The equivalence of human induced pluripotent stem cells (hiPSCs) and human embryonic
stem cells (hESCs) remains controversial. Here we use genetically matched hESC and
hiPSC lines to assess the contribution of cellular origin (hESC vs hiPSC), the Sendai
virus (SeV) reprogramming method and, genetic background to transcriptional patterns
while controlling for cell-line clonality and sex. We find that transcriptional variation
originating from genetic background dominates over variation due to cellular origin
or SeV infection. Moreover, the 49 differentially expressed genes we detected between
isogenic hESCs and hiPSCs neither predicted functional outcome nor distinguished an
independently derived, larger set of unmatched hESC and hiPSC lines. We conclude that
hESCs and hiPSCs are molecularly and functionally equivalent and cannot be distinguished
by a consistent gene expression signature. Our data further imply that genetic background
variation is a major confounding factor for transcriptional comparisons of pluripotent
cell lines, explaining some of the previously observed expression differences between
unmatched hESCs and hiPSCs.
The question of whether hiPSCs, derived from somatic cells by overexpression of the
transcription factors Oct4, Klf4, Sox2 and c-Myc (OKSM)
1
, are equivalent to hESCs, the gold standard of pluripotent cell lines, is becoming
increasingly urgent as patient-specific hiPSCs are advanced toward clinical application
1-4
. Initial studies showed that hESC and hiPSC lines are fundamentally different at
the transcriptional level, whereas subsequent work concluded that they are virtually
indistinguishable when comparing larger sample sets
5-7
. More recent reports using refined gene expression analyses found small sets of differentially
expressed genes (DEGs)
8-10
. However, the origins of these DEGs, their consistency across independent studies
and their impact on the differentiation potential of hiPSC lines remain unclear. Transcriptional
patterns are influenced by numerous biological and technical parameters that may confound
results. The reprogramming method, including the choice of integrating versus non-integrating
factor delivery systems, can alter gene expression in iPSCs
11-13
. Likewise, genetic background may influence transcriptional signatures in pluripotent
cell lines since iPSCs derived from different individuals are reportedly more divergent
than iPSCs derived from the same individual. The difference between the clonal origin
of hiPSC lines, derived from single somatic cells, and the polyclonal origin of most
hESC lines may also introduce transcriptional variation
14
. An additional consideration is the sex of cell lines and defects in X chromosome
reactivation in female hiPSCs
17,18
. Some of these variables have been addressed in previous reports
11,12,15,16
, but, to our knowledge, no comparative study of hESCs and hiPSCs has accounted for
all of them.
We previously showed that comparing genetically matched mouse ESC and integration-free
iPSC lines eliminates most of the transcriptional variation observed between unmatched
cell lines
16
. Although we could not identify consistent transcriptional differences between mouse
ESC and iPSC lines, we discovered a small group of transcripts that was aberrantly
silenced in a subset of iPSC lines, which adversely affected their developmental potential.
Here we extend our analyses to the human system and ask whether molecular differences
can be identified in hiPSC lines relative to hESC lines that cannot be attributed
to the SeV reprogramming method, genetic background, clonal origin or sex, and whether
any such differences impact functional outcomes.
RESULTS
Approach to generate isogenic hESCs and hiPSCs
To compare hESCs with genetically matched hiPSC lines devoid of viral integrations,
we generated hiPSCs from in vitro-differentiated hESCs using a non-integrating Sendai
virus (SeV)-based reprogramming system
19
; SeV is an RNA virus that is diluted from infected cells in a replication-dependent
manner, leaving no genetic footprint behind (Fig. 1A,B). We chose two well-characterized
hESC lines, HUES2 and HUES3
20
, for these experiments. We selected male hESC lines because female iPSCs can exhibit
defects in X chromosome reactivation
17,18
, which might confound subsequent interpretations
9,21
.
First, we subcloned each line in order to ensure genetic and epigenetic homogeneity
of cells and to properly control for the clonal origin of hiPSCs (Fig. 1A). We differentiated
one hESC subclone from each background by switching cells to serum-containing medium
without basic fibroblast growth factor (bFGF), which is critical for the maintenance
of hESCs, and sorting fibroblast-like cells based on CD90+/TRA-1-81− expression (Fig.
