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Probing the effects of transmembrane domains on the continuum mechanics of lipid bilayers
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Probing the effects of transmembrane domains on the continuum mechanics of lipid bilayers
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Content
Probing the Effects of Transmembrane Domains on the Continuum Mechanics of Lipid Bilayers
by
Lucia Caterina Dalle Ore
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 PHILOSOPY
(MATERIALS SCIENCE)
December 2023
Copyright 2023 Lucia Caterina Dalle Ore
ii
Dedication
Per ardua ad astra
Conducting research and surviving graduate school takes a village. I would like to dedicate this
work to the members of that village who made it possible. Namely, my parents Cristina and
Luciano Dalle Ore, who instilled in me since childhood an unwavering dedication to gaining a
deep and fundamental understanding and appreciation for science and the scientific process, as
well as provided support and encouragement during the times I needed it most. Another person
I’d like to dedicate this work to is my partner, Nicholas Humphrey, without whom I would not be
the person I am today. And last but definitely not least, my friends and colleagues from my time
at Mount Holyoke College, AstraZeneca, and USC, who, through commiseration sessions, voice
and video calls, much-needed coffee runs, and late nights in lab, made surviving graduate school
possible.
iii
Acknowledgements
I would like to formally acknowledge those who made a significant contribution to the
development and execution of this body of work. Firstly, I would like to acknowledge my
advisor, Prof. Noah Malmstadt, for his mentorship and guidance throughout this process, and two
of my committee members, Prof. Wade Zeno and Prof. Peter Chung, for their expertise and
advice. I’d also like to acknowledge Lehao Zhang for his instrumental contributions to the
development and optimization of the VFA pipeline, Chris Hughes and Justin Ong for their hard
work in synthesizing, purifying, and labeling the peptides used herein, and David Johnson for his
assistance with the execution and analysis of various spectroscopic techniques. I’d also like to
thank the rest of the Malmstadt lab members, past and present, for their camaraderie, support,
and inspiration. Finally, I’d like to thank my mentors, Krisna Bhargava and Gertrude Gutierrez
for their invaluable advice and guidance throughout the entire process.
iv
Table of Contents
DEDICATION ..............................................................................................................................II
ACKNOWLEDGEMENTS.......................................................................................................III
LIST OF TABLES ........................................................................................................................V
LIST OF FIGURES.................................................................................................................... VI
ABSTRACT ...............................................................................................................................VII
CHAPTER ONE: INTRODUCTION..........................................................................................1
CHAPTER TWO: DEVELOPMENT OF THE VESICLE FLUCTUATION ANALYSIS
PIPELINE ......................................................................................................................................6
INTRODUCTION.............................................................................................................................6
IMAGE ACQUISITION AND VESICLE SELECTION ..............................................................................7
IMAGE PROCESSING AND THE FLUCTUATION ANALYSIS PIPELINE...................................................8
CHAPTER THREE: CONSTRUCTION OF MODEL LIPID MEMBRANES
INCORPORATING G-PROTEIN COUPLED RECEPTORS (GPCRS)..............................24
INTRODUCTION...........................................................................................................................24
METHODOLOGY..........................................................................................................................26
RESULTS .....................................................................................................................................32
DISCUSSION................................................................................................................................34
CHAPTER FOUR: INCORPORATION OF A MODEL PEPTIDE FOR INDUCING
TRANS-BILAYER MEMBRANE TENSION ..........................................................................39
INTRODUCTION...........................................................................................................................39
METHODOLOGY:.........................................................................................................................41
RESULTS AND DISCUSSION..........................................................................................................49
CONCLUSION ..............................................................................................................................63
CHAPTER FIVE: FUTURE DIRECTIONS............................................................................65
REFERENCES ............................................................................................................................70
APPENDIX ..................................................................................................................................86
METHODOLOGY..........................................................................................................................86
SUPPLEMENTAL FIGURES ............................................................................................................88
v
List of Tables
Table 1. Summary of generated � values for lipid composition and technique. ...........................17
Table 2. List of � values obtained via VFA in this work and in the literature...............................52
vi
List of Figures
Figure 1. An example of an image intensity profile line. ..............................................................10
Figure 2. Examples of the results of Sobel and DoG filtering. .....................................................11
Figure 3. ISS edge detection, image center, and calculated vesicle center....................................14
Figure 4. Fluctuation spectra of a 90:10 DPhPC:Chol GUV.........................................................17
Figure 5. Fluctuation spectra of a 25-micron radius glass bead.. ..................................................19
Figure 6. Fluctuation spectrum of a large GUV displaying gravitational effects..........................20
Figure 7. Outlier correction and number of measurements effects on fluctuation spectra............21
Figure 8. Effects of number of frames analyzed on fluctuation spectra........................................23
Figure 9. Illustration of the detailed protocol steps.......................................................................32
Figure 10. Micrographs comparing protein incorporated GUVs and GUVs without protein.......33
Figure 11. Micrographs of protein incorporated GUVs with line intensity profiles. ....................34
Figure 12. Schematic and results of the fluorescence quenching assay. .......................................50
Figure 13. Demonstrative fluctuation spectrum of a 90:10 mol% DPhPC:Chol GUV. ................51
Figure 14. Third harmonic mode MSAs of control and peptide-exposed GUVs..........................53
Figure 15. Dynamic fluctuation spectra and third mode MSAs for each condition......................54
Figure 16. Phase Contrast micrographs of dTMX-induced GUV membrane distortion...............55
Figure 17. Phase Contrast micrographs and distributions of vesicle morphology subtypes. ........56
Figure 18. Confocal micrographs of hemifusion in control and cTMX-exposed GUVs. .............58
Figure 19. Confocal FRAP studies of interfacial and bilayer regions...........................................59
Figure 20. Fluorescence polarization studies of cTMX and DPhPC:Chol SUVs. ........................62
Figure 21. CD spectra of Wedge1..................................................................................................66
Figure 22. Symmetric insertion behavior of dW1 in POPC GUVs...............................................67
Figure 23. CD Spectra of Wedge2.................................................................................................67
Figure 24. CD Spectra of c- and d-TMX.......................................................................................88
Figure 25. Demonstrative fluctuation spectrum of a nonfluctuating glass bead. ..........................89
Figure 26. Effects of osmolarity changes on DPhPC:Chol SUVs.................................................90
vii
Abstract
The plasma membrane (PM) serves as a multifaceted pillar of the biology, chemistry, and
physics that occurs within, around, and to a mammalian cell, though our understanding of the
relationship between its constituents is still being elucidated. There is a profound asymmetry
imbued throughout the PM, such as lipid acyl chain composition and headgroup charge. While
extensive work has been done to investigate the rich biophysics present in the lipid bilayer,
comparatively less biophysical research exists elucidating the role that transmembrane protein
domains (TMD) have on the fundamental mechanics of the bilayer. Given that transmembrane
proteins constitute up to 50% of the plasma membrane area, we sought to probe whether there
was a biophysical impetus for this sustained TMD asymmetry and to decouple the role that
various biochemical factors have on the fundamental forces shaping the membrane. We first
established a method to reconstitute functional transmembrane protein receptors. We then
developed model peptides to probe their effects on bilayer continuum mechanics. We also
modified a field-standard technique to probe these dynamic interactions. We found that one of
our model peptides significantly suppresses membrane fluctuations at very low bulk solution
concentrations, quantifiable by our modified fluctuation analysis method and corroborated by
microscopy and spectroscopy. This work provides an initial exploration and characterization of
protein-lipid interactions, and a novel perspective on fluctuation analysis. Further work utilizing
our other model peptides and other prototypical transmembrane proteins could deepen our
understanding of the relationship between protein biochemical properties and their subsequent
effects on membrane continuum mechanics.
1
Chapter One: Introduction
The plasma membrane (PM) serves as a multifaceted pillar of the biology, chemistry, and
physics that occurs within, around, and to a mammalian cell, though our understanding of this
structure and its hidden complexities are still being uncovered today. Previously established
schools of thought considered the lipid bilayer as a structural element to be acted on and
reshaped by proteins and provide scaffolding for proteins to act within and upon it1–4
. The
membrane undergoes significant restructuring and remodeling processes, such as endo- and
exocytosis, which are central to cellular function and survival. These processes are largely
protein-mediated and are of great interest to biophysicists, who have replicated some of these
active restructuring mechanisms in model systems5
. However, half of the PM area consists of
proteins, many of which do not have a primary function of membrane restructuring6
. Some
previous biophysical studies have suggested that these intrinsic proteins may modify the
mechanics of the membrane7–10. In addition, peptides with antimicrobial properties have also
been shown to affect the bilayer in disparate ways10–15. However, there is little agreement or
understanding of the seemingly contradictory effects of these inclusions in the bilayer. I seek to
explore a slightly different angle, one that focuses on the effects of passive protein inclusions on
bilayer mechanics through three main aims: first, to create a method that results in functional
transmembrane protein insertion into a minimal cell; then, to develop a tool to analyze the
material properties of these model cells with peptides whose transmembrane structure has been
designed to deconvolute effects of tension, dipole effects, and helix radius on bilayer properties;
and finally, to combine the now validated tool with our previously mentioned formation method
2
to obtain the effects on the continuum mechanics of model cells with select model
transmembrane proteins.
Since the introduction of the fluid mosaic model, we’ve also known that the membrane is
asymmetric in lateral composition, with regions of increased lipid order known as “saturations”,
or acyl tails without double bonds16,17 However, as our experimental techniques have improved,
so has our awareness that yet unanswered questions persist. One such example was highlighted
recently by Lorent et al18. Using advanced lipidomic and proteomic techniques, these authors
showed that the PM is massively asymmetric in both degree of lipid saturation across the exo- or
cytoplasmic leaflets, and also in surface area differential of the proteins that inhabit those
leaflets18. These protein asymmetries are conserved across various eukaryotic phyla, indicating a
strong evolutionary selection pressure for maintaining these asymmetries. Conventional thinking
regarding lipid compositional asymmetry in the plasma membrane focused on trans-bilayer polar
headgroup asymmetry, and under strain due to curvature, while the internal leaflet was under
lateral compression on the convex side of the membrane.
Lorent et al’s paper demonstrated several key findings, and the polar headgroup
asymmetry was just the beginning; there is wealth of asymmetry in biological membranes,
including a numerical imbalance of lipids, where the cytoplasmic contains nearly double the
number of lipids than the exoplasmic leaflet. Many of these cytoplasmic leaflet lipids are
polyunsaturated and anionic in charge. The exoplasmic leaf had far more neutrally charged and
saturated lipids than the cytoplasmic leaf, and finally that the exoplasmic leaflet was under
compression while the cytoplasmic leaflet was under tension. The cytoplasmic leaf averaged
approximately 3.5 unsaturations, or double bonds in the acyl tail region of the lipid molecule. In
addition, in single-pass transmembrane protein domains, there was a significant bias towards a
3
larger protein surface area in the part of the transmembrane domain (TMD) in the cytoplasmicfacing leaflet. The existence of these contradictory asymmetries has energized the field to
examine more closely the relationship between the bilayer and its protein inhabitants. However,
the involvement of the degree of TMD asymmetry indicates a strong biophysical motivation for
the maintaining of this seemingly unfavorable arrangement. We are interested in investigating if
there a biophysical impetus for these phenomena.
Given that transmembrane proteins constitute up to 50% of the plasma membrane area,
we began to probe whether there was a biophysical impetus for this sustained TMD asymmetry
and sought to decouple the role that various biochemical factors have on the membrane6
. We first
established a method to reconstitute functional transmembrane protein receptors. We then
developed model peptides to probe their effects on bilayer continuum mechanics. We also
modified a field-standard technique to probe these dynamic interactions. We found that one of
our model peptides significantly suppresses membrane fluctuations at very low bulk solution
concentrations, quantifiable by our modified fluctuation analysis method and corroborated by
microscopy and spectroscopy. This work provides an initial exploration and characterization of
protein-lipid interactions, and a novel perspective on fluctuation analysis. Further work utilizing
our other model peptides and other prototypical transmembrane proteins could deepen our
understanding of the relationship between protein biochemical properties and their subsequent
effects on membrane continuum mechanics.
One of the tools we can use to begin to probe this question has been used in the
biophysical field since the 1970s 19–22. Vesicle fluctuation analysis is the process of recording an
undulating vesicle using phase contrast microscopy to generate a decomposition of the harmonic
4
modes that contribute to the undulations. This dimensionless fluctuation spectrum contains
information such as how stiff the bilayer is, and under how much tension the vesicle is under.
Bending rigidity (BR), the former property, is what is known as a material property;
under the same compositions and conditions, it should always be the same number (within
reason) and is defined as the energy it takes to bend the bilayer. Vesicle fluctuation analysis
(VFA) studies in giant unilamellar vesicles (GUVs) have shown that vesicles made leaflet-byleaflet via the phase-transfer protocol with asymmetric leaflets showed markedly higher BRs
when compared to their symmetric counterparts23. Hossein and Deserno argue that it’s not just a
change in composition that causes this odd increase in BR, but rather the fact that each individual
leaflet now has different material properties than their symmetric counterparts24. In symmetric
membranes, there is a lateral tension between the two layers that vanishes. However, in
asymmetric systems, this may not be the case, these authors argue, due to a difference in stress
between the two leaflets, leading to a non-equilibrium family of states where one can swap out
the contribution of spontaneous curvature to the overall energy state of the bilayer for this
differential stress.
However, the latter property, membrane tension, can vary wildly within the same sample
under normal vesicle formation conditions as a result of the heterogeneous vesicle quality
population generated by these methods. Tension can be broadly modified in many different ways,
most commonly through an osmotic differential—the imbalance of solutes on one side of the
vesicle than the other. A hyper-osmotic solution is frequently utilized in the field to reduce
average membrane tension in their samples. The benefit of this is twofold. The first benefit is
that a reduction of tension results in more amplified surface undulations in the equatorial plane
of the vesicle, which corresponds to the focal plane where we record these fluctuations. The
5
second is to slightly increase the refractive index differential to take better advantage of the
phase contrast reporting method, which results in a larger and brighter “halo” around the vesicle
edge, as well as the bilayer itself being the darkest point in a sort of “anti-halo”. This allows for
sub-pixel level accuracy in the contour detection algorithms utilized in this work25,26. However,
vesicle formation protocols normally generate many flawed vesicles, which can also have high
tensions as a result of multilamellarity or budded vesicles27. Tubules that bud off the vesicle
surface have also been shown to increase the bending rigidity of the vesicle28. It is typical to
disregard these flawed samples in favor of unilamellar, unperturbed vesicles. It is important to
note that even in these unilamellar, high-quality vesicles, it is typical to see orders-of-magnitude
variations in tension.
