with human induced pluripotent stem cells used to study contraction of cardiac myocytes. Source: [101]. B:
Micromyocardium system developed as a higher throughput system for studying contraction of myocytes using TFM.
Source: [106]. C: Micropatterning of single cell islands in polyacrylamide hydrogels to study myofibril and
mitochondrial organization, in addition to perform TFM. Source: [14].
1.5. Outlook
As described above, remodeling of myocardial tissue architecture, the ECM, and cardiac
myocyte metabolism are coupled in cardiac development and disease. Within the ECM, changes
range from an increasing in elastic modulus, to an increase in the quantity of collagen fibers versus
glycoproteins, to an increased organization of protein fibers. On the cellular level, the aspect ratio
of myocytes increases from development to adulthood, and either regresses to a lower ratio or
increases even more in the case of different types of disease. These processes are coupled with
metabolic behaviors that change from purely glycolytic to increased reliance on oxidative
24

phosphorylation, which can retrograde to more glycolytic if needed. Furthermore, the architecture
of the mitochondrial network changes significantly: embryonic cardiac myocytes contain highly
fused and non-specialized mitochondria, whereas adult cells present three different mitochondrial
populations with varying degrees of fragmentation. Diseased myocytes will have even more
fragmented mitochondria. However, little is known about whether the observed
microenvironmental remodeling drives the changes in mitochondrial function and structure, and
what the most important regulators are in this process.
Previous model systems were incapable of answering these questions in cardiac
metabolism due to their intrinsic limitations. However, with the use of tissue engineering
techniques, we can address some of the constraints of in vivo and in vitro models to start clarifying
the mechanisms pertaining to microenvironmental regulation of mitochondrial function and
structure. With microphysiological systems, we can combine tissue-relevant platform engineering
with functional and structural assays to study mitochondrial properties. By controlling ECM
elasticity and tissue architecture, we can study of how these elements regulate mitochondrial
function. Through the control of ECM elasticity and cell aspect ratio, we can identify important
parameters associated with mitochondrial structure and organization in cardiac myocytes and
possibly identify some of the reasons behind the metabolic changes the heart experiences.
Therefore, with data from these new engineered platforms, we can start answering the question:
how does the tissue microenvironment regulate mitochondrial function and structure in cardiac
myocytes?
1.6. Outline
As described above, microenvironmental remodeling occurs in parallel with metabolic
remodeling. However, little is currently known about the individual contribution of different
25

microenvironmental factors in the regulation of mitochondrial function and structure. Thus, our
goal is to utilize microphysiological system approaches to engineer models of cardiac tissue that
are more biomimetic than conventional cell culture and implement them to identify specific
mechanisms of how the extracellular environment regulates mitochondrial function and structure.
With this dissertation, we expand upon the hypothesis that the extracellular
microenvironment regulates mitochondrial function in cardiac myocytes. To achieve this goal, we
developed platforms to identify the effects of multiple parameters within the extracellular
microenvironment on mitochondrial structure and function in cardiac myocytes. In Chapter 3: , we
determine the effects of extracellular matrix elasticity and tissue alignment on mitochondrial
function in engineered cardiac tissues. In Chapter 2: , we delineate the effects of extracellular
matrix elasticity and protein composition, in addition to biomaterial class on mitochondrial
function in engineered cardiac tissues. Chapter 4 presents our concluding remarks and some
possible future directions for this project. This work contributes to elucidate different interplay
mechanisms between the mitochondrial network and the extracellular environment of cardiac
myocytes, providing novel insights into myocardial tissue development and disease.

26

Chapter 2: Regulation of Mitochondrial Function in Engineered
Cardiac Myocyte Tissues by ECM Elasticity and Protein
Composition
Partially adapted from: D.M. Lyra-Leite, A.M. Andres, N. Cho, A.P. Petersen, N.R.
Ariyasinghe, S.S. Kim, R.A. Gottlieb, M.L. McCain, Matrix Guided Regulation of Mitochondrial
Function in Cardiac Myocytes, submitted to Acta Biomaterialia.

2.1. Introduction
In native myocardium, cardiac myocytes are embedded in extracellular matrix (ECM), a
compliant, porous mesh of proteins, polysaccharides, and other macromolecules. The ECM
provides mechanical support, resists contraction, and activates intracellular signaling pathways via
integrin receptors [8, 119]. In fetal and neonatal hearts, the ECM is composed largely of
glycoproteins, such as fibronectin, that are associated with an immature network of relatively thin
collagen fibers . As the myocardium matures, cardiac fibroblasts become more prevalent and
increase ECM production and secretion [16, 20]. As a result, the ECM becomes dominated by
thicker and denser collagen fibers that form a more rigid network, reducing overall tissue
compliance [20-22]. Myocardial ECM also undergoes significant remodeling in many pathological
settings. For example, after an infarction, necrotic cardiac myocytes are replaced by fibroblasts
and myofibroblasts that deposit collagenous scar tissue, leading to local stiffening of the
myocardium [1, 61]. In parallel to these developmental and pathological changes to the
extracellular matrix, cardiac myocytes undergo distinct changes in their metabolism. At birth,
oxygen availability increases and metabolism becomes preferentially oxidative [2, 41-43].
27

Conversely, after injuries that deprive cells of oxygen, such as myocardial infarction [40, 57, 58]
or ischemia/reperfusion [71, 72], cardiac myocytes increase their reliance on glycolysis and
glucose oxidative phosphorylation. Thus, in both physiological and pathological settings, cardiac
myocyte metabolism is rewired by mechanisms thought to be regulated predominantly by nutrient
availability. However, because the ECM is a source of both biochemical signals and mechanical
resistance, the diverse changes in the ECM during physiological and pathological growth could
also contribute to alterations in cardiac myocyte metabolism. However, the direct impact of the
biochemical and mechanical properties of the ECM on metabolic function in cardiac myocytes is
poorly understood.
Tunable biomaterials offer flexibility in recapitulating the diverse mechanical and
biochemical cues present in the native ECM in a controlled in vitro setting. For example,
polydimethylsiloxane (PDMS), an organic silicone-based elastomeric polymer, is widely used as
a substrate for culturing cardiac myocytes due to its optical clarity and ease of fabrication. The
rigidity of PDMS can be easily tuned by altering the ratio of base to curing agent [120] or by
blending with other silicone polymers [107, 121]. ECM molecules can also be attached to the
surface of PDMS uniformly or via microcontact printing [109, 122-125], allowing for independent
modulation of bulk mechanical and surface chemical properties. Previously, we microcontact
printed tunable PDMS substrates with fibronectin to determine if mitochondrial function in cardiac
myocytes is regulated by ECM elasticity and/or tissue alignment. Our results demonstrated that
baseline oxygen consumption rate (OCR) is increased when ECM rigidity increases, whereas the
ability of tissues to respond to stress is regulated by both tissue alignment and ECM elasticity
[121]. Although PDMS is advantageous due to its tunability, it is a highly synthetic material. For
this reason, hydrogels derived from ECM molecules, such as Matrigel [126] or gelatin [127, 128],
28

are another popular culture substrate because they more closely replicate the bulk biochemical,
mechanical, and architectural properties of native cardiac ECM and generally have lower toxicity
compared to synthetic materials [129-131]. Previously, we reported that certain aspects of
metabolism in cardiac myocytes are upregulated when cells are cultured on gelatin hydrogels
compared to fibronectin-coated PDMS [127]. However, this study was limited to a single
formulation of gelatin hydrogel and fibronectin-coated PDMS, providing limited insight into
which physical aspect of the substrate was responsible for the differences in mitochondrial
function. Fibronectin-coated polyacrylamide hydrogels have also been used to correlate ECM
elasticity to mitochondrial function in cardiac myocytes [132]. Thus, several studies have
demonstrated that mitochondrial function in cardiac myocytes is modulated by the ECM.
However, there are still many unanswered questions related to which specific physical features of
the ECM dominate distinct mitochondrial functions.
In this study, we systematically tested the impact of multiple types of substrates on
mitochondrial function in cardiac myocytes. To achieve this, we coated the wells of specialized
XF24 cell culture microplates with fibronectin- or gelatin-coated PDMS of three distinct elastic
moduli or gelatin hydrogels with four distinct elastic moduli. Next, we cultured neonatal rat
ventricular myocytes on the functionalized microplates and characterized mitochondrial function
by measuring OCR and extracellular acidification rate (ECAR) using a Seahorse Biosciences
XFe24 Extracellular Flux Analyzer. In most comparisons, basal respiration, basal glycolytic
activity, ATP production, and maximum respiration were higher in tissues on all gelatin hydrogels
compared to those on all PDMS substrates, irrespective of rigidity or PDMS coating. PDMS
substrates were slightly more cytotoxic than gelatin hydrogels, which likely contributed to the
reduced metabolism on PDMS substrates compared to gelatin hydrogels. Cell size and
29

mitochondrial content was preserved across all substrates, suggesting that any differences in
metabolism were not caused by an increase in mitochondrial quantity. Collectively, these results
suggest that diverse cues within the ECM impact the metabolism of cardiac myocytes, which is
important for understanding mechanisms of cardiac remodeling in physiological and pathological
settings. These data also have implications for selecting appropriate biomaterial scaffolds for
engineering physiologically-relevant cardiac tissues for in vitro modeling and drug screening.
2.2. Methods
2.2.1. PDMS and Gelatin Hydrogel Substrate Preparation
Three formulations of PDMS with distinct elastic moduli were prepared, similar to
previous studies [92, 107, 121]. Sylgard 184 silicone elastomer (Dow Corning) curing agent and
base were combined at a 1:10 mass ratio and mixed and degassed for two minutes each using a
planetary centrifugal mixer (Thinky AR-100 Conditioning Mixer). Sylgard 527 (Dow Corning)
was prepared by mixing and degassing components A and B in a 1:1 mass ratio. Sylgard 184 and
Sylgard 527 were also mixed and degassed in a 1:20 mass ratio.
Four formulations of gelatin hydrogels were prepared: 5% or 10% w/v solutions of gelatin
from porcine skin (175 g bloom, Sigma Aldrich) with 2% or 4% w/v transglutaminase (TG,
Ajinomoto) [127]. Gelatin was dissolved in ultrapure water at 65
o
C until homogenous. TG powder
was then added to the gelatin solution and the mixture was vortexed.
For mitochondrial respirometry studies, 10 µL of either PDMS or gelatin hydrogel
prepolymer solution was pipetted into wells of XF24 cell culture microplates, similar to previous
studies [127]. For LDH cytotoxicity assay and mtDNA:nucDNA quantification, 120 µL of either
PDMS or gelatin hydrogels were pipetted into the wells of 12-well plates. For both types of plates,
PDMS-coated wells were incubated overnight at 65
o
C. Wells with cured PDMS were treated in a
30

UVO cleaner (Jelight Company Inc.) for eight minutes to sterilize and oxidize the surface followed
by incubation with 100 (for XF24 plates) or 500 (for 12-well plates) µL (for XF24 plates) of human
fibronectin (Corning, 50 µg/mL) or porcine gelatin (Sigma Aldrich, 200 µg/mL) for at least five
minutes. Wells were rinsed with sterile PBS and stored at 4
o
C until cell seeding (>24 hours).
Hydrogel-coated wells were cured overnight at room temperature in a vacuum desiccator. These
wells were then treated in a UVO cleaner for one minute for sterilization, rinsed with sterile PBS,
and stored at 4
o
C until cell seeding.
For measuring cell size, PDMS-coated coverslips were prepared by spin-coating 25 mm
diameter glass with the 1:20 blend of Sylgard 184:527 and curing overnight at 65
o
C, similar to
previous protocols [133]. Next, coverslips were UVO-treated for 8 minutes, coated with either
fibronectin (50 µg/mL) or gelatin (200 µg/mL) solutions, transferred to 6-well plates, rinsed with
sterile PBS, and stored at 4
o
C until cell seeding. To fabricate gelatin hydrogel-coated coverslips,
25 mm diameter glass coverslips were covered with low-adhesive tape, leaving the center exposed.
Next, they were treated with sodium hydroxide, (3-aminipropyl)triethoxysilane (APTES), and
glutaraldehyde to increase gelatin hydrogel adherence [127]. Flat slabs of PDMS were then
sonicated for at least 30 minutes in ethanol and blown dry. 5% gelatin/4% TG hydrogel was
pipetted onto the center of the coverslips and compressed with the slab of PDMS. After curing
overnight at room temperature, hydrogels were re-hydrated with sterile ultrapure water, stamps
were removed using tweezers, and the outer-ring of tape was removed. Coverslips were rinsed
with PBS, dried, sterilized for 1 minute with UVO treatment, rinsed with sterile PBS, and stored
at 4
o
C until cell seeding.
31

2.2.2. Bulk Compressive Elastic Modulus Measurements
Gelatin hydrogels were prepared as described above, cast into 35 mm Petri dishes (6
mL/dish), and cured overnight in a vacuum desiccator. Cylinders of 6 mm diameter were cut and
removed using a biopsy punch and mounted on an Instron 5942 Mechanical Testing System
(Norwood, MA). Each cylinder was compressed to 40% of its initial height. The 9-19% range of
compressive strain was used for elastic modulus calculations, accounting for cylinder height and
radius. To determine if hydrogel elasticity changes in culture-like conditions, as observed
previously [92], we prepared hydrogels in 35 mm Petri dishes as described above, added PBS to
the Petri dishes, and incubated the samples overnight in a 37
o
C desktop incubator. Cylinders of 6
mm diameter were cut and removed, and elastic moduli were measured using the same procedure
described above. For each type of hydrogel, at least four independent batches of hydrogel were
fabricated and measured in triplicate.
2.2.3. Neonatal Rat Ventricular Myocyte Harvest and Culture
Neonatal rat ventricular myocytes were isolated from two-day old neonatal Sprague-
Dawley rats, as previously described [106, 121, 127]. Euthanization, harvesting, and cell isolation
procedures were approved by the University of Southern California Institutional Animal Care and
Use Committee. Ventricular tissues were extracted from rat pups, incubated in Trypsin solution (1
mg/mL, Affymetrix) at 4
o
C for 11-13 hours, and subjected to four 1-2 minute collagenase (1
mg/mL, Worthington Biochemical Corp, in HBSS) digestions at 37
o
C, each followed by manual
pipette agitation to dissociate tissues into a single cell suspension. Cells were strained using a 40
µm cell strainer, resuspended in cell culture media, and pre-plated twice for 45 minutes each to
minimize fibroblast contamination.
32

For PDMS-coated microplates, 50,000 cells/50 µL/well were seeded in each well of the
XF24 Cell Culture Microplates. For gelatin hydrogel microplates, 25,000 cells/50 µL/well were
seeded in each well. These seeding densities were selected to maximize the range of measurements
for mitochondrial respirometry without saturating the signal, based on calibration experiments.
After seeding, plates were maintained in the biosafety cabinet for 30 minutes to prevent cell
aggregation at the wells’ edges, as suggested by the manufacturer. Plates were then placed in a
37
o
C, 5% CO2 incubator. After 1-4 h, an additional 450 µL of media was added to each well.
For 12-well plates, 500,000 cells/well were seeded in each PDMS-coated well and 250,000
cells/well were seeded in each gelatin hydrogel-coated well. These seeding densities were chosen
to match the cell coverage of the XF24 cell culture microplates. Coverslips were seeded at a density
of 150,000 cells/well in a 6-well plate so that cells would be sparsely distributed, enabling
individual cell imaging.
Media consisted of M199 culture medium supplemented with 10% heat-inactivated FBS,
10 mM HEPES, 0.1 mM MEM nonessential amino acids, 20 mM glucose, 2 mM L-glutamine, 1.5
µM vitamin B-12, and 50 U/mL penicillin and was exchanged after one day. After two and four
days, media was exchanged again, with FBS concentration reduced to 2%.
2.2.4. Immunostaining, Microscopy, and Image Analysis
After five days in culture, cells on coverslips or within the XF24 cell culture microplates
wells were fixed with 4% paraformaldehyde and permeabilized with 0.1% Triton X-100 solution
for ten minutes. Fixed tissues were incubated with monoclonal mouse anti-sarcomeric α-actinin
(Sigma, 1:1000 for plates or 1:200 for coverslips) primary antibody for one to two hours at room
temperature. After PBS rinsing, samples were incubated with chemical stains DAPI (1:1000 for
plates or 1:200 for coverslips), Alexa Fluor 488 Phalloidin (Life Technologies, 1:1000 for plates
33

or 1:200 for coverslips), and Alexa Fluor 546 goat anti-mouse secondary antibody (Life
Technologies, 1:1000 for plates or 1:200 for coverslips) for one to two hours at room temperature.
Cells in microplates were then coated with a drop of ProLong Gold Anti-Fade Mountant, sealed
with Parafilm, and stored at room temperature. Coverslips were mounted on a glass slide with a
drop of ProLong Gold Anti-Fade Mountant, sealed with nail-polish, and stored at -20
o
C. For cells
in wells, a Nikon C2 point-scanning confocal microscope with a 20x air objective, using 2x digital
zoom (total magnification of 40x), was used to acquire images of at least five locations dispersed
across each well. ImageJ was used to count the total number of nuclei per field of view (based on
DAPI stain) [121]. For coverslips, a Nikon C2 point-scanning confocal microscope with a 60x oil
(n = 1.515) objective, using 2x digital zoom (total magnification of 120x), was used to acquire z-
stacks (0.25 µm thick optical sections) of at least three cells dispersed across each coverslip.
ImageJ, Cell Profiler, and MATLAB were used to measure individual cell cross-section area,
height and volume based on the α-actinin signal, based on previously published protocols [134].
2.2.5. Pierce LDH Cytotoxicity Assay
Three, four (before media change), and five days after seeding, 150 µL of media was
collected from each well of a 12-well plate. In addition, on the fifth day after seeding, 100 µL of
lysis solution (Thermo Fisher Scientific) was added to some wells, followed by an incubation for
30 minutes in 37
o
C and collection of 150 µL of media. The samples were stored at room
temperature for up to three days until a Pierce LDH assay (Thermo Fisher Scientific) was
performed according to manufacturer’s instructions. The absorbance values at 490 nm and 680 nm
were measured using a plater reader (Varioskan Lux, Thermo Fisher Scientific) and cytotoxicity
was computed according to [135]:
𝐶𝑦𝑡𝑜𝑡𝑜𝑥𝑖𝑐𝑖𝑡𝑦 (%) = 100×
𝐸𝑥𝑝𝑒𝑟𝑖𝑚𝑒𝑛𝑡𝑎𝑙 𝐿𝐷𝐻 𝑟𝑒𝑙𝑒𝑎𝑠𝑒
𝑀𝑎𝑥𝑖𝑚𝑢𝑚 𝐿𝐷𝐻 𝑟𝑒𝑙𝑒𝑎𝑠𝑒

34

The experimental LDH release is the difference of absorbance values (490 nm – 690 nm) measured
from live samples after subtracting the absorbance of media alone. The maximum LDH release is
the same measurement obtained from lysed samples.
2.2.6. Extracellular Flux Analysis
After five days in culture, OCR and ECAR was measured using a Seahorse Bioscience
XFe24 Extracellular Flux Analyzer, as previously described [121, 127, 136]. Assay medium was
prepared by supplementing XF Assay Medium (Seahorse Bioscience) with 10 mM glucose, 2 mM
L-glutamine, and 1 mM sodium pyruvate (pH 7.4). After acquiring baseline measurements, the
following drugs were used for a mitochondrial stress test: 2 µM oligomycin (port A), 1 µM FCCP
(port B), and a mixture of 1 µM antimycin A and 1 µM rotenone (port C). Mitochondrial functional
metrics were computed as previously described [121]. All OCR and ECAR measurements were
normalized to total protein content, determined as described below. Lastly, we computed the
bioenergetic health index (BHI) from the normalized OCR values according to the following
formula [104, 121]:
𝐵𝐻𝐼 =
(𝐴𝑇𝑃 𝑝𝑟𝑜𝑑𝑢𝑐𝑡𝑖𝑜𝑛)×(𝑆𝑝𝑎𝑟𝑒 𝑟𝑒𝑠𝑝𝑖𝑟𝑎𝑡𝑜𝑟𝑦 𝑐𝑎𝑝𝑎𝑐𝑖𝑡𝑦)
(𝑃𝑟𝑜𝑡𝑜𝑛 𝑙𝑒𝑎𝑘)×(𝑁𝑜𝑛−𝑚𝑖𝑡𝑜𝑐ℎ𝑜𝑛𝑑𝑟𝑖𝑎𝑙 𝑟𝑒𝑠𝑝𝑖𝑟𝑎𝑡𝑖𝑜𝑛)

2.2.7. Measurements of Protein Concentration
Tissues from duplicate plates or plates after OCR measurements were rinsed twice with
PBS and incubated with 100 µL of radioimmunoprecipitation assay (RIPA) buffer and 400 µL of
PBS to lyse the tissues. Protein lysates were stored at -20
o
C. BCA protein assay was performed
per manufacturer instructions. Absorbance was measured using a plater reader (Varioskan Lux,
Thermo Fisher Scientific) and values were fit to a BSA protein standard curve. Average total
protein value per harvest for each condition was used to normalize the OCR and ECAR values.
35

2.2.8. mtDNA:nucDNA Quantification
After five days in culture, samples in 12-well plates were lysed and lysates were stored at
-80
o
C until measurements. DNA was then isolated using a DNeasy Blood and Tissue kit (Qiagen).
260/280 ratios were measured using a Nanodrop (Thermo Fisher Scientific) to verify sample purity
(all samples had ratios greater than 1.9). DNA samples were then diluted to 15 ng/µL. qPCR was
performed by mixing SsoAdvanced Universal SYBR Green Supermix (Bio-Rad), DNA, and
primers (Integrated DNA Technologies) into a 384-well PCR plate. A CFX384 Touch Real-Time
PCR Detection System (Bio-Rad) was used to obtain the cycle threshold (Ct) values for each
condition. PCR conditions were: 95
o
C for 10 minutes, followed by 40 cycles of 95
o
C for 15 sec,
55
o
C for 15 sec, 72
o
C for 20 sec. Housekeeping nuclear target was ApoB: forward 5’–
CACGTGGGCTCCAGCATT – 3’, reverse 5’ – TCACCAGTCATTTCTGCCTTTG – 3’.
Mitochondrial target was 235bp: forward 5’ – CCTCCCATTCATTATCGCCGCCCTTGC – 3’,
reverse 5’ – GTCTGGGTCTCCTAGTAGGTCTGGGAA – 3’. mtDNA:nucDNA ratio was
quantified using the formula [137, 138]:
𝑚𝑡𝐷𝑁𝐴:𝑛𝑢𝑐𝐷𝑁𝐴 = 2×2
∆LM

In which ∆𝐶𝑡 is the difference between Ct values for housekeeping gene ApoB and the 235bp
mitochondrial gene.
2.2.9. Statistical Analysis
Normality for all measurements was first validated using the Kolmogorov-Smirnov Test.
Elastic moduli data were analyzed using a paired two sample student’s t-test, with α set to 0.05.
The remaining data were analyzed using one-way and/or two-way ANOVA followed by Tukey’s
test for multiple comparisons in MATLAB, with α set to 0.05. Each biological parameter was
measured using cells from at least three independent harvests, and multiple wells per harvest per
36

condition were used. Data is displayed as box plots, where the solid line indicates the median value
and the bottom and top edges of the boxes indicate the 25
th
and 75
th
percentiles, respectively.
Whiskers extend to the most extreme points not considered outliers, which are represented by
crosses.
2.3. Results
2.3.1. Characterization and Preparation of Biomaterials
To determine the impact of different substrates on mitochondrial function in cardiac
myocytes, we first coated the wells of XF24 cell culture microplates with three different blends of
PDMS, as shown in Figure 2-1A. As reported previously, these three PDMS blends have elastic
moduli of 1.6 kPa, 27 kPa, and 2.7 MPa [121], a range that corresponds to developing myocardium,
healthy adult myocardium [21, 22], and the mechanical load applied to cardiac myocytes in
pathological conditions [139]. These PDMS blends were coated with fibronectin or gelatin
solution, which we will refer to as PDMS-fibronectin or PDMS-gelatin, respectively. These
conditions were selected to roughly mimic developing myocardium, which is richer in fibronectin
and other glycoproteins [140], and healthy [140] or fibrotic [141] myocardium, which is richer in
collagen and its derivatives. We also coated microplate wells with four gelatin hydrogel
formulations: 5% or 10% gelatin with 2% or 4% TG [92, 127], which we will refer to as 5%/2%,
5%/4%, 10%/2%, and 10%/4%. Because the elastic moduli of gelatin hydrogels can change in
culture-like conditions [92], we fabricated gelatin hydrogels and measured their elastic moduli
before and after overnight incubation in PBS at 37
o
C. As shown in Figure 2-1B, the elastic modulus
increased for each condition by 5-15 kPa after overnight incubation in culture-like conditions,
although this was not statistically significant for 10%/2%. Elastic modulus also increased as gelatin
or TG concentration increased (5%/2%: 17.1 ± 2.2 kPa, 5%/4%: 26.7 ± 2.7 kPa, 10%/2%: 58.3 ±
37

6.0 kPa, and 10%/4%: 71.8 ± 3.9 kPa, all values after incubation in culture-like conditions), similar
to previous measurements [127].

