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An induced neuron cell model derived from human olfactory epithelial cells

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An induced neuron cell model derived from human olfactory epithelial cells

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Content An Induced Neuron Cell Model Derived
from Human Olfactory Epithelial Cells
by
Edder Lopez
A Dissertation Presented to the
FACULTY OF THE GRADUATE SCHOOL
UNIVERSITY OF SOUTHERN CALIFORNIA
In Partial Fulfillment of the Requirements for the Degree
DOCTOR OF PHILOSOPHY
SYSTEMS BIOLOGY AND DISEASE
May 2020
ii
Contents
List of Figures…………………………………………………….…. iv
List of Tables………………………………………………………..... v
Chapter 1: Introduction and Background…………………….….… 1
A. The problem of schizophrenia…………………………………...………..1
B. The state of research on schizophrenia…………………………………..3
Genetic studies on schizophrenia………………………………..….….......3
Imaging studies on schizophrenia………………………….……………....5
Animal models of schizophrenia……………………….…………………..6
Cell models of schizophrenia……………………………………….........…8
C. iNs provide a unique strategy to study schizophrenia……....…………10
Induced neurons generated from a novel source…………………..…….12
Chapter 2: Induced Neurons from CNON Cells………………….. 17
A. Generating CNON lines……………………………….………………....17
B. Establishing a protocol to make iNs from CNON cells …....…………..18
C. The protocol to generate CiNs……………………………….…………. 23
Chapter 3: Characterization of CiNs ...…………………….…….... 31
A. CiNs express markers of mature neurons and synapses...……....……. 31
B. CiNs are primarily GABAergic………………………….………..……. 32
C. CiNs are electrically active ...……………………………………....….... 33
D. CiNs form functional synapses ……...…………………………………. 35
E. RNAseq on iNs generated with a preliminary protocol ...…………..... 36
Chapter 4: Discussion …...…………………………………………. 40
A. GABAergic neurons in neuropsychiatric diseases ………...……......... 40
B. The type of induced neurons that are generated depends
on the starting cells and which factors are transduced………...…….. 43
C. CNON are one of only three cell types isolated from
human adults which have been used to generated
induced neurons have been generated……………………..…..…........ 44
D. All models are wrong, but some are useful – the upside
of CiNs as a model to study neuropsychiatric diseases……....…….… 44

iii
E. Caveats and future directions………………………….……………… 47
Chapter 5: Projects Not Involving CiNs……………….…………. 49
A. “Odorant receptive” cells within CNON cultures….…………….. 49
B. Attempts to differentiate CNON into neurons
within two- and three-dimensional culture systems ..……………. 50
Bibliography……………………………………………………… 175

iv
List of Figures
Figure 1……................................................................................................... 55
Figure 2A........................................................................................................ 56
Figure 2B…...........................................................................................……. 61
Figure 2C…...........................................................................................……. 64
Figure 3…...........................................................................................……… 75
Figure 4A……...........................................................................................…. 85
Figure 4B…...........................................................................................……. 88
Figure 4C…...........................................................................................……. 92
Figure 5A…...........................................................................................……. 96
Figure 5B….................................................................................................. 100
Figure 5C…..........................................................................................…… 104
Figure 6………........................................................................................…. 107
Figure 7A…….............................................................................................. 113
Figure 7B….................................................................................................. 118
Figure 8A…….............................................................................................. 120
Figure 8B….............................................................................…… ……… 125
Figure 9….................................................................................................... 127
Figure 10…............................................................................…………….. 130
Figure 11…............................................................................…….............. 133
Figure 12…...........................................................................……………... 134
Figure 13A... ............................................................................................... 138
Figure 13B... ......................................................................................…..... 140
Figure 14……........................................................................................….. 142
Figure 15…...…........................................................................................... 145
Figure 16…............................................................................…….............. 148
Figure 17…............................................................................…………….. 150
Figure 18….................................................................................................. 154
Figure 19A.... .............................................................................................. 156
Figure 19B…............................................................................................... 159
Figure 20…….............................................................................…………. 164

v
List of Tables
Table 1: 4506 Media Components……………………………………… 168
Table 2: Transgene Plasmids Used in Screen……….…………….…… 169
Table 3: Plasmids Used to Generate Viral Particles..…………………. 169
Table 4: Screening Conditions Without EtO-N2A…………….……… 170
Table 5: Screening Conditions With EtO-N2A…………………….….. 170
Table 6: Small Molecules Used in Screen…………………….….….…. 171
Table 7: Small Molecules in Each Media Mixture………….……...…. 171
Table 8: Transgene And Media Mixtures Tested In Screen………..… 172
Table 9: External Solution Components…………………….….……… 174
Table 10: Internal Solution Components……………………….……… 174
Table 11: High K
+
Solution Components…………………….………… 174

1

Chapter 1: Introduction and Background
A. The Problem of Schizophrenia
Schizophrenia is a neuropsychiatric syndrome of unknown etiology (National
Health Service, 2016; NIMH, 2018) that affects approximately 0.5%-1% of the US
population (McGrath, Saha, Chant, & Welham, 2008; Ritsner, 2011; Wu, Shi, Birnbaum,
Hudson, & Kessler, 2006). It has a heritability of approximately 80%, though the
majority of cases are sporadic (Cardno & Gottesman, 2000; Patrick F. Sullivan, Kendler,
& Neale, 2003). It is characterized by symptoms that fall into three general categories:
“positive”, “negative”, and “cognitive” (NIMH, 2018). Positive symptoms of this
disease include delusions, hallucinations, and disorganized thoughts. Negative symptoms
include anhedonia, difficulty speaking, and flat affect. Finally, cognitive symptoms
include poor executive function, problems with memory and troubles with learning or
focusing. The range and severity of symptoms experienced by each patient can vary and
change throughout a patient’s life. Furthermore, these symptoms cause patients with
schizophrenia to face numerous social and economic challenges throughout their lifetime
that include an inability to live independently or form meaningful relationships with
other people, a life expectancy that is reduced by nearly thirty years (Olfson, Gerhard,
Huang, Crystal, & Stroup, 2015), and an employment rate of only about 10% (Evensen et
al., 2016). For these reasons, schizophrenia is a significant cause of human suffering, as
it is listed in the top 10 worldwide causes of disability (Rossler, Salize, Os, & Riecher-
Rossler, 2005), despite its relatively low prevalence. Unfortunately, there is currently no
cure for this disease and treatments are mostly ineffective, especially against alleviating
2

the negative and cognitive symptoms (Fusar-Poli et al., 2015). It is not surprising,
therefore, that schizophrenia enacts a great cost to both individual patients and societies
as a whole (McEvoy, 2007). Thus, there is a large incentive to reduce the impact of
schizophrenia, since this will benefit society and individual patients and their families.
One of the modalities to treat schizophrenia is with the use of antipsychotic
pharmacotherapy (Stroup & Marder, 2018). These medications primarily reduce the
positive symptoms of the disease (Buchanan et al., 2010) but do little to reduce the
impact of negative or cognitive symptoms (Fusar-Poli et al., 2015). These medications
also come with numerous deleterious side effects, such as agranulocytosis, which can be
fatal, and occurs in 1-2% of patients who take the potent antipsychotic Clozapine (Alvir,
Lieberman, Safferman, Schwimmer, & Schaaf, 1993). Side effects can also affect the
nervous system, such as the so-called “extrapyramidal symptoms”, which consist of
akathisia (sudden urges to move and an inability to sit still), parkinsonism (a resting
tremor and a stop-and-go type rigidity), and tardive dyskinesia (involuntary movement
of various facial muscles). Extrapyramidal symptoms are generally irreversible and are
caused by most antipsychotics (Marder, Stroup, & Stein Murray, 2018). Weight gain,
hyperlipidemia, hypertension, and other cardiac problems are also side effects, as well
(Jibson Michael & Marder Stephen, 2018). Thus, there remains a need for the
development of more efficacious pharmacotherapy that can better treat the symptoms
suffered by patients with this disease. A significant obstacle of this goal is the fact that
the etiology of schizophrenia is largely unknown and, as such, there are few, if any,
good targets of choice to which new pharmacotherapies can hone in. Thus, the use and
creation of platforms that can help elucidate etiological features of schizophrenia, such
3

as in vitro cell models, animal models, imaging, and genetic studies, can be of great
benefit since they may help with the discovery of new pharmacotherapies to treat the
millions of people with this disease.

B. The State of Research on Schizophrenia
The current body of knowledge indicates schizophrenia is a complex illness that
is influenced by both genetic and environmental factors (St Clair et al., 1990; Patrick F.
Sullivan et al., 2003). However, the exact mechanism of how genetic or environmental
factors, as well as any interactions between them or among them, leads to an individual
person developing schizophrenia remains unclear. Although much work remains to be
done, many recent findings, some of which are discussed below, have shed a bit of light
on this mystery.

Genetic studies on schizophrenia
In 2014, a large Genome Wide Association Study (GWAS) found 108 linkage
disequilibrium independent genetic loci that were associated with schizophrenia
(Schizophrenia Working Group of the Psychiatric Genomics Consortium, 2014). Many
of these loci colocalized with genes that are essential to neuronal physiology, including
glutamate neurotransmission, synaptic plasticity, and calcium channel function.
Astonishingly, the most statistically significant area associated with schizophrenia is in
and around the Major Histocompatibility Complex (MHC) locus, suggesting that
components of the immune system play a role in the development of schizophrenia
(Matzaraki, Kumar, Wijmenga, & Zhernakova, 2017; Shiina, Hosomichi, Inoko, &
4

Kulski, 2009). Other statistically significant loci that have a function in immunity
included enhancers that are active in CD19 and CD20 cells, which have a role in the
adaptive immune system (Kuijpers et al., 2010; K. Wang, Wei, & Liu, 2012). These data
support the hypothesis that interactions between neurons and the immune system can
influence schizophrenia. This GWAS study also revealed that most of the 108 loci
spanned areas of the genome which did not code for proteins, which suggests that non-
protein coding regulatory elements of the genome have a role in this disease. Exactly
how interactions between the immune system, neuronal function, and non-protein
coding elements affect the probability of acquiring schizophrenia remains an area of
research.
Earlier genetic studies have revealed that the gene DISC1 has a role in
developing several neuropsychiatric disorders, including schizophrenia (Blackwood et
al., 2001; JACOBS et al., 1970; Millar et al., 2000; Sachs et al., 2005; St Clair et al.,
1990). Remarkably, some frameshift mutations in DISC1 increases the risk of
developing schizophrenia by up to 50-fold, an astonishing increase considering the
complex nature of the disease (Blackwood et al., 2001). As significant as this finding
may be, there are only a few families worldwide in whom deletions in DISC1 are
present, thus the overwhelming majority of patients with this disease do not have such a
mutation, which brings into question how applicable these findings are to the general
pool of patients with schizophrenia (P F Sullivan, 2013). Furthermore, patients with
DISC1 deletions are also heavily predisposed to developing other neuropsychiatric
illnesses, including depression, bipolar disorder, schizoaffective disorder, autism, and
Asperger syndrome (Hikida, Gamo, & Sawa, 2012), which confounds the role this gene
5

has in the development of schizophrenia. Follow up studies have revealed DISC1 has a
role in several functions, including neurite outgrowth, mitochondrial function, and
microtubule stability (James et al., 2004; Kamiya et al., 2005; Ozeki et al., 2003). The
diverse functions of this gene, and the numerous splice isoforms that have been
discovered, have made it difficult to pinpoint the mechanism by which perturbation
leads to the various illnesses to which it is linked.

Imaging studies on patients with schizophrenia
Imaging studies of patients with schizophrenia have revealed that, on average,
there are anatomical differences in the brains of patients with schizophrenia when
compared to healthy controls. These differences included a smaller hippocampus,
amygdala, and thalamus, but larger caudate, pallidum, and putamen. Schizophrenia
patients also had more pronounced asymmetries in the amygdala, pallidum, putamen,
and lateral ventricles (Okada et al., 2016; van Erp et al., 2016). Another imaging study
found that patients with schizophrenia had widespread reduction in microstructural
white matter fractional anisotropy in various regions of the brain, including the anterior
corona radiata and corpus callosum (Kelly et al., 2017). Other anatomical findings
associated with schizophrenia include reduced dendritic spine density in cortical
pyramidal neurons in the prefrontal cortex (L A Glantz & Lewis, 2000; Leisa A. Glantz
et al., 2000) and a thinner temporal cortex (van Haren et al., 2011). These findings
indicate that, on average, people with schizophrenia have alterations in numerous areas
of the brain in both white matter and gray matter. Such global alterations imply that the
underlying etiology of the disease is related to something that has a systemic effect on
6

the brains of patients with schizophrenia. Importantly, these studies all had large sample
sizes of at least 2000 subjects, which indicates the findings presented therein may be
generalized to the general population of patients with schizophrenia.