Most of our understanding of membrane remodeling is rooted in a biological perspective:
where the various classes of proteins act on the bilayer to reshape it, and where the bilayer plays
a secondary, more structural role. While there has been a great focus on proteins that actively
deform or cause mechanics-shifting changes in local composition, much less work has been done
on the multitudes of proteins that do not fall into these two categories. In this work, we hope to
begin to address some of these gaps in the literature.
6
Chapter Two: Development of the Vesicle Fluctuation Analysis
Pipeline
Introduction
Lipid bilayers are spontaneously self-assembled quasi-two-dimensional structures that are
comprised of amphiphilic lipid molecules. These continuous structures can by characterized by a
number of physical parameters, including bending rigidity, an quantity that describes how much
energy is needed to bend the membrane, and membrane tension, the energy per area required to
change the area of a section of the membrane29–31. Bending rigidity (BR) is considered a material
property, and thus is sensitive to composition, not limited to the phospholipid species present in
the membrane, but also of any additional components, such as cholesterol32–34. GUVs are
commonly utilized as the minimal model system by which one can determine these properties.
GUVs are an excellent platform for these studies as one has exquisite control over vesicle
composition and experimental conditions, allowing for studies on changes in BR as a result of
changes in bilayer composition. One such technique that allows us to measure the BR and
tension of a vesicle in solution is vesicle fluctuation analysis.
Vesicle fluctuation analysis (VFA) is a straightforward experimental technique that
allows for the direct measurement of membrane properties of interest, such as intrinsic curvature,
bending rigidity, and effective tension. In short, VFA accomplishes this through the recording of
shape fluctuations of free-standing, micron-scale GUVs via optical microscopy, resulting in
videos consisting of at least 1500 frames each. The videos are then analyzed by a home-made
software program that then traces the contours of the vesicle. These contours are then Fourier
Transformed (FT) and the variance of the mean square amplitudes (MSAs) of the weights of
each Fourier mode are generated, plotted, and fit against theory to generate the BR and tension
for the vesicle of interest25–27. VFA presents a straightforward method of measuring BR and
7
tension in quasi-spherical, thermally fluctuating GUVs. Other methods of obtaining bulk
mechanical properties require advanced setups or equipment, including micropipette aspiration
(MPA)25,35–38 or small angle x-ray scattering (SAXS)39. However, VFA requires excellent sample
quality and requires special considerations for out-of-equilibrium processes.
The theory motivating VFA was formalized in the 1970’s by Canham and Helfrich,
establishing the connection between the thermal fluctuations driving bilayer shape changes and
the underlying physical properties that control them: curvature, bilayer tension, and
rigidity19,22,40. Helfrich’s method involved FT deviations from a planar membrane, and later, in
1987, Milner and Safran introduced the spherical approach41. This process was verified
experimentally the same year by Zilker, Engelhardt and Sackmann in red blood cells
(RBCs)
42.This was done in parallel to the MPA approach of generating BR and tension from
RBCs, completed by Evans and Rawicz in 199037.
Image acquisition and vesicle selection
Equatorial fluctuations were recorded in phase contrast on a Nikon Eclipse Ti-S using an
40x magnification objective with a 0.60 NA. Vesicles were recorded using µManager software,
specifically the multidimensional acquisition functionality of µManager. The time series was set
to 0 ms to allow the software to capture the images at the highest possible acquisition rate with a
10 ms exposure time for each individual image to allow for adequate contrast. This results in
approximately 1500 images collected per vesicle, collected at approximately 10-25 fps. The low
framerate is acceptable as earlier methods used much slower acquisition rates, and VFA
conducted with spinning disc confocal microscopy (SDCM) acquires images at much lower
framerates (e.g. 10 fps) but results in BR and tension values that are equivalent to PC images
8
acquired at much higher framerates (100+ fps)28. Thus, the framerate is acceptable just so long as
we record the vesicle for a long enough time such that all modes of interest to be able to span
their entire phase space. Vesicles were selected for imaging and subsequent using the following
criteria:
1. No visible surface defects26,28,34,43
i. No budding, tethers
ii. No elongated/ellipsoidal or multilamellar vesicles
2. No near neighbors 28
3. Large enough to resolve surface fluctuations, but not so large that gravity has an effect
26,44
4. Visible fluctuations (no stiff, tense vesicles, oftentimes are multilamellar) 45
5. Freshly made 46,47
Videos were exported as .tif files and analyzed as described in the upcoming subsection.
Image processing and the fluctuation analysis pipeline
Videos of vesicles were checked one final time to ensure that they were good candidates
for our image processing algorithm prior to uploading the .tif files to the Center for Advanced
Research Computing (CARC) at USC. In summary, our videos were analyzed with a home built
VFA image processing and analysis software as follows: we first execute some image
corrections, then we calculate the center of the vesicle using our contour detection algorithm,
then use that calculated center as the starting point for our fluctuation measurements. The precise
location of the bilayer is calculated following a similar algorithm outlined in Pecreaux, 200426.
Once we have acquired the radial measurements, we calculate the time-averaged radius, discrete
9
Fourier coefficients, and mean squared amplitude (MSA) of these coefficients. We then use
either a reduced, order-of-magnitude equation, or a much more robust fitting equation to generate
the bending rigidity and tension.
The first step in our analysis pipeline begins with image corrections, consisting of first a
dark current correction, where we subtract the pixel value corresponding to stochastic noise from
our CMOS camera from each pixel grey value. We then apply the flatfield correction, dividing
the image by a flat field correction image, generated as follows. To obtain the flatfield correction
image, we take 15 images under the same microscopy conditions as our VFA videos at various
arbitrary locations around the glass slide. We then compile all the images into an image stack
using FIJI. FIJI stands for “FIJI is Just ImageJ” and is a version of ImageJ that comes with a lot
of helpful packages pre-installed. We then Z-project the images to the median intensity value. We
subtract the dark current value from the Z-projected image, and then measure the mean grey
value for the resulting image. We take that value and then divide the entire image by it, resulting
in what initially looks like a black image. Adjusting the brightness and contrast reveals the
flatfield correction, which should consist of an image constructed with grey values between 0.8-
1.3 a.u., that contains variable lighting of the sample due to lens biases and any artefacts
introduced by nonspecific lens issues, such as debris. We save this image as our flatfield
correction image and implement the correction by dividing our sample image by this correction
image to generate a sample image that should be free of lens biases or artefacts. We then crop the
image to improve data processing efficiency and remove any debris or other vesicles that may
interfere with the contour detection algorithm.
We use our edge detection algorithm twice, the first to find the center of the vesicle, and
the second to obtain the radial measurements of the bilayer. However, prior to detailing the
10
center-finding and radial measurements algorithms, we must first describe the contour detection
method that serves as the basis for our edge detection.
Our method was designed similarly to Pecreaux’s and Dobereiner’s method, where the
principle of detection is based on a well-established property of phase contrast imaging, where
the refractive index differential between the interior and exterior solutions results in an image
where the vesicle has a lower intensity value, and therefore looks darker, while a “halo”, or a
peak in grey level, appears on the external edge of the vesicle. The grey level intensity line
drawn across this region appears as a sigmoidal shape as a result of the solution asymmetry
across the bilayer, as seen in Figure 1 below.
Figure 1. An example of an image intensity profile line. Gray image intensity is plotted as a function of pixel distance.
Preceaux has shown that we can define the refractive index discontinuity modeled in one
dimension as the following: in short, we define the bilayer as the boundary between two semiinfinite regions with differing refractive indices. We find that the boundary between these two
regions is not actually at the minima of the sigmoidal shape, but rather at the halfway point
between the minima and maxima of the line. However, Pecreaux’s method uses a combination of
11
the slope of the intensity line and the mean intensity of the section as a positional estimate for the
subpixel edge detection. Additionally, edge detection methods reported in the literature
frequently utilize Sobel filtering48. This filter method applies the Sobel-Feldman operator, which
is a discrete differentiation operator which computes the gradient of the image intensity
function49. However, as demonstrated in Figure 1 above, our data does not lend itself to Sobel
filtering due to the amount of high-slope noise that would correspond to edges. We also
considered an additional filtering method, Difference of Gaussians (DoG), which is defined as
the difference of an image with a high radius gaussian filter applied and a smaller radius gaussian
filter to produce an image with emphasized edges. The benefits of DoG filtering are noise
reduction and emphasis of relevant edges but comes at a consequence of image contrast
reduction. Figure 2 demonstrates the results of the applications of the Sobel and DoG filters,
respectively, on an example image.
Figure 2. Examples of the results of Sobel and DoG filtering on an example vesicle. A red arrow highlighting confounding edge
artifacts from DoG filtering has been added.
12
As demonstrated in the figure, the use of the Sobel-Feldman operator results in multiple edges
detected, shown as the speckling of teal dots in the Figure above. In comparison, DoG filtering
results in a much more robust edge detection, but still results in the generation of confounding
edge artifacts, highlighted with a red arrow in Figure 2.
Given that typical edge detection methods commonly utilized in the literature do not
appear to be a good fit for our data, so we had to define and develop our own edge detection
method that addressed the challenges that our data presented. It is important to emphasize that
the edge of the vesicle is not the local minima of the intensity line, but rather closer to the midpoint of the sigmoidal boundary between the lumen and exterior of the vesicle. We have taken
this approach and applied it to our data in the following manner. We know that the bilayer can be
inherently described as a very sharp increase in intensity, going from the anti-halo, or dark region
on the interior of the vesicle, to the exterior halo. Conceptually, we can detect this very sharp
increase by segmenting the profile line into “increasing subsequences” (IS), which are runs of
intensity values that increase. We then take the IS with the largest difference from bottom to top,
assume that’s the anti-halo/halo boundary, and return the middle of the subsequence as the
boundary point. Our implementation is a little more complicated than this because our input
images are relatively noisy, so some of our increasing subsequences are interrupted by
decreasing values. We accommodate this noise by adding a configurable threshold that a
subsequent value can decrease by without breaking a subsequence. Thus, in practice, we are
computing mostly increasing subsequences. We then compute the boundary position using the
halfway point between the min and max of the subsequence, which are the points of maximum
intensity of the anti-halo and halo.
13
The edge detection algorithm samples the bilayer 250 times to ensure adequate
visualization of the contours. The results of the first edge detection are then fed in our center
finding algorithm. This algorithm assumes the following: first that the vesicle is roughly a circle,
and the second that the image center is located within the vesicle. We also know the following:
first, that the vesicle’s true center is a bit off from the image center, and that the polar definition
of a circle with radius ‘a’ centered at coordinates (�!,�) is given by
�(�) = �! cos(� − �) + ./�" − �!
" sin(� − �)"3 (1)
Where `�!` and `ϕ` are the position of the true center of the vesicle. Profile lines are
drawn from center to edge of image, and we use a simple curve fit to the polar equation of a
circle to determine the center coordinates. The radial measurements use the calculated center of
vesicle coordinates as the new start point, rather than the image center as is the case for the
center-finding algorithm, to draw new profile lines to the edge of cropped image. This process
generates a list of 250 radial measurements. An example of our ISS edge detection and centerfinding algorithms has been shown below.
14
Figure 3. Image of a vesicle with the ISS edge detection, image center, and calculated vesicle center overlaid. Figure 3. Image of
a vesicle with the ISS edge detection, image center, and calculated vesicle center highlighted with red, black, and blue X's
respectively..
We then take these measurements to calculate the following. First, we calculate the timeaveraged mean radius, �. Let point � = 0 be � = � and � = � + 1 the point � = 1, with � defined
as the radial measurement, � the corresponding angle of �, and � as the number of
measurements.
� = 1
2�=>
�# + �#$%
2 ? (�#$% − �#)
&
#'%
(2)
Since we have 250 equally spaced radial measurements, (�#$% − �#) is a constant value. We can
then use the time-averaged mean radius to obtain the adimensional Fourier coefficients:
15
�( = 1
��=[�# cos(��#) + �#$% cos(��#$%)] C
(�#$% − �#)
2 D
&
#'%
(3)
�( = 1
��=[�# sin(��#) + �#$% sin(��#$%)] C
(�#$% − �#)
2 D
&
#'%
(4)
We can then calculate the experimentally determined HI�)I
"
K as, where ⟨�⟩ stands for the average
of all contours and is defined as: ⟨�⟩ = (1⁄�) ∑ �(�#) &
# :
HI�)I
"
K = 1
4 RHS�)(�#) − T�)UV
"
K + HS�)(�#) − T�)UV
"
KW (5)
We have replicated Gracia, et al.’s technique for taking our radial measurements,
executing the discrete Fourier analysis, and finally determining bending rigidity, which I will
explain in more detail below. As shown by Milner and Safran41, the equilibrium fluctuations of a
quasi-spherical vesicle can be described as:
�(�,�) = � Y1 +=�*+�*+(�,�)
*+
\ (6)
Where � and � are the polar and azimuthal angles, respectively, � is the time-averaged radius,
and �*+ are the weights of each spherical harmonic mode, �*+. The dimensionless MSAs of the
harmonic modes can be described as
⟨|�*+|"⟩ = �,�
�(� + 2)(� − 1)[�(� + 1) + �c] (7)
Where �,� is the thermal energy, and �c = �-..�"⁄�, where � is the time-averaged mean radius,
�-.. is the effective tension, and � is the bending rigidity of the bilayer.
Experimentally speaking, we can only access the variance of the harmonic modes,
HI�)I
"
K, and the sharpest image of the vesicle contours can be found at the equator of the vesicle,
16
as we can only access the fluctuations in the focal plane. We account for this by setting the polar
angle to � = �⁄2, or the zeroth order azimuthal angle, resulting in the following equation:
HI�)I
"
K = �,�
� = C /�*)[�*)(��� �⁄23]
"]
(� + 2)(� − 1)[�(� + 1) + �c]
D
*!"#
*')
(8)
Where �*) is a normalization factor and is expressed as �*) = (2� + 1)(� − �)!/4�(1 + �)! and
�*) are the associated Legendre polynomials.
