bioRxiv preprint doi: https://doi.org/10.1101/146696; this version posted July 4, 2017. The copyright holder for this preprint (which was not certified by peer review) is the author/funder. All rights reserved. No reuse allowed without permission.
Barakat et al. - hESC enhancer repertoire
Functional dissection of the enhancer repertoire in human embryonic stem cells
Tahsin Stefan Barakat1,5,7, Florian Halbritter2,5, Man Zhang1,6, André F.
Rendeiro2,6, Christoph Bock2,3,4 and Ian Chambers1,7,8
1 MRC Centre for Regenerative Medicine, Institute for Stem Cell Research, School of Biolog-
ical Sciences, University of Edinburgh, Edinburgh, EH16 4UU, United Kingdom
2 CeMM Research Center for Molecular Medicine of the Austrian Academy of Sciences, Laz-
arettgasse 14, AKH BT 25.3, 1090 Vienna, Austria
3 Department of Laboratory Medicine, Medical University of Vienna, 1090 Vienna, Austria
4 Max Planck Institute for Informatics, Saarland Informatics Campus, 66123 Saarbrücken,
Germany
5 Co-first authors
6 Co-second authors
7 Corresponding authors: e-mail: [email protected] or [email protected]
8 Lead contact: Phone: (44) 131 651 9500; Fax: (44) 131 651 9501
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Barakat et al. - hESC enhancer repertoire
Summary
Enhancers are genetic elements that regulate spatiotemporal gene expression. Enhancer func-
tion requires transcription factor (TF) binding and correlates with histone modifications. How-
ever, the extent to which TF binding and histone modifications can functionally define active
enhancers remains unclear. Here we combine chromatin immunoprecipitation with a massively
parallel reporter assay to identify functional enhancers in human embryonic stem cells (hESCs)
genome-wide in a quantitative unbiased manner. While active enhancers associate with TFs,
only a minority of regions marked by NANOG, OCT4, H3K27ac and H3K4me1 function as
enhancers, with activity changing markedly with culture conditions. Our analysis also reveals
a novel enhancer set associated with housekeeping genes. Moreover, while transposable ele-
ments associate with putative enhancers only some exhibit activity. Similarly, within super-
enhancers, large tracts are non-functional, with activity restricted to small sub-domains. This
catalogue of validated enhancers provides a valuable resource for further functional dissection
of the regulatory genome.
Keywords
ChIP-STARR-seq, genome-wide functional enhancer map, housekeeping enhancers, super-en-
hancers, transposable elements, naive pluripotency, NANOG, OCT4, H3K27ac, H3K4me1.
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Barakat et al. - hESC enhancer repertoire
Highlights
A catalog of functional enhancers in hESCs including a novel housekeeping class
Active enhancers feature specific transcription factors and transposable elements
Major shifts in enhancer activity occur during induction of naive pluripotency
Super-enhancers consist of small units with enhancer function
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Barakat et al. - hESC enhancer repertoire
Introduction
Human embryonic stem cells (hESC) are a genetically tractable developmental model system
with tremendous potential for stem-cell-based therapeutics. Understanding how hESC pluripo-
tency is regulated and how extrinsic signals influence chromatin via transcription factors (TFs)
to direct cell-specific gene expression is central to achieving this promise. Gene expression is
modulated by cis-regulatory elements such as enhancers (Banerji et al., 1981) which can stim-
ulate target gene expression in a position and orientation-independent manner and independent
of their genomic context (Spitz and Furlong, 2012). hESCs use a network of pluripotency TFs
including OCT4, SOX2 and NANOG, to direct a hESC-specific gene expression programme
(Yeo and Ng, 2013). Compared to mouse ESCs, hESCs are a more developmentally advanced
state with characteristics of post-implantation stage embryos (Weinberger et al., 2016). Re-
cently, so-called naive human ESCs have been derived from established hESCs either by tran-
sient ectopic transgene expression (Buecker et al., 2010; Hanna et al., 2010; Takashima et al.,
2014) or by altering culture conditions (Chan et al., 2013; Gafni et al., 2013; Theunissen et al.,
2014; Ware et al., 2014). Naive hESCs are thought to mirror cells of the pre-implantation em-
bryo differing from primed hESCs in several ways: increased clonogenicity, different growth
factor requirements, distinct energy metabolism, and altered morphology (Sperber et al., 2015).
How naïve and primed hESC states are regulated, and how this is affected by differences in
enhancer usage is currently not well understood.
The past decade of mammalian genomics research has focused on cataloguing cis-regulatory
elements within the non-coding genome (ENCODE, 2012). Technological advances have al-
lowed genome-wide occupancy by TFs to be measured by chromatin immunoprecipitation
(ChIP) followed by sequencing (ChIP-seq) (Kagey et al., 2010; Robertson et al., 2007). Putative
enhancer locations have been obtained by mapping histone modifications (e.g. H3K27ac,
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Barakat et al. - hESC enhancer repertoire
H3K4me1) (Heintzman et al., 2007; Rada-Iglesias et al., 2011) and by measuring chromatin
accessibility (Buenrostro et al., 2013). However, not all predicted enhancers can be validated
functionally. To assay enhancer activity, plasmid-based cell transfections can be used but these
have low throughput. More recently, massively parallel reporter assays (MPRAs) have enabled
thousands of sequences to be tested simultaneously (Arnold et al., 2013; Kwasnieski et al.,
2012; Melnikov et al., 2012; Patwardhan et al., 2012; Smith et al., 2013). For instance, with
Self-Transcribing Active Regulatory Region Sequencing (STARR-seq) compact, non-mamma-
lian genomes can be screened quantitatively for enhancer activity by cloning randomly sheared
DNA between a minimal-promoter-driven GFP open reading frame and a downstream polyA
sequence. If an enhancer is active, this results in transcription of the enhancer sequence (Arnold
et al., 2014; Arnold et al., 2013; Shlyueva et al., 2014). Similar MPRA approaches have recently
been adapted to test chosen sequences with putative enhancer features (Kwasnieski et al., 2014;
Shen et al., 2016; Vanhille et al., 2015), predicted TF binding sites (Verfaillie et al., 2016),
features of quantitative trait loci (Tewhey et al., 2016; Ulirsch et al., 2016; Vockley et al., 2015)
or nucleosome-depleted sequences (Murtha et al., 2014).
Application of STARR-seq to explore mammalian genomes is hindered by genome size which
means enhancer sequences would be infrequently sampled and transfection of plasmid libraries
would require huge numbers of cells. Here we alleviate this issue by combining STARR-seq
with ChIP, in a technique we refer to as ChIP-STARR-seq to generate a resource of genome-
wide activity maps of functional enhancers in hESCs. In these maps we identify highly active
enhancers and observe major changes in activity patterns between primed and naive hESCs.
Moreover, some transposable element families are enriched at highly active enhancers. Our
data also identify the functional components within super-enhancers and uncover a novel class
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Barakat et al. - hESC enhancer repertoire
of enhancers associated with housekeeping gene expression. The resource presented here en-
compasses a comprehensive collection of functional enhancer sequences in hESCs, providing
a valuable knowledge base for systematic analysis of the core transcriptional network circuitry
underlying hESC maintenance and differentiation. Enhancer data are available from the Sup-
plemental Materials to this paper and from a supplemental website (http://hesc-enhancers.com-
putational-epigenetics.org).
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Barakat et al. - hESC enhancer repertoire
Results
ChIP-STARR-seq: an effective strategy for genome-wide identification of functional en-
hancers
To generate a comprehensive catalogue of regulatory genomic elements relevant to mammalian
stem cell biology we used a massively parallel reporter assay, called ChIP-STARR-seq. In
ChIP-STARR-seq, DNA is co-immunoprecipitated and cloned en masse within the transcrip-
tion unit of a STARR-seq plasmid, downstream of GFP driven by a minimal promoter and
upstream of a polyA sequence (Figure 1A)(Arnold et al., 2013). The resulting libraries, each
consisting of millions of individual plasmids containing DNA sequences that were bound by a
factor of interest, can be tested for enhancer activity by cell transfection. If a cloned sequence
functions as an enhancer, GFP will be expressed and the transfected cells can be purified by
FACS. Since the assayed sequences are located upstream of the polyA signal, the transcribed
mRNA will contain the enhancer sequence itself. Therefore, both the identity and activity of
captured regions can be determined quantitatively by sequencing mRNA (RNA-seq) from GFP-
positive cells.
To investigate the functional potential of enhancers in hESCs, we first focused on H9 hESCs
cultured on Matrigel, in medium supplemented with b-FGF. In these primed culture conditions
hESCs grew as flat colonies expressing NANOG and OCT4 (Figure S1A, B). ChIP-qPCR con-
firmed enrichment of sequences known to be bound by NANOG, OCT4, H3K4me1 and
H3K27ac in hESCs (Figure S1C) (Kunarso et al., 2010; Rada-Iglesias et al., 2011). Subsequent
ChIP-seq confirmed a high overlap with peaks from previous datasets (Figure S1D) (Gafni et
al., 2013; Gifford et al., 2013; Ji et al., 2016; Kunarso et al., 2010; Lister et al., 2009).
7 bioRxiv preprint doi: https://doi.org/10.1101/146696; this version posted July 4, 2017. The copyright holder for this preprint (which was not certified by peer review) is the author/funder. All rights reserved. No reuse allowed without permission.
Barakat et al. - hESC enhancer repertoire
We generated ChIP-STARR-seq libraries from the same end-repaired, adapter-ligated ChIP
DNA used for the ChIP-seq experiments (Figure 1A). Total genomic DNA was also used to
carry out STARR-seq in hESCs (Arnold et al., 2013). To generate reporter plasmid libraries,
we cloned ChIP DNA en masse into the STARR-seq plasmid by Gibson assembly. Sequencing
the resulting plasmid libraries produced 1.9x109 reads. Counting unique read ends mapping to
the human genome, indicated that each library consisted of 1.9-3.1x107 unique plasmids, with
a mean insert size of 230bp (Table S1). Figure S2A summarises the sequenced samples ana-
lysed in this study.
To validate the comprehensiveness of our reporter assay, we first assessed whether the plasmid
libraries achieved a good representation of the binding events captured by ChIP-seq (File S1).
A high correlation between ChIP-seq coverage and the corresponding plasmid libraries was
seen both before and after transfection (Figure 1B,C S2B,C). Next, the ability of the plasmid
libraries to drive GFP expression in primed hESCs was tested. Library transfections produced
up to 20% GFP-positive cells compared to <1% GFP-positive cells obtained by transfection of
the empty STARR-seq vector or ~50% in control transfections with a constitutively expressed
mCherry plasmid (Figure 1D and data not shown). Therefore, a considerable proportion of cells
contained plasmids with enhancer activity. 24h after transfection RNA was prepared from
FACS-purified GFP-positive cells and DNA from unsorted cells. Plasmids were recovered and
GFP-derived mRNAs amplified by PCR for RNA-seq. DNA sequencing confirmed high con-
sistency between the original plasmid libraries and plasmids re-isolated after transfection (Fig-
ure 1B, S2C). Positive correlations were also observed between read coverage from STARR-
RNA-seq and the respective plasmid libraries (Figure 1E, S2D) and between replicate STARR-
RNA-seq datasets, with an increase for expressed plasmids sampled (read count > 0) in both
replicates (Figure S2E). Taken together, these results show that while abundant plasmids can
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Barakat et al. - hESC enhancer repertoire
produce more RNA, some plasmids produce RNA in excess of the plasmid count, indicating
high enhancer activity. However, many plasmids transfected into cells did not produce RNA
indicating that the ChIP-enriched DNA in these plasmids lacked enhancer activity.
Visual inspection of selected genomic regions illustrates the broad spectrum of enhancer activ-
ity measured by ChIP-STARR-seq (Figure 1F,G). For instance, ChIP-seq for NANOG indi-
cates two strong binding sites upstream and downstream of SOX2 (Figure 1F) but only the
downstream binding site resulted in ChIP-STARR-seq RNA in excess of plasmid abundance.
In summary, we used ChIP-STARR-seq for a functional enhancer analysis of DNA fragments
bound by NANOG, OCT4, H3K4me1, and H3K27ac in hESCs.
Activity levels define classes of enhancers bound by distinct transcription factors
Based on our ChIP-STARR-seq dataset, we assessed the functional capacity of 296,034 ge-
nomic regions represented in our plasmid libraries. Enhancer activity was defined as the ratio
of RNA reads relative to plasmid reads scaled to account for differences in sequencing library
size (RPPM: reads per plasmid and per million sequenced reads). Paired-end sequencing ena-
bled unequivocal assignment of RNA reads to plasmids. The activity level of each region was
recorded as the activity generated by the most active plasmid (from any library) within this
region. Thresholds for discriminating enhancer activity from the activity of the minimal pro-
moter in the STARR-seq vector were obtained by comparison to data from inactive regions (see
Methods). This calculation defined a threshold for high and low activity elements of ≥220 and
≥144 RPPM, respectively. Based on these thresholds, ChIP-STARR-seq identified 21,200 high
activity enhancers and 18,422 low activity enhancers (Figure 2A, File S1). However, the ma-
jority of peaks showed no evidence of enhancer activity (Figure 2A,B).
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Barakat et al. - hESC enhancer repertoire
To assess the biological relevance of these thresholds, sixty-eight genomic regions covering the
full activity range were tested in luciferase assays. DNAs from regions of <144 RPPM had
luciferase activities indistinguishable from empty vector. In contrast, regions with increasingly
high ChIP-STARR-seq activity showed increasingly higher luciferase activity (Figure 2C) sup-
porting the utility of the RPPM-based enhancer classification. These thresholds indicate that
only a minority of peaks bound by NANOG, OCT4, H3K4me1 or H3K27ac showed enhancer
activity (Figure 2B), with regions bound by OCT4 having the highest proportion of high activ-
ity enhancers.
To assess the relationship of activity classifications to gene expression, each peak was assigned
to a putative target gene based on genomic distance (Figure 2D, S3B). ChIP-STARR-seq re-
gions with enhancer activity were associated with genes that showed significantly higher gene
expression values than genes associated with peaks lacking enhancer activity. Regions with
different ChIP-STARR-seq activity levels were next assessed for association with distinct his-
tone modifications in an H9 chromatin segmentation (Kundaje et al., 2015) (Figure 2E). Chro-
matin segments marked as enhancers, transcription start sites (TSSs), sites flanking transcrip-
tion and repeat sequences were most overrepresented in the high activity group. Together, these
results indicate that ChIP-STARR-seq can distinguish ChIP-seq peaks on the basis of enhancer
activity and this enhancer activity reflects expression of the endogenous sequences.
The occurrence of TF binding sites in the three activity classes was next assessed. The relative
representation of TFs from 190 ChIP-seq datasets in the CODEX database were assessed by
LOLA enrichment analysis (Sanchez-Castillo et al., 2015; Sheffield and Bock, 2016) (Figure
2F, Table S2). High activity enhancers were preferentially associated with pluripotency-related
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Barakat et al. - hESC enhancer repertoire
TFs (SOX2, SMAD3 and NANOG). Significant overlaps were also seen for regions bound in
non-hESCs by STAT5 and NCOR1. Low activity enhancers were weakly enriched for plurip-
otency-related TFs (SOX2 and SMAD3) but also for proteins with more generic functions
(NFE2, CHD1, RBPJ, ZNF143). In contrast, no TF in the reference database were enriched at
inactive regions. Similar results were obtained by extending LOLA analysis to 690 ChIP-seq
datasets for TFs from ENCODE (2012) (Figure S3D). Enhancer activity was strongest close to
the binding peaks of the enriched factors with activity lost quickly with increasing distance
from the peak center (Figure 2G, S3C). These results suggest that binding of distinct TFs in
close proximity might contribute to robust enhancer activity. How enhancer classes relate to
chromatin state was further examined by LOLA analysis of ENCODE chromatin segmentations
from H1 hESCs and various non-pluripotent cell types (Figures S3E,F). This confirmed that
high activity enhancers were enriched in segments annotated as H1 enhancers and promoters,
while inactive regions occurred primarily in closed chromatin. Interestingly, low activity en-
hancers were often annotated in ENCODE as non-cell-specific promoters. We also interrogated
the DNA sequences underlying enhancers for the occurrence of known TF binding motifs (Fig-
ures 2H, S3G). The most enriched motif in the highly active enhancers was the designated
NANOG motif in the reference database, which we note actually corresponds closely to the
Oct/Sox motif from the literature (Chen et al., 2008). The percentage of peaks that contained
this motif was higher in the high activity group. These results indicate that functional classes of
enhancers differ in TF binding sites and motif occurrence.
Active enhancers include ESC-specific and housekeeping modules
Previous high-throughput sequencing studies have attempted to predict hESC enhancers on the
basis of histone marks, TF binding sites or DNAseI hypersensitivity (Hawkins et al., 2011;
Rada-Iglesias et al., 2011; Xie et al., 2013). However, the overlap between enhancers predicted
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Barakat et al. - hESC enhancer repertoire
from these studies is limited (Figure S4A). We sought to exploit the catalogue of functional
enhancers to ratify enhancer predictions. Comparing the combination of three previously de-
scribed enhancer maps with our dataset, 7,596 of the 21,200 high activity enhancers identified
by ChIP-STARR-seq were among these predicted enhancers (n = 76,666; union of all datasets;
Table S3). Several putative enhancers predicted by these previous studies that were inactive by
ChIP-STARR-seq were tested in luciferase assays but none possessed enhancer activity in this
assay (Figure S4C). Functional enrichment analysis using GREAT (McLean et al., 2010)
showed that the high activity ChIP-STARR-seq enhancer subset overlapping with previously
predicted enhancers had stronger enrichment for gene ontology (GO) terms related to ESC bi-
ology than terms identified from all predicted enhancers (Table S2). This “ESC module” (Fig-
ure 3A) includes enhancers in close proximity to hESC TFs (NANOG, OCT4) and signaling
pathway genes (TGF-b, FGF, and WNT signaling). The remaining 13,604 enhancers with high
ChIP-STARR-seq activity, that were not predicted previously, had GO terms reflecting generic
biological processes, for instance, transcription, energy metabolism and DNA repair. Hence,
we refer to these enhancers as the “housekeeping (HK) module”.