Animal models of schizophrenia
There are currently at least 24 rodent models that have been generated to
investigate the pathophysiology of schizophrenia (C. Jones, Watson, & Fone, 2011).
Generally, these models fall into one of four categories: developmental (when
neurogenesis is perturbed during gestation), lesion induced (when a lesion is done to
the brains of rodents), drug induced (such as administering a psychotic agent), or
genetic manipulation (such as making Disc1 transgenic or knockout mice) (Tomoda,
Sumitomo, Jaaro-Peled, & Sawa, 2016).
Disc1 rodent models have shown this gene is involved in working memory
(Hiroko, Arguello, Kvajo, Karayiorgou, & Gogos, 2006), impulse control and cognition,
(Kuroda et al., 2011), regulates trafficking and degradation of amyloid precursor protein
and Aβ (Shahani et al., 2015), aids in spatial memory formation (Hikida et al., 2007),
and even social interactions (Pletnikov et al., 2008). Other types of models have also
revealed how other factors, such as social isolation and parental separation (Fone &
Porkess, 2008; Lapiz et al., 2003), exposure to environmental toxins (Lodge & Grace,
2009; H. Moore, Jentsch, Ghajarnia, Geyer, & Grace, 2006), and lesions to the ventral
hippocampus (Lipska, Aultman, Verma, Weinberger, & Moghaddam, 2002; Marquis,
Goulet, & Doré, 2006) can lead to many features seen in human patients with
schizophrenia including: working memory deficits (Lipska et al., 2002), hyperactivity
7

(Fabricius, Helboe, Fink-Jensen, Wörtwein, & Steiniger-Brach, 2011), aversion to novel
stimuli (neophobia), reduced prefrontal cortex synaptic plasticity, reduced volume of the
prefrontal cortex (Fone & Porkess, 2008), and how some of these features can be
alleviated by giving antipsychotics (Fabricius et al., 2011; Lapiz et al., 2003).
One significant weakness of rodent models is that each one displays only a small
subset of the positive symptoms seen in patients with schizophrenia. Such
inconsistencies raise the question of how applicable the findings from rodent models are
to humans with the disease. Another, and perhaps more significant, weakness when
using rodent models is that none of the models perform well when recapitulating the
negative symptoms of the disease (C. A. Jones, Watson, & Fone, 2011). This is
obviously a big shortcoming of these models, since medications currently available to
treat schizophrenia are least effective at treating negative symptoms (Fusar-Poli et al.,
2015). Thus, their utility to screen medications that can alleviate negative symptoms is
most likely minimal.
Yet another drawback of these animal models is that, in some instances, the
same genetic mutations or manipulations that led to behavioral effects in one rodent
strain had no effect on rodents of a different strain (Shoji et al., 2012). This makes it
possible that some of the findings from studies done using these animal models may be
artifacts related to the genetic backgrounds that are unique to different mouse or rodent
strains. (Tomoda et al., 2016)
One further drawback is rodents are, obviously, not humans. As such, many of
the findings present in rodents will not translate into humans. Although this is a
drawback of all animal models of disease, it is especially relevant when attempting to
8

model neuropsychiatric conditions such as schizophrenia because many of the
symptoms of these diseases are nearly impossible to assess in non-human animals. For
example, symptoms including hallucinations, delusions, anhedonia, flat affect,
disorganized thoughts and poor executive function can, at best, be only inferred from
altered rodent behavior. This will likely always remain a weakness of animal models of
human neuropsychiatric diseases.
Yet despite these numerous drawbacks, animal models have been critical to
study something that is extremely difficult using other modalities: How risk factors that
predispose humans to schizophrenia can lead to alterations in behavior. For this reason,
they will continue to be an important tool to find new treatments for, and investigate the
pathophysiology of, neuropsychiatric diseases including schizophrenia.

Cell models of schizophrenia
Other insights related to the etiology of schizophrenia have been found by
analyzing neurons derived from induced pluripotent stem cells (iPSCs) that were
generated from patients with schizophrenia patients and controls (K. J. Brennand et
al., 2011). Data from these samples revealed that iPSC-derived neurons from patients
with schizophrenia had numerous differences when compared to iPSC-derived
neurons from the control group. Among these differences were that SCZ-patient
derived neurons had less connectivity to each other, lower number of neurites, lower
expression of the synaptic protein PSD95, and lower expression of glutamate
receptors. These findings suggest it may be possible to model schizophrenia using in-
vitro patient derived neurons and, perhaps, that further studies using this type of
9

model can be used to investigate cellular mechanisms associated with the disease.
Though certainly noteworthy, the findings of this study cannot be generalized to the
general population of SCZ patients. This is because of the exceedingly small sample
size that only had three samples from patients with schizophrenia and four samples
from controls. In addition, the three SCZ patients used in this study are not a good
representative sample of SCZ patients, as one of the patients was diagnosed at age 6,
and two were siblings from a family with a significant history of schizophrenia. All of
these features make the patients outliers with respect to SCZ, as most SCZ patients do
not have siblings with the disease, and are diagnosed between ages 16 to 30 years of
age (NIMH, 2018). Brennand’s group has also published other studies using iPSC-
derived neuron from patients with schizophrenia. However, these studies have the
same drawbacks as described above, since they were done using samples that
exclusively had childhood onset schizophrenia (Hoffman et al., 2017), or were done
on the same four patients used in Brennand et. al, 2011 (K. Brennand et al., 2015;
Topol et al., 2015).
Other studies have been done using iPSC-derived neurons from patients with a
DISC1 frameshift mutation (Wen et al., 2014). iPSC-derived neurons from these
patients had numerous dysfunctions, including reduced production synaptic vesicles,
deficits in the release of synaptic vesicles, and reduced numbers of functional synapses.
These dysfunctions were rescued when the DISC1 mutation was repaired. This study
indicates DISC1 has a role in synaptic function, and that alterations in this gene can be
one of the reasons for the observed symptoms in patients with schizophrenia, at least in
the patients which have mutations in the gene. Though once again noteworthy, the
10

findings presented from this research may not be applicable to the generalized
population of patients with schizophrenia because, as discussed previously, the
overwhelming majority of people with schizophrenia do not have such deleterious
mutations in DISC1. Furthermore, the mutation present in the samples used by Wen et
al only occurs in one specific family.
The biggest drawback to cell models of schizophrenia is that, unlike the findings
from imaging and GWAS studies, the sample sizes used for these studies are inherently
small due to the high costs associated with generating and maintaining patient-derived
cell samples. For this reason, it remains to be seen if the findings presented by using cell
models can be applied to the larger population of patients with schizophrenia. To
explore this question, generating patient-derived neurons from a larger sample size is
required.
One strategy that can provide a larger sample size is with the use of patient-
derived-induced Neurons (iNs), which, as will be in the next section, have several
advantages over iPSC-derived neurons. Furthermore, since there have been no studies
that have explored schizophrenia using iNs, there remains a niche which can be
explored.

C. iNs Provide a Unique Strategy to Study Schizophrenia
Induced neurons (iNs) are a type of in vitro neuron that does not require the
initial formation of iPSCs (Gao et al., 2017; Huh et al., 2016; Mertens, Marchetto,
Bardy, & Gage, 2016; Tang, Liu, Zang, & Zhang, 2017; Xu, Du, & Deng, 2015a; Yang
et al., 2015). These iNs are generated by directly transdifferentiating a patient-derived
11

somatic cell source into a neuron (notably, neurons are not the only type of cell that can
be generated by direct transdifferentiation, as protocols have been published that yield
cells including oligodendrocytes, cardiomyocytes, melanocytes, chondrocytes, and
others (Mertens et al., 2016; Xu, Du, & Deng, 2015b). However, my discussion will
focus solely on the generation of induced neurons). Typically the somatic cell source is
a fibroblast, but other cell types, including hepatocytes, adipocytes, and pericytes can be
used (Mertens et al., 2016; Xu et al., 2015b). Bypassing the need to first generate iPSCs
greatly reduces the time and costs associated with culturing patient derived neurons
(Mertens et al., 2016). For instance, when starting from fibroblasts, generation of
neurons requires about 6-7 months when iPSCs first need to be made, whereas the
formation of induced neurons takes 1-3 weeks when starting from the same cell source.
Such a striking difference in the time required to generate neurons is thus an obvious
advantage of using iNs, since they can provide patient-derived neurons in a less time
consuming, less labor-intensive, and more cost-effective manner. These features make it
more likely to have in vitro cell model studies with larger sample sizes. In addition, and
perhaps more importantly, various studies suggest that iNs retain age-associated factors,
such as transcriptomic signatures and cellular functions, from the donor from which they
were derived, while such factors are completely lost when a sample is first converted
into iPSCs (Huh et al., 2016; Mertens et al., 2015a; Soliman, Aboharb, Zeltner, &
Studer, 2017; Tanabe et al., 2018; Tang et al., 2017; Xu et al., 2015a; Yang et al., 2015).
This suggest iNs form a more faithful cellular model of adult neurons than those derived
from iPSCs, and thus they may reveal insights into age-associated diseases that may not
be possible with other modalities. For this reason, iNs may be a more suitable model
12

with which to study schizophrenia than iPSC-derived neurons, since symptoms of this
disease almost always appear in adulthood. In my project, I set out to generate iNs from
neural progenitor cells derived from the olfactory epithelium from patients with
schizophrenia and healthy controls.

Induced neurons generated from a novel cell source
Here, I present a robust and reproducible method to generate iNs from cell
lines termed Cultured Neuronal cells from the Olfactory Neuroepithelium (Evgrafov
et al., 2011 and 2017). CNON lines are somatic neural progenitors derived from
biopsies from the olfactory neural epithelium of living human donors (Figure 1 and
Evgrafov et al., 2011; Rhie et al., 2018). CNON lines have been generated from 144
SCZ patients and 111 from controls. These lines have been well characterized: they
have all undergone RNA-Seq (total mRNA and miRNA) and have either Whole-
Genome genotyping or 30X Whole Genome Sequencing (WGS). In addition, a subset
of these CNON lines are in the USC PsychENCODE Consortium Project
(R01MH103346) and have ChIP-Seq and Hi-C seq data available (Rhie et al., 2018).
Notably, studies on these cells have found differentially expressed genes (Evgrafov et
al., 2017) and differentially enriched enhancers (Rhie et al., 2018) between samples
derived from schizophrenia patients and controls
CNON samples are neural progenitors and not terminally differentiated neurons
based on the presence of immunostain markers, gene expression, and cellular functions.
For example, they progress through the cell cycle and proliferate when grown with
appropriate media, generally for at least 30-35 passages at a 1:3 ratio. Furthermore,
13

CNON cultures are devoid of any fully differentiated neurons when immunostaining
with mature neuronal marker MAP2. Notably, CNON samples also fail to differentiate
into neurons when using published protocols that are effective on iPSC or other neural
progenitor cell lines (Frega et al., 2017; M. A. Lancaster et al., 2013; Salimi, Nadri,
Ghollasi, Khajeh, & Soleimani, 2014; ThermoFisher, n.d.; Y. Zhang et al., 2013a).
However, I have developed a protocol to induce CNON cells to become terminally
differentiated neurons by transfecting them with neurotrophic genes and a cocktail
of small molecules. I call these cells induced neurons CNON-derived induced
Neurons (CiNs). My protocol is a modified version of previous methods used to
generate induced neurons from mouse or human samples (Gao et al., 2017; Huh et
al., 2016; Mertens et al., 2015a, 2016; Tang et al., 2017; Vadodaria et al., 2016;
Vierbuchen et al., 2010a; Xu et al., 2015a; Yang et al., 2015; Yoo et al., 2011). My
data indicate CiNs are terminally differentiated, functional neurons, the majority of
which (>90%) are GABAergic inhibitory neurons. This is only the third tissue from
which adult iNs have ever been generated (Lau, Mertens, Gage, Kim, & Reid, 2018;
Tanabe et al., 2018), and the first time iNs have been generated from cells derived
from the olfactory epithelium. It is also the first time that human donor-derived
inhibitory neurons have been generated from cells other than iPSCs. CiNs are also
the first iNs that have been generated from patients with schizophrenia.
The use of CiNs have several advantages:
1) Induced neurons tend to generate a more faithful model of an aged neuron
when compared to iPSC derived neurons from the same donor (Mertens et
al., 2015a; Tang et al., 2017; Yang et al., 2015). And although it is not
14

currently known if CiNs will have this same property, they may reveal
different insights into the gene expression and functional characteristics of
neurons of schizophrenic patients than the corresponding iPSC-derived
neurons.
2) Since CNON are from a neuronal lineage, CiNs may retain epigenetic
signatures that are closer to those of actual neurons when compared to induced
neurons from fibroblasts (Lau et al., 2018). Since this is the first time iNs that
have been generated from patients with schizophrenia, CiNs can reveal
insights into the disease that either have not been done or may not be possible
with other modalities.
3) It is practical – there are currently approximately 255 available cell lines that
have been generated (144 from patients with schizophrenia and 111 controls).
Thus, there is already a large pool of samples from which neurons can be
generated. So far, I have generated CiNs from 5 CNON lines from schizophrenia
patients, and 6 from controls. Furthermore, it should be possible to generate data
from a far larger number of samples by transdifferentiating a mixture of different
CNON lines together in a common cell culture dish, and after a number of
weeks, perform single cell RNA-Seq with a portion of either the sorted or
unsorted cells, and assign the data from each cell to its progenitor CNON line
using each donors’ unique combination of expressed SNPs. This is possible
because, as mentioned previously, all CNON samples have been either Whole-
Genome Genotyped, 30X whole-genome sequenced, and thus all have SNP data
available. This approach should substantially reduce variance due to batch effects
15

that can affect in vitro cell studies.
4) Because CNON lines are mitotically active, a large quantity of CiNs can be
generated whenever needed.
5) Unlike some of the other cellular studies of the disease, which collected unusual
or extreme phenotypic outliers of schizophrenia, the 144 CNON samples from
donors with schizophrenia were ascertained from a large community cohort of
patients (Pato et al., 2013), with no further selection criteria other than an ability
to consent and participate. As such, none of these samples have known rare
variants, such as mutations in DISC1. Thus, the CiNs generated from these
samples are a better representation than previous studies in which schizophrenia-
patient derived neurons were generated. The 111 controls were, to as great an
extent possible, matched for age, race, and sex.
6) Generating CiNs is both less costly and less time consuming than generating
iPSC- derived neurons (Lau et al., 2018), therefore opening the possibility to
generate induced neurons from hundreds of patient samples in a cost effective
manner. Further adding to the cost-effectiveness of this method is that CNON
samples from hundreds of individuals have already been generated and thus there is no
need to deal with logistics and costs recruit donors.
7) Gene expression in CNON, which are genetically unmodified, are highly
correlated between individuals (approximately 98%), and is indistinguishable
between the major racial groups (Evgrafov et al., 2017). This is in contrast to the large
variation in iPSCs, where it is typical to make a least three lines because of the large
variability introduced by reprogramming. Hence, CiN’s start with less variability and
this is likely to lead to less noise in determination of differences between CiN samples
16

generated from patients and controls.