1A,C). These fibroblast-like cells, which resemble primary human fibroblasts by morphological
criteria (Fig. 1C), did not form Alkaline Phosphatase (AP)-positive colonies in hESC
media, indicating successful differentiation and the absence of residual pluripotent
cells in the culture (Supplementary Fig. 1A). Analysis of global gene expression by
RNA-sequencing revealed that the fibroblast-like cells were highly similar to dermal
fibroblasts but distinct from pluripotent stem cell lines (Supplementary Fig. 1B).
Pluripotency-associated promoters, such as POU5F1, LEFTY1, TDGF1, and SCNN1A, were
re-methylated and decreased in expression levels whereas fibroblast-specific promoters
such as TMEM173, EMILIN1, LMNA, and RIN2 were demethylated and regained expression
in fibroblast-like cells (Fig. 1D). In a final step, the fibroblast-like cultures
were reprogrammed into hiPSCs by infecting the cells with SeV vectors expressing OKSM,
as previously reported
19
(Fig. 1A). Emerging colonies were isolated after ~3 weeks, expanded and confirmed
to be positive for AP activity and endogenous OCT4 expression, indicating successful
reprogramming (Fig. 1C). Moreover, we ensured loss of SeV expression in all lines,
demonstrating reprogramming factor independent self-renewal (Supplementary Fig. 1C,D).
Genetic background drives transcriptional variation
First we studied whether the SeV reprogramming method affects global transcription.
The parental hESC subclones were infected with GFP-expressing SeV (SeV-GFP) and passaged
until GFP fluorescence was no longer detectable before analyzing cell lines by RNA-sequencing
(Fig. 1A and 2A). We found a common set of 63 genes that was differentially expressed
between three uninfected hESC subclones (hESC SCs) and three SeV-GFP infected hESC
subclones (hESC GFPs) from each genetic background, which demonstrates that viral
infection itself leads to subtle but statistically significant transcriptional changes
that persist after viral loss (Fig. 2B). This 63-DEG set consistently separated hESC
SC lines from hESC GFP lines (Fig. 2C). Gene Ontology terms significantly enriched
among these 63 DEGs are related to transcription, DNA binding, and development (Supplementary
Fig. 1E). Based on these observations, we decided to use expression data from hESC
GFP lines as controls for all subsequent comparisons with SeV-generated hiPSC lines.
A comparison of the transcriptional profiles of hESC subclones (hESC SCs and hESC
GFPs), in vitro-differentiated fibroblasts and derivative hiPSCs by unsupervised clustering
showed the largest differences between pluripotent cell lines and differentiated cell
types, consistent with previous observations
5,15,22,23
(Supplementary Fig. 2A). Likewise, global methylation analysis of representative samples
by reduced representation bisulfite sequencing (RRBS) separated pluripotent cells
from in vitro-differentiated fibroblasts, indicating distinct epigenetic states (Supplementary
Fig. 1F). Notably, we observed a clear segregation of all pluripotent samples into
two transcriptionally related groups, irrespective of whether cell lines were infected
with SeV or not (Fig. 2D, expanded from Supplementary Fig. 2A). This segregation could
not be explained by the cellular origin of cell lines from embryos (hESCs) or somatic
cells (hiPSCs) but instead correlated with the genetic background of each line. That
is, HUES2-derived hESC subclones clustered with HUES2-derived hiPSC lines whereas
HUES3-derived hESC subclones clustered with HUES3-derived hiPSC lines. Consistent
with this finding, overall transcriptional variation between groups of genetically
matched hESC and hiPSC lines was significantly lower than that between unmatched cell
lines (Supplementary Fig. 2B). Moreover, transcriptional variation within groups of
genetically matched hiPSC or hESC lines was similar, indicating that hiPSCs and hESCs
are equally variable (Supplementary Fig. 2C). Of note, the number of promoters differentially
methylated between unmatched pluripotent cell lines was approximately twice as high
(2,610) as that between matched pluripotent cell lines (1,205), suggesting that genetic
background also influences epigenetic patterns in hESCs and hiPSCs (Supplementary
Fig. 2D). We conclude that genetic background is a major driver of transcriptional
and epigenetic differences between pluripotent cell lines, whereas the SeV reprogramming
method introduces more subtle yet stable transcriptional changes in hiPSCs.