A comparison of the ChIP-seq signal intensity for all peaks, or peaks associated with either the
ESC or HK modules, indicates that HK enhancers generally, had lower affinity for H3K4me1,
H3K27ac, NANOG and OCT4 (Figure 3B). Therefore, these HK enhancers may have escaped
detection previously due to the thresholds used. Applying instead a direct functional assay en-
abled their discovery. The lower levels of NANOG and OCT4 at the HK enhancers may suggest
that these enhancer sequences rely less on ESC-specific TFs. Nonetheless, these sequences
function as bona fide enhancers, as the RPPM of these sequences is higher than those of all
sequences assessed (Figure 3C) and only slightly lower than those seen in the ESC module. In
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Barakat et al. - hESC enhancer repertoire
addition, the expression of genes associated with the ESC and HK module is similar and sig-
nificantly above the average hESC gene expression levels (Figure 3D).
Consistent with function in many cell types, expression of genes associated with the HK module
was higher than expression of genes associated with the ESC module in data from various tis-
sues of the RNA-seq Atlas (Krupp et al., 2012) (Figure 3E) and from the GTEx portal (2013)
(Figure S4B,D). Furthermore, functional enrichment analysis using the Enrichr software (Chen
et al., 2013) with data from ENCODE (2012) or ChEA (Lachmann et al., 2010) showed that
ESC module enhancers were enriched for binding of NANOG, TCF3, SOX2 and OCT4,
whereas HK module enhancers showed preferential enrichment of more broadly expressed fac-
tors, such as BRCA1 and MYC (Figure 3F, Table S2). For instance, one of the HK enhancers
that we identified is located upstream of GAPDH, in a region that has been shown to interact
with the GAPDH promoter in various cell types (Figure 3G). The majority of HK and ESC
enhancers showed a similar distribution of distances from TSSs, although a subset of HK en-
hancers lie closer to TSSs (Figure 3H). ESC enhancers were often found in regions associated
with enhancer-like chromatin features in H9 hESCs (Kundaje et al., 2015) (Figure 3I). In con-
trast, HK enhancers were more often annotated as heterochromatic or bivalent. ChIP-STARR-
seq therefore identified previously unappreciated genomic sequences characterized by lower
enrichment of enhancer-associated histone modifications and pluripotency-related TFs but with
comparable enhancer activity.
Major changes in enhancer activity upon induction of naive pluripotency
To augment the catalogue of functional enhancers in hESCs and to gauge the dynamics of en-
hancer activity we applied ChIP-STARR-seq to a closely related cell type. To this end, primed
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Barakat et al. - hESC enhancer repertoire
H9 hESCs were passaged in Naive Human Stem Cell (NHSM) medium to establish phenotyp-
ically altered, “naïve” hESCs (Gafni et al., 2013). Dome-shaped colonies expressing NANOG
and OCT4 appeared after 3 days that could be passaged more than 10 times (Figure S5A, B).
Relative to primed hESCs these naive hESCs expressed similar levels of OCT4, REX1 and
STAT3 but with higher NANOG, TEAD4, KLF4, DUSP10, IL6R, TBX3 and lower XIST and
DNMT3B (Figure S5C,D). ChIP-qPCR showed that selected loci were similarly bound by
NANOG, OCT4, H3K4me1 and H3K27ac in both primed and naive hESCs (Figure S5E). A
high overlap of H3K4me1 and H3K27ac ChIP-seq peaks with previous data was seen (Gafni et
al., 2013) (Figure S5F,G). NANOG and OCT4 ChIP-seq data identified similar motifs in both
naive and primed hESCs (Figure S5H,I). These results agree with prior studies (Barakat et al.,
2015; Gafni et al., 2013) confirming conversion of primed hESCs to a naive hESC state.
ChIP-STARR-seq plasmid libraries generated from naive hESCs (Figures 4A, S6A-C) were
transfected into naive hESCs and for comparison, into primed hESCs (Figure S6C). Enhancer
activity was categorized with the same calculation as in primed hESCs into high activity (RPPM
≥96) and low activity (RPPM ≥62) (Figure S6D, S2E, File S1). 337,178 peaks covered by
plasmids in naive hESCs (Figure S6E) were analysed, identifying 31,303 high activity and
40,732 low activity enhancers. Again, only a fraction of ChIP-seq peaks had high activity (Fig-
ure S6F). LOLA enrichment analysis of TFs from CODEX for the naive enhancer class (Figure
4B, Table S2), identified a distinct TF profile from primed hESCs (compare to Figure 2F).
Sites bound by pluripotency-related TFs (e.g., NANOG) in primed hESCs were not strongly
represented in the high activity enhancer category. Instead, genomic regions bound by repres-
sive chromatin interactors (CHD1, HDAC1/2) in primed hESCs showed high activity in naive
hESCs. The altered protein binding landscape was also seen by enrichment analysis of EN-
CODE ChIP-seq datasets (Figure S6G) and chromatin segmentations (Figure S6H) (Ernst et
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Barakat et al. - hESC enhancer repertoire
al., 2011). Enhancers with high activity in naive cells occurred mainly in regions that, in primed
cells, were bound by TFs linked to proliferation and often to cancer (BRCA1, FOSL1, MYC).
These enhancers overlap with active promoters in various cell types more than with primed
enhancers (Figure S6H, I).
Having a comprehensive genome-wide enhancer maps for both pluripotent states allowed a
global comparison of enhancer usage in both primed and naive hESCs (Figure 4C). We focused
on regions that maintained (H→H) or lost high enhancer activity (H→I), that remained inactive
(I→I) or that gained high activity (I→H) in the primed to naive switch. Functional enrichment
analysis of these four groups with Enrichr (Figure 4D, Table S2) revealed that enhancers with
high activity in both cell states (H→H) were related to stem cell maintenance and suppression
of differentiation, whereas enhancers that lost activity (H→I) were associated with genes with
GO terms related to differentiation. No significant GO terms were associated with enhancers
that gained activity (I→H) or regions that remained inactive (I→I), though this may be due to
lack of annotation in naive hESCs. However, examining ENCODE and ChEA indicated that
enhancers that gained activity in naive hESCs were enriched for transcriptional activators such
as ATF2 and TAF1 that occur near target gene promoters.
To relate changes in enhancer activity to differences in the expression of regulated genes, we
plotted the average difference in enhancer RPPM levels between naive and primed hESCs ver-
sus the expression of nearby genes (Figure 4E). Several genes previously found to be more
strongly expressed in naive hESCs (such as members of the WNT pathway) showed increased
enhancer activity and vice versa.
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Barakat et al. - hESC enhancer repertoire
To further compare enhancer activity in primed and naive hESCs, two loci were examined in
more detail (Figure 4F). Two adjacent enhancers proximal to the NODAL promoter exhibit a
pattern typical of the changing activity landscape (Figure 4F). In primed hESCs, enhancer A
is highly active, yet the NODAL gene is weakly expressed. Enhancer A is marked by H3K4me1
but lacks H3K27ac suggesting that site A may function as an enhancer rather than as the pro-
moter of the adjacent gene. In naive hESCs, enhancer A activity is lost but enhancer B is acti-
vated. Concordantly, NODAL expression is elevated and an increase in H3K27ac intensity is
observed at the promoter.
At OCT4, a similar binding of NANOG to the proximal (PE) and distal (DE) enhancers was
seen in primed and naïve hESCs (Figure 4G). However, in primed hESCs, ChIP-STARR-seq
activity was mainly detected from the PE. In naïve hESCs, PE activity was strongly reduced,
whereas DE activity was similar in both cell states (Figure 4G). Independent luciferase assays
confirmed these findings (Figure S7D). To determine the biological relevance of this switch in
enhancer usage, primed hESCs with heterozygous OCT4 DE deletions were generated by
CRISPR-Cas9 (Figure S7). These primed OCT4 ΔDE+/- hESCs expressed similar levels of
OCT4 mRNA to wild type clones. However, OCT4 mRNA dropped to 50% upon conversion
to the naive state without affecting flanking gene expression (Figure 4H). This indicates that
OCT4 DE is indeed a functional enhancer that regulates OCT4 in naive hESCs. We conclude
that enhancer activity is remarkably dynamic even in closely related cell types.
The occurrence of various transposable elements is associated with enhancer activity
As chromatin segments associated with repetitive DNA were found in high activity enhancers
(Figure 2E), we sought to exploit the functional enhancer catalogue presented here to examine
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Barakat et al. - hESC enhancer repertoire
the link between repeats and enhancer activity more closely. Large portions of mammalian ge-
nomes are derived from transposable elements (TEs) which have been reported to be linked to
TF binding sites and enhancers (Bourque et al., 2008; Glinsky, 2015; Kunarso et al., 2010; Teng
et al., 2011). In hESC, human endogenous retrovirus (HERV) TEs are enriched in NANOG and
OCT4 binding sites (Glinsky, 2015; Kunarso et al., 2010) but whether this enrichment reflects
enhancer activity has not been determined genome-wide. To assess ChIP-STARR-seq enhanc-
ers for the occurrence of TE sequences, we used the RepeatMasker annotation in the UCSC
Genome Browser. The number of TE-derived sequences in regions of distinct activity was com-
pared to the number detected in all genomic regions (Figure 5, Table S4). LTR-containing
TEs, such as HERV1, were found in high activity enhancers more often than expected (Figure
5A). However, not all LTR-containing TEs were enriched at active enhancers. The most en-
riched repeats were dominated by satellite repeats and LTR family members (Figure 5B). For
TEs enriched for NANOG and OCT4 binding (e.g., LTR9B) (Kunarso et al., 2010) or TEs
enriched at candidate human-specific regulatory loci (e.g., LTR7) (Glinsky, 2015) the observed
enrichment increases further with increasing enhancer activity (Figure 5C, D). Indeed LTR7B,
LTR7 and HERVH-int show the strongest enrichment at the highest activity enhancers. In con-
trast, other TE families that have been previously linked to human-specific TF binding sites
(Glinsky, 2015), were either not (L1HS) or only weakly (L1PA2) enriched at high activity en-
hancers. These results indicate that certain families of TEs are overrepresented at active en-
hancers and that their enrichment correlates with enhancer activity However, not all TEs of the
same TE type are associated with active enhancers, nor do all TEs enriched in pluripotency TF
binding sites occupy active enhancers.
ChIP-STARR-seq dissects super-enhancers into small functional units
17 bioRxiv preprint doi: https://doi.org/10.1101/146696; this version posted July 4, 2017. The copyright holder for this preprint (which was not certified by peer review) is the author/funder. All rights reserved. No reuse allowed without permission.
Barakat et al. - hESC enhancer repertoire
Recently, large linear tracts of chromatin, referred to as super-enhancers (SEs) have been iden-
tified that function to regulate lineage-specific gene expression (Hnisz et al., 2013; Whyte et
al., 2013). Compared to traditional enhancers, SEs have increased binding of Mediator, specific
histone marks and lineage-specific TFs. Whether the full length of SEs is required for biological
activity has become a matter of debate (Dukler and Gulko, 2016; Hay and Hughes, 2016;
Moorthy et al., 2017; Shin et al., 2016). As a further application of our enhancer catalogue, we
used the data to dissect the regulatory potential of DNA underlying SE regions. SEs were first
identified by H3K27ac enrichment in primed (Figure 6A, File S1) and naive (Figure S8A)
hESCs. Alignment of ChIP-STARR-seq data to these SEs showed that the H3K27ac intensity
used to define SEs correlated to RPPM levels (Figures 6B, S8B), supporting the notion that
SE-likeness is an indicator of functional potential. However, as exemplified by the SE covering
the FGFR1 gene, detailed examination indicates that strong RPPM signals originate from only
a small region within the entire SE (Figure 6C). Therefore, luciferase assays were used to de-
termine the enhancer activity of DNA in the neighborhood of this active region. Strong activity
was confined to a 596bp region with other DNA elements from this SE devoid of enhancer
activity (Figure 6D). This indicates that the FGFR1 SE is composed of small units with en-
hancer activity. To test whether this finding is valid globally, the relative abundance of highly
active plasmids (RPPM ≥220) in SEs compared to “normal” enhancers (NEs) was examined.
Most enhancers contained only a small percentage of active plasmids within their bounds (Fig-
ures 6E). Although this fraction was slightly higher in SEs than in NEs, it accounted for only
a minority (~3%) of the genome annotated as SEs. Therefore, only a small part of the large SEs
has enhancer function (Figure 6F). These conclusions also apply to naive hESC SEs (Figure
S8). Although H3K27ac intensities in naive hESCs were, in general, slightly less than in primed
hESCs (Figure S8C), the majority of SEs (n=2,597) were found in both cell states (Figure
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Barakat et al. - hESC enhancer repertoire
S8D). As for primed hESCs, only a minor portion of naive hESC SEs possessed high enhancer
activity (mean across SEs =1.9%; Figure S8E).
19 bioRxiv preprint doi: https://doi.org/10.1101/146696; this version posted July 4, 2017. The copyright holder for this preprint (which was not certified by peer review) is the author/funder. All rights reserved. No reuse allowed without permission.
Barakat et al. - hESC enhancer repertoire
Discussion
We present here a large-scale analysis of enhancer activity in human embryonic stem cells.
Previous studies to elucidate the cis-regulatory network of hESCs used correlations between
the presence of histone modifications, to predict potential enhancers (Hawkins et al., 2011;
Rada-Iglesias et al., 2011; Xie et al., 2013). Only a small proportion of predicted enhancers
sequences were functionally validated (Attanasio et al., 2013; Rada-Iglesias et al., 2011; Visel
et al., 2007). Therefore, the degree to which correlations with histone marks can predict en-
hancer function genome-wide has remained unclear. Indeed, sequences not marked by
H3K4me1 and H3K27ac can act as transcriptional enhancers (Pradeepa et al., 2016). Here we
have combined ChIP with STARR-seq as a direct test of the ability of DNA sequences bound
by OCT4, NANOG or by the histone marks H3K4me1 and H3K27ac to function as enhancers.
The glucocorticoid receptor network in lung epithelial cells was recently assessed similarly
(Vockley et al., 2016). Importantly, we found that only a subset of these sequences displayed
enhancer activity. We find that TF binding is closely linked with enhancer activity, in line with
recent reports (Kwasnieski et al., 2014) (Dickel et al., 2014; Ernst et al., 2016; Kheradpour et
al., 2013) (Vockley et al., 2016). However, assigning enhancer potential based on the presence
of histone marks or TF binding alone identifies putative enhancers that cannot enhance tran-
scription when tested functionally. In addition, previous approaches did not identify some en-
hancer classes, as illustrated by our discovery of a previously unrecognized group of functional
enhancers associated with housekeeping genes. The HK module is characterized by reduced
binding of pluripotency-associated TFs and histone marks. This reduced binding likely placed
these regions below the detection threshold in previous ChIP-seq studies that lacked a func-
tional read-out.
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Barakat et al. - hESC enhancer repertoire
Previous studies identified crucial roles for OCT4, NANOG and SMAD3, the latter of which
are downstream mediators of TGF-β signaling in the maintenance of hESC pluripotency (James
et al., 2005; Mullen et al., 2011; Xu et al., 2008). High enhancer activity is enriched near the
binding peaks of these TFs, suggesting that they contribute directly to enhancer function. Other
MPRA studies have shown that heterotypic clusters of different TF binding sites can drive
higher enhancer activity (Smith et al., 2013) and it will be of future interest to decipher the
contributions of individual TF binding to these active enhancers.
Several classes of TEs were also enriched at active enhancers, as reported recently (Ernst et al.,
2016). TEs are enriched in species-specific TF binding sites and have been hypothesized to
shape the enhancer network in hESCs (Glinsky, 2015; Kunarso et al., 2010). Our data indicate
that only a limited number of TEs contribute to enhancer function providing the means to fur-
ther refine the rewiring hypothesis.
A further way in which our findings identify additional features of enhancers is in the position
of enhancers relative to transcription units. Most enhancers studied to date lie within distal el-
ements or intronic sequences. However, some sequences near TSSs are detected by the ChIP-
STARR-seq assay. As test enhancers are inserted downstream of the GFP ORF in the STARR-
seq plasmid (Figure 1A) GFP-positive transcripts cannot be made by initiating transcription in
situ from the inserted TSS. Therefore, sequences near a TSS can exert enhancer activity, in line
with a recent report (Engreitz et al., 2016). Furthermore, a subset of housekeeping enhancers
lie close to a TSS, suggesting that nearby enhancers may regulate some human housekeeping
genes. It will be interesting to determine whether the specific links identified between enhancers
and core-promoters that distinguish housekeeping genes from developmental genes in Dro-
sophila also exist in mammalian cells (Cubenas-Potts et al., 2016; Zabidi et al., 2015).
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Barakat et al. - hESC enhancer repertoire
Several groups have recently developed cultures supporting a more naive hESC state (Gafni et
al., 2013; Takashima et al., 2014; Theunissen et al., 2014) with cells cultured in some of these
conditions able to contribute to interspecies chimaeras (Gafni et al., 2013; Wu et al., 2017).
Here we have used one such culture condition to compare enhancer activity in primed and naive
cells. Enhancer activity alters substantially between primed and naïve hESCs. Pluripotency in
both states is established by differential use of regulatory elements that is partly reflected in
gene expression changes. Many active enhancers in naive cells are located close to TSSs, which
may relate to the reported decrease in bivalent marks near TSSs in hESCs cultured in NHSM
(Gafni et al., 2013). Further studies should clarify differences between different states of naive
pluripotency and how these relate to differences in enhancer usage.