17

Chapter 2: Induced Neurons from CNON cells
A. Generating CNON lines
The acquisition and expansion of CNON cells is similar to the protocol described
in described by Ghanbari et al. (2004) and Wolozin et al. (1992). A complete description
is in Evgrafov et al., 2011; A brief outline of this protocol is given below and shown in
figure 1:
1) Biopsies from patients with schizophrenia and controls were taken from the
most superior-posterior aspect of the nasal septum epithelium and from the
superior- medial aspect of the middle turbinate. The collected specimens were
immediately placed in L15 media at 4C
2) Within 1-2 hours after acquisition, the biopsy samples were triturated in the
same media and tube from step 1. In one instance, however, a CNON line was
successfully established when trituration occurred 12 hours after biopsy
3) Samples fragmented in step 2 were embedded in liquid droplets of non-
growth factor reduced Matrigel Basement Membrane (BD Biosciences) held
at 4
o
C. These were then incubated at 37
o
C for 20 minutes to allow the
Matrigel to solidify. After incubation, 4506 media was added to the culture
(Ambesi- Impiombato, Parks, & Coon, 1980; Coon, Curcio, Sakaguchif,
Brandi, & Swerdlow, 1989). The reagents for 4506 are shown in Table 1.
4) 1-4 weeks after embedding, CNON cells will grow out of the biopsy and will
eventually exit from the droplet of Matrigel (figure 1, panel B), while non-
CNON cells remain trapped. Once they exit the Matrigel, CNON cells were
18

passaged using dispase (Jager et al., 2016). Cloning cylinders ensures that the
dispase only
dislodges the cells that exited from one of the Matrigel droplets, leaving the rest
of the droplets undisturbed. The collected cells were then placed onto a 35mm
cell-culture treated dish coated with 10ul of 50% Matrigel diluted in Coon’s
base medium. Hereafter, only 4506 media was used to maintain CNON cells.
5) Once CNON cells from step 4 are approximately 90% confluent, passage them
onto a 60mm cell culture treated dish coated with 25ul of 50% Matrigel diluted
in Conns media
6) Thereafter, CNON cells were passaged at a 1:3 ratio onto 60mm cell culture
treated dishes coated with 25ul 10% Matrigel diluted in Coon’s base
medium. This passaging pattern is repeated for all subsequent passages.

B. Establishing a protocol to make induced neurons from CNON cells
CNON cells do not differentiate into neurons when using protocols that have
been shown to work on other types of neuronal progenitors, including iPSCs, neural
stem cells derived from iPSCs, and neural stem cells from the subventricular zone
(Frega et al., 2017; M. A. Lancaster et al., 2013; Salimi et al., 2014; ThermoFisher, n.d.;
Y. Zhang et al., 2013a). In addition, and despite rigorous attempts, I was unable to
replicate findings made by other labs, which claimed MAP2 positive neurons could be
generated from olfactory derived epithelial cells (Girard et al., 2011a; Matigian et al.,
2010; Roisen et al., 2001). Thus, I set out to generate a new method that would allow for
the conversion of CNON cells into functional neurons. It was not until I virally
19

transduced CNON cells with pro-neural genes that I was able to achieve this goal.
The final protocol to generate CiNs has CNON cells be transduced with four
transgenes: Ngn2, Brn2, Ascl1, and Myt1l. Each of these factors has a role in the
development or maintenance of neurons; Ascl1 is required for development of olfactory
and autonomic neurons (Guillemot et al., 1993), Brn2 is required to establish neuronal
cell lineages during development (Fujii & Hamada, 1993; Nakai et al., 1995), Myt1l
represses non-neuronal cell fates (Mall et al., 2017), and Ngn2 promotes formation of
cortical neurons while repressing the formation of glia (Nieto, Schuurmans, Britz,
& Guillemot, 2001; Y. Sun et al., 2001).
The route I took to get this protocol began when I transduced CNON with the
factors, Brn2, Ascl1, and Myt1l (also known as BAM factors) because they were shown
to be capable of generating induced neurons from mouse and human fibroblasts
(Pfisterer et al., 2011; Vierbuchen et al., 2010b). However, unlike with fibroblasts,
transducing BAM factors alone was not sufficient to convert CNON into induced
neurons. I then decided to transduce with two other factors: 1) a construct that codes
p53dd, which inhibits cell apoptosis by acting as a dominant-negative repressor of p53
(Bowman et al., 1996). This inhibition promotes cellular reprogramming (H. Hong et
al., 2009). 2) a construct that produces the micro RNAs miRNA-9 and miRNA-124
(called miR-9/9*- 124), which suppress non-neuronal fates (Yoo, Staahl, Chen, &
Crabtree, 2009). The number of induced neurons that formed when transducing with
BAM factors, miR9/9*- 124, and p53dd were an improvement over just using BAM
factors alone but were nevertheless underwhelming: the conversion rate was at most
only 1-2%, which is significantly lower than is seen in fibroblasts. The conversion rate
20

was also inconsistent, as I found that despite using the same viral preps and reagents,
approximately 1-2% of CNON converting to CiNs, but only about half the time. The
rest of the attempts to generate CiNs resulted in formation of no neuronal cells at all.
There were also some CNON lines that persistently resulted in no induced neurons
being generated. I made numerous changes in an attempt to improve results, which
included using different combinations of pro-neural small molecules (table 6), using
new batches of reagents, different media components, and growing CNON on protein-
coated which are thought to promote neuronal survival. Despite these changes, I
continued to get the same poor results, which led me to seek different factors that may
aid in the formation of induced neurons. One such factor was Ngn2, which had been
shown, by itself, to generate induced neurons from iPSCs (Y. Zhang et al., 2013b). In
addition, the two factors Ngn2 and Ascl1 were also capable of generating induced
neurons from human fibroblasts (Mertens et al., 2015a). Once again, however, neither
transducing CNON cells with Ngn2 by itself, nor with Ngn2 combined with Ascl1,
resulted in the formation of CiNs. These results persisted despite implementing the
different ways to improve results as discussed above.
After these failed attempts, I decided to set up a screen to see which
combination of transcription factors, if any, were capable of consistently generating
induced neurons multiple CNON lines. I decided to do such a screen in order to test
numerous conditions at the same time, thus reducing possible batch effects, while also
saving time and minimizing the use of reagents. The total number of transgene
combinations, as well as the plasmid constructs used to generate each virus, are shown
in tables 2-5, and 8. The results of this screen indicated that transducing CNON with
21

Brn2, Ascl1, Myt1l, and Ngn2 gave the highest yield of CiNs.
As mentioned earlier, one of the ways I attempted to improve the yield of
induced neurons was by using pro-neural small molecules. These were used because
they had been shown to support the survival of any induced neurons that may be
formed, and also capable of generating induced neurons without needing to transduce
transcription factors (Gascón, Masserdotti, Russo, & Götz, 2017; Hu et al., 2015;
Ladewig et al., 2012a; M.-L. Liu et al., 2013a; Tang et al., 2017; L. Zhang et al., 2015).
In my screen, I included 14 small molecules to make four different combinations of
small molecules, each of which has been shown to aid in the formation of induced
neurons generated from mouse or human fibroblasts (tables 6 and 7). Notably, omitting
small molecules resulted no induced neurons being formed, regardless of the
combination of factors that were virally transduced into CNON cells.
I also tried different combinations and concentrations of proteins coat the
surfaces on which CiNs would be cultured. These coatings aid in the survival of
neurons. The different proteins I tested were Poly-L-Lysine, laminin, gelatin, and
matrigel. However, I found no differences in the formation of induced neurons when
using different proteins. For this reason, I decided to coat the growth surfaces with the
protein that required the least amount of time to establish a coated surface, which was
matrigel. These time differences are significant, as poly-L-lysine and laminin require at
least 24 hours to coat a growth surface, gelatin requires up to 4 hours, while matrigel
requires only 30 minutes.
The success of the screen was determined by which condition yielded the highest
percentage of cells that had visually distinct neuronal morphology and immunostained
22

positive for MAP2, which is a marker for mature neurons (Caceres et al., 1984; De
Camilli, Miller, Navone, Theurkauf, & Vallee, 1984; S. A. Lewis, Villasante, Sherline,
& Cowan, 1986; Miller, Walter, Theurkauf, Vallee, & De Camilli, 1982; Tucker, 1990),
and the amount of time the induced neurons could survive in culture. Not surprisingly,
most conditions tested in the screen yielded few or no induced neurons, as shown by a
lack of neuronal morphology and/or immunostains that were negative for MAP2 (figures
2 and 3). Remarkably, however, there were a few conditions that did yield a surprisingly
high number of neurons, all of which included Ngn2 (table 8, the highest CiN production
was given by conditions 66-69. Note: the term “N2A” denotes a construct that codes for
both Ngn2 and Ascl1. More details on the N2A construct are discussed in Chapter 2,
Section C. In addition, conditions that have “Ascl1” are denoted as such since I used a
construct that codes for Ascl1 by itself. Furthermore, conditions that included N2A did
not have the construct that codes for only Ascl1, since this gene is coded by the N2A
construct).
I then repeated my experiments for these conditions, and, as negative controls, I
also repeated some conditions that resulted in lower, or even zero, yields of induced
neurons. Fortunately, the repeat experiments all gave the same results. I once again
repeated these conditions, this time also including multiple CNON lines, which gave
results that were similar to prior experiments for each of the CNON lines I tested. From
the data generated in this screen and the numerous repeats, it became evident that
condition 67 – which was transducing CNON with Brn2, Myt1l, Ascl1, and Ngn2 while
growing them in media combination #3 – gave the highest number of induced neurons
that were also capable of surviving several weeks.
23

The yield of CiN conversion given by condition 67 was 30-40%, as measured
by the percentage of cells that are positive for MAP2. By far, the biggest factor that
determined the efficiency of conversion is the health of the CNON cells used to derive
CiNs. Generally, healthier CNON cell cultures gave higher conversion rate (the highest
being ~50%) than CNON cells that have a poorer condition (the lowest was ~15%). In
this case, the health of CNON cultures was determined by the appearance of the cells
themselves, such as a lack of vacuoles, no cells sloughing off the bottom of the culture
plate, little to no debris, as well as doubling rate between one to two days. The rate of
conversion did not seem to differ significantly across CNON samples derived from
different donors. Increased passages of CNON also did not affect conversion rate, so
long as the CNON remained in a healthy state. The details of the protocol are discussed
next.

C. The protocol to generate CiNs
Viral preps used to generate CiNs were prepared using Lenti-X Concentrator
(Clonetech #631232), which concentrates virus by approximately 80X and resuspended
in phosphate buffered saline (PBS). One microliter of the resuspended virus is
sufficient to transduce 5,000 CNON cells and scaling up was done accordingly. This
volume was acquired via a titration in which I added increasing amounts of
resuspended virus that coded for a red fluorescent reporter (dsRed, in my case) to see
how much viral concentrate was needed to have nearly 100% of cells in the culture
fluoresce.
The protocol for CiN production is separated into two parts, each requiring its
24

own round of viral transduction. The first part of the protocol is to transduce CNON
cells with a Tet-On system that uses antibiotic resistance markers. This type of system is
advantageous because it allows one to control the timing of expression of the genes that
were transduced into target cells, while also killing the cells which did not take up the
transgenes carried by the viral particles. Such control allows for the expansion and
formation of a population of cells which are ensured to carry the genes delivered by
theviral constructs, while also allowing for their expansion without expressing the
transgenes delivered by the viral particles. This bypasses the need to generate new viral
preps and reduced potential batch effects when needing to do multiple transductions on
separate cell lines (Das, Tenenbaum, & Berkhout, 2016; Gossen et al., 1995; Gossen &
Bujard, 1992). The Tet-On system used for my protocol consists of the following two
constructs provided by Dr Fred Gage (Ladewig et al., 2012b; Mertens et al., 2015b):
A) A Tet-On vector modified by replacing the endogenous CMV promoter
with EF1α-on (this construct is referred to as EtO), which enhances
translation transgenes under control of this promoter (Magnusson, Haase,
Schleef, Wagner, & Ogris, 2011). This construct constitutively codes for
neomycin resistance and the rtTA protein, which binds to a Tet Response Element
in the presence of doxycycline.
B) A construct that codes for Ngn2 and Ascl1 under the control of one promoter.
These two proteins are separated by two amino acids (referred to as N2A), as
well as for puromycin resistance under control of a separate promoter. For this
construct, puromycin resistance is constitutively expressed. However, A Tet
Response Element is upstream of the region coding for Ngn2 and Ascl1. Thus,
Ngn2 and Ascl1 will only be expressed if rtTA binds to this region, which will
25

only occur if doxycycline is in the media and the Tet-On vector was
transduced into the same cell.
In my case, this system ensures that only CNON cells transduced with both EtO and
N2A remain in the culture, as discussed below, while killing all other cells that were
transduced with either one or none of the viruses. The transduction of CNON cells with
EtO and N2A as follows:
1) Grow CNON under normal conditions until they are near confluency.
2) Remove media and wash cells with 1X PBS.
3) Add 1mL of trypsin per 6-cm cell culture dish. Scale up or down if using dishes
of different sizes. Place in cell incubator for 5 minutes.
4) For each 6-cm dish, add 1mL of 0.05% trypsin inhibitor or 1mL of 4506 media
to stop trypsin reaction. Scale the amount of trypsin inhibitor or 4506 media up
or down as needed if using different sized culture dishes.
5) Place cells into a 15mL conical tube and spin for 5 minutes at 1000RPM.
Aspirate and discard the supernatant.
6) Resuspend cells from step 5 in 1ml of 4506 media. Count 20,000 cells and add
these cells into ten wells of a 24-well dish (20,000 cells are added to each well,
thus totaling 200,000 cells) that has been coated with 50% Matrigel (diluted in
4506 media) at an amount of 1.25uL per cm2 surface area of plastic. One of
these wells will be transduced with EtO and N2A, while the rest will be
controls to determine selection with puromycin and neomycin (referred to as
G418).
7) The next day, transduce the cells from one well in step 6 with lentiviral vectors
26

coding for EtO and N2A. Mark the well which has been transduced. Once
viruses have been added, it is important allow the transduced cells to begin
expressing
resistance to puromycin and G418. This typically takes at least 48 hours. Leave
the other nine wells undisturbed.
8) Change media on all ten wells with normal 4506.
9) 48 hours after transduction, add 4506 media containing .5μg/ml and 100μg/ml
of G418 to the cells that were transduced in step 7. To the other nine wells, add
puromycin OR neomycin (but not both) as follows:
A) To four wells add puromycin with the following final concentrations in
μg/ml: 1, .5, .25, .1.
B) To another four wells add G418 with the following final concentrations
in μg/ml: 200, 100, 50, 25.
C) Leave the remaining well with only 4506 to serve as a negative control.
D) It is important to monitor the effects of both G418 and puromycin
separately. This ensures that only cells that have resistance to both
antibiotics (and thus were transduced by both EtO and N2A) survive. This is
important because expression of Ngn2 and Ascl1 requires the transduction of
both viral vectors in the same cell. This analysis is confounded if
untransduced cells are exposed to both antibiotics at the same time.
10) At this point, it is critical to monitor the health of both transduced and
untransduced cells – check for cell blebbing, intracellular vacuoles, slower
doubling times, and formation of debris in the culture, as these are all signs
27

that cells in culture are under stress and may soon die. To increase the viability
of transduced CNON, it is necessary to reduce the concentration of puromycin
and G418 as follows: once the untransduced CNON cells at a given
concentration begin to die, reduce the concentration of ONLY that antibiotic
for the transduced cells. As an example, once the untransduced cells in
growing in .5μg/ml of puromycin start to die, reduce the concentration of
puromycin in the media of the transduced cells to .25μg/ml of puromycin, while not
changing the concentration of G418. Conversely, when untransduced cells in
100μg/ml of G418 begin to die, reduce the concentration for transduced cells to
50μg/ml of G418, while not changing the concentration of puromycin. Repeat this
process until untransduced
CNON growing in .1μg/ml of puromycin and 25μg/ml of G418 begin to die,
which will take between 5 to 10 days, depending on the CNON cell line. By this
time, nearly all CNON that were not transduced by both EtO and N2A viruses
have died. Thereafter, maintain concentration of puromycin at 0.05μg/ml
and G418 at 10μg/ml.
NOTE: This type of titration is required because CNON seem to be particularly
sensitive to the effects of puromycin and G418. For instance, the same selection
process, when using fibroblasts, includes puromycin and G418 at double the
concentrations used on CNON cells (thus puromycin is at 1ug/ml and G418 at
200ug/ml (Mertens et al., 2015a)) and is never reduced during the selection
procedure. In addition, exposure to .5μg/ml of puromycin and 100μg/ml of G418
for one week will cause even transduced cells to divide slowly and senesce, even
if the antibiotics are completely removed after the selection. Furthermore, about
28

