Genome-wide epigenomic maps have revealed millions of putative enhancers and promoters, but experimental validation of their function and high-resolution dissection of their driver nucleotides remain limited. Here, we present HiDRA (High-resolution Dissection of Regulatory Activity), a combined experimental and computational method for high-resolution genome-wide testing and dissection of putative regulatory regions. We test ~7 million accessible DNA fragments in a single experiment, by coupling accessible chromatin extraction with self-transcribing episomal reporters (ATAC-STARR-seq). By design, fragments are highly overlapping in densely-sampled accessible regions, enabling us to pinpoint driver regulatory nucleotides by exploiting differences in activity between partially-overlapping fragments using a machine learning model (SHARPR-RE). In GM12878 lymphoblastoid cells, we find ~65,000 regions showing enhancer function, and pinpoint ~13,000 high-resolution driver elements. These are enriched for regulatory motifs, evolutionarily-conserved nucleotides, and disease-associated genetic variants from genome-wide association studies. Overall, HiDRA provides a high-throughput, high-resolution approach for dissecting regulatory regions and driver nucleotides. Millions of enhancers are predicted, but their validation remains challenging. Here, the authors report genome-wide enhancer function quantification and high-resolution dissection for millions of accessible DNA fragments, revealing driver nucleotides and helping interpret non-coding disease variants. Unlike protein-coding genes, which can be readily identified by their sequence properties and evolutionary signatures, gene-regulatory elements lack highly predictive sequence patterns and show only modest evolutionary conservation at the nucleotide level4. Thus, systematic recognition of gene-regulatory elements has relied on mapping of their epigenomic signatures, including DNA accessibility, histone modifications, and DNA methylation7. For example, both enhancers and promoters have high DNA accessibility and low H3K27me3, but distal enhancers show relatively higher H3K27ac and H3K4me1 while promoters show relatively higher H3K9ac and H3K4me39. However, many regions showing such epigenomic marks do not experimentally drive reporter gene expression, and some regions driving gene expression lack endogenous signatures12. Moreover, epigenomic signatures are often low-resolution, with important driver regulatory nucleotides comprising only a small subset of the larger regions showing epigenomic signatures13.Precise spatiotemporal control of gene expression is achieved by the interplay between non-coding regulatory elements, including distal enhancers and proximal promoters, and the transcriptional regulators they help recruit or repel, thus modulating the expression of nearby genes14, CRE-seq15) use oligonucleotide synthesis technology to generate elements and coupled barcodes. Genome-fragmentation approaches  use DNA fragments collected or captured from genomic DNA. For synthesis-based approaches, technical limitations of oligonucleotide synthesis technology restrict the tested DNA fragment lengths to 130–230 nucleotides (nt), and the number of tested constructs to 100,000–200,000 sequences per array. For genome-fragmentation approaches, random fragmentation of the entire genome results in only shallow coverage of regulatory elements, while synthesis-based capture is limited in the number of regions interrogated due to its reliance on oligonucleotide synthesis, and ChIP-based capture is limited in only one or few transcription factors at a time. To recognize driver nucleotides within tested regions, synthesis-based approaches have used systematic mutagenesis20 or tiling at regularly-spaced intervals13, but both require synthesis of many constructs for fine-mapping each region, thus reducing the number of regions that can be dissected at high resolution.Experimental dissection of enhancer and promoter regions has been traditionally expensive, laborious, low-throughput, and low-resolution, lacking the resolution to pinpoint individual regulatory driver nucleotides without recourse to extensive mutagenesis. Several recent high-throughput reporter assays for enhancer function enable testing of thousands of distinct DNA sequences simultaneously, by cloning variable DNA fragments into common reporter constructs, and using high-throughput sequencing to quantify fragment activity. Synthesis-based