SEs are characterized by large domains marked by H3K27ac with increased binding of Media-
tor and other TFs. ChIP-STARR-seq shows that the majority of sequences covered by SEs lack
enhancer activity. Rather, enhancer activity is limited to small domains within the SEs that
frequently overlap with TF binding sites. This suggests that the observed chromatin signatures
at SEs might be a consequence of enhancer activity from much smaller units. Recent reports
suggest that SE constituents may function alternatively as either independent and additive en-
hancers (Hay and Hughes, 2016; Moorthy et al., 2017), as constituents in a temporal and func-
tional enhancer hierarchy (Shin et al., 2016), or as interdependent units (Hnisz et al., 2015)
exhibiting synergy (Suzuki et al., 2017). The large scale identification of such small active
constituents within SEs reported here will be a valuable resource to further decipher the regu-
latory mechanisms contributing to SE formation, evolution and function.
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Barakat et al. - hESC enhancer repertoire
The catalogue of functional enhancers presented here provides the means to refine models of
the regulatory circuitry of hESCs and a framework for deepening understanding of transcrip-
tional regulation in humans. Given the increasing appreciation of the importance of the regula-
tory genome in health and disease we expect that this resource and the more widespread use of
MPRAs such as ChIP-STARR-seq will advance basic and translational research alike.
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Barakat et al. - hESC enhancer repertoire
Author contribution
TSB and IC conceived the study. TSB performed the molecular biology and contributed to the
bioinformatics analysis and cell culture. AR developed the primary data analysis pipeline. FH
performed bioinformatics analysis, visualization and interpretation. MZ performed cell culture,
immunofluorescence and generated CRISPR/Cas9 deleted clones. CB supervised the bioinfor-
matics analysis. TSB, FH and IC wrote the paper, with input from all authors.
Acknowledgements
We thank S. Pollard, D. O’Carroll, A. Soufi and S. Tomlinson for comments on the manuscript,
E. Hall-Ponsele and F. Rossi for technical support and R. Pantier, J. Zhang and other members
of the Chambers’ lab for helpful discussions. We thank A. Stark (IMP) for the STARR-seq
plasmid, F. Zhang (Broad Institute) for eSpCas9(1.1) and Y. Wang and D. Hay for H9 hESC
cells. IC’s lab is supported by the Medical Research Council (UK) and The Wellcome Trust.
TSB was supported by fellowships from Niels Stensen, EMBO (EMBO-LTF) and Marie
Sklodowska-Curie (H2020 MSCA-IF). FH was supported by the DFG (grant HA 7723/1-1).
CB is supported by a New Frontiers Group award of the Austrian Academy of Sciences and by
an ERC Starting Grant (no. 679146). Sequencing was done by Edinburgh Genomics.
Conflict of interest
The authors declare no conflict of interest.
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Barakat et al. - hESC enhancer repertoire
Figure Legends
Figure 1: ChIP-STARR-seq in human embryonic stem cells.
A) To prepare ChIP-STARR-seq plasmid libraries, ESC chromatin is sonicated to between 200-
600bp and immunoprecipitated with antibodies against TFs or histone modifications (coloured
balls). DNA is end-repaired, adapter-ligated and used to make a plasmid library by Gibson
assembly. In the STARR-seq plasmid (Arnold et al., 2013) a multiple cloning site (MCS; dark
blue) lies downstream from a minimal promoter (purple) and an intron-containing EGFP ORF
(green) and upstream from a UTR/polyA sequence (light blue). Plasmids libraries are then
transfected into target cells. If cloned DNA has enhancer activity, transcription from the mini-
mal promoter is activated, yielding GFP mRNAs with enhancer sequences embedded in the
3’UTR. Enhancers can then be identified by FACS purification of GFP-positive cells and RNA-
seq.
B) ChIP-STARR-seq for NANOG in H9 hESCs. Each scatterplot contrasts the normalized read
count (reads per million) per peak between two datasets, obtained from ChIP-seq or DNA-seq
of the generated plasmid libraries before or after transfection and recovery from hESCs (n=2).
The Pearson correlation (r) for each comparison is indicated.
C) Doughnut charts of the genomic distribution of peaks called for ChIP-seq (outer chart) and
corresponding plasmid libraries (inner chart); TSS, transcription start sites; UTR, untranslated
region.
D) FACS plots of single, DAPI-negative hESCs. Left, untransfected cells; right, cells trans-
fected with a NANOG ChIP-STARR-seq plasmid library.
E) Scatterplot (as in panel B) contrasting the NANOG plasmid library and corresponding ChIP-
STARR-seq RNA. The dense cluster of points in the lower left corner corresponds to library
plasmids that did not produce RNAs. RPM, reads per million.
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Barakat et al. - hESC enhancer repertoire
F) Genome browser plot of the SOX2 locus showing tracks for ChIP-seq, DNA-seq of plasmid
libraries, and ChIP-STARR-seq from RNA-seq of GFP+ cells transfected with the indicated
libraries.
G) Genome browser shots of the KLF15 and LEFTY loci and HOXB cluster, illustrating a broad
variety of enhancers profiled in this catalogue of functional enhancers.
Figure 2: Activity levels define functional classes of enhancers.
A) Plot showing enhancer activity (enrichment of ChIP-STARR-seq RNA over plasmids; log2)
ranked from lowest to highest across all measured enhancers (union of all peak calls). Three
groups of enhancers were distinguished based on their activity and the thresholds (θ) are indi-
cated by dashed lines. RPPM, reads per plasmid and per million sequenced reads.
B) Distribution of high activity (RPPM ≥220), low activity (RPPM 144-220) and inactive se-
quences (RPPM <144) in peaks called for the indicated factors.
C) Luciferase activities of 68 tested genomic sequences in primed hESCs grouped by ChIP-
STARR-seq activity. Activity is the fold enrichment relative to empty vector (in log2), normal-
ised to a Renilla transfection control. Boxes are interquartile range, line is median, whiskers are
10th to 90th percentile. ***= p<0.001, unpaired t-test; n=2.
D) Distribution of gene expression values (Takashima et al., 2014) of genes associated with
enhancers grouped by activity level. Boxplots represent interquartile range (IQR), line is the
median, whiskers extend to 1.5xIQR, dots indicate outliers. All, all genes in RNA-seq dataset;
inactive, RPPM <144; low, RPPM 144-220; high, RPPM ≥220; RPKM, reads per kilobase mil-
lion.
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Barakat et al. - hESC enhancer repertoire
E) Heatmap of the relative enrichment of H9 chromatin segments from the Roadmap project
(Kundaje et al., 2015) in regions with different ChIP-STARR-seq activities (see panel A). Col-
ors report log-odds ratios. Rows and columns are arranged by hierarchical clustering with com-
plete linkage to put similar segment types together. TSS, transcription start site; enh, enhancer;
ZNF, zinc-finger protein.
F) Relative enrichment of TFs from the CODEX database (Sanchez-Castillo et al., 2015) in
inactive regions, low activity and high activity enhancers. Shown are the log2-odds ratios be-
tween observed percentages of enhancers overlapping binding sites for each TF in the respec-
tive groups relative to the percentage in the entire region set. Each dot represents a TF ChIP-
seq dataset with lines connecting the most extreme dot to zero for visualization. For each cate-
gory, the eight most enriched TFs are ranked by their mean log-odds ratio. ChIP-seq datasets
from hESCs are indicated as dots and those from other cells as crosses. Enrichments were cal-
culated using LOLA (Sheffield and Bock, 2016).
G) Smooth line plots showing proportion of active plasmids (RPPM ≥220) of all plasmids
measured at the indicated distance from the peak center for ChIP-seq binding sites for SOX2,
SMAD3 and NANOG averaged across all binding sites for the respective factor.
H) Top motifs from the HOCOMOCO database (Kulakovskiy et al., 2016) ranked by the hit
frequency within the high activity enhancers compared to low and inactive enhancers (strongest
enrichment is at the left). Numbers indicate the proportion of peaks with ≥1 motif hit. Motif
matches were determined using FIMO (Grant et al., 2011). On the right, the DNA sequence
logo of the top motif is shown. ***, p < 0.001, Fisher’s exact test.
Figure 3: Active enhancers include ESC-specific and housekeeping modules.
A) Illustration of the overlap between previously published putative enhancers (Hawkins et al.,
2011; Rada-Iglesias et al., 2011; Xie et al., 2013) (light blue circle) and regions assessed in
27 bioRxiv preprint doi: https://doi.org/10.1101/146696; this version posted July 4, 2017. The copyright holder for this preprint (which was not certified by peer review) is the author/funder. All rights reserved. No reuse allowed without permission.
Barakat et al. - hESC enhancer repertoire
ChIP-STARR-seq in hESCs (white circle). Highly active ChIP-STARR-seq regions (RPPM
≥220) are indicated by the blue circle. ChIP-STARR-seq enhancers overlapping with previously
published putative enhancers are referred to as the ESC module and non-overlapping ChIP-
STARR-seq enhancers as the housekeeping (HK) module.
B) Kernel density plots of the distribution of enrichment values (ratio of ChIP-seq signal over
input, log2) in hESCs for NANOG, OCT4, H3K27ac and H3K4me1 for all peaks called or for
peaks associated with ESC or HK module enhancers.
C) Distribution of measured RPPM values for all assessed genomic regions compared to en-
hancers from the ESC and HK modules. Boxplots represent interquartile range (IQR), line is
the median, whiskers extend to 1.5xIQR. RPPM, reads per plasmid million.
D Gene expression (RNA-Seq; log2) in H9 hESCs (Takashima et al., 2014) for all genes com-
pared to genes associated with either ESC or HK module enhancers. Boxplots as in C. RPKM,
reads per kilobase million. *** = p<0.001 (t-test).
E) Gene expression (RNA-Seq; log2) in different tissues from the RNA-seq Atlas (Krupp et al.,
2012) for all genes linked to ESC or HK module enhancers or both. Boxplots as in C.
F) Functional enrichment analysis using Enrichr testing the relative over-representation in ESC
(top) and HK (bottom) module enhancers near genes associated with different annotation cate-
gories (Chen et al., 2013). Shown are the top 10 results each for TF binding sites from ENCODE
and ChEA (left) and genes down-regulated (middle) or up-regulated (right) upon single-gene
perturbations from the GEO database. The x-axis reports the combined score calculated by En-
richr.
G) Genome browser of GAPDH locus including neighbouring genes. The olive-green box high-
lights one HK enhancer with high activity. Chromosome confirmation data from other cell types
((Mifsud et al., 2015) indicates that this region interacts with the promoter of GAPDH.
28 bioRxiv preprint doi: https://doi.org/10.1101/146696; this version posted July 4, 2017. The copyright holder for this preprint (which was not certified by peer review) is the author/funder. All rights reserved. No reuse allowed without permission.
Barakat et al. - hESC enhancer repertoire
H) Kernel density plot showing the distance to associated genes for ESC and HK module en-
hancers. Distance was calculated between both boundaries of the enhancer region and the start
site of the linked gene with the shorter absolute distance recorded.
I) Bar plot indicating the relative enrichment (log-odds ratio in ESCs compared to all) of H9
chromatin segments from the Roadmap Epigenomics project (Kundaje et al., 2015) in ESC and
HK enhancers. Segment types more commonly overlapping with HK modules (left) or ESC
enhancers (right) are shown.
Figure 4: Major changes in enhancer activity upon induction of naive pluripotency.
A) Overview of the primed to naive conversion and ChIP-STARR-seq cross-over design.
B) Relative enrichment of TFs from CODEX (Sanchez-Castillo et al., 2015) in inactive, low
activity and high activity enhancers in naive H9 hESCs. Plots as in Figure 2F. ChIP-seq datasets
produced from primed hESCs are indicated as dots and those from other cell sources as crosses.
C) Sankey river plot illustrating relative changes in enhancer activity between primed (left) and
naive (right) hESCs. Percentages refer to the total number of peaks of the respective group in
primed hESCs. Percentages and groups are indicated only for groups with the most prominent
changes or those that maintain activity.
D) Functional enrichment analysis using Enrichr to test the relative over-representation of en-
hancers near genes with certain GO assignments (left) or occurring near binding sites from
ENCODE and ChEA ChIP-seq experiments (right). Shown are the top 10 results for each en-
hancer group highlighted in panel F. The x-axis reports the combined score calculated by Enri-
chr.
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Barakat et al. - hESC enhancer repertoire
E) Scatterplot contrasting changes in enhancer activity with changes in gene expression of as-
sociated genes. RPPM values are rescaled by dividing them by the cell-state-specific high-ac-
tivity threshold prior to calculating the difference between primed and naive hESCs. Outliers
and assorted genes are highlighted with dots and examples are labeled.
F) Genome browser shot of NODAL. ChIP-seq tracks for NANOG, OCT4. H3K27ac, and
H3K4me1 in primed (top) and naive (middle) hESCs are shown, together with the ChIP-
STARR-seq RNA (combination of all datasets) in the respective cell states. A switch of en-
hancer activity from the promoter-overlapping enhancer A to the upstream enhancer B is ob-
served in the primed to naive transition.
G) Genome browser shot of the OCT4 locus. Display equivalent to E. The proximal enhancer
(PE) and distal enhancer (DE) display different activity patterns in naive and primed hESCs.
H) qRT-PCR analysis of wild type (wt) H9 hESCs or H9 hESCs with a heterozygous OCT4
distal enhancer deletion (DE+/-) for two amplicons detecting OCT4 mRNA or mRNAs from
flanking genes. The average ratio of gene expression in cells cultured in naive conditions (8
passages) relative to primed conditions is shown for three wt or three OCT4 ΔDE+/- cell lines,
normalized for TBP and one wt set to 1. * =p<0.05 (2-way ANOVA with Bonferoni post-test),
error bars are SD.
Figure 5: Distinct transposable elements are associated with enhancers of differing activ-
ity in hESCs.
A) Bar graph showing enrichment (observed over expected; O/E) ratio for the occurrence of
distinct transposable element (TE) families (LTR, DNA, SINE and LINE) in high activity ChIP-
STARR-seq enhancers (RPPM ≥220). TEs with a high O/E (≥2; green) or low O/E (≤0.5; red)
are shown.
B) Bar graph of the top-25 most enriched TE families in high activity enhancers (RPPM ≥220).
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Barakat et al. - hESC enhancer repertoire
C) Line graph of the enrichment ratio for distinct transposable element families in enhancers
with various activity levels.
D) As panel C but for the top-10 most enriched families of transposable elements in B.
Figure 6: ChIP-STARR-seq dissects super-enhancers into functional elements.
A) Super-enhancers (SEs) were called from H3K27ac ChIP-seq data using ROSE (Whyte et
al., 2013).
B) Scatterplot contrasting SE intensity (H3K27ac enrichment over input) with ChIP-STARR-
seq activity. The Pearson correlation (r) is shown with the blue line indicating a generalised
additive model fit to the data.
C) Genome browser view of the FGFR1 super-enhancer, with ChIP-seq tracks for NANOG,
OCT4, H3K27ac, and H3K4me1 in primed and naive hESCs. The top plot shows the SE locus,
the bottom plot zooms into the indicated region within the second intron of FGFR1. Also shown
are the positions of all regions assessed by ChIP-STARR-seq (grey) and active enhancers (blue)
from this study and the coordinates of eight luciferase constructs matching selected enhancers
(labeled A-H). Also included are public data available via the UCSC genome browser (from
top to bottom): H1 DNase hypersensitivity, ChIP-seq for NANOG and POLR2A, ENCODE
chromatin segmentation and DNA sequence motif occurrences. Enhancer activities are concen-
trated at a single position within the FGFR1 super-enhancer.
D) Luciferase assays of DNA sequences depicted in green in C). Enrichment in luciferase ac-
tivity relative to empty vector, normalized to the Renilla transfection control. N=2, error bars
represent SD.
E) Violin plots showing the proportion of active plasmids (RPPM ≥220) for 3,521 super-en-
hancers (SE) compared to normal enhancers (NE). Each data point is one SE or NE region.
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Barakat et al. - hESC enhancer repertoire
F) Venn diagram of the active subspace (covered by plasmids with RPPM ≥220) of the entire
SE space (all plasmids occurring within SEs).
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Barakat et al. - hESC enhancer repertoire
References
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Barakat et al. - hESC enhancer repertoire
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Barakat et al. - hESC enhancer repertoire
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Barakat et al. - hESC enhancer repertoire
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37 bioRxiv preprint doi: https://doi.org/10.1101/146696; this version posted July 4, 2017. The copyright holder for this preprint (which was not certified by peer review) is the author/funder. All rights reserved. No reuse allowed without permission.
Barakat et al. - hESC enhancer repertoire
Materials & Methods
Cell Culture
H9 human embryonic stem cells were cultured on Matrigel coated cell culture plates, using
mTesR1 medium (Stem Cell Technology, 05850). Cells were routinely split (ratio 1:3-1:4) us-
ing 0.5mM EDTA (Invitrogen, 15575020). For transfection, single cells were obtained by Ac-
cutase treatment (Invitrogen, A1110501), in the presence of Rock inhibitor, Y-27632 (10uM,
Cambridge bioscience, SM02-10). For conversion to the naive state, cells were split on irradi-
ated MEFs on gelatin coated plates and media was changed to NHSM media, as described by
Gafni et al. (Gafni et al., 2013), containing knockout DMEM (Invitrogen ), 20% knockout se-
rum (Invitrogen), human insulin (Sigma, 12.5g ml-1 final concentration), ng ml-1 recom-
binant human LIF (Millipore), 8 ng ml-1 recombinant bFGF (Peprotech) and 1 ng ml-1 recom-
binant TGF-1 (Peprotech), 1 mM glutamine (Invitrogen), 1% nonessential amino acids (Invi-
trogen), 0.1 mM beta-mercaptoethanol (Invitrogen), penicillin-streptomycin (Invitrogen) and
small molecule inhibitors: PD0325901 (1M, ERK1/2i, Axon Medchem); CHIR99021 (3M,
GSKi, Axon Medchem); SP600125 (10M, JNKi, Abcam ab120065) and SB203580 (10 M,
p38i,Abcam ab120638) Y-27632 (5M, ROCKi) and protein kinase C inhibitor G06983 (5 M,
PKCi, Abcam, ab144414). Cells were 1:10 passaged using TrypLETM (Invitrogen, 12604021)
in the presence of Rock inhibitor and maintained for more than 10 passages in NHSM media
prior to analysis. All cells were regularly karyotyped and checked for the presence of myco-
plasm.