Figure 2-1: Characterization and Fabrication of Biomaterial Substrates. A: XF24 cell culture microplates were
coated with either 10 µL of PDMS (top row) or gelatin hydrogel (bottom row) solution (i). After overnight curing, the
plates were treated with UVO (ii). PDMS-coated wells were coated with 100 µL of ECM protein solution (iii, top
row). Both types of wells were rinsed with sterile PBS (iv, top row, iii, bottom row) and seeded with neonatal rat
ventricular myocytes (v, top row, iv, bottom row). B: Average elastic moduli for the four different formulations of
gelatin hydrogels, before and after incubation in culture-like conditions. Values are means ± SE; n = 4 for all
conditions. * p<0.05. Refer to Table II-1 for details related to statistical analysis.
2.3.2. Substrate Effects on Cardiac Myocyte Adhesion, Viability, and Morphology
To evaluate the effects of each substrate on cardiac myocyte adhesion and viability, we
cultured neonatal rat ventricular myocytes within the wells of our modified microplates and
immunostained tissues after five days. On all gelatin hydrogel (Figure 2-2A) and PDMS (Figure
2-2B), myocytes self-assembled into confluent, isotropic tissues. Tissues on all substrates were
composed primarily of cardiac myocytes, as validated by positive sarcomeric α-actinin staining in
the majority of cells. To quantify cell density, we counted the number of nuclei in our stained
tissues (Figure 2-3A). Although we did not detect any statistical differences within PDMS
substrates or gelatin hydrogels, select hydrogels had statistically lower cell density compared to
select PDMS substrates. This is expected because gelatin hydrogels were seeded with half as many
cells, which we chose based on the results of OCR calibration experiments. However, the final
38

number of nuclei on PDMS was less than twice that measured for gelatin hydrogels, suggesting
that myocyte adhesion and/or viability is higher on gelatin hydrogels. To further compare tissue
composition, we quantified total protein content per well. As shown in Figure 2-3B, tissues on 72
kPa gelatin hydrogels and 1.6 kPa PDMS-fibronectin had higher protein content than those on 27
kPa PDMS-fibronectin and 2.7 MPa PDMS-gelatin. All other conditions were not statistically
different. Thus, tissues on most substrates had similar protein content, although cell density was
lower on select gelatin hydrogels compared to select PDMS substrates.

Figure 2-2: Cardiac tissues cultured on PDMS substrates within XF24 Cell Culture Microplates. Composite
images of neonatal rat ventricular myocyte tissues cultured on indicated (A) gelatin hydrogels or (B) PDMS. Blue:
nuclei; white: α-actinin; scale bars: 25 µm.
39

Figure 2-3: Cell adhesion and viability. (A) Nuclei per 0.1 mm2 and (B) total protein content per well in cell culture
microplates. n indicated below each box. Cytotoxicity on day 4 (C) and day 5 (D) after seeding. n = 8 for all conditions.
Letters above each box indicate a statistical difference (p<0.05) with the condition represented by the letter on the x-
axis. For example, “a” indicates p<0.05 compared to gelatin hydrogel, 17 kPa. Refer to Table II-2 to Table II-4 for
details related to statistical analysis.
Previous studies have reported that PDMS has some cytotoxic effects due to the leaching
of uncured oligomers [129-131]. To account for this, we performed a series of LDH cytotoxicity
assays for tissues on each substrate. On the third day post-seeding (Figure I-1), cytotoxicity was,
on average, lower than 2% on all substrates. The only statistical difference at this timepoint was
between tissues on 17 kPa gelatin hydrogel and 2.7 MPa PDMS-fibronectin. On the fourth day
post-seeding (Figure 2-3C), cytotoxicity measurements were higher compared to the third day due
40

to the accumulation of LDH over this two-day period without media exchange. At this timepoint,
cytotoxicity in tissues on all gelatin hydrogels were significantly lower than 2.7 MPa PDMS-
gelatin. Additionally, cytotoxicity in tissues on 17 kPa and 58 kPa gelatin hydrogels was
significantly lower than those on 27 kPa and 2.7 MPa PDMS-fibronectin. However, average
cytotoxicity values were still below 5% on all substrates. On the next day (Figure 2-3D), which is
the same timepoint when mitochondrial respirometry studies were performed, average cytotoxicity
was below 2% in all conditions. These values are lower than the previous day due to media
exchange. On this day, cytotoxicity was statistically similar between all conditions, except for
lower values on gelatin hydrogels and 27 kPa PDMS-gelatin compared to 1.6 kPa PDMS-
fibronectin. Thus, although cytotoxicity on all substrates was relatively low (<5% in most cases),
PDMS substrates did have a more cytotoxic effect compared to gelatin hydrogels in many
comparisons. This effect likely contributed to the lower number of nuclei detected on PDMS
substrates relative to the initial seeding density compared to gelatin hydrogels.
Because of the observed substrate-dependent differences in cell density and protein
content, we also measured differences in cardiac myocyte size to determine if cells were
undergoing hypertrophy on gelatin hydrogels. To quantify and compare myocyte size, we sparsely
seeded myocytes on coverslips with 27 kPa gelatin hydrogels, 27 kPa PDMS-fibronectin, and 27
kPa PDMS-gelatin (Figure 2-4A) to control for elastic modulus while altering the other properties
of the substrates. We then immunostained cells for sarcomeric α-actinin, collected 3-D image
stacks using confocal microscopy, and computed cell area, height, and volume (Figure 2-4B-D).
No statistical differences were observed across the conditions for any of the parameters, indicating
that cell size was mostly preserved across substrates. Although not statistically different, cardiac
myocytes on gelatin hydrogels had a slightly higher area and lower height compared to PDMS,
41

suggestive of more cell spreading. This subtle difference in cell spreading likely also contributed
to the observation that nuclei number is lower, but protein content is equal, on gelatin hydrogels
compared to PDMS substrates.

Figure 2-4: Cell geometry. (A) Composite images of single cardiac myocytes seeded on coverslips. Blue: nuclei;
white: α-actinin; scale bars: 10 µm. Myocyte area (B), height (C), and volume (D). n indicated below the boxes on
(C). Refer to Table II-5 for details related to statistical analysis.
2.3.3. Substrate Regulation of Mitochondrial Function
To characterize mitochondrial function, we performed a standard mitochondrial stress test
using a Seahorse Extracellular Flux Analyzer on tissues cultured on each substrate. In this assay,
basal OCR is first measured. Then, OCR is measured after the addition of oligomycin, FCCP, and
antimycin/rotenone in series to analyze specific mitochondrial and non-mitochondrial functions
[121, 127, 136], as shown in Figure 5. The Seahorse Extracellular Flux Analyzer also
simultaneously measures ECAR, which reflects glycolytic activity (Figure I-2). All OCR and
ECAR measurements were normalized to total protein content. Based on a one-way ANOVA and
multiple comparisons, basal respiration was higher for tissues on 17 kPa, 27 kPa, and 58 kPa
42

gelatin hydrogels compared to all PDMS substrates, while tissues on 72 kPa gelatin hydrogels had
higher basal respiration than select PDMS substrates (Figure 2-6A, Table II-6). Tissues on a few
PDMS substrates (27 kPa PDMS-fibronectin, 1.6 kPa PDMS-gelatin, and 27 kPa PDMS-gelatin)
also had higher basal respiration than those on 2.7 MPa PDMS-gelatin. Basal glycolytic activity
was higher for 17 kPa, 27 kPa and 58 kPa gelatin hydrogels, 27 kPa PDMS-fibronectin, and 1.6
kPa PDMS-gelatin compared to 2.7 MPa PDMS-fibronectin. Additionally, ECAR was
significantly lower for tissues on 2.7 MPa PDMS-gelatin compared to all other conditions, except
for 2.7 MPa PDMS-fibronectin. To further compare these responses, we plotted basal ECAR as a
function of basal OCR (Figure 2-6C). In this energy plot, tissues on gelatin hydrogels clustered in
the energetic quadrant due to their high OCR and ECAR. Tissues on both 2.7 MPa PDMS
conditions were in the quiescent quadrant due to their low OCR and ECAR. Tissues on 1.6 kPa
and 27 kPa PDMS were in the glycolytic quadrant due to their relatively high ECAR but low OCR.
Together, these data indicate that tissues on gelatin hydrogels have more energetic metabolic
Figure 2-5: OCR measurements in engineered cardiac tissues. Average experimental OCR measurements for
tissues on gelatin hydrogels (A), tissues on PDMS-fibronectin (B), and tissues on PDMS-gelatin (C). Measurements
at baseline and after addition of oligomycin, FCCP, and antimycin and rotenone. Data are presented as mean ± s.e.m.,
n = 16 for all conditions.
43

activity in terms of both mitochondrial respiration and glycolysis compared to those on PDMS
substrates, especially those on the most rigid PDMS.

Figure 2-6: Baseline oxidative and glycolytic activity. Average OCR (A) and ECAR (B) associated with basal
respiration. n = 16 for all conditions. Letters above each box indicate a statistical difference (p<0.05) with the condition
represented by the letter on the x-axis. For example, “a” indicates p<0.05 compared to gelatin hydrogel, 17 kPa. (C)
Energy map at baseline for cardiac tissues culture on the different substrates, colors correspond to the bars in (A) and
(B). Values are presented as mean ± s.e.m., n = 16 for all. For details related to statistical analysis, refer to Table II-6
and Table II-7.
For the remainder of the mitochondrial stress test measurements, we focused only on the
OCR measurements because the added compounds are mitochondrial inhibitors and thus less
relevant for glycolysis. On all gelatin hydrogels, tissues had higher mitochondrial ATP production
than those on all PDMS substrates (Figure 2-7A). Additionally, tissues on 27 kPa PDMS-
44

fibronectin had higher mitochondrial ATP production than those on 2.7 MPa PDMS-gelatin.
Proton leak was mostly preserved across all conditions (Figure 2-7B). The only statistical
differences were a higher proton leak for tissues on 17 kPa gelatin hydrogels and 1.6 kPa PDMS-
gelatin compared to those on 2.7 MPa PDMS-gelatin. Non-mitochondrial respiration was higher
for select tissues on gelatin hydrogels compared to select PDMS substrates (Figure 2-7C). Thus,
in most cases, basal respiration, ATP production, proton leak, and non-mitochondrial respiration
were similar within tissues on gelatin hydrogels and within tissues on PDMS substrates. However,
in many comparisons, tissues on gelatin hydrogels had higher values for baseline metabolic metrics
compared to PDMS substrates, irrespective of substrate elasticity or ECM protein composition.
45

Figure 2-7: Baseline metabolic functions. Average OCR associated with (A) ATP production, (B) proton leak, and
(C) non-mitochondrial respiration. n = 16 for all conditions. Letters above each box indicate a statistical difference
(p<0.05) with the condition represented by the letter on the x-axis. For example, “a” indicates p<0.05 compared to
gelatin hydrogel, 17 kPa. For details related to statistical analysis, refer to Table II-8 to Table II-10.

46

Figure 2-8: Metabolic stress responses. Average OCR associated with (A) maximum respiration and (B) spare
respiratory capacity. n = 16 for all conditions. Letters above each box indicate a statistical difference (p<0.05) with
the condition represented by the same letter on the x-axis. For example, “a” indicates p<0.05 compared to gelatin
hydrogel, 17 kPa. For details related to statistical analysis, refer to Table II-11 to Table II-12.
Next, we compared the response of tissues to metabolic stress. Maximum respiration
followed a similar trend to basal respiration (Figure 2-8A). Specifically, tissues on the three softer
gelatin hydrogels had higher OCR values than those on all PDMS substrates, whereas those on 72
kPa gelatin hydrogels were higher than select PDMS substrates. Because tissues on gelatin
hydrogels generally had higher basal and maximum respiration, spare respiratory capacity was
mostly preserved across the different conditions (Figure 2-8B). The only statistical differences
were an increase in spare respiratory capacity in tissues on 17 kPa, 27 kPa, and 58 kPa gelatin
hydrogels compared to those on 27 kPa PDMS-fibronectin, 27 kPa PDMS-gelatin, and 2.7 MPa
PDMS-gelatin. Together, these data indicate that tissues on gelatin hydrogels generally have
higher maximum respiration compared to those on all PDMS substrates, but spare respiratory
capacity is mostly conserved due to parallel increases in basal and maximum respiration.
To determine if gelatin and/or TG concentrations have an independent effect on
mitochondrial function, we next performed a two-way ANOVA analysis to determine if any OCR
47

and ECAR measurements are regulated by one or both of these variables (Table 2-1). Based on
these results, basal respiration, ATP production, proton leak, and maximum respiration were
higher for tissues on 2% TG compared to 4% TG, whereas spare respiratory capacity, non-
mitochondrial respiration, and basal glycolytic activity were preserved across the conditions (see
Table 2-1). Gelatin concentration did not have a significant impact on any OCR or ECAR values.
Thus, for the ranges we tested, TG concentration, but not gelatin concentration, impacted some
metabolic parameters.
We also performed a similar two-way ANOVA analysis to investigate the independent
effects of ECM elasticity and ECM protein ligand on mitochondrial function for tissues on PDMS
substrates (Table 2-2). Based on these results, basal respiration, ATP production, proton leak, and
basal glycolytic activity were regulated by ECM elasticity, but not ECM protein ligand.
Specifically, basal respiration and basal glycolytic activity were higher in tissues on 1.6 kPa and
27 kPa PDMS compared to those on 2.7 MPa PDMS. ATP production was higher in tissues on 27
kPa compared to those on 2.7 MPa PDMS. Lastly, proton leak was higher in tissues on 1.6 kPa
compared to those on 2.7 MPa PDMS.
Next, we computed the BHI for tissues based on OCR measurements [104, 121] and
compared values using a one-way ANOVA analysis (Figure 2-9A). The BHI combines positive
and negative aspects of oxygen consumption into a single overall parameter by dividing the
product of spare respiratory capacity and ATP production by the product of proton-leak and non-
mitochondrial respiration. BHI was significantly higher for tissues on all gelatin hydrogels
compared to those on all PDMS-gelatin substrates and 27 kPa PDMS-fibronectin. Additionally,
tissues on 17 kPa hydrogels had higher BHI than tissues cultured on all PDMS-fibronectin
substrates. No statistical differences were observed within hydrogels or within PDMS substrates.
48

Two-way ANOVA analysis did not indicate independent regulation of BHI by gelatin or TG
concentration for tissues on gelatin hydrogels (Table 2-1). Two-way ANOVA analysis also
indicated that ECM ligand, but not PDMS elasticity, regulates BHI, with tissues on PDMS-
fibronectin substrates having higher BHI than those on PDMS-gelatin. Collectively, these data
indicate that BHI in cardiac tissues is regulated by multiple biomaterial properties, with tissues on
gelatin hydrogels demonstrating the highest BHI values, followed by those on PDMS-fibronectin
and finally PDMS-gelatin.
Because we observed substrate-dependent differences in OCR and ECAR, we next
investigated if the quantity of mitochondria per cell was regulated by the substrate. To compare
mitochondrial content, we quantified the ratio of mitochondrial DNA to nuclear DNA copy
number (mtDNA:nucDNA). As seen in Figure 2-9B, there were no statistical differences in
mtDNA:nucDNA across the conditions. Thus, the differences in metabolism that we observed
were not caused by mitochondrial biogenesis, suggesting that other mechanisms, such as
difference in mitochondrial efficiency, underlie the observed differences in OCR.

Figure 2-9: Bioenergetic Health Index and quantity of mitochondria. (A) Average Bioenergetic Health Index for
all conditions. n = 16 for all conditions. (B) Mitochondrial DNA to nuclear DNA copy number ratio. n indicated below
each box. Letters above each box indicate a statistical difference (p<0.05) with the condition represented by the same
49

letter on the x-axis. For example, “a” indicates p<0.05 compared to gelatin hydrogel, 17 kPa. For details related to
statistical analysis, refer to Table II-13 and Table II-14.

2.4. Discussion
Myocardial tissue remodeling during both development and disease is characterized by
distinct changes in ECM composition and rigidity, as well as rewiring of cardiac myocyte
metabolism. However, the direct impact of the ECM on cardiac myocyte metabolism is unclear.
Additionally, the types of biomaterials used to model different stages of myocardial growth in vitro
might have direct impacts on mitochondrial function that are important to characterize and
consider for engineered tissue models. To address these questions, we cultured cardiac myocytes
on a variety of gelatin hydrogel and PDMS substrates and evaluated their mitochondrial function
with a Seahorse XFe24 Extracellular Flux Analyzer. Our results suggest that several metabolic
parameters, including basal respiration, ATP production, and maximum respiration, are generally
higher on gelatin hydrogels compared to PDMS-gelatin and PDMS-fibronectin substrates,
independent of substrate rigidity. Thus, mitochondrial function in cardiac myocytes is sensitive to
the biomaterial culture substrate, indicative of ECM regulation of metabolism.
For this study, one category of biomaterials that we fabricated were gelatin hydrogels
cross-linked with TG for thermostability [142, 143]. Similar to previous studies, the elastic moduli
of these hydrogels increased with increasing gelatin and/or TG concentration [127]. Importantly,
we found that elastic moduli continued to increase during overnight incubation in culture-like
conditions. Thus, the elastic moduli experienced by the cells was higher than that of the hydrogels
as initially fabricated. Ultimately, gelatin hydrogels demonstrated a range of elastic moduli that
mimic passive measurements of healthy adult myocardial tissue (17 kPa, 27 kPa) [21, 22] to
fibrotic myocardial tissue (58 kPa, 72 kPa) [20, 139, 140]. One limitation of gelatin hydrogels is
50

their limited range of elasticity. Fabricating gelatin hydrogels with substantially higher rigidity
than these values is problematic due to the limits of gelatin solubility, while fabricating gelatin
hydrogels with substantially lower rigidity is problematic due to the fragility of the hydrogel. This
could potentially be overcome by adding other stabilizing proteins, such as tropoelastin [144].
Conversely, PDMS substrates, which are the second category of biomaterial that we fabricated,
offer a much wider range of elastic moduli, spanning multiple orders of magnitude. By blending
Sylgard 184 and Sylgard 527, we achieved a range of 1.6 kPa to 2.7 MPa, as reported in previous
studies [107, 121, 145]. Depending on the research question, an additional benefit of PDMS is that
it can be easily coated with specific ECM proteins to independently modulate the mechanical and
biochemical properties of the substrate. However, the ECM protein forms a relatively thin layer
on the surface, which can contribute to delamination over long-term culture due to the
hydrophobic, non-fouling nature of the underlying PDMS [146, 147]. In contrast, gelatin hydrogels
are more permissive for long-term culture, likely because the entire bulk of the substrate is
comprised of cell-adhesive ECM proteins [92, 127]. In addition, we found in this study that PDMS
substrates are slightly cytotoxic compared to gelatin hydrogels, another drawback of PDMS. Thus,
gelatin hydrogels and ECM-coated PDMS substrates have distinct advantages and disadvantages
in terms of fabrication, mechanical properties, and long-term biocompatibility. Our results here
indicate that these biomaterial substrates also have unique impacts on the metabolic function of
cardiac myocytes. These are important factors to weigh when selecting biomaterial substrates for
engineering in vitro models of the myocardium or other muscle tissues.

51

Table 2-1: Two-way ANOVA analysis of mitochondrial respirometry data for tissues on gelatin hydrogels. Data
for all conditions was normally distributed, as determined by the Kolmogorov-Smirnov test. p-values for each
comparison are indicated, with * indicating p<0.05. n = 16 for all conditions.

Table 2-2: Two-way ANOVA analysis of mitochondrial respirometry data for tissues on PDMS substrates. Data
for all conditions was normally distributed, as determined by the Kolmogorov-Smirnov test. p-values for each
comparison are indicated, with * indicating p<0.05. n = 16 for all conditions.

To measure biomaterial-mediated differences in metabolic function, we coated XF24 cell
culture microplate wells with gelatin hydrogels, PDMS-fibronectin, or PDMS-gelatin. On all
substrates, neonatal rat ventricular myocytes attached and formed confluent tissues. Interestingly,
although we seeded PDMS-coated wells with twice as many cells, we did not observe twice as
many nuclei per field of view on PDMS substrates compared to gelatin hydrogels. This is likely
Comparison
Parameter Gelatin
Concentration
TG Concentration Interaction
Basal Respiration 0.3454 0.0135 * 0.5784
ATP Production 0.6247 0.0434 * 0.7316
Proton Leak 0.1124 0.0322 * 0.4337
Maximum Respiration 0.0778 0.0240 * 0.2631
Spare Respiratory Capacity 0.0846 0.1378 0.2642
Non-mitochondrial Respiration 0.9539 0.3350 0.8474
BHI 0.6922 0.6009 0.6099
Basal Glycolytic Activity 0.0874 0.1931 0.8567
Comparison
Parameter PDMS Elasticity Coating Protein Interaction
Basal Respiration 0.0005 * 0.5510 0.0098 *
ATP Production 0.0065 * 0.4992 0.0381 *
Proton Leak 0.0052 * 0.9208 0.2948
Maximum Respiration 0.1367 0.2010 0.4865
Spare Respiratory Capacity 0.1188 0.2528 0.8952
Non-mitochondrial Respiration 0.2509 0.7911 0.4523
BHI 0.5137 0.0392 * 0.3072
Basal Glycolytic Activity 1.1056e-08* 0.2059 0.0023*
52

because myocytes initially adhered more strongly to gelatin hydrogels and/or experienced more
delamination or apoptosis on PDMS, consistent with the higher level of cytotoxicity that we
measured on PDMS. We also found that tissues on all substrates had similar protein content. By
very rough calculation (average protein content/average cell density), cardiac myocytes on gelatin
hydrogels contained 2.1 ng of protein per cell while those on PDMS substrates contained 1.3 ng
of protein per cell. These values are within the order of magnitude of more direct measurements
of cell mass [148]. This increase in total protein content per cell could indicate that myocytes on
gelatin hydrogels underwent hypertrophy. However, our analysis of the area and volume of
isolated cardiac myocytes on gelatin hydrogels, PDMS-gelatin, and PDMS-fibronectin with
preserved elastic modulus (27 kPa) revealed that cell volume was consistent across these
substrates. Although not statistically significant, cells on gelatin hydrogels had a slightly higher
area and lower height than those on PDMS, suggestive of more cell spreading on gelatin hydrogels.
This phenotype could partially explain why protein content was similar, but cell number was
lower, on gelatin hydrogels. Additionally, we did not quantify the quantity, length, etc. of
sarcomeres or myofibrils, which could alter protein content, force generation [149, 150], and
potentially metabolism by increasing energetic demands and/or rearranging mitochondria. For
example, in our previous study, we found that maximum respiration was regulated by tissue
alignment [121], which increases myofibril length [109, 133]. Our tissues also have a low level of
fibroblast contamination. The adhesion or proliferation of fibroblasts could also be regulated by
the substrate, but their contribution was neglected in this study. Overall, a more detailed analysis
of proteins related to the cytoskeleton and other intercellular structures in cardiac myocytes could
explain these differences in protein level per cell, which could contribute to the differences in
mitochondrial function we observed.
53

Our OCR data revealed new relationships between mitochondrial function and biomaterial
substrate. In most comparisons, basal respiration, ATP production, and maximum respiration were
higher in tissues on gelatin hydrogels compared to those on PDMS-fibronectin and PDMS-gelatin
substrates. Although cytotoxicity was slightly higher on PDMS, average cytotoxicity was below
4% for all conditions and thus cytotoxicity alone cannot explain these differences in metabolism.
Furthermore, mtDNA:nucDNA was preserved across all conditions. Together, these results
suggest that mitochondrial activity is generally higher on gelatin hydrogels. Because tissues on
gelatin hydrogels exhibited higher basal and maximum respiration, spare respiratory capacity,
which reflects respiratory capacity that is utilized only in times of increased metabolic demand,
was mostly conserved across all substrates. Proton leak, a metric that can indicate mitochondrial
damage [151, 152], was also preserved across all conditions. On average, non-mitochondrial
respiration was higher for tissues on gelatin hydrogels compared to those on PDMS substrates.
This could indicate increased rates of oxidative activity caused by the production of reactive
oxygen species (ROS) by nitric oxide synthases or NADPH oxidases in the mitochondria [153,
154]. Additionally, higher values of non-mitochondrial respiration could represent upregulated
enzymatic activities of many oxygenases, such as Heme oxygenase-1 [155, 156], or increased
oxygen use in late stages of protein folding [157]. Quantifying ROS production as a function of
biomaterial substrate could provide further insight into this relationship, especially considering
that muscles with robust mitochondrial output have to develop equally robust systems to deal with
oxidative stress [158, 159] and misfolding of proteins [160, 161]. The Bioenergetic Health Index
(BHI), a parameter that combines positive and negative metrics of mitochondrial function into a
single number to indicate overall mitochondrial health [104], was also significantly higher for
tissues on gelatin hydrogels compared to all tissues on PDMS-gelatin and many tissues on PDMS-
54

fibronectin. Collectively, these data indicate that gelatin hydrogels are, at least in the short-term,
beneficial to overall mitochondrial function compared to PDMS-gelatin and PDMS-fibronectin
substrates.
Similar to the OCR data, the basal ECAR values indicated that tissues on gelatin hydrogels
were more metabolically active in glycolysis compared to those on PDMS substrates. The
energetic map (OCR versus ECAR) generated for our tissues roughly clustered the tissues into
three groups: tissues on hydrogels in the energetic quadrant, tissues on softer PDMS (1.6 kPa and
27 kPa) in the glycolytic quadrant, and tissues on 2.7 MPa PDMS in the quiescent quadrant. These
results suggest that substrates with high elastic moduli reduce the metabolic activity of cardiac
myocytes, which is in-line with other studies showing that stiff substrates reduce the ability of
cardiac myocytes to shorten and contract [15, 22, 106, 117]. The high OCR and ECAR values
measured from tissues on gelatin hydrogels compared to PDMS suggests that both oxidative and
glycolytic activity is relatively high on gelatin hydrogels, which may explain why tissues on gelatin
hydrogels maintain their contractility longer than on PDMS, as previously described [127].Among
tissues on gelatin hydrogels, we did not observe any statistical differences in any metabolic metric
based on elastic modulus. However, our two-way ANOVA analysis indicated that tissues on
hydrogels with 4% TG had lower basal respiration, ATP production, protein leak, and maximum
respiration compared to tissues on hydrogels with 2% TG. These differences cannot be explained
solely by the increase in hydrogel rigidity due to TG because elastic modulus increased in this
order: 5%/2%, 5%/4%, 10%/2%, 10%/4%. Thus, these differences are likely linked to TG-
mediated changes in surface chemistry, porosity, toxicity, and/or other mechanisms that have yet
to be established, which could limit the use of this platform for some applications. Alternative
55

fabrication methods, such as UV-crosslinking of methacrylated gelatin [162], could potentially
ameliorate any concerns related to TG toxicity.
Within tissues on PDMS substrates, we did not observe a clear relationship between ECM
rigidity and OCRs associated with basal respiration and ATP production, as reported in our
previous study with cardiac tissues cultured on micropatterned PDMS discs [121]. A possible
explanation is related to differences in cell distribution on the substrates. In our previous study, we
fabricated PDMS discs by first spin-coating, curing, and laser-engraving thin layers of PDMS. We
then microcontact printed the PDMS and transferred the discs to the XF24 microplate wells. In the
present study, uncured PDMS was pipetted directly into the bottom of the wells, cured, and coated
with ECM proteins. Due to surface tension and interactions with the walls of the microplate, a
meniscus formed in the PDMS. This non-uniform surface profile likely resulted in some subtle
variation in the mechanical properties of the substrates as well as layering of cardiac myocytes.
These confounding factors could have reduced the sensitivity of our mitochondrial respirometry
measurements, which were more controlled in our previous report, leading to clearer trends in
basal respiration. However, similar to our previous study [121] and others [132], spare respiratory
capacity and non-mitochondrial respiration was independent of ECM elasticity for tissues on
substrates with uniform coating of ECM protein.
Our data for tissues on PDMS-gelatin and PDMS-fibronectin demonstrate that, in some
conditions, both ECM elasticity and ECM ligand can impact cardiac metabolism, which has
implications for understanding physiological and pathological cardiac growth. As described above,
ECM stiffening and increasing collagen fiber density are hallmarks of both cardiac development
[16, 20-22, 140] and disease [1, 61]. Our softest PDMS substrate (1.6 kPa) resembles the rigidity
of a developing heart, whereas our intermediate PDMS substrate (27 kPa) is closest in rigidity to
56

an adult heart [21, 22]. For all OCR measurements, tissues on PDMS-gelatin and PDMS-
fibronectin did not present any significant differences between the lowest and moderate rigidities,
indicating that neither ECM ligand nor PDMS rigidity impacted mitochondrial function for these
conditions. This is likely because neonatal rat cardiac myocytes express integrin receptors for both
proteins (fibronectin and collagen) [163] and thus the cells could adhere and maintain a similar
phenotype, irrespective of ECM ligand, on both the softest and intermediate PDMS.
Our stiffest PDMS substrate (2.7 MPa) roughly mimics the increase in mechanical load
experienced by cardiac myocytes in certain pathological conditions, such as pressure overload
[164, 165] or fibrosis [139, 166]. Interestingly, we observed in several instances that tissues on 2.7
MPa PDMS-gelatin, but not 2.7 MPa PDMS-fibronectin, had lower OCR and ECAR values
compared to tissues on other PDMS substrates. This indicates that metabolic activity was
compromised in tissues on 2.7 MPa PDMS-gelatin, but not 2.7 MPa PDMS-fibronectin, suggesting
that the combination of a stiff ECM and collagen-based ligand could be deleterious to
mitochondrial health. Hence, the biochemical cues provided by the ECM ligand could impact
mitochondrial function when ECM stiffness is elevated, as in diseased tissues. Similar ECM
ligand-mediated changes in cardiac myocyte function have previously been observed. For
example, the viability of HL-1 cardiac myocytes is higher on glass coverslips coated with
fibronectin compared to gelatin and collagen [167]. Additionally, neonatal rat cardiac myocytes
express higher levels of Cx43 and N-cadherin on fibronectin compared to collagen [168]. In this
same study, the expression of Cx43 and N-cadherin increased due to cyclic stretch only for
myocytes on collagen, but not fibronectin. The electrophysiological maturation of cardiac
myocytes differentiated from human induced pluripotent stem cells (iPSCs) has also been shown
to be regulated by both substrate type (glass, PDMS) and coating (fibronectin, Matrigel) [169].
57