approaches , by insertion in the 3′ untranslated region (3′ UTR) of reporter genes, thus enabling them to drive their own transcription and serve as their own barcodes, providing a quantitative readout of their activity. We then exploit the dense sampling of accessible regions and the partially-overlapping nature of tested fragments for high-resolution inferences using a machine learning approach (SHARPR-RE). Our approach overcomes the construct-length and region-count limitations of synthesis-based technologies at substantially lower cost, and our ATAC-based selection of open chromatin regions concentrates the signal on likely regulatory regions and enables high-resolution inferences. Altogether, in a single experiment we test millions of enhancer constructs of comparable length to low-throughput studies while achieving the high-resolution dissection of systematic perturbation studies. We apply HiDRA to infer genome-wide regulatory activity across ~7 million DNA fragments preferentially selected from accessible chromatin in the GM12878 lymphoblastoid cell line, resulting in ~65,000 discrete genomic regions showing significant regulatory function. These are enriched for endogenous active histone marks , regulatory sequence motifs, and regions bound by immune regulators. Our selection approach resulted in highly-overlapping fragments , enabling us to pinpoint “driver” regulatory nucleotides that are critical for transcriptional enhancer activity. We discover ~13,000 of these high-resolution driver elements, which are enriched for regulatory motifs and evolutionarily conserved nucleotides, and help predict causal genetic variants underlying disease from genome-wide association studies. Overall, HiDRA provides a general, scalable, and high-throughput approach for the high-resolution experimental dissection of regulatory regions and driver nucleotides in the context of human biology and disease.Here, we present High-resolution Dissection of Regulatory Activity (HiDRA), a method for high-resolution inference of transcriptional regulatory activity across all accessible regions of the genome. HiDRA overcomes some limitations of previous technologies and combine their advantages, enabling high-throughput and high-resolution inference of regulatory activity. Briefly, we first extract accessible DNA regions using ATAC-seq21. Fragments with transcriptional regulatory activity promote self-transcription such that active segments of DNA can be identified and quantified by high–throughput RNA sequencing to produce a quantitative readout of enhancer activity : fragments are enriched from open chromatin and regulatory regions using ATAC-seq (Assay for TransposaseAccessible Chromatin with high-throughput sequencing) and subsequently cloned into the 3′ UTR of a reporter gene on the self-transcribing enhancer reporter vector used in Self-Transcribing Active Regulatory Region sequencing (STARR-seq)ity Fig. . Library14) with more than 10 unique fragments . More than 99% of fragments had lengths between 169 nt and 477 nt (median: 337 nt), with the fragment length distribution showing two peaks spaced by ~147 nt, corresponding to the length of DNA wrapped around each nucleosome  geneays Fig. . To asseach Fig. . A visuaach Fig. .We surveyed the 95,481 active HiDRA fragments identified in GM12878 to assess shared common genomic or epigenomic characteristics. In comparison with the set of all HiDRA fragments tested, active fragments were eight times more likely to overlap regions annotated as active promoter chromatin states by ChromHMM based on the presence of H3K4me3 and H3K27ac, and five times more likely to overlap annotated Active Enhancer chromatin states, marked by H3K4me1 and H3K27ac Fig. . By cont7. However, 64% of its occurrences are > 2 kb from the nearest transcription start site, suggesting that a portion of genomic regions annotated as “TSS Flanking Upstream” may function biologically as distal enhancers . This chromatin state is defined by the presence of both promoter and enhancer histone marks H3K4me1, H3K4me3, and H3K27ac, and was named “TSS Flanking” due its depletion at exactly the TSS position, but its enrichment 400 nt-1kb upstream of annotated transcription start sitesp = 9.3 × 10–102, Mann–Whitney U test, Supplementary Fig. When we computed enrichment of chromatin states as a function of HiDRA activity strength, we found a linear quantitative relationship for HiDRA activity levels up to ~2.5-fold RNA/DNA