38 bioRxiv preprint doi: https://doi.org/10.1101/146696; this version posted July 4, 2017. The copyright holder for this preprint (which was not certified by peer review) is the author/funder. All rights reserved. No reuse allowed without permission.
Barakat et al. - hESC enhancer repertoire
Chromatin immunoprecipitation
For chromatin immunoprecipitation, 2x10^7 H9 primed or naive hESC were harvested in 9 ml
of medium and cross-linked by addition of 270 l 37% Formaldehyde (Sigma, final concentra-
tion of 1%), for 10 min at room temperature under rotation. 1 ml of 1.25 M Glycine was added,
cells were incubated on ice for 5 min and 3x washed with ice cold PBS. At this point, cross-
linked cell pellets were snap-frozen and stored at -80ᴼC, or immediately processed for soni-
cation. Prior to sonication, cells were resuspended in 1ml TE-I-NP40 (10mM TRIS-HCl pH 8,
1mM EDTA, 0.5% NP40, 1mM PMSF, 1x Protease inhibitor complex (PIC, Complete tablets,
04693116001, Roche)) incubated on ice for 5 min and centrifuged for 5 min at 2500 rpm at 4ᴼC
in a refrigerated bench top centrifuge (Eppendorf). Supernatant was removed and nuclei were
resuspended in 1 ml ice-cold lysis buffer (50mM TRIS-HCl pH 8, 10mM EDTA, 1% SDS,
1mM PMSF, 1x PIC) and transferred to a 15 ml Falcon tube for sonication, using a Diagenode
Bioruptor Next Gen (40 cycles of 30” on, 30” off). After transfer to an Eppendorf tube and
centrifugation for 10 min at 13200 rpm at 4ᴼC, chromatin solution was aliquoted and used for
immunoprecipitation or snap-frozen and stored at -80ᴼC. A 20 µl sample was taken and served
as a total input control. For immunoprecipitation, Protein Dynabeads G (10004D, Life Techol-
ogy) were washed with PBS and incubated for 6 hours with 5 g of antibody, at 4ᴼC on a
rotating wheel. Antibodies used were: goat-anti-NANOG (AF1997, R&D Systems), rabbit-
anti-OCT4 (AB19857, Abcam), rabbit-anti-H3K4me1 (AB8895, Abcam) and rabbit-anti-
H3K27ac (AB4729, Abcam); as a control, respective IgG antibodies were used (rabbit-IgG:
10500C, Life Technology, goat-IgG: SC-2028, Santa Cruz Biotechnology). After washing with
PBS, antibody-coupled beads were incubated with 200 l chromatin solution, diluted to a final
volume of 2 ml with dilution buffer (167mM NaCl, 16.7mM TRIS-HCl pH 8.1, 1.2mM EDTA,
0.01% SDS, 1.1% Triton-X100, 1mM PMSF, 1x PIC), overnight at 4ᴼC on a rotating wheel.
Washing of beads was performed by incubation with ice-cold 1 ml of washing buffer, for 5 min,
39 bioRxiv preprint doi: https://doi.org/10.1101/146696; this version posted July 4, 2017. The copyright holder for this preprint (which was not certified by peer review) is the author/funder. All rights reserved. No reuse allowed without permission.
Barakat et al. - hESC enhancer repertoire
at 4ᴼC on a rotating wheel, followed by removal of supernatant using a magnetic stand, for each
of the following: 2x with wash buffer 1 (10mM TRIS-HCl pH 7.6, 1mM EDTA, 0.1% SDS,
1% Triton-X100, 0.1% NaDeoxychloate), 2x with wash buffer 2 (10mM TRIS-HCl pH 7.6,
1mM EDTA, 0.1% SDS, 1% Triton-X100, 0.1% NaDeoxychloate, 150mM NaCl), 2x with
wash buffer 3 (250mM LiCl, 0.5% NP40, 0.1% NaDeoxychloate), 1x with TE 1x with 0.2%
TritonX-100 and 1x with TE 1x, after which beads were resuspended in 100ul TE1x. Immuno-
precipitated chromatin and total input control were decross-linked, by addition of 3 l of 10%
SDS and 5 l Proteinase K (20 g/l, Roche) and 10 l RNAse A (50 g/l, Roche) to each
tube and incubation overnight at 65°C on a shaking thermomixer block, 1400 rpm (Eppendorf).
The next day, beads were briefly vortexed and supernatants were transferred to new tubes using
the magnetic stand. 100l of TE1x containing 500mM NaCl was added to the beads and briefly
vortexed, after which the supernatant was added to the first fraction of collected supernatant.
Following Phenol / chloroform extraction, DNA was precipitated using 1l glycogen
(20mg/ml), 1/10 vol NaOAc (3M) and 100% ice-cold Ethanol, at -20°C for 1 hour, followed by
centrifugation at 13200 rpm for 1 hour at 4ᴼC. After a final wash with 70% ethanol, the DNA
pellet was dried and resuspended in 50l H2O. Concentration of ChIP DNA was determined by
Qubit measurement following manufacturer’s instructions and sonication was assessed by gel-
electrophoresis of total input DNA (target fragment size between 200 and 600 bp).
ChIP-qPCR
Concentration of ChIP and total input control DNA was assessed by Qubit measurement (Life-
Tech) according to manufacturer’s instructions and was diluted to 2 ng/l. 2 l of DNA was
used per qPCR reaction, using a 2x Takyon qPCR master mix (No ROX SYBR, UF-NSMT-
B0701, Takyon). qPCR reactions were run on a Roche Lightcycler 480 II (Roche), using the
following cycle conditions: 95ᴼC 3 min, (95ᴼC 10 sec, 60ᴼC 30 sec, 72ᴼC 25 sec) x45, followed
40 bioRxiv preprint doi: https://doi.org/10.1101/146696; this version posted July 4, 2017. The copyright holder for this preprint (which was not certified by peer review) is the author/funder. All rights reserved. No reuse allowed without permission.
Barakat et al. - hESC enhancer repertoire
by a melting curve from 95ᴼ to 65ᴼC. All data shown are averages of at least 2 biological rep-
licates and 3 technical replicates. All primers used are shown in Table S5.
ChIP-seq library and ChIP-STARR-seq plasmid library preparation
For ChIP-seq and ChIP-STARR-seq plasmid library generation, 10 ng of ChIP DNA was used
as starting material. Using NEB Next ChIP-seq library preparation kit (E6200 or E6240, NEB),
DNA was end-repaired, dA-tailed and adapter-ligated according to manufacturer’s instructions.
After adapter ligation and purification using AMPure-XP beads (0.8x, Beckman Coulter) and
elution into 30l of 0.1xTE, 25 l of the reaction product was used for ChIP-seq library prepa-
ration, by PCR amplification with Illumina index primers (7335 and 7500, NEB) using the NEB
Next Q Hot start high fidelity master mix (M0543S, NEB) according to manufactures instruc-
tions (cycle conditions: 98ᴼC 30 sec, (98ᴼC 10 sec, 65ᴼC 75 sec) x15, 65ᴼC 5 min, 4ᴼC hold).
After an additional round of AMPureXP bead purification, DNA was eluted in 0.1xTE without
further size selection. Quality and quantity of the prepared ChIP-seq libraries was assessed on
an Agilent Tapestation. All sequencing occurred on an Illumina HiSeq 2500 platform, using
50bp single-end sequencing.
The remaining 5 l of purified adapter ligated DNA were used for ChIP-STARR-seq plasmid
library generation. Therefore, DNA was diluted to a total volume of 10 l in 0.1xTE and used
as an input in 8 x 50l PCR reactions using Phusion Polymerase, High-fidelity buffer
(M0530L, NEB) and primers 147 STARRseq libr FW (TAGAGCATGCACCGGACAC-
TCTTTCCCTACACGACGCTCTTCCGATCT) and 148 STARRseq libr RV
(GGCCGAATTCGTCGAGTGACTGGAGTTCAGACGTGTGCTCTTCCGATCT) (Arnold
et al., 2013), which prime on the adapter sequences and add a 5’and 3’ 15 nucleotide homology
sequence to the reaction products which are used for Gibson assembly. After PCR amplification
(cycle conditions: 98ᴼ 2 min, (98ᴼC 10 sec, 62ᴼC 30 sec, 72ᴼC 30 sec) x 15, 72ᴼC 5 min, 4ᴼC
41 bioRxiv preprint doi: https://doi.org/10.1101/146696; this version posted July 4, 2017. The copyright holder for this preprint (which was not certified by peer review) is the author/funder. All rights reserved. No reuse allowed without permission.
Barakat et al. - hESC enhancer repertoire
hold), PCR reactions were pooled, purified using AMPure XP beads (1.8x), eluted in 30 l
0.1xTE and used for Gibson assembly. Therefore, 15 g of the mammalian STARRseq plasmid
(a kind gift of A.Stark) (Arnold et al., 2013) were digested with AgeI-HF and SalI-HF (NEB)
for 8h at 37ᴼC, column purified (Nucleospin purification columns, 740609250, Machery-
Nagel), eluted in 30 l elution buffer and used as a vector in a Gibson reaction, using 2 l of
digested plasmid, 5 l purified PCR product, 3 l H20 and 10 l of a home-made Gibson reac-
tion (100mM Tris-HCl, 10mM MgCl2, 0.2 mM dNTP (each), 0.5U Phusion DNA polymerase
(NEB), 0.16U 5’ T5 exonuclease (Epicentre), 2 Gibson reactions per library. After incubation
at 50ᴼC for 1 hour, Gibson reaction were pooled and precipitated by addition of 1 l Glycogen
(20 g/l, Roche, 1090139300), 5 l NaOAc (3M) and 125 l ice-cold 100% ethanol, incuba-
tion at -20ᴼC for 1 hour and centrifugation for 1 hour at 13200 rpm at 4ᴼC, followed by a final
wash in 70% ethanol. After air drying, DNA pellet was dissolved in 10 l water and used for
electroporation into electrocompetent MegaX DH10 E.coli bacteria (Invitrogen), according to
manufacturer’s instructions, using a Biorad pulser. A total of 5 electroporations per library were
performed with each 2 l of DNA. After recovery in 1 ml SOCS medium each, bacteria were
grown for 1 hour at 37ᴼC in a bacterial shaker in the absence of antibiotics. Then, bacteria were
pooled together and 50 l of a 1:100 and 1:10000 dilution was plated on Ampicillin containing
Agar plates to enable estimation of the number of transformants after overnight growth at 37ᴼC
(Control electroporations with Mock-Gibson without addition of PCR product plated on Am-
picillin, or digested STARRseq plasmid transformations on Ampicillin- and Ampicillin/Chlo-
ramphenicol-containing Agar plates were negative, confirming complete digestion of the
STARR-seq plasmid and a functional Ccdb counter-selection in DH10βE.Coli). The remaining
5 ml of bacteria culture were incubated in a total volume of 2 liter of LB-media supplemented
with Ampicillin and allowed to grow for 16 hours in a bacterial shaker at 37ᴼC. Plasmid DNA
42 bioRxiv preprint doi: https://doi.org/10.1101/146696; this version posted July 4, 2017. The copyright holder for this preprint (which was not certified by peer review) is the author/funder. All rights reserved. No reuse allowed without permission.
Barakat et al. - hESC enhancer repertoire
was isolated using a Qiagen Maxiprep kit according to manufacturer’s instructions and eluted
in 500 l 10mM Tris-HCl, pH 7.4. Concentration was determined by Nanodrop measurement.
Transfection of plasmid libraries
Primed and naive H9 hESCs were transfected using either Nucleofection (Lonza, VPH-5022),
or using Lipofectamin3000 according to manufacturer’s instructions. For each transfection, six
million cells were used and transfected with 8 g of plasmid library DNA and 500 ng
pmCherry-N1 plasmid (Clonetech) as transfection control. Cells were incubated in 10 cm dishes
and 24h post-transfection, single cells were harvested and subjected to FACS. Non-transfected
cells were used to set sorting gates, DAPI was used as a marker for dead cells. All percentages
mentioned are relative to the fraction of DAPI-negative, single cells.
Preparation of ChIP-STARR-seq RNA and DNA samples for sequencing
A minimum of 400,000 GFP-positive, sorted cells were used to isolate total RNA using Trizol
(Thermo Fisher) according to manufacturer’s instructions. The mRNA fraction was captured
using Oligo (dT)25 beads (61002, Life Technologies) and DNAseI treated (18068-015, Life
Technologies), followed by reverse transcription using 2 l SuperscriptIII (18080-044, Life
Technologies) using a GFP-mRNA specific primer (149 STARRseq rep RNA cDNA synth,
CAAACTCATCAATGTATCTTATCATG) at 50ᴼC for 90 minutes, in a total reaction volume
of 21 l. To repress residual plasmid DNA contamination, cDNA was PCR amplified using a
combination of primers (152 STARR reporter specific primer 2 fw, GGGCCAGCTGTT-
GGGGTG*T*C*C*A*C and 153 STARR reporter specific primer 2 rv,
CTTATCATGTCTGCTCGA*A*G*C, where * represent phosphorothioate bonds) spanning a
synthetic intron in the STARR-seq plasmid, as previously described (Arnold et al., 2013). PCR
43 bioRxiv preprint doi: https://doi.org/10.1101/146696; this version posted July 4, 2017. The copyright holder for this preprint (which was not certified by peer review) is the author/funder. All rights reserved. No reuse allowed without permission.
Barakat et al. - hESC enhancer repertoire
was performed with Phusion polymerase and High-fidelity buffer, in 6 x 50 l reactions (cy-
cling conditions: 98ᴼC 2 min, (98ᴼC 10 sec, 62ᴼC 30 sec, 72ᴼC 70 sec) x15, 72ᴼC 5 min, 4ᴼC
hold). PCR reactions were pooled, purified using AMPureXP beads (1.0x) and eluted in 18 l
0.1xTE. Absence of significant plasmid contamination in the PCR amplified cDNA was as-
sessed by qPCR using a primer-set amplifying an amplicon from the STARR-seq plasmid back-
bone (161 STARRseq detect plasmid backbone qPCR fw, CATCATCGGGAATCGTTCTT,
and 162 STARRSeq detect plasmid backbone qPCR rv, TGAAGATCAACTGGGTGCAA),
relative to a primer-set amplifying GFP (154 STARRseq GFP fw, AC-
GGCCACAAGTTCTCTGTC, and 155 STARRseq GFP rv, GCAGTTTGCCAGTAGTG-
CAG). PCR amplified cDNA was then used in a second round of PCR to add Illumina index
primers (7335, 7500, NEB) using priming on the adapter sequences added during the plasmid
library generation. PCR was performed in 1-4x 50 l reactions using Phusion polymerase and
High-fidelity buffer (NEB)(cycling conditions: 98ᴼC 2 min, (98ᴼC 10 sec, 65ᴼC 30 sec, 72ᴼC
30 sec) x13, 72ᴼC 5 min, 4ᴼC hold), after which PCR reactions were pooled, purified using
AMPureXP beads (1.0x) and eluted in 15 l 0.1xTE. Corresponding plasmid libraries were
similarly amplified in a nested PCR, using primers detecting the STARR-seq plasmid (160
STARR reporter specific primer for plasmid DNA fw, GGGCCAGCTGTTGGGGTG, and 153
STARR reporter specific primer 2 rv, CTTATCATGTCTGCTCGA*A*G*C, where * repre-
sent phosphorothioate bonds) and Illumina index primers. In addition to sequencing libraries
prepared from plasmid maxiprep DNA, we also sequenced plasmid libraries reisolated from
transfected hESCs. For this, we transfected H9 hESCs as described above and harvested non-
sorted cells 24h post-transfection, followed by plasmid reisolation using a Qiagen miniprep
isolation kit and sequencing library preparation. Quantity and quality of generated sequencing
libraries was assessed on an Agilent Tapestation. All sequencing occurred on an Illumina HiSeq
2500 platform, using 50bp or 125 bp paired-end sequencing. Up to 22 RNA samples were
44 bioRxiv preprint doi: https://doi.org/10.1101/146696; this version posted July 4, 2017. The copyright holder for this preprint (which was not certified by peer review) is the author/funder. All rights reserved. No reuse allowed without permission.
Barakat et al. - hESC enhancer repertoire
pooled on a single lane. During data-processing all reads were trimmed to 50bp length to im-
prove consistency.
RT-qPCR
For RNA analysis of complete cultures, cells were lysed in Trizol (Thermo Fisher) and RNA
was prepared according to manufacturer’s instructions. 1 g of RNA was treated with DNAseI
(Invitrogen) to remove genomic DNA contamination and cDNA was obtained through reverse
transcription using SuperScriptIII (Invitrogen) in the presence of RNAseOUT (Invitrogen).
cDNA was diluted in DEPC-treated water to a final volume of 200 l and 2 l of cDNA was
used per qPCR reaction, using a 2x Takyon qPCR master mix (No ROX SYBR, UF-NSMT-
B0701, Takyon). qPCR reactions were run on a Roche Lightcycler 480 II (Roche), using the
following cycle conditions: 95ᴼC 3 min, (95ᴼC 10 sec, 60ᴼC 30 sec, 72ᴼC 25 sec) x45, followed
by a melting curve from 95ᴼ to 65ᴼC. All data shown are averages of at least 2 biological rep-
licates and 3 technical replicates, normalized to TBP. All primers used are shown in Table S5.