We can also use the reduced equation:
HI�)I
"
K = �,� (�� ⁄ / + �c�) (9)
All viable spectra in this work were fit to the above reduced equation using the LevenbergMarquardt algorithm. Since we are using the spherical harmonics approach, we can comfortably
include mode numbers lower than 5 in our fit. The analytical method utilized in Pecreaux’s
publication is based on a planar membrane approximation which deviates significantly from the
spherical approximation below mode 5, and thus modes below 5 must be discarded if you are to
use Pecreaux’s method.
Our algorithm can also generate negative BR values in the case of tense vesicles. Such
cases are thus not considered for analysis, and any spectra that are presented that generate these
values are purely for qualitative comparison purposes.
We validated our VFA pipeline by generating fluctuation spectra of pure DPhPC and
90:10 mol% DPhPC:Chol GUVs. Vesicles were fabricated using the EF method as outlined
above. Only vesicles that were visibly fluctuating, i.e.- have an effective tension within the range
of −5 × 1001 N m-1 and 2 × 1002 N m-1 were selected for analysis27.
17
Figure 4. Fluctuation spectra of a 90:10 DPhPC:Chol GUV. A) Plot of Fourier modes 1-35, inset image is of the analyzed
vesicle. Scale bar is 8 microns. B) The fitted spectra of modes of interest. Green dots and orange line represent the fitted modes.
Figure 4 shows the generated fluctuation spectra for a pure DPhPC:Chol vesicle, shown
in the inset in Fig. 4. The time-averaged vesicle radius is 7.57 microns, and the fit in Fig. 4
generates a bending rigidity � of 35.32 k3T, or 1.44 × 100%1 J and an effective tension �c of
4.23 × 100%4 N m-1
.
Table 1. Summary of generated � values for lipid composition and technique.
10-3
100
Fluctuation Spectrum of a DPhPC:Chol GUV
10-4
10-5
!
!
"
(dimensionless)
101
Mode Number (q)
(dimensionless)
18
As shown in Table 1, the values that we have generated agree with the literature values of the
bending rigidity for pure DPhPC GUVs that range from 1.17 ± 0.10 × 100%1 to 1.29 ±
0.37 × 100%1 J11,50–52. The slight increase in bending rigidity for our population could be a result
of the inclusion of cholesterol in our sample. It is conventionally thought that the inclusion of
cholesterol to lipid bilayers results in acyl tail stabilization, leading to increases in bending
rigidity, supported by studies on DMPC32,53, SOPC37,54, and POPC34. However, it is important to
note that that the interaction is not so straightforward. Pan et al. note that the effect is dependent
on length and saturation of the acyl tails; where the short, saturated lipids had the most
pronounced increases in bending rigidity, while unsaturated lipids like DOPC had a small
increase at low cholesterol mol%’s, but overall were largely unaffected by increases in
cholesterol content55. Gracia et al. showed that the addition of cholesterol in membranes
containing DOPC results in a very slight increase in bending rigidity as cholesterol content
increases from 0-10 mol%27. In addition, while there are no other studies that generated the
bending rigidity value for specifically DPhPC:Chol GUVs, Bakht et al. note that while DPhPC
packs poorly with cholesterol, but still results in a slight increase of anisotropy, indicating a very
slight level of ordering56. Thus, the slight increase in bending rigidity could be explained by the
addition of cholesterol.
We also wanted to establish the fluctuations generated as a result of noise from our image
processing algorithm. To this point, we recorded an approximately 25-micron radius glass bead
that was suspended in water to increase the contrast and decrease bead-bead adhesion and fed the
resulting contours into our image processing and analysis algorithm. The resulting fluctuation
spectra is shown below.
19
Figure 5. A) Fluctuation spectra of a 25-micron radius glass bead. The inset micrograph is the glass bead in question. Scale bar
is 25 microns. B) Result of attempted fit for modes of interest.
Since the glass bead is not fluctuating, we can reasonably assume that any measured
fluctuations are artifacts of the image processing and analysis algorithm. The highest value of the
variance of the fluctuations is approximately 10-8 a.u., and any variances found in the same order
of magnitude were discarded. The fit in Figure 5b results a significantly negative BR value and
has only been included to demonstrate the suppression of modes for a non-fluctuating sample.
We also wanted to investigate and determine gravitational effects, as well as outlier
correction, number of numerical measurements and number of frames analyzed had on the
subsequently generated fluctuation spectra.
20
Starting with gravitational effects. We initially had gated the maximum analyzed vesicle
radius to 20-25 microns, in line with the literature recommendations for similar compositions. An
example of a larger analyzed GUV is shown in Figure 6 below.
Figure 6. Example of a fluctuation spectrum of a large GUV displaying gravitational effects. Scale bar is 20 microns.
However, generated spectra of vesicles that were approaching or above 15 microns
showed massive increases in the generated BR values, with the curve fitting algorithm providing
BR guesses that were scale of hundreds of kBT. Using the equation provided in Pecreaux et al.,
2004, that states that, neglecting spontaneous curvature, gravitational effects are negligible if
�4 ≲ (12 + Σ) (10)
With �4 = ∆���5⁄� and Σ = ��"⁄�. This is equivalent to the following condition for
the radius:
21
� ≲ �+67 = y
�
� + .z
�
�{
"
+ 4∆��
�
12
2∆��
�
(11)
Using the � and � from our control samples, we find that our �+67 value is close to 15.8
µm, demonstrating that any GUVs analyzed above this radius will have gravitational effects
confounding the data and resulting in significant overestimations of BR.
We also wanted to investigate the ideal number of numerical measurements to take for a
single vesicle, as well as the effects of the number of frames on capturing an accurate fluctuation
spectrum for analysis.
We determined that 250 numerical measurements were enough to capture the
fluctuations, as demonstrated with the example spectra in Figure 7.
Figure 7. Effects of outlier correction (O.C.) and number of measurements (N.M.) on subsequently generated fluctuation spectra.
The orange “Nov Data” corresponds to our current method, and we looked at 150, 250,
and 350 numerical measurements with outlier corrections (150/250/350 O.C. samples), while the
outlier-containing data corresponds to the N.M. samples. We are comparing against the ideal
0 10 20 30
1×10-7
1×10-6
1×10-5
1×10-4
1×10-3
1×10-2
q
<|uq|
2>
150 N.M.
Nov Data
250 N.M.
350 N.M.
150 O.C.
250 O.C.
350 O.C.
Generated Data
22
spectrum, the light blue Generated Data line, which corresponds to generated MSA data under
ideal conditions. We find that the best agreement to our generated data is our 250 and 350
outlier-corrected (O.C.) procedures, and our current operating protocol, represented by the
orange Nov Data line. The Generated Data line continues to evolve at higher mode numbers (q),
but our setup is limited in its detection of small wavelengths, corresponding to higher mode
numbers, above which our spectra result in noise levels in the form of equivalent MSAs above a
particular mode number. In the above case, we are not able to resolve fluctuations above mode
number 10-11.
We also wanted to investigate the effects of number of frames captured on the
subsequent spectra. It is well known that for longer exposure times, usually in the case of
confocal microscopy or older systems, you must capture more frames in order to make sure that
the fluctuation phase space is fully represented28,57. Thus, we compared two systems of interest; a
fully fluctuating control GUV, titled “GUV” in Figure 8 below, and a nonfluctuating GUV
exposed to 80 nM of a peptide that severely suppresses fluctuations, titled “80 nM”. We analyzed
full length videos, corresponding to the 1400 frame conditions, and compared them to just the
first 600 frames of the same video. The corresponding number of frames is noted in the figure
legend.
23
Figure 8. Effects of number of frames analyzed on fluctuation spectra.
Figure 8 demonstrates two crucial points; first, that we need to make sure that we are
accurately capturing the full phase space of the fluctuations when it comes to our fluctuating
samples, as demonstrated by the difference in depth and number of resolvable modes between
the two GUV spectra. The second, and arguably more crucial point to set the stage for our
dynamic VFA data, is that there is effectively no difference in spectra between the number of
frames for our nonfluctuating conditions, illustrating that truncated datasets for these populations
still generates an accurate spectrum.
We have validated our VFA method and pipeline by forming GUVs via electroformation,
then feeding them through our image processing and analysis algorithms, successfully generating
BR and tension values that agree with the literature. We have also used a stationary glass bead to
determine the behavior of a non-fluctuating sample and establish the artifacts generated from our
algorithm. We also established our upper bound for analyzable vesicles, below which
gravitational effects can be ignored, and also validated our outlier correction algorithm, ideal
number of measurements per frame, and total number of frames on the reliability and accuracy of
our VFA pipeline.
24
Chapter Three: Construction of Model Lipid Membranes
Incorporating G-Protein Coupled Receptors (GPCRs)1
Introduction
Synthetic model membranes are powerful tools in the investigation of the fundamental
properties and functions of biomembranes. Giant unilamellar vesicles (GUVs) are one of the
most prominent platforms to study a variety of plasma membrane properties and can be
engineered to mimic different physiological conditions33,58–64. It is well established that the
plasma membrane and its organization play a key role in a multitude of cellular processes, such
as signal transduction, adhesion, endocytosis, and transport65–71.
GUVs have been fabricated using various methods, including gentle hydration72, hydrogel
swelling73, electroformation74, microfluidic techniques75–78, jetting79, and solvent exchange80–82.
Due to challenges in handling integral membrane proteins (IMPs), in vitro platforms to study
them have been limited. GUVs present a simplified platform for studying IMPs in an
environment that mimics their native environment. Although there have been several approaches
for protein reconstitution in GUVs, challenges arise from incorporating proteins with the correct
orientation and maintaining protein functionality83.
Most successful protein-reconstitution in GUVs requires the detergent exchange method;
which involves solubilizing the proteins from their native environment by detergents, followed
by protein purification, then replacing the detergent molecules with lipids through various
methods84. While detergents serve to stabilize the tertiary structure of IMPs during purification,
detergent micelles are a relatively unnatural environment for these proteins, which are better
stabilized, particularly for functional studies, in lipid bilayers85–87. Moreover, incorporating
1 Chapter adapted from published work: ElBaradei, A.*, Dalle Ore, L.C.,* and Malmstadt, N. "Construction of
Model Lipid Membranes Incorporating G-protein Coupled Receptors (GPCRs)." JoVE (Journal of Visualized
Experiments) 180 (2022): e62830. [Asterisks indicate co-first author]
25
functional transmembrane proteins into the lipid bilayer using traditional GUV fabrication
techniques has been difficult due to the size, the delicacy of these proteins, and the additional
detergent exchange steps that would be needed83,88–90. The use of organic solvent to remove
detergents causes protein aggregation and denaturing91. An improved detergent-mediated method
has been promising, however, caution is needed for the detergent removal step and optimization
might be needed for specific proteins84,88. Additionally, methods that utilize electroformation
could restrict the choice of protein and may not be suitable for all lipid compositions especially
charged lipids889293. Another technique that has been used is peptide-induced fusion of large
unilamellar vesicles (LUVs) containing the desired protein with GUVs, though it was found to
be laborious and can lead to the insertion of foreign molecules —the fusogenic peptides90,94,95.
Giant plasma membrane vesicles (GPMVs), which are derived from living cells, can be used to
overcome some of these issues, however they allow minimal control of the resultant lipid and
protein composition70,96,97. Therefore, the integration of IMPs in the bilipid layer of GUVs using
our modified agarose swelling method presents a reliable method to further examine these
proteins in the membrane environment98–101.
Cellular signaling and communication involves a family of proteins known as G proteincoupled receptors (GPCRs); GPCRs are among the largest family of proteins and are associated
with modulating mood, appetite, blood pressure, cardiovascular function, respiration, and sleep
among many other physiological functions102. In this study, we used human serotonin 1A
receptor (5-HT1AR) which is a prototypical member of the GPCR family. 5-HT1AR can be found
in the central nervous system (CNS) and blood vessels; it influences numerous functions such as
cardiovascular, gastrointestinal, endocrine functions, as well as participating in the regulation of
mood103. A large barrier to GPCR research arises from their complex amphiphilic structure, and
26
GUVs present a promising platform for the investigation of various properties of interest,
ranging from protein functionality, lipid-protein interactions, and protein-protein interactions.
Various approaches have been utilized to study lipid-protein interactions such as surface plasmon
resonance (SPR)104,105, nuclear magnetic resonance spectroscopy (NMR)
106,107, protein lipid
overlay (PLO) assay107–110, native mass spectrometry111, isothermal titration calorimetry
(ITC)112,113, and liposome sedimentation assay114,115. Our lab has used the simplified GUV
approach to investigate the lipid-protein interactions effect on protein functionality by
incapsulating BODIPY-GTPγS which binds with the Giα subunit in the active state of the
receptor. Their binding unquenches the fluorophore producing a fluorescence signal that could be
detected over time101. Moreover, various studies investigated Lipid-protein interactions and the
role of proteins in sensing or stabilizing membrane curvature116,117, and utilizing a feasible GUV
approach could be a key advantage.
This protocol demonstrates a straightforward method to incorporate GPCRs into the
membrane of GUVs using a modified agarose hydrogel system73,98. Furthermore, based on our
previous work our method could be suitable for IMPs that can bear short-term exposure to 30-40
°C. Briefly, we spread a thin film of agarose combined with membrane fragments containing the
GPCR of interest. Following gelation of this layer, we deposit a lipid solution atop the agarose
and allow the solvent to evaporate. We then rehydrate the system with an aqueous buffer,
resulting in the formation of GUVs with protein incorporated in the lipid bilayer.
Methodology
PROTOCOL:
1. Protein labeling
1.1. Allow NHS-Rhodamine, 5-HT1A membrane fragments, and one 7 K MWCO spin
desalting column to equilibrate at room temperature.
27
1.2. Dissolve 1 mg of NHS-rhodamine in 100 µL of dimethyl sulfoxide (DMSO).
1.3. Add 5 µL of 1 M sodium bicarbonate solution to increase the pH of 5-HT1AR solution to
pH 8.