ratios, with increasing activity showing increasing chromatin state enrichment for both promoter and enhancer chromatin states Fig. . SurprisSurprisingly, fragments from heterochromatin-associated chromatin states showed a modest enrichment in active elements, indicating that DNA kept in an endogenous heterochromatic state may contain regulatory signals that become active once taken outside their repressive endogenous chromosomal context. The ZNF/repeats-associated chromatin state (marked by H3K36me3 and H3K9me3) showed a modest enrichment for lower HiDRA activity levels, but continued to increase linearly even at the highest activity levels, possibly due to active repetitive elements, as we discuss below. In contrast, quiescent and polycomb-repressed chromatin states showed a two-fold relative depletion for HiDRA active elements, accounting for the enrichments found in other states. The depletion of polycomb-repressed chromatin states may also reflect polycomb repression on the episomal plasmid.7. Active promoter- and active-enhancer-associated acetylation marks H3K9ac and H3K27ac, histone turnover-associated H2A.Z, promoter- and enhancer-associated H3K4me3 and H3K4me1, and DNase I accessible chromatin were the most enriched individual marks within active HiDRA regions, while polycomb-repression-associated H3K27me3, heterochromatin-associated H3K9me3, and transcription-associated H3K36me3 were the least enriched compared with the input library  showed endogenous epigenomic signatures characteristic of repressed and inactive chromatin states, including quiescent, repressed polycomb, weak repressed polycomb, and heterochromatin.Even though HiDRA active regions were most enriched for enhancer and promoter states, they were not exclusive to them. In fact, consistent with recent studies12. While these marks were not profiled in GM12878, inactive chromatin states that showed HiDRA activity were 8-fold to 13-fold more likely to be bound by transcription factors in ChIP-seq experiments in GM12878 than inactive chromatin states that lacked HiDRA activity  retrotransposons, which have been previously shown to have regulatory activity potential and were enriched in the set of all active HiDRA regions unlike other repetitive elements in the genome , a graphical probabilistic model that inferred high-resolution activity from MPRA tiling experiments by reasoning about the differential activity of partially-overlapping microarray spots. Intuitively, SHARPR allowed us to transform measurements from the 145-bp resolution of individually tested tiles to the 5-bp resolution of the offset between consecutive tiles. The SHARPR algorithm relies on synthesized oligos that uniformly tile regions at regularly spaced intervals, and thus is not applicable for the random fragmentation nature of HiDRA experiments where both the length and the spacing of neighboring fragments can vary. To address this challenge, we developed a new algorithm, SHARPR-RE (for SHARPR with Random Endpoints), which estimates regulatory scores underlying any set of randomly positioned and variable-length segments, by appropriately scaling the segments by their varying lengths, and enabling inferences at variable-length offsets between them . Driver elements are also more evolutionarily conserved than equally sized segments residing directly upstream or downstream . The enriched motifs consisted of regulators known to be active in GM12878, including several critical B-cell and immune transcription factor including NF-kB and the IRF family Fig. . A total13). As the original SHARPR algorithm does not include the functionality to call discrete driver elements, we compared Sharpr-MPRA activity scores within driver elements identified in this study compared with equally sized segments shifted upstream and downstream. We found that HiDRA driver elements are much more likely to show Sharpr-MPRA activity than these shifted segments. Sharpr-MPRA activity scores peaked for HiDRA driver elements, and were lower in flanking regions , and also compared with all tested variants inside HiDRA-tiled regions but outside driver elements   across the genomest) Fig. , supportTaken together, these results indicate that our high-resolution inferences are biologically meaningful and can help pinpoint driver nucleotides among larger regions.29. We studied the overlap between genetic variants associated with immune disorders and our high-resolution predicted driver nucleotides. Even though driver nucleotides