Immunostaining
Cells were grown on culture dishes suitable for confocal microscopy (Ibidi, 81156) and fixed
using 4% v/v Paraformaldehyde at room temperature for 10 min. After permeabilisation using
0.3% Triton/PBS and incubation with blocking solution (1% BSA, 3% Donkey serum, 0.1%
triton in PBS), cells were incubated with primary antibody O/N at 4ᴼC. After washing with
PBS, cells were incubated with secondary antibody at RT for 1h, washed and counterstained
with DAPI. Imaging occurred on a Leica SP8 STED-CW confocal microscope and images were
processed using ImageJ software. Antibodies used are: goat-anti-NANOG (1: 200, AF1997,
45 bioRxiv preprint doi: https://doi.org/10.1101/146696; this version posted July 4, 2017. The copyright holder for this preprint (which was not certified by peer review) is the author/funder. All rights reserved. No reuse allowed without permission.
Barakat et al. - hESC enhancer repertoire
R&D Systems), rabbit-anti-OCT4 (1: 200, AB19857, Abcam). Secondary antibodies were Don-
key-anti-goat conjugated to Alexa fluor488 (1:800, A11055, Invitrogen) and Donkey-anti-rab-
bit conjugated to Alexa fluor568 (1:1000, A10042, Invitrogen).
Western blotting
Whole cell protein extracts were isolated and Western blotting was performed using standard
procedures using pre-cast 10% Bis-Tris Bolt gels (Invitrogen). Primary antibody used was goat-
anti-NANOG (1: 500, 1g/ml, AF1997, R&D Systems), secondary antibody conjugated to
fluorophores was donkey-anti-goat-IRDey680 (1:500, 926-68074, Li-cor). Rabbit-anti-Lam-
inin B (1:1000, AB16048, Abcam) served as a loading control and was detected by chemi-
iluminescence. Imaging occurred on an Odyssey imager (Li-cor).
Luciferase assays
Enhancer sequences were PCR amplified from human genomic DNA using Phusion polymer-
ase and cloned by Gibson assembly into a KpnI-NheI linearized Pgl3 promoter luciferase vec-
tor. For primer sequences, see Table S5. All constructs were sequence-verified by Sanger se-
quencing and co-transfected with a Renilla expressing plasmid using Lipofectamin 3000 into
H9 hESCs. 48h post-transfection illuminescence was assessed using the Dual Glo luciferase kit
(E2920, Promega) according to manufacturer’s instructions, on a Promega Glumax Multidec-
tion system. All data shown are average from at least two biological replicates and two technical
replicates, representing fold-change in luciferase activity compared to empty vector controls
and normalized for Renilla transfection control.
46 bioRxiv preprint doi: https://doi.org/10.1101/146696; this version posted July 4, 2017. The copyright holder for this preprint (which was not certified by peer review) is the author/funder. All rights reserved. No reuse allowed without permission.
Barakat et al. - hESC enhancer repertoire
CRISPR/Cas9 genome editing
Oligonucleotides for gRNAs flanking the OCT4 distal enhancer (OCT4 DE human gRNA1 fw:
CACCGGAGATGGGCACACGAACAG, OCT4 DE human gRNA1 rv: AAAC-
CTGTTCGTGTGCCCATCTCC, OCT4 DE human gRNA2 fw: CAC-
CGTCTGCGTCCCTCTCGGGAA, OCT4 DE human gRNA2 rv: AAACTTCCCGA-
GAGGGACGCAGAC) were annealed and cloned into a BbsI digested spCas9 plasmid, from
which the gRNAs are separately expressed together with a eSpCas9(1.1)-t2a-mCherry or eS-
pCas9(1.1)-t2a-GFP (modified from Addgene plasmid #71814, (Slaymaker et al., 2016)). All
plasmids were sequence verified and 1 g of each gRNA was used to transfect primed H9
hESCs in a 6-well plate using Lipofectamine 3000. 48h post-transfection, mCherry and GFP
double positive cells were FAC sorted and cells were plated at low density in 10 cm dishes
coated with Matrigel in conventional mTesR1 hESC medium. Emerging clones were expanded
and genotyped by PCR using primers flanking the gRNA targets (440: OCT4 DE genotyping 2
fw, GGGTCAGTGGCTCTATCTGC; 441: OCT4 DE genotyping 2 rv, TTCAACCAAACAG-
CACCTCA) to detect the approximately 650 bp deletion of OCT4 DE enhancer. Candidate
clones after PCR screening were Sanger sequenced and correct clones were expanded and used
for conversion experiments to the naive hESC state.
ChIP-seq and ChIP-STARR-seq plasmid and RNA data processing
We trimmed possible adapter contaminants from reads using Skewer (Jiang et al., 2014).
Trimmed reads were then aligned to the GRCh37/hg19 assembly of the human genome using
Bowtie2 (Langmead and Salzberg, 2012) with the "--very-sensitive" parameter. For ChIP-
STARR-seq plasmid and RNA libraries, only properly paired, concordantly aligning and
uniquely mapping fragments were kept. Genome browser tracks were created with the genome-
CoverageBed command in BEDTools (Quinlan and Hall, 2010) and normalized such that each
47 bioRxiv preprint doi: https://doi.org/10.1101/146696; this version posted July 4, 2017. The copyright holder for this preprint (which was not certified by peer review) is the author/funder. All rights reserved. No reuse allowed without permission.
Barakat et al. - hESC enhancer repertoire
value represents the read count per base pair per million uniquely mapped reads. Finally, the
UCSC Genome Browser's bedGraphToBigWig tool was used to produce a bigWig file.
Definition of ChIP-STARR-seq enhancer and activity levels
For ChIP-seq and plasmid DNA-seq libraries, peak calling was performed with MACS2 (Zhang
et al., 2008) with default parameters, using the respective input samples as background. In ad-
dition, we called peaks with MACS2 without the background samples (“—nomodel --extsize
147” parameters). For each peak set, we fixed the peak width to 500 bp from the peak summit
for transcription factors and 1000 bp for histone modifications and removed peaks that over-
lapped blacklisted features as defined by the ENCODE project (Hoffman et al., 2013). ChIP-
seq peaks are given in File S1.
To define a set of enhancers to compare in our analysis of ChIP-seq, plasmid DNA-seq and
ChIP-STARR RNA-seq samples, we produced a set of peaks by merging (i.e. computing the
union) of peaks for the same factor across cell types and experiment types (ChIP-seq and plas-
mid DNA-seq). Furthermore, we generated a set of ”false positive” peaks for each factor to be
used as a background dataset in our subsequent analyses. We defined this dataset as those peaks
that were called by MACS2 without the total input control and that did not overlap with the
peaks called with the control sample.
We initially quantified the intensity of ChIP-seq, plasmid DNA-seq and ChIP-STARR RNA-
seq datasets in the enhancer peak regions by counting the number of aligned reads overlapping
each enhancer region. To get a more accurate and precise measure of plasmid reporter intensity
for further analysis, we then made use of our paired-end sequencing data to unequivocally link
RNA-seq reads to the plasmid that they came from. To do so, we matched RNA-seq reads to
plasmid reads with the exact same start coordinate of the first read and the exact same end
48 bioRxiv preprint doi: https://doi.org/10.1101/146696; this version posted July 4, 2017. The copyright holder for this preprint (which was not certified by peer review) is the author/funder. All rights reserved. No reuse allowed without permission.
Barakat et al. - hESC enhancer repertoire
coordinate of the second read. Comparing the counts for both made it possible to define a meas-
ure of RNA-seq activity relative to the abundance of plasmids in the library (reads per plasmid).
To avoid distortion by differences in sequencing depth, we first scaled RNA-seq read counts
by library size (RNA-seq reads per million (RPM), R) and plasmid counts by library size (plas-
mid RPM, P) to define our final measure of activity level as reads per plasmid million (RPPM
= R / P). We then used the maximum observed RPPM value as an estimate of enhancer-peak-
level activity. Since our individual replicate datasets were sparse, with the same plasmids in-
frequently measured in both replicates, but our overall coverage of enhancers was much better,
we used RPPM from all datasets generated in the same cell type (so specific to either primed
or naive H9 hESCs) for this purpose. We could do so because the ChIP-STARR-seq plasmid
libraries are independent from the antibody target used to pull down the enriched DNA frag-
ments, thus the plasmids in all libraries jointly report the activity of the same genome. To ob-
jectively define thresholds to distinguish highly active, lowly active and inactive genome re-
gions, we made use of “false positive” background peaks defined earlier (from the MACS2
software). These are peaks usually removed from the analysis in other publications, because
they display peak-like coverage not only in the DNA enriched for ChIP-seq targets but also in
unenriched sonicated DNA (input control). The reasoning is generally that these regions repre-
sent DNA that is particularly well accessible, however, is not of genuine regulatory importance.
We make the assumption that these peaks, even if they may contain regulatory enhancers, were
slightly less likely to contain genuine functional enhancers than “true positive” peaks (that is,
peaks in ChIP-seq dataset without concordant peak in the input control). We therefore fitted a
log-normal statistical model to the distribution of RPPM values in the false positive peaks and
determined thresholds at which 5% (p < 0.05) or 10% (p < 0.1) of these peaks would have been
called “active”. We did this separately in primed and naive hESCs because the distributions of
RPPM values were different, and we then used the threshold determined (primed: θhigh = 220,
49 bioRxiv preprint doi: https://doi.org/10.1101/146696; this version posted July 4, 2017. The copyright holder for this preprint (which was not certified by peer review) is the author/funder. All rights reserved. No reuse allowed without permission.
Barakat et al. - hESC enhancer repertoire
θlow=144; naive: θhigh=96, θlow=62) throughout our analysis (see Figures 2A, S6D). The coor-
dinates of all genome regions assessed with activity calls are given in File S1.
Motif enrichment analysis for generated ChIP-seq data sets
BED files of ChIP-seq data sets were generated with 500 bp sequences centered on the narrow
ChIP-seq peak, and used for motif enrichment analysis using CentriMo (http://meme-
suite.org/)(Bailey and Machanick, 2012), using default settings.
Assignment of enhancers to genes
We used GREAT, version 3.0.0 (McLean et al., 2010) to assign regulatory elements identified
in ChIP-STARR-seq to their putative target genes, using the following settings: basal plus ex-
tension, proximal 5kb upstream and 1kb downstream, plus distal up to 100kb. Publically avail-
able, processed RNA-seq data from primed human ESCs were downloaded (Gifford et al.,
2013; Ji et al., 2016; Takashima et al., 2014) and their RPKM value distribution was plotted for
the various ChIP-STARR-seq regions grouped by activity in RPPM. For naive hESCs, we used
publically available microarray data from the original study describing gene expression in naive
cells cultured under NHSM conditions (Gafni et al., 2013).
Comparison to previously published ESC enhancers
The coordinates of putative enhancers were obtained from the supplementary data of Hawkins
et al, Rada-Iglesias et al and Xi et al (Hawkins et al., 2011; Rada-Iglesias et al., 2011; Xie et
al., 2013), and when necessary converted to the hg19 version of the human genome using the
liftOver tool. Overlapping enhancers were merged into 76,666 putative enhancers and joint to
our ChIP-STARR-seq enhancers using GenomicRanges (Lawrence et al., 2013) in R (see Fig-
ure S4A,B, Table S3). We refer to those enhancers that overlapped with previously published
50 bioRxiv preprint doi: https://doi.org/10.1101/146696; this version posted July 4, 2017. The copyright holder for this preprint (which was not certified by peer review) is the author/funder. All rights reserved. No reuse allowed without permission.
Barakat et al. - hESC enhancer repertoire
enhancers and showed a ChIP-STARR-seq activity of RPPM>=220 as the ESC enhancer mod-
ule (n=7,596). Conversely, we refer to active enhancers (RPPM >= 220) that did not overlap
with the previously published enhancers as the housekeeping (HK) enhancer module (n =
13,604).
Functional enrichment analysis
To help understand the function and relevance of different groups of enhancers, we used three
types of functional enrichment analysis (Table S2).
(a) We used LOLA (Sheffield and Bock, 2016) to determine the relative over-representation of
ChIP-seq peaks related transcription factor binding and other elements of known regulatory
function. To this end, we used the codex, encode_tfbs, and encode_segmentation databases con-
tained in the LOLA Core database and tested for the enrichment of overlap in genome regions
with a specific level of activity (high, low or inactive) over the background of all ChIP-STARR-
seq peaks.
(b) We also used the Enrichr web interface (February 2017 version) (Chen et al., 2013) to test
genes linked to enhancers of interest for significant enrichment in numerous functional catego-
ries. In all plots, we report the “combined score” calculated by Enrichr, which is a product of
the significance estimate and the magnitude of enrichment (combined score c = log(p) * z,
where p is the Fisher’s exact test p-value and z is the z-score deviation from the expected rank).
(c) We additionally used the GREAT web interface (version 3.0.0) (McLean et al., 2010) for
gene ontology analysis, using the following settings: basal plus extension, proximal 5kb up-
stream and 1kb downstream, plus distal up to 100kb, including curated regulatory domains, and
whole genome (hg19) as background.
51 bioRxiv preprint doi: https://doi.org/10.1101/146696; this version posted July 4, 2017. The copyright holder for this preprint (which was not certified by peer review) is the author/funder. All rights reserved. No reuse allowed without permission.
Barakat et al. - hESC enhancer repertoire
Motif over-representation analysis
To find motifs that occurred more frequently in one enhancer group than in the others, we used
FIMO (v4.10.2) (Grant et al., 2011) to scan the DNA sequences of all ChIP-STARR-seq en-
hancers for occurrences of known DNA motifs from the HOCOMOCO database (v10)
(Kulakovskiy et al., 2016) using default parameters. We than compared the count of motif hits
at a significance threshold of p<=0.05).
Enrichment analysis for transposable elements
The UCSC RepeatMask (hg19) was downloaded from the UCSC Table Browser, imported into
Galaxy (usegalaxy.org) (Afgan et al., 2016) and joined to the ChIP-STARR-seq activity calls
for primed hESCs. The frequency of the various repeat sequences was counted for either all
ChIP-STARR-seq regions that could be measured, or for the various subgroups binned accord-
ing to their activity. To calculate the expected number of repeats present in each of the various
activity groups, we divided the observed repeat counts in the total group by the number of
regions that could be measured in all ChIP-STARR-seq regions, and multiplied this for the
number of regions present in each activity subgroup (Table S4). We then calculated the ratio
between observed and expected (O/E), and considered repeats with O/E<0.5 as depleted, or
O/E>2 as enriched. For the subsequent data interpretation, we only focused on transposable
elements that were >15 times present in all ChIP-STARR-seq regions.
Super-enhancer analysis
To call super-enhancers in primed and naive H9 hESCs, we used the ROSE software (v0.1)
(Whyte et al., 2013) to combine (“stitch”) ChIP-STARR-seq enhancers within 12.5 kb of each
other and excluding 2.5 kb around known transcription start sites. We then asked the software
to quantify the ratio of the H3K27ac ChIP-seq signal in primed and naive hESCs over the total
52 bioRxiv preprint doi: https://doi.org/10.1101/146696; this version posted July 4, 2017. The copyright holder for this preprint (which was not certified by peer review) is the author/funder. All rights reserved. No reuse allowed without permission.
Barakat et al. - hESC enhancer repertoire
input control and to call super-enhancers. The coordinates of all stitched enhancers, as well as
primed and naive super-enhancers are given in File S1.
Statistics for qPCR and luciferase assays
qPCR and luciferase assay figures were plotted and statistics were calculated using GraphPad
Prism 5 software, p<0.05 was considered significant.
Data availability
High-throughput sequencing data generated in this study have been submitted to the Gene Ex-
pression Omnibus (GEO) under accession code GSE99631. Additional data, genome browser
tracks and an interactive search tool for active enhancers in the proximity of genes are available
from a supplementary website under the following URL: http://hesc-enhancers.computational-
epigenetics.org
53 bioRxiv preprint doi: https://doi.org/10.1101/146696; this version posted July 4, 2017. The copyright holder for this preprint (which was not certified by peer review) is the author/funder. All rights reserved. No reuse allowed without permission.
Barakat et al. - hESC enhancer repertoire
Supplemental Figure legends
Figure S1, related to Figure 1: ChIP-seq in primed H9 human embryonic stem cells
A) Brightfield microscopy of a representative colony of H9 hESCs cultured on Matrigel in
standard hESC culture conditions.
B) Immunofluorescence of primed H9 hESCs for NANOG (green) or OCT4 (red); DNA is
stained with DAPI (blue).
C) ChIP-qPCR in primed H9 hESC with anti-NANOG, anti-OCT4 or rabbit IgG at known
OCT4 and NANOG binding sites in SCGB3A2, SMARCA (Kunarso et al., 2010) and XIST (left)
or with anti-H3K4me1, anti-H3K27ac or rabbit IgG at binding sites near FGFR1 (at central and
flanking locations), POU5F1 (at central and flanking locations), CD9, SCGB3A2, and SMARCA
(Rada-Iglesias et al., 2011) (right). The mean fold-enrichment is shown relative to total input
control DNA, normalized to a non-bound site in ACTB (for OCT4 and NANOG), or NCAPD2
(for H3K4me1 and H3K27ac). Error bars indicate standard deviations; n= 3.
D) Venn diagrams of the overlap between ChIP-seq peaks in primed hESCs, indicating the cell
line and study for NANOG, OCT4, H3K4me1 and H3K27ac. The numbers indicate overlapping
peaks.
Figure S2, related to Figure 1: Overview of generated datasets
A) Summary table of the high-throughput sequencing datasets generated in this study, indicat-
ing the number of ChIP-seq, plasmid (corresponding to ChIP-STARR-seq plasmid libraries
prior to transfection) and isolated plasmid (plasmid libraries 24h after transfection) samples, as
well as RNA-seq data from GFP-positive primed and/or naive hESCs (RNA: primed and RNA:
naive, respectively).