1.4. Add 3.66 µL of the NHS-rhodamine solution to 50 µL of the 5-HT1AR solution and
pipette gently up and down in microcentrifuge tube.
NOTE: Ensure to have at least 10x molar excess of NHS-rhodamine.
1.5. Keep the mixture protected from light and put on rotator at room temperature for 1 h.
1.6. Wash a 7k MWCO spin column with 200 µL of 1x phosphate buffer saline (1x PBS)
three times for 1.5 min at 1.5 RCF for each wash.
1.7. Add the labeled protein to one column and balance the amount in another
microcentrifuge tube.
1.8. Spin down labeled protein once for 5 min at 1.5 RCF.
1.9. Take a UV-vis spectrum using a nanodrop spectrophotometer at 280 nm and 554 nm and
calculate the labeling efficiency following the manufacturer’s manual.
1.10 Store the labeled protein covered at 5 °C until further use. In our experience, the solution
is stable for approximately a week after labelling.
2. GUVs with membrane-incorporated 5-HT1A
2.1. Preparation of Materials and Reagents
2.1.1. Allow the protein, lipids and BSA (Bovine serum albumin) to equilibrate to room
temperature.
28
2.1.2. During this time clean the coverslips by placing them in methanol and sonicating for 30
min at 40 °C. Ensure that the methanol completely covers the coverslips and the water level in
the water bath is above the level of the methanol in the container.
NOTE: Methanol is toxic and should be handled in appropriate chemical hood.
2.1.3. Dry off the excess methanol on the coverslips with a gentle stream of air. Place the
coverslip rack in a 40 °C oven for 15 min to ensure that the excess coverslips dry off.
2.1.4. Begin the plasma cleaning process. First place the coverslips into the plasma cleaner and
close the air intake valve to evacuate all the air inside the chamber.
2.1.5. Once the chamber is under vacuum, clean the coverslips for 5 min using high RF power
setting and a near complete vacuum, with only a slight air intake into the vacuum chamber. To
ensure the proper level of plasma, adjust the opening of the vacuum chamber such that
the resultant color of the plasma is a steady, bright pink.
NOTE: It is crucial when using air that the plasma remains a bright pink color for the duration of
the plasma treatment step, as a darker purple color indicates that there is an improper amount of
air in the chamber and will result in a suboptimal plasma treatment.
2.1.6. Once the 5 min is passed shut off the power and release the vacuum.
NOTE: Upon removal from the plasma chamber please ensure that the coverslips remain
covered.
2.2. Hydrogel preparation
2.2.1. Combine 6 mg of ultra-low melting temperature agarose to 300 µL of ultrapure water (i.e.,
2% (w/v) agarose).
NOTE: 2% agarose will be used to make protein-free GUVs. Agarose solution can be kept at 45
°C for two days.
29
2.2.2. Combine 9 mg of ultra-low temperature agarose in 300 µL of ultrapure water for 3 w/v%
agarose by as prepared in step 3.1. 3% agarose will be used to make protein incorporated GUVs.
2.2.3. Vortex the solution briefly before placing them on the 90 °C heat block for 10 min. Then
vortex the tube again before transferring it to 45 °C heat block to keep it in the molten form until
further use.
2.3. Agarose and protein mixing
2.3.1. Mix 21 µL of 3% agarose with the 7 µL of 5-HT1AR membrane fragments. Pipette up and
down slowly many times to ensure adequate mixing. Then incubate at 45 °C for 1 min.
2.4. Hydrogel and Lipid Deposition
2.4.1. For protein-free GUVs: Make a thin film on freshly plasma cleaned coverslips using 20
µL of 2% agarose. Quickly drop another coverslip on top of the agarose droplet and gently slide
the coverslips apart to make a thin film on both coverslips.
NOTE: This step is tricky in that the sliding of the droplet must occur while the agarose is still in
the molten form.
2.4.2. For protein-incorporated GUVs: Pipette the protein/agarose mixture up and down one
more time, then deposit 20 µL of the 2% agarose on a plasma-cleaned coverslip. Follow the slipcasting directions as described above.
2.4.3. Allow the agarose to gel protected from light for 30 min at room temperature.
2.4.4. Deposit the lipids dropwise on top of the agarose layer. Use a total of 10 µL of 2 mg/mL
of 1-palmitoyl-2-oleoyl-glycero-3-phosphocholine (POPC) with 0.4 Mol% 1,2-Dipalmitoyl-snglycero-3-phosphoethanolamine (DPPE) labeled with ATTO 488 (ATTO-488-
DPPE) (or lipid mixture of interest) in chloroform on top of the agarose film. Deposit
the droplets using a gas chromatography needle and spread one droplet at a time around via a
30
gentle air stream.
NOTE: Caution is needed with this step to make a relatively uniform layer of lipids on top of the
hydrogel. Also, chloroform is toxic and should be handled in appropriate chemical hood.
2.4.5. Assemble the Sykes-Moore (S-M) chambers by placing an o-ring on top of the coverslip,
and then placing the top component of the chamber on top of the o-ring. Use the key
provided by the manufacturer to assemble the chamber by screwing the chamber components
together to seal the chamber and prevent any leakage.
NOTE: The top of the chamber should be tightened on the O-ring but caution is
needed to ensure the coverslip stays intact as the coverslip can crack if the O-ring doesn’t sit
properly in the chamber. Also ensure that the chamber is sealed tight enough such that the
chamber does not leak when the swelling solution is added. Failure to tighten the chamber
enough will result in leaks and loss of sample.
2.5. Swelling and Harvesting of Vesicles
2.5.1. Hydrate the entire system by gently pipetting 450 µL of 200 mM sucrose in 1X PBS
and gently tapping the chambers to ensure adequate buffer coverage of the hydrogel-lipid
layers.
NOTE: The sucrose solution can be replaced with a rehydration buffer containing biological
probes of interest.
2.5.2. Place the chambers at 45 °C and cover the top part of the chamber with a coverslip to
prevent evaporation. Allow the sample to swell protected from debris and light for 1 hour.
2.5.3. Add 100 µL of 1 mg/mL BSA in Milli-Q water into each well of a 96-well plate intended
to be used. Incubate at room temperature for one hour.
2.5.4. Wash three times with Milli-Q water and once with 200 mM sucrose in 1X PBS.
31
2.5.5. Finally add 200 mM of glucose in 1x PBS until the addition of the GUV sample solution.
NOTE: BSA was used to block GUV adsorption.
2.5.6. After allowing the hydrogel to swell, gently shake and tap the chamber to dislodge any
GUVs that may remain attached to the hydrogel surface. Then, carefully pipette up the GUVsucrose solution.
NOTE: As an optional step to ensure all vesicles are detached from the surface, gently pipette
some of the sucrose suspension back onto the hydrogel surface
2.5.7. Move the suspension into a previously prepared microcentrifuge tube containing 700 µL
of 200 mM glucose in 1x PBS.
NOTE: The density gradient will lead to settling of the vesicles to the bottom of
the centrifuge tube.
2.5.8. Allow the vesicles to settle for another hour to ensure that the vesicles can sink to the
bottom of the microcentrifuge tube, allowing for optimal collection.
2.5.9. After the settling of GUVs in glucose, transfer 300 µL from the bottom of the centrifuge
tube (the settled vesicles) into the previously prepared and BSA-treated 96-well plate to
examine the vesicles under the confocal microscope.
NOTE: Be sure to avoid the very bottom of the microcentrifuge tube to minimize the amount of
debris collected in the final sample.
2.6.Check the samples under the microscope.
2.6.1. Shine a 488 nm laser on the sample (That allows us to visualize the membrane, as the
bilayer has been labeled with ATTO-488-DPPE)
2.6.2. Shine a 561 nm laser on the sample (That allows us to visualize the protein, since it has
been labeled with NHS-Rhodamine).
32
NOTE: Caution is needed while imaging the sample as photooxidation can destabilize the
vesicles. Vesicles were observed in the same day.
Figure 9. Illustration of the detailed protocol steps. Created with BioRender.com.
Results
The concentration of protein was measured, and the degree of labeling was calculated as the
molar ratio between the dye and the protein to be 1.1. By examining the GUVs using confocal
microscopy, we were able to confirm successful formation and protein integration of the
vesicles. The lipids were labeled with 0.4 mol% ATTO 488-DPPE, and the protein was
covalently labeled via rhodamine NHS-ester modification of primary amines. Figure 2 (a) and
(b) show a protein-incorporated vesicle in the ATTO 488 and rhodamine channels respectively.
All micrographs have been dark current and flatfield corrected. Micrographs (c) and (d) show a
33
negative control GUV with no protein incorporated. Figure 3 (a) and (b) show a proteinincorporated GUV with line intensity profiles given by the dashed white line of the same vesicle
in both channels. The line intensity profile shows a two-dimensional plot of the intensities of the
pixels along the white drawn line within the image. The x-axis is the distance along the line and
the y-axis is the pixel intensity. ImageJ software was used to plot the profile intensity of the
indicated line.
Figure 10. Micrographs comparing protein incorporated GUVs and GUVs without protein (control). Micrographs (a)and (b)
show protein incorporated GUV fluorescence with the respective ATTO 488 and rhodamine channels, respectively. Micrographs
(c) and (d) show a protein omitted GUV when excited with ATTO 488 and rhodamine channels, respectively.
34
Figure 11. Top row shows micrographs of protein incorporated GUVs in ATTO 488 (a) and rhodamine (b) channels. Line
intensity profiles for the indicated white-dashed lines are below. The analysis was performed using ImageJ software.
Discussion
We have identified two steps that are critical to the success of the overall protocol:
plasma treatment and lipid deposition. Plasma cleaning of the coverslips is essential in ensuring
that there is adequate coverage and adhesion of the agarose hydrogel to the glass coverslip.
Plasma cleaning accomplishes two things: first, it removes traces of organic matter from the
glass surface; second, it activates the coverslip surface, allowing for an increase in wettability
as the glass surface hydrophilicity increases118,119. Touching the coverslip surface post-plasma
cleaning will inactivate and contaminate the ultraclean surface and is strongly advised against.
Our recommendation is to only touch the very edges and undersides of the coverslip when
handling the coverslips for the agarose slip casting step. The second critical step is the deposition
of lipids onto the “dry” hydrogel surface. This method uses a dropwise lipid deposition, which
requires a gas chromatography (GC) needle and an air stream to deposit a few microliters of lipid
solution at a time, allowing for precise control of the amount of lipid added and the placement of
35
the lipid film on the hydrogel surface. The drawback of this method is that if not done
carefully, it can result in a few select areas with a thicker lipid film, resulting in reduced GUV
yields. Thus, it is critical to ensure that there is as uniformly thin of a lipid layer as possible on
the surface of the agarose.
One of the most significant benefits of this protocol is the flexibility of the platform
itself; this method lends itself very well to changes in protein and lipid composition, as well as
encapsulation and buffer modifications. This protocol can in principle include any
transmembrane protein, as we have been able to successfully incorporate a number of different
transmembrane proteins, ranging from the adenosine receptor (A2AR) to plant
aquaporins without sacrificing functionality98,100,101. Traditionally, proteins have been
incorporated into GUVs following solubilization by detergents or incorporation into proteoliposomes or small unilamellar vesicles that can be subsequently integrated into a preformed
GUV120. The advantage of our modified hydrogel swelling method is that it removes the
dependency of detergents or intermediate vesicles and provides an intermediate hydrated
scaffold. The benefits of this are twofold: we can stably incorporate functional GPCRs into the
membrane in a more physiologically relevant buffer without relying on detergent exchange
methods that require more preparation and care regarding the concentration of said detergents,
and that the process by which GUVs bud off the surface of the hydrogel allows for the correct
orientation of the proteins in the bilayer121. We have shown that the GUV budding process
involves the coalescence of many smaller nanometer-scale vesicles into larger, micron-scale
vesicles, which encourages correct protein orientation from the beginning. We have shown this
to be the case in previous work; in short, we covalently labelled an antibody targeting a specific
cytosolic loop of the Adenosine receptor and incubated the labeled antibody with the protein,
36
then incorporated the labeled protein into lipid-dye-labeled GUVs using the modified hydrogel
swelling method. We then exposed the protein-incorporated vesicles to a charged quencher,
which is unable to cross the bilayer. We subsequently see a 50% reduction in fluorescence of the
lipid dye, but the fluorescence of the labeled protein remains unaffected by the quencher,
demonstrating proper orientation100.
Previous work out of our lab has investigated the role in which lipid headgroup charge,
zwitterionic and net-ionic charged lipids, as well as buffer and hydrogel properties such as pH,
ionic strength, osmolarity, and hydrogel concentration have on the dynamics of GUV
formation122. In short, lipid charge does not largely affect GUV formation, while buffer
properties such as increases in sucrose concentration (e.g., 500 mM Sucrose in 185 mM ionic
strength PBS buffer) negatively affect GUV formation, resulting in irregularly shaped vesicles
that most likely will not readily lend themselves to harvesting. Acidic solutions (pH = 3) increase
rate of formation, while a more basic solution (pH = 8) suppresses the rate of GUV formation.
GUVs still form at both the acidic and basic buffers, with only marginal differences in vesicle
size. Low agarose concentrations (~0.1 – 1 w/v%) also negatively affect GUV formation due to a
lack of homogenous surface coverage and a decrease in hydrogel swelling, a necessary force in
the coalescence and budding of GUVs off the hydrogel surface. Thus, we have determined that a
2 w/v% final agarose concentration with a sucrose/glucose solution of 100 - 200 mM, combined
with a buffer ionic strength of 185 mM PBS at pH 7.4 achieves a good balance of agarose
swelling, GUV formation rate and subsequent vesicle size. For vesicles that contain protein,
increasing the initial agarose concentration to 3 w/v% allows for a final agarose concentration of
2 w/v% after the addition of the protein solution. In addition to formation dynamics, the
37
sucrose/glucose buffer system also facilitates the sedimentation and subsequent collection of
formed GUVs, as well as visualization under phase contrast microscopy120,123.