only cover 0.032% of the genome, we found 12 cases where they overlap fine-mapped SNPs associated with 21 immune-related traits30 predicted to be causal  than the typical sequencing reads we used for quantification , and thus 78% of genetic variants will not be covered by our sequencing reads . To overcome this limitation and to determine allele-specific activity scores for all our fragments, we used low-depth re-sequencing of our input library using long reads, thus revealing the genotype associated with each start/end position in our library Fig. . We augmp-value cutoff of 0.05, we found 880 “allelic” HiDRA SNPs where paternal and maternal alleles showed differences in activity, 25 of which had a corrected FDR < 0.1 , shows differential HiDRA activity between paternal and maternal alleles. The risk allele shows increased regulatory activity upstream of a HiDRA-annotated active region . By concentrating the signal on open-chromatin regions, HiDRA enables high-resolution inference of driver nucleotides within these regions using a machine-learning model (SHARPR-RE), by exploiting subtle differences in the reporter activity driven by partially overlapping tested fragments. We refer to the combined SHARPR-RE-ATAC-STARR-seq method as the “High-resolution Dissection of Regulatory Activity”, or HiDRA. By capturing putative regulatory regions directly from open chromatin regions, HiDRA has the advantage of foregoing oligonucleotide synthesis, and thus enabling testing of much longer fragments, and testing many more regions in a single experiment.We applied HiDRA on the GM12878 lymphoblastoid cell line, revealing a global map of regulatory elements and their sequence-driven effects on transcription. We showed that HiDRA provides a quantitative assay with strong sensitivity and specificity, compared to low-throughput luciferase assays. We showed that fragments with the strongest activity show endogenous promoter and enhancer signatures, contain motifs for immune transcription factors, and show in vivo binding by immune regulators. We also showed that driver nucleotides inferred by our high-resolution mapping are enriched for evolutionarily-conserved regions, known regulatory motifs for immune regulators, and for genetic variants associated with immune traits. We also showed that long-read resequencing of the HiDRA library can distinguish allele-specific activity of risk vs. non-risk fragments derived from heterozygous loci associated with disease, enabling directionality-of-effect inference and providing mechanistic insights on disease loci.13. Both Sharpr-MPRA and HiDRA seek to map regulatory regions at high resolution, but they differ in several key respects: Sharpr-MPRA selects regions based on prior computational predictions, requiring microarray-based synthesis. This in vitro synthesis step limits the number of nucleotides and regions tested by Sharpr-MPRA . Moreover, Sharpr-MPRA uses fixed 5-bp increments , 145-bp fragments , and Sharpr-MPRA tests elements upstream of the TSS using 3′ UTR barcodes .Currently, the only other method that enables the high-resolution dissection of thousands of putative regulatory regions is Sharpr-MPRA9 cells per tissue, and high depth of sequencing to quantify a library with ~20-fold more fragments).The ATAC-STARR-seq step in HiDRA has many benefits compared with regular STARR-seq without fragment selection. ATAC-STARR-seq preferentially tests the activity of fragments derived from open chromatin, and the HiDRA library we developed achieves 130–220X more highly covered regulatory elements than random genome fragmentation Fig. . This fa35. Moreover, integrating reporter techniques can require cell sorting using fluorescent reporter activity, which limits the number of constructs that can be tested36.The HiDRA approach is general and can be readily adapted for other applications. While we performed our study using ATAC regions from the GM12878 cell line and re-transfection of constructs in the same cell line, the approach is applicable to any cell type, and to combining of different source and target cells. For example, libraries could be generated from limited patient tissue, or a pool of multiple donors to increase heterozygous loci, and subsequently transfected into a relevant immortalized cell line that can be easily grown to high cell quantities. Here, we used transfection to introduce episomal plasmid reporters, but lentiviral methods can be used in cell lines with lower transfection