Together, these studies are indicative of ECM ligand-mediated changes in mechanosensitivity,
similar to our results reported here.
Our study has some limitations that are important to note. First, we measured OCRs from
isotropic cardiac tissues, which is more characteristic of diseased [170] or immature [9] cardiac
tissues. As shown by our previous study, tissue alignment can impact mitochondrial function
[121], but this was not tested in the current study. We also used neonatal rat cardiac myocytes, the
gold standard for in vitro cardiac myocyte research due to their plasticity and longevity in culture.
However, these cells are non-human and relatively immature. The immaturity of these cells likely
impacts our results because integrin expression profiles are known to change with age. For
example, studies have shown that neonatal rat cardiac myocytes can adhere to fibronectin, laminin,
and collagen types I, II, III, IV, and V [163, 171]. Adult rat cardiac myocytes, however, adhere
only to laminin and collagen type IV [163, 171], indicating that neonatal and adult cardiac myocyte
express distinct integrin receptors. For these reasons, we would expect slightly different results
from adult cardiac myocytes. In our experiments, we also minimized supporting cell populations,
although cardiac myocytes in the heart are surrounded by fibroblasts, endothelial cells, and other
supporting cell types. All of these features limit the translation of our results to cardiac myocytes
in adult human myocardium. To increase human relevance, future work will focus on cardiac
myocytes differentiated from human iPSCs. However, these cells are known to express immature
phenotypes [172, 173], which could also limit translation. Another limitation is that the relatively
short timescale of our experiments captured only the early responses of cardiac myocytes to ECM
cues and thus do not reflect any long-term remodeling. However, long-term studies are difficult to
perform in cardiac myocytes on PDMS-coated substrates due to cell delamination [127], limiting
our experimental time frame. Furthermore, our OCR measurements were performed within intact
58

cardiac tissues instead of isolated mitochondria. Although we verified that mitochondrial quantity
per cell was preserved across the conditions, it is not clear if our observed results were caused by
changes in mitochondrial morphology and/or mitochondrial structure within the cardiac myocytes
[31, 174]. In the present study, we tested the impact of isolated proteins, specifically fibronectin
and gelatin (denatured collagen). However, in the native heart, the ECM is comprised of proteins
and macromolecules, including hyaluronans. Hyaluronans have also been shown to mediate cell
adhesion and signaling in cardiac myocytes [175, 176], but these and other ECM molecules were
excluded in this study. Lastly, we did not replicate the different nutrient sources that cardiac
myocytes are exposed to in vivo. Our media was rich in glucose and amino acids, but lacked the
fatty acids that are known to be the primary energy source for healthy adult cardiac myocytes in
vivo [57, 59]. Thus, future studies will focus on determining how mitochondrial function is altered
by the ECM in more human- and physiologically-relevant cardiac tissues in more native-like
biochemical environments.
Due to recent breakthroughs in generating iPSC-derived cardiac myocytes from patient
somatic cells [177, 178], there is now significant interest in engineering functional, micro-scale
tissue constructs from iPSC-derived cardiac myocytes for personalized disease modeling and
medium-throughput drug screening [179-181]. However, the success of these “Heart on a Chip”
platforms is highly dependent on the long-term survival and health of the cardiac myocytes,
especially for modeling slowly progressing diseases or screening the chronic cardiotoxic effects
of drugs, including common chemotherapies such as doxorubicin [182]. Because contractility
imposes high energetic demands [183], ensuring the metabolic health of cardiac myocytes in
culture is critical for maintaining a stable, contractile phenotype. Here, our results indicate that
gelatin hydrogels enhance the metabolic activity of cardiac myocytes compared to a variety of
59

PDMS substrates, complementing our earlier report that gelatin hydrogels extend the culture
lifetime of cardiac myocytes [127] and skeletal myotubes [92] compared to PDMS substrates.
Previous studies from us and others have also demonstrated that gelatin hydrogels can be
micromolded or photopatterned to induce tissue alignment and recapitulate the native architecture
of striated muscle [92, 127, 184]. Additionally, we and others have shown that gelatin hydrogels
are compatible with several assays for quantifying cardiac tissue function, including
characterization of calcium wave propagation velocity [133], contractility [127], and extracellular
field potentials [185]. Thus, gelatin hydrogels are a versatile substrate for engineering functional
“Heart on a Chip” platforms that also enhance the metabolic activity of cardiac myocytes, as shown
here, which helps maintain a healthy, robust phenotype for cardiac disease modeling and drug
screening.
2.5. Conclusions
In conclusion, we characterized mitochondrial function in cardiac myocytes cultured on a
variety of culture substrates to delineate the impact of diverse ECM cues on metabolism. Our
results indicate that, in general, baseline mitochondrial function, baseline glycolysis, and
maximum respiration are increased in tissues on gelatin hydrogels compared to PDMS substrates
coated with gelatin or fibronectin, independent of elastic modulus. These differences could be
partially, but not fully, attributed to slight increases in cytotoxicity on PDMS. We also found that
ECM protein composition can reduce mitochondrial function in rigid microenvironments,
mimicking those found in many disease settings. Collectively, our data demonstrate that the ECM
can directly impact the metabolic phenotype of cardiac myocytes, which is important for
understanding how ECM remodeling contributes to physiological and pathological cardiac growth.
Our study also highlights how the properties of the biomaterial substrate can have a direct impact
60

on the metabolism of cardiac myocytes, which is an important consideration for engineering in
vitro cardiac tissue models.

61

Chapter 3: Mitochondrial Function in Engineered Cardiac Tissues
is Regulated by Extracellular Matrix Elasticity and Tissue Alignment

Partially adapted from: D.M. Lyra-Leite, A.M. Andres, A.P. Petersen, N.R. Ariyasinghe,
N. Cho, J.A. Lee, R.A. Gottlieb, M.L. McCain, Mitochondrial Function in Engineered Cardiac
Tissues is Regulated by Extracellular Matrix Elasticity and Tissue Alignment, American Journal
of Physiology Heart Circulatory Physiology (2017), ajpheart.00290.2017.

3.1. Introduction
Due to the significant amount of energy required for sarcomere shortening, mitochondria
are especially prevalent in cardiac myocytes, occupying approximately 35% of cell volume [28,
29]. Mitochondria in cardiac myocytes remodel both structurally and functionally throughout
development, health, and disease [43, 48, 71]. For example, neonatal rat and mouse cardiac
myocytes are mostly glycolytic [186, 187] and mitochondria are randomly distributed throughout
the cell [188, 189]. As cardiac myocytes mature, mitochondria become highly organized and
associate closely with sarcomeres [188, 189], which mature on a similar time-scale. During this
time, myocytes also become more reliant on oxidative phosphorylation instead of glycolysis [2,
41, 43, 186, 187]. Similarly, as human embryonic stem cells differentiate into cardiac myocytes in
vitro, mitochondria become more organized and metabolism switches from glycolysis to oxidative
phosphorylation [42, 190]. Mitochondria and metabolism in cardiac myocytes also remodel in
many pathological settings. For example, healthy cardiac myocytes rely primarily on fatty acids,
but in ischemic heart disease, myocytes switch to glucose [57, 58]. Similarly, fatty acid oxidation
62

decreases in rodent models of pressure-overload hypertrophy, leading to impaired mitochondrial
function [191, 192]. Thus, metabolic remodeling is a key component of many physiological and
pathological processes in cardiac myocytes, although the factors driving metabolic remodeling are
not completely understood.
Cardiac development and disease are also associated with diverse changes in the cardiac
myocyte microenvironment [8]. During development, cardiac myocytes gradually elongate and
self-assemble into an aligned tissue [9]. Concurrently, the elastic modulus of the extracellular
matrix (ECM), and therefore the load on cardiac myocytes, also increases [21, 22]. Many cardiac
diseases are associated with fibrosis, which further increases the elastic modulus of the tissue [139]
and can lead to disruption of tissue alignment [170]. Several in vitro studies have demonstrated
that remodeling of tissue architecture and the elasticity of the ECM impact the electrical and
contractile function of cardiac myocytes. For example, engineered cardiac tissues aligned by
microcontact printing generate higher contractile stresses, propagate action potentials more
rapidly, and have more mature calcium transients compared to un-aligned tissues [13, 108, 109,
193, 194]. Furthermore, both myocyte shape and ECM elasticity affect contractility and sarcomere
formation [15, 21, 22, 117]. However, few studies have investigated relationships between the
tissue microenvironment and metabolism in cardiac myocytes. Previously, we showed that cardiac
myocytes cultured uniformly on gelatin hydrogels have higher spare respiratory capacity compared
to those cultured uniformly on fibronectin-coated polydimethylsiloxane (PDMS) [105], suggesting
potential links between the ECM and mitochondrial function. However, due to the multiple
differences between gelatin hydrogels and fibronectin-coated PDMS, this study provides limited
insight into which microenvironmental factors regulate mitochondrial function in cardiac
myocytes. This study also did not include tissue alignment as a variable, which is likely important
63

due to the tight spatial relationship between sarcomeres and mitochondria [188, 189]. Thus,
relationships between the cardiac myocyte microenvironment and metabolism are still mostly
unknown.
Our goal for this study was to test the hypothesis that ECM elasticity and tissue architecture
impact mitochondrial function in cardiac myocytes. Mitochondrial function is commonly
characterized by measuring oxygen consumption rate (OCR) with a Seahorse Biosciences
Extracellular Flux Analyzer. However, this device requires cells to be cultured within specialized
XF24 Cell Culture Microplates, which poses a significant obstacle for modifying ECM elasticity
and tissue alignment. To overcome this limitation, we developed a technique for transferring
PDMS discs with tunable elasticity and micropatterned with fibronectin into the bottom of XF24
Cell Culture Microplates. We then cultured neonatal rat cardiac myocytes within the wells,
validated tissue structure and alignment, and performed mitochondrial respirometry experiments.
Our results suggest that select mitochondrial functions are regulated solely by ECM elasticity,
while others are co-regulated by both ECM elasticity and tissue alignment. These results provide
new insights into links between mitochondrial function, ECM elasticity, and tissue architecture in
cardiac myocytes, which has many implications for understanding how extracellular and
intracellular structures are coordinated during cardiac development and disease. Additionally, our
engineered platform enables a variety of new studies into mechanisms associated with the
regulation of metabolism by the ECM in other cell and tissue systems.
3.2. Materials and Methods
3.2.1. Fabrication of Mechanically Tunable PDMS
Three types of PDMS with distinct elastic moduli were prepared using Sylgard 184 silicone
elastomer and Sylgard 527 silicone dielectric gel (Dow Corning). Sylgard 184 was prepared by
64

mixing a 1:10 mass ratio of elastomer curing agent to base. Sylgard 527 was prepared by mixing
A and B components in a 1:1 mass ratio. An intermediate PDMS was prepared by mixing Sylgard
184 with Sylgard 527 in a 1:20 mass ratio, similar to previous studies [92, 107]. All polymers were
mixed for two minutes and degassed for two minutes using a planetary centrifugal mixer (Thinky
AR-100 Conditioning Mixer).
3.2.2. Bulk Compressive Elastic Modulus Measurements
Sylgard 184 and the 1:20 mixture of Sylgard 184 and Sylgard 527 were prepared as
described above, poured into Petri dishes, and cured at 65
o
C overnight. Cylinders of 6 mm diameter
were then cut and removed using a biopsy punch. To fabricate cylinders of Sylgard 527, 1.7 mL
centrifuge tubes were coated with Poly(N-isopropylacrilacrylamide) (PNIPAm) dissolved in
butanol at 10% w/v. Next, Sylgard 527 was prepared as described above, poured into PNIPAm-
coated tubes, and cured at 65
o
C overnight. Tubes were then incubated in room temperature water
to liquefy the PNIPAm and release the PDMS, which was cut into cylinders using a razor blade.
For each type of PDMS, cylinders were mounted on an Instron 5942 Mechanical Testing
System. Each cylinder was compressed 40% of its initial height. The 1-4% range of compressive
strain was used for elastic modulus calculations, taking into account the height and radius of each
cylinder. For each type of PDMS, at least four independent batches of PDMS were fabricated and
measured in triplicate.
3.2.3. Master Wafer and PDMS Stamp Fabrication
Standard photolithography and soft lithography techniques [109, 194, 195] were used to
fabricate master wafers and PDMS stamps. Briefly, to fabricate wafers for aligned stamps, silicon
wafers were cleaned using a Nitrogen gun, spin-coated with a layer of Hexamethyldisilazane
(HDMS), spin-coated with a 2µm-thick layer of the negative photoresist SU-8 2002 (MicroChem),
65

and baked according to manufacturer instructions. Next, a photolithographic mask with 15 µm-
wide lines separated by 2 µm (referred to as 15x2) was positioned over the wafer using a standard
mask aligner (Karl-Suss MJB3 Contact Aligner). The masked wafer was then exposed to high-
energy UV light, baked, and immersed in developer solution to remove un-exposed photoresist.
The wafer was then silanized by incubating it with a drop of trichloro-(1H, 1H, 2H, 2H-
perfluorooctyl)-silane overnight in a vacuum desiccator. To fabricate featureless wafers for
isotropic stamps, virgin silicon wafers were silanized. Sylgard 184 PDMS was prepared as
described above, poured over master wafers in 150 mm Petri dishes, polymerized at 65°C
overnight, and peeled off the wafer. Cured PDMS was removed from the wafer and individual
PDMS stamps measuring approximately 2cm x 2cm were then cut.
3.2.4. Fabrication of Micropatterned PDMS Discs
To fabricate PDMS discs, we adapted previously published protocols for fabricating PDMS
muscular thin films [108, 117] . Briefly, PNIPAm was dissolved in butanol at 10% w/v and spin-
coated onto 22 mm square coverslips using a Specialty Coating Systems G3P-8 spin coater. Next,
coverslips were spin-coated with a layer of PDMS Sylgard 184 and incubated at 65
o
C for at least
four hours. Select constructs were then spin-coated with a layer of Sylgard 527 or the 1:20 blend
of Sylgard 184 and Sylgard 527. These constructs were then cured again at 65
o
C for at least four
hours. Next, an Epilog Mini 24 Laser Engraver (30 Watt) was used to etch nine circular discs with
6.5 mm diameter into each construct (Power: 4, Speed: 10, Frequency: 300).
PDMS stamps (aligned or isotropic) were sonicated in 95% ethanol and dried using
compressed air in a sterilized biosafety cabinet. Human fibronectin in distilled, de-ionized water
(50µg/mL) was pipetted onto the surface of each stamp and incubated for one hour at room
temperature. PDMS-coated, laser-engraved coverslips were treated in a UVO cleaner Model 342
66

(Jelight Company Inc.) for eight minutes to sterilize and oxidize the surface. Stamps were then
blown dry under compressed air, placed gently onto treated coverslips to transfer the fibronectin,
and carefully removed. XF24 Cell Culture Microplates were treated in a Plasma Cleaner (Harrick
Plasma) at High Power for ten minutes and transferred to the biosafety cabinet. Micropatterned
PDMS discs were then carefully peeled from the glass coverslip using tweezers and transferred to
the bottoms of the microplate wells. Pressure was applied to remove any bubbles trapped between
the discs and the microplate, especially near the edges. The plates were then rinsed in sterile PBS
and stored at 4
o
C until cell seeding.
3.2.5. Neonatal Rat Ventricular Myocyte Harvest and Culture
Neonatal rat ventricular myocytes were isolated from two-day old neonatal Sprague-
Dawley rats, similar to previously published protocols . Harvest procedures were approved by the
University of Southern California Institutional Animal Care and Use Committee. Briefly,
ventricles were extracted from rat pups and incubated in Trypsin solution (1mg/mL, Affymetrix)
in HBSS overnight at 4
o
C. Ventricles were then subjected to four collagenase (1mg/mL,
Worthington Biochemical Corp, in HBSS) digestions for 1-2 minutes each at 37
o
C, followed by
manual pipette agitation. Cells were then strained, resuspended in M199 culture medium
supplemented with 10% heat-inactivated FBS, 10 mM HEPES, 0.1 mM MEM nonessential amino
acids, 20 mM glucose, 2 mM L-glutamine, 1.5 µM vitamin B-12, and 50 U/mL penicillin. The
cells were pre-plated twice for 45 minutes each to minimize fibroblast contamination.
For mitochondrial respirometry studies, 50,000 cells/50µL/well were seeded in each well
of the XF24 Cell Culture Microplates containing the micropatterned discs. After seeding, plates
were left in the biosafety cabinet for 30 minutes to prevent cell aggregation at the well’s edges, as
suggested by manufacturer. Plates were then placed in a 37
o
C, 5% CO2 incubator. After 1-4 h, an
67

additional 450 µL of media was added to each well. For all wells, FBS concentration in the media
was reduced to 2% after two days in culture and media was exchanged every other day.
3.2.6. Immunostaining
Tissues within XF24 Cell Culture Microplates were fixed with 4% paraformaldehyde and
permeabilized with 0.1% Triton X-100 solution for ten minutes. Fixed tissues or micropatterned
coverslips (without cells) were incubated with monoclonal mouse anti-sarcomeric α-actinin
(Sigma, 1:1000) or monoclonal rabbit anti-fibronectin (Sigma, 1:200) primary antibodies,
respectively, for one hour at room temperature. After PBS rinsing, samples were incubated with
chemical stains DAPI (1:1000) and Alexa Fluor 488 Phalloidin (Life Technologies, 1:1000), and
either Alexa Fluor 546 goat anti-mouse secondary antibody (Life Technologies, 1:1000) or Alexa
Fluor 546 goat anti-rabbit secondary antibody (Life Technologies, 1:200), for one hour at room
temperature. Tissues were then coated with a drop of ProLong Gold Anti-Fade Mountant, sealed
with Parafilm, and stored at room temperature. Coverslips were mounted on a glass slide with
ProLong Gold Anti-Fade Mountant and sealed with nail-polish.
3.2.7. Microscopy and Image Analysis
For each XF24 Cell Culture Microplate, confocal images of at least five locations dispersed
across each well were collected using a 20x air objective (with 2X digital zoom to a total
magnification of 40X) on a Nikon C2 point-scanning confocal microscope system. Using ImageJ,
the total number of nuclei per field of view (based on DAPI stain) was counted. Actin fiber
alignment was computed using a software based on fingerprint detection algorithms, as previously
described [13, 62, 108, 109]. The orientational order parameter, which varies from zero for
completely isotropic systems to one for completely aligned systems, was calculated from the actin
fiber alignment data [13].
68

3.2.8. Mitochondrial Respirometry
Cellular metabolism was measured using a Seahorse Bioscience XFe24 Extracellular Flux
Analyzer, as previously described [105, 136]. After five days in culture, cell media was replaced
with XF Assay Medium (Seahorse Bioscience) supplemented with 10 mM glucose, 2 µM L-
glutamine, and 1 mM sodium pyruvate (pH 7.4) and the plate was incubated for one hour in a
37
o
C, non-CO2 incubator. The wells of a hydrated sensor cartridge were then loaded with 2µM
oligomycin (port A), 1µM FCCP (port B), and a mixture of 1µM antimycin A and 1µM rotenone
(port C), according to manufacturer’s instructions. A standard mitochondrial stress test was then
conducted. Mitochondria-related ATP production was calculated by subtracting the oxygen
consumption rate (OCR) after oligomycin injection from the baseline OCR. Spare respiratory
capacity was calculated by subtracting the baseline OCR from the OCR after FCCP injection. Non-
mitochondrial respiration was determined from the OCR value after antimycin/rotenone injection.
Lastly, basal respiration was determined by subtracting the OCR after antimycin/rotenone
injection from the OCR at baseline. For each well, the OCR measurements were normalized to
total protein content, determined with a bicinchoninic acid (BCA) protein assay (Thermo Fisher
Scientific), as described below. In addition, the bioenergetic health index (BHI) was calculated
using the normalized OCR values obtained from mitochondrial respirometry using the following
formula [104]:
𝐵𝐻𝐼 =
(𝐴𝑇𝑃 𝑝𝑟𝑜𝑑𝑢𝑐𝑡𝑖𝑜𝑛)×(𝑆𝑝𝑎𝑟𝑒 𝑟𝑒𝑠𝑝𝑖𝑟𝑎𝑡𝑜𝑟𝑦 𝑐𝑎𝑝𝑎𝑐𝑖𝑡𝑦)
(𝑃𝑟𝑜𝑡𝑜𝑛 𝑙𝑒𝑎𝑘)×(𝑁𝑜𝑛−𝑚𝑖𝑡𝑜𝑐ℎ𝑜𝑛𝑑𝑟𝑖𝑎𝑙 𝑟𝑒𝑠𝑝𝑖𝑟𝑎𝑡𝑖𝑜𝑛)

3.2.9. Measurements of Protein Concentration
After OCR measurements, wells were rinsed twice with PBS and 100 µL of
radioimmunoprecipitation assay (RIPA) buffer and 400µL of PBS were added to each well. Cell
69

lysates were transferred to 1.7 mL centrifuge tubes and stored at -20
o
C. BCA protein assay was
performed according to manufacturer’s instructions. Briefly, 50 parts of BCA reagent A and 1 part
of BCA reagent B were mixed in a conical tube to obtain the working reagent. 200 µL of working
reagent was added to the wells of a 96-well plate, followed by cell lysates at a ratio of 1:8 samples
or standards:working reagent. After 1 h incubation at 37
o
C, absorbance was read using a plate
reader (Varioskan Lux, Thermo Fisher Scientific) and values were fit to a protein standard curve
(using BSA) to calculate protein concentrations. Total protein mass per well was computed by
multiplying the protein concentration by the total volume of the lysate (500µL).
3.2.10. Statistical Analysis
Normality for all measurements was first validated using the Lilliefors Test. Elastic moduli
data were analyzed using student’s t-test, with α set to 0.05. The remaining data were analyzed
using one-way and/or two-way ANOVA followed by Tukey’s test for multiple comparisons in
MATLAB, with α set to 0.05. Each biological parameter was tested in at least three independent
harvests, and multiple wells per harvest per condition were used in the analysis.
3.3. Results
3.3.1. Tuning Tissue Alignment and ECM Elasticity within XF24 Cell Culture Microplates
In ventricular myocardium, dynamic remodeling of tissue architecture and ECM elasticity
occur in both cardiac development and disease [8]. We hypothesized that both microenvironmental
factors impact the function of mitochondria in cardiac myocytes. To test this hypothesis in vitro,
we utilized the Seahorse XFe24 extracellular flux analyzer, which measures OCR of cells cultured
in XF 24 Cell Culture Microplates as an indicator of mitochondrial respiration. However, many
conventional techniques for engineering properties of the ECM for cell culture, such as
70

microcontact printing, are incompatible with these microplates due to the physical restrictions of
the wells. To overcome this, we developed a technique to engineer ECM surfaces outside of the
microplate and transfer them into the wells as the final step before cell seeding. As shown in Figure
3-1A, we first spin-coated 22 mm square glass coverslips with PNIPAm, followed by Sylgard 184
PDMS. After curing the PDMS, we laser-engraved the coated coverslips with 6.5 mm-diameter
circles (Figure 3-1B), which is just slightly larger than the wells of the XF 24 Cell Culture
Microplates (diameter: 6.3 mm). Due to slight loss of material during the etching process, the final
discs were 6.35-6.4 mm in diameter. Using discs slightly larger than the bottom of the wells was
advantageous because the wells have three pillars that restrict the disc from laying perfectly flat.
Furthermore, discs would attach slightly to the walls of the wells, guaranteeing full coverage of
the bottom of the well with the disc and minimizing the dislodgement of discs during rinses and
media changes. Next, we used microcontact printing to transfer either uniform or 15x2 lines of
fibronectin (Figure 3-1C) to the laser-engraved coverslips. We then used tweezers to detach
micropatterned discs from the coverslips, which released easily due to the PNIPAm layer, and
transferred them to the wells of XF24 Cell Culture Microplates treated in a plasma oxidizer. Next,
we seeded neonatal rat ventricular myocytes into wells containing PDMS discs. Myocytes seeded
in wells with uniform fibronectin formed isotropic tissues (Figure 3-1D), while myocytes in wells
with 15x2 fibronectin formed aligned tissues (Figure 3-1E). Thus, we successfully engineered
isotropic and aligned neonatal rat ventricular myocyte tissues within the wells of XF24 Cell
Culture Microplates.
71

Figure 3-1: Engineering cardiac tissues within XF24 Cell Culture Microplates. (A) Glass coverslips were spin-
coated with layers of PNIPAm and PDMS (i), laser-engraved into discs (ii), and microcontact printed (iii-v) with
fibronectin. Micropatterned discs were then detached from the coverslip, transferred into a XF24 Cell Culture
Microplate (vi), and seeded with neonatal rat ventricular myocytes (vii). (B) Laser-engraved PDMS discs on a square
glass coverslip (22 mm × 22 mm) obtained after step (ii) in Panel A. Scale bar: 5 mm. (C) Immunostained 15x2
fibronectin on a micropatterned PDMS disc obtained after step (v) in Panel A. Scale bar: 50µm. (D) Isotropic cardiac
tissue on PDMS disc patterned with uniform fibronectin and (E) aligned cardiac tissue on PDMS disc patterned with
15x2 fibronectin. Scale bars: 50µm.
To tune the elasticity of the ECM within the XF24 Cell Culture Microplate, we next
fabricated three formulations of PDMS with distinct elastic moduli: pure Sylgard 184, pure
Sylgard 527, and a 1:20 blend of Sylgard 184:Sylgard 527, similar to previous studies [92, 107].
Representative stress-strain curves for these three formulations (Figure 3-2A and 9B) demonstrate
that each is approximately linearly elastic under the range of strain 1-4%. As shown in Figure 2C,
the average elastic modulus of pure Sylgard 527 was measured as 1.61 ± 0.37 kPa (s.e.m., n=4),
similar to ex vivo passive measurements of developing myocardium [21, 22]. The elastic modulus
of blended PDMS was measured as 27.4 ± 2.3 kPa (s.e.m., n=5), similar to ex vivo passive
measurements of adult myocardium [21, 22]. Finally, the elastic modulus of pure Sylgard 184 was
measured as 2686.7 ± 143.6 kPa (s.e.m., n=7), which is supraphysiological, but may recapitulate
72

the high mechanical load applied to cardiac myocytes in pathological situations, such as pressure
overload [139]. To simplify our terminology, we will subsequently refer to pure Sylgard 527 as
low, 1:20 Sylgard 184:Sylgard 527 as moderate, and pure Sylgard 184 as high.