There are some points of caution regarding this protocol, specifically regarding the
agarose and the selection of vesicles. For instance, while we use an ultralow melting temperature
agarose, the agarose-water suspension needs to reach at least 60°C to become molten, and
the agarose-protein mixture is incubated at 45 °C. In our experience, this temperature does not
eliminate the activity of 5-HT1AR, but caution is warranted for other proteins. In general, the
agarose we use begins to gel at 20 °C and thus the swelling reaction can take place at
temperatures above 20 °C, but this process cannot function below that temperature. It should also
be noted that the closer the temperature gets to 20 °C, the less efficient the swelling step
becomes, leading to subsequent decreases in GUV yields. The agarose can also present an issue
during the settling and visualization steps, as it can persist at the bottom of the settling/collection
tube as debris. Thus, caution is required for the temperature required to maintain the molten
agarose and ensuring that said temperature will not denature the protein of interest as well
as aspirating the settling solution to avoid any excess suspended agarose from being included in
the final sample. This method in its current state also results in a heterogeneous GUV population
size, with some vesicles displaying multilamellarity and other flawed vesicle phenomena such as
vesicles within vesicles. This is typical of common GUV formation methods and requires
vigilance and discretion when selecting vesicles for microscopy and analysis. GUVs that display
unusually high levels of fluorescence are also not recommended for analysis, as agarose can be
found on the interior of some of these vesicles. Unpublished work out of our lab has been able to
run micropipette aspiration experiments using vesicles made using this technique, illustrating
that the agarose method produces vesicles without mechanics-altering agarose in the lumen.
38
Limitations aside, this protocol presents a robust and straightforward method for generating
protein-incorporated GUVs. It can generate high yields of GUVs in physiologically relevant
conditions that incorporate properly oriented transmembrane proteins into the bilayer without
compromising their functionality. This is a departure from other methods of vesicle formation,
which involve electric currents or gentle hydration, that would significantly damage the structure
of the protein and render it nonfunctional or require further detergent solubilization and removal
steps. Given that GPCRs represent upwards of a third of all pharmaceutical targets, there is
significant interest in being able to study this family of proteins in a highly tunable, highthroughput, biomimetic platform. More specifically, the applications of this work range from the
study of protein-lipid interactions, how the lipid microenvironment influences protein
functionality and localization, and other basic biophysical questions that can inform
pharmaceutical drug development and discovery. An example of this can be found in the work
completed within our lab which has been able to discern variances in receptor functionality as a
result of lipid oxidation.
39
Chapter Four: Incorporation of a model peptide for inducing transbilayer membrane tension
Introduction
Biological membranes delineate regions of biochemical interest and are themselves hosts
and active participants in many vital biological processes. Signal transduction, endo- and
exocytosis, and viral infections are some examples of cellular processes that are mediated
through complex protein-protein, lipid-protein, and lipid-lipid interactions124–126. It is becoming
well-established that these aforementioned ensemble interactions can include modification of
bilayer mechanical properties, which in turn, affect the organization, dynamics, and function of
the proteins residing within the bilayer127.
Several recent biophysical studies have demonstrated that transmembrane receptor
function and localization is intimately connected with lipid microenvironment99,100,128.
Additionally, experimental work has demonstrated that the function of dilute concentrations of
specific types of transmembrane proteins significantly modifies the continuum properties of the
bilayer129,130. However, comparatively less is known about the ensemble effect of passive
transmembrane domain (TMD) inclusion on bilayer continuum mechanics. These studies have
hinted towards the sensitivity of bilayer properties to TMD conformational changes, which is of
great relevance as some estimates place the protein contribution to the interleaflet area of the
plasma membrane around 50%6,16,17. Furthermore, recent shotgun proteomic studies have also
demonstrated a highly conserved surface area asymmetry of single-pass protein TMDs that is
maintained across eukaryotes, suggesting a substantial selective pressure that favors this TMD
construction18. We sought out to develop a tool to evaluate the geometric effects of these TMDs
have on bilayer mechanics.
40
To accomplish this, we modified an idealized alpha helical synthetic peptide first
introduced by Wimley and White131. Membrane-inserting peptides have been widely deployed as
tools to investigate how changes in biomembranes are linked to the structure of membrane
proteins. However, a significant portion of peptides currently found in the literature either derive
from or consist of membrane-active moieties, such as antimicrobial peptides (AMPs). Common
examples of these membrane-active peptides include melittin, alamethicin, and gramicidin.
However, these peptides result in membrane-disruptive pore formation at high concentrations,
which prevent them from approaching a PM-like peptide concentration while maintaining bilayer
stability11,12,132–137. Additionally, other peptides commonly found in the literature also affect
bilayer properties through shallow inclusion restricted to the polar headgroup region or forced
rearrangement of anionic lipids138–140. Other studies have used synthetic peptides that are
monotonically shaped with the goal of observing the effects of TMD surface roughness on
bilayer properties141. The question of TMD length and lateral domain mismatch and sorting has
also been investigated at length142. However, the role that the leaflet area mismatch arising as a
result of the asymmetrical surface area single-pass TMD domains has on bilayer properties is
still an open question.
In this work, we characterize a model peptide that induces an area differential between
bilayer leaflets upon binding, resulting in an increase in apparent membrane tension at low bulk
peptide solution concentrations, and large-scale vesicle restructuring at higher peptide
concentrations. We modified an idealized transmembrane (TM) alpha helical peptide developed
by Wimley and White (TMX) to investigate the effect on bilayer properties induced by
asymmetric trans-bilayer protein surface area distribution through peptide insertion deep into the
hydrophobic region of the outer leaflet within a model membrane131. Based on studies of giant
41
unilamellar vesicle (GUV) fluctuation, morphology, and fluidity, we determined that these model
peptides increased membrane tension and led to the formation of vesicle-vesicle junctions in
which the peptide was concentrated. Fluorescence polarization anisotropy studies showed that
peptide insertion also increased membrane order / decreased fluidity. The effects induced by
single leaflet peptide insertion demonstrate that the membrane is highly sensitive to leaflet area
differentials, as indicated by changes in membrane mechanical properties and morphologies even
at low peptide concentrations.
Methodology:
Peptide design, synthesis, and labeling
Peptides were synthesized by the University of Southern California Center for Protein
and Peptide Engineering using Fmoc chemistry on Rink amide resin and purified using reverse
phase HPLC. A portion of the peptides were then labeled with Cy5 azide using copper-catalyzed
alkyne-azide cycloaddition and purified using reverse phase HPLC. Peptide sequence is AcALAAALAAAVAAGKSKSKSKSW1-NH2, where the N terminus has been acetylated, and the
C-terminus amidated for peptide stability and encouragement of alpha helix formation. The 1 in
the sequence is a propargylglycine group for click chemistry fluorescent label conjugation. In
order to control for any artifacts from label addition, unlabeled peptides were also used in control
experiments. Unlabeled peptides are denoted dTMX for dark, and cTMX for Cy5-conjugated
peptides.
Vesicle formation method
GUVs were formed by electroformation (EF)74. We obtained our stock solutions of 1,2-
diphytanoyl-sn-glycero-3-phosphocholine (DPhPC) from Avanti Polar Lipids, and cholesterol
42
solids, sucrose, glucose, and bovine serum albumin (BSA) from Sigma-Aldrich, all reagents
were used without further purification. Lipid stocks were prepared at a final concentration of 2
mg/mL of either 90:10 mol% DPhPC:Chol or pure DPhPC in CHCl3. Buffers consisted of 50
mM sucrose in MilliQ (MQ) water in the lumen of the GUV, and an external solution of 52 mM
glucose in MQ to provide index contrast in vesicle imaging. All buffers were filtered with a 0.45
µm pore PTFE syringe filter (Avantor) to remove any dust or debris prior to use. The
osmolarities of the internal and external solutions were measured 3x using an Osmomat Basic
3000 (Gonotec) to ensure no more than a 2 mOsmol/kg trans-bilayer osmotic gradient. Indium
tin oxide (ITO, Delta Technologies) glass slides were cleaned with an 80% EtOH solution, then
acetone, then EtOH, and then plasma cleaned for 5-7 min prior to lipid deposition. After every
third use, ITO glasses were reannealed at 150°C in air to maintain ITO functionality and ensure
vesicle quality143,144. 20-40 µL of the lipid solution was deposited dropwise on the cleaned ITO
slides using a 25 µL Hamilton gas-tight gas chromatography needle with excess solvent
evaporated off with a compressed nitrogen air stream, and then both glass plates were placed
under vacuum for at least an hour to remove any excess solvent. Plates were then sandwiched
together, conductive side facing inwards, and separated with a Teflon O-ring (13/16″ ID, 1″ OD,
Sterling Seal & Supply) and connected to an 8116A pulse/function generator (HewlettPackard/Agilent Technologies). EF conditions were as follows: 10Hz sinusoidal alternating
current, 1.5 V for 2 hours at RT with 700 µL of 50 mM Sucrose in MQ. After EF, 350 µL of the
sucrose GUV solution was aliquoted and settled in 700 µL 52 mM glucose. Imaging chambers
were fabricated using #1 thickness 24 x 60 mm rectangular cover glass, precleaned with Alconox
(Alconox Inc.), rinsed well with DI water, and then plasma treated for 5 minutes. Silicone Pressto-Seal wells (ThermoFisher) were cut up to isolate individual wells and then adhered onto the
43
cover glass. Surface treatment of the glass to discourage vesicle adhesion was completed as
follows: after the wells were assembled, 60 µL of 2 mg/mL filtered BSA in MQ were deposited
into the wells and allowed to incubate at RT for at least an hour. The BSA solution was then
removed, and the wells were rinsed 3x with at least 100 µL of MQ per chamber per wash, and
then rinsed twice with fresh 52 mM glucose solution. After this, 55 µL of fresh 52 mM glucose
buffer was added, and 5 µL of the GUV solution was added. Chambers were then sealed from air
by placing another cleaned square cover glass (dimensions: 24x24 mm, #1 thickness glass,
Chemglass Life Sciences) on top of the well to prevent additional evaporation during the
imaging process.
Preparation of Peptide GUVs
Peptide stock solutions were prepared in the same solution used for GUV settling, vortexed well
to ensure adequate peptide suspension, and then centrifuged for 5-10 minutes to pellet any large
aggregates. Concentrations were determined via solution absorbance at 280 nm (A280), and the
peptide solutions were then further diluted to their final concentrations with fresh settling buffer
to a final volume of 55 µL. Osmolarity measurements were taken to ensure that the osmolarity of
the external solution did not deviate from the set 2 mOsmol/kg differential. We then added 5 µL
of the GUV solution and allowed the solution to incubate protected from light for at least 20
minutes. GUVs were then imaged immediately after.
Peptide insertion validation experiments
GUV samples incubated with cTMX peptide were then split into two groups. The first group was
loaded onto a Nikon TI-E inverted microscope and imaged with S Plan Fluor 20X 0.45 NA and
44
40X 0.60 NA objective. Samples were excited with a 640 nm laser (Coherent). The second group
was exposed to a 5´ molar excess of Black Hole Quencher 3 functionalized with carboxylic acid
(BHQ-3, BioSearch Technologies) and allowed to incubate protected from light for at least 20
minutes prior to imaging. For each group of vesicles, Cy5 fluorescence intensity values of at
least 250 vesicles were measured 100. The maximum fluorescence intensity of the equatorial
plane of each GUV was measured for each condition using the radial profile angle plug-in in FIJI
and dark current corrected. P-values were obtained via a student’s t-test in GraphPad Prism.
Vesicle Fluctuation Analysis
Vesicles were formed and loaded into the imaging chamber as described above. After the vesicles
were allowed to settle, only vesicles that passed the selection criteria were recorded. These
criteria, as outlined elsewhere, are summarized as follows; the analyzed vesicles must have no
visible surface defects such as buds or tethers, and no elongated, ellipsoidal, or multilamellar
vesicles; no near neighbors; large enough to resolve the surface fluctuations but not so large
where gravitational effects begin to emerge; visibly fluctuating (i.e.- no stiff, tense vesicles), and
finally must be freshly made26,34,42,44–46. Chambers were loaded onto a Nikon TI-e Eclipse
inverse light microscope and vesicles were imaged under phase contrast with a 40X objective,
0.6 NA and recorded with a scientific CMOS camera (Photometrics). Camera settings were set at
a 10ms exposure time and recorded as fast as the software could support. To ensure that the
entire phase space of the lower mode fluctuations is represented, 1200-1400 frames were
recorded per vesicle. All samples were collected at room temperature. Temperature variations
within the chamber during the sample collection method were measured with a high-accuracy
45
type K thermocouple (Omega) to validate minimal (<0.5°C) increases in chamber temperature
during the collection period.
Videos were then fed into a Python script. The script is designed as follows; a frame is loaded,
and the image is cropped such that the vesicle of interest is centered in the ROI. Flatfield
corrections are then applied by dividing the frame by a correction image to remove lens artifacts.
After this, 250 radial measurements are taken using a increasing subsequence algorithm to
determine the location of the bilayer with subpixel accuracy26,28. We then use a center-finding
algorithm by fitting these measurements to the polar definition of a circle with radius ‘a’ centered
at coordinates (�!,�) is given by:
�(�) = �! cos(� − �) + ./�" − �!
" sin(� − �)"3 (12)
Where `�!` and `ϕ` are the position of the true center of the vesicle. We then compute the
average vesicle radius for each frame, let point � = 0 be � = � and � = � + 1 the point � = 1.
Thus, the mean radius is:
� = 1
2�=>
�# + �#$%
2 ? (�#$% − �#)
&
#'%
(13)
Where � is the measured distance in pixels, � is the corresponding angle. We then feed these
measurements into the following equations to obtain the adimensional discrete Fourier
coefficients26,27:
�( = 1
��=[�# cos(��#) + �#$% cos(��#$%)] C
(�#$% − �#)
2 D
&
#'%
(14)
�( = 1
��=[�# sin(��#) + �#$% sin(��#$%)] C
(�#$% − �#)
2 D
&
#'%
(15)
46
Where � is the mode number, and �( and �( are the corresponding Fourier coefficients. We then
obtain the mean square amplitudes (MSAs) of each mode by computing the variance:
HI�)I
"
K = 1
4 RHS�)(�#) − T�)UV
"
K + HS�)(�#) − T�)UV
"
KW (16)
The values for bending rigidity and tension were then determined through a fit to a Helfrich
Hamiltonian such that:
HI�)I
"
K = �,�
(��/ + �c�) (17)
Where �,� is the Boltzmann constant multiplied by temperature, � is the bending rigidity, q is
the dimensionless mode number, and �c = �-..�"⁄�, where �-.. is the effective tension, and R is
the time-averaged mean radius of the vesicle. The starting mode fit was selected according to the
crossover mode, defined as |�⁄�. Vesicles that generated fluctuation spectra which indicated
subpar samples were discarded.