54 bioRxiv preprint doi: https://doi.org/10.1101/146696; this version posted July 4, 2017. The copyright holder for this preprint (which was not certified by peer review) is the author/funder. All rights reserved. No reuse allowed without permission.
Barakat et al. - hESC enhancer repertoire
B) Pearson correlation matrix of samples from the indicated ChIP-seq, plasmid libraries and
isolated plasmid libraries from primed hESCs. Rows and columns have been arranged by hier-
archical clustering with complete linkage.
C-D) Scatterplots contrasting normalized read counts (reads per million) per peak for different
datasets. The Pearson correlation coefficient (r) for each comparison is indicated for each plot.
C) Comparison between ChIP-seq, DNA-seq of ChIP-STARR-seq plasmid libraries and two
replicates of DNA-seq for isolated plasmid libraries post transfection, for OCT4, NANOG,
H3K37ac, H3K27ac and genomic DNA (input).
D) Comparison between ChIP-STARR-seq plasmid libraries prior to transfection and the cor-
responding RNA-seq read counts generated from two replicates of GFP-positive cells after
transfection.
E) Pearson correlation coefficients (r) between replicate STARR-RNA-seq measurements from
the same pool of hESCs transfected with the same ChIP-STARR-seq library, shown as a func-
tion of minimum read count in both replicates (0 = unfiltered, 1 = at least one read from the
same plasmid measured in each replicate, etc.).
Figure S3, related to Figure 2: ChIP-STARR-seq in primed H9 hESCs
(Zhang et al., 2008)A) Boxplots showing the distribution of RNA-seq RPKM values of genes
associated with enhancers grouped by activity level. Boxplots represent the interquartile range
(IQR), the line is the median, whiskers extend to 1.5xIQR and outliers are indicated as dots.
The numbers on the x-axis indicate thresholds on the RPPM activity level; RPKM, reads per
kilobase million. RNA-seq datasets were from the following studies: HUES64 (Gifford et al.,
2013); H1 (Ji et al., 2016); H9 (Takashima et al., 2014).
55 bioRxiv preprint doi: https://doi.org/10.1101/146696; this version posted July 4, 2017. The copyright holder for this preprint (which was not certified by peer review) is the author/funder. All rights reserved. No reuse allowed without permission.
Barakat et al. - hESC enhancer repertoire
B) Smooth line plots showing the proportion of active plasmids (RPPM ≥220) of all plasmids
measured at the indicated distance from the peak center for ChIP-seq binding sites of factors
from the CODEX database (Sanchez-Castillo et al., 2015) found preferentially associated at
highly active enhancers (compare to Fig. 2F), averaged across all binding sites for the respec-
tive factor. The number of peaks (n) is indicated in each plot.
C) Relative enrichment of DNA-binding proteins (DBPs) from the ENCODE database (2012)
in inactive genome regions, as well as lowly active and highly active enhancers (compare to
Fig. 2A). Shown are the log2-odds ratios between observed percentages of enhancers overlap-
ping binding sites of each given DBP in the respective groups over the percentage of overlaps
in the entire enhancer dataset. Each dot represents one ChIP-seq dataset for the given DBP and
the lines connect the most extreme dot with zero for visualization. For each category, the eight
most enriched DBPs are shown ranked by their mean log-odds ratio. ChIP-seq datasets pro-
duced from hESCs are indicated as dots and those from other cell sources as crosses. Enrich-
ments were calculated using LOLA (Sheffield and Bock, 2016).
D) LOLA enrichment plots as in panel C, but showing instead the relative over-presentation of
ENCODE chromatin segments from different cell lines in enhancers with different activity lev-
els. E, enhancer; PF, promoter-flanking region; R, repressed; T, transcribed; TSS, transcription
start site; WE, weak enhancer.
E) Line plots as in panel B, showing the proportion of active plasmids in a window around the
center of repressed chromatin segments and enhancer chromatin segments from the ENCODE
H1 chromatin segmentation (Hoffman et al., 2013).
F) Top motifs from the HOCOMOCO database (Kulakovskiy et al., 2016) ranked by the fre-
quency of hits within the lowly active enhancers compared to highly active enhancers and in-
active genome regions (left) or within the inactive genome regions compared to lowly and
56 bioRxiv preprint doi: https://doi.org/10.1101/146696; this version posted July 4, 2017. The copyright holder for this preprint (which was not certified by peer review) is the author/funder. All rights reserved. No reuse allowed without permission.
Barakat et al. - hESC enhancer repertoire
highly active enhancers (right). The numbers indicate the proportion of peaks with at least one
motif hit. Motif matches were determined using FIMO (Grant et al., 2011).
Figure S4, related to Figure 3: ESC-specific and housekeeping enhancer module
A) Illustration showing three source datasets (Hawkins et al., 2011; Rada-Iglesias et al., 2011;
Xie et al., 2013) that contributed to the catalogue of putative ESC enhancers used in this study.
We converted all enhancer coordinates to the same assembly (hg19) and then merged overlap-
ping peaks resulting in a list of 76,666 putative enhancers.
B) Illustration of the number of genes found in the proximity of ESC module enhancers (pink,
left circle), HK module enhancers (olive, right), or with enhancers of both types (white, over-
lap). Enhancer-gene assignments were performed with GREAT (McLean et al., 2010).
C) Luciferase assay in primed hESCs for eight putative enhancers that did not show activity in
ChIP-STARR-seq. The OCT4 proximal enhancer (PE) is tested as a positive control. Luciferase
activity is reported as the fold enrichment in luciferase counts over empty vector, normalised
to the Renilla transfection control. Error bars indicate standard deviations, n=2.
D) Boxplots of gene expression (RNA-Seq; log2) in different tissues from the GTEx database
(2013)for all genes linked to ESC (pink) or HK (olive) module enhancers or both (white). Box-
plots represent the interquartile range (IQR), the line is the median, whiskers extend to 1.5xIQR
and outliers are indicated as dots. RPKM, reads per kilobase million.
Figure S5, related to Figure 4: Conversion of primed to naive H9 human embryonic stem
cells
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Barakat et al. - hESC enhancer repertoire
A) Brightfield microscopy of a representative colony of H9 hESCs cultured in naive culture
conditions on feeders for 10 passages showing a more dome-shaped colony morphology (com-
pare to Figure S1A).
B) Immunofluorescence of naive H9 hESCs for NANOG (green) and OCT4 (red); DNA is
stained with DAPI (blue).
C) Immunoblot analysis of NANOG and LAMININ Bin primed and naive hESCs.
D) qRT-PCR of pluripotency-related genes in H9 hESCs cultured in primed or naive conditions
(10 passages). Error bars indicate standard deviations; n=3.
E) ChIP-qPCR in naive H9 hESC with anti-NANOG, anti-OCT4 or rabbit IgG at three OCT4
and NANOG binding sites (from primed ChIP-seq data) in SCGB3A2, SMARCA (Kunarso et
al., 2010) and XIST (left) or with anti-H3K4me1, anti-H3K27ac or rabbit IgG at binding sites
near FGFR1 (at central and flanking locations), POU5F1 (at central and flanking locations),
CD9, SCGB3A2 and SMARCA (Rada-Iglesias et al., 2011) (right). The mean fold-enrichment
is shown relative to total input control DNA, normalized to a non-bound site in ACTB (for
NANOG and OCT4) or NCAPD2 (for H3K4me1 and H3K27ac). Error bars indicate standard
deviations; n= 3.
F-G) Venn diagrams of the overlap between ChIP-seq peaks in naive hESCs, indicating the cell
line and study for F) H3K4me1 and G) H3K27ac. The numbers indicate overlapping peaks.
H) Local motif enrichment analysis using CentriMo (Bailey and Machanick, 2012) for OCT4
ChIP-seq data generated in this study for primed (upper panel) and naive H9 hESCs (lower
panel). Top-3 identified motifs and their p-values are indicated.
I) as H, but now for NANOG.
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Barakat et al. - hESC enhancer repertoire
Figure S6, related to Figure 4: ChIP-STARR-seq in naive hESCs
A-C) Scatterplots contrasting normalized read counts (reads per million) in naive H9 hESCs
per peak for different datasets. The Pearson correlation coefficient (r) for each comparison is
indicated for each plot.
A) Comparison of ChIP-seq datasets and the corresponding ChIP-STARR-seq plasmid library.
B) Comparison between ChIP-STARR-seq plasmid libraries prior to transfection and the cor-
responding RNA-seq read counts generated from two replicates of GFP-positive naive H9
hESCs after transfection.
C) Comparison between two replicates of GFP-positive naive H9 hESCs after transfection with
libraries generated in naive H9 hESCs (top), with libraries generated in primed H9 hESCs (mid-
dle), or of GFP-positive primed H9 hESCs after transfection with libraries generated in naive
H9 hESCs (bottom).
(Zhang et al., 2008)D) Plot showing enhancer activity (ratio of ChIP-STARR RNA over plas-
mids; log2) in naive H9 hESCs ranked from lowest to highest across all measured enhancers
(union of all peak calls). Three groups of enhancers were distinguished based on their activity
and the thresholds (θ) are indicated in the plot by dashed lines. RPPM, reads per plasmid mil-
lion.
E) Relative distribution of highly active enhancers (RPPM ≥96), lowly active enhancers (RPPM
62-96) and inactive genomic regions (RPPM <62) in peaks called for the indicated factors.
F) Relative enrichment of DNA-binding proteins (DBPs) from the ENCODE database (2012)
in inactive genome regions, as well as lowly active and highly active enhancers in naive H9
hESCs (compare to Fig. 4B). Shown are the log2-odds ratios between observed percentages of
enhancers overlapping binding sites of each given DBP in the respective groups over the per-
centage of overlaps in the entire enhancer dataset. Each dot represents one ChIP-seq dataset for
the given DBP and the lines connect the most extreme dot with zero for visualization. For each
59 bioRxiv preprint doi: https://doi.org/10.1101/146696; this version posted July 4, 2017. The copyright holder for this preprint (which was not certified by peer review) is the author/funder. All rights reserved. No reuse allowed without permission.
Barakat et al. - hESC enhancer repertoire
category, the eight most enriched DBPs are shown ranked by their mean log-odds ratio. ChIP-
seq datasets produced from hESCs are indicated as dots and those from other cell sources as
crosses. Enrichments were calculated using LOLA (Sheffield and Bock, 2016).
G) LOLA enrichment plots as in panel F, but showing instead the relative over-presentation of
ENCODE chromatin segments from different cell lines in enhancers with different activity lev-
els. E, enhancer; PF, promoter-flanking region; R, repressed; T, transcribed; TSS, transcription
start site; WE, weak enhancer.
H) Heatmap showing the relative enrichment of H9 chromatin segments from the Roadmap
project (Kundaje et al., 2015) in regions with different levels of ChIP-STARR-seq activity from
naïve hESCs. Heatmap colors report log-odds ratios. Rows and columns have been arranged by
hierarchical clustering with complete linkage to put similar segment types together. TSS, tran-
scription start site; enh, enhancer; ZNF, zinc-finger protein.
Figure S7, related to Figure 4: Generation of H9 hESCs cell lines with OCT4 distal en-
hancer deletion
A) Genome browser view of the OCT4 (Pou5F1) locus and upstream region, with tracks from
ChIP-seq for NANOG (blue) and OCT4 (red) in primed and naive H9 hESCs. The OCT4 tran-
scriptional start site (TSS), proximal enhancer (PE), distal enhancer (DE) are indicated, as are
the locations of gRNA1 and 2 used for DE deletion and genotyping primers A and B. Black
bars represent sequences tested in luciferase assays.
B) PCR genotyping using primers A and B confirms a 650 bp heterozygous deletion of the
OCT4 DE in clones 2, 7 and 8.
C) The sequences of gRNAs and flanking sequences are shown at the top. The line is the Cas9
cut site. Below is the dideoxy sequence traces of the deleted alleles in OCT4 DE+/- clones 2, 7
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Barakat et al. - hESC enhancer repertoire
and 8, confirming expected sequence upon deletion. In the wt allele, the OCT4 DE sequence
was still present (data not shown).
D) Luciferase assay for proximal and distal OCT4 enhancer in primed (blue) and naive (red)
H9 hESCs. Luciferase activity is reported as the fold enrichment in luciferase counts over empty
vector, normalised to the Renilla transfection control. Error bars indicate standard deviations,
n=2.
Figure S8, related to Figure 6: Super-enhancers and ChIP-STARR-seq in naive hESCs
A) Super-enhancers in naive H9 hESCs (SEs) were called from H3K27ac ChIP-seq data on
enhancers stitched within 12.5kb windows using the ROSE software (Whyte et al., 2013).
B) Scatterplot contrasting SE intensity in naive H9 hESCs (H3K27ac signal divided by input)
with ChIP-STARR-seq activity. The Pearson correlation coefficient (r) is indicated and the red
line represents a generalised additive model fit to the data.
C) Kernel density plots showing the distribution of SE intensity values (H3K27ac signal di-
vided by input) in primed and naive H9 hESCs.
D) Scatterplot contrasting SE intensity values (H3K27ac signal divided by input) in primed and
naive H9 hESCs. Regions called as SEs in primed, naive, or both hESCs are indicated in blue,
red, or purple, respectively.
E) Violin plots showing the proportion of active plasmids (RPPM ≥ 96) for 3,408 super-en-
hancers (SE) compared to normal enhancers (NE).
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Barakat et al. - hESC enhancer repertoire
Overview of Supplemental Tables
Table S1 related to Figure 1: Data overview Table S2 related to Figures 2, 3 and 4: LOLA and GREAT enrichments Table S3 related to Figure 3: ESC and HK enhancer modules Table S4 related to Figure 5: transposable elements observed/expected ratios Table S5, related to Figures 1, 2, 4 and 6: Oligonucleotides used in this study
File S1 related to Figure 1, 2, 4 and 6: Genomic coordinates (BED files) of ChIP-seq peaks, ChIP-STARR-seq enhancer with activity level and of super-enhancers called in this study
62 bioRxiv preprint doi: https://doi.org/10.1101/146696; this version posted July 4, 2017. The copyright holder for this preprint (which was not certified by peer review) is the author/funder. All rights reserved. No reuse allowed without permission.
Barakat et al. - hESC enhancer repertoire
Supplemental References
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64
bioRxiv preprint doi: https://doi.org/10.1101/146696; this version posted July 4, 2017. The copyright holder for this preprint (which was not certified by peer review) is the author/funder. All rights reserved. No reuse allowed without permission. Figure 1. ChIP-STARR-seq in human embryonic stem cells.
A B NANOG H9 ChIP-STARR-seq library Gene TF/histone Source cell r = 0.65 r = 0.62 chromatin
Chromatin shearing and ChIP-seq ChIP-seq (bound DNA) Plasmid library
Cloning of ChIP fragments into plasmids 0 5 10 15 0 5 10 15 Isolated plasmid 1 Intron Cloned 0 5 10 15 0 5 10 15 Minimal binding site ChIP-seq ChIP-seq promoter EGFP pA
Library preparation r = 0.87 r = 0.83 DNA-seq (plasmids)
Transfect target cells 0 5 10 15 0 5 10 15 Isolated plasmid 1 Isolated plasmid 1 0 5 10 15 0 5 10 15 Plasmid library Isolated plasmid 2
C ChIP-seq Plasmid Active reporters produce GFP+ RNAs OCT4 NANOG Synthesis of cDNA from GFP+ cell extracts RNA-seq Intergenic (STARR RNA) (STARR Introns
Library application TSS Amplification and RNA-seq UTR H3K27ac H3K4me1 Exons D E F UntransfectedUntransfected NANOG library NANOG chr3:181,425,000-181,435,000 ChIP-seq 0.2 ) 2 12 Plasmid 0.3 0.3 8 Active Isolated plasmid SSC-A --> SSC-A NANOG 4.0
SSC-A STARR-seq RNA 4 Measured but inactive ChIP-seq 0.2
0.0% 16.3% RPM (log RNA 0 Plasmid 0.3 0 4 8 12 0.3 GFP --> GFP GFP --> Isolated plasmid Plasmid RPM (log2) OCT4 STARR-seq RNA 4.0 G ChIP-seq 0.2 KLF15 locus 1kb 0.3 ChIP-seq 0.2 Plasmid 0.3 RNA / plasmid 469 Isolated plasmid 4.0 H3K27ac STARR-seq RNA Weak ChIP-seq, Strong ChIP-seq at 0.1 strong STARR-seq signal 3' end, no STARR-seq ChIP-seq Plasmid 0.3 0.3 LEFTY1 locus 1kb Isolated plasmid ChIP-seq 0.2 4.0 H3K4me1 STARR-seq RNA 1.2k RNA / plasmid ChIP-seq 0.1 TMEM63A 0.2 Spread-out ChIP-seq peaks, Plasmid focal enhancer activity Isolated plasmid 0.2 Input 4.0 HOXB cluster STARR-seq RNA STARR-seq ~ plasmid 10kb 4.0 ChIP-seq 0.2 Combined RNA RNA / plasmid 2.1k Comb. RNA / plasmid 150
Strong and weak enhancers ChIP-seq peaks 1kb SOX2 STARR-seq without activity > plasmid bioRxiv preprint doi: https://doi.org/10.1101/146696; this version posted July 4, 2017. The copyright holder for this preprint (which was not certified by peer review) is the author/funder. All rights reserved. No reuse allowed without permission. Figure 2. Activity levels define functional classes of enhancers.