Expression differences between matched hESCs and hiPSCs
Although genetic background accounted for most transcriptional differences among the
analyzed pluripotent cell lines, we noticed that hESCs clustered with each other and
separately from hiPSCs within a given background, suggesting subtle but consistent
transcriptional differences that reflect distinct cellular origins (Fig. 2D). To identify
any DEGs that distinguish hESC from hiPSC lines independent of SeV infection and genetic
background, we compared transcriptional profiles of hiPSC lines with those of genetically
matched hESC GFP lines. This analysis revealed that 52 and 91 genes were up- and down-
regulated, respectively, in hiPSC lines derived from the HUES2 background, whereas
77 and 426 genes were up- and down-regulated in hiPSC lines derived from the HUES3
background, respectively. Forty-nine genes were commonly dysregulated in both genetic
backgrounds (Fig. 3A). Considering the good depth of our RNA-seq data (~40 million
mapped reads per sample on average) (Supplementary Fig. 2E), it is highly unlikely
that this small number of DEGs was due to low sensitivity. As expected, the 49-DEG
signature reliably separated our hiPSC lines from our hESC lines (Fig. 3B).
We did not detect any Gene Ontology term that was significantly enriched among the
49 DEGs. A comparison of our DEG set with 8 different protein interaction databases,
including BIND, DIP, MINT and REACTOME INTERACTION using DAVID, also showed no significant
enrichment (data not shown). Notably, 48 of 49 DEGs were downregulated in hiPSCs relative
to hESCs (Fig. 3C). This raised the possibility that the DEGs were silenced in fibroblast-like
cells and were not properly reactivated in derivative hiPSCs. However, the expression
levels of the DEGs in fibroblast-like cells did not show a consistent pattern, excluding
incomplete reprogramming or the retention of ‘epigenetic memory’ (Fig. 3C).
We next asked whether the DEGs have functional consequences. We focused on two DEGs,
LDHA and SLC2A1 (also known as GLUT1), because of their strong basal expression in
hESCs and reduced expression in all hiPSCs (Fig.3C,D). Both gene products are involved
in energy metabolism; LDHA plays an important role in glycolysis by catalyzing the
conversion of pyruvate to lactate
24,25
, whereas SLC2A1 facilitates glucose uptake in cells
26,27
. Accordingly, LDHA and SLC2A1 are abundantly expressed in pluripotent cells, which
produce energy through glycolysis
28
(Fig. 3C). Based on the down-regulation of these two genes in all examined hiPSC lines
compared to hESC lines by RNA-seq and qPCR analyses (Fig. 3E), we hypothesized that
hiPSC lines might be less glycolytic than hESC GFP lines. However, neither lactate
production nor glucose uptake levels differed between isogenic hiPSC and hESC GFP
lines (Fig. 3F). Further, there was no difference in LDHA protein levels despite the
observed transcriptional differences (Fig. 3G). Thus, at least two of the 49 DEGs
seem not to translate into functional differences, possibly owing to posttranscriptional
compensatory mechanisms.
The low level of transcriptional differences between undifferentiated hESCs and hiPSCs
does not exclude the existence of iPSC-specific aberrations that become detectable
only after differentiation. We performed RNA-sequencing of fibroblast-like cells derived
from 8 hESC subclones (2 hESC SC and 6 hESC GFP lines) and 6 hiPSC subclones using
the same in vitro differentiation protocol as described above (Fig. 1A). Only two
genes were consistently upregulated in hiPSC-derived fibroblast-like cells compared
to hESC-derived fibroblast-like cells from both genetic backgrounds, and they did
not overlap with the 49 DEGs between undifferentiated hESC and hiPSC lines (Supplementary
Fig. 3A,B). However, HUES2-derived fibroblast-like cells tended to cluster together
and apart from HUES3-derived fibroblast-like cells using PCA analysis (Supplementary
Fig. 1B), which is consistent with the segregation of undifferentiated cells by genetic
background. We infer that genetic background also drives transcriptional variation
in differentiated cell populations, and that any transcriptional differences observed
between undifferentiated hESC and hiPSC lines do not persist in differentiated fibroblast-like
cells.
Dysregulation of genes in a subset of hiPSC lines
As most of the DEGs between undifferentiated hESC GFP and hiPSC lines produced low-abundance
transcripts that were not obviously connected through a common biological process
(Fig. 3C), we examined genes that were dysregulated in only a subset of hiPSC lines,
which we refer to as inconsistently differentially expressed genes (iDEGs) (Supplementary
Fig. 3C). We have previously shown that iDEGs between isogenic mouse ESCs and iPSCs
could predict full developmental potential of subsets of iPSC lines
16
. Applying the same principle to our human data set, we found that 34 genes were upregulated,
whereas 27 genes were downregulated in some of the HUES2-derived hiPSC lines when
compared to genetically matched hESC GFP lines. Similarly, 9 genes were upregulated
and 32 genes were downregulated in some of the HUES3-derived hiPSC lines relative
to matched hESC GFP controls (Supplementary Fig. 3C). Only eight iDEGs were dysregulated
in both genetic backgrounds, and these were thus selected for further analysis (Fig.