Figure 3-2: Elastic moduli of PDMS formulations. (A-B) Stress-strain curves for the three PDMS formulations (low:
pure Sylgard 527; moderate: 1:20 Sylgard 184:Sylgard 527; and high: pure Sylgard 184), with different y-axis scales.
(C) Average elastic moduli for the three different formulations of PDMS. Data are presented as mean ± s.e.m., n=4
for low, n=5 for moderate, n=7 for high. *p<0.001.
To determine the impact of substrate elasticity on mitochondrial function, we followed the
same procedure described above, with one additional step. After spin-coating high PDMS on
PNIPAm-coated coverslips, we spin-coated an additional layer of either low or moderate PDMS.
The constructs were then similarly laser-engraved, microcontact-printed, and transferred to
microplate wells. The high PDMS was needed as a support layer for the low and moderate PDMS
to provide structural stability during the disc transfer process. With this technique, we can
independently control both ECM patterning and elasticity within XF 24 Cell Culture Microplates.
3.3.2. Engineering Isotropic and Aligned Cardiac Tissues within XF24 Cell Culture Microplates
To determine the impact of ECM micropatterning and elasticity on cardiac tissue
architecture, we next immunostained neonatal rat ventricular myocyte tissues cultured within the
modified XF24 Cell Culture Microplates for five days. As shown in Figure 3-3, myocytes self-
73

assembled into isotropic and aligned tissues as expected based on the fibronectin micropatterning,
regardless of ECM elasticity. We also detected sarcomeric α-actinin-positive striations within the
majority of cells for all conditions, indicating that tissues consisted primarily of cardiac myocytes
with minimal fibroblast contamination. However, it was often difficult to clearly resolve individual
sarcomeres because these images were collected through the bottom of the microplate, which is
fabricated from thick polystyrene plastic incompatible with imaging at high magnification. To
quantify tissue alignment, we measured the orientational order parameter of stained actin
filaments. For each elasticity, aligned tissues had significantly higher actin alignment compared to
isotropic tissues (Figure 3-4A, p<0.001 for aligned vs isotropic, all substrates). For both isotropic
and aligned tissues, actin alignment was independent of ECM elasticity, indicating the robustness
of the microcontact printing process for the different PDMS formulations and the limited impact
of ECM elasticity on myofibril alignment.

Figure 3-3: Cardiac tissues engineered within XF24 Cell Culture Microplates. Composite images of neonatal rat
ventricular myocyte tissues cultured on the indicated conditions. (D) and (H) are zoomed-in images of the white boxes
in (C) and (G), respectively. Blue: nuclei; green: actin, red: α-actinin; scale bars: 100µm.
74

To determine if cell density was conserved between conditions, we also counted the
number of nuclei in our immunostained images. We did not detect any statistical differences in the
overall number of cells (Figure 3-4B) between any conditions, suggesting that cell density was
similar for all conditions. We also measured total protein content and did not identify any statistical
differences (Figure 3-4C). Thus, we engineered neonatal rat ventricular myocyte tissues within
XF24 Cell Culture Microplates with uniform density and protein content, and alignment dictated
by the fibronectin micropatterning. These measurements were all independent of ECM elasticity.

Figure 3-4: Quantification of tissue architecture. (A) Actin alignment (calculated as the orientational order
parameter) (n=9 for all conditions), (B) cells per 0.1mm
2
(n=9 for all conditions), and (C) total protein content per
well measured using BCA assay (n= 16 for all conditions). Data are presented as mean ± s.e.m.. ╪ p<0.05 compared
to isotropic tissues, same elasticity.
75

3.3.3. Co-Regulation of Mitochondrial Function by ECM Elasticity and Tissue Alignment

Figure 3-5: OCR measurements in engineered cardiac tissues. Average experimental OCR measurements for
isotropic (left) and aligned (right) tissues at baseline and after addition of oligomycin, FCCP, and antimycin and
rotenone. Data are presented as mean ± s.e.m., n=16 for all conditions.
Next, we utilized a Seahorse extracellular flux analyzer to quantify OCR in cardiac tissues
engineered within our modified XF24 Cell Culture Microplates. As shown in Figure 3-5, we added
oligomycin, FCCP, and antimycin/rotenone in series to alter mitochondrial function, as previously
described [105, 136]. This assay, known as the mitochondrial stress test, allowed us to determine
mitochondria-related basal respiration, ATP production, proton leak, maximum respiration, spare
respiratory capacity, and non-mitochondrial respiration, as shown in Figure 3-6. To determine the
independent effects of tissue alignment and ECM elasticity on mitochondrial function, we first
performed two-way ANOVA followed by multiple comparisons (Table 3-1. These tests are used
to identify the individual and combined impact of two independent variables on an outcome. Based
on this analysis, basal respiration, ATP production, maximum respiration, and spare respiratory
capacity were each regulated by ECM elasticity, but not tissue architecture (Figure 3-6).
Specifically, tissues on low substrates had lower basal respiration compared to those on moderate
and high substrates (p<0.001 and p<0.001, respectively). Similarly, tissues on low substrates had
76

lower ATP production compared to those on moderate and high substrates (p<0.001 and p<0.001,
respectively). In contrast, tissues on both low and moderate substrates had lower maximum
respiration compared to high substrates (p<0.05 and p<0.003, respectively), as shown in Figure
3-6. As a consequence of these differences in basal and maximum respiration, tissues on moderate
substrates had lower spare respiratory capacity than those on high substrates (p<0.006). Proton
leak was independent of ECM elasticity and was the only parameter regulated solely by tissue
architecture, with lower values in aligned tissues (p<0.04). The OCR associated with non-
mitochondrial respiration was not regulated by ECM elasticity or tissue alignment, which is a
strong indicator that the cells in each condition were similar in overall health, density, and
composition (Figure 3-6).
77

Figure 3-6: Metabolic function in engineered cardiac tissues. Average OCR associated with (A) basal respiration,
(B) ATP production, (C) proton leak, (D) maximum respiration, (E) spare respiratory capacity, and (F) non-
mitochondrial respiration. Data are presented as mean ± s.e.m., n=16 for all conditions. * p<0.05 compared to tissues
on low PDMS, same architecture. ┼ p<0.05 compared to tissues on moderate PDMS, same architecture. ╪ p<0.05
compared to isotropic tissues, same elasticity.
To further delineate the impact of tissue alignment and ECM elasticity on mitochondrial
function, we next performed one-way ANOVA followed by multiple comparisons on sub-sets of
our data, classified by either tissue alignment or ECM elasticity (Figure 3-6). In isotropic tissues,
basal respiration was higher only on the moderate substrate compared to the low substrate
(p<0.008), while ATP production was higher only for the moderate and high substrates compared
to the low substrate (p<0.009 and p<0.05, respectively). Maximum respiration and spare
respiratory capacity were not significantly different at each elastic modulus in isotropic tissues.
For aligned tissues, basal respiration increased with each increase in elastic modulus (p<0.02 for
78

low vs moderate, p<0.001 for low vs high, and p<0.05 for moderate vs high). Similarly, ATP
production in aligned tissues increased with each increase in elastic modulus (p<0.02 for low vs
moderate, p<0.001 for low vs high, and p<0.05 for moderate vs high). For aligned tissues,
maximum respiration was higher on high substrates compared to both low and moderate substrates
(p<0.001 and p<0.001, respectively). Likewise, spare respiratory capacity was significantly higher
on high substrates compared to both low and moderate substrates (p<0.03 and p<0.001,
respectively). Interestingly, spare respiratory capacity in aligned tissues was lowest for the
moderate substrate compared to both low and high substrates (p<0.04 and p<0.001, respectively).
Proton leak and non-mitochondrial respiration did not show statistical differences for any sub-set
of data. Together, these data suggest that certain aspects of mitochondrial function are more
sensitive to ECM elasticity in aligned tissues compared to
isotropic tissues.
Next, we calculated the Bioenergetic Health Index
of our tissues based on the OCR measurements, as shown in
Figure 3-7. This value reflects the combined impact of both
the positive aspects of oxygen consumption (ATP
production and spare respiratory capacity) and the negative
aspects of oxygen consumption (proton leak and non-
mitochondrial respiration) [104]. Based on two-way
ANOVA statistical analysis (Table 3-1), we observed that
the BHI is regulated by both ECM elasticity and tissue
architecture. Specifically, aligned tissues had higher BHI than isotropic tissues (p<0.006), and
tissues on high substrates had higher BHI than those on low and moderate substrates (p<0.001 and
Figure 3-7: Bioenergetic Health Index in
engineered cardiac tissues. Average
Bioenergetic Health Index for all
conditions. Data are presented as mean ±
s.e.m., n=16 for all conditions. * p<0.05
compared to tissues on low PDMS, same
architecture. ╪ p<0.05 compared to
isotropic tissues, same elasticity.
79

p<0.01, respectively). Based on one-way ANOVA, we identified no differences in BHI based on
ECM elasticity in isotropic tissues. However, for aligned tissues, BHI was higher on high
substrates compared to low and moderate substrates (p<0.001 and p<0.003, respectively). We also
found that, on high substrates, the BHI for aligned tissues was higher than that for isotropic tissues
(p<0.006). Thus, overall, this data indicates that BHI is sensitive to both ECM elasticity and tissue
alignment, as this parameter was maximized in aligned tissues on substrates with the highest elastic
modulus.
Table 3-1: Two-way ANOVA analysis for mitochondrial respirometry data. Data for all conditions was normally
distributed, as determined by the Lilliefors test. P-values for each comparison are indicated. *p<0.05.

3.4. Discussion
Remodeling of tissue architecture, ECM elasticity, and mitochondrial function are each
associated with distinct phases of cardiac development and disease, but relationships between these
phenomena have not yet been clearly established. In this study, we developed a method to robustly
control ECM elasticity and cardiac tissue alignment within cell culture microplates. This approach
enabled us to utilize a Seahorse XFe24 Extracellular Flux Analyzer to establish how these two
variables independently and jointly impact mitochondrial function in engineered cardiac tissues.
Our results suggest that different aspects of mitochondrial function are uniquely regulated by tissue
Comparison
Parameter Elasticity Alignment Interaction
Basal Respiration <0.0001* 0.8702 0.0894
ATP Production <0.0001* 0.4125 0.1246
Proton Leak 0.0667 0.0308* 0.2886
Maximum Respiration 0.0027* 0.9150 0.0008*
Spare Respiratory Capacity 0.0057* 0.9083 0.0080*
Non-mitochondrial Respiration 0.1310 0.6530 0.3293
BHI 0.0001* 0.0057* 0.0413*
80

architecture and/or ECM elasticity, indicating that mitochondrial function is sensitive to
remodeling of the tissue microenvironment. These data are important for understanding the factors
that regulate mitochondrial function during cardiac development and disease, which can help
improve the differentiation of cardiac myocytes from pluripotent stem cells and lead to novel
therapeutic approaches targeted to the mitochondria.
To independently control tissue alignment and ECM elasticity, we microcontact printed
fibronectin onto tunable PDMS discs with elastic moduli ranging from developmental to
supraphysiological ranges. Although these polymer surfaces are highly synthetic, the ability to
independently control ECM patterning and elasticity is a clear advantage compared to natural,
ECM-derived biomaterials, such as Matrigel or gelatin hydrogels. For example, the elastic
modulus of gelatin hydrogels is dictated by the percentage of gelatin [105], and thus it is impossible
to de-couple ECM ligand concentration from the elastic modulus. PDMS is also relatively easy to
fabricate and handle compared to other synthetic biomaterials, such as polyacrylamide hydrogels
[15, 117]. Thus, our approach was relatively simple while also facilitating a high degree of control
over the two parameters of interest. Importantly, many other biochemical assays beyond those
reported here rely on measuring properties, such as absorbance or luminescence, from cells
cultured within microwell plates. Our approach for regulating ECM elasticity and patterning can
easily be adapted for many assays that are performed within standard plate readers, broadly
expanding the applications for our technology.
Our OCR measurements revealed a variety of unique relationships between tissue
architecture, ECM elasticity, and different aspects of mitochondrial function. Basal respiration and
ATP production are both associated with baseline mitochondrial function and showed relatively
similar trends: both parameters showed increases with increasing elastic modulus, with more
81

pronounced trends in aligned tissues compared to isotropic tissues. Correlations between basal
respiration, ATP production, and elastic modulus are likely due to the increased demand for ATP
in more rigid microenvironments, which increases the resistance to myocyte shortening. However,
tissue alignment has relatively minimal impact on these parameters, indicating that ECM elasticity
dominates over tissue alignment for the regulation of baseline mitochondrial function.
Maximum respiration and spare respiratory capacity reflect the ability of myocytes to adapt
to increased metabolic demands in times of stress. Interestingly, we observed differences in these
parameters only in aligned tissues, not isotropic tissues. Specifically, in aligned tissues, both
maximum respiration and spare respiratory capacity were highest on the most rigid substrate. This
suggests that the ability of mitochondria to adapt to metabolic stress and increased demand for
ATP is maximized when both tissue alignment and ECM rigidity are high. We also observed non-
monotonic increases in maximum respiration and spare respiratory capacity with ECM elasticity
in aligned tissues. This is an intriguing result and is suggestive of non-linear relationships between
ECM elasticity and these two metabolic parameters in aligned tissues, which can be explored in
future studies.
Proton leak is indicative of mitochondrial efficiency [151] and was the only parameter
regulated by tissue alignment based on our two-way ANOVA analysis, with lower values in
aligned tissues. However, our one-way ANOVA analysis showed no differences due solely to
tissue alignment or ECM elasticity, if the other parameter is kept constant. Thus, proton leak seems
to be slightly affected by tissue alignment, but not as robustly as most of the other parameters we
measured. Importantly, non-mitochondrial respiration was preserved across all conditions,
suggesting that tissues were relatively consistent in terms of cell density, composition, and overall
health. This conclusion is also supported by our immunostaining and protein content data. To
82

characterize overall mitochondrial health, we also calculated the Bioenergetic Health Index. This
parameter combines the positive (ATP production and spare respiratory capacity) and negative
(proton leak and non-mitochondrial respiration) metrics of mitochondrial function [104]. We
found that this value was maximized in aligned tissues on the most rigid substrates, suggesting that
both ECM elasticity and tissue alignment play a role in overall mitochondrial health.
Our data has important biological significance to both cardiac development and disease.
During cardiac development, myocytes gradually elongate and self-assemble into aligned tissues
[9]. Concurrently, the elastic modulus of the ECM [22] and the hemodynamic load [196] increases,
which both elevate the afterload on cardiac myocytes. Our experimental conditions most relevant
to these transitions are the isotropic and aligned tissues on low and moderate substrates. Within
this subset of data, basal respiration and ATP production both increase with ECM rigidity,
suggesting that myocytes increase their baseline metabolism as their afterload increases during
development. However, maximum respiration and spare respiratory capacity were less affected by
ECM elasticity, suggesting that mitochondrial adaptation to stress is not regulated by ECM
elasticity or potentially other increases in afterload during development. In general, tissue
alignment had minimal impact on any parameter on low and moderate substrates, suggesting that
the elongation and alignment of cardiac myocytes does not play a predominant role in the
functional maturation of mitochondria during development.
Many cardiac diseases are associated with increased hemodynamic load [164, 165],
increased ECM rigidity [139, 166], and/or disorganization of cardiac myocytes [170], often
secondary to fibrosis [197]. Our measurements from isotropic and aligned tissues on moderate and
high substrates can be used to understand the impact of these pathological transitions on
mitochondrial function. In isotropic tissues, all mitochondrial parameters were similar on moderate
83

and high substrates. However, in aligned tissues, basal respiration, ATP production, maximum
respiration, and spare respiratory capacity each increased on the high substrate compared to the
moderate substrate. Thus, aligned tissues appear to be more adaptable to increases in ECM rigidity
and/or afterload in pathological settings, which could be a compensatory response to generate
sufficient ATP to increase contractile output in response to increased load. Thus, preservation of
tissue alignment seems to be most critical for maintaining, or even enhancing, mitochondrial
function in diseased settings.
In many forms of pathological hypertrophy, compensatory mechanisms are temporary and
eventually transition to pathological remodeling and heart failure [198, 199]. Importantly, reactive
oxygen species are a natural by-product of mitochondrial respiration, which can lead to oxidative
stress long-term [72]. Oxidative stress has been observed in many cardiac diseases [200-202]. This
could be in part due to increased ATP production due to increased load in pathological
environments. Due to the timescale of our experiments, we are likely capturing responses more
similar to early compensatory stages and thus may not observe the deleterious effects of oxidative
stress. Thus, correlating mitochondrial function to oxidative stress at extended timepoints is an
important subject for future studies.
Mitochondria are dynamic organelles that undergo biogenesis, fusion, fission, and
mitophagy [26, 72, 203, 204]. Relative to other cell types, mitochondria in cardiac myocytes are
relatively static and highly fragmented because they must pack tightly next to myofibrils for
efficient delivery of ATP to sarcomeres [27, 205]. Thus, myofibril architecture, mitochondrial
structure, and mitochondrial function are closely related. We and others have previously shown
that sarcomere alignment and force generation are regulated by both myocyte shape and tissue
alignment, but not always linearly [13, 15, 108, 117, 206]. Altered mitochondrial structure and/or
84

function secondary to differences in sarcomere and myofibril architecture could be a missing piece
of this puzzle. Furthermore, alterations in biogenesis, fusion, fission, and mitophagy are associated
with cardiac differentiation and maturation [42, 188, 189] as well as many cardiac diseases [40,
41, 43, 71]. Thus, characterizing mitochondrial fragmentation and turnover due to ECM elasticity
and tissue architecture could provide mechanistic insight into some of our results.
Our study has many inherent limitations. For example, our engineered cardiac tissues
lacked important components of native myocardium, such as supporting cell populations,
vascularization, three-dimensional architecture, and mechanical stimulation. However, for this
study, we chose to minimize complexity such that we could delineate the impact of ECM elasticity
and tissue alignment on cardiac myocytes specifically. Future studies can focus on determining
the metabolic impact of additional features of native myocardium, such as cardiac fibroblasts, and
modeling more complex pathophysiological conditions, such as ischemia or hypertrophy. We also
used fibronectin as the sole ECM protein, but the native basal lamina in the myocardium consists
of many diverse proteins and macromolecules [207], which also remodel in development and
disease [8] and could potentially impact mitochondrial function as well. Additionally, we could
not fully replicate the nutrient sources present in native blood. Our media contained glucose and
amino acids, but no fatty acids, which are known to be present in vivo. Furthermore, we measured
OCR within intact engineered tissues instead of isolated mitochondria. Hence, we cannot conclude
if the differences we observed are caused by intrinsic changes to mitochondria, the total quantity
of mitochondria, or the architecture of the mitochondria within the cell [31, 174], which is known
to impact mitochondrial function.
Another limitation of our study is the use of neonatal rat ventricular myocytes, which are
non-human. However, the only renewable sources of human cardiac myocytes are those
85

differentiated from pluripotent stem cells, which are relatively heterogeneous and immature [101,
172] and therefore would likely not provide clear results. Nevertheless, our platform should be
compatible with identifying how mitochondrial function in human induced pluripotent stem cell
(iPSC)-derived cardiac myocytes with disease-relevant genetic mutations is also impacted by ECM
elasticity and tissue architecture. This is especially relevant for mitochondrial cardiomyopathies,
such as Barth Syndrome [101], which are often characterized by mitochondrial dysfunction as well
as fibrosis. Thus, our platform could provide new insights into how genetic mutations impair
mitochondrial function and identify new avenues for therapeutic interventions that can recover the
function of these organelles.
In conclusion, we developed a novel approach to tune both ECM elasticity and tissue
alignment within cell culture microplates. This approach allowed us to measure OCR in engineered
cardiac tissues due to these two variables. Our findings demonstrate that baseline mitochondrial
function is predominantly regulated by ECM elasticity, but the ability of mitochondria to adapt to
stress is regulated by both ECM elasticity and tissue alignment. Our data complement existing in
vivo studies that have reported remodeling of mitochondrial function in developmental and
pathological settings. Our results also provide further evidence that ECM elasticity, myofibril
architecture, contractility, and mitochondrial function are all interrelated, emphasizing that each
of these factors are important to consider in the context of cardiac development, maturation, and
disease.
86

Chapter 4: Concluding Remarks and Future Work
Microenvironment remodeling is a hallmark of myocardial tissue development and disease
and is directly correlated with changes in metabolic demands by the heart. During development,
the heart is loosely organized [208], with cardiac myocytes presenting hexagonal shapes [9] and
forming an isotropic mesh that demands little to no oxygen for producing ATP. The myocardial
matrix is primarily composed of glycoproteins such as fibronectin and laminin [20, 140, 209], and
cardiomyocytes primarily obtain their energy through glycolysis [2]. As the tissue develops, cells
elongate and become more aligned [9]. In parallel, the ECM remodels becoming heavily
collagenous [20], while myocytes start to rely primarily on oxidative phosphorylation of fatty acids
and sugars to produce ATP [2]. When this transition happens, the mitochondrial network also
reorganizes with organelles going from elongated and loosely packed to bean-shaped sarcomere-
interleaved structures[42, 43, 210].
During the transition from healthy heart to heart failure, either following concentric or
eccentric hypertrophy, the ECM stiffens significantly, the protein network becomes even more
collagenous [141, 211], and myocardial tissue loses organization [20]. This is accompanied by
myocytes changing their aspect ratio [66, 212] in order to comply with increased mechanical
demands [15]. In addition, there is an adaptative phase where the myocardium becomes reliant on
glucose, either for glycolysis of oxidative phosphorylation [58], while the mitochondrial network
fragments, with organelles becoming smaller and distributing more around the cells [41, 213].
Creating models to probe the different changes in cardiac microenvironment is an
important task of tissue engineering, as it enables more physiologically-relevant in vitro studies
(Figure 4-1) [214]. In this dissertation, we evaluated how controlled microenvironmental changes
would affect mitochondrial function in cardiac myocytes. Specifically, we evaluated if cardiac
87

myocytes would increase their metabolic demands in matrices that resemble natural cardiac tissue
or diseased tissue, by changing ECM protein composition, rigidity, and tissue alignment. We used
tissue engineering techniques in the form of biomaterials and microfabrication to dictate
mechanical and biochemical cues to our myocytes and induce some phenotypical changes that
were evaluated using extracellular flux analysis, imaging, and molecular biology assays.