A B C Enhancer activity groups OCT4 NANOG *** High 7 *** 10.0 19% 11% ) (n = 21,200) 6% 2 6 *** ) 8%
2 θhigh = 220 5 Low 73% 83% *** 7.5 (n = 18,422) 4 θlow = 144 High 3 Low 5.0 2 Inactive H3K27ac H3K4me1 Inactive 1 RPPM (log (n = 256,412) 6% 8% 0 over empty (log 2.5 6% 7% Luciferase/Renilla -1 88% 85% Ranked genomic regions <144 > 1000 (n = 296,034) 144 - 220220 - 500500-1000 RPPM D E H9 chromatin segmentation 1.5 Expression of genes linked to ChIP-STARR enhancer Inactive ● ● ● ● ● ●
All n.s. Low *** ● ● ● Inactive *** High *** (log-odds ratio) Low ● ● -1.5 Relative abundance
High ● ● Enhancer Quiescent
0 5 10 TSS Active Transcription Gene expression: RPKM (log2) TSS Bivalent Genic enhancer Active TSS flank Active Heterochromatin Bivalent enhancer Biv. TSS/enh flank Biv. Transcription flank Transcription Weak transcription Weak ZNF genes / repeats Weak repr. polycomb repr. Weak Repressed polycomb F G Inactive Low High ChIP-STARR-seq activity near binding sites EZH2 ● P53 ● STAT5 25 CODEX ChIP-seq data: SMC3 ● SMAD3 ● SOX2 ●● CTCF NFE2 SMAD3 ● 20 SOX2 (5,638 peaks) CBX2 CHD1 ● NANOG ●● 15 SMAD3 (947 peaks) TAL1 SOX2 ● NCOR1 10 NANOG (19,861 peaks) SUZ12 ● RBPJ OCT4 ● EZH2, SMC3, CTCF TCF7L2 ZNF143 P300 ● 5 CODEX peaks GATA1 POLR2A ●● P53 ●● 0 0 1 2 3 4 0 1 2 3 4 0 1 2 3 4 plasmids (%) Active −1000 −500 0 500 1000 Observed/expected ratio (log2) Distance from peak center (bp) Cell type: ● ESC Other H
G CC T CA T A TT T C T TG T CA C G G A GT GA A A A T A CG A G A G G G G GA T AG T G CA CCACC 50 TT A T T High Low Inactive NANOG motif from HOCOMOCO 40 (NANOG_HUMAN.H10MO.A) 30 High 8,191 * *** *** 20 38.6% *** 10 Low 6,044 32.8%
Peaks with motif (%) 0 Inactive 74,848 TEF IRF7
TCF7 29.2% SOX2 SOX3 SOX8 OCT4 OCT6 PITX3 NFAC1 GATA2 GATA6 FOXP2 NANOG TCF7L2
HOCOMOCO motifs sorted by ratio in High / (Low + Inactive) bioRxiv preprint doi: https://doi.org/10.1101/146696; this version posted July 4, 2017. The copyright holder for this preprint (which was not certified by peer review) is the author/funder. All rights reserved. No reuse allowed without permission. Figure 3. Active enhancers include ESC-specific and housekeeping modules.
Putative AllESCHK A enhancers B ESC module NANOG OCT4 H3K27ac H3K4me1 (n = 7,596) 0.8 0.8 2.0 1.0
0.4 0.4 1.0 regions 0.5
ChIP-STARR HK module
(n = 13,604) Kerneldensity 0.0 0.0 0.0 0.0 Active enhancers 789 10 89 10 7 8 9 10 7 8 9 10 ChIP / input (log2)
C ChIP-STARR-seq D H9 RNA-seq E RNA-seq Atlas Genes near: ESC ESC+HK HK 10 ***
*** )
10 2 6
)
)
2 2
5 3
RPKM(log RPKM(log
RPPM (log RPPM 5 0
0 Colon Liver Lung Heart Kidney Ovary Spleen Testes
Adipose
All
All
HK
HK
ESC ESC Hypothalamus Skeletalmuscle
F ENCODE and ChEA Genes with decreased expression Genes with increased expression consensus TFs in response to perturbation in response to perturbation GATA2 (C) *** SOX2 KO *** OCT4 KD ***
NANOG (C) *** OCT4 KD *** HHLA1 KD *** ESC TCF3 (C) *** AKAP12 OE ** OCT4 KD ** SOX2 (C) *** OCT4 KD ** MYB oncogenic mutation ** TP63 (C) *** Nfe2l2 KO ** NANOG OE ** OCT4 (C) *** TP63 KD ** Htt knock−in mouse ** GATA1 (C) *** VGLL4 OE ** Rnh1 KO ** TRIM28 (C) *** OCT4 OE ** RAF1 active mutant ** RUNX1 (C) ** SOX2 KD ** Ncoa3 KD ** SALL4 (C) ** Mkl2 KO ** GRN deficiency ** *** SPI1 (C) 2030 *** FOXM1 KD 1015 *** ATM KD 10 15 20 *** MYC (E) *** Rp1 KO ** YY1 KD PML (E) Pdpn KO JAK2 KD Enrichedin: *** *** *** *** NELFE (E) *** DPP3 OE *** ERBB2 OE *** TAF7 (E) *** NRAS KD *** NRAS depletion *** CEBPD (E) *** SUZ12 depletion *** MAP4K5 KD *** SOX2 (C) *** NME2 induction *** DLX4 OE *** BRCA1 (E) *** BRCA1 depletion *** TP53 KD *** TAF1 (E) *** AMER1 OE *** IGF1 OE
*** ATF2 (E) *** ABL1 inhibition *** AMER1 OE HK 30 20 10 0 1520 510 5101520 Enrichr combined score Enrichr combined score Enrichr combined score
G H Enhancer-gene association I H9 chromatin segmentation
HK enhancer 1 ChIP-seq ESCHK OCT4 0.9 NANOG H3K27ac H3K4me1 0.6 0 RNA / plasmid
0.3
odds− Log ratio Kernel density Kernel −1 NCAPD2 GAPDHIFFO1NOP2 0.0 010 2 104 106
Distance to gene (bp) Enhancer
Capture Hi-C TSS Active
1kb TSS Bivalent
(CD34+ cells) Quiescent / low / Quiescent
|min(d ,d )| enhancer Genic Active TSSflank Active
1 2 Heterochromatin
Bivalentenhancer
Transcription flank Transcription
Weak transcription Weak
Strongtranscription
ZNF genes ZNF repeats /
Weak repr. polycomb repr. Weak
Repressedpolycomb Bivalent TSS/Enhflank Bivalent HK ESC Segments sorted by relative abundance bioRxiv preprint doi: https://doi.org/10.1101/146696; this version posted July 4, 2017. The copyright holder for this preprint (which was not certified by peer review) is the author/funder. All rights reserved. No reuse allowed without permission. Figure 4. Major changes in enhancer activity upon induction of naive pluripotency.
A B H9 primed ChIP-STARR-seq activity in naive H9 hESCs Inactive Low High NANOG ●● STAT5 NCOR1 CBX2 CHD1 ● SIRT6 CBX8 MED21 KDM5C ChIP-STARR-seq OCT4 ● POLR2A ●● CHD1 ● library creation TCF7L2 SAP30 ● HDAC2 ● and transfection SMARCA4 PHF8 ● +NHSM HDAC1 TAL1 HDAC1 POLR2A ● GATA1 NOTCH1 P300 ● ChIP-seq inprimed
hESCs andothercells -1 0 1 2 3 -1 0 1 2 3 -1 0 1 2 3 Observed/expected ratio (log2) H9 naive Cell type: ● ESC (primed) Other
Gene Ontology: ENCODE and ChEA C H→H (17%) D High High Biological Process Consensus TFs High High High High H→I Regulation of gastrulation ** ZBTB7A (E) *** (65%) Somatic stem cell maintenance ** TCF3 (C) *** Stem cell maintenance ** SOX2 (C) *** Low Low Regulation of cell fate specification ** TP63 (C) *** Tube formation ** OCT4 (C) *** Cell fate specification ** TP53 (C) *** Establishment/maintenance of cell bipolarity ** GATA2(C) *** Establishment/maintenance of apical/basal polarity ** TCF3 (E) *** Neg. regulation of intracellular signal transduction ** KLF4 (C) *** I→H Neg. regulation of stem cell differentiation ** FOSL2 (E) ** (9%) High Inactive High Inactive Inactive Inactive Embryonic morphogenesis *** TCF3 (C) *** Regionalization ** SOX2 (C) *** I→I (78%) Pattern specification process ** NANOG (C) ** Skeletal system development ** SUZ12 (C) ** Anterior/posterior pattern specification ** GATA2 (C) ** Regulation of organ morphogenesis ** KLF4 (C) ** Primed → naive Ureteric bud development ** NELFE (E) Kidney development ** OCT4 (C) Planar cell polarity involved in neural tube closure SALL4 (C) E Axis specification ** EZH2 (C) Inactive High Inactive High Heme transport ATF2 (E) *** 4 GCNT4 Iron coordination entity transport RASGRF2 TAF1 (E) *** CD44 Pos. regulation of extrinsic apoptotic signaling PML (E) *** Cofactor transport BRCA1 (E) *** NODAL Ovarian follicle development CREB1 (C) *** 2 IL6 Linoleic acid metabolic process MAX (E) *** DUSP5 SMAD3 TEAD4 Pos. regulation of mRNA metabolic process YY1 (E) ** MYC FZD10 Purine−containing compound salvage CHD1 (E) KLF4 WNT3 ** NNMT STAT3 WNT7A Fear response TAF7 (E) ** OCT4 IL6R Pos. reg. of extrinsic apoptotic signaling w/o ligand USF2 (E) DICER1 CDX2 KLF10 WNT7B ** 0 JARID2 MAX Inactive Inactive Inactive Inactive OTX2 ID3 TGFBR3 ZIC1 DLK1 Citrulline biosynthetic process ZKSCAN1 (E) FGFR2 HNF4A Citrulline metabolic process GATA1 (C) DLL1 ZIC2 ZNF521 Peptidyl−arginine modification GATA2 (C) −2 Organic hydroxy compound transport SMAD4 (C) BMP2 Xenobiotic catabolic process UBTF (E) Cellular response to hydrogen peroxide TP63 (C) DLX5 Regulation of vascular permeability IRF1 (E) HAND1 Glutathione derivative metabolic process FOS (E) TTR Glutathione derivative biosynthetic process EZH2 (C)
Expression: Naive−primed (RMA) −4 APOA2
Δ Regulation of cell fate commitment ESR1 (C) 0 5 10 15 20 0 5 10 15 20 25 −6 −4 −2 0 2 4 6 Enrichr combined score Enrichr combined score Δ Scaled RPPM: Naive − primed (log2)
F chr10:72,174,136-72,230,535 G chr6:31,125,776-31,144,789 H WT 0.2 0.2 OCT4 DE+/- Combined ChIP-seq 5kb Combined ChIP-seq 2kb 0.0 0.0 1.5 NANOG NANOG OCT4 OCT4 H3K27ac H3K27ac H3K4me1 H3K4me1 1.0 Primed (P) Primed (P) 0.2 0.2 Combined ChIP-seq Combined ChIP-seq 0.0 0.0 NANOG NANOG 0.5 OCT4 OCT4 H3K27ac H3K27ac H3K4me1 H3K4me1 normalized to TBP normalized to Naive (N) Naive (N) 12 12 Combined RNA (P) Enhancer A Combined RNA (P) Ratio naive /primed, 0.0 12 12 Combined RNA (N) Combined RNA (N) 0.0 0.0 Enhancer B PE DE HCG27 EIF4EBP2 CR627373 TCF19 PSORS1C3 NODAL OCT4 OCT4 ex1-ex2 OCT4 ex2-ex3 TCF19 ex1-ex2 TCF19 CCHCR1 ex1-ex3 PSORS1C1 ex3-ex5 bioRxiv preprint doi: https://doi.org/10.1101/146696; this version posted July 4, 2017. The copyright holder for this preprint (which was not certified by peer review) is the author/funder. All rights reserved. No reuse allowed without permission. Figure 5. Distinct transposable elements are associated with enhancers of differing activity in hESCs.
A C Depleted O/E ≤ 0.5 Enriched O/E ≥ 2 ESC-related TEs from literature ● 6 ● 6 6 ● ● HERVH−int Eulor8, MER97a, L1MEg2, X8_LINE ● 4 MamRep1894, ... 4 ● LTR7 ● LTR7B 2 2 4 ● ● LTR9B ● ● ● LTR5_Hs 0 0 ● DNA (n = 171) LINE (n = 131) ● ● ● L1PA2 ● ● ● 6 6 ● MER74A ● 2 ● ● MER20 MLT1K-int, LTR10B1, AluYa8 ● ● ● ● ● 4 4 ● ● ● ● LTR7Y HERVK14-int, ... ● ● ● ● ● ● ● Observed/expected ratio ● ● ● ● L1HS Observed/expected ratio 2 2 0 0 LTR (n = 443) 0 SINE (n = 44) <144 144−220 ≥220 ≥500 ≥1000 ChIP−STARR−seq activity (RPPM) B D Top-20 (all classes) 40 Top-10 TEs in RPPM≥220 enhancers ●
● Eulor8 5 ● HSATII 30 ● ● (CATTC)n ● HERVK14−int ● MER83C 0 20 ● ● ● (GAATG)n ●
Observed/expected ratio Observed/expected ● ● LTR10B1 LTR7 ● HSATII ● (GAGTG)n LTR47B LTR10A 10 ● LTR10B1 ● MLT1K−int ● ● ● ●
DNA Eulor8 ● Observed/expected ratio ● MER97a LTR MER61E LTR LTR MER83C LTR LTR MER34C LTR DNA MER97a ● ● ● ● ● ● − int MLT1F LTR ● − int MLT1K LTR ● LTR MER65 − int LTR ● − int HERVH LTR ● tRNA − Asn AAC 0 ● ● Satellite BSR/Beta − int HERVK14 LTR Satellite (CATTC)n Repeat (GAGTG)n Satellite (GAATG)n <144 144−220 ≥220 ≥500 ≥1000 ChIP−STARR−seq activity (RPPM) bioRxiv preprint doi: https://doi.org/10.1101/146696; this version posted July 4, 2017. The copyright holder for this preprint (which was not certified by peer review) is the author/funder. All rights reserved. No reuse allowed without permission. Figure 6. ChIP-STARR-seq dissects super-enhancers into functional elements.
A B C
) LETM2 FGFR1 C8ORF86 RNF5P1
2 16 H9 hESC 15 g ChIP-seq (P) lo Super-enhancers ( )
t 12 2 ChIP-seq (N)
u 10
p STARR-seq RNA (P) n I (log 8 STARR-seq RNA (N) −
5 c
a Super-enhancer chr8:38,253,977-38,470,901 7
4 RPPM 2 0 K G B H ChIP- STARR-seq regions
3 n = 3,521 r = 0.52 A C H 0 D Luciferase constructs E Ranked stitched enhancers 0 5 10 15 F (n = 77,118) H3K27ac − Input (log2)
0.4 ChIP-seq (P) 0 D E NANOG OCT4 200 H3K27ac a 100 H3K4me1 0.4 ChIP-seq (N) 180 0 NANOG ids (%) 75 OCT4 3 = 3.0% H3K27ac
se/Renill H3K4me1 50 SE 12 STARR-seq RNA (P) 2 0 12 over em pty STARR-seq RNA (N) 1 25 mean 0
Lucifera High Low 0 High
0 Active plasm High A B C D E F G H NE SE Enhancers (P) Luciferase construct Low Empty Enhancers (N)
A C E F Luciferase constructs B D F
H1 DNase
Active plasmids H1 NANOG H1 POLR2A
H1 chromatin segmentation TT TT TSS TSS TT TT
Human/mouse/rat conserved binding sites RFX1 MZF1 All plasmids in SEs FAC1 E4BP4 NKX61 MEIS1 OCT OCT1 BRN2 HEN1 FREAC2 TCF11MAFG FOXO1 NF1 STAT1 bioRxiv preprint doi: https://doi.org/10.1101/146696; this version posted July 4, 2017. The copyright holder for this preprint (which was not certified by peer review) is the author/funder. All rights reserved. No reuse allowed without permission. Figure S1. Related to Figure 1.
Total input IgG NANOG OCT4 H3K4me1 H3K27ac A H9 hESC C 32 8 16 4 8 4 2 2 enrichment 1 ChIP-qPCR fold 1 0.5 0.5
XIST
SCGB3A2SMARCA2 SCGB3A2SMARCA2 CD9 enh. flank OCT4OCT4 enh. flank enh. center FGFR1FGFR1 enh. flank enh. center
B H9 hESC H9 hESC D NANOG OCT4 H9 Barakat H9 Barakat H9 Kunarso HUES64 2386 13305 Gifford 6028 0 278 5139 848 529 H1 Lister 3
OCT4 1341 1014 Kunarso H9 2464 14126
NANOG 4935 H1 Lister4639 181 124 657 200 1766 47309 279 237 750 957 3547 672 358 55 528 7580 281 524 435 1141 408 4848 1061 HUES64 rep2 2364 1214 2102 Gifford 1798 DAPI DAPI 494 2593 1816 2940
HUES64 rep1 Gifford H3K4me1 H3K27ac H9 Barakat H1 Ji
30234 6416 1742
2708 3751 1017 H9 Barakat H9 170 Gafni LIS2 3406 725 581 1701 C1 Gafni12222 2534 82 C1 Gafni 1248 167 1177 304 215469 1959 54 1359 509 359 2235 2368 1197 165 515 3245 39 636 1566 167 290 5849 81 624 477 272 410 341 91 1191 855 56 WIBR3_Gafni 1768 WIS2 Gafni 185 85 1941 931 538 1552 2492 2077 2020 4769 16790 2724 1921 5865 3441
WIBR3 Gafni LIS2 Gafni bioRxiv preprint doi: https://doi.org/10.1101/146696; this version posted July 4, 2017. The copyright holder for this preprint (which was not certified by peer review) is the author/funder. All rights reserved. No reuse allowed without permission. Figure S2. Related to Figure 1.