Dynamic vesicle fluctuation analysis
General Experimental Execution
GUVs were added to the observation wells without prior exposure to cTMX and allowed to
settle. For our high concentration experiments, once an appropriate vesicle was located, an
isosmotic cTMX solution was carefully added to the well with a micropipette and image
collection was initiated.
Low concentration experiments were executed as follows: an isosmotic reservoir containing 200
nM dTMX was exposed to a sample well containing peptide naive D:C GUVs and allowed to
diffuse freely between wells, resulting in a final peptide solution concentration of 100 nM. We
then located vesicles that were suitable for VFA analysis and initiated image collection.
47
The degrees of cTMX exposure were delineated phenomenologically as follows: “visibly
fluctuating” is defined as the fluctuation regime in which the bilayer is still visibly fluctuating,
and “fully dampened” is defined as no discernable membrane fluctuations. Degrees of exposure
were determined on a frame-by-frame basis, and divided into separate videos which then fed into
our VFA image processing and analysis pipeline.
Fluorescence polarization anisotropy
SUVs were formed at room temperature via extrusion in fresh 52 mM glucose and diluted to 50
µM final concentration using a mini extruder (Avanti Polar Lipids) at room temperature with a
100 nm pore filter (Whatman). SUV solutions were extruded 21 times to ensure a tight SUV
diameter distribution. Diphenylhexatriene (DPH) suspended in anhydrous ethanol was added to
the SUV solution in a 1:1000 dilution regime to a final concentration of 0.5 µM. Peptides were
suspended in an isosmotic glucose solution and either further diluted to the final concentration or
added to the SUV solution and were allowed to incubate in the dark for at least 20 minutes prior
to measurement. FP measurements were taken on a Jasco FP-8500 Spectrofluorometer (Jasco
Analytical Instruments) at room temperature, with a 5 nm excitation and emission bandwidth and
1 second response time. Anisotropy measurements are an average of three accumulations, and
three measurements were taken per condition. P-values were determined via a student’s t-test or
multiple comparison one-way ANOVA.
Morphological changes upon TMX exposure
GUVs were fabricated following our standard formation protocol, and then divided into three
groups: a control population, vesicles exposed to 0.2 µM cTMX, and vesicles exposed to 2 µM
48
cTMX. We then imaged each population in phase contrast and quantified using the FIJI Cell
Counter function. Populations were binned to four independent groups: freestanding GUVs,
defined as unilamellar vesicles that do not display any of the other morphologies listed.
Conjoined vesicles were defined as vesicles that had a hemifusion-like interface, with a
pronounced, near-flat interface between two or more vesicles. We also made note of vesicle(s)-
within-vesicles and associated vesicle buds, to account for other common vesicle morphologies
that typically arise during standard vesicle formation procedures.
Fluorescence recovery after photobleaching experiments
GUVs with cTMX were prepared as described above. The observation wells were loaded onto a
Leica laser scanning confocal microscope (Leica Microsystems) and the equatorial plane of the
vesicle was brought into focus. For the bleaching process, bilayer and interfacial regions were
selected, and laser power and percent intensity were maxed out. FRAP images were acquired as
follows: 3-5 pre-bleach frames, 30 bleach frames, and 40-90 post-bleach frames were collected.
Images were then processed in FIJI using the Radial Profile Angle plugin to determine the
integrated intensity for the bilayer region. The intensity values corresponding to the bilayer were
selected, determined as the peak values. Two types of regions of interest (ROIs) were selected,
interfacial regions and bilayer regions. ROIs were selected such that there was a corresponding,
equivalent reference region for each type within the same sample. These reference regions were
then used to correct for photobleaching that occurred during the post-bleach imaging process,
such that the resulting change in intensity was only due to FRAP-induced fluorescence recovery.
Subsequent FRAP curves were fit to a one-phase association model of the form �8 = �4 +
(�9 − �4) ∗ [1 − exp (− �⁄� ∗ �), where �4,�8, and �9 are the intensities immediately after
49
bleaching, at some time point �, and after infinite time, respectively, and � as the characteristic
diffusion time. To obtain diffusion coefficients, the following equation was used: � = �"⁄4�.
The bleached area � was measured in FIJI, resulting in diffusion coefficients of units µm2
/s.
Results and Discussion
We modified a peptide sequence based on an “ideal” alpha-helical TMD peptide design,
first introduced by Wimley and White to investigate the changes in bilayer mechanics
corresponding to TMD geometric shape and insertion depth131. We selected this peptide as it
demonstrates good aqueous solubility and robust binding to the bilayer that favors insertion
normal to the plane of the bilayer, into the acyl tail region. More specifically, their
transmembrane peptide, TMX-1, is highly energetically localized to one leaflet through a highly
polar and cationic C-terminus and features an asymmetric arrangement of bulky hydrophobic
residues to encourage deeper insertion into the interleaflet space/acyl tail region, which
maximizes water solubility without penalizing potential deep transmembrane alpha helix
insertion.
The TMX peptide is designed to insert in lipid bilayers with the N-terminus buried in the
hydrophobic region of the bilayer. Multiple charged residues on the C-terminal side of the
peptide prevent spontaneous translocation through the bilayer in accordance with the chargedistribution hypothesis145–149. Figure S1 consists of the circular dichroism (CD) spectra of Cy5-
labeled TMX (cTMX) in the absence (0:1 lipid:peptide mole ratio) and presence (25:1 and 50:1)
of 90:10 mol% DPhPC:cholesterol SUVs, and Table S1 consists of the percent alpha helical
content. There is a modest increase in percent alpha helicity of cTMX in the presence of SUVs.
This data agrees with behavior noted by Wimley and White, who observed the formation of
50
micelle-like TMX aggregates resulting in a solution alpha helical structure that increases
modestly upon exposure to SUVs131.
Figure 12. A) Schematic of the fluorescence quenching assay, with the top schematic demonstrating a non-quenched system, and
below showing the fluorescence quenching of Cy5 upon exposure to BHQ-3. Figure made with BioRender.com. B) The Cy5
fluorescence intensity values for the unquenched and quenched populations.
To probe peptide insertion topology, we performed fluorescence quenching
experiments100. Figure 12A is a schematic of fluorescence quenching assay used to validate the
peptide orientation. Figure 12B shows the subsequent distribution of fluorescence intensity (FI)
for the two independent vesicle populations, where the quenched population (right, Fig. 12B)
displays a significantly quenched mean FI of approximately 120 a.u. when compared to the
unquenched population, which has a mean FI of nearly 700 a.u. (left, Fig. 12B). Because the
quencher carries a formal charge at pH 7.4, it cannot spontaneously translocate through the
hydrophobic region of the bilayer to access any fluorophores on the lumen of the vesicle. Thus,
the near-complete extinction of fluorescence in the quenched population demonstrates the
localization of the peptide to the outer leaflet of our GUVs. Persistence of a minimal amount of
fluorescence could be due to localization of the peptide in regions where the quencher is not
A
Unquenched
Quenched
B
51
readily able to access, such as in cases where peptide insertion results in the generation of
lamellar lipid/peptide structures that would insulate the fluorophore from quencher exposure131.
Membrane bending rigidity and tension determine the frequency and amplitude of spatial
fluctuations of a membrane at thermal equilibrium; these properties can be quantified by
analyzing the membrane fluctuation spectrum130. The process by which to measure these
fluctuations is vesicle fluctuation analysis (VFA), which uses video microscopy to capture the
fluctuations of a freestanding, quasi-spherical vesicle in the equatorial plane (zeroth order
azimuthal angle). Time series images of a fluctuating vesicle can be analyzed to generate
fluctuation spectrum. Here, we use fluctuation analysis to track changes in GUV mechanics
associated with peptide insertion. To validate our VFA methodology, we first measured the
bending moduli of vesicles without peptides.
Figure 13. Demonstrative fluctuation spectrum of a 90:10 mol% DPhPC:Chol GUV, shown in inset. Green data points
correspond to the fitted modes. Scale bar is 10 microns.
52
Figure 13 shows a typical fluctuation spectrum for a 90:10 mol% DPhPC:Chol vesicle,
shown in the inset. The mean square amplitudes (MSAs, 〈I�)I
"
〉) of each harmonic mode are
plotted as a function of the mode number �. For this vesicle the crossover mode is 9, and thus
modes 9-14 were fit to a reduced Helfrich Hamiltonian equation to generate a bending modulus
(�) of 1.34 × 100%1 J. We analyzed eight 90:10 DPhPC:Chol vesicles as well as eight pure
DPhPC vesicles. Table 2 shows the average bending modulus for each composition together with
results generated from previous studies using the VFA method. We find strong agreement with
previously established � values for pure DPhPC bilayers. Additionally, we find that � for
DPhPC:Chol membranes is modestly higher than that for pure DPhPC, which we can cautiously
attest to the addition of cholesterol, as previous X-ray scattering studies have demonstrated a
modest rigidifying effect of cholesterol in DPhPC-based bilayers150.
Table 2. List of � values obtained via VFA in this work and in the literature.
Lipid species κ (J) Citations
Pure DPhPC 1.17 ± 0.10 × 100%1 Viktova et al., 2006
Pure DPhPC 1.29 ± 0.37 × 100%1 Karamdad et al., 2015
Pure DPhPC 1.29 ± 0.23 × 100%1 This work
90:10 DPhPC:Chol 1.44 ± 0.13 × 100%1 This work
Compared to these peptide-free GUVs, GUVs with TMX inserted have suppressed
fluctuation spectra across all observable modes, reflecting a significant increase in membrane
tension. The suppressed fluctuation spectra could not be analyzed to obtain accurate bending
modulus values. However, the behavior of individual modes provides insight on the nature of the
53
peptide-induced membrane stiffening. The first and second harmonic modes correspond to the
vesicle radius and center of mass, respectively. We focused on the third mode as it corresponds to
membrane-spanning waves, which are dominated by membrane tension.
Figure 14. Comparison of the values of the third harmonic mode for our control populations and GUVs exposed to varying
peptide concentrations. Asterisks correspond to statistical significance and are delineated as follows:
**** for p-value of < 0.0001, *** for 0.0001, and ** for 0.0013.
Figure 14 shows the suppression of the third mode MSAs when compared to the control
populations for GUVs exposed to 60, 80, 200, and 2,000 nM dTMX, with lipid:peptide ratios of
114, 84, and 34 respectively. The dotted line represents the mean MSA value for the peptide-free
control. Significance is based on a one-way ANOVA. This demonstrates that the effect of cTMX
insertion is highly pronounced in the low mode-number regime where membrane tension
dominates. These data also demonstrate that the increases in bilayer tension quickly saturates at
high lipid:peptide ratios. This approach to interpreting changes in low modes of the fluctuation
sample is similar to that implemented by Faris and coworkers, who observed that a reduction of
tension resulted in an enhancement of the magnitude of the measured MSAs, especially in the
lower mode number regimes130.
Control
60 nM
80 nM
200 nM
2 uM
-6.5
-6.0
-5.5
-5.0
-4.5
-4.0
log <|u
q|
2>
Mean Square Amplitudes
Third Mode
**** **** *** **
54
Figure 15. A) Representative micrographs of the two regimes, and the corresponding spectra below. In order from left to right,
the first two micrographs are “Visibly fluctuating”, where the vesicle displays discernable fluctuations and the final micrograph
is“Fully Dampened”, where there are no visible vesicle fluctuations. Scale bars are 8 microns. B) Fluctuation spectra of a
control GUV (dashed line), and the generated fluctuation spectrum for the vesicle pictured in A). C) Mean square amplitudes of
the third harmonic mode. ** Corresponds to a p-value of 0.0011.
Figure 15A shows three phase contrast micrographs displaying a time course of a GUV
exposed to 100 nM dTMX. The observed behavior as the vesicle stiffens can be classified into
three regimes. Initially, the GUV is fluctuation visible, though these fluctuations are suppressed
relative to the peptide-free control (see SI Movie 1 for the recording). Then, the membrane
A
B C
55
fluctuations diminish, stabilizing at a slightly smaller radius and fluctuations are no longer
discernable. In this experimental trial, a tubule appears (white arrow in the leftmost micrograph
in Fig. 15A). The fluctuation spectra in Fig. 15B show the evolution of the vesicle as it interacts
with the peptide. The refractive index difference across the membrane does not change,
suggesting that the bilayer remains intact and impermeable. Fig 15C shows the comparison of
the third mode from our control populations to the GUV before and after the fluctuations have
been suppressed by dTMX insertion. These results demonstrate that the dTMX peptide rapidly
suppresses fluctuations in the membrane, likely by increasing membrane tension. The time
course observed here likely reflects the time it took for peptide molecules pipetted into the
observation chamber to diffuse to the membrane.
Additionally, the inclusion of a small amount of cholesterol is not enough to offset the
stress differential in these bilayer, as recent simulation work has also demonstrated that
cholesterol inclusion in asymmetrically tense bilayers may not necessarily overcome the stress
differential profile by flipping to the relatively under stressed leaflet151. Despite also resulting in
highly tense vesicles, the membrane retains its refractive index differential, demonstrating that
the bilayer is able to find a balance between excess lipid area ejected into daughter vesicles or
tubules while still maintaining bilayer integrity.
Figure 16. Phase contrast micrographs of a time course displaying significant vesicle restructuring after the addition of 2 µM
dTMX to the external GUV solution with a micropipette. Scale bar is 10 microns. Video available online.