10-20% of CNON lines did not survive the transduction and selection protocol,
as it seems these cells are extremely sensitive to the effects of the selection
antibiotircs. In my case, three out of the 14 cell lines that have been transduced
with EtO and N2A senesced quickly and eventually died.
11) Passaging of both transduced and untransduced cells will still need to be done
during selection process discussed in step 10. Once cells in a given well have
reached confluence, passage cells at a 1:3 ratio. Cells that are untransduced can
be discarded but keep all cells that have been transduced by either freezing
them or growing them in culture. The selection process, which is the amount
of time it takes for the untransduced cells to die when exposed to each of the
antibiotics, is typically 5-8 days.
12) CNON cells that have been transduced with EtO and N2A are labeled as
having the letter “i” after their individual unique ID (ex: SEP25.16i).

The second part of the protocol is to transduce two additional viruses into cells
that already been transduced with EtO and N2A. One virus codes for Brn2 (Addgene
32925), while the other codes for Myt1l (Addgene 32926). These two viruses have
constitutive expression of the transgenes they carry, which means they are
immediately expressed once the constructs enter the target cell. This second
transduction is done as follows:
1) Add 1.25μl of 50% Matrigel (diluted in 4506) per 1cm
2
of a cell culture treated
dish. Allow the Matrigel to polymerize for at least 20 minutes in the cell
incubator at 37C. It is important to use cell culture treated plastic since CiNs will not
survive if grown on glass surfaces, even if they have been coated with proteins including
29

laminin, gelatin, and poly-L-lysine. This is also the case for plastic surfaces that have
not been cell culture treated.
2) Add 10,000 CNON cells that have been transduced with EtO and N2A per 1cm
2

of a cell culture treated plastic dish. Note that these cells are growing in 4506
that contains .05μg/ml of puromycin and 10μg/ml of G418
3) The next day, transduce cells from step 2 with Brn2 and Myt1l.
4) 24 hours after step 3, change the media to 4506 that contains .05μg/ml
of puromycin and 10μg/ml of G418
5) 24 hours after step 4, change to media #3, which is 4506 media supplemented
with the following: N2, B27, GDNF, BDNF, CNTF, doxycycline, forskolin,
dbCAMP, SB431542, CHIR99021, LDN193189, A83-01. The concentrations
of each component are shown in table 7.
6) Thereafter, change this media every 48 hours. Neuronal morphology should
be apparent within 2-3 days after adding media #3.
7) Two weeks after the second transduction, change to neuronal maturation
media, which is media 4506 supplemented with only N2, B27, GDNF, BDNF,
CNTF, doxycycline, and dbCAMP (thus excluding Forskolin, SB431542,
CHIR99021, LDN193189, and A83-01).
8) The protocol discussed above yields a neuronal conversion rate of about 30-40%.
This conversion rate not differ significantly across the different cell lines from
which I made CiNs. These cells remained viable for up to ten weeks after media
#3 was added.
NOTE: Though it may be possible to increase the efficiency of CiN conversion by
30

transducing Myt1l and Brn2 in an dox-inducible rtTA vector with antibiotic
resistance, the fact that CNON cells’ sensitivity to G418 and puromycin suggests that
exposing them to more selective antibiotics would further damage the transduced
CNON. As previously discussed, the health of CNON cultures prior to becoming
CiNs was the biggest factor determining efficiency of conversion. Thus, risking the
viability of CNON cells by exposing them to more selection antibiotics was not
worth the potential increase in CiN conversion efficiency. For this reason, I did not
pursue this strategy to generate CiNs.

31

Chapter 3: Characterization of CiNs
A. CiNs express markers of mature neurons and synapses
Representative images of CiNs made using this protocol are shown in figures 4
through 7. As mentioned previously, the protocol discussed above results in 30-40% of
CNON cells becoming CiNs that immunostained positive for MAP2 (figure 4). Cells
which did not convert into CiNs little to no MAP2 signal. Cells began having a neuronal
morphology within 2-3 days and were MAP2 positive within one week. CiNs remained
viable for as long as ten weeks without needing to coculture with mouse neurons or glia.
However, some CiNs begin to die within two weeks after being made. Typically, CiNs
are produced in wells with a 1cm
2
surface area (a description of the wells used to
produce CiNs is discussed later) and, once a few cells begin to die within in any given
well, the remainder of the cells in the culture will also die within 2-4 days (this die-off
occurs to both CiNs and CNON that did not convert). This occurs in about 10 to 30
percent of the wells with CiNs. The die-off tended to occur during a time when fungal
contamination was present in various cell lines being cultured by various people in the
lab, which eventually affected some of my CiN culture. This leads me to believe
contamination was the responsible, since the sudden death was no longer seen once the
contamination problem was resolved.
Nearly every cell that was positive for MAP2 was also positive for NeuN, which
is another marker of mature neurons that is localized to the soma of neurons (Duan et
al., 2016; Mullen, Buck, & Smith, 1992; H.-Y. Wang et al., 2015; Wolf et al., 1996). As
shown in figure 5, NeuN expression in CiNs is localized to the soma, which further
32

suggests these cells are mature neurons. Notably, although some NeuN signal is present
in cells that are not CiNs, it is nevertheless at a much lower intensity than in CiNs and
is also not localized to the soma of the cells. Furthermore, only cells with neuronal
morphology are the only cells which stain positive for both MAP2 and NeuN.
Figure 6 shows CiNs also stain positive for SYN1, which is a protein that
coats the outer leaflet of synaptic vesicles and has a role in the release of
neurotransmitters at synapses (Ferreira & Rapoport, 2002; Gitler et al., 2004; Hilfiker
et al., 2005; Thiel, 1993). CiNs also stain positive for PSD-95 (also known as DLG4,
figure 7), which is a scaffolding protein found at the post synaptic density and serves
as a marker for functional synapses in neurons (Chen et al., 2011; Chih, Engelman, &
Scheiffele, 2005; Ting et al., 2011; W. Zhang, Vazquez, Apperson, & Kennedy,
1999). Taken together, these data indicate CiNs are mature neurons and should be
capable of forming synapses.

B. CiNs are primarily GABAergic
The vast majority (>90%) of CiNs stained positive for GAD1 (also called
GAD67, figure 8). GAD1 is a marker of neurons that produce gamma-amino butyric
acid (GABA), which itself is the most prevalent inhibitory neurotransmitter in the brain
(Erlander, Tillakaratne, Feldblum, Patel, & Tobin, 1991; Kanaani et al., 2015; Kaufman,
Houser, & Tobin, 1991). Approximately the same proportion of CiNs also stained
positive for GABA itself (figure 9). Conversely, only a small percentage of CiNs stained
positive the excitatory neuron maker VGLU1 (also called SLC17A7, figure 10).
VGLU1 is a protein that transports glutamate, an excitatory neurotransmitter, into
33

synaptic vesicles (Bellocchio, Reimer, & Edwards, 2000; Takamori, Rhee, Rosenmund,
& Jahn, 2000).

C. CiNs are electrically active
All patch clamp experiments were done using the HEKA EPC-9 with fire
polished borosilicate glass pipets with a resistance between 3 to 6 Mega Ohm. The
components of internal and external solutions are shown in tables 9-11. Importantly,
recordings could only be done using pipets that were fire polished, since no giga seals,
let alone in whole-cell configurations, were accomplished when non-fire polished pipets
were used.
CiNs are capable of firing action potentials within 4-5 days after being
differentiated (which is after adding media #3 described previously). However, action
potentials at this time point lasted between 20-40ms, indicating CiNs have not yet
reached full maturity by this time. Action potentials resembling those of mature neurons
began to be displayed when CiNs were 2-3 weeks old (figure 11). For CiNs which were
successfully patch clamped in whole-cell configuration, 74 out of 79 showed some kind
of action potential. Out of those 74 CiNs, 19 had action potentials which lasted less than
4ms (notably, all 19 of these CiNs were at least 2 weeks old) and an additional 8 had
action potentials lasting less than 9ms. All measurements done on cells with action
potentials lasting less than 4ms had a series resistance less than 10MΩ. Conversely,
almost all measurements on cells with action potentials lasting more than 10ms had
series resistance values ranging from 15-60MΩ. This leads me to believe the reasons for
detecting such long action potentials (in CiNs that are at least two weeks old) is because
34

of technical reasons, such as a high series resistance, which can affect voltage and
current clamp recordings (J. W. Moore, Hines, & Harris, 1984; Sigworth, 1995), rather
than mature CiNs having long action potentials. Evidence for this is shown in figure 12,
where current clamp recordings are initially aberrant tracings with action potentials with
half-wavelengths near 10ms, if they were present at all. Application of gentle suction
when these recordings were being made suddenly led to the final recorded tracing
showing an action potential with a half-width of 2ms, suggesting something preventing
accurate current clamp readings was removed once the suction was applied.
Unfortunately, most of the time such a suction was applied when I saw these aberrant
recordings would lead to loss of the gigaseal or cell death. Thus, it is possible that
some of the CiN action potential recordings were affected by technical issues, such as
a high series resistance.
CiNs have an action potential firing pattern was remarkably consistent; 70 out of
the 74 cells had “single-spike” firing pattern (figure 11), which suggests CiNs are a
mostly homogenous population of neurons (the other four displayed non-
accommodating a firing pattern with wide action potentials). Single-spike firing pattern
has been reported by other groups which generated in vitro GABAergic neurons from
induced pluripotent stem cells (A. X. Sun et al., 2016). However, the neurons in their
study were more functionally heterogenous as they observed four main types of firing
patterns, and only 9% of which were “single-spike”.
The ability of CiNs to fire action potentials was confirmed by imaging using the
calcium sensitive dye FLUO-4AM. Upon the firing of an action potential, cytoplasmic
concentrations of calcium within a neuron increases (Stosiek, Garaschuk, Holthoff, &
35

Konnerth, 2003). FLUO-4AM binds to this calcium, which causes the dye to fluoresce
at a higher intensity (Stosiek et al., 2003). CiNs labeled with this dye had transient
increase in fluorescence when puffed with a solution containing 100mM K+, indicating
CiNs had fired an action potential (see movie 1). Such increases in fluorescence was not
detected in normal CNON cells.
CiNs also display sodium-channel and potassium-channel mediated currents in
voltage-clamp (table, figure 13). This is in stark contrast to normal CNON cells, which,
although display potassium-channel mediated currents, have minimal sodium channel
mediated currents (figure 14). Furthermore, CNON cells also display no action
potentials on current clamp. These data indicate that CiNs are functionally distinct from
the CNON cells from which they are derived. Specifically, CiNs display functional
characteristics of mature neurons, whereas CNON cells do not.

D. CiNs form functional synapses
Post-synaptic currents and potentials indicate functional synapses have formed
between different neurons. This is significant for in-vitro neurons as it indicates,
among other things, that presynaptic neurons are forming and releasing synaptic
vesicles containing neurotransmitters, and that postsynaptic neurons have
neurotransmitter receptors that respond appropriately when bound by a ligand (Byrne,
2014; Deutch & Roth, 2014; Waxham, 2014). This is a requirement if a neuron is to
send information to, or receive information from, other neurons.
Out of the 74 CiNs which fired action potentials, 18 displayed synaptic
potentials or currents; all were detected in CiNs that were at least two weeks old. Three
36

of these cells showed predominantly excitatory post synaptic input in the form of
excitatory post synaptic potentials and currents (EPSPs and EPSCs, respectively). The
remaining 15 cells displayed either predominantly, or exclusively, inhibitory post
synaptic input in the form of inhibitory post synaptic potentials and currents (IPSPs or
IPSCs, respectively).
These findings indicate that most (15/18, approximately 83%) presynaptic
neurons released inhibitory neurotransmitters onto the postsynaptic cell (see figures 15
through 18). Not surprisingly, these data coincide with immunostain images showing
the vast majority (approximately 90%) of CiNs stain positive for GABA, which is the
most widely expressed inhibitory neurotransmitter in the brain (Capogna & Pearce,
2011). An equally large percentage of CiNs also express GAD1, an important enzyme
in the biochemical pathway that synthesizes GABA. Notably, inhibitory PSCs
displayed by CiNs had two waveforms: “slow” and “fast” (figure 18). Previous studies
indicate these types of currents arise in mature GABAergic neurons expressing two
distinct isoforms of the GABAA receptor (Banks, Li, & Pearce, 1998; Capogna &
Pearce, 2011; Ling & Benardo, 1994; Pearce, 1993). They have also been observed in
GABAergic neurons in vivo (Banks, Hardie, & Pearce, 2002; Ling & Benardo, 1994)
and in vitro (A. X. Sun et al., 2016; Yuan et al., 2018). Overall, these findings further
indicate CiNs have functional properties that occur in bonafide GABAergic neurons.