4A and Supplementary Fig. 3C).
The iDEGs IRX2 and DPP10 have been linked to neural development or psychiatric disease
29-32
and IRX2 suppression reportedly impairs hESC differentiation into neural progenitors.
Silencing of IRX2 and DPP10 in some of the hiPSC lines and none of the hESC lines
(Fig. 4B) was confirmed by qPCR (Fig. 4C). However, the iDEGs did not affect the cells’
potential to differentiate into neuroectodermal cells using a published protocol
33
(Fig. 4D), as determined by RNA expression analysis for NESTIN, SOX1, PAX6, and FOXG1,
well- established markers of neuroectoderm differentiation from human pluripotent
stem cells
34
(Fig. 4E). Consistent with this, PAX6 and SOX1 were equally expressed at the protein
level during neural differentiation from hiPSC and hESC GFP lines (Fig. 4F and Supplementary
Fig. 3D).
To determine whether hiPSCs exhibit biases in differentiation into other lineages,
we evaluated their ability to generate ectodermal, endodermal and mesodermal derivatives
by the Score card assay
6
. Briefly, hiPSC and hESC GFP lines from both genetic backgrounds were differentiated
into embryoid bodies (EBs) before scoring for the expression of 77 developmental marker
genes by qPCR. Hierarchical clustering of these data showed that all markers were
expressed at similar levels in genetically matched cell lines (Fig. 4G). Thus, isogenic
hESCs and hiPSCs appear to have equivalent potential to differentiate into cell types
of the three germ layers.
Genetic background explains previous expression differences
We asked whether the 49 genes differentially expressed between our isogenic hESCs
and hiPSCs are also dysregulated in hiPSC lines derived from primary somatic cells
as well as in other published datasets. First, we reanalyzed a published set of unmatched
hESC (n=18) and hiPSC (n=12) lines generated from primary fibroblasts using retroviral
vectors, whose gene expression patterns were previously analyzed by microarrays
6
. Since many of the 49 DEGs were not covered in the available microarray data, we
performed RNA-sequencing of these hESC and hiPSC lines, which offers increased sensitivity,
especially for low-abundance transcripts
35,36
. However, unsupervised clustering was unable to separate these hESCs from hiPSCs
(Fig. 5A). Although 3 DEGs (RP11-1, MEG3, AL1327) were identified between unmatched
hESCs and hiPSCs, these were likely false positives based on permutation analysis.
Indeed, supervised clustering of all samples with these 3 DEGs (data not shown) or
an extended set of 16 DEGs using loosened criteria could not distinguish hESCs from
hiPSCs (Fig. 5A and Supplementary Fig. 4A,D). Notably, our stringently defined 49-DEG
signature was also unable to segregate the transcriptomes of this extended set of
hESC and hiPSC lines (Fig. 5B).
Next, we determined the potential overlap between DEGs identified within our isogenic
and unmatched hESC/hiPSC lines, and two previously reported sets of DEGs
8,10
. There was little to no overlap between DEGs discovered by independent laboratories
(Fig. 5C and Supplementary Fig. 4B) and these DEGs could not distinguish hiPSC and
hESC lines from the respective other data sets using supervised clustering (Supplementary
Fig. 4C-I). Only two of our 49 DEGs (MT1E, S100A14) and two of our 8 iDEGs (IRX2 and
DPP10) overlapped with DEGs reported in ref. 10. Collectively, these data support
the view that other parameters, such as reprogramming method, genetic background or
sex, account for the majority of previously reported transcriptional differences between
hESCs and hiPSCs.
In agreement with this conclusion, DEGs reported in ref. 10 distinguished our hESC
and hiPSC cell lines by genetic background rather than cellular origin (Fig. 5D, left
panel). In that study, multiple hiPSC lines generated from one man were compared to
male and female hESC lines of different genetic backgrounds. Conversely, genes that
distinguish our HUES2-derived and HUES3-derived pluripotent cell lines were able to
separate the hESCs and hiPSCs in ref. 10 (Fig. 5D, right panel; Fig. 5E; Supplementary
Fig. 4J,K).