0 s 33 s 52 s 106 s 166 s
56
The TMX peptide induces membrane restructuring. Some of these vesicles, an example
of which shown in Figure 16, resulted in the generation of a conjoined-vesicle morphology,
where upon exposure to dTMX, the mother vesicle rejects excess membrane area, in this case, in
the form of internal blebs, to first form a daughter vesicle at the end of a tubule, which then
retracts and subsequently adheres to the surface of the mother vesicle. Vesicle budding can arise
as a consequence of the insertion of an amphipathic molecule into the bilayer as a result of the
increase in bilayer spontaneous curvature to resolve the stress arising from the area difference
between the two monolayers.152–154. Other examples in the literature of other amphipathic
molecules, such as detergents, result in a recovery of a single vesicle152. However, TMX remains
energetically pinned to the outer leaflet of a GUV, resulting in a persistent leaflet area differential
as evidenced by the generation of a daughter vesicle. Additionally, as demonstrated in SI Movie
3, the peptide also induces a large-scale reorganization of the vesicles to form a stable interface
between the newly ejected daughter vesicle and the mother vesicle.
Figure 17. ) Demonstrative Phase Contrast micrographs of vesicle morphology subtypes. B) Morphological distribution of
control GUVs and GUVs exposed to 0.2 and 2 µM cTMX. For our control population, the vesicle morphological distribution is
A
Morphological Population Distribution
B
Freestanding
Conjoined
Vesicles within
vesicles
Associated vesicle
buds
Demonstrative Vesicle Morphologies
57
67.25% Freestanding, 2.61% conjoined, 20% Vesicles w/in vesicles, and 10.14% Associated vesicle buds. For our 0.2 µM cTMX
population, the Freestanding population drops slightly to 51.69%, 26.09% display Conjoined morphologies, 20.77% have
Vesicles within vesicles, and 1.45% have Associated vesicle buds. The 2 µM population displayed 41.03% Freestanding vesicles,
53.85% Conjoined, 2.07% Vesicles within vesicles, and 2.05% had Associated vesicle buds.
As demonstrated in Fig. 16, dTMX insertion into the outer leaflet of the bilayer results in
a significant trans-bilayer leaflet area differential, reconciled through a stepwise process resulting
in a stable conjoined vesicle morphology. Figure 17 shows that the degree of morphological
distortion depends on the concentration of peptide. Fig. 17A consists of demonstrative Phase
Contrast micrographs of the different morphological subtypes, Fig. 17B summarizes the
distribution of vesicle morphologies in control and peptide-exposed GUV populations. Mode of
vesicle distortion appears to favor continuous membranes with stored excess membrane area
rather than the fusion of two distinct vesicles, as associated vesicle bud populations are nearly
entirely diminished upon exposure to low cTMX concentrations, while freestanding GUVs
display a more stepwise decrease in prevalence as cTMX concentration increases. In comparison,
the population prevalence of GUVs that contain vesicles decreases only after the addition of 2
µM cTMX, suggesting that high enough concentrations of cTMX insertion could also mediate
vesicle-vesicle fusion.
58
Figure 18. Confocal micrographs of hemifusion domains in control GUVs and a graph of the corresponding intensities along the
yellow profile line (A-C) and GUVs exposed to 2 uM cTMX (D-G). H) Summary of bilayer-normalized 488 nm (ATTO-488, lipid
channel) and 640 nm (Cy5, peptide channel) FI at the hemifusion interfaces of GUVs exposed to 2 µM cTMX. Scale bars are 10
microns.
Further evidence that this fusion behavior is peptide-mediated can be found in confocal
micrographs of peptide fluorescence which demonstrate that the peptide is concentrated at these
interfaces. Figures 18A and B are confocal micrographs of conjoined GUVs from a peptide-free
sample. Figure 18A and B are the 488 nm (lipid label) and 640 nm (peptide label) channels,
respectively. Fig. 18C shows the pixel intensity along the profile line in Figs. 18A and B. As
expected for the control population, lipid fluorescence intensity of the conjoined vesicle interface
is twice that of single-GUV membrane, indicating that there are two lipid bilayers at the
interface. The lack of signal in the 640 nm channel demonstrates a lack of crosstalk between the
lipid and peptide fluorophore channels. Figs. 18D and E are micrographs of a GUVs that became
conjoined after being exposed to 2 µM cTMX. Fig. 18F is the overlay of the two channels and
A
B
C
D
E
F G
n=15
H
Merge
488 nm 640 nm 488 nm 640 nm
GUV Fluorescence Profile
Control GUV
GUV Fluorescence Profile
2 µM cTMX
488 nm
640 nm
488 nm
640 nm
488 nm 640 nm
Laser Wavelength
59
Fig. 18G is the intensity along the line profile in 18F. In contrast to the peptide-free conjoined
vesicles, the lipid intensity is not significantly increased at the interface. However, the peptide is
concentrated by nearly a factor of four relative to non-interfacial regions. Fig. 18H shows that
consistently over many such hemifusion interfaces the lipid intensity remains as expected for a
bilayer while the peptide is significantly concentrated.
Figure 19. A) Example of a bilayer FRAP study of a GUV exposed to 2 µM cTMX. From left to right, 640 nm excited confocal
micrograph of the GUV immediately after bleaching, with the bleach ROI and corresponding reference area highlighted. B)
Normalized fluorescence recovery of two interfacial and bilayer regions. C) Comparison of peptide diffusion coefficients between
three bilayer samples and three interfacial samples.
We used FRAP to measure the mobility of peptide at and away from the interface. Figure
19A shows representative time series micrographs for a vesicle exposed to 2 µM cTMX, starting
with the image immediately after bleaching and every 0.98s after. The white box containing
“Bleach” designates the bleached area, and the opposing bilayer region “Ref” was used as a
photobleaching reference for normalization. The corresponding graph in Fig. 19B shows
Seconds post-bleach: 10 20 40 60
Bleached
Time post-bleach (s)
A
B C
Ref
Bleach
60
representative fluorescence recovery plots for two interfacial and two bilayer regions. Note that
recovery is slower at the interface. This is reflected by the calculated diffusivities in the two
regions (Fig. 19C), which shows the comparison of diffusion coefficients for bilayer and
interfacial peptide FRAP. The interfacial regions have lower diffusivities but reach a lower
fluorescence recovery fraction. We can reconcile this seemingly disparate phenomena by
considering the highly peptide enriched context of these regions. Fig. 18 has demonstrated an
enrichment of cTMX at the hemifusion/interfacial region, which could reasonably explain why
the interfacial regions result in an overall higher recovery when compared to the bilayer regions.
Additionally, we postulate that the lower diffusion coefficient at the interfacial region could be
due and the enrichment mechanism that results in peptide accumulation at the interface—
Wimley and White note that the longer form of TMX can result in charge-based aggregation131.
Thus, the enrichment of the peptide at this interface could be the result of peptide-peptide and
peptide-bilayer electrostatic interactions, which would result in the reduced diffusion. This is
also in line with other work that has demonstrated significantly reduced diffusion coefficients as
a function of membrane crowding through the multimerization of TM peptides155,156.
Furthermore, another model peptide developed by Sharpe and coworkers (2002) with a similar
hydrophobic helix design also reported significant and reversible peptide-peptide interactions157.
The shallow insertion of TMX induces dramatic changes in GUV morphology. These
morphological changes include the generation of planar hemifusion domains, largely indicative
of high membrane tension158. Furthermore, we find morphologies of TMX-exposed vesicles
appear to resemble other work regarding peptide-bilayer interactions in the field, such as
agglomeration, vesicle budding, and highly planar surfaces at the region of contact, supporting
our initial hypothesis of significantly increased outer leaflet area, and a subsequent increase in
61
membrane tension as a result of peptide binding158. Some other studies that feature peptides that
induce hemifusion show a depletion of peptide signal at the hemifusion domain interface, which
we do not see in our work159. In our work, the interface is enriched. This mechanism could be the
result of sequestration of peptide aggregates to the hemifusion domain interface to minimize the
area differential induced by peptide insertion in the rest of the bilayer. Especially coupled with
the fact that this peptide is designed to prevent flipping through the bilayer, a common modality
by which lipid bilayers resolve asymmetric stress distributions.
We used fluorescence polarization (FP) anisotropy experiments to examine how peptide
insertion alters membrane fluidity. Diphenylhexatriene (DPH) anisotropy was used to monitor
lipid interleaflet fluidity and peptide fluorophore (Cy5) anisotropy was used to monitor peptide
mobility.
62
Figure 20. Fluorescence polarization studies of cTMX and D:C SUVs. Error bars correspond to one standard deviation, solid
horizontal line in each condition corresponds to the average anisotropy value. A) Cy5 anisotropy of free peptide in solution.
Average anisotropy value for all conditions is indicated by a purple horizontal dotted line to guide the eye. B) Cy5 anisotropy of
cTMX exposed to 50 µM D:C SUVs. Y-axis begins at the average anisotropy value from 5(A) to demonstrate an overall increase
in peptide anisotropy. C) DPH anisotropy of control SUVs and SUVs exposed to cTMX. Horizontal purple dotted line
corresponds to the average anisotropy value of control SUVs to guide the eye. D) Time course measurement of changes in DPH
anisotropy upon exposure to 0.1 and 10 µM cTMX. E) Grouped DPH anisotropy values before and after cTMX exposure.
**** corresponds to a p-value <0.0001.
Figure 20A shows Cy5 anisotropy of different peptide concentrations in solution, with the
average anisotropy value indicated by the dotted purple line. We then exposed the same
concentrations of cTMX to 50 µM SUVs (Fig. 20B). Note that the origin of the y-axis in 20B
corresponds to the average value in 20A, i.e., all samples have greater anisotropy (lower
mobility) than the samples without SUVs due to peptide inserting in the SUV membranes. The
0 0.1 0.25 0.5 0.75 1
0.100
0.102
0.104
0.106
0.108
0.110
0.112
cTMX concentration (uM)
Anisotropy
cTMX + 50 µM SUVs
DPH Anisotropy
0.1 0.25 0.5 0.75 1
0.13
0.14
0.15
0.16
cTMX concentration (µM)
Anisotropy
Free cTMX in solution
Cy5 Anisotropy
0.1 0.25 0.5 0.75 1
0.14
0.16
0.18
0.20
0.22
0.24
cTMX + 50 µM SUVs
Cy5 Anisotropy
cTMX concentration (µM)
Anisotropy
0 500 1000
0.09
0.10
0.11
0.12
0.13
0.14
time (s)
Anisotropy
cTMX Dynamic Addition Experiment
DPH Anisotropy
10 µM
0.1 µM
Peptide added
Peptide added
10 µM: before
10 µM: after
0.1 µM: before
0.1 µM: after
0.09
0.10
0.11
0.12
0.13
0.14
Anisotropy
Grouped Dynamic Experiment Values
DPH Anisotropy
✱✱✱✱ ✱✱✱✱
A B C
D E
63
fact that peptide mobility increases with concentration suggests that the SUV membranes are
saturated with peptide at the lowest concentration and that peptide molecules added above this
concentration remain free in solution.
The changes in the bilayer interleaflet fluidity as a function of cTMX concentration are
reported in Fig. 20C. The dotted line corresponds to the mean anisotropy for peptide-free
samples. Fig. 20C demonstrates that even low peptide concentrations such as 0.1 µM, which
corresponds to a lipid:peptide ratio of 500:1, result in a significant increase in DPH anisotropy
indicating a reduction in interleaflet fluidity as a direct result of peptide binding. This loss of
membrane fluidity is consistent with the increase in membrane tension observed in VFA
experiments.
Figure 20D shows the time course behavior of DPH anisotropy, with a vertical dotted line
to indicate when the peptide was added to the cuvette. The decrease in membrane fluidity is rapid
and, as with Fig. 20C, depends on peptide concentration. Fig. 20E groups the pre- and postpeptide addition anisotropy values over many trials. TMX concentrations of 10 µM and 0.1 µm
both effected statistically significant changes in anisotropy. The immediate increase in anisotropy
which substantiates our hypothesis that this phenomenon was diffusion-limited, as our
fluorimeter cell contained a mixer to ensure that the peptide was immediately well-distributed
upon addition. While the regime transition in the low-concentration dynamic VFA was slower
than the high concentration dynamic VFA experiments, Fig. 20D and E show that it is possible
for this effect to occur rapidly upon peptide introduction to a well-mixed cell.
Conclusion
To mimic the shape asymmetry of single-pass transmembrane domain proteins, we
developed a truncated model peptide that serves as a geometric analogue for TM domain shape
64
that inserts deep into the hydrophobic region. The peptide spontaneously inserts into the
membrane of GUVs, remaining highly localized to the outer leaflet. Upon insertion, even low
concentrations of peptide radically increase membrane tension, as is clear from observation of
vesicle fluctuation modes. This is accompanied in many cases by the formation of stable
hemifusion interfaces between vesicles in which the peptide is enriched. Based on fluorescence
anisotropy studies, these phenomena are accompanied by peptide-induced membrane ordering.
65
Chapter Five: Future Directions
The work in previous chapter outlined the preliminary biophysical studies that we
conducted with TMX. Other experimental techniques could also be implemented to deepen our
understanding and characterization of TMX’s peculiar and potent effect on lipid bilayers, an
example of which is neutron spin echo (NSE) experiments. NSE is a field-standard technique
and allows for more quantitative studies of the effects of peptide insertion on bilayer properties.
Using this technique, we can probe the effects of TMX insertion on membrane properties, such
as thickness, and evaluate changes in bending rigidity.
Additionally, we can further decouple biochemical factors such as dipole moment and
helix radius using the designed peptides Wedge1 and Wedge2. These peptides are two full-length
(i.e.- membrane-spanning) TMD peptides with identical but mirrored amino acid sequences to
delineate dipole moment with helix radius. As noted above, some preliminary work has been
done, but issues with synthesis and purity have stalled this project.
Wedge1
We sought to replicate the above characterization with Wedge1 (W1), beginning with
suspension characterization: W1 showed little to no suspension behavior, in the form of a viable
A280 trace or by visual inspection (e.g.- determining if the peptide able to suspend in the solvent
without the generation of large aggregates) in pure water, chloroform, or acetonitrile
suspensions. A 2:1 combination of anhydrous ethanol and water (EtOH:MQ) was able to produce
a reliable and effective suspension with minimal visual aggregates and a clean A280 trace. We
decided to proceed with this buffer for the rest of our experiments.