efficiencies. We chose non-integrating episomal reporters to focus more directly on sequence function independently of its broader chromatin context, however the HiDRA approach can be used with an integrating lentiviral vector to also incorporate the effect of chromosomal context, with the understanding that previous analyses used up to ~100 integration sites per tested element to accurately quantify activity25. Thus, to improve detection of modestly-active enhancer elements, future studies may forgo the SCP1 promoter altogether and utilize the ORI site exclusively. Zabidi et al. previously demonstrated that different promoters in Drosophila STARR-seq experiments will uncover different classes of enhancer elements 37. Based on Muerdter et al., the pSTARR-seq_human plasmid employed here drives transcriptional initiation from two promoter elements: the SCP1 synthetic core promoter and the bacterial plasmid origin of replication, suggesting that the original pSTARR-seq_human plasmid may identify more active enhancer elements than newer STARR-seq plasmid backbones lacking SCP125. Future HiDRA studies can also increase the diversity of promoter sequences tested to improve the detection of subclasses of enhancer elements. In addition, transfection can induce interferon response in some cell types, which can alter the transcriptional landscape if they do not already express interferon, as is the case for HeLa-S3 cells25. The GM12878 cell line used here already expresses interferon-stimulated genes such as IFIT1, ISG15, and IRF7, similarly to other EBV-transformed lymphocyte lines, and thus interferon response does not greatly alter the regulatory landscape. For other cell lines, we recommend following the recommendation by Muerdter et al. and inhibiting relevant IRF signaling kinases during plasmid transfection25.Several improvements are possible on the existing HiDRA experimental approach. In addition to the minimal SCP1 promoter, the STARR-seq vector has a second active promoter at the bacterial ORI site, whose transcripts are both more abundant and have stronger signal-to-noise ratios at regulatory elements38. For systematic mutagenesis, HiDRA libraries can also processed to introduce new mutations through error-prone PCR or introduction of mutagens during amplification.The HiDRA approach can also be specifically tuned for mapping differential allele activity in regulatory regions associated with human disease SNPs from GWAS. At the library construction stage, capture probes can be used to further increase coverage at known polymorphic SNPs of interest, thus increasing fine-mapping resolution, facilitating comparison of fragments with alternate alleles but matching start and end positions, and increasing statistical power to detect differential activity between risk and non-risk alleles. To increase the number of heterozygous sites, future studies can pool cells or tissue from multiple individuals, thus ensuring polymorphic representation of many disease-associated loci. For cancer mutations and other somatic mutations, HiDRA may also be applied to pools of tumor samples, or pools of disease tissue, to identify variants that alter regulatory activity and gene expression39. To test regions associated with specific chromatin states, capture could be performed using the output of chromatin immunoprecipitation experiments (ChIP) using histone modifications, thus preferentially sequencing genomic regions that were also pulled down with ChIP. Finally, the SHARPR-RE high-resolution mapping algorithm developed here can be applied to perform high-resolution mapping of genomic regions for high-complexity libraries with sufficient fragment density . We envision that HiDRA and such modified approaches can be used to quantify the transcriptional regulatory landscape of DNA sequences for a variety of tissues from multiple organisms.We also envision modifications of the HiDRA assay presented here for testing specific subsets of the genome. A modified HiDRA assay may be used to enrich for fragments from active regulatory regions, by coupling HiDRA with a fragment capture technology similar to those used in Capture Hi-C to selectively test a subset of enhancers or promoters at higher resolution while retaining the advantages of having larger fragment sizes and high library complexity21 ; a pellet was collected by spinning at 500 g for 10 min, and was resuspended in 25 µL of TD buffer (Illumina #FC-121–1030), 2.5 µL Tn5 transposase (Illumina #FC-121–1030), and 22.5 µL of dH2O; the transposition reaction proceeded for 30 min at 37 °C on a shaker (300 rpm). Tn5-fragmented DNA was cleaned up using a MinElute PCR purification kit  and the resulting 80 µL of eluate was split into 16 PCR reactions  and NEBNext Ultra II Q5 DNA polymerase master mix (NEB #M0544L). Thermocycler conditions were: 65 °C for 5 min, 98 °C for 30 s, 8 cycles of: 98 °C for 10 s and 65 °C for 90 s. PCR reactions were pooled and cleaned up with a Qiagen MinElute PCR purification kit (two PCR reactions per column eluted in 20 µL of EB buffer) and run on a 1% agarose E-Gel EX with SYBR Gold II stain (Thermo Fisher #G402001). Size selection of ATAC-seq fragments was performed by gel excision using a razor blade to select fragments between 150–500 nt. Gel slabs were pooled into < 300 mg groups and DNA was purified using a MinElute Gel Extraction kit (Qiagen #28604), and eluted in 20 µL of buffer EB per column following modified guidelines described in Box 2 of Taiwo et al.40. The resulting size-selected ATAC-seq fragment library was treated with an anti-mitochondrial DNA CRISPR/Cas9 library following the protocol outlined in Montefiori et al. using 10X excess of Cas9 protein (Supplementary Note 41. We cleaned up the reaction with a Qiagen MinElute PCR purification kit and split into eight PCR reactions for a second round of PCR using the same conditions and primers described above. PCR products were cleaned up using two rounds of AMPure bead selection (0.8X ratio of beads to input) to size-select against small (<150 nt) fragments, eluted in 40 µL of dH2O and quantified using a Qubit dsDNA HS Assay kit (Thermo Fisher #Q32854).We performed 16 ATAC-seq reactions on 50,000 GM12878 cells, each using a modified protocol based upon Buenrostro et al.16. For each library, we performed 20 individual InFusion HD cloning reactions (Takara Bio #638911) using a 3.5:1 molar ratio of insert to vector backbone, following manufacturer’s instructions . In total, 22 µL of bacteria/DNA mixture were pipetted into a 0.1 cm electroporation cuvette (Thermo Fisher Scientific #P41050) and tapped repeatedly against a hard surface to remove bubbles. Cuvettes were electroporated using a Bio-Rad Gene Pulser Xcell Microbial Electroporation System (Bio-Rad #1652662) using the conditions: 2.0 kV, 200 Ω, 25 µF (Supplementary Note The pSTARR-seq_human plasmid used for generating the plasmid library was a gift from Alexander Stark (Addgene plasmid #71509). The linear backbone used for the subsequent cloning steps was generated by digesting 4 µg of circular pSTARR-seq_human for 4–6 h with AgeI and SalI restriction enzymes (NEB #R3552S and R3138S), followed by gel excision under a dark reader transilluminator  to extract a linear 3.5 kb fragment corresponding to the human STARR-seq plasmid backbone. We performed cloning of the fragment library into the plasmid backbone approximately following the Methods section from Arnold et al.5 and 1 × 106 cells/mL with regular media changes every 2–3 days. Approximately 24 h before transfection, GM12878 cells were split to a density of 4 × 105 cells/mL to ensure the presence of actively dividing cells for increased transfection efficiency. For transfection, cells were collected by centrifugation for 5 min at 300 g, washed once with pre-warmed PBS, and collected again for 5 min at 300 g. PBS was aspirated and cell pellets were re-suspended in Resuspension Buffer R (Thermo Fisher Scientific #MPK10096) at a concentration of 7.5 million cells per 100 µL. DNA was added to cells at a concentration of 5 µg of plasmid per 1 million cells. In total, we transfected five replicates with 120–130 M million cells per replicate using 100 µL tips from the Neon Transfection System at 1200 V with three pulses of 20 ms. Replicate number was chosen based on other high-throughput reporter assay studies . Transfected cells were immediately recovered in pre-warmed GM12878 media without antibiotic and recovered at a density of 1 × 106 cells/mL for 24 h. In parallel, we performed two transfections of GM12878 cells with a positive control GFP plasmid to assess transfection efficiency using the same conditions.GM12878 cells were obtained from the Coriell biorepository and grown in RPMI 1640 Medium with GlutaMAX Supplement (Thermo Fisher #61870127), 15% fetal bovine serum (Sigma Aldrich #F2442), and 1% pen/strep at a density of between 2 × 10g) and RNA was purified using the Qiagen RNEasy Maxi kit (Qiagen #75162) following