In further support of the notion that genetic background profoundly influences transcriptional
patterns in pluripotent cell lines, we found that DEGs that distinguish our HUES2-derived
and HUES3-derived cell lines account for more transcriptional variation among our
30 unmatched hESCs and hiPSCs than do all genes or DEGs that distinguish our isogenic
hESCs and hiPSCs or SeV-infected and uninfected hESCs (Fig. 5F). Taken together, these
meta-analyses suggest that the main transcriptional differences between genetically
unrelated hESC and hiPSC lines are primarily driven by genetic background rather than
cellular origin or reprogramming method.
DISCUSSION
Here we show that isogenic male hESC and hiPSC lines are transcriptionally highly
similar to one another, suggesting that genetic background variability and possibly
sex differences account for most of the previously reported gene expression differences
between hESCs and hiPSCs. This conclusion is particularly relevant to studies where
only a limited number of hESC lines or a single iPSC donor individual was used, as
imbalances in genetic background and sex may further inflate transcriptional differences
9,10,37,38
. Our finding that a previously reported set of DEGs between 4 hiPSC lines derived
from a single individual and 4 hESC lines separated our hESC and hiPSC lines by genetic
background rather than cellular origin supports this conclusion (Fig. 5D).
Our study reveals that a commonly used non- integrating reprogramming method can subtly
but stably alter transcriptional patterns in iPSCs (Fig. 2B,C). However, the transcriptional
signature introduced by SeV infection (63 DEGs) did not separate hESCs from hiPSCs
previously generated with retroviral or episomal vectors, suggesting that each reprogramming
system may introduce unique transcriptional alterations into iPSCs (Supplementary
Fig. 4F,I). Whereas the molecular mechanisms of this observation remain to be elucidated,
our findings highlight the importance of controlling for the method of iPSC induction
when studying transcriptional patterns in iPSCs. Indeed, a recent comparison of hiPSCs
generated with different OKSM delivery systems showed that hiPSCs derived with integrating
vectors (e.g., retroviral transgenes) more often exhibit expression, methylation and
differentiation defects compared to hiPSCs produced with non-integrating approaches
39
.
We identified only 49 DEGs that could distinguish hESCs and hiPSCs and 8 iDEGs that
were dysregulated in a subset of hiPSC lines (Fig. 3C and 4A). This small number of
genes contrasts with previous studies, which identified much larger numbers of DEGs
when comparing unmatched hESCs and hiPSCs using a similar cutoff
5-7,10,13,22,23
. Of note, we found no evidence that two of the tested DEGs (LDHA and SLC2A1) and
two of the tested iDEGs (IRX2 and DPP10) predict functional outcome, i.e., energy
production or differentiation potential into neural cells or EBs. These results therefore
suggest that hESC and hiPSC lines are equivalent after accounting for genetic background
differences. We surmise that the remaining DEGs we detected between isogenic hESCs
and hiPSCs might represent transcriptional noise from weakly expressed genes. In support
of this notion, the vast majority of the 49 DEGs was expressed at relatively low levels
in our hESCs and hiPSCs and showed no overlap with previously reported gene expression
signatures. However, we cannot exclude that the lack of an obvious phenotype with
the abovementioned assays could be due to insufficient expression of the analyzed
genes in undifferentiated hiPSCs or compensation by posttranscriptional mechanisms,
as appears to be the case with LDHA (Fig. 3G). Alternatively, our metabolic and in
vitro-differentiation assays may not have been sensitive enough to detect functional
differences. Another possibility is that hiPSCs are distinguished from hESCs by epigenetic
or genetic differences that do not manifest in the pluripotent state. However, our
finding that fibroblast-like cells derived from all examined hESC and hiPSC lines
show no discernable transcriptional differences argues against this explanation (Supplementary
Fig. 3A,B). The fact that isogenic hESC and hiPSC lines exhibit equivalent differentiation
potentials using either a directed or spontaneous differentiation paradigm further
supports this interpretation (Fig. 4D-G). Critically, hiPSCs were derived from in
vitro- differentiated fibroblasts in this study and, we can therefore not rule out
that hiPSCs produced from primary cells accrue additional aberrations that cannot
be recapitulated with our in vitro differentiation approach.