Figure 4-1: Engineering Cardiac Microphysiological Systems to Define the Functional Impacts of Pathological
Extracellular Matrix Remodeling. (A) Distinct features of native healthy and diseased/fibrotic myocardium, such
as myocyte shape, tissue alignment, ECM rigidity, and cell demographics, are used as design templates for engineering
cardiac microphysiological systems. (B) Features of native healthy and diseased/fibrotic myocardium are replicated
by combining appropriate biomaterials and microfabrication techniques. (C) Engineered cardiac cells and tissues are
interrogated with functional assays to quantify contractility, electrophysiology, and metabolism as a function of their
microenvironment. Collectively, these approaches implemented as cardiac microphysiological systems can identify
the functional impact of ECM remodeling to streamline mechanistic studies and therapeutic development. Images in
(C) adapted from References [15], [215], [216], and [217]. Figure and caption from [214].

88

4.1. Biomaterial class, ECM protein composition and rigidity regulate metabolic demands in
isotropic cardiac tissues
A variety of tunable biomaterials are employed to control tissue culture microenvironment
and maintain the viability and functionality of cardiac myocytes in vitro. With them, it is possible
to either define ECM rigidity or protein composition, dictating different mechanical and
biochemical cues to myocytes. Hence, they can be used as tools to elucidate the direct impacts of
the biochemical and mechanical properties of the ECM on the metabolic function in cardiac
myocytes. In addition, as they are important elements in tissue engineering, understanding the
impact of distinct biomaterials on cardiac myocyte phenotype, including metabolism, is critical for
engineering physiologically-relevant models of healthy and diseased myocardium.
For these reasons, in Chapter 2, we systematically tested the impact of multiple varieties
of commonly-used biomaterial substrates on mitochondrial function in cardiac myocytes. First, we
coated the wells of specialized cell culture microplates with fibronectin- or gelatin-coated
polydimethylsiloxane (PDMS) of three elastic moduli (ranging from developmental to
pathological ECM mechanical loads) or gelatin hydrogels with four elastic moduli (from healthy
adult ECM to post myocardial infarction ECM), as described in Figure 2-1A. We then cultured
neonatal rat ventricular myocytes within the wells, which consistently assembled into confluent,
isotropic tissues on all substrates, with PDMS inducing slight cytotoxicity (Figure 2-2 and Figure
2-3). Throughout our culture, myocytes were spontaneously contracting irrespective of culture
substrate, however potential differences in force or contractile work might have been present due
to mechanical load and biochemical cues.
After five days in culture, we quantified oxygen consumption rates and extracellular
acidification rates using a Seahorse extracellular flux analyzer. Our results indicate that, in general,
89

baseline mitochondrial function, baseline glycolysis, and maximum respiration are increased in
tissues on gelatin hydrogels compared to PDMS substrates coated with gelatin or fibronectin
(Figure 2-6 and Figure 2-8). Interestingly, we found that ECM protein composition can reduce
mitochondrial function in rigid microenvironments, mimicking microenvironment conditions
found in many disease settings [141].
Together, our results in Chapter 2 demonstrate that the ECM can directly impact the
metabolic phenotype of cardiac myocytes, which is important for understanding how ECM
remodeling contributes to physiological and pathological cardiac growth. We also highlighted how
the properties of the biomaterial substrate can have a direct impact on the metabolism of cardiac
myocytes, which is an important consideration for engineering in vitro cardiac tissue models.
These data complement previous reports in non-cardiac and cardiac cell types suggesting ECM
rigidity and composition as potential regulators of mitochondrial function.
4.2. Tissue alignment and mitochondrial function
In Chapter 3, we developed a new method to engineer the bottom of our cell culture
microplates that enable the control of both rigidity and tissue alignment using microfabrication
techniques. Our biomaterial of choice was PDMS, due to its tunability and easiness of patterning.
We chose a range of elasticities that ranges from developmental to high pressure overload heart,
combined with isotropic or parallel protein distribution to create isotropic or anisotropic cardiac
tissues. Prior studies had already shown a relationship between matrix rigidity and mitochondrial
function in engineered cardiac myocytes and non-cardiac cells [132, 218]. However, ours was the
first to identify the interplay between these two variables.
We were able to independently control ECM rigidity and tissue alignment for cardiac
myocytes within the wells of cell culture microplates (Figure 3-4). We also found that parameters
90

associated with baseline metabolism are predominantly regulated by ECM elasticity, whereas the
ability of tissues to adapt to metabolic stress is regulated by both ECM elasticity and tissue
alignment (Figure 3-6). Combined, these results demonstrate that mitochondrial function is
regulated by both ECM rigidity and myofibril architecture in cardiac myocytes.
The methodology present in Chapter 3 is more robust in controlling tissue distribution
across the wells of our microplates, maintaining a more uniform monolayer of cardiac myocytes
due to reduced meniscus on the substrate. In addition, the system can be further expanded to use
different types of matrix proteins in order to study the possible co-regulation between alignment
and biochemical cues, especially in light of our results from Chapter 2. Furthermore, this platform
can be applied on the study of cardiomyopathies that present a strong metabolic component, by
testing the effects of combined alterations in ECM rigidity and tissue alignment. Lastly, it could
be combined with human iPSC technology to provide human-relevant models with applications
for drug development in cardiac tissues.
4.3. Limitations and future directions
With the work presented in this dissertation, we contributed to the understanding of
different components pertaining mitochondrial function regulation in cardiac myocytes. We
provided further evidence that matrix rigidity is an important component on metabolic regulation,
but also presented first in vitro evidence of co-regulation due to rigidity and tissue alignment,
however our studies present important limitations.
One important limitation is the fact that we solely used in vitro models based on neonatal
cells to understand the different mechanisms. Even though NRVMs are a powerful tool to
understand cardiac function, these cells present different phenotype than adult ones, remarkably
the expression of integrins for both glycoproteins and collagens [163, 171, 219]. As heart attack is
91

a phenomenon that usually afflicts adults, translating some of our results might be difficult. Hence,
we should expand our systems to either incorporate strategies to mature the cells to match
phenotypical characteristics, or use myocytes isolated from adult models to further validate our
findings.
Furthermore, our systems present limited human-relevancy as we used primary harvest rat
cells, which possess different ion channel and contractility patterns than humans. Harvesting
primary tissues from humans is unrealistic, but the incorporation of induced pluripotent stem cell
technologies [214, 220] would be a step further in these studies and could provide new insights on
how mechanotransduction- and biochemical-driven regulation of mitochondrial function occurs.
However, more validation is necessary to secure the differentiation and maturation of these cells,
and their possible applicability in different studies.

Figure 4-2: Adult somatic cells from patients can be reprogrammed into induced pluripotent stem cells (iPSCs) and
differentiated in vitro into different cell types for applications in disease modelling and drug screening. Adapted from
[220].
We also presented results solely on a tissue level. Mitochondrial remodeling happens both
in higher scale, with the reorganization of the network both in terms of localization and
fragmentation inside the cells, and inside the organelles with increased folding of the cristae that
92

provides increased area for mitochondrial complexes anchoring and ATP production. It is
important to validate our findings with isolated mitochondria studies [88], possibly culturing cells
on substrates similar to those we described here, isolating the organelles, and quantifying their
function independently. Lastly, more robust studies should combine in vivo and ex vivo
components to validate our findings, especially characterizing the changes in mitochondrial
function due to tissue alignment at different stages of heart remodeling. More robust in vivo
models, such as zebrafish [221-223], could provide a platform for testing the translatability of
these results. Combined with isolated mitochondria studies, in vivo models would further validate
our findings, and provide new investigative avenues to understand the metabolic remodeling of
the heart.
One future study already in development is the characterization of mitochondrial
distribution and fragmentation in single cardiac myocytes as a function of matrix rigidity and cell
aspect ratio. Mitophagy, mitochondrial biogenesis, and mitochondrial fragmentation play an
important role in cardiac tissue maturation and disease remodeling. It is known that mitochondrial
phenotypes change from a network of long and filamentous organelles in neonatal tissue to that of
spherically shaped organelles in adult tissue, interleaved with sarcomeres [42, 43]. Additionally,
during heart failure, mitochondria change their behavior again, down-regulating fusion and
mitophagy [40, 71] and becoming even more fragmented. However, little is known about how this
change in mechanical cues, specifically the ECM elasticity and tissue alignment/cell shape,
regulate mitochondrial structure. Using microfabrication techniques, we are probing these
elements to elucidate this relationship between microenvironment and mitochondrial structure.
93

4.4. Final conclusions
The heart is a highly metabolic organ, needing significant amounts of ATP to continuously
pump blood. Additionally, microenvironmental and metabolic remodeling of the heart are parallel
processes happening from development to disease. Understanding how some of these processes
are interconnected can provide important therapeutic avenues for improving patients’ lives. Our
data from this dissertation elucidates some of the interaction between of the components associated
with higher scale tissue remodeling (ECM rigidity, protein composition, and tissue alignment) and
their effects in mitochondrial function.
As mentioned above, our work indicates that macroscale tissue remodeling regulates
mitochondrial function in cardiac myocytes, with an interplay of the different components. Protein
composition seems to be more important with increased mechanical load, as the greatest
differences were in tissues on stiff gelatin-coated PDMS compared to other PDMS conditions.
Biomaterial composition was important on studying this regulation mechanisms, as cardiac
myocytes presented higher metabolic demands on gelatin hydrogels. We should pay attention to
this element when designing future experiments that envision probing the relationship between
matrix composition and rigidity, and mitochondrial function or possibly even contractility.
On the other hand, tissue alignment is an important cue when matrix stiffens, allowing
cardiac myocytes to properly function. The remodeling of the mitochondrial network might be one
of the mechanisms behind the observed phenomena and is an area of research deserving further
probing, especially in light of in vivo data. The ability to study in parallel mechanical and alignment
cues might provide a better understanding of organelle behavior and tissue level function in cardiac
myocytes in the context of health and disease.
94

The work present in this dissertation provides insights on the relationship of
microenvironment remodeling and cardiac myocyte mitochondrial function. In addition, we
evaluated elements sometimes overlooked in experimental design that might directly affect the
results from studies. It is a building block on understating how cardiac metabolism is regulated in
the context of physiological and pathological myocardium remodeling.

95

Chapter 5: References:
[1] J. Heineke, J.D. Molkentin, Regulation of cardiac hypertrophy by intracellular
signalling pathways, Nat Rev Mol Cell Biol 7(8) (2006) 589-600.
[2] G.D. Lopaschuk, J.S. Jaswal, Energy metabolic phenotype of the cardiomyocyte during
development, differentiation, and postnatal maturation., Journal of cardiovascular pharmacology
56(2) (2010) 130-40.
[3] K.M. Trybus, No strain, no gain, Nat Cell Biol 7(9) (2005) 854-6.
[4] D. Mozaffarian, E.J. Benjamin, A.S. Go, D.K. Arnett, M.J. Blaha, M. Cushman, S.R.
Das, S. de Ferranti, J.P. Després, H.J. Fullerton, V.J. Howard, M.D. Huffman, C.R. Isasi, M.C.
Jiménez, S.E. Judd, B.M. Kissela, J.H. Lichtman, L.D. Lisabeth, S. Liu, R.H. Mackey, D.J. Magid,
D.K. McGuire, E.R. Mohler, C.S. Moy, P. Muntner, M.E. Mussolino, K. Nasir, R.W. Neumar, G.
Nichol, L. Palaniappan, D.K. Pandey, M.J. Reeves, C.J. Rodriguez, W. Rosamond, P.D. Sorlie, J.
Stein, A. Towfighi, T.N. Turan, S.S. Virani, D. Woo, R.W. Yeh, M.B. Turner, W.G. Members,
A.H.A.S. Committee, S.S. Subcommittee, Heart Disease and Stroke Statistics-2016 Update: A
Report From the American Heart Association, Circulation 133(4) (2016) e38-360.
[5] O. Khavjou, D. Phelps, A. Leib, Projections of Cardiovascular Disease Prevalence and
Costs: 2015-2035, RTI International, 2016, p. 54.
[6] A.V. Khera, C.A. Emdin, I. Drake, P. Natarajan, A.G. Bick, N.R. Cook, D.I. Chasman,
U. Baber, R. Mehran, D.J. Rader, V. Fuster, E. Boerwinkle, O. Melander, M. Orho-Melander, P.M.
Ridker, S. Kathiresan, Genetic Risk, Adherence to a Healthy Lifestyle, and Coronary Disease, N
Engl J Med 375(24) (2016) 2349-2358.
[7] G. Vunjak Novakovic, T. Eschenhagen, C. Mummery, Myocardial tissue engineering:
in vitro models, Cold Spring Harb Perspect Med 4(3) (2014).
96

[8] M.L. McCain, K.K. Parker, Mechanotransduction: The role of mechanical stress,
myocyte shape, and cytoskeletal architecture on cardiac function, Pflugers Archiv European
Journal of Physiology 462(1) (2011) 89-104.
[9] A. Hirschy, F. Schatzmann, E. Ehler, J.C. Perriard, Establishment of cardiac
cytoarchitecture in the developing mouse heart, Dev Biol 289(2) (2006) 430-41.
[10] J.G. Jacot, A.D. McCulloch, J.H. Omens, Substrate stiffness affects the functional
maturation of neonatal rat ventricular myocytes, Biophys J 95(7) (2008) 3479-87.
[11] A.G. Szent-Györgyi, Calcium regulation of muscle contraction, Biophys J 15(7)
(1975) 707-23.
[12] M. Song, K. Mihara, Y. Chen, L. Scorrano, G.W. Dorn, Mitochondrial fission and
fusion factors reciprocally orchestrate mitophagic culling in mouse hearts and cultured fibroblasts,
Cell Metabolism 21(2) (2015) 273-285.
[13] A. Grosberg, P.W. Alford, M.L. McCain, K.K. Parker, Ensembles of engineered
cardiac tissues for physiological and pharmacological study: Heart on a chip, Lab on a Chip 11(24)
(2011) 4165.
[14] A.J. Ribeiro, Y.S. Ang, J.D. Fu, R.N. Rivas, T.M. Mohamed, G.C. Higgs, D.
Srivastava, B.L. Pruitt, Contractility of single cardiomyocytes differentiated from pluripotent stem
cells depends on physiological shape and substrate stiffness, Proc Natl Acad Sci U S A 112(41)
(2015) 12705-10.
[15] M.L. McCain, H. Yuan, F.S. Pasqualini, P.H. Campbell, K.K. Parker, Matrix elasticity
regulates the optimal cardiac myocyte shape for contractility, Am J Physiol Heart Circ Physiol
306(11) (2014) H1525-39.
97

[16] R. Kakkar, R.T. Lee, Intramyocardial fibroblast myocyte communication, Circ Res
106(1) (2010) 47-57.
[17] A.J. Engler, S. Sen, H.L. Sweeney, D.E. Discher, Matrix Elasticity Directs Stem Cell
Lineage Specification, Cell 126(4) (2006) 677-689.
[18] R.A. Gottlieb, D. Bernstein, METABOLISM. Mitochondria shape cardiac
metabolism, Science 350(6265) (2015) 1162-3.
[19] F.M. Watt, W.T.S. Huck, Role of the extracellular matrix in regulating stem cell fate.,
Nature reviews. Molecular cell biology 14(8) (2013) 467-73.
[20] J.R. Gershlak, J.I. Resnikoff, K.E. Sullivan, C. Williams, R.M. Wang, L.D. Black,
Mesenchymal stem cells ability to generate traction stress in response to substrate stiffness is
modulated by the changing extracellular matrix composition of the heart during development,
Biochem Biophys Res Commun 439(2) (2013) 161-6.
[21] S. Majkut, T. Idema, J. Swift, C. Krieger, A. Liu, D.E. Discher, Heart-specific
stiffening in early embryos parallels matrix and myosin expression to optimize beating, Curr Biol
23(23) (2013) 2434-9.
[22] A.J. Engler, C. Carag-Krieger, C.P. Johnson, M. Raab, H.Y. Tang, D.W. Speicher,
J.W. Sanger, J.M. Sanger, D.E. Discher, Embryonic cardiomyocytes beat best on a matrix with
heart-like elasticity: scar-like rigidity inhibits beating, J Cell Sci 121(Pt 22) (2008) 3794-802.
[23] J.G. Jacot, J.C. Martin, D.L. Hunt, Mechanobiology of cardiomyocyte development,
J Biomech 43(1) (2010) 93-8.
[24] D.H. Kim, E.A. Lipke, P. Kim, R. Cheong, S. Thompson, M. Delannoy, K.Y. Suh, L.
Tung, A. Levchenko, Nanoscale cues regulate the structure and function of macroscopic cardiac
tissue constructs, Proc Natl Acad Sci U S A 107(2) (2010) 565-70.
98

[25] X. Wang, J. Chang, T. Tian, B. Ma, Preparation of calcium silicate/decellularized
porcine myocardial matrix crosslinked by procyanidins for cardiac tissue engineering, RSC
Advances 6(41) (2016) 35091-35101.
[26] G.W. Dorn, Mitochondrial dynamics in heart disease, Biochimica et biophysica acta
1833(1) (2013) 233-41.
[27] G.W. Dorn, Mitochondrial dynamism and heart disease: changing shape and shaping
change, EMBO Mol Med 7(7) (2015) 865-77.
[28] E.N. Dedkova, L.A. Blatter, Measuring mitochondrial function in intact cardiac
myocytes, Journal of Molecular and Cellular Cardiology 52(1) (2012) 48-61.
[29] R.A. Gottlieb, A. Stotland, MitoTimer: a novel protein for monitoring mitochondrial
turnover in the heart, Journal of Molecular Medicine 93(3) (2015) 271-278.
[30] M. Song, G.W. Dorn, Mitoconfusion: Noncanonical functioning of dynamism factors
in static mitochondria of the heart, Cell Metabolism 21(2) (2015) 195-205.
[31] M. Miragoli, J.L. Sanchez-Alonso, A. Bhargava, P.T. Wright, M. Sikkel, S.
Schobesberger, I. Diakonov, P. Novak, A. Castaldi, P. Cattaneo, A.R. Lyon, M.J. Lab, J. Gorelik,
Microtubule-Dependent Mitochondria Alignment Regulates Calcium Release in Response to
Nanomechanical Stimulus in Heart Myocytes, Cell Rep 14(1) (2016) 140-51.
[32] P.A. Gorski, D.K. Ceholski, R.J. Hajjar, Altered myocardial calcium cycling and
energetics in heart failure--a rational approach for disease treatment, Cell Metab 21(2) (2015) 183-
94.
[33] E. Gottlieb, S.M. Armour, M.H. Harris, C.B. Thompson, Mitochondrial membrane
potential regulates matrix configuration and cytochrome c release during apoptosis, Cell Death
and Differentiation 10 (2003) 709-717.
99

[34] V. Parra, V. Eisner, M. Chiong, A. Criollo, F. Moraga, A. Garcia, S. Härtel, E.
Jaimovich, A. Zorzano, C. Hidalgo, S. Lavandero, Changes in mitochondrial dynamics during
ceramide-induced cardiomyocyte early apoptosis, Cardiovascular Research 77(2) (2008) 387-397.
[35] M.P. Murphy, How mitochondria produce reactive oxygen species., The Biochemical
journal 417(1) (2009) 1-13.
[36] G. Lenaz, The mitochondrial production of reactive oxygen species: mechanisms and
implications in human pathology, IUBMB Life 52(3-5) (2001) 159-64.
[37] S.S. Sabharwal, P.T. Schumacker, Mitochondrial ROS in cancer: initiators, amplifiers
or an Achilles' heel?, Nat Rev Cancer 14(11) (2014) 709-21.
[38] G.S. Shadel, T.L. Horvath, Mitochondrial ROS Signaling in Organismal Homeostasis,
Cell 163(3) (2015) 560-569.
[39] H.U. Simon, A. Haj-Yehia, F. Levi-Schaffer, Role of reactive oxygen species (ROS)
in apoptosis induction, Apoptosis 5(5) (2000) 415-8.
[40] C. Vasquez-Trincado, I. García-Carvajal, C. Pennanen, V. Parra, J.A. Hill, B.A.
Rothermel, S. Lavandero, Mitochondrial dynamics, mitophagy and cardiovascular disease., The
Journal of physiology 3(March 2016) (2015) 509-525.
[41] T. Wai, J. García-Prieto, M.J. Baker, C. Merkwirth, P. Benit, P. Rustin, F.J. Rupérez,
C. Barbas, B. Ibañez, T. Langer, Imbalanced OPA1 processing and mitochondrial fragmentation
cause heart failure in mice, Science 350(6265) (2015) aad0116.
[42] S. Chung, P.P. Dzeja, R.S. Faustino, C. Perez-Terzic, A. Behfar, A. Terzic,
Mitochondrial oxidative metabolism is required for the cardiac differentiation of stem cells, Nature
Clinical Practice Cardiovascular Medicine 4(Suppl 1) (2007) S60-S67.
100

[43] G. Gong, M. Song, G. Csordas, D.P. Kelly, S.J. Matkovich, G.W. Dorn, Parkin-
mediated mitophagy directs perinatal cardiac metabolic maturation in mice, Science 350(6265)
(2015) aad2459.
[44] W.C. Stanley, M.P. Chandler, Energy metabolism in the normal and failing heart:
Potential for therapeutic interventions, Heart Failure Reviews 7(2) (2002) 115-130.
[45] C. Pennanen, V. Parra, C. López-Crisosto, P.E. Morales, A. Del Campo, T. Gutierrez,
P. Rivera-Mejías, J. Kuzmicic, M. Chiong, A. Zorzano, B.A. Rothermel, S. Lavandero,
Mitochondrial fission is required for cardiomyocyte hypertrophy mediated by a Ca2+-calcineurin
signaling pathway, J Cell Sci 127(Pt 12) (2014) 2659-71.
[46] P. Mishra, D.C. Chan, Metabolic regulation of mitochondrial dynamics, J Cell Biol
212(4) (2016) 379-87.
[47] F.R. Jornayvaz, G.I. Shulman, Regulation of mitochondrial biogenesis, Essays
Biochem 47 (2010) 69-84.
[48] A.M. Andres, A. Stotland, B.B. Queliconi, R.A. Gottlieb, A time to reap, a time to
sow: mitophagy and biogenesis in cardiac pathophysiology, J Mol Cell Cardiol 78 (2015) 62-72.
[49] A.V. Kuznetsov, J. Troppmair, R. Sucher, M. Hermann, V. Saks, R. Margreiter,
Mitochondrial subpopulations and heterogeneity revealed by confocal imaging: Possible
physiological role?, Biochimica et Biophysica Acta - Bioenergetics 1757(5-6) (2006) 686-691.
[50] R. Ferreira, R. Vitorino, R.M. Alves, H.J. Appell, S.K. Powers, J.A. Duarte, F. Amado,
Subsarcolemmal and intermyofibrillar mitochondria proteome differences disclose functional
specializations in skeletal muscle, Proteomics 10(17) (2010) 3142-54.
[51] J. Kuzmicic, A. Del Campo, C. López-Crisosto, P. Morales, C. Pennanen, R. Bravo-
Sagua, J. Hechenleitner, R. Zepeda, P. Castro, H. Verdejo, V. Parra, M. Chiong, S.e. Lavandero,
101

Mitochondrial dynamics: a potential new therapeutic target for heart failure, Revista española de
cardiología 64(10) (2011) 916-923.
[52] J.G. Betts, O. College, P. Desaix, J.E. Johnson, E.W. Johnson, O. Korol, D. Kruse, B.
Poe, J. Wise, M.D. Womble, Anatomy and Physiology, OpenStax College2013.
[53] B. Westermann, Mitochondrial fusion and fission in cell life and death, Nat Rev Mol
Cell Biol 11(12) (2010) 872-84.
[54] M.G. Rosca, C.L. Hoppel, Mitochondria in heart failure, Cardiovasc Res 88(1) (2010)
40-50.
[55] OpenStax, Oxidative Phosphorylation, 2013.
https://cnx.org/contents/7oTVAgrZ@7/Oxidative-Phosphorylation. (Accessed 08/03/2017 2017).
[56] O.V. Savinova, A.M. Gerdes, Myocyte Changes in Heart Failure, Heart Failure Clinics
8(1) (2012) 1-6.
[57] S. Neubauer, The Failing Heart — An Engine Out of Fuel, New England Journal of
Medicine 356(11) (2007) 1140-1151.
[58] J.S. Jaswal, V.J.J. Cadete, G.D. Lopaschuk, Optimizing cardiac energy substrate
metabolism : a novel therapeutic intervention for ischemic heart disease, Heart Metabolism 38
(2008) 5-14.
[59] H. Taegtmeyer, M.E. Young, G.D. Lopaschuk, E.D. Abel, H. Brunengraber, V.
Darley-Usmar, C. des Rosiers, R. Gerszten, J.F. Glatz, J.L. Griffin, R.J. Gropler, H.-G.
Holzhuetter, J.R. Kizer, E.D. Lewandowski, C.R. Malloy, S. Neubauer, L.R. Peterson, M.A.
Portman, F.A. Recchia, J.E. van Eyk, T.J. Wang, Assessing Cardiac Metabolism, 2016.
[60] A.V. Khera, S. Kathiresan, Genetics of coronary artery disease: discovery, biology
and clinical translation, Nat Rev Genet 18(6) (2017) 331-344.
102