E. RNAseq on induced neurons generated with a preliminary protocol
As previously discussed, the method that gave the highest and most consistent
yield of CiNs included transducing CNON with viruses coding for Brn2, Myt1l,
37

Ascl1, and Ngn2. Unfortunately, I have been unable to acquire RNAseq data on the
neurons produced with said protocol. However, I did have an opportunity to acquire
RNAseq data on induced neurons generated with a protocol that I developed early on
in my attempts to make neurons out of CNON lines. I call the induced neurons
generated with this preliminary protocol “CiN-Prelims”, in order to distinguish them
from the induced neurons made with the more efficient protocol, which are the
aforementioned CiNs. CiN-Prelims were made by transducing CNON with Brn2,
Myt1l, Ascl1, miR9/9*-124, and p53dd (see tables 2, 4 and 8). The conversion rate of
this protocol was approximately 1-2%, but at time it was the protocol that had given
the highest yield.
RNAseq on these cells was done as follows:
1) CiN-Prelims and an equal number of CNON cells on the same culture dish but had
not converted, were picked one at a time using boro-silicate glass patch clamp
pipets with a resistance of 2-5 MΩ. CiNs were chosen based on fluorescence of a
MAP2:YPET reporter as well as appropriate neuronal morphology. CNON that had
not converted were picked based on the normal morphology these cells possess.
Once identified, one cell, either a CiN-Prelim or a CNON, was aspirated into the tip
of the pipet. This caused the cells to be clog the opening of the pipet, since the
opening is far too small for cells to pass through. The tips of these pipets were
broken into Single Cells-to-CT buffer (Fisher Scientific #445827), which released
the RNA contents of the cells into the buffer. The pipets used to pick the CiN-
Prelims and CNON were broken into their own individual tube containing the
Single Cells-to-CT buffer, and thus RNA from each of these cells was never mixed.
38

2) RNA within Single Cells-to-CT was then amplified using the aRNA
amplification protocol (Morris, Bell, Buckley, & Eberwine, 2014).
3) Amplified RNA was converted into libraries was done using illumina TruSeq
mRNA library preparation kit and sequenced on an illumine HiSeq2500 according to
manufacturer protocol. Sequencing data was mapped to GenCODE 28 using GTFAR
pipeline (https://genomics.isi.edu/gtfar)
4) Importantly, library preparations were done using the same kit and all sequencing
was done on one single flow cell. This minimized batch effects that may have
appeared from library prep and sequencing. Data also passed sequencing quality
control metrics. Differential gene expression between CiNs and CNON that had not
converted was done using DESeq2 (Love, Huber, & Anders, 2014)
The data from this experiment revealed that CiN-Prelims had high expression of
various GABA receptor subunits and markers of GABAergic neurons, such as GAD1. I
made this discovery after I had noticed CiNs largely exhibited inhibitory PSCs and
PSPs. Based on electrophysiological data from CiNs, and the RNAseq from CiN-
Prelims, I began to suspect CiNs were mostly inhibitory GABAergic neurons. This was
confirmed by immunostain images discussed in sections A and B of this chapter.
Interestingly, the RNAseq data also showed CiN-Prelims have high expression of the
GABRB3, which is the subunit of the GABA receptor that is required to observe slow
IPSCs (Capogna & Pearce, 2011). The RNAseq data also revealed CiN-Prelims express
high levels of SST, which is the gene that codes for somatostatin. SST-expressing
GABAergic neurons are one of the three major types of inhibitory neurons in the human
brain (the other two types are PV-expressing and 5HT3aR-expressgin GABAergic
39

neurons). However, CiN- Prelims do not express the genes that serve as markers for the
other two types of GABAergic neurons: PV, which is the gene that codes for
pavalbumin, and 5HT3aR, which is a gene that codes for a subunit for a specific
serotonin. Based on this CiN- Prelim data, it is possible that CiNs are SST-expressing
GABAergic neurons. Obviously, this is yet to be confirmed, whether it is with RNAseq
of CiNs or immunostaining them for SST.

40

Chapter 4: Discussion
A. GABAergic neurons in neuropsychiatric diseases
Ample evidence has accumulated which suggests excitatory glutamatergic
neurons have a role in the pathophysiology of schizophrenia (Fromer et al., 2014;
Harrison & Eastwood, 1998; Pathania et al., 2014). Intuitively, this makes sense because
excitatory neurons make up the majority of neurons in the human brain (Marín, 2012a;
Wonders & Anderson, 2006). However, and perhaps due to the various etiological
factors that seem to have a role in schizophrenia, there is also a plethora of evidence
suggesting GABAergic neurons also have a role in this disease. Given the complexity of
schizophrenia and the numerous risk factors that appear to have a role in its
development, it is not surprising to find evidence suggesting both excitatory and
inhibitory neurons may be involved in its pathophysiology. Since CiNs appear to be
predominantly inhibitory GABAergic neurons, I will review some of the evidence
suggesting the role these types neurons have in the development of schizophrenia and
other neuropsychiatric diseases.
The hypothesis that GABAergic neurons are involved in schizophrenia was
initially proposed decades ago (Roberts, 1972). Since then, many studies have presented
evidence suggesting reduced signaling parvalbumin-positive GABAergic neurons in the
dorso-lateral prefrontal cortex are altered in patients with schizophrenia (Benes &
Berretta, 2001; Marín, 2012b; Rao, Williams, & Goldman-Rakic, 2000; Uhlhaas &
Singer, 2010a; Woo, Whitehead, Melchitzky, & Lewis, 1998). In addition, other groups
have found SCZ patients have reduced concentrations of GABA in the visual cortex
41

(Yoon et al., 2010), or increased GABA concentrations the occipital-parietal cortex and
anterior cingulate gyrus (Ongür, Prescot, McCarthy, Cohen, & Renshaw, 2010). As
important as these findings may be, the mechanisms causing these alterations, and how
such alterations lead to the symptoms experienced by patients with schizophrenia,
remain to be understood. A significant obstacle to discover said mechanisms is the fact
that, although there are only three major categories of GABAergic neurons
(somatostatin- positive, parvalbumin-positive, and 5HT3aR-positive), there is a wide
heterogeneity within each type – up 50 different subtypes are thought to exist in the
cortex (Lim, Mi, Llorca, & Marín, 2018), and at least 21 different subtypes have been
identified in the CA1 region of the hippocampus alone (Capogna & Pearce, 2011). Thus,
the mechanisms linking GABAergic neurons to symptoms seen in schizophrenia are
unlikely to be elucidated any time soon.
But despite this shortcoming, the findings of numerous groups across multiple
decades suggest alterations in GABAergic neuronal signaling is likely to have a role
in the pathophysiology of schizophrenia. For this reason, studying CiNs may add to
the body of knowledge of how such neurons are involved in the disease. Given the
existence of a large series of CNON lines from cases and controls, such a study is
warranted.
Additional studies have suggested that inhibitory neurons are involved in other
neuropsychiatric illnesses, including bipolar disorder. (Hattori, Kuchibhotla, Froemke,
& Komiyama, 2017; D. A. Lewis, 2014; Marín, 2012b; Tremblay, Lee, & Rudy, 2016).
This widens the potential use of CiNs, as they could be used to study these illnesses if
CNON lines from such patients are acquired. The design of studies of the CiN model
42

system will undoubtedly vary, but it would likely encompass analyzing differences in
gene expression, morphology, and functional characteristics between CiNs from
controls and patients with a disease.
Furthermore, the presence of slow IPSCs in CiNs is noteworthy because studies
have indicated these currents have a role in the formation of theta and gamma wave
oscillations, which are detected in EEGs during periods intense focus or concentration
(Banks, White, & Pearce, 2000; Gonzalez-Burgos & Lewis, 2008; Rotstein et al., 2005;
White, Banks, Pearce, & Kopell, 2000). These, theta and gamma oscillations have been
reported to be altered in patients with schizophrenia (L. E. Hong et al., 2008; Moran &
Hong, 2011; Siekmeier & Stufflebeam, 2010; Sponheim, Clementz, Iacono, & Beiser,
1994; Uhlhaas & Singer, 2010b). Despite an extensive search, I have been unable to find
any articles in which induced neurons display these types of IPSCs. Though other
groups have been able to generate GABAergic neurons that display slow and fast IPSCs
(A. X. Sun et al., 2016; Yuan et al., 2018), the starting cells have always been induced
pluripotent stem cells, which, as previously discussed, removes any age-associated
characteristics that are preserved when starting from a somatic cell. For these reasons,
CiNs may be the first induced neuron model in which slow IPSCs have been detected,
and thus they provide an opportunity to study these types of synaptic currents in
neurons from patients with schizophrenia.

43

B. The type of induced neurons that are generated depends on
the starting cells and which factors are transduced

Previous work to generate induced neurons revealed that the types of neurons
that are generated can vary depending on the starting cell type and which pro-neural
factors are virally transduced (Masserdotti, Gascó n, & Gö tz, 2016). For instance,
transducing Ascl1 into in fibroblasts promotes the formation of glutamatergic induced
neurons (Chanda et al., 2014). In astrocytes from the cerebral cortex Ascl1 leads to the
formation of GABAergic neurons (Masserdotti et al., 2015). However, in astrocytes
from the dorsal midbrain region, Ascl1 expression leads to a mixture of GABAergic and
glutamatergic induced neurons (Achim, Salminen, & Partanen, 2014; Y. Liu et al.,
2015). Similarly, for Ngn2, it leads to the formation of cholinergic neurons when
transduced into fibroblasts (M.-L. Liu et al., 2013b), but promotes the formation of
glutamatergic neurons when expressed in astrocytes from the cerebral cortex (Heinrich
et al., 2010).
One possible explanation for this observation is that each transduced factor
has different effects that depend on the proteins present in the starting cells. For
instance, Dlx2, when interacting with Ascl1, promote the formation of GABAergic
neurons (Heinrich et al., 2010; Petryniak, Potter, Rowitch, & Rubenstein, 2007; Torii
et al., 1999). Dlx2 is highly expressed in astrocytes from the cerebral cortex, but not
in fibroblasts. Thus, when transduced, Ascl1 will interact with Dlx2 that is already
present within said astrocytes, leading to the formation of GABAergic neurons. In
contrast, when Dlx2 is not expressed (as is the case in fibroblasts), Ascl1 promotes
the formation a “default” glutamatergic fate. These results indicate that the types of
44

induced neurons generated with different protocols at least partly influenced by the
gene expression profile of the starting cell source, and, given this context, it is thus
not surprising that CiNs are GABAergic, as the starting CNON lines express Dlx2
mRNA, and presumably the Dlx2 protein, at high levels.

C. CNON are one of only three cell types isolated from
human adults which have been used to generate induced
neurons have been generated

As discussed above, it is evident that induced neurons can be generated from
numerous cell types, including fibroblasts, astrocytes, hepatocytes, and many others.
However, it is important to mention that the majority of these cells were acquired from
human fetal tissues or mice. As far as generating induced neurons from adult tissues,
only two starting cell types have successfully used: pericytes and fibroblast (Mertens,
Reid, Lau, Kim, & Gage, 2018). This means CNON cells only the third tissue from
which human adult induced neurons have been generated. In my opinion, this alone
makes the formation of CiNs a noteworthy finding. However, there are many
advantages
that can make CiNs a better model to study schizophrenia and other
neuropsychiatric diseases, some of which are discussed next.

D. All models are wrong, but some are useful - the upside of
CiNs as a model to study neuropsychiatric diseases

The epigenetic state of a cell, such as the presence or absence of histone
45

modifications and DNA methylation, have an influence on the gene expression patterns
within that cell (Dolinoy, Huang, & Jirtle, 2007; P. A. Jones & Baylin, 2002). This
leads to an important corollary with respect to induced neurons - namely, that it is
possible that induced neurons from cells that are of a neuronal lineage have an
epigenetic state, and thus gene expression landscape, that is more similar to actual
neurons in vivo when compared to induced neurons derived from cells that are not from
such a lineage (Masserdotti et al., 2016; Mertens et al., 2018). Since CiNs are induced
from a population of cells that have a neuronal origin, it is possible that they are a more
accurate model of neurons within a human brain than induced neurons from fibroblasts.
With respect to pericytes, evidence has appeared that at least some pericytes in
the brain are derived from the neural crest and are thus from a neuronal lineage (Korn,
Christ, & Kurz, 2002). However, pericytes have marked developmental heterogeneity,
even when they are from the same organ or anatomical region. For this reason, it has not
been possible to isolate a pure population of pericytes that originated from the neural
crest (Yamazaki & Mukouyama, 2018). Such heterogeneity can be carried over to
induced neurons derived from them (Karow et al., 2018). For comparison, the
correlation of gene expression between CNON samples is approximately 98%,
indicating these cell lines are highly homogenous and, therefore, CiNs derived from
them are likely more homogenous than induced neurons from pericytes. This
homogeneity likely contributed to the observation that >90% of CiNs are GABAergic.
In many contexts, such homogeneity is advantageous since many confounders and
issues with sample sizes can arise when analyzing various types of neurons in a given
culture. However, if desired, CNON may be transduced with factors promoting the
46