66
Figure 21. CD spectra of Wedge1. A) shows the CD spectra of a final peptide concentration of 40 µM with variable lipid
concentrations. B) shows the dose-dependence of the strength of the CD spectral signal, and the effects of the sodium fluoride
buffer on the cleanliness of the low-UV area of the spectra.
Our CD spectra data shows a dose-dependent and environment-sensitive strengthening of
the alpha helical and arginine traces of dW1 in both 20 mM MOPS, 100 mM NaCl, pH 7.01
(MOPS) buffer, as well as the 15 mM sodium phosphate, 100 mM sodium fluoride, pH 7.0 (NaF)
buffer. Samples investigated with the MOPS buffer showed significant noise below 210 nm, as is
typical for chlorine-containing buffers. Figure 21 is a comparison of three different
concentrations, as well illustrates that samples in NaF buffers showed clean traces through the
low-UV region, demonstrated by the relatively clean orange line below 200 nm, when compared
to the nosier blue and purple data, which were acquired with chlorine-containing MOPS buffer.
The traces for W1 are indicative of an alpha helical structure with a strong arginine turn
contribution that conceals the distinctive secondary ~205nm alpha helical shouldering. The alpha
helical content of the CD spectra also increases upon incubation with SUVs, indicating
successful insertion.
200 250 300
-10
0
10
20
30
wavelength (nm)
CD (mdeg)
dW1 CD Spectra
0:1
25:1
50:1
200 250 300
-10
0
10
20
30
wavelength (nm)
CD (mdeg)
dW1 CD Spectra
(25:1 lipid:peptide ratio)
20 uM
10 uM
40 uM (NaF)
67
Figure 22. Symmetric insertion behavior of dW1 in POPC GUVs.
As of writing, we do not have access to the labeled form of W1. Instead, we looked at
whether replicating the concentration of TMX for W1 caused any morphological changes. Figure
22 shows a statistically dose-dependent effect of vesicle diameter that is inversely proportional to
peptide content. In essence, as peptide concentration increases, average vesicle diameter
decreases, and the size heterogeneity is also reduced, indicating that the peptide is inserting into
the bilayer and enforcing a maximum radius of curvature.
Wedge2
Figure 23. CD Spectra of Wedge2. A) The spectra of 40 uM dW2 in NaF buffer, B) The comparison of equivalent final peptide
and SUV concentrations for dW1 versus dW2.
Wedge2 followed a similar suspension behavior as Wedge1, where there was decent
suspension in 2:1 EtOH:MQ. Despite showing a strong A280 signal (data not shown), the CD
spectra of 40 µM dW2 in NaF buffer showed a very slight alpha helical signal as seen in Figure
Control
0.1 uM
1 uM
2 uM
0
20
40
60
80
Symmetric Insertion in POPC GUVs
dW1 Final Concentration
Diameter (micron)
✱✱✱
✱✱✱✱
✱✱✱✱
200 250 300
-4
-2
0
2
4
wavelength
CD (mdeg)
dW2
0:1
25:1
50:1
200 250 300
-20
-10
0
10
20
30
Comparison of dW1 and dW2
Identical CD Spec conditions
wavelength
CD (mdeg)
dW1
dW2
68
23A. However, the overall magnitude of that signal is exceedingly small. For comparison and
added context, Figure 23B shows the equivalent behavior of dW1 for the exact same CD spec
conditions, including final peptide and lipid concentration. The cause of the lack of signal
strength of dW2 is still currently unknown. As of writing, we do not have the labeled form of
W2.
We also plan on probing W1-bilayer interactions more deeply. While concentrations of W1
above approximately 1 µM result in morphological changes that prevent robust VFA studies,
there are other interesting results that were not included in this manuscript having to do with
asymmetric vs symmetric insertion of W1, including the generation of nanometer-scale lipid
fragments upon asymmetric insertion of W1 to preformed GUVs. We aim on troubleshooting the
lack of W2 CD spectral signal and then characterizing its interactions with GUVs similar to what
we have done with W1. Once the labeled form of W2 becomes available, we will also explore
the insertion behavior compared to W1. We would also like to see if the maximum radius effects
noticed in W1 are balanced by the inclusion of W2. We also plan on implementing a
modification to our VFA analysis script to calculate and quantify the changes in intrinsic
curvature that arise as a result of peptide insertion into the membrane.
Some additional future projects could extend our perspective to functional
transmembrane proteins. Previous work has shown that proteins are sensitive to their local lipid
environment, including recent crystallographic structures showing that GPCRs have cholesterolspecific binding sites. Previous work out of our lab has also shown that 5HT1A function is
dependent on lipid phase, and that A2AR has a phase preference that is dependent on GPCR
binding state99,100,128. However, as shown in the previous chapter, there is significant interest in
elucidating the effects on membrane properties in the context of protein inclusion. Previous
69
studies of active proteins being reconstituted and subjected to this type of biophysical analysis
also show little agreement in effects on bending rigidity.
We seek to introduce not only a wider variety of transmembrane proteins, but also to
investigate how the flexibility of the transmembrane domain affects the mechanics of the bilayer
it resides in. We have a number of proteins of interest with varying flexibilities that we are able
to robustly reconstitute into the bilayer. We have previously described the structure and function
of 5HT1A in Chapter 3, in addition, 5HT1A has a highly conformationally flexible alpha helical
bundle of seven alpha helices. The other transmembrane proteins we will be investigating is the
human adenosine receptor 2A (A2AR); a spinach aquaporin whose function is to transport water
(SoPIP2;1), which is another alpha helical bundle TM protein with low conformational
flexibility; and OmpF, a beta-barrel monomer of a bacterial porin protein trimer that transports
small, hydrophilic cations across the outer membrane of gram-negative bacteria160,161. We plan to
run VFA on these samples and elucidate if there are any differences in BR as a function of TMD
conformational flexibility and TM tertiary structure. Through these studies, we hope to deepen
the understanding of the effects that these different types of TMDs have on membrane continuum
mechanics.
70
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Appendix
Methodology
Circular Dichroism Spectroscopy
SUVs were prepared as follows, 2 mM final concentration of 90:10 mol% DPhPC:Chol SUVs
were extruded in either 20 mM sodium phosphate, 100 mM NaF buffer, pH 7.0, or 10 mM NaCl,
110 HEPES buffer, pH 7.0 using a mini extruder kit from Avanti with a 0.1 µm pore membrane.
The SUV solution was extruded 21 times to ensure a tight SUV size distribution. SUVs were
then added to fresh CD buffer containing 20 µM dTMX or 40 µM cTMX and allowed to
incubate for at least 20 minutes prior to CD spectroscopy measurements. CD spectra were
obtained at room temperature in reduced volume 0.1 mm quartz cuvettes with a Jasco J-110
spectropolarimeter (Jasco Analytical Instruments) with the following settings: 190 – 280 nm
spectral range, continuous mode, 100 nm/min scan rate, 1 nm bandwidth, data pitch of 0.5 nm,
and two accumulations at 20°C. Percent alpha helicity were generated from mean residue
ellipticity ([θ]) values at 222 nm.
Fluctuation Analysis of a Glass Bead
Coverslips were cleaned and passivated as described previously. Approximately 100 µL of MQ
water was added to a small amount of glass beads to increase the refractive index and discourage
bead aggregation in solution. Isolated beads were selected and recorded with the same
parameters as our standard VFA protocol. Videos were then fed into our VFA pipeline and
generated tension and bending rigidity values were discarded.
Dynamic VFA
87
High concentration Dynamic VFA protocol was executed as described previously. In short, 90:10
mol% DPhPC:Chol GUVs were formed in the absence of dTMX with the standard 50 mM
sucrose internal solution and 52 mM glucose external solution. Imaging chambers were
assembled and passivated, and once an appropriate vesicle was located, an isosmotic cTMX
solution was carefully added to the well with a micropipette and image collection was initiated.
Osmotic Effects Determined by Fluorescence Anisotropy
SUVs were formed in fresh 52 mM glucose and diluted to 50 µM final concentration as
described above. Diphenylhexatriene (DPH) was added to the SUV solution to a final
concentration of 0.5 µM. Peptides were suspended in an isosmotic glucose solution and either
further diluted to the final concentration or added to the SUV solution and were allowed to
incubate in the dark for at least 20 minutes prior to measurement. FP measurements were taken
on a Jasco spectrofluorometer 8900 (Jasco Analytical Instruments) at room temperature, with a 5
nm excitation and emission bandwidth and 1 second response time. Anisotropy measurements
are an average of three accumulations, and three measurements were taken per condition. SUV
solution was loaded into a cuvette with a mixer to ensure rapid attenuation. A time course was
initiated, and MQ water was added sequentially to the SUV solution. Anistropy values were
grouped by dilution condition and evaluated against the control (starting) data via a one-way
ANOVA with multiple comparisons.
88
Supplemental Figures
Figure 24. CD Spectra of c- and d-TMX.
SI Figure 1 demonstrates two key points of interest. First, that the insertion behavior of TMX
matches what is described in Wimley and White’s work. Namely, that there is a solution alpha
helical structure that persists despite centrifugation, shown in the Figure above as the 0:1
samples for both the c- and d-TMX populations. Peptide binding is represented by the increase in
peptide alpha helical structure in the 25:1 and 50:1 samples when compared to the 0:1 samples.
For the dTMX spectra, the reduced concentration results in a reduced spectral signal.
Additionally, the chlorine-containing buffer resulted in a noiser spectrum. There is effectively no
difference in structure as a result of the Cy5 fluorophore conjugation.
210 220 230 240 250 260
-5
0
5
nm
CD (mdeg)
dTMX CD Spectra
0:1
25:1
50:1
~98:1
200 220 240 260 280
-10
-5
0
5
10
15
nm
CD (mdeg)
CD Spectra of cTMX with DPhPC:Chol SUVs
0:1
25:1
50:1
89
Figure 25. Demonstrative fluctuation spectrum of a nonfluctuating glass bead.
We also sought to evaluate the spectrum generated as a result of a non-fluctuating glass bead,
pictured in the inset. There is some noise as a result of the Brownian motion of the particle in
solution. The MSAs demonstrate a highly linear behavior, as all measured MSAs fall between
3 − 6 × 100: a.u..
For the SI Movie (see attached), we selected a vesicle that had an internal bleb for analysis.
Upon introduction of 2 µM final solution concentration of dTMX, we found that after an initial
expansion and subsequent contraction due to the fluid flow upon pipetting, we see the distinct
vesicle budding phenomena. Curiously, the budding does not just stop at the generation of a
daughter vesicle at the end of a tubule or neck, but rather continues on to form a conjoined
vesicle structure with the newly ejected daughter vesicle, forming a distinct hemifusion domain.
90
Figure 26. Effects of osmolarity changes on DPhPC:Chol SUVs.
We also wanted to evaluate the effects that changes in external solution osmolarity have on DPH
anisotropy. We have made sure that the peptide solution was always isosmotic with the external
vesicle solution. This figure demonstrates that sequentially diluting the external solution that
would one would expect to result in more tense vesicles, has no effect on DPH anisotropy.
Starting
10% dilution
30% dilution
70% dilution
120% dilution
0.095
0.100
0.105
0.110
Effects of Osmolarity Changes on DPhPC Anisotropy r (dimensionless)
ns
ns
ns
ns
Abstract (if available)
Abstract
The plasma membrane (PM) serves as a multifaceted pillar of the biology, chemistry, and physics that occurs within, around, and to a mammalian cell, though our understanding of the relationship between its constituents is still being elucidated. There is a profound asymmetry imbued throughout the PM, such as lipid acyl chain composition and headgroup charge. While extensive work has been done to investigate the rich biophysics present in the lipid domain, comparatively less biophysical research exists elucidating the role that transmembrane protein domains (TMD) have on the fundamental mechanics of the bilayer. Given that transmembrane proteins constitute up to 50% of the plasma membrane area, we began to probe whether there was a biophysical impetus for this sustained TMD asymmetry and sought to decouple the role that various biochemical factors have on the membrane. We first established a functional method to reconstitute functional transmembrane protein receptors. We then developed model peptides to probe their effects on bilayer continuum mechanics. We also modified a field-standard technique to probe these dynamic interactions. We found that one of our model peptides significantly suppresses membrane fluctuations at very low bulk solution concentrations, quantifiable by our modified fluctuation analysis method and corroborated by microscopy and spectroscopy. This work provides an initial exploration and characterization of protein-lipid interactions, and a novel perspective on fluctuation analysis. Further work utilizing our other model peptides and other prototypical transmembrane proteins could deepen our understanding of the relationship between protein biochemical properties and their subsequent effects on membrane continuum mechanics.
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Asset Metadata
Creator
Dalle Ore, Lucia Caterina
(author)
Core Title
Probing the effects of transmembrane domains on the continuum mechanics of lipid bilayers
School
Viterbi School of Engineering
Degree
Doctor of Philosophy
Degree Program
Materials Science
Degree Conferral Date
2023-12
Publication Date
12/13/2024
Defense Date
10/25/2023
Publisher
Los Angeles, California
(original),
University of Southern California
(original),
University of Southern California. Libraries
(digital)
Tag
lipid biophysics,lipid membrane mechanics,OAI-PMH Harvest,peptide-lipid interactions
Format
theses
(aat)
Language
English
Contributor
Electronically uploaded by the author
(provenance)
Advisor
(
Chung, Peter
), Malmstadt, Noah (
committee chair
), Graham, Nicholas (
committee member
), (
Zeno, Wade
)
Creator Email
dalleore@usc.edu,lcdalleore@gmail.com
Permanent Link (DOI)
https://doi.org/10.25549/usctheses-oUC113792524
Unique identifier
UC113792524
Identifier
etd-DalleOreLu-12553.pdf (filename)
Legacy Identifier
etd-DalleOreLu-12553
Document Type
Dissertation
Format
theses (aat)
Rights
Dalle Ore, Lucia Caterina
Internet Media Type
application/pdf
Type
texts
Source
20231214-usctheses-batch-1115
(batch),
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 author, as the original true and official version of the work, but does not grant the reader permission to use the work if the desired use is covered by copyright. It is the author, as rights holder, who must provide use permission if such use is covered by copyright.
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
Repository Email
cisadmin@lib.usc.edu
Tags
lipid biophysics
lipid membrane mechanics
peptide-lipid interactions