[61] C.Y. Ho, Hypertrophic cardiomyopathy: Preclinical and early phenotype, Journal of
Cardiovascular Translational Research 2(4) (2009) 462-470.
[62] A. Agarwal, Y. Farouz, A.P. Nesmith, L.F. Deravi, M.L. McCain, K.K. Parker,
Micropatterning alginate substrates for in vitro cardiovascular muscle on a chip, Advanced
Functional Materials 23(30) (2013) 3738-3746.
[63] B.H. Lorell, B.A. Carabello, Left ventricular hypertrophy: pathogenesis, detection,
and prognosis, Circulation 102(4) (2000) 470-9.
[64] O. Gjesdal, D.A. Bluemke, J.A. Lima, Cardiac remodeling at the population level--
risk factors, screening, and outcomes, Nat Rev Cardiol 8(12) (2011) 673-85.
[65] P. Dimroth, G. Kaim, U. Matthey, Crucial role of the membrane potential for ATP
synthesis by F(1)F(o) ATP synthases., The Journal of experimental biology 203(Pt 1) (2000) 51-
59.
[66] A.M. Gerdes, S.E. Kellerman, J.A. Moore, K.E. Muffly, L.C. Clark, P.Y. Reaves, K.B.
Malec, P.P. McKeown, D.D. Schocken, Structural remodeling of cardiac myocytes in patients with
ischemic cardiomyopathy, Circulation 86(2) (1992) 426-30.
[67] J. Fujita, Y. Itabashi, T. Seki, S. Tohyama, Y. Tamura, M. Sano, K. Fukuda,
Myocardial cell sheet therapy and cardiac function, Am J Physiol Heart Circ Physiol 303(10)
(2012) H1169-82.
[68] C. Mihl, W.R. Dassen, H. Kuipers, Cardiac remodelling: concentric versus eccentric
hypertrophy in strength and endurance athletes, Neth Heart J 16(4) (2008) 129-33.
[69] Y. Mizuguchi, Y. Oishi, H. Miyoshi, A. Iuchi, N. Nagase, T. Oki, Concentric left
ventricular hypertrophy brings deterioration of systolic longitudinal, circumferential, and radial
103

myocardial deformation in hypertensive patients with preserved left ventricular pump function, J
Cardiol 55(1) (2010) 23-33.
[70] A.M. Gerdes, A reliable, efficient, and comprehensive approach to assess myocyte
remodeling in cardiac hypertrophy and failure, Journal of Cardiac Failure 3(1) (1997) 63-68.
[71] A.M. Andres, K.C. Tucker, A. Thomas, D.J. Taylor, D. Sengstock, S.M. Jahania, R.
Dabir, S. Pourpirali, J.A. Brown, D.G. Westbrook, S.W. Ballinger, R.M. Mentzer, R.A. Gottlieb,
Mitophagy and mitochondrial biogenesis in atrial tissue of patients undergoing heart surgery with
cardiopulmonary bypass, JCI Insight 2(4) (2017) e89303.
[72] R.A. Gottlieb, D. Bernstein, Mitochondrial remodeling: Rearranging, recycling, and
reprogramming, Cell Calcium 60(2) (2016) 88-101.
[73] J.C. Chatham, M.E. Young, Metabolic remodeling in the hypertrophic heart: fuel for
thought, Circ Res 111(6) (2012) 666-8.
[74] L.M. Delbridge, K.M. Mellor, D.J. Taylor, R.A. Gottlieb, Myocardial autophagic
energy stress responses--macroautophagy, mitophagy, and glycophagy, Am J Physiol Heart Circ
Physiol 308(10) (2015) H1194-204.
[75] T. Doenst, T.D. Nguyen, E.D. Abel, Cardiac metabolism in heart failure: Implications
beyond atp production, Circulation Research 113(6) (2013) 709-724.
[76] D. Huh, Y.-S. Torisawa, G.A. Hamilton, H.J. Kim, D.E. Ingber, Microengineered
physiological biomimicry: Organs-on-Chips, Lab on a Chip 12(12) (2012) 2156.
[77] M.R.M. Harrison, J. Bussmann, Y. Huang, L. Zhao, A. Osorio, C.G. Burns, C.E.
Burns, H.M. Sucov, A.F. Siekmann, C.L. Lien, Chemokine-Guided Angiogenesis Directs
Coronary Vasculature Formation in Zebrafish, Developmental Cell 33(4) (2015) 442-454.
104

[78] Y. Chen, G.W. Dorn, PINK1-phosphorylated mitofusin 2 is a Parkin receptor for
culling damaged mitochondria, Science 340(6131) (2013) 471-5.
[79] S. Schaller, K. Henriksen, P. Hoegh-Andersen, B.C. Søndergaard, E.U. Sumer, L.B.
Tanko, P. Qvist, M.A. Karsdal, In vitro, ex vivo, and in vivo methodological approaches for
studying therapeutic targets of osteoporosis and degenerative joint diseases: how biomarkers can
assist?, Assay Drug Dev Technol 3(5) (2005) 553-80.
[80] R. Arora, S. Verheule, L. Scott, A. Navarrete, V. Katari, E. Wilson, D. Vaz, J.E. Olgin,
Arrhythmogenic substrate of the pulmonary veins assessed by high-resolution optical mapping,
Circulation 107(13) (2003) 1816-21.
[81] P. Lee, F. Taghavi, P. Yan, P. Ewart, E.A. Ashley, L.M. Loew, P. Kohl, C.
Bollensdorff, C.E. Woods, In situ optical mapping of voltage and calcium in the heart, PLoS One
7(8) (2012) e42562.
[82] J.B. Treweek, K.Y. Chan, N.C. Flytzanis, B. Yang, B.E. Deverman, A. Greenbaum,
A. Lignell, C. Xiao, L. Cai, M.S. Ladinsky, P.J. Bjorkman, C.C. Fowlkes, V. Gradinaru, Whole-
body tissue stabilization and selective extractions via tissue-hydrogel hybrids for high-resolution
intact circuit mapping and phenotyping, Nature Protocols 10(11) (2015) 1860-1896.
[83] M. Attin, C.W. T., Basic Concepts of Optical Mapping Techniques in Cardiac
Electrophysiology, Biol Res Nurs 11(2) (2009) 195-207.
[84] A.D. Corben, M.M. Uddin, B. Crawford, M. Farooq, S. Modi, J. Gerecitano, G.
Chiosis, M.L. Alpaugh, Ex vivo treatment response of primary tumors and/or associated
metastases for preclinical and clinical development of therapeutics, J Vis Exp (92) (2014) e52157.
[85] J.W. Scannell, A. Blanckley, H. Boldon, B. Warrington, Diagnosing the decline in
pharmaceutical R& D efficiency., Nature reviews. Drug discovery 11(3) (2012) 191-200.
105

[86] J. Michaelson, V. Hariharan, H. Huang, Hyperglycemic and hyperlipidemic
conditions alter cardiac cell biomechanical properties, Biophysical Journal 106(11) (2014) 2322-
2329.
[87] J.E. Michaelson, Quantifying Structural and Functional Changes in Cardiac Cells in
an In Vitro Model of Diabetic Cardiomyopathy, Biomedical Engineering, Columbia University,
2014.
[88] J. Sin, A.M. Andres, D.J. Taylor, T. Weston, Y. Hiraumi, A. Stotland, B.J. Kim, C.
Huang, K.S. Doran, R.A. Gottlieb, Mitophagy is required for mitochondrial biogenesis and
myogenic differentiation of C2C12 myoblasts, Autophagy 12(2) (2016) 369-80.
[89] A.M. Andres, G. Hernandez, P. Lee, C. Huang, E.P. Ratliff, J. Sin, C.A. Thornton,
M.V. Damasco, R.A. Gottlieb, Mitophagy is required for acute cardioprotection by simvastatin,
Antioxid Redox Signal 21(14) (2014) 1960-73.
[90] H. Zhang, M.G. Badur, A.S. Divakaruni, S.J. Parker, C. Jäger, K. Hiller, A.N. Murphy,
C.M. Metallo, Distinct Metabolic States Can Support Self-Renewal and Lipogenesis in Human
Pluripotent Stem Cells under Different Culture Conditions, Cell Rep 16(6) (2016) 1536-47.
[91] S.J. Parker, R.U. Svensson, A.S. Divakaruni, A.E. Lefebvre, A.N. Murphy, R.J. Shaw,
C.M. Metallo, LKB1 promotes metabolic flexibility in response to energy stress, Metab Eng
(2016).
[92] A. Bettadapur, G.C. Suh, N.A. Geisse, E.R. Wang, C. Hua, H.A. Huber, A.A. Viscio,
J.Y. Kim, J.B. Strickland, M.L. McCain, Prolonged Culture of Aligned Skeletal Myotubes on
Micromolded Gelatin Hydrogels, Scientific Reports 6 (2016) 28855.
[93] V. García-Rúa, M.F. Otero, P.V. Lear, D. Rodreiguez-Penas, S. Feijóo-Bandín, T.
Noguera-Moreno, M. Calaza, M. Álvarez-Barredo, A. Mosquera-Leal, J. Parrington, J. Brugada,
106

M. Portolés, M. Rivera, J.R. González-Juanatey, F. Lago, Increased Expression of Fatty-Acid and
Calcium Metabolism Genes in Failing Human Heart, PLoS ONE 7(6) (2012) e37505.
[94] K.B. Kaufmann, H. Büning, A. Galy, A. Schambach, M. Grez, Gene therapy on the
move, EMBO Mol Med 5(11) (2013) 1642-61.
[95] L. Naldini, Ex vivo gene transfer and correction for cell-based therapies, Nat Rev
Genet 12(5) (2011) 301-15.
[96] K.M. Holzem, K.C. Vinnakota, V.K. Ravikumar, E.J. Madden, G.A. Ewald, K.
Dikranian, D.A. Beard, I.R. Efimov, Mitochondrial structure and function are not different
between nonfailing donor and end-stage failing human hearts, FASEB J 30(8) (2016) 2698-707.
[97] N.C. Walsh, L.L. Kenney, S. Jangalwe, K.E. Aryee, D.L. Greiner, M.A. Brehm, L.D.
Shultz, Humanized Mouse Models of Clinical Disease, Annu Rev Pathol 12 (2017) 187-215.
[98] M.A. Brehm, L.D. Shultz, D.L. Greiner, Humanized mouse models to study human
diseases, Curr Opin Endocrinol Diabetes Obes 17(2) (2010) 120-5.
[99] R. Ito, T. Takahashi, I. Katano, M. Ito, Current advances in humanized mouse models,
Cell Mol Immunol 9(3) (2012) 208-14.
[100] J.R. Masters, Human cancer cell lines: fact and fantasy, Nat Rev Mol Cell Biol 1(3)
(2000) 233-6.
[101] G. Wang, M.L. McCain, L. Yang, A. He, F.S. Pasqualini, A. Agarwal, H. Yuan, D.
Jiang, D. Zhang, L. Zangi, J. Geva, A.E. Roberts, Q. Ma, J. Ding, J. Chen, D.-Z. Wang, K. Li, J.
Wang, R.J.A. Wanders, W. Kulik, F.M. Vaz, M.A. Laflamme, C.E. Murry, K.R. Chien, R.I.
Kelley, G.M. Church, K.K. Parker, W.T. Pu, Modeling the mitochondrial cardiomyopathy of Barth
syndrome with induced pluripotent stem cell and heart-on-chip technologies., Nature medicine
20(6) (2014) 616-23.
107

[102] E. Poole, M. Reeves, J.H. Sinclair, The use of primary human cells (fibroblasts,
monocytes, and others) to assess human cytomegalovirus function, Methods Mol Biol 1119 (2014)
81-98.
[103] B.K. Chacko, P.A. Kramer, S. Ravi, M.S. Johnson, R.W. Hardy, S.W. Ballinger,
V.M. Darley-Usmar, Methods for defining distinct bioenergetic profiles in platelets, lymphocytes,
monocytes, and neutrophils, and the oxidative burst from human blood, Lab Invest 93(6) (2013)
690-700.
[104] B.K. Chacko, D. Zhi, V.M. Darley-Usmar, T. Mitchell, The Bioenergetic Health
Index is a sensitive measure of oxidative stress in human monocytes, Redox Biology 8 (2016) 43-
50.
[105] M.L. McCain, A. Agarwal, H.W. Nesmith, A.P. Nesmith, K.K. Parker, Micromolded
gelatin hydrogels for extended culture of engineered cardiac tissues, Biomaterials 35(21) (2014)
5462-71.
[106] N.R. Ariyasinghe, C.H. Reck, A.A. Viscio, A.P. Petersen, D.M. Lyra-Leite, N. Cho,
M.L. McCain, Engineering micromyocardium to delineate cellular and extracellular regulation of
myocardial tissue contractility, Integr Biol (Camb) 9(9) (2017) 730-741.
[107] R.N. Palchesko, L. Zhang, Y. Sun, A.W. Feinberg, Development of
Polydimethylsiloxane Substrates with Tunable Elastic Modulus to Study Cell Mechanobiology in
Muscle and Nerve, PLoS ONE 7(12) (2012).
[108] A.W. Feinberg, P.W. Alford, H. Jin, C.M. Ripplinger, A.A. Werdich, S.P. Sheehy,
A. Grosberg, K.K. Parker, Controlling the contractile strength of engineered cardiac muscle by
hierarchal tissue architecture, Biomaterials 33(23) (2012) 5732-5741.
108

[109] M.L. McCain, S.P. Sheehy, A. Grosberg, J.A. Goss, K.K. Parker, Recapitulating
maladaptive, multiscale remodeling of failing myocardium on a chip., Proceedings of the National
Academy of Sciences of the United States of America 110(24) (2013) 9770-5.
[110] M.L. McCain, T. Desplantez, N.A. Geisse, B. Rothen-Rutishauser, H. Oberer, K.K.
Parker, A.G. Kleber, Cell-to-cell coupling in engineered pairs of rat ventricular cardiomyocytes:
relation between Cx43 immunofluorescence and intercellular electrical conductance., American
journal of physiology. Heart and circulatory physiology 302(2) (2012) H443-50.
[111] M.A. Bray, S.P. Sheehy, K.K. Parker, Sarcomere alignment is regulated by myocyte
shape, Cell Motility and the Cytoskeleton 65(8) (2008) 641-651.
[112] F.S. Pasqualini, S.P. Sheehy, A. Agarwal, Y. Aratyn-Schaus, K.K. Parker, Structural
phenotyping of stem cell-derived cardiomyocytes, Stem Cell Reports 4(3) (2015) 340-7.
[113] K.E. Steucke, P.V. Tracy, E.S. Hald, J.L. Hall, P.W. Alford, Vascular smooth muscle
cell functional contractility depends on extracellular mechanical properties, J Biomech 48(12)
(2015) 3044-51.
[114] N. Ojha, S. Roy, J. Radtke, O. Simonetti, S. Gnyawali, J.L. Zweier, P. Kuppusamy,
C.K. Sen, Characterization of the structural and functional changes in the myocardium following
focal ischemia-reperfusion injury., American journal of physiology. Heart and circulatory
physiology 294(6) (2008) H2435-H2443.
[115] S.J. Park, M. Gazzola, K.S. Park, S. Park, V. Di Santo, E.L. Blevins, J.U. Lind, P.H.
Campbell, S. Dauth, A.K. Capulli, F.S. Pasqualini, S. Ahn, A. Cho, H. Yuan, B.M. Maoz, R.
Vijaykumar, J.W. Choi, K. Deisseroth, G.V. Lauder, L. Mahadevan, K.K. Parker, Phototactic
guidance of a tissue-engineered soft-robotic ray, Science 353(6295) (2016) 158-62.
109

[116] S. Hinderer, J. Seifert, M. Votteler, N. Shen, J. Rheinlaender, T.E. Schäffer, K.
Schenke-Layland, Engineering of a bio-functionalized hybrid off-the-shelf heart valve,
Biomaterials 35(7) (2014) 2130-2139.
[117] M.L. McCain, H. Lee, Y. Aratyn-Schaus, A.G. Kleber, K.K. Parker, Cooperative
coupling of cell-matrix and cell-cell adhesions in cardiac muscle, Proc Natl Acad Sci U S A
109(25) (2012) 9881-6.
[118] M.S. Kane, J. Alban, V. Desquiret-Dumas, N. Gueguen, L. Ishak, M. Ferre, P. Amati-
Bonneau, V. Procaccio, D. Bonneau, G. Lenaers, P. Reynier, A. Chevrollier, Autophagy controls
the pathogenicity of OPA1 mutations in dominant optic atrophy, J Cell Mol Med (2017).
[119] R.S. Ross, T.K. Borg, Integrins and the myocardium, Circ Res 88(11) (2001) 1112-
9.
[120] Z. Wang, A.A. Volinsky, N.D. Gallant, Crosslinking effect on polydimethylsiloxane
elastic modulus measured by custom-built compression instrument, Journal of Applied Polymer
Science 131(22) (2014).
[121] D.M. Lyra-Leite, A.M. Andres, A.P. Petersen, N.R. Ariyasinghe, N. Cho, J.A. Lee,
R.A. Gottlieb, M.L. McCain, Mitochondrial function in engineered cardiac tissues is co-regulated
by extracellular matrix elasticity and tissue alignment, Am J Physiol Heart Circ Physiol (2017).
[122] P.W. Alford, A.P. Nesmith, J.N. Seywerd, A. Grosberg, K.K. Parker, Vascular
smooth muscle contractility depends on cell shape, Integr Biol (Camb) 3(11) (2011) 1063-70.
[123] A.W. Feinberg, A. Feigel, S.S. Shevkoplyas, S. Sheehy, G.M. Whitesides, K.K.
Parker, Muscular thin films for building actuators and powering devices, Science 317(5843)
(2007) 1366-70.
110

[124] K.C. Chaw, M. Manimaran, F.E. Tay, S. Swaminathan, Matrigel coated
polydimethylsiloxane based microfluidic devices for studying metastatic and non-metastatic
cancer cell invasion and migration, Biomed Microdevices 9(4) (2007) 597-602.
[125] A.J. Ribeiro, K. Zaleta-Rivera, E.A. Ashley, B.L. Pruitt, Stable, covalent attachment
of laminin to microposts improves the contractility of mouse neonatal cardiomyocytes, ACS Appl
Mater Interfaces 6(17) (2014) 15516-26.
[126] T.K. Feaster, A.G. Cadar, L. Wang, C.H. Williams, Y.W. Chun, J.E. Hempel, N.
Bloodworth, W.D. Merryman, C.C. Lim, J.C. Wu, B.C. Knollmann, C.C. Hong, Matrigel Mattress:
A Method for the Generation of Single Contracting Human-Induced Pluripotent Stem Cell-
Derived Cardiomyocytes, Circ Res 117(12) (2015) 995-1000.
[127] M.L. McCain, A. Agarwal, H.W. Nesmith, A.P. Nesmith, K.K. Parker, Micromolded
gelatin hydrogels for extended culture of engineered cardiac tissues, Biomaterials 35(21) (2014)
5462-5471.
[128] J.W. Nichol, S.T. Koshy, H. Bae, C.M. Hwang, S. Yamanlar, A. Khademhosseini,
Cell-laden microengineered gelatin methacrylate hydrogels, Biomaterials 31(21) (2010) 5536-
5544.
[129] S.I. Ertel, B.D. Ratner, A. Kaul, M.B. Schway, T.A. Horbett, In vitro study of the
intrinsic toxicity of synthetic surfaces to cells, J Biomed Mater Res 28(6) (1994) 667-75.
[130] S. Halldorsson, E. Lucumi, R. Gómez-Sjöberg, R.M.T. Fleming, Advantages and
challenges of microfluidic cell culture in polydimethylsiloxane devices, Biosens Bioelectron 63
(2015) 218-231.
111

[131] K.J. Regehr, M. Domenech, J.T. Koepsel, K.C. Carver, S.J. Ellison-Zelski, W.L.
Murphy, L.A. Schuler, E.T. Alarid, D.J. Beebe, Biological implications of polydimethylsiloxane-
based microfluidic cell culture, Lab Chip 9(15) (2009) 2132-9.
[132] F.S. Pasqualini, A. Agarwal, B.B. O'Connor, Q. Liu, S.P. Sheehy, K.K. Parker,
Traction force microscopy of engineered cardiac tissues, PLoS One 13(3) (2018) e0194706.
[133] A.P. Petersen, D.M. Lyra-Leite, N.R. Ariyasinghe, N. Cho, C.M. Goodwin, J.Y.
Kim, M.L. McCain, Microenvironmental Modulation of Calcium Wave Propagation Velocity in
Engineered Cardiac Tissues, Cellular and Molecular Bioengineering 11(5) (2018) 337-352.
[134] C.E. Rupert, H.H. Chang, K.L.K. Coulombe, Hypertrophy Changes 3D Shape of
hiPSC-Cardiomyocytes: Implications for Cellular Maturation in Regenerative Medicine, Cellular
and Molecular Bioengineering 10(1) (2017) 54-62.
[135] P. Kumar, A. Nagarajan, P.D. Uchil, Analysis of Cell Viability by the Lactate
Dehydrogenase Assay, Cold Spring Harb Protoc 2018(6) (2018) pdb.prot095497.
[136] D. Taylor, R.A. Gottlieb, Parkin-mediated mitophagy is downregulated in browning
of white adipose tissue, Obesity (Silver Spring) 25(4) (2017) 704-712.
[137] C.P. Gonzalez-Hunt, J.P. Rooney, I.T. Ryde, C. Anbalagan, R. Joglekar, J.N. Meyer,
PCR-Based Analysis of Mitochondrial DNA Copy Number, Mitochondrial DNA Damage, and
Nuclear DNA Damage, Curr Protoc Toxicol 67 (2016) 20.11.1-20.11.25.
[138] J.P. Rooney, I.T. Ryde, L.H. Sanders, E.H. Howlett, M.D. Colton, K.E. Germ, G.D.
Mayer, J.T. Greenamyre, J.N. Meyer, PCR Based Determination of Mitochondrial DNA Copy
Number in Multiple Species, in: C.M. Palmeira, A.P. Rolo (Eds.), Mitochondrial Regulation:
Methods and Protocols, Springer New York, New York, NY, 2015, pp. 23-38.
112

[139] M.F. Berry, A.J. Engler, Y.J. Woo, T.J. Pirolli, L.T. Bish, V. Jayasankar, K.J.
Morine, T.J. Gardner, D.E. Discher, H.L. Sweeney, Mesenchymal stem cell injection after
myocardial infarction improves myocardial compliance, Am J Physiol Heart Circ Physiol 290(6)
(2006) H2196-203.
[140] C. Williams, K.P. Quinn, I. Georgakoudi, L.D. Black, 3rd, Young developmental age
cardiac extracellular matrix promotes the expansion of neonatal cardiomyocytes in vitro, Acta
Biomater 10(1) (2014) 194-204.
[141] K.E. Sullivan, K.P. Quinn, K.M. Tang, I. Georgakoudi, L.D. Black, Extracellular
matrix remodeling following myocardial infarction influences the therapeutic potential of
mesenchymal stem cells, Stem Cell Res Ther 5(1) (2014) 14.
[142] M.K. McDermott, T. Chen, C.M. Williams, K.M. Markley, G.F. Payne, Mechanical
properties of biomimetic tissue adhesive based on the microbial transglutaminase-catalyzed
crosslinking of gelatin, Biomacromolecules 5(4) (2004) 1270-9.
[143] C.W. Yung, L.Q. Wu, J.A. Tullman, G.F. Payne, W.E. Bentley, T.A. Barbari,
Transglutaminase crosslinked gelatin as a tissue engineering scaffold, J Biomed Mater Res A 83(4)
(2007) 1039-46.
[144] N. Annabi, K. Tsang, S.M. Mithieux, M. Nikkhah, A. Ameri, A. Khademhosseini,
A.S. Weiss, Highly Elastic Micropatterned Hydrogel for Engineering Functional Cardiac Tissue,
Adv Funct Mater 23(39) (2013).
[145] N. Cho, S.E. Razipour, M.L. McCain, Featured Article: TGF-β1 dominates
extracellular matrix rigidity for inducing differentiation of human cardiac fibroblasts to
myofibroblasts, Exp Biol Med (Maywood) 243(7) (2018) 601-612.
113

[146] E.S. Hald, K.E. Steucke, J.A. Reeves, Z. Win, P.W. Alford, Long-term vascular
contractility assay using genipin-modified muscular thin films, Biofabrication 6(4) (2014) 045005.
[147] S. Kidambi, N. Udpa, S.A. Schroeder, R. Findlan, I. Lee, C. Chan, Cell adhesion on
polyelectrolyte multilayer coated polydimethylsiloxane surfaces with varying topographies,
Tissue Eng 13(8) (2007) 2105-17.
[148] D. Martínez-Martín, G. Fläschner, B. Gaub, S. Martin, R. Newton, C. Beerli, J.
Mercer, C. Gerber, D.J. Müller, Inertial picobalance reveals fast mass fluctuations in mammalian
cells, Nature 550(7677) (2017) 500-505.
[149] L.M. Hanft, K.S. McDonald, Length dependence of force generation exhibit
similarities between rat cardiac myocytes and skeletal muscle fibres, J Physiol 588(Pt 15) (2010)
2891-903.
[150] L.M. Hanft, F.S. Korte, K.S. McDonald, Cardiac function and modulation of
sarcomeric function by length, Cardiovasc Res 77(4) (2008) 627-36.
[151] M. Jastroch, A.S. Divakaruni, S. Mookerjee, J.R. Treberg, M.D. Brand,
Mitochondrial proton and electron leaks., Essays in biochemistry 47 (2010) 53-67.
[152] M.D.D. Brand, D.G.G. Nicholls, Assessing mitochondrial dysfunction in cells,
Biochemical Journal 435(2) (2011) 297-312.
[153] A. Nickel, M. Kohlhaas, C. Maack, Mitochondrial reactive oxygen species
production and elimination, J Mol Cell Cardiol 73 (2014) 26-33.
[154] C.X. Santos, N. Anilkumar, M. Zhang, A.C. Brewer, A.M. Shah, Redox signaling in
cardiac myocytes, Free Radic Biol Med 50(7) (2011) 777-93.
[155] J. Tongers, B. Fiedler, D. König, T. Kempf, G. Klein, J. Heineke, T. Kraft, S.
Gambaryan, S.M. Lohmann, H. Drexler, K.C. Wollert, Heme oxygenase-1 inhibition of MAP
114

kinases, calcineurin/NFAT signaling, and hypertrophy in cardiac myocytes, Cardiovasc Res 63(3)
(2004) 545-52.
[156] X. Long, G. Wu, D.J. Rozanski, M.O. Boluyt, M.T. Crow, E.G. Lakatta, Hypoxia-
induced Haem Oxygenase-1 gene expression in neonatal rat cardiac myocytes, Heart Lung Circ
10(3) (2001) 121-9.
[157] M. Koritzinsky, F. Levitin, T. van den Beucken, R.A. Rumantir, N.J. Harding, K.C.
Chu, P.C. Boutros, I. Braakman, B.G. Wouters, Two phases of disulfide bond formation have
differing requirements for oxygen, J Cell Biol 203(4) (2013) 615-27.
[158] J. Bouitbir, A.L. Charles, A. Echaniz-Laguna, M. Kindo, F. Daussin, J. Auwerx, F.
Piquard, B. Geny, J. Zoll, Opposite effects of statins on mitochondria of cardiac and skeletal
muscles: a 'mitohormesis' mechanism involving reactive oxygen species and PGC-1, Eur Heart J
33(11) (2012) 1397-407.
[159] J. Bouitbir, F. Singh, A.L. Charles, A.I. Schlagowski, A. Bonifacio, A. Echaniz-
Laguna, B. Geny, S. Krähenbühl, J. Zoll, Statins Trigger Mitochondrial Reactive Oxygen Species-
Induced Apoptosis in Glycolytic Skeletal Muscle, Antioxid Redox Signal 24(2) (2016) 84-98.
[160] P.M. Herst, M.R. Rowe, G.M. Carson, M.V. Berridge, Functional Mitochondria in
Health and Disease, Front Endocrinol (Lausanne) 8 (2017) 296.
[161] M. Valera-Alberni, C. Cantó, Mitochondrial stress management: a dynamic journey,
Cell Stress 2(10) (2018) 253-274.
[162] J.W. Nichol, S.T. Koshy, H. Bae, C.M. Hwang, S. Yamanlar, A. Khademhosseini,
Cell-laden microengineered gelatin methacrylate hydrogels, Biomaterials 31(21) (2010) 5536-44.
[163] T.K. Borg, K. Rubin, E. Lundgren, K. Borg, B. Obrink, Recognition of extracellular
matrix components by neonatal and adult cardiac myocytes, Dev Biol 104(1) (1984) 86-96.
115