manufacturer’s instructions and performing the optional on-column DNase treatment step (Qiagen #79254). Poly A+RNA was extracted from total RNA using the Oligotex mRNA Midi kit , and any remaining DNA was digested with a second DNase treatment step using Turbo DNase (Thermo Fisher #AM2238) following manufacturer’s instructions  with thermocycler conditions: 98 °C for 30 s, 20 cycles of: 98 °C for 10 s, 65 °C for 90 s. We proceeded to perform eight regular 50 µL PCR reactions  using the same thermocycler conditions except using the Ct value for the cycle number . PCR reactions were cleaned up using Qiagen MinElute PCR purification kits and balanced for sequencing using the Kapa Library Quantification Kit  was sequenced by the Broad Institute Walk-Up Sequencing Facility on four flowcells on a NextSeq 500 machine using the 75-cycle kit as per manufacturer’s instructions for 2 × 37 nt paired-end reads with 2 × 8 nt barcodes.Reads were labeled by a random 4 nt P7 barcode and an 8 nt P5 barcode for sample ID. Reads were split into the ten samples (five plasmid replicates and five RNA replicates) by P5 barcode and aligned to the human genome (hg19 assembly) using bowtie2 v2.2.9. Alignment files were filtered to (i) keep only aligned fragments, (ii) remove reads mapping to chrM, (iii) select reads passing the -q 30 filter in samtools, and (iv) remove reads aligning to the ENCODE hg19 blacklist regions  and output (RNA). We grouped fragments into “fragment groups” by 75% mutual overlap , removed redundant fragment groups and summed counts of all fragments per fragment group. As we detect active fragments by comparing RNA signal to the non-transfected DNA library, we controlled for the possible length-dependent biases in transfection efficiency of plasmids by splitting fragment groups into separate bins of 100nt  and used DEseq2 (v1.10.1) to identify FDR < 0.05 significantly up-regulated fragment groups in each bin43. Most colors for plots chosen with guidance from the wesanderson R package (https://github.com/karthik/wesanderson). For chromatin state annotations, we used the 18-state output model generated by the Roadmap Epigenomics Consortium6. Active enhancer states were merged from states #9 and #10 (EnhA1 and EnhA2). ATAC-seq peaks positions were obtained from Buenrostro et al.21.All overlap and shuffle analyses performed using the BEDTools suite, v2.26.0Signal tracks: Signal tracks for regulatory activity calculated as (RNA-DNA)/DNA after adding a pseudocount of 0.1 to both plasmid and RNA samples, so that fragments with no activity have a regulatory activity value of 0. Signal tracks were drawn in UCSC Genome browser showing only means (no whiskers) and with 5-pixel smoothing.Correlation between RNA samples: We show correlations for fragments selected by four different cutoffs of minimum RPM. Pearson and Spearman correlations were calculated on log2-transformed data. Matrix of graphs drawn using layout and grid.arrange functions in R from the gridExtra library. Scatterplot between RNA samples drawn using the hexbinplot function from the hexbin library in R with xbins = 100.Proximal vs. distal: TSS regions were defined using the UCSC Genome Browser’s Table Browser tool for hg19. Distances to nearest annotated TSS were taken using closestBed tool in the BEDTools2 suite.7. We only considered motifs corresponding to transcription factors expressed in GM12878 (RPKM > 5 using processed GM12878 RNA-seq data from the Roadmap Epigenomics Consortium). TF motifs in driver elements were compared against motifs found in shuffled driver elements within the same set of tested tiled regions (regions with at least 10 HiDRA fragments).TF motif enrichment: We obtained the hg19 TF motif catalog from the ENCODE projectz-score of true driver elements compared to shuffled driver elements. The p-value of this difference was calculated in R from this z-score under a normal distribution (two-sided) with mean and standard deviation calculated from random shuffles.Random shuffling of driver elements: To assess significance of TF motifs, evolutionary conservation and fine-mapped GWAS SNPs in driver elements, we shuffled the positions of driver elements within tiled regions (genomic segments with at least 10 HiDRA fragments) using shuffleBed with the -incl flag to force driver elements to be shuffled within tiled regions. To assess the significance of enrichment, we performed 1000 shuffles of driver elements and calculated Activity of HiDRA regions in