Our results may have implications for the use of iPSC technology in disease modeling
approaches, where hiPSC lines from healthy individuals are usually compared to hiPSC
lines from affected individuals. Because of the apparent influence of genetic background
on gene expression patterns in both undifferentiated and differentiated cells, it
will be critical to study a sufficient number of hiPSC lines to detect robust phenotypes;
this is particularly relevant in complex diseases where the causal mutation(s) are
not known. When studying monogenic diseases, it may be necessary to introduce mutations
into wild- type hESCs or rescue mutations in patient-derived hiPSCs, as different
backgrounds may mask subtle transcriptional differences
40
.
METHODS
Cell culture
hESC lines and hiPSC lines were cultured with mouse embryonic fibroblasts (MEFs, Globalstem)
pre-plated at 12-15,000 cells/cm2. Medium containing DMEM/F12, 20% knockout serum
replacement, 1mM L-glutamine, 100 uM MEM non-essential amino acids, and 0.1 mM beta-mercaptoethanol
was used. 10 ng/ml of FGF-2 was added after sterile filtration and cells were fed
daily and passaged weekly using 6 U/mL dispase or mechanically.
hiPSC generation
hESC lines were cultured in fibroblast medium without FGF-2 containing DMEM, 10% FBS,
1 mM L-glutamine, 100 uM MEM non-essential amino acids, and 0.1 mM beta-mercaptoethanol,
for a week. Cells were passaged three times using 0.25% trypsin and then sorted for
hThy1+/hTRA-1-81− populations. Sorted fibroblast-like cells were plated, passaged
one more time, and then reprogrammed by using CytoTune®-iPS Sendai Reprogramming Kit
(Invitrogen) following manufacturer’s instructions.
RNA-sequencing
Undifferentiated hESC/hiPSC cells were sorted for hTRA-1-81+ to control for the homogeneity
of cells before RNA extraction. The quality and quantity of total input mRNA was determined
on an Agilent BioAnalyzer 2100 using Agilent RNA 6000 Nano kit. One microgram of total
RNA from each sample was then used as input for library preparation using Illumina
TruSeq RNA Sample Prep Kit, following manufacturer’s instructions. Each paired-end
library was prepared with an adaptor with unique index sequence. The size profile
and quantity of resulting libraries were than determined on the BioAnalyzer 2100 with
Agilent High Sensitivity DNA kit. These libraries were then pooled together at equal
molar concentration and sequenced on an Illumina HiSeq 2000. All hESC and hiPSC samples
for RNA-Seq analysis were prepared on the same day by the same person, and then sequenced
simultaneously on the same run (except for hiPSC lines 1, 2 and 3; this did not affect
the clustering). All fibroblasts samples were prepared and sequenced in the same manner
as the pluripotent samples but on different days.
RNA-seq reads were mapped using Bowtie 0.12.7 allowing up to 2 mismatches, to the
library of human transcriptome sequences obtained from ENSEMBL (GRCh37.67) reference
chromosomes, then entries with identical gene symbols were merged. The transcriptome
includes both protein-coding genes and non-coding genes such as lincRNAs. EMSAR was
used to quantify the expression levels in TPM (transcripts per million) and to infer
read counts for individual genes. Differentially expressed genes were identified using
edgeR 3.4.2 and confirmed using DESeq 1.8.3.
Methylation analysis
Methylation of individual CpGs was derived by observing bisulfite conversion of unmethylated
cytosines in RRBS reads when compared to the reference genome. Methylation maps of
individual CpGs show the average methylation value obtained by dividing the number
of reads on which the CpG was methylated by the total times the CpG was covered by
a read. Promoters were defined as 1 Kb up- and downstream of Refseq gene transcription
start sites. Methylation values of individual CpGs in promoters were pooled in a weighted
manner (i.e. proportional to the number of reads covering that CpG).
To count differentially-methylated promoters that supported variance due to cellular
origin or genetic background, within-sample methylation difference was compared to
the between-sample methylation difference for each promoter in sets based on cellular
origin (hESC/hiPSC) and cell background (HUES2/HUES3). The promoter was assigned to
the set with the lesser methylation difference, such that promoters in the hESC/hiPSC
set showed greater methylation difference between hESCs and hiPSCs and lesser metylation
difference between HUES2 and HUES3.
Global methylation clustering was performed by first pooling individual CpG methylation
levels into 1 Kb non-overlapping tiles using weighted averages as with promoters,
and then using Pearson’s correlation to compute distance between samples. Ward’s method
was used for hierarchical clustering analysis. Analyses were performed using R and
Perl.