formation of excitatory induced neurons, though such an experiment will need to be
optimized to ensure a high conversion rate and long survival of the resulting neurons.
Furthermore, the relative ease with which CNON can be acquired is another tremendous
advantage of CiNs, as CNON cells can be generated from a biopsy that is acquired
during an out-patient procedure under local anesthesia. This makes it possible to recruit
healthy controls and patients with schizophrenia or other diseases of interest. For
comparison, human pericytes are acquired using two ways: from resected brain tissue
from patients undergoing brain surgery (Karow et al., 2012), or isolation from post-
mortem tissues. Both of these methods are significant barriers when isolating pericytes
from numerous donors; regarding the surgery, is unlikely pericytes from healthy
controls could ever be acquired since such procedures are only done on people suffering
from serious brain or neurological diseases, while the postmortem interval is always a
confounding factor that can greatly affect the viability of samples acquired from post-
mortem tissues (Blair et al., 2016).
Preserving the epigenetic state of patient-derived neurons is especially relevant
with respect to neuropsychiatric diseases, since increasing evidence indicates epigenetic
mechanisms have a role in the pathophysiology of such diseases (Jakovcevski &
Akbarian, 2012; Kyzar & Banerjee, 2016; Nestler, Peña, Kundakovic, Mitchell, &
Akbarian, 2016), including schizophrenia (Akbarian, 2014; Bryois et al., 2018; Gavin &
Floreani, 2014; Jaffe et al., 2016; Wockner et al., 2015). Remarkably, induced neurons
also appear to retain age-associated epigenetic markers (Huh et al., 2016; Mertens et al.,
2018), which demonstrates iNs preserve at least some of the epigenetic status of their
parental cells. This is yet another indicator suggesting iNs can be a better model to study
47

neuropsychiatric diseases than iPSC-derived neurons, which have no retention of an
age- associated state. Herein CiNs have another potential advantage, since they may
preserve at least some of the epigenetic status of the patients (and controls) from which
they were derived.
E. Caveats and future directions
Unfortunately, there remain numerous caveats to the use of CiNs, and it
remains to be seen if they provide the advantages discussed above. For example, I have
not determined if CiNs belong to a specific subtype of GABAergic neurons found in
the human brain. As discussed above, I believe CiNs are SST-expressing GABAergic
neurons, although experimental confirmation of this has not been done. Furthermore, I
also don’t have data to determine if CiNs can be grouped into a specific subtype of
SST- expressing GABAergic neurons.
I have also not established if CiNs retain age-associated transcriptomic
signatures nor age-associated epigenetic markers. To answer these questions,
experiments involving RNAseq, methylation status, analysis of histone marks, and
functional studies, among others, need to be done on CiN samples from several donors
with a wide range of ages.
Although these experiments will be difficult, there is reason to be optimistic,
since there has not yet been a study that found induced neurons did not retained age
associated characteristics. I believe this is possible to answer these questions in a timely
manner because the CNON lines have already been acquired and the protocol to
generate CiNs is robust. These experiments will also help determine what subtype of
neurons CiNs belong to.
48

Although I believe it is unlikely, it is possible that CiNs do not retain age-
associated characteristics. Were this to be the case, then such a discovery would be
perhaps be even more significant since such findings would be contrary to results
presented by various labs the past 5-6 years, thus potentially adding a new branch of
research into studies involving induced neurons. For these reasons, I believe CiNs have
the capability of being very useful as a model system.
Furthermore, the primary goal of my project was to find differences in functional
properties and differentially expressed genes between CiNs from CNON samples
derived from donors with, and without, schizophrenia. To achieve this, I originally
hoped to generate and analyze CiNs from at least 20 samples from cases and controls.
Obviously, this was not the case, as I was able to generate CiNs from only 6 controls
and 5 cases. Furthermore, I acquired immunostain data from two cases and two controls,
and electrophysiological data from only one case and one control. Thus, much work
remains to be done in order to determine the potential complexity of the CiN model
system

49

Chapter 5: Projects Not Involving CiNs
A. “Odorant receptive” cells within CNON cultures
The discussion in this section revolves around following up on a publication
whose authors claimed to have found odorant responsive cells present in cell lines
derived from biopsies of the olfactory neural epithelium (Gomez, Rawson, Hahn,
Michaels, & Restrepo, 2000). The responses were measured by increases or decreases in
intracellular calcium when odorant molecules were added to the media. The authors
reported that although only 1-2% of the cells in the culture had such a response to
odorant molecules, these cells were easily identified by their strong resemblance to
bipolar neurons: a round, phase-bright cell bodies which had one or two processes
emanating from said cell body (figure 19A).
Finding such odorant responsive cells is noteworthy because numerous studies
have found that patients with schizophrenia have olfaction deficits as indicated by their
lower scores on smell identification tests such as the University of Pennsylvania Smell
Identification Test (Ishizuka, Tajinda, Colantuoni, & Cascella, 2010; Kimberley, Rui, &
Lili, 2006; Kopala et al., 2001; Malaspina et al., 2012; Moberg & Turetsky, 2003;
Wilcox & Hirshkowitz, 2011). Thus, having a cell that is odorant responsive can provide
a means to study the cellular processes that may cause patients with schizophrenia to
have olfactory deficits.
The cells used by Gomez et al are strikingly similar to the CNON lines
discussed in previous sections and may actually be the same type of culture system. For
instance, the biopsy site, culture conditions and reagents used to maintain the cells were
50

all the same. The morphology of the cells in CNON cultures, which consisted of largely
flat, wide cells and about 1-2% of the cells having a round, phase-bright appearance
with processes, is also the same as morphology of the cells used by Gomez. Thus, the
presence of these phase-bright cells presented an exciting opportunity to study odorant
responsive cells derived from patients with schizophrenia, since more approximately
150 CNON samples had already been isolated in the Knowles lab.
Unfortunately, the results I acquired when trying to confirm whether these
phase- bright cells could respond to odorants were completely different from the results
presented by Gomez. Firstly, I found that these cells had zero expression of all known
genes that coded for odorant receptors. They also had zero expression for Olfactory
Marker Protein (OMP), a gene which Gomez claimed these phase-bright cells stained
positive when using immunocytochemistry. I also found these cells to stain negative for
Beta Tubulin III (which is a marker for immature neurons), which Gomez claimed they
would stain positive. Furthermore, genes that were differentially expressed between the
phase-bright cells and the surrounding flat cells were generally involved in DNA
replication, cell cycle progression, and DNA repair. Putting this set of genes into Gene
Ontology confirmed this observation. As such, I began to suspect the phase bright cells
were merely flat cells that were actively undergoing mitosis. This suspicion was
confirmed when time-lapse imaging of these supposed odorant responsive cells
revealed they are just flat cells undergoing mitosis (figure 19B).
This revelation was obviously a huge disappointment, since studying odorant-
responsive cells present within CNON cultures could have provided insights into why
patients with schizophrenia have olfactory deficits. How it is that Gomez and his
51

colleagues got the findings they presented in their paper remains a mystery to me. My
suspicions range from carelessly carrying out their experimental protocols, using low
quality antibodies, to outright falsification of their images and graphs. One obvious
omission from their paper is any patch clamp data, which would have definitively
demonstrated if these bipolar can fire action potentials and have calcium channel
mediated currents (as well as currents mediated by sodium and potassium channels).
Many months, several hundred hours, and thousands of dollars were wasted because of
such faulty science. Sadly, what could have been an impactful study that propelled
research on schizophrenia forward was reduced to merely realizing a group of cells
were undergoing mitosis. Needless to say, this experience ended up giving me a much
more skeptical eye for findings in scientific journals.

B. Attempts to differentiate CNON into neurons within two-
and three- dimensional culture systems

In a sensational article published by Lancaster et al (2013), findings were
presented that showed iPSC-derived neural stem cells can form different structures of
the developing brain, such as the midbrain, forebrain, hindbrain, and cortex, with
strikingly accurate orientations with respect to each other as they differentiate in a three-
dimensional culture within a 20uL droplet of Matrigel. Amazingly, this orientation
occurred without the need for outside signals to influence which cells in the culture to
become which structures. This suggests that at least some part of the blue prints for
developing structures of the brain is carried by neural stem cells themselves and are not
wholly dependent on the presence of outside signals to develop into appropriate
52

structures.
Since some groups had reported they could generate terminally differentiated
neurons from cultures that, like Gomez et al., were very similar to CNON (Girard et al.,
2011b; Matigian et al., 2010). Thus, I set out to see if CNON also had the capacity to
differentiate into neurons both in traditional 2D cultures and 3D cultures as well. With
respect to 3D cultures, I attempted to differentiate CNON cells within 20uL droplets of
Matrigel using the same protocol used by Lancaster (M. Lancaster & Knoblich, 2014)
to make cerebral organoids when starting from iPSCs. This protocol is as follows:
1) Grow one full dish of CNON cells to full confluence under normal
conditions as discussed in previously
2) Remove media and wash cells with 1X PBS
3) Add 1mL of trypsin per 6-cm cell culture dish. Scale up or down if using
dishes of different sizes. Place in cell incubator for 5 minutes.
4) Add 1mL of trypsin inhibitor or 1mL of 4506 media to stop trypsin reaction
5) Place cells into a 15mL conical tube and spin for 5 minutes at 1000RPM.
Aspirate supernatant and discard
6) Dislodge cell pellet by gently tapping the bottom of the tube. Tapping will
cause the cells to resuspend into the residual media from step 5. This results
in a high concentration of cells
7) Place a piece of parafilm against an empty pipet tip tray. Make divots on
the parafilm by pressing it against the holes on the pipet tip tray. Place
20uL of Matrigel on the divots of the parafilm (how many droplets are
made depends on how many three-dimensional cultures are
53

desired.Typically 10-20 cultures suffice). Take 2uL of resuspended cells
from step 6 and dispense it into this droplet.
8) Place the droplet from step 7 into a cell incubator for 20-25 minutes. This
incubation will cause the Matrigel to form into a gel and trap the cells
within the matrigel.
9) Remove each droplet from step 8 the parafilm by gently agitating the
droplets with 4506 media. Take care to not let the droplets dry out. The
droplets will settle to the bottom of the well. Place cultures into incubator at
37C
10) Three days later, change media to neural differentiation media. For 250mL
of this media, the reagents were: 125ml DMEM-F12, 125ml Neurobasal
medium, 62.5ul of human insulin, 1X Glutamax, 1X MEM-NEAA, N2,
B27 with vitamin A, and 87.5ul of a 1:100 dilution (diluted in DMEM-F12)
beta-mercaptoethanol. After adding this media, place the cultures onto an
orbital shaker. Media was changed every 2 days, taking care to not aspirate
Matrigel cultures when changing the media.

Using the conditions above, the CNON three-dimensional cultures grew
with varying efficiencies. Most of the time the CNON cells adhered to the
surrounding Matrigel and sprouted processes in numerous directions (figure 20,
panels A-C). Typically, the cultures survived for up to one week and rapidly died
off thereafter (figure 20, panels E and F). This survival was also not dependent on
the type of media the cultures were in, since those grown in only the native 4506
did not seem to survive loner than those grown in the neural differentiation media.
54

However, about 10-20% of the cultures survived for one month (figure 20, panel G
and H). Though not optimal, this survival rate was still enough for downstream
analyses since numerous droplets are made at the same time.
Despite the growth of cells within the droplets, I unfortunately saw no reliable
signs of neuronal differentiation of CNON within these cultures, as none of the cells
stained positive for TUBB3 (a marker for immature neurons), MAP2 (a marker for
mature neurons), or NEFM (another marker for mature neurons) whether they were
grown in the normal 4506 media or the neural differentiation media. Surprisingly, some
cells present within the Matrigel droplets were able to move onto the bottom of the
plastic well in which the three-dimensional cultures were located (figure 20, panel D).
Even when grown in neural differentiation media, the cells that made their way out of
the droplet had no change in their overall morphology compared to CNON grown in
normal conditions, as they continued to divide and did not stain positive for TUBB3,
MAP2 or NEFM, indicating these cells had undergone no neuronal differentiation
despite growing in a traditional two-dimensional format. This finding was one of the
first signs that CNON cells were not classical neuronal progenitors, since the neural
differentiation media discussed here has been used by numerous labs to differentiate
neurons from iPSCs or neural stem cells in both 3D and 2D culture conditions. After
these results I attempted numerous other protocols to see if CNON cells could be
differentiated into neurons in 2-dimensional culture (Frega et al., 2017; M. A. Lancaster
et al., 2013; Salimi et al., 2014; ThermoFisher, n.d.; Y. Zhang et al., 2013a), but I was
unsuccessful in all attempts. These results led me to use virally transduced pro-neural
factors to attempt to generate neurons from CNON.
55

Figure 1 - acquisition and morphology of CNON cells. A: Location of nasal cavity from which biopsies were
taken (black arrow). B: CNON cells (blue arrows) growing from a piece of biopsy (black arrow) tissue
embedded in matrigel. After 1-2 weeks, CNON cells have migrated out of the matrigel and can be passaged.
Cells which are not CNON (red arrow) remain stuck within matrigel. Scale bar = 200um. C: CNON cells
growing in culture. The round, phase-bright cells are CNON during mitosis (orange arrow). D: CNON cells in
culture when they reach confluency. When left in culture, CNON cells will acquire a cobble stone-like
appearance shown in panel E

Figure 2A: Selected representative images of four-day-old CiNs immunostained for MAP2
(green). CiNs were generated by transducing CNON with different neurotropic factors and
incubated in different media formulations. None of the conditions depicted here include
transduction with N2A. Negative control consists of CNON cells grown for 4 days in media
4506. Scale bar = 200um or 400um as indicated in each image

Condition 2 – p53dd with media #2. Notice the faint MAP2 signal (arrow)

Condition 4 – Transduced with p53dd and grown in media #4 for two days, then switched to
media #1. this condition has cells that look like neurons but are only faintly positive for MAP2

Condition 5 – transduced with p53dd, grown in media #4 for two days then switched to media
#2. Cells with neuronal morphology are still only faintly positive for MAP2

Condition 6 – transduced with p53dd. Grown in media #4 for two days, then switched to media
#3

Condition 7 – transduced with Brn2, Ascl1, MYT1L, and p53dd and grown in media #1. Notice
the strong MAP2 signal of one cell. However, the majority of cells are either completely
negative for MAP2 or faintly positive.

CNON grown only in 4506 only. The left image is DIC. The right image is MAP2. Notice the
extremely faint positive signal compared to CNON grown in other conditions with same
exposure time, contrast, and brightness settings

Figure 2B: Selected images of nine-day-old CiNs immunostained for MAP2 (red). CiNs were
generated by transducing CNON with different neurotropic factors and incubated in different
media formulations. Negative control consists of CNON cells grown for 9 days in media 4506.
Scale bar = 200um

Condition 7 - transduced with Brn2, Ascl1, MYT1L, and p53dd and grown in media #1. Notice
the strong MAP2 signal of one cell. However, the majority of cells are either completely
negative for MAP2 or faintly positive.