[164] W. Nadruz, Myocardial remodeling in hypertension, J Hum Hypertens 29(1) (2015)
1-6.
[165] W. Grossman, D. Jones, L.P. McLaurin, Wall stress and patterns of hypertrophy in
the human left ventricle, J Clin Invest 56(1) (1975) 56-64.
[166] C.W. Doering, J.E. Jalil, J.S. Janicki, R. Pick, S. Aghili, C. Abrahams, K.T. Weber,
Collagen network remodelling and diastolic stiffness of the rat left ventricle with pressure overload
hypertrophy, Cardiovasc Res 22(10) (1988) 686-95.
[167] S. Choi, Y. Hong, I. Lee, D. Huh, T.J. Jeon, S.M. Kim, Effects of various
extracellular matrix proteins on the growth of HL-1 cardiomyocytes, Cells Tissues Organs 198(5)
(2013) 349-56.
[168] A.J. Shanker, K. Yamada, K.G. Green, K.A. Yamada, J.E. Saffitz, Matrix-protein-
specific regulation of Cx43 expression in cardiac myocytes subjected to mechanical load, Circ Res
96(5) (2005) 558-66.
[169] T.J. Herron, A.M. Rocha, K.F. Campbell, D. Ponce-Balbuena, B.C. Willis, G.
Guerrero-Serna, Q. Liu, M. Klos, H. Musa, M. Zarzoso, A. Bizy, J. Furness, J. Anumonwo, S.
Mironov, J. Jalife, Extracellular Matrix-Mediated Maturation of Human Pluripotent Stem Cell-
Derived Cardiac Monolayer Structure and Electrophysiological Function, Circ Arrhythm
Electrophysiol 9(4) (2016) e003638.
[170] T. Matsushita, M. Oyamada, K. Fujimoto, Y. Yasuda, S. Masuda, Y. Wada, T. Oka,
T. Takamatsu, Remodeling of cell-cell and cell-extracellular matrix interactions at the border zone
of rat myocardial infarcts, Circ Res 85(11) (1999) 1046-55.
[171] E. Lundgren, L. Terracio, T.K. Borg, Adhesion of cardiac myocytes to extracellular
matrix components, Basic Res Cardiol 80 Suppl 1 (1985) 69-74.
116

[172] S.P. Sheehy, F. Pasqualini, A. Grosberg, S.J. Park, Y. Aratyn-Schaus, K.K. Parker,
Quality metrics for stem cell-derived cardiac myocytes, Stem Cell Reports 2(3) (2014) 282-94.
[173] X. Yang, L. Pabon, C.E. Murry, Engineering adolescence: maturation of human
pluripotent stem cell-derived cardiomyocytes, Circ Res 114(3) (2014) 511-23.
[174] R. Guzun, M. Gonzalez-Granillo, M. Karu-Varikmaa, A. Grichine, Y. Usson, T.
Kaambre, K. Guerrero-Roesch, A. Kuznetsov, U. Schlattner, V. Saks, Regulation of respiration in
muscle cells in vivo by VDAC through interaction with the cytoskeleton and MtCK within
Mitochondrial Interactosome, Biochimica et Biophysica Acta - Biomembranes 1818(6) (2012)
1545-1554.
[175] F. Bonafè, M. Govoni, E. Giordano, C.M. Caldarera, C. Guarnieri, C. Muscari,
Hyaluronan and cardiac regeneration, J Biomed Sci 21 (2014) 100.
[176] A. Chopra, V. Lin, A. McCollough, S. Atzet, G.D. Prestwich, A.S. Wechsler, M.E.
Murray, S.A. Oake, J.Y. Kresh, P.A. Janmey, Reprogramming cardiomyocyte mechanosensing by
crosstalk between integrins and hyaluronic acid receptors, J Biomech 45(5) (2012) 824-31.
[177] P.W. Burridge, E. Matsa, P. Shukla, Z.C. Lin, J.M. Churko, A.D. Ebert, F. Lan, S.
Diecke, B. Huber, N.M. Mordwinkin, J.R. Plews, O.J. Abilez, B. Cui, J.D. Gold, J.C. Wu,
Chemically defined generation of human cardiomyocytes., Nature methods 11(8) (2014) 855-60.
[178] X. Lian, J. Zhang, S.M. Azarin, K. Zhu, L.B. Hazeltine, X. Bao, C. Hsiao, T.J. Kamp,
S.P. Palecek, Directed cardiomyocyte differentiation from human pluripotent stem cells by
modulating Wnt/β-catenin signaling under fully defined conditions., Nature protocols 8(1) (2013)
162-75.
[179] G. Wang, M.L. McCain, L. Yang, A. He, F.S. Pasqualini, A. Agarwal, H. Yuan, D.
Jiang, D. Zhang, L. Zangi, J. Geva, A.E. Roberts, Q. Ma, J. Ding, J. Chen, D.Z. Wang, K. Li, J.
117

Wang, R.J. Wanders, W. Kulik, F.M. Vaz, M.A. Laflamme, C.E. Murry, K.R. Chien, R.I. Kelley,
G.M. Church, K.K. Parker, W.T. Pu, Modeling the mitochondrial cardiomyopathy of Barth
syndrome with induced pluripotent stem cell and heart-on-chip technologies, Nat Med 20(6)
(2014) 616-23.
[180] J.T. Hinson, A. Chopra, N. Nafissi, W.J. Polacheck, C.C. Benson, S. Swist, J.
Gorham, L. Yang, S. Schafer, C.C. Sheng, A. Haghighi, J. Homsy, N. Hubner, G. Church, S.A.
Cook, W.A. Linke, C.S. Chen, J.G. Seidman, C.E. Seidman, HEART DISEASE. Titin mutations
in iPS cells define sarcomere insufficiency as a cause of dilated cardiomyopathy, Science
349(6251) (2015) 982-6.
[181] N. Huebsch, P. Loskill, N. Deveshwar, C.I. Spencer, L.M. Judge, M.A. Mandegar,
C.B. Fox, T.M. Mohamed, Z. Ma, A. Mathur, A.M. Sheehan, A. Truong, M. Saxton, J. Yoo, D.
Srivastava, T.A. Desai, P.L. So, K.E. Healy, B.R. Conklin, Miniaturized iPS-Cell-Derived Cardiac
Muscles for Physiologically Relevant Drug Response Analyses, Sci Rep 6 (2016) 24726.
[182] P.W. Burridge, Y.F. Li, E. Matsa, H. Wu, S.G. Ong, A. Sharma, A. Holmström, A.C.
Chang, M.J. Coronado, A.D. Ebert, J.W. Knowles, M.L. Telli, R.M. Witteles, H.M. Blau, D.
Bernstein, R.B. Altman, J.C. Wu, Human induced pluripotent stem cell-derived cardiomyocytes
recapitulate the predilection of breast cancer patients to doxorubicin-induced cardiotoxicity, Nat
Med 22(5) (2016) 547-56.
[183] S. Givvimani, S.B. Pushpakumar, N. Metreveli, S. Veeranki, S. Kundu, S.C. Tyagi,
Role of mitochondrial fission and fusion in cardiomyocyte contractility, Int J Cardiol 187 (2015)
325-33.
[184] J.C. Nawroth, L.L. Scudder, R.T. Halvorson, J. Tresback, J.P. Ferrier, S.P. Sheehy,
A. Cho, S. Kannan, I. Sunyovszki, J.A. Goss, P.H. Campbell, K.K. Parker, Automated fabrication
118

of photopatterned gelatin hydrogels for organ-on-chips applications, Biofabrication 10(2) (2018)
025004.
[185] V.J. Kujala, F.S. Pasqualini, J.A. Goss, J.C. Nawroth, K.K. Parker, Laminar
ventricular myocardium on a microelectrode array-based chip, Journal of Materials Chemistry B
4(20) (2016) 3534-3543.
[186] K.S. Mdaki, T.D. Larsen, L.J. Weaver, M.L. Baack, Age Related Bioenergetics
Profiles in Isolated Rat Cardiomyocytes Using Extracellular Flux Analyses, PLoS One 11(2)
(2016) e0149002.
[187] T. Tiivel, L. Kadaya, A. Kuznetsov, T. Kaambre, N. Peet, P. Sikk, U. Braun, R.
Ventura-Clapier, V. Saks, E.K. Seppet, Developmental changes in regulation of mitochondrial
respiration by ADP and creatine in rat heart in vivo, Mol Cell Biochem 208(1-2) (2000) 119-28.
[188] T. Anmann, M. Varikmaa, N. Timohhina, K. Tepp, I. Shevchuk, V. Chekulayev, V.
Saks, T. Kaambre, Formation of highly organized intracellular structure and energy metabolism in
cardiac muscle cells during postnatal development of rat heart, Biochim Biophys Acta 1837(8)
(2014) 1350-61.
[189] T. Ishihara, R. Ban-Ishihara, M. Maeda, Y. Matsunaga, A. Ichimura, S. Kyogoku, H.
Aoki, S. Katada, K. Nakada, M. Nomura, N. Mizushima, K. Mihara, N. Ishihara, Dynamics of
mitochondrial DNA nucleoids regulated by mitochondrial fission is essential for maintenance of
homogeneously active mitochondria during neonatal heart development, Mol Cell Biol 35(1)
(2015) 211-23.
[190] S. Chung, D.K. Arrell, R.S. Faustino, A. Terzic, P.P. Dzeja, Glycolytic network
restructuring integral to the energetics of embryonic stem cell cardiac differentiation, J Mol Cell
Cardiol 48(4) (2010) 725-34.
119

[191] H. Bugger, M. Schwarzer, D. Chen, A. Schrepper, P.A. Amorim, M. Schoepe, T.D.
Nguyen, F.W. Mohr, O. Khalimonchuk, B.C. Weimer, T. Doenst, Proteomic remodelling of
mitochondrial oxidative pathways in pressure overload-induced heart failure, Cardiovasc Res
85(2) (2010) 376-84.
[192] T. Doenst, G. Pytel, A. Schrepper, P. Amorim, G. Färber, Y. Shingu, F.W. Mohr, M.
Schwarzer, Decreased rates of substrate oxidation ex vivo predict the onset of heart failure and
contractile dysfunction in rats with pressure overload, Cardiovasc Res 86(3) (2010) 461-70.
[193] G. Kaushik, A.J. Engler, From stem cells to cardiomyocytes: The role of forces in
cardiac maturation, aging, and disease, 1 ed., Elsevier Inc.2014.
[194] N. Bursac, K.K. Parker, S. Iravanian, L. Tung, Cardiomyocyte cultures with
controlled macroscopic anisotropy: a model for functional electrophysiological studies of cardiac
muscle, Circ Res 91(12) (2002) e45-54.
[195] D. Qin, Y. Xia, G.M. Whitesides, Soft lithography for micro- and nanoscale
patterning, Nat Protoc 5(3) (2010) 491-502.
[196] N. Hu, E.B. Clark, Hemodynamics of the stage 12 to stage 29 chick embryo, Circ
Res 65(6) (1989) 1665-70.
[197] A.M. Segura, O.H. Frazier, L.M. Buja, Fibrosis and heart failure, Heart Failure
Reviews 19(2) (2014) 173-185.
[198] P. Anversa, R. Ricci, G. Olivetti, Quantitative structural analysis of the myocardium
during physiologic growth and induced cardiac hypertrophy: a review, J Am Coll Cardiol 7(5)
(1986) 1140-9.
[199] N. Frey, H.A. Katus, E.N. Olson, J.A. Hill, Hypertrophy of the heart: a new
therapeutic target?, Circulation 109(13) (2004) 1580-9.
120

[200] D.J. Grieve, J.A. Byrne, A.C. Cave, A.M. Shah, Role of oxidative stress in cardiac
remodelling after myocardial infarction, Heart Lung Circ 13(2) (2004) 132-8.
[201] K. Sugamura, J.F. Keaney, Reactive oxygen species in cardiovascular disease, Free
Radical Biology and Medicine 51(5) (2011) 978-992.
[202] M. Hori, K. Nishida, Oxidative stress and left ventricular remodelling after
myocardial infarction, Cardiovasc Res 81(3) (2009) 457-64.
[203] Y. Chen, Y. Liu, G.W. Dorn, Mitochondrial fusion is essential for organelle function
and cardiac homeostasis, Circulation Research 109(12) (2011) 1327-1331.
[204] A.R. Hall, N. Burke, R.K. Dongworth, D.J. Hausenloy, Mitochondrial fusion and
fission proteins: Novel therapeutic targets for combating cardiovascular disease, British Journal of
Pharmacology 171(8) (2014) 1890-1906.
[205] T. Andrienko, A. Kuznetsov, T. Kaambre, Y. Usson, A. Orosco, F. Appaix, T. Tiivel,
P. Sikk, M. Vendelin, R. Margreiter, S. VA., Metabolic consequences of functional complexes of
mitochondria, myofibrils and sarcoplasmic reticulum in muscle cells, Journal of Experimental
Biology 206 (2003) 2059-2072.
[206] M.B. Knight, N.K. Drew, L.A. McCarthy, A. Grosberg, Emergent Global Contractile
Force in Cardiac Tissues, Biophys J 110(7) (2016) 1615-24.
[207] C.A. Walker, The structure and function of the cardiac myocyte: A review of
fundamental concepts, The Journal of Thoracic and Cardiovascular Surgery 118(2) (1999) 375-
382.
[208] G.H. Karunamuni, S. Gu, M.R. Ford, L.M. Peterson, P. Ma, Y.T. Wang, A.M.
Rollins, M.W. Jenkins, M. Watanabe, Capturing structure and function in an embryonic heart with
biophotonic tools, Front Physiol 5 (2014) 351.
121

[209] M. Rienks, A.P. Papageorgiou, N.G. Frangogiannis, S. Heymans, Myocardial
extracellular matrix: An ever-changing and diverse entity, Circulation Research 114(5) (2014)
872-888.
[210] C.K. Phoon, D. Acehan, M. Schlame, D.L. Stokes, I. Edelman-Novemsky, D. Yu,
Y. Xu, N. Viswanathan, M. Ren, Tafazzin knockdown in mice leads to a developmental
cardiomyopathy with early diastolic dysfunction preceding myocardial noncompaction, J Am
Heart Assoc 1(2) (2012).
[211] C. Jourdan-Lesaux, J. Zhang, M.L. Lindsey, Extracellular matrix roles during cardiac
repair, Life Sci 87(13-14) (2010) 391-400.
[212] P.A. Harvey, L.A. Leinwand, Cellular mechanisms of cardiomyopathy, Journal of
Cell Biology 194(3) (2011) 355-365.
[213] L. Leatherbury, Q. Yu, B. Chatterjee, D.L. Walker, Z. Yu, X. Tian, C.W. Lo, A novel
mouse model of X-linked cardiac hypertrophy, Am J Physiol Heart Circ Physiol 294(6) (2008)
H2701-11.
[214] N.R. Ariyasinghe, D.M. Lyra-Leite, M.L. McCain, Engineering cardiac
microphysiological systems to model pathological extracellular matrix remodeling, American
Journal of Physiology-Heart and Circulatory Physiology 315(4) (2018) H771-H789.
[215] A. Agarwal, J.A. Goss, A. Cho, M.L. McCain, K.K. Parker, Microfluidic heart on a
chip for higher throughput pharmacological studies, Lab Chip 13(18) (2013) 3599-608.
[216] M.L. McCain, T. Desplantez, N.A. Geisse, B. Rothen-Rutishauser, H. Oberer, K.K.
Parker, A.G. Kleber, Cell-to-cell coupling in engineered pairs of rat ventricular cardiomyocytes:
relation between Cx43 immunofluorescence and intercellular electrical conductance, Am J Physiol
Heart Circ Physiol 302(2) (2012) H443-50.
122

[217] D. Zhang, I.Y. Shadrin, J. Lam, H.Q. Xian, H.R. Snodgrass, N. Bursac, Tissue-
engineered cardiac patch for advanced functional maturation of human ESC-derived
cardiomyocytes, Biomaterials 34(23) (2013) 5813-20.
[218] E. Bartolák-Suki, J. Imsirovic, H. Parameswaran, T.J. Wellman, N. Martinez, P.G.
Allen, U. Frey, B. Suki, Fluctuation-driven mechanotransduction regulates mitochondrial-network
structure and function, Nat Mater 14(10) (2015) 1049-57.
[219] T.K. Borg, R.E. Gay, L.D. Johnson, Changes in the distribution of fibronectin and
collagen during development of the neonatal rat heart, Coll Relat Res 2(3) (1982) 211-8.
[220] M. Bellin, M.C. Marchetto, F.H. Gage, C.L. Mummery, Induced pluripotent stem
cells: the new patient?, Nat Rev Mol Cell Biol 13(11) (2012) 713-26.
[221] L. Gamba, A. Amin-Javaheri, J. Kim, D. Warburton, C.L. Lien, Collagenolytic
Activity Is Associated with Scar Resolution in Zebrafish Hearts after Cryoinjury, J Cardiovasc
Dev Dis 4(1) (2017).
[222] M.R. Harrison, J. Bussmann, Y. Huang, L. Zhao, A. Osorio, C.G. Burns, C.E. Burns,
H.M. Sucov, A.F. Siekmann, C.L. Lien, Chemokine-guided angiogenesis directs coronary
vasculature formation in zebrafish, Dev Cell 33(4) (2015) 442-54.
[223] C.L. Lien, M. Schebesta, S. Makino, G.J. Weber, M.T. Keating, Gene expression
analysis of zebrafish heart regeneration, PLoS Biol 4(8) (2006) e260.

123

Appendix I: Supplementary Figures for Enhanced Mitochondrial
Function in Cardiac Myocytes on Matrix-Derived Hydrogels
This appendix contains the two supplementary figures referred to in Chapter 2: .

Figure I-1: Cytotoxicity for different substrates. Average cytotoxicity values for tissues after 3 days in culture. n =
8 for all conditions. Letters above each box indicate a statistical difference (p<0.05) with the condition represented by
the same letter on the x-axis. For example, “a” indicates p<0.05 compared to gelatin hydrogel, 17 kPa. For details
related to statistical analysis, refer to Table II-4.

124

Figure I-2: ECAR measurements in cardiac myocytes. Average experimental ECAR measurements for tissues on
gelatin hydrogels (A), PDMS-fibronectin (B), and PDMS-gelatin (C) at baseline and after addition of oligomycin,
FCCP, and antimycin and rotenone during the mitochondrial stress test. Data are presented as mean ± s.e.m., n = 16
for all conditions.

125

Appendix II: Mitochondrial Function in Engineered Cardiac
Tissues is Regulated by Extracellular Matrix Elasticity and Tissue
Alignment
This appendix contains the expanded statistical comparisons for the different conditions
presented in Chapter 2: . For the One-Way ANOVAs with all datasets, only the statistically
significant results are show in the tables. All statistical analyses were initially performed using
either MATLAB 2017b or MATLAB 2018a, followed by a cross-validation of the results with the
different version of the software.
Table II-1: Statistical analysis for compressive elastic moduli of hydrogel cylinders. Data were normally
distributed, as determined by the Kolmogorov-Smirnov test. For comparisons of samples of different composition,
Before and After PBS incubation, an un-paired t-test was performed. For comparisons of samples of same composition
Before and After PBS incubation, a paired t-test was performed.
Comparison p-value
Before Incubation in PBS
5% gelatin + 2% TG vs 5% gelatin + 4% TG 0.0328
5% gelatin + 2% TG vs 10% gelatin + 2% TG 2.6793e-07
5% gelatin + 2% TG vs 10% gelatin + 4% TG 1.8295e-06
5% gelatin + 4% TG vs 10% gelatin + 2% TG 3.0634e-07
5% gelatin + 4% TG vs 10% gelatin + 4% TG 2.7980e-06
10% gelatin + 2% TG vs 10% gelatin + 4% TG 0.001
After Incubation in PBS
5% gelatin + 2% TG vs 5% gelatin + 4% TG 0.0320
5% gelatin + 2% TG vs 10% gelatin + 2% TG 6.4134e-04
5% gelatin + 2% TG vs 10% gelatin + 4% TG 1.7709e-05
5% gelatin + 4% TG vs 10% gelatin + 2% TG 0.003
5% gelatin + 4% TG vs 10% gelatin + 4% TG 7.7043e-05
10% gelatin + 2% TG vs 10% gelatin + 4% TG 0.1076
Before versus After
5% gelatin + 2% TG vs 5% gelatin + 2% TG 0.0296
5% gelatin + 4% TG vs 10% gelatin + 4% TG 0.0144
10% gelatin + 2% TG vs 10% gelatin + 2% TG 0.1189
10% gelatin + 4% TG vs 10% gelatin + 4% TG 0.0173

126

Table II-2: Statistical analysis for cell number per 0.1mm
2
. All data was normally distributed, as determined by the
Kolmogorov-Smirnov test. Multiple comparisons were performed using Tukey’s test, with the p-values indicated in
the table below.
Comparison p-value
Two-Way ANOVA: Hydrogels
5% gelatin vs 10% gelatin 0.2821
2% TG vs 4% TG 0.2211
Interaction 0.7012
Two-Way ANOVA: PDMS
Elasticity 0.4115
Protein 0.2749
Interaction 0.9716
1.6 kPa vs 27 kPa 0.5967
1.6 kPa vs 2.7 MPa 0.4076
27 kPa vs 2.7 MPa 0.9469
One-Way ANOVA: Subsets
Subset: gelatin hydrogels 0.4205
17 kPa vs 27 kPa 0.6598
17 kPa vs 58 kPa 0.7247
17 kPa vs 72 kPa 0.3632
27 kPa vs 58 kPa 0.9995
27 kPa vs 72 kPa 0.9591
58 kPa vs 72 kPa 0.9297
Subset: PDMS-fibronectin 0.5142
1.6 kPa vs 27 kPa 0.6043
1.6 kPa vs 2.7 MPa 0.5520
27 kPa vs 2.7 MPa 0.9960
Subset: PDMS-gelatin 0.7163
1.6 kPa vs 27 kPa 0.8790
1.6 kPa vs 2.7 MPa 0.6957
27 kPa vs 2.7 MPa 0.9412
One-Way ANOVA: All
All Conditions 5.2404e-05
Hydrogel (17 kPa) vs PDMS-fibronectin (1.6 kPa) 8.4507e-04
Hydrogel (17 kPa) vs PDMS-fibronectin (27 kPa) 0.0209
Hydrogel (17 kPa) vs PDMS-fibronectin (2.7 MPa) 0.0267
Hydrogel (17 kPa) vs PDMS-gelatin (1.6 kPa) 0.0146
Hydrogel (27 kPa) vs PDMS-fibronectin (1.6 kPa) 0.0195
Hydrogel (58 kPa) vs PDMS-fibronectin (1.6 kPa) 0.0150

Table II-3: Statistical analysis for total protein content per well. All data was normally distributed, as determined
by the Kolmogorov-Smirnov test. The p-value for the one-way ANOVA test was 3.4 x 10e-4. Multiple comparisons
127

were performed using Tukey’s test, with the p-values indicated in the table below. Comparisons not listed were not
statistically significant.
Comparison p-value
One-Way ANOVA: Subsets
Subset: gelatin hydrogels 0.0102
17 kPa vs 27 kPa 0.9962
17 kPa vs 58 kPa 0.4120
17 kPa vs 72 kPa 0.0133
27 kPa vs 58 kPa 0.5751
27 kPa vs 72 kPa 0.0304
58 kPa vs 72 kPa 0.3642
Subset: PDMS-fibronectin 0.0168
1.6 kPa vs 27 kPa 0.0707
1.6 kPa vs 2.7 MPa 0.8442
27 kPa vs 2.7 MPa 0.0189
Subset: PDMS-gelatin 0.0315
1.6 kPa vs 27 kPa 0.2565
1.6 kPa vs 2.7 MPa 0.0237
27 kPa vs 2.7 MPa 0.5813
One-Way ANOVA: All
Hydrogel (72 kPa) vs PDMS-fibronectin (27 kPa) 0.0025
Hydrogel (72 kPa) vs PDMS-gelatin (2.7 MPa) 8.0903e-04
PDMS-fibronectin (1.6 kPa) vs PDMS-fibronectin (27 kPa) 0.0238
PDMS-fibronectin (1.6 kPa) vs PDMS-gelatin (2.7 MPa) 0.0137