A H3 H3 B OCT4 NANOG K27ac K4me1 Input NANOG ChIP 1 1 NANOG iso. plasmid 1 0.8 ChIP-seq 1 1 1 1 1 1 1 1 1 1 NANOG plasmid 1 NANOG iso. plasmid 2 OCT4 ChIP 1 0.6 Plasmid 1 1 1 1 1 1 1 1 0 1 OCT4 iso. plasmid 1 OCT4 plasmid 1 0.4 Isolated plasmid 0 2 0 2 0 2 0 2 0 2 OCT4 iso. plasmid 2 H3K4me1 ChIP 1 0.2 H3K4me1 iso. plasmid 2 RNA: primed 2 2 2 2 0 2 0 2 0 2 H3K4me1 plasmid 1 0 H3K4me1 iso. plasmid 1 Pearson correlation RNA: naive 2 2 2 2 2 0 2 0 0 2 Input ChIP 1 −0.2 Input iso. plasmid 1 Input plasmid 1 Input iso. plasmid 2 H3K27ac ChIP 1 naive naive naive naive naive H3K27ac plasmid 1 primed primed primed primed primed H3K27ac iso. plasmid 1 Library source (H9 hESCs) H3K27ac iso. plasmid 2 C D ChIP-seq vs. plasmid libraries Plasmid vs. RNA RNA 1 RNA 2 RNA OCT4 Plasmid Iso. plasmid 1 Iso. plasmid 1 Iso. plasmid 1
ChIP ChIP Plasmid Isolated plasmid 2 Plasmid Plasmid RNA 1 RNA RNA 2 RNA Plasmid H3K27ac Iso. plasmid 1 Iso. plasmid 1 Iso. plasmid 1
ChIP ChIP Plasmid Isolated plasmid 2 Plasmid Plasmid RNA 1 RNA 2 RNA Plasmid H3K4me1 Iso. plasmid 1 Iso. plasmid 1 Iso. plasmid 1
ChIP ChIP Plasmid Isolated plasmid 2 Plasmid Plasmid Input RNA 1 RNA 2 RNA Plasmid Iso. plasmid 1 Iso. plasmid 1 Iso. plasmid 1
ChIP ChIP Plasmid Isolated plasmid 2 Plasmid Plasmid E ChIP-STARR-seq RNA replicates 1.0 1.0 1.0 0.9
0.6 0.6 0.6 0.7
NANOG OCT4 0.3 H3K27ac H3K4me1 0.2 0.2 0.4 20 4 1086 20 4 1086 20 4 1086 20 4 1086 Pearson correlation (r) Minimum read count in replicates bioRxiv preprint doi: https://doi.org/10.1101/146696; this version posted July 4, 2017. The copyright holder for this preprint (which was not certified by peer review) is the author/funder. All rights reserved. No reuse allowed without permission. Figure S3. Related to Figure 2.
A Gifford Ji Takashima B CODEX database *** *** *** 20 STAT5 *** **n .s . ***
) ** *** ● ● 2 ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● 10 ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● n = 128 ● ● ● ● ● ● ● 10 ● 10 ● ● 10 ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● 0 ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● 20 SOX2 n = 5,638 10 5 5 5 0
Gene expression: RPKM (log 20 SMAD3 n = 947 0 0 0 10 All All All
< 144 < 144 < 144 0 >= 220 >= 220 >= 220 >= 1000 >= 1000 >= 1000 144 − 220 144 − 220 144 − 220 Genes linked to ChIP-STARR-seq peak region 20 NANOG n = 19,861 C ENCODE ChIP-seq peaks 10 Inactive Low High BRF1 ZNF274 NANOG ● POL3 GRP20 OCT4 ● 0 SUZ12 ● NANOG ● ZNF274 EZH2 ● BRCA1 ● BCL11A ● NCOR1 BRF2 CHD1 ●● RXRA ● 20 GATA3 GTF2F1 ● HDAC2 ● n = 624 CTCF ●● ATF3 ● TCF12 ● ZNF274 POL2 ● P300 ● 10 −1 0 1 −1 0 1 2 3 4 −1 0 1 2 3
Observed/expected ratio (log2) Proportion of active plasmids (%) 0 Cell type: ● ESC Other 20 OCT4 D ENCODE chromatin segmentation n = 10,380 Low High Inactive 10 H1 R ● Helas3 TSS H1 E ● K562 R H1 TSS ● H1 TSS ● Helas3 R Huvec TSS H1 PF ● 0 Hepg2 R K562 TSS H1 WE ● Gm12878 R Hepg2 TSS Helas3 TSS Huvec R H1 E ● Huvec TSS 20 P300 H1 T ● Gm12878 TSS K562 TSS n = 41,821 Huvec T K562 PF Hepg2 TSS 0 1 2 3 0 1 2 3 0 1 2 3 10 Observed/expected ratio (log2) E Cell type: ● ESC Other 0 H1 R (repressed chromatin) H1 E (enhancer chromatin) 2.8 20 P53 1.95 n = 5,946 2.6 1.90 10 2.4 1.85 2.2 1.80 0 n = 543,759 n = 52,106 2.0 −1000 −500 0 500 1000 Active plasmids (%) Active −1000 −500 0 500 1000 −1000 −500 0 500 1000 Distance from peak center (bp) Distance from peak center (bp)
F High Low Inactive 80 60
60 40 40
20 20
Peaks with motif (%) Peaks 0 0 SP2 SRF KLF6 RFX5 OTX2 ETV1 RFX2 AP2B NRF1 EGR1 IKZF1 REST HEN1 PITX1 PITX2 EGR1 MBD2 KLF13 ZN639 PLAL1 PO6F1 HXC10 ZBT7A MEF2B MEF2A THAP1 MEF2C MEF2D GABPA HMGA1 HOCOMOCO motifs sorted by HOCOMOCO motifs sorted by ratio in Low / (High + Inactive) ratio in Inactive / (Low + High) bioRxiv preprint doi: https://doi.org/10.1101/146696; this version posted July 4, 2017. The copyright holder for this preprint (which was not certified by peer review) is the author/funder. All rights reserved. No reuse allowed without permission. Figure S4. Related to Figure 3.
A B C Putative enhancers Genes in proximity of Luciferase: published enhancers nunion = 76,666 enhancer modules with low ChIP-STARR-seq activity
60 Xie ESC HK 50 Xi (hESC) 40 Hawkins 3 901 3,312 2
over empty 1
Luciferase/Renilla 0 1,006 Rada-Iglesias
Empty vector
OCT4 proximal enhancer
chr2:11,529,990-11,530,991chr3:68,715,529-68,716,078 chr22:31,737,166-31,738,167 chr6:144,284,360-144,284,861chr2:171,620,395-171,621,396chr2:202,737,766-202,738,767chr2:202,737,913-202,738,914chr2:175,833,616-175,834,617 D GTEx RNA-seq Genes near: ESC ESC+HK HK
15 ● ● ●
● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● 10 ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● 5 RPKM (log2)
0 Liver Lung Testis Ovary Uterus Vagina Spleen Thyroid Bladder Pituitary Prostate Stomach Pancreas Whole Blood Nerve − Tibial Artery − Tibial Artery − Aorta Adrenal Gland Brain − Cortex Brain Fallopian Tube Fallopian Kidney − Cortex Kidney Colon − Sigmoid Muscle − Skeletal Brain − Amygdala Brain Artery − Coronary Brain − Cerebellum Brain Colon − Transverse Cervix − Ectocervix Cervix − Endocervix Minor Salivary Gland Heart − Left Ventricle Brain − Hippocampus Brain Esophagus − Mucosa Brain − Hypothalamus Brain Brain − Substantia nigra Brain Esophagus − Muscularis Adipose − Subcutaneous Heart − Atrial Appendage Breast − Mammary Tissue Brain − Frontal Cortex (BA9) Brain Visceral (Omentum) Adipose − Visceral Transformed fibroblasts Cells − Transformed Brain − Cerebellar Hemisphere Brain Brain − Caudate (basal ganglia) Brain Terminal Ileum Small Intestine − Terminal Sun Exposed (Lower leg) Skin − Sun Exposed (Lower Brain − Putamen (basal ganglia) Brain Brain − Spinal cord (cervicalBrain − 1) c Skin − Not Sun Exposed (Suprapubic) transformed lymphocytes Cells − EBV transformed Brain − AnteriorBrain cingulate cortex (BA24) Esophagus − Gastroesophageal Junction Brain − Nucleus accumbens (basal ganglia) Brain bioRxiv preprint doi: https://doi.org/10.1101/146696; this version posted July 4, 2017. The copyright holder for this preprint (which was not certified by peer review) is the author/funder. All rights reserved. No reuse allowed without permission. Figure S5. Related to Figure 4.
A H9 hESC naive B H9 hESC naive H9 hESC naive C OCT4 H9 naive H9 primed NANOG NANOG
Laminin B DAPI DAPI
D H9 primed H9 naive H 4 1500 OCT4 (H9 primed)
0.008 3 1450 0.006 TBP ression 1400 ability 0.004 2 30
Prob 0.002
alized to 20 1 0.000 -250 0 250 10 Position of Best Site in Sequence norm normalized to TBP normalized to Relative exp Relative expression Pou5f1::Sox2 MA0142.1 p=2.4e-310 0 0 Pou3f3_3235.2 UP00211_1 p=4.0e-142 POU2F2_DBD_1 p=6.5e-109 10 IL6R XIST KLF4 OCT4STAT3 REX1 SOX2 TBX3 OCT4 (H9 naive) TEAD4 PRKCI 0.008 NANOG DNMT3B DUSP
0.006 Total input IgG NANOG OCT4 H3K4me1 H3K27ac
E ability 0.004 128 16
64 Prob 0.002 8 32 0.000 -250 0 250 16 4 Position of Best Site in Sequence Pou5f1::Sox2 MA0142.1 p=4.2e-112 8 Pou3f3_3235.2 UP00211_1 p=7.1e-57 4 2 POU2F2 MA0507.1 p=1.2e-44 enrichment 2 ChIP-qPCR fold 1 1 I NANOG (H9 primed) 0.5 0.5 0.005
0.004 XIST 0.003 SMARCA SCGB3A2SMARCA SCGB3A2 ability 0.002 CD9 enh. flank OCT4OCT4 enh. flank enh. center Prob 0.001 FGFR1FGFR1 enh. flank enh. center 0.000 H3K4me1 (hESC naive) G H3K27ac (hESC naive) -250 0 250 F C1 Gafni H9 Barakat Position of Best Site in Sequence Pou5f1::Sox2 MA0142.1 p=3.7e-735 ZIC1_full p=2.0e-436 11952 17052 ZIC4_DBD p=7.1e-417 5941 6090 3541 540 NANOG (H9 naive) 528 C1 Gafni 153
BGO116205 Gafni 9046 398 17609 401 240 0.005 H9 Barakat H9 129 1458 2184 130 979 Gafni LIS2 179 461 1087 3009 161 0.004 1395 265 395 415 383 0.003 193 8 1368 891
41 ability 5 725 568 8104 0.002 10 1109 626 13 326 193 160 Prob 8 18 400 0.001 438 3 440 419 WIBR3 Gafni 89 WIS2 Gafni 391 1440 0.000 117 2954 -250 0 250 12319 134 761 Position of Best Site in Sequence 5727 574 Pou5f1::Sox2 MA0142.1 p=3.2e-979 ZIC1_full p=6.0e-696 LIS2 Gafni WIBR3 Gafni Zic3_secondary UP00006_2 p=3.6e-612 bioRxiv preprint doi: https://doi.org/10.1101/146696; this version posted July 4, 2017. The copyright holder for this preprint (which was not certified by peer review) is the author/funder. All rights reserved. No reuse allowed without permission. Figure S6. Related to Figure 4.
A D Enhancer activity groups ChIP-seq vs. plasmid libraries 8 High (n = 31,303) ) ) ) )
2 2 2 2 θhigh = 96 NANOG OCT4 H3K27ac H3K4me1 ) r = 0.83 r = 0.57 r = 0.92 r = 0.94 2 Low 6 (n = 40,732) θlow = 62
Inactive Plasmid (log Plasmid (log Plasmid (log Plasmid (log 4
RPPM (log (n = 265,143)
ChIP (log2) ChIP (log2) ChIP (log2) ChIP (log2) 2 B Ranked enhancers Plasmid vs. RNA (n = 337,178)
) NANOG ) OCT4 ) H3K27ac ) H3K4me1 2 r = 0.65 2 r = 0.44 2 r = 0.63 2 r = 0.77 OCT4 NANOG E 7% 4% 9% 7%
RNA 2 (log RNA 2 (log RNA 2 (log RNA 2 (log RNA 84% 89% High Plasmid (log ) Plasmid (log ) Plasmid (log ) Plasmid (log ) 2 2 2 2 Low H3K27ac H3K4me1 ) NANOG ) OCT4 ) H3K27ac ) H3K4me1 Inactive 2 r = 0.70 2 r = 0.44 2 r = 0.76 2 r = 0.84 9% 14% 12% 17% 79% 69% RNA 1 (log RNA 1 (log RNA 1 (log RNA 1 (log RNA
Plasmid (log ) Plasmid (log ) Plasmid (log ) Plasmid (log ) 2 2 2 2 F C ENCODE ChIP-seq peaks Inactive Low High Libraries from naive hESCs transfected into naive hESCs: ZNF274 GRP20 NELFE GATA3 NELFE BRCA1 ● IKZF1 ● ) NANOG ) OCT4 ) H3K27ac ) H3K4me1 GTF2B GTF2F1 ● 2 r = 0.50 2 r = 0.94 2 r = 0.49 2 r = 0.69 BRF2 TAF7 ● ZBTB33 ZZZ3 TR4 FOSL1 ● FOXA1 GTF2F1 ● MYC ● JUND BRCA1 ● ATF3 ● EZH2 IRF3 ZNF274 RNA 2 (log RNA RNA 2 (log RNA 2 (log RNA 2 (log RNA −1 0 1 2 3 4 −1 0 1 2 −1 0 1 2 Observed/expected ratio (log2) Cell type: ● ESC (primed) Other RNA 1 (log2) RNA 1 (log2) RNA 1 (log2) RNA 1 (log2) Libraries from primed hESCs transfected into naive hESCs: G
) NANOG ) OCT4 ) Input
2 2 2 ENCODE chromatin segmentation r = 0.53 r = 0.27 r = 0.92 Inactive Low High H1 R ● Helas3 TSS Helas3 TSS K562 R Huvec TSS Huvec TSS Helas3 R H1 TSS ● H1 TSS ● RNA 2 (log RNA 2 (log RNA 2 (log RNA Hepg2 R K562 TSS K562 TSS Huvec R Hepg2 TSS Hepg2 TSS Gm12878 R Gm12878 TSS Gm12878 TSS RNA 1 (log2) RNA 1 (log2) RNA 1 (log2) Huvec T Huvec PF Huvec PF Helas3 T Helas3 PF H1 E ● Libraries from naive hESCs transfected into primed hESCs: −1 0 1 −1 0 1 −1 0 1 Observed/expected ratio (log2)
) NANOG ) OCT4 2 r = 0.23 2 r = 0.08 Cell type: ● ESC (primed) Other H RNA 2 (log RNA 2 (log RNA H9 chromatin segmentation 1.2 RNA 1 (log2) RNA 1 (log2) Inactive Low High
(log-odds ratio) -1.2 Relative abundance Enhancer Quiescent Active TSS Active Transcription Bivalent TSS Bivalent Genic enhancer Active TSS flank Active Heterochromatin Bivalent enhancer Biv. TSS/enh flank Biv. flank Transcription Weak transcription Weak ZNF genes /repeats Weak repr. polycomb repr. Weak Repressed polycomb bioRxiv preprint doi: https://doi.org/10.1101/146696; this version posted July 4, 2017. The copyright holder for this preprint (which was not certified by peer review) is the author/funder. All rights reserved. No reuse allowed without permission. Figure S7. Related to Figure 4.
A chr6:31,135,000 chr6:31,114,000 PSORS1C3 TCF19 OCT4 OCT4 OCT4 ChIP-seq NANOG (H9 hESC primed)
ChIP-seq NANOG (H9 hESC naive)
ChIP-seq OCT4 (H9 hESC primed)
ChIP-seq OCT4 (H9 hESC naive)
TSS PE DE
AB
gRNA 1 gRNA 2 B C OCT4 DE +/- WT 2 7 8 gRNA 1 CTG C G TTCGTGTGCCCATCTCC CG G G CCC A C CC Flanking sequence TTATGTTGCCTCTG A G GCC C G TTCGTGTGCCCATCTCC C G G GCCC A C CC AxB AxB gRNA 2 TTC TT C CCGAGAGGGACGCAGACCC A A AC C A AC Flanking sequence CCCATTCCCC A TT C CCGAGAGGGACGCAGACCC A A AC C A AC
D 60 Clone 2
40
20 Clone 7 Luciferase/Renilla 0
Clone 8 OCT4 PE OCT4 PE OCT4 DE OCT4 DE Empty vector Empty vector Primed Naive bioRxiv preprint doi: https://doi.org/10.1101/146696; this version posted July 4, 2017. The copyright holder for this preprint (which was not certified by peer review) is the author/funder. All rights reserved. No reuse allowed without permission. Figure S8. Related to Figure 6.
A B C 16 ) H9 hESC naive Primed (P) Naive (N) 2 Super-enhancers
12 ) 12 30 2
8 8 20 4 4 RPPM (log 10 0 Density (%)
H3K27ac − Input (log n = 3,408 r = 0.57 0 0 Ranked stitched enhancers 0 5 10 15 0 5 10 15 (n = 77,118) H3K27ac − Input (log2) H3K27ac − Input (log2)
D E )
2 P (n = 924) N (n = 811) P+N (n = 2,597) 100 15 75 10 = 1.9% 50 SE 5
25 mean 0 0 Active plasmids (%) 0 5 10 15 NE SE
Input: Naive (log H3K27ac − Input: Naive H3K27ac − Input: Primed (log2)