Condition 19 – Brn2, Ascl1 and Myt1l in media #1

CNON cells grown in regular 4506. Notice the lack of fluorescence signal

Figure 2C: Selected representative images of 15-day-old CiNs immunostained for MAP2
(green). CiNs were generated by transducing CNON with different neurotropic factors and
incubated in different media formulations. None of these conditions depicted here include
transduction with Ngn2. Arrows denote cells with a neuronal morphology but were not MAP2+.
Negative control consists of CNON cells grown for 15 days in media 4506. Scale bars are either
400 or 200um, as indicated in each image.

Condition 7 - transduced with Brn2, Ascl1, MYT1L, and p53dd and grown in media #1. Many
more cells were positive for MAP2 than previous time points for this same condition. However,
the majority of cells are still negative for MAP2 or faintly positive.

Condition 7 continued

Condition 8 – transduced with Br2, Ascl1, MYT1L, and p53dd, and grown in media #2. This
condition had very few MAP2+ cells.

Condition 13 – transduced with Brn2, Ascl1, MYT1L, p53dd, and miRNA9/9*-124, and grown
in media #1. Notice some cells with neuronal morphology are not MAP2+ (arrows)

Condition 13 continued

Condition 15 - transduced with Brn2, Ascl1, MYT1L, p53dd, and miRNA9/9*-124, and grown
in media #3.

Condition 19 - transduced with Brn2, Ascl1, and MYT1L, and grown in media #1.

Condition 21 – transduced with Brn2, Ascl1, MYT1L, p53dd, and miRNA9/9*-124, and grown
in media #3. Notice the presence of cells with neuronal morphology but are very weakly MAP2+
(arrows)

Condition 25 - transduced with Brn2, Ascl1, MYT1L, and miRNA9/9*-124, and grown in media
#1. Once again, some cells with neuronal morphology are very weakly MAP2+ (arrows)

Condition 27 – transduced with Brn2, Ascl1, MYT1L, and miRNA9/9*-124, and grown in media
#3.

Negative control: CNON cells grown in regular 4506 only for 15 days. Notice the high cell
density that causes CNON to acquire a cobble stone appearance. MAP2 signal is negative or very
faintly positive.

Figure 3 – Selected images of different time points from different conditions used in the screen
to make CiNs. The green images were generated by transducing cells with a MAP2-YPET
reporter, which will fluoresce if MAP2 is being expressed at high levels. The red images were
acquired by transducing cells with construct that constitutively expresses the fluorophore dsRed
in all cells within the culture, which was used to more easily identify the morphology of all cells
within the culture. Cells expressing both constructs appear yellow.

10 days old cultures in mix 3 and miRNA+Ascl1+p53dd – red was just to more clearly see
demarcations of cells. Green is MAP2YPET reporter

10-day old cultures in mix 2 and miRNAs+p53dd.

12 days cultures in mix 1 with BAM+p53dd. Notice how few cells express the MAP2 reporter
despite having a neuronal morphology.

3 weeks old culture in mix 1 p53dd only, with little expression of the MAP2 reporter

10-day old cultures in mix 3 with miRNAs+p53dd+ASCL1

Mix 3 with miRNAs+BAM+P53dd – 20 days old. Notice how only one cell with a neuronal
morphology is expressing MAP2

20 day old cultures in mix 3 with miRNAs+BAM+P53dd

20- day old cultures Mix 1 with miRNAs+p53dd

Mix 2 with BRN2 only – 17 days old - here, although the neuronal-like cells are MAP2 positive,
the culture itself looks extremely unhealthy. In addition, the neurons in this condition rarely
survived past 2-3 weeks

Another set of images of 17-day old cultures in mix 2 with BRN2 only

Figure 4A: two-week old CiNs immunostain positive for MAP2 (green). The sample shown here was derived from a CNON donor
with schizophrenia (CNON25). Notice that cells which did not convert to CiNs have either no fluorescence or low fluorescence
localized to the nucleus (white arrows). Many of the cells with a non-neuronal morphology and high fluorescence are CiNs that had
their processes sloughed off during the immunostaining procedure (yellow arrows). There are also a small percentage of cells that are
positive for MAP2 but did not convert (red arrows). scale bar = 200um or 400um as denoted within each panel.

Figure 4A Continued: scale bar = 400um

Figure 4B: two-week old CiNs immunostain positive for MAP2 (green). The sample shown here was derived from
a CNON donor that does not have schizophrenia (CNON60). White, yellow, and red arrows denote the same types
of cells described in figure 4A scale bar = 200um or 400um as denoted within each panel.

Figure 2B continued: MAP2(green), scale bar = 400um

Figure 4C: two-week old CiNs immunostain positive for MAP2 (green). The sample shown here was derived from
a CNON donor with schizophrenia (CNON110). White, yellow, and red arrows denote the same types of cells
described in figure 4A scale bar = 200um or 400um as denoted within each panel.

Figure 4C continued: MAP2 (green), scale bar = 400um

A
Figure 5A:Close-up images of three-week-old CiNs immunostained for MAP2 (red) and NeuN (green). NeuN
appears yellow in some images due to overlap of red and green signal. Note that panel D has a cell that does not
express either marker (red arrow)

C

B

E

D

G

F

Figure 5B: Three-week-old CiNs stain positive for MAP2 (red) and NeuN (green). NeuN appears yellow in some
images due to overlap of red and green signal. The streak seen in some images is an artifact from the microscope
(white arrows). Although some many cells that did not convert to CiNs stain positive for NeuN, it is at a lower
intensity (red arrow). Furthermore, these cells do not stain positive for MAP2

Figure 5C: Five-week-old CiNs stain positive for MAP2 (red) and NeuN (green). NeuN appears yellow in some
images due to overlap of red and green signal. Although some many cells that did not convert to CiNs stain positive
for NeuN or MAP2, the signal for both for both markers is at a lower intensity (red arrow). Furthermore, these cells
do not stain positive for MAP2. Panel A has cell that appears to have lost a part of the soma (white arrow)

B
Figure 6: four-week-old CiNs immunostained for MAP2 (red) and SYN1 (green puncta). Panel C shows a cell that is only
weakly positive for synapsin (white arrow). However, a process belonging to a different cell appears to be strongly positive
(red arrow arrow). Unfortunately the rest of this cell was dislodged during the immunostaining procedure. Panel E shows two
cells - one is positive for synapsin, the adjacent cell appears to be negative (white arrow). Panel F also shows two cells. One
stains positive for synapsin and MAP2 (white arrow), while the other cells is weakly positive for MAP2 and negative for
synapsin (red arrow) that may be a CNON cell that did not convert. Panels J and K shows a CiN with little to no signal for
SYN1 despite being surrounded by debris. This serves as a control to ensure the punctate synapsin pattern is no caused by
debris that is lodged on CiNs.

A

C

D

F
110

E

G
I

J
K

Figure 7A: three-week old CiNs immunostaining for MAP2 (green), PSD-95(red puncta), and DAPI (blue). Boxes
demarcate areas that have been zoomed in.

Next Page

Figure 7B: another three-week-old CiN immunostained for MAP2 (red) and PSD95 (green).
Different secondary antibodies were used to ensure puncta were not artifacts from the secondary
antibodies. Notice a large number of puncta are present where processes from three neurons
intersect (white box, white arrows).

A B
C
Figure 8A: four-week-old CiNs immunostained for GAD1 (green) and MAP2 (red). The images for each individual
marker are shown in panels A and B, while panel C is the resulting overlaid image. Panel D shows one CiN that
was negative for GAD1 (blue arrow) between two CiNs that are positive (white arrows). The green streak is debris
formed during immunostain procedure (red arrow). Panel E shows a group of CiNs that are GAD1 (within dotted
box) positive and one CiN that is negative (blue arrow), as well as debris (red arrow). Panels F and G show more
GAD1-positive CiNs (white arrows) and debris (red arrows). Panels H, I and J show another set of images for each
individual marker and the resulting overlay

D

E

G

F

I J

H

A
B C
Figure 8B: three-week-old CiNs staining for GAD1 (red) and MAP2 (green). Note that the fluorescence pattern is
opposite that in 8A. Panels A, B and C show individual markers and the overlay. Note how there is one CiN that
does not stain positive for GAD1 (white arrow). Some CNON cells that did not convert are also shown (red
arrows). Panels D, E, and F also show the markers and resulting overlay with two CiNs staining positive for GAD1

F G

E

A
B
C
Figure 9: five-week-old CiNs staining for GABA (red) and MAP2(green). Panels A-C show fluorescent images of
the individual markers and the resulting image that occurs when they are overlaid. The vast majority of CiNs in the
culture stained positive for GABA (panels D, E, and F). Panel G shows one of very few CiNs that was not positive
for GABA (white arrow) along with another cell that is GABA positive (red arrow).

F

F E

G

Figure 10: 5-week-old CiNs stained for MAP2 (green) and VGLU1 (red puncta, white arrows). Only a small
number of stained positive for VGLU1

133

Figure 11: Representative current clamp recordings on three-week-old CiNs. Inlet represents current steps taken to
elicit voltage changes. The bottom panel shows selected tracings to more easily view the shape of the elicited action
potentials.

134

A
B
Figure 12: Current clamp recordings changed when gentle suction was applied when in whole-
cell. A: Out of 20 steps while increasing current (see inlet), the first 19 had high noise and long
action potentials, if any (gray traces). Gentle suction was applied shortly before the last trace was
recorded, which resulted in a recording with a short action potential and low noise (black trace).
B: Some selected tracings are shown in panel B to give a better view of the aberrant morphology
and nigh noise from the traces preceding trace 20. A clearer depiction of the sudden change in the
quality of the traces is represented in panel C, which shows traces 1-10, and panel D, which shows
11-20. The overlaid traces demarcated by the red bracket in panel D are individually shown in
panel E

135

D

C
136

Panel E: Individual tracings from panel D. Note long half-width
and small magnitude of the action potential (arrows), if it is even
present at all. A magnified scale bar that is depicted for each trace
present in each tracing is shown on the right

Panel F: These recordings from the same cell were acquired by repeating the same current
injection steps that gave the traces in panel A. Here, however, notice that several traces show a
fast action potential. The only trace that has a slow action potential resulted from injecting a
small current just above the minimum required to cause the CiN to fire.
137

F
138

A
Figure 13A – The two panels below show representative currents during voltage clamp recordings of CiNs. A: Sodium
(downward deflections) and potassium (upward deflections) channel-mediated currents and outward currents elicited by
CiNs. Area within dashed box is shown in next page. Inlet denotes the voltage steps. Lower panel: selected tracings to more
easily see the shapes of recordings. Step-ups in in voltage are shown in inlet on panel A

B
139

C
140

-100
K- IV curve
1400
1200
1000
800
600
400
200
0
-50 0 50 100
current-voltage for cell 5
400
200
0
-100 -50
-200
0

-400
-600
-800
50 100 150
K IV curve
2500

2000

1500

1000

500

0
-100 -50 0 50 100 150
Na current-voltage cell 4
800
600
400
200
0
-100 -50
-200
0

-400
-600
-800
-1000
50 100 150
60/291 = 20.6
133/378 = 35.2
74/132 = 56.0
30%-40%, with cell viability prior to differentiation being the biggest determinant factor in how many
CiNs were made

141

K IV curve
9000
8000
7000
6000
5000
4000
3000
2000
1000
0
-100 -50 0 50 100 150
Na IV curve
2000
1500
1000
500
0
-100 -50 -500 0
-1000
-1500
-2000
-2500
-3000
50 100 150
142

A
Figure 14: Patch clamp recordings on normal CNON Cells. A: Coltage clamp recordings oCNON cells are
significantly different than CiNs. Small to no currents are detected until -50mV, indicating these cells have few
voltage gated sodium channels. This is in stark contrast to currents seen in CiNs undergoing the same voltage steps.
Area within dashed box is shown in panel B. Panel C shows current clamp recordings, which show CNON don’t fire
action potentials despite increasing current steps.

143

B
144

C
145

a1 b1
Figure 15: Representative voltage clamp tracings of CiNs showing upward deflections indicative of inhibitory post
synaptic currents. This figure has two panels, A and B, with their respective subpanels. Tracings were recorded
when the CiN was held at voltages indicated within each panel. All recordings shown here lasted 75 milliseconds.

A) Currents detected when holding voltage = -74mV. Subpanels a1 through a4 showing a “slow” inhibitory
PSC (black arrows). Subpanel a5 shows four tracings overlaid on top of each other (gray) as well as other
traces not showing IPSCs (black). Note that the average amplitude of these IPSCs is approximately 20pA.

d1 c1
146

e1
147

b1 b2
B) Holding potential at -54mV – Each panel represents recordings lasting 75 milliseconds. Top two and bottom left
panels: tracing showing an IPSC (black) with typical morphology (rapid upward deflection followed by a slow
decay). Lower panel right panel: all three tracings overlaid on top of each other (gray) as well as tracings
showing no IPSCs (black). Note that the average amplitude of IPSCs at this holding potential is approximately
40-50pA, a larger magnitude than those that occur when holding potential is -74mV

b4 b3
148

Figure 16: Voltage clamp recordings on a CiNs held at -66mV – The recordings here show CiNs
have both inhibitory (top two panels) and excitatory (two panels on next page) post synaptic
currents. Notably, there is a marked difference in the shape, magnitude, and duration between the
inhibitory and excitatory currents

149

150

A
Figure 17: Voltage clamp recordings on a 5-week old CiN held at -65mV depicting EPSCs and IPSCs – 4 out of the
18 CiNs that had post synaptic currents would sometimes display an IPSC (upward deflection) that was followed
immediately by an EPSC (downward deflection). Panel A shows an IPSC preceding an EPSC (black arrow), then
what appears to be a solitary IPSC (red arrow). Notice that both the IPSC and EPSC still have a fast upward (or
downward) deflection followed by a slower decay back to equilibrium. Panel B shows another IPSC-EPSC pair,
which is enlarged in panel C. Notably, the amount of time it took for an EPSC to occur varried, as shown in panels
D and E. Notably, CiNs that showed this of synaptic currents pattern only exhibited the “fast” type of IPSC.
Furthermore, most IPSCs still occurred without a following EPSC (panels F through H). This particular CiN had no
EPSCs that were not initially preceded by an IPSC.