Immunostaining
Immunostaining was performed using the following antibodies: α-hTRA-1-81 (330704,
BioLegend), Streptavidin APC (17-4317-82, eBioscience) α-hCD90 (328118, BioLegend),
α-Sendai viral protein (PD029, MBL International), and α- OCT4 (ASK-3006, Applied
StemCell), α-PAX6 (Cat. no. PAX6, DSHB), and α- SOX1 (Cat. no. 4194, Cell Signaling).
Lactate production assay
Lactate production assay was done according to Zhong et at
41
. Lactate concentration was determined with the Lactate Assay Kit (BioVision). O.D.
was measured at 570nm, 30 min. after addition of substrate.
Glucose uptake assay
The glucose uptake assay was done according to Sebastián et al.
42
. Cells were grown under normal conditions for 24 hr and 100 mM 2-NBDG (Invitrogen)
was added to the media for 2 hr. Fluorescence was measured in a FACSCalibur Analyzer
(BD).
Neural differentiation
Neural induction was performed as previously reported
33
. Briefly, cells were dissociated to single cells using Accutase and plated on gelatin
for 10 minutes to remove MEFs. Non-adherent cells were collected and plated on Geltrex-treated
dishes at a density of 150-200k cells per well of a 24-well plate in the presence
of MEF-conditioned hESC media containing 10 ng/ml of FGF-2 (Life Tech) and 10 uM of
Y-27632 (Tocris). Neural differentiation was initiated when cells were confluent using
KSR media containing 820 ml of Knockout DMEM (Life Tech), 150 ml Knockout Serum Replacement
(Life Tech), 1 mM L-glutamine (Life Tech), 100 uM MEM non-essential amino acids (Life
Tech), and 0.1 mM beta- mercaptoethanol (Life Tech) to inhibit SMAD signaling, 100
nM of LDN-193189 (Cat. no. ab142186, Abcam) and 5 uM of SB431542 (Cat. No. 13031,
Cayman Chemical) were added on Days 0 through 9. Cells were fed daily, and N2 media
(Life Tech) was added in increasing 25% increments every other day starting on Day
4 (100% N2 on Day 10).
Western blot analysis
For Western blot analysis of PAX6, 10 ug of whole cell lysates was loaded to 4-20%
gradient SDS-PAGE gels and then transferred to nitrocellulose membranes (BIO-RAD)
by using Trans-Blot® Turbo™ Transfer System (BIO-RAD). Blocked membranes were incubated
with antibodies against PAX6 (Cat. no. 5790, Abcam) or GAPDH (Cat. no. 2118, Cell
Signaling), respectively. For Western blot analysis of LDHA, undifferentiated hESC/hiPSC
cells were sorted for hTRA- 1-81+ in order to control for the homogeneity of the cells,
and then the rest of the procedure ensued as above. LDHA (Cat. no. 2012S, Cell Signaling),
β-ACTIN (Cat. no. MA5-15739-HRP, Thermo Scientific).
RNA extraction and qPCR
Total RNA was extracted from differentiating hESC/hiPSC lines using the TRIzol Reagent
(Life Tech), and 0.51 ug of RNA was reverse transcribed by High Capacity cDNA Reverse
Transcription Kit RT2 first strand kit (ABIQiagen). Primer sequences are provided
below. qRT-PCR mixtures were prepared with SYBR Green PCR Master Mix Universal (Applied
BiosystemsKapabiosystem) and reactions were done with the Eppendorf Realplex2.
EB scorecard assay
EB differentiation was performed as described previously
6
. On day 7, EBs were lysed and total RNA was extracted before analyzing differentiation
markers using qPCR.
Primer sequences
GAPDH
Forward
AGG TCG GAG TCA ACG
Reverse
GTG ATG GCA TGG ACT
SOX1
Forward
GCG GAA AGC GTT TTC
Reverse
TAA TCT GAC TTC TCC
NESTIN
Forward
GAA ACA GCC ATA GAG
Reverse
TGG TTT TCC AGA GTC TTC
PAX6
Forward
CTT TGC TTG GGA AAT CCG
Reverse
AGC CAG GTT GCG AAG
FOXG1
Forward
CCC TCC CAT TTC TGT
Reverse
CTG GCG GCT CTT AGA
OTX2
Forward
AAG CAC TGT TTG CCA
Reverse
CAG GAA GAG GAG GTG
Supplementary Material
1
2