Table II-4: Statistical analysis for Cytotoxicity. All data was normally distributed, as determined by the
Kolmogorov-Smirnov test. Multiple comparisons were performed using Tukey’s test, with the p-values indicated in
the table below. Comparisons not listed were not statistically significant.
Comparison p-value
Day 3
All Conditions 4.9425e-04
Hydrogel (17 kPa) vs PDMS-fibronectin (2.7 MPa) 0.0375
Day 4
All Conditions 3.54608e-05
Hydrogel (17 kPa) vs PDMS-fibronectin (27 kPa) 0.0229
Hydrogel (17 kPa) vs PDMS-fibronectin (2.7 MPa) 0.0346
Hydrogel (17 kPa) vs PDMS-gelatin (2.7 MPa) 0.0020
Hydrogel (27 kPa) vs PDMS-gelatin (2.7 MPa) 0.0071
Hydrogel (58 kPa) vs PDMS-fibronectin (27 kPa) 0.0316
Hydrogel (58 kPa) vs PDMS-fibronectin (2.7 MPa) 0.0471
Hydrogel (58 kPa) vs PDMS-gelatin (2.7 MPa) 0.0029
Hydrogel (72 kPa) vs PDMS-gelatin (2.7 MPa) 0.0327
Day 5
128

All Conditions 0.0021
Hydrogel (17 kPa) vs PDMS-fibronectin (1.6 kPa) 0.0107
Hydrogel (27 kPa) vs PDMS-fibronectin (1.6 kPa) 0.0227
Hydrogel (58 kPa) vs PDMS-fibronectin (1.6 kPa) 0.0006
Hydrogel (72 kPa) vs PDMS-fibronectin (1.6 kPa) 0.0212
PDMS-fibronectin (1.6 kPa) vs PDMS-gelatin (27 kPa) 0.0291

Table II-5: Statistical analysis for cell height and cell volume. All data was normally distributed, as determined by
the Kolmogorov-Smirnov test. Multiple comparisons were performed using Tukey’s test, with the p-values indicated
in the table below.
Comparison p-value
Cell area
Hydrogel (27 kPa) vs PDMS-fibronectin (27 kPa) 0.6551
Hydrogel (27 kPa) vs PDMS-gelatin (27 kPa) 0.3070
PDMS-fibronectin (27 kPa) vs PDMS-gelatin (27 kPa) 0.7555
Cell height
Hydrogel (27 kPa) vs PDMS-fibronectin (27 kPa) 0.4836
Hydrogel (27 kPa) vs PDMS-gelatin (27 kPa) 0.1787
PDMS-fibronectin (27 kPa) vs PDMS-gelatin (27 kPa) 0.7229
Cell volume
Hydrogel (27 kPa) vs PDMS-fibronectin (27 kPa) 0.6625
Hydrogel (27 kPa) vs PDMS-gelatin (27 kPa) 0.6657
PDMS-fibronectin (27 kPa) vs PDMS-gelatin (27 kPa) 1.0000

Table II-6: Statistical analysis for Basal Respiration OCR. All data was normally distributed, as determined by the
Kolmogorov-Smirnov test. Multiple comparisons were performed using Tukey’s test, with the p-values indicated in
the table below. Comparisons not listed were not statistically significant.
Comparison p-value
Two-Way ANOVA: PDMS
1.6 kPa vs 27 kPa 0.6215
1.6 kPa vs 2.7 MPa 0.0105
27 kPa vs 2.7 MPa 0.0005
One-Way ANOVA: Subsets
Subset: PDMS-gelatin 2.31108e-05
1.6 kPa vs 2.7 MPa 5.4505e-05
27 kPa vs 2.7 MPa 3.4889e-04
Subset: PDMS 0.0002
PDMS-fibronectin (27 kPa) vs PDMS-gelatin (2.7 MPa) 7.6749e-04
PDMS-gelatin (1.6 kPa) vs PDMS-gelatin (2.7 MPa) 5.9013e-04
PDMS-gelatin (27 kPa) vs PDMS-gelatin (2.7 MPa) 0.0036
One-Way ANOVA: All
All Conditions 8.30901e-20
129

Hydrogel (17 kPa) vs PDMS-fibronectin (1.6 kPa) 1.2743e-07
Hydrogel (17 kPa) vs PDMS-fibronectin (27 kPa) 1.6197e-05
Hydrogel (17 kPa) vs PDMS-fibronectin (2.7 MPa) 1.2792e-07
Hydrogel (17 kPa) vs PDMS-gelatin (1.6 kPa) 2.2764e-05
Hydrogel (17 kPa) vs PDMS-gelatin (27 kPa) 1.8589e-06
Hydrogel (17 kPa) vs PDMS-gelatin (2.7 MPa) 1.2692e-07
Hydrogel (27 kPa) vs PDMS-fibronectin (1.6 kPa) 9.5686e-06
Hydrogel (27 kPa) vs PDMS-fibronectin (27 kPa) 0.0161
Hydrogel (27 kPa) vs PDMS-fibronectin (2.7 MPa) 1.5660e-05
Hydrogel (27 kPa) vs PDMS-gelatin (1.6 kPa) 0.0202
Hydrogel (27 kPa) vs PDMS-gelatin (27 kPa) 0.0035
Hydrogel (27 kPa) vs PDMS-gelatin (2.7 MPa) 1.2692e-07
Hydrogel (58 kPa) vs PDMS-fibronectin (1.6 kPa) 1.3141e-07
Hydrogel (58 kPa) vs PDMS-fibronectin (27 kPa) 7.8121e-05
Hydrogel (58 kPa) vs PDMS-fibronectin (2.7 MPa) 1.3537e-07
Hydrogel (58 kPa) vs PDMS-gelatin (1.6 kPa) 1.0764e-04
Hydrogel (58 kPa) vs PDMS-gelatin (27 kPa) 9.7452e-06
Hydrogel (58 kPa) vs PDMS-gelatin (2.7 MPa) 1.2692e-07
Hydrogel (72 kPa) vs PDMS-fibronectin (1.6 kPa) 0.0027
Hydrogel (72 kPa) vs PDMS-fibronectin (2.7 MPa) 0.0039
Hydrogel (72 kPa) vs PDMS-gelatin (2.7 MPa) 1.5479e-07
PDMS-fibronectin (27 kPa) vs PDMS-gelatin (2.7 MPa) 0.0045
PDMS-gelatin (1.6 kPa) vs PDMS-gelatin (2.7 MPa) 0.0035
PDMS-gelatin (27 kPa) vs PDMS-gelatin (2.7 MPa) 0.0199

Table II-7: Statistical analysis for Basal Glycolytic Activity. All data was normally distributed, as determined by
the Kolmogorov-Smirnov test. Multiple comparisons were performed using Tukey’s test, with the p-values indicated
in the table below. Comparisons not listed were not statistically significant.
Comparison p-value
Two-Way ANOVA: PDMS
1.6 kPa vs 27 kPa 0.8984
1.6 kPa vs 2.7 MPa 7.1582e-07
27 kPa vs 2.7 MPa 1.0647e-07
One-Way ANOVA: All
All Conditions 1.2239e-11
Hydrogel (17 kPa) vs PDMS-fibronectin (2.7 MPa) 2.6738e-04
Hydrogel (17 kPa) vs PDMS-gelatin (2.7 MPa) 1.2692e-07
Hydrogel (27 kPa) vs PDMS-fibronectin (2.7 MPa) 0.0182
Hydrogel (27 kPa) vs PDMS-gelatin (2.7 MPa) 1.3056e-07
Hydrogel (58 kPa) vs PDMS-fibronectin (2.7 MPa) 0.0471
Hydrogel (58 kPa) vs PDMS-gelatin (2.7 MPa) 1.5253e-07
130

Hydrogel (72 kPa) vs PDMS-gelatin (2.7 MPa) 3.0906e-06
PDMS-fibronectin (1.6 kPa) vs PDMS-gelatin (2.7 MPa) 3.4556e-04
PDMS-fibronectin (27 kPa) vs PDMS-fibronectin (2.7 MPa) 0.0195
PDMS-fibronectin (27 kPa) vs PDMS-gelatin (2.7 MPa) 1.3108e-07
PDMS-fibronectin (2.7 MPa) vs PDMS-gelatin (1.6 kPa) 0.0152
PDMS-gelatin (1.6 kPa) vs PDMS-gelatin (2.7 MPa) 1.2946e-07
PDMS-gelatin (27 kPa) vs PDMS-gelatin (2.7 MPa) 1.0742e-05

Table II-8: Statistical analysis for ATP Production OCR. All data was normally distributed, as determined by the
Kolmogorov-Smirnov test. Multiple comparisons were performed using Tukey’s test, with the p-values indicated in
the table below. Comparisons not listed were not statistically significant.
Comparison p-value
Two-Way ANOVA: PDMS
1.6 kPa vs 27 kPa 0.2480
1.6 kPa vs 2.7 MPa 0.2279
27 kPa vs 2.7 MPa 0.0044
One-Way ANOVA: Subsets
Subset: PDMS-gelatin 0.0012
1.6 kPa vs 2.7 MPa 0.0044
27 kPa vs 2.7 MPa 0.0031
Subset: PDMS 0.0054
PDMS-fibronectin (27 kPa) vs PDMS-gelatin (2.7 MPa) 0.0056
PDMS-gelatin (1.6 kPa) vs PDMS-gelatin (2.7 MPa) 0.0388
PDMS-gelatin (27 kPa) vs PDMS-gelatin (2.7 MPa) 0.0287
One-Way ANOVA: All
All Conditions 3.06259e-19
Hydrogel (17 kPa) vs PDMS-fibronectin (1.6 kPa) 1.2725e-07
Hydrogel (17 kPa) vs PDMS-fibronectin (27 kPa) 6.9758e-05
Hydrogel (17 kPa) vs PDMS-fibronectin (2.7 MPa) 1.5093e-07
Hydrogel (17 kPa) vs PDMS-gelatin (1.6 kPa) 3.0307e-06
Hydrogel (17 kPa) vs PDMS-gelatin (27 kPa) 5.2152e-06
Hydrogel (17 kPa) vs PDMS-gelatin (2.7 MPa) 1.2692e-07
Hydrogel (27 kPa) vs PDMS-fibronectin (1.6 kPa) 1.8693e-06
Hydrogel (27 kPa) vs PDMS-fibronectin (27 kPa) 0.0185
Hydrogel (27 kPa) vs PDMS-fibronectin (2.7 MPa) 4.8700e-05
Hydrogel (27 kPa) vs PDMS-gelatin (1.6 kPa) 0.0019
Hydrogel (27 kPa) vs PDMS-gelatin (27 kPa) 0.0028
Hydrogel (27 kPa) vs PDMS-gelatin (2.7 MPa) 1.2713e-07
Hydrogel (58 kPa) vs PDMS-fibronectin (1.6 kPa) 1.2768e-07
Hydrogel (58 kPa) vs PDMS-fibronectin (27 kPa) 1.2138e-04
Hydrogel (58 kPa) vs PDMS-fibronectin (2.7 MPa) 1.7668e-07
Hydrogel (58 kPa) vs PDMS-gelatin (1.6 kPa) 5.5657e-06
Hydrogel (58 kPa) vs PDMS-gelatin (27 kPa) 9.5406e-06
131

Hydrogel (58 kPa) vs PDMS-gelatin (2.7 MPa) 1.2692e-07
Hydrogel (72 kPa) vs PDMS-fibronectin (1.6 kPa) 5.4876e-05
Hydrogel (72 kPa) vs PDMS-fibronectin (27 kPa) 0.1276
Hydrogel (72 kPa) vs PDMS-fibronectin (2.7 MPa) 9.9387e-04
Hydrogel (72 kPa) vs PDMS-gelatin (1.6 kPa) 0.0214
Hydrogel (72 kPa) vs PDMS-gelatin (27 kPa) 0.0299
Hydrogel (72 kPa) vs PDMS-gelatin (2.7 MPa) 1.4538e-07
PDMS-fibronectin (27 kPa) vs PDMS-gelatin (2.7 MPa) 0.0227

Table II-9: Statistical analysis for Proton Leak OCR. All data was normally distributed, as determined by the
Kolmogorov-Smirnov test. Multiple comparisons were performed using Tukey’s test, with the p-values indicated in
the table below. Comparisons not listed were not statistically significant.
Comparison p-value
Two-Way ANOVA: PDMS
1.6 kPa vs 27 kPa 0.3543
1.6 kPa vs 2.7 MPa 0.0036
27 kPa vs 2.7 MPa 0.1324
One-Way ANOVA: Subsets
Subset: gelatin hydrogels 0.0552
17 kPa vs 72 kPa 0.0445
Subset: PDMS-gelatin 0.0045
1.6 kPa vs 2.7 MPa 0.0032
Subset: PDMS 0.0241
PDMS-gelatin (1.6 kPa) vs PDMS-gelatin (2.7 MPa) 0.0103
One-Way ANOVA: All
All Conditions 0.0047
Hydrogel (17 kPa) vs PDMS-gelatin (2.7 MPa) 0.0085
PDMS-gelatin (1.6 kPa) vs PDMS-gelatin (2.7 MPa) 0.0128

Table II-10: Statistical analysis for Non-Mitochondrial Respiration. All data was normally distributed, as
determined by the Kolmogorov-Smirnov test. Multiple comparisons were performed using Tukey’s test, with the p-
values indicated in the table below. Comparisons not listed were not statistically significant.
Comparison p-value
Two-Way ANOVA: PDMS
1.6 kPa vs 27 kPa 0.3040
1.6 kPa vs 2.7 MPa 0.3362
27 kPa vs 2.7 MPa 0.9977
One-Way ANOVA: All
All Conditions 1.17818e-07
Hydrogel (17 kPa) vs PDMS-fibronectin (1.6 kPa) 1.2440e-04
Hydrogel (17 kPa) vs PDMS-fibronectin (2.7 MPa) 0.0244
Hydrogel (17 kPa) vs PDMS-gelatin (1.6 kPa) 0.0017
132

Hydrogel (17 kPa) vs PDMS-gelatin (27 kPa) 0.0041
Hydrogel (17 kPa) vs PDMS-gelatin (2.7 MPa) 0.0435
Hydrogel (27 kPa) vs PDMS-fibronectin (1.6 kPa) 0.0028
Hydrogel (27 kPa) vs PDMS-gelatin (1.6 kPa) 0.0242
Hydrogel (27 kPa) vs PDMS-gelatin (27 kPa) 0.0479
Hydrogel (58 kPa) vs PDMS-fibronectin (1.6 kPa) 2.5536e-04
Hydrogel (58 kPa) vs PDMS-fibronectin (2.7 MPa) 0.0397
Hydrogel (58 kPa) vs PDMS-gelatin (1.6 kPa) 0.0032
Hydrogel (58 kPa) vs PDMS-gelatin (27 kPa) 0.0073
Hydrogel (72 kPa) vs PDMS-fibronectin (1.6 kPa) 0.0020
Hydrogel (72 kPa) vs PDMS-gelatin (1.6 kPa) 0.0184
Hydrogel (72 kPa) vs PDMS-gelatin (27 kPa) 0.0372

Table II-11: Statistical analysis for Maximum Respiration OCR. All data was normally distributed, as determined
by the Kolmogorov-Smirnov test. Multiple comparisons were performed using Tukey’s test, with the p-values
indicated in the table below. Comparisons not listed were not statistically significant.
Comparison p-value
Two-Way ANOVA: PDMS
1.6 kPa vs 27 kPa 0.2208
1.6 kPa vs 2.7 MPa 0.1715
27 kPa vs 2.7 MPa 0.9896
One-Way ANOVA: Subsets
Subset: gelatin hydrogels 0.0135
17 kPa vs 72 kPa 0.0100
One-Way ANOVA: All
All Conditions 3.59986e-14
Hydrogel (17 kPa) vs PDMS-fibronectin (1.6 kPa) 1.6697e-04
Hydrogel (17 kPa) vs PDMS-fibronectin (27 kPa) 1.2205e-05
Hydrogel (17 kPa) vs PDMS-fibronectin (2.7 MPa) 1.2268e-05
Hydrogel (17 kPa) vs PDMS-gelatin (1.6 kPa) 5.1587e-04
Hydrogel (17 kPa) vs PDMS-gelatin (27 kPa) 1.3736e-07
Hydrogel (17 kPa) vs PDMS-gelatin (2.7 MPa) 1.2953e-07
Hydrogel (27 kPa) vs PDMS-fibronectin (1.6 kPa) 0.0049
Hydrogel (27 kPa) vs PDMS-fibronectin (27 kPa) 5.4877e-04
Hydrogel (27 kPa) vs PDMS-fibronectin (2.7 MPa) 5.5120e-04
Hydrogel (27 kPa) vs PDMS-gelatin (1.6 kPa) 0.0121
Hydrogel (27 kPa) vs PDMS-gelatin (27 kPa) 1.4223e-06
Hydrogel (27 kPa) vs PDMS-gelatin (2.7 MPa) 5.1775e-07
Hydrogel (58 kPa) vs PDMS-fibronectin (1.6 kPa) 0.0012
Hydrogel (58 kPa) vs PDMS-fibronectin (27 kPa) 1.1100e-04
Hydrogel (58 kPa) vs PDMS-fibronectin (2.7 MPa) 1.1153e-04
Hydrogel (58 kPa) vs PDMS-gelatin (1.6 kPa) 0.0033
Hydrogel (58 kPa) vs PDMS-gelatin (27 kPa) 2.9610e-07
133

Hydrogel (58 kPa) vs PDMS-gelatin (2.7 MPa) 1.7408e-08
Hydrogel (72 kPa) vs PDMS-gelatin (27 kPa) 0.0109
Hydrogel (72 kPa) vs PDMS-gelatin (2.7 MPa) 0.0051

Table II-12: Statistical analysis for Spare Respiratory Capacity. All data was normally distributed, as determined
by the Kolmogorov-Smirnov test. Multiple comparisons were performed using Tukey’s test, with the p-values
indicated in the table below. Comparisons not listed were not statistically significant.
Comparison p-value
Two-Way ANOVA: PDMS
1.6 kPa vs 27 kPa 0.0995
1.6 kPa vs 2.7 MPa 0.6481
27 kPa vs 2.7 MPa 0.4613
One-Way ANOVA: All
All Conditions 2.95756e-06
Hydrogel (17 kPa) vs PDMS-fibronectin (27 kPa) 0.0108
Hydrogel (17 kPa) vs PDMS-gelatin (27 kPa) 7.6590e-05
Hydrogel (17 kPa) vs PDMS-gelatin (2.7 MPa) 0.0131
Hydrogel (27 kPa) vs PDMS-fibronectin (27 kPa) 0.0228
Hydrogel (27 kPa) vs PDMS-gelatin (27 kPa) 2.1615e-04
Hydrogel (27 kPa) vs PDMS-gelatin (2.7 MPa) 0.0274
Hydrogel (58 kPa) vs PDMS-fibronectin (27 kPa) 0.0363
Hydrogel (58 kPa) vs PDMS-gelatin (27 kPa) 4.1770e-04
Hydrogel (58 kPa) vs PDMS-gelatin (2.7 MPa) 0.0431

Table II-13: Statistical analysis for Bioenergetic Health Index. All data was normally distributed, as determined by
the Kolmogorov-Smirnov test. Multiple comparisons were performed using Tukey’s test, with the p-values indicated
in the table below. Comparisons not listed were not statistically significant.
Comparison p-value
Two-Way ANOVA: PDMS
1.6 kPa vs 27 kPa 0.5267
1.6 kPa vs 2.7 MPa 0.6438
27 kPa vs 2.7 MPa 0.9809
One-Way ANOVA: All
All Conditions 1.88501e-11
Hydrogel (17 kPa) vs PDMS-fibronectin (1.6 kPa) 0.0067
Hydrogel (17 kPa) vs PDMS-fibronectin (27 kPa) 1.1938e-04
Hydrogel (17 kPa) vs PDMS-fibronectin (2.7 MPa) 0.0125
Hydrogel (17 kPa) vs PDMS-gelatin (1.6 kPa) 2.6260e-04
Hydrogel (17 kPa) vs PDMS-gelatin (27 kPa) 2.1061e-05
Hydrogel (17 kPa) vs PDMS-gelatin (2.7 MPa) 2.9313e-07
Hydrogel (27 kPa) vs PDMS-fibronectin (27 kPa) 0.0039
Hydrogel (27 kPa) vs PDMS-gelatin (1.6 kPa) 0.0074
134

Hydrogel (27 kPa) vs PDMS-gelatin (27 kPa) 9.2008e-04
Hydrogel (27 kPa) vs PDMS-gelatin (2.7 MPa) 1.5227e-05
Hydrogel (58 kPa) vs PDMS-fibronectin (27 kPa) 0.0026
Hydrogel (58 kPa) vs PDMS-gelatin (1.6 kPa) 0.0051
Hydrogel (58 kPa) vs PDMS-gelatin (27 kPa) 5.9851e-04
Hydrogel (58 kPa) vs PDMS-gelatin (2.7 MPa) 9.1048e-06
Hydrogel (72 kPa) vs PDMS-fibronectin (27 kPa) 0.0027
Hydrogel (72 kPa) vs PDMS-gelatin (1.6 kPa) 0.0053
Hydrogel (72 kPa) vs PDMS-gelatin (27 kPa) 6.2548e-04
Hydrogel (72 kPa) vs PDMS-gelatin (2.7 MPa) 9.5951e-06

Table II-14: Statistical analysis for mtDNA:nucDNA. All data was normally distributed, as determined by the
Kolmogorov-Smirnov test. Multiple comparisons were performed using Tukey’s test, with the p-values indicated in
the table below. Comparisons not listed were not statistically significant.
Comparison p-value
One-way ANOVA
All Conditions 0.9974

Abstract (if available)

Abstract The heart is the most metabolically demanding organ in our body due to the energy required for cardiac myocytes to continuously contract from birth to death. Mitochondria are the organelles that provide the energy needed to maintain this contractile activity in the form of ATP. From development to adulthood, and from healthy to diseased states, mitochondrial function, substrate preference, and structure changes in cardiac myocytes. Simultaneously, myocardial tissue remodels, with progressive increases in extracellular matrix (ECM) rigidity and alterations in tissue alignment and cardiac myocyte elongation. Based on these observations, we hypothesize that changes in mitochondrial structure and function are dictated by these physical changes in the tissue. To address this, our goal is to engineer new platforms to identify the effects of multiple parameters within the extracellular microenvironment on mitochondrial structure and function in cardiac myocytes. First, we engineered cell culture microplates with layers of synthetic and natural biomaterials to delineate the effects of ECM elasticity and protein composition on mitochondrial function in engineered NRVM tissues. Our data indicate that gelatin hydrogels enhance several metrics associated with baseline and maximum mitochondrial function in cardiac myocytes, irrespective of substrate rigidity. Second, we created microcontact printed polydimethylsiloxane (PDMS) discs of varying elasticities that were combined with an extracellular flux analysis assay to determine the effects of ECM elasticity and tissue alignment on mitochondrial function in engineered neonatal rat ventricular myocytes (NRVMs). Our data indicate that parameters associated with baseline metabolism are predominantly regulated by ECM elasticity, whereas the ability of tissues to adapt to metabolic stress is regulated by both ECM elasticity and tissue alignment. Collectively, our results provide new understanding of the microenvironmental factors that regulate mitochondrial function and structure in cardiac myocytes, validating the hypothesis that microenvironment regulates mitochondrial function in cardiac myocytes.

Linked assets

University of Southern California Dissertations and Theses

Conceptually similar

PDF

New approaches for precisely engineering heterotypic muscle tissues by naturally and synthetically controlling cell fate

PDF

Engineering a post-infarct human myocardium on a chip to reveal oxygen-dependent crosstalk in cardiac fibroblasts and myocytes

PDF

Engineering scalable two- and three-dimensional striated muscle microtissues for human disease modeling

PDF

Renal extracellular matrix bioactivity in a range of physiological states: novel insights for kidney regeneration

PDF

The regulation, roles, and mechanism of action of mitochondrial-derived-peptides (MDPs) in aging

PDF

Microenvironmental and biomechanical regulation of mitochondrial membrane potential in cancer cells

PDF

Membrane-bound regulation of hematopoietic stem cells

PDF

Metabolic profiling of single hematopoietic stem cells for developing novel ex vivo culture strategies

PDF

Role of the autonomic nervous system in vasoconstriction responses to mental stress in sickle cell disease: a bioengineering perspective

PDF

Improving antitumor efficacy of chimeric antigen receptor-engineered immune cell therapy with synthetic biology and combination therapy approaches

PDF

Multiscale modeling of cilia mechanics and function

Asset Metadata

Creator Lyra Leite, Davi Marco (author)

Core Title Extracellular matrix regulation of mitochondrial function in engineered cardiac myocytes

School Viterbi School of Engineering

Degree Doctor of Philosophy

Degree Program Biomedical Engineering

Publication Date 07/30/2019

Defense Date 06/19/2019

Publisher University of Southern California (original), University of Southern California. Libraries (digital)

Tag biomaterials,extracellular flux analyzer,hydrogels,mechanotransduction,metabolism,microfabrication,OAI-PMH Harvest,PDMS

Format application/pdf (imt)

Language English

Contributor Electronically uploaded by the author (provenance)

Advisor McCain, Megan Laura (committee chair), Khoo, Michael C. K. (committee member), Lien, Ching-Ling Ellen (committee member)

Creator Email davilyra@gmail.com,leite@usc.edu

Permanent Link (DOI) https://doi.org/10.25549/usctheses-c89-200780

Unique identifier UC11662922

Identifier etd-LyraLeiteD-7689.pdf (filename),usctheses-c89-200780 (legacy record id)

Legacy Identifier etd-LyraLeiteD-7689.pdf

Dmrecord 200780

Document Type Dissertation

Format application/pdf (imt)

Rights Lyra Leite, Davi Marco

Type texts

Source University of Southern California (contributing entity), University of Southern California Dissertations and Theses (collection)

Access Conditions The author retains rights to his/her dissertation, thesis or other graduate work according to U.S. copyright law. Electronic access is being provided by the USC Libraries in agreement with the a...

Repository Name University of Southern California Digital Library

Repository Location USC Digital Library, University of Southern California, University Park Campus MC 2810, 3434 South Grand Avenue, 2nd Floor, Los Angeles, California 90089-2810, USA