151

C

B
152

E

D
153

G
H

F
154

A B
C D
Figure 18 – IPSCs displayed by CiNs had two distinct waveforms designated as either “fast” or “slow”. In both
instances the IPSC is depicted by the typical fast, upward deflection that is followed by a slower decay back to
baseline. However, for “fast” IPSCs (panels A-D), both upward deflection and decay occur faster when compared to
the “slow” IPSC (panels E-H), as shown below

155

G H

F E

Figure 19A – Below are figures 1-3 from Gomez et al. Notice the bipolar morphology of the
cells in each figure

B
A
C
Figure 19B: Panels A and B show cells with a bipolar morphology are also detected in CNON cultures.
Panels C – E show images taken at different time intervals with two cells with bipolar morphologies (white
arrows) that become two flat cells (black boxes) by the last time point. Panel F shows a close up of one cell
over different time intervals which also becomes two flat cells.

Time = 0 minutes

Time 60 minutes
C

Time = 30 minutes

D

Time = 45 minutes

E

Time = 60 minutes

Time = 0 minutes Time = 30 minutes
Time = 45 minutes Time = 60 minutes
Panel F – images of the same cell (white arrow) taken at different time intervals. Notice how 60 minutes after
imaging begins there are now two cells with a flat morphology

A
B
Figure 20: three-dimensional cultures. Panel A shows CNON two days after being seeded into a 20uL Matrigel droplet; the
edge of the matrigel droplet is denoted by a light-gray edge (black arrow). By day 4, numerous processes have emanated
from cells within the droplet (panel B). By day 9, cellular processes reach the outer edges of the droplet (panel C, white
arrow). Panel D demonstrates how CNON cells can migrate out of the matrigel droplet and begin growing on the bottom
surface of the plates in which three-dimensional cultures are grown (black arrow). Within two weeks, there is noticeable
death of cells within most of the three-dimensional cultures, which results in phase-dark debris being stuck within the
matrigel droplets (panel E and F, black arrows). However, some of the cultures survived and had cells grow within them and
the entire area of the matrigel droplet (panels G and H)

D

C

F

E

H

G
168

Tables

Table 1: 4506 Media Components
Reagent Concentration
Coon’s Modified Ham’s F12 Base Media
Fetal Calf Serum 6% by volume
Bovine Hypotalamic Extract 150ug/ml
Bovine Pituitary Extract 50ug/ml
Na-Insulin 1ug/ml
Human Transferrin 5ug/ml
Hydrocortisone 10nM
Thyroxine 40pg/ml
Sodium Selenite 2.5ng/ml
Antibiotic-Antimycotic 1X
169

Table 2: Transgene plasmids used in screen
Plasmid Name Format Species Source Notes
pMXs-Brn2 Retroviral Mouse
Addgene
32925
Codes for BRN2
pMXs-MYT1L Retroviral Human
Addgene
32926
Codes for MYT1L
pMXs-Ascl1 Retroviral Mouse
(Son et al.,
2011)
Codes for Ascl1
miRNA9/9*-124 Lentiviral Human
Addgene
31874
Codes for miRNA-9 and
miRNA-124
pLVXTP-Ngn2-
Ascl1
Lentiviral Mouse
Addgene
84777
Codes for Ngn2 and
Ascl1
pLVX-EtO Lentiviral N/A
Addgene
84776
Codes for rtTA
pMXs-p53dd Lentiviral Human
Addgene
22729
Codes for a protein that
inhibits p53 activity

Table 3: Plasmids used to generate viral particles
Plasmid
Name
Format Plasmid Type Source Notes

psPAX2

Lentiviral

Packaging

Addgene 12260
Codes for
lentiviral
packaging
proteins

pMD2.G

Lentiviral

Envelope

Addgene 12259
Codes for
lentiviral envelope
protein

pMD-MLVogp

Retroviral

Packaging
Dana-Farber/Harvard
Cancer Ctr
(EvNO00061613)
Codes for
retroviral
packaging protein

pVSV-G

Retroviral

Envelope

Addgene 8454
Codes for
retroviral
envelope protein
170

Table 4: Screening conditions without EtO-N2A
Number of
Transduced
Viruses

Genes Coded by Viruses
Total Number
of Conditions
1
Brn2, Ascl1, Myt1l, p53dd, miRNA-9/124 each by
themselves
5
2 Brn2, Ascl1, MYT1L, miRNA-9/124 each with p53dd 4
3 Brn2, Ascl1, MYT1L 3
4
Brn2, Ascl1, MYT1L, p53dd;
Brn2, Ascl1, MYT1L, miRNA-9/124
2
5 Brn2, Ascl1, MYT1L, miRNA-9/124, p53dd 1

Table 5: Screening conditions with EtO-N2A
Additional Number of
Transduced Viruses
Genes Coded by Viruses
Total Number
of Conditions
0 EtO-N2Aonly 1

1
p53dd, EtO-N2A;
miRNA-9/124, EtO-N2A;
Brn2, EtO-N2A;
MYT1L, EtO-N2A

4
2 Brn2, MYT1L, EtO-N2A 1
3
Brn2, MYT1L, p53dd EtO-N2A;
Brn2, MYT1L, miRNA-9/124, EtO-N2A
2
4 Brn2, MYT1L, miRNA-9/124, p53dd, EtO-N2A 1
171

Table 6: Small molecules used in screen
Molecule
Concentration in
solution (uM)
Valproic Acid 500
CHIR-99021 3
RepSox 1
Forskolin 10
SP600125 10
GO6983 10
Y-27632 5
Dorsomorphin 1
SB431542 10
LDN193189 0.5
A83-01 0.5
ISX-9 0.5
IBET151 0.5
dbCAMP .5mg per ml

Table 7: Small molecules in each media mixture
Media
Mix
Small Molecules Added Reference
1 ISX9, CHIR99021, SB431542, Forskolin Hu et al., 2015
2
Valproic acid, CHIR99021, RepSox, Forskolin,
SP600125, GO6983, Rock Inhibitor Y-27632
Li et al., 2015
3
Forskolin, dbCAMP, SB431542, CHIR99021,
LDN193189, A83-01
Mertens et al., 2015
4 All 14 N/A
5 None N/A
172

Table 8: Transgene and media mixtures tested in screen
Condition Transgenes Added Media Added
1 p53dd 1
2 p53dd 2
3 p53dd 3
4 p53dd 4 to 1
5 p53dd 4 to 2
6 p53dd 4 to 3
7 Brn2, Ascl1, MYT1L, p53dd 1
8 Brn2, Ascl1, MYT1L, p53dd 2
9 Brn2, Ascl1, MYT1L, p53dd 3
10 Brn2, Ascl1, MYT1L, p53dd 4 to 1
11 Brn2, Ascl1, MYT1L, p53dd 4 to 2
12 Brn2, Ascl1, MYT1L, p53dd 4 to 3
13 Brn2, Ascl1, MYT1L, miRNA-9/124, p53dd 1
14 Brn2, Ascl1, MYT1L, miRNA-9/124, p53dd 2
15 Brn2, Ascl1, MYT1L, miRNA-9/124, p53dd 3
16 Brn2, Ascl1, MYT1L, miRNA-9/124, p53dd 4 to 1
17 Brn2, Ascl1, MYT1L, miRNA-9/124, p53dd 4 to 2
18 Brn2, Ascl1, MYT1L, miRNA-9/124, p53dd 4 to 3
19 Brn2, Ascl1, MYT1L 1
20 Brn2, Ascl1, MYT1L 2
21 Brn2, Ascl1, MYT1L 3
22 Brn2, Ascl1, MYT1L 4 to 1
23 Brn2, Ascl1, MYT1L 4 to 2
24 Brn2, Ascl1, MYT1L 4 to 3
25 Brn2, Ascl1, MYT1L, miRNA-9/124 1
26 Brn2, Ascl1, MYT1L, miRNA-9/124 2
27 Brn2, Ascl1, MYT1L, miRNA-9/124 3
28 Brn2, Ascl1, MYT1L, miRNA-9/124 4 to 1
29 Brn2, Ascl1, MYT1L, miRNA-9/124 4 to 2
30 Brn2, Ascl1, MYT1L, miRNA-9/124 4 to 3
31 miRNA-9/124, p53dd 1
32 miRNA-9/124, p53dd 2
33 miRNA-9/124, p53dd 3
34 miRNA-9/124, p53dd 4 to 1
35 miRNA-9/124, p53dd 4 to 2
36 miRNA-9/124, p53dd 4 to 3
37 miRNA-9/124 1
38 miRNA-9/124 2
39 miRNA-9/124 3
40 miRNA-9/124 4 to 1
41 miRNA-9/124 4 to 2
42 miRNA-9/124 4 to 3
173

43 Brn2, Ascl1, MYT1L 5
44 Brn2, Ascl1, MYT1L, miRNA-9/124 5
45 Brn2, Ascl1, MYT1L, p53dd, miRNA-9/124 5
46 EtO-N2Aonly 1
47 EtO-N2Aonly 3
48 EtO-N2Aonly 4 to 1
49 EtO-N2Aonly 4 to 3
50 p53dd, EtO-N2A 1
51 p53dd, EtO-N2A 3
52 p53dd, EtO-N2A 4 to 1
53 p53dd, EtO-N2A 4 to 3
54 miRNA-9/124, EtO-N2A 1
55 miRNA-9/124, EtO-N2A 3
56 miRNA-9/124, EtO-N2A 4 to 1
57 miRNA-9/124, EtO-N2A 4 to 3
58 Brn2, EtO-N2A 1
59 Brn2, EtO-N2A 3
60 Brn2, EtO-N2A 4 to 1
61 Brn2, EtO-N2A 4 to 3
62 MYT1L, EtO-N2A 1
63 MYT1L, EtO-N2A 3
64 MYT1L, EtO-N2A 4 to 1
65 MYT1L, EtO-N2A 4 to 3
66 Brn2, MYT1L, EtO-N2A 1
67 Brn2, MYT1L, EtO-N2A 3
68 Brn2, MYT1L, EtO-N2A 4 to 1
69 Brn2, MYT1L, EtO-N2A 4 to 3
70 Brn2, MYT1L, p53dd EtO-N2A 1
71 Brn2, MYT1L , p53dd EtO-N2A 3
72 Brn2, MYT1L, p53dd EtO-N2A 4 to 1
73 Brn2, MYT1L, p53dd EtO-N2A 4 to 3
74 Brn2, MYT1L, miRNA-9/124, EtO-N2A 1
75 Brn2, MYT1L, miRNA-9/124, EtO-N2A 3
76 Brn2, MYT1L, miRNA-9/124, EtO-N2A 4 to 1
77 Brn2, MYT1L, miRNA-9/124, EtO-N2A 4 to 3
78
Brn2, MYT1L, miRNA-9/124, p53dd,
EtO-N2A
1
79
Brn2, MYT1L, miRNA-9/124, p53dd,
EtO-N2A
3
80
Brn2, MYT1L, miRNA-9/124, p53dd,
EtO-N2A
4 to 1
81
Brn2, MYT1L, miRNA-9/124, p53dd,
EtO-N2A
4 to 3
174

Table 9: External Solution Components
(pH = 7.3)
Reagent Concentration Notes
NaCl 140mM
KCl 2.8mM
HEPES 10mM
MgCl2*6H20 1mM

CaCl2*2H2O 2mM
D (+) Glucose 300mM
Added Immediately
Before Use

Table 10: Internal Solution Components
(pH=7.3)
Reagent Concentration Notes
NaCl 10mM
KCl 145mM
HEPES 10mM
MgCl2*6H20 1mM
EGTA .1mM
Added Immediately
Before Use
ATP 2mM
Added Immediately
Before Use
GTP .3mM
Added Immediately
Before Use

Table 11: High K
+
Solution Components
(pH = 7.3)
Reagent Concentration Notes
NaCl 40mM

KCl 100mM
HEPES 10mM
MgCl2*6H20 1mM
CaCl2*2H2O 2mM
D (+) Glucose 300mM
Added Immediately
Before Use
175

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Abstract (if available)

Abstract Induced neurons (iNs) are in vitro neuron models that are generated via direct transdifferentiation from patient-derived somatic cells. These iNs can be generated without initially generating induced pluripotent stem cells (iPSCs). Bypassing the need to first generate iPSCs greatly reduces the time and costs associated with culturing patient derived neurons Furthermore, iNs retain age-associated factors, such as transcriptomic signatures and cellular functions, from the donor from which they were derived, while such factors are completely lost when a sample is first converted into iPSCs. Thus, iNs may be a more suitable model to study schizophrenia than iPSC-derived neurons, since symptoms of this disease almost always appear in adulthood. In this project, I generated iNs from neural progenitor cells derived from the olfactory epithelium from patients with schizophrenia and healthy controls, which I termed CiNs. CiNs provide a unique opportunity to study schizophrenia, as they express markers of mature, terminally differentiated, GABAergic neurons, including NeuN, MAP2, and GAD1 as well as markers of synapses, including SYN1 and PSD95. CiNs are also electrically active, as they display induced and spontaneous action potentials, as well as excitatory, and inhibitory post synaptic potentials and currents.

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Asset Metadata

Creator Lopez, Edder (author)

Core Title An induced neuron cell model derived from human olfactory epithelial cells

School Keck School of Medicine

Degree Doctor of Philosophy

Degree Program Systems Biology and Disease

Publication Date 05/15/2020

Defense Date 06/07/2019

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

Tag induced neurons,neuron,OAI-PMH Harvest,schizophrenia

Language English

Contributor Electronically uploaded by the author (provenance)

Advisor Sieburth, Derek (committee chair), Chow, Robert (committee member), Knowles, James (committee member), Siegmund, Kimberly (committee member)

Creator Email edderlop@usc.edu,elopez22@ucla.edu

Permanent Link (DOI) https://doi.org/10.25549/usctheses-c89-310135

Unique identifier UC11663322

Identifier etd-LopezEdder-8534.pdf (filename),usctheses-c89-310135 (legacy record id)

Legacy Identifier etd-LopezEdder-8534.pdf

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Rights Lopez, Edder

Type texts

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

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

Repository Name University of Southern California Digital Library

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