other tissues: We set a lenient definition for active in other tissues as the union of regions annotated in 97 non-GM12878 tissues from epigenome roadmap predicted with 18-state ChromHMM model. For active regions we considered states “TssA” (state #1), “TssFlnkU” (state #3), and “EnhA” (states #9 and 10).SHARPR-RE activity plots: Tracks were drawn in the UCSC Genome Browser using “Custom Tracks”. Coloring of individual fragments was performed by setting maximum and minimum colors  to log2(RNA/DNA) values of 3rd lowest and 3rd highest fragments (two strongest and weakest fragments were removed to avoid strong outliers), and scaling colors of all other fragments linearly between these extremes. We chose to include only ChIP-seq bound TF bars for ChIP-seq experiments performed in GM12878 cells by the ENCODE project and where the motif (green bar) overlapped driver nucleotides.27. In total, ~39,500 SNPs were tested by Tewhey et al.27 for allelic activity, of these 4335 SNPs had enhancer activity in MPRA fragments containing either allele so that allelic skew can be calculated. 3291 SNPs remained after using dbSNP142 and the corresponding RsMergeArch file to assign coordinates for these SNPs. We used this set of 3291 SNPs to assess the degree of allelic skew inside driver elements.Comparison of driver elements vs. MPRA allelic skew: We use allelic skew data from Supplemental Table 13. We identified the top Sharpr-MPRA activity score per driver element and compared these to activity scores for control, shifted elements.Comparison of driver elements vs. Sharpr-MPRA activity: We used Sharpr-MPRA activity scores from the basepredictions_*_ScaleUpDesign1and2_combinedP.txt files provided by Ernst et al.See Supplementary 34.We used vcf-consensus (VCFTools) to mask the hg19 genome assembly by replacing heterozygous nucleotides identified by the Illumina NA12878 Platinum Genome with N’s. 250 nt paired-end MiSeq reads were trimmed using cutadapt to remove Illumina primer sequences, mapped to the NA12878-masked hg19 assembly using bowtie2 v2.2.9 , and filtered using the steps described above for 37 nt reads. As some long reads have poor quality scores at their 3′ end, we trimmed low quality sequences  to reduce the proportion of sequencing errors at SNPs that could lead to incorrect allelic assignment of fragments. Fragments were then assigned to a SNP based on genotype at the position. For comparisons of SNP activity, we only considered fragments with 90% mutual overlap to reduce the confounding effect of fragments that differ by both allele and position. We also removed fragments if either end was within 25 nt of a driver element, as in these cases small differences in end position could artifically lead to large effects. After assigning fragment abundances (from high-depth 37 nt PE read sequencing) to each allele of a SNP, we identified SNPs with significant differential activity using QuASAR-MPRA. CENTIPEDE SNPs were identified by Moyerbrailean et al. using an effect-size cutoff of > 3 or < −3, following the cutoffs used by Kalita et al.Additional information and considerations in applying the method are are provided in the Supplementary Notes.Supplementary InformationPeer Review FileDescription of Additional Supplementary FilesSupplementary Data 1Supplementary Data 2Supplementary Data3Supplementary Data 4


Xenopus oocytes. Genes that are silenced, but have pre-existing open transcription start sites in donor cells, are prone to be activated after nuclear transfer, suggesting that the chromatin signature of somatic nuclei influences transcriptional reprogramming. There are also activated genes associated with new open chromatin sites, and transcription factors in oocytes play an important role in transcriptional reprogramming from such genes. Finally, we show that genes resistant to reprogramming are associated with closed chromatin configurations. We conclude that chromatin accessibility is a central factor for successful transcriptional reprogramming in oocytes.Oocytes have a remarkable ability to reactivate silenced genes in somatic cells. However, it is not clear how the chromatin architecture of somatic cells affects this transcriptional reprogramming. Here, we investigated the relationship between the chromatin opening and transcriptional activation. We reveal changes in chromatin accessibility and their relevance to transcriptional reprogramming after transplantation of somatic nuclei into   •ATAC-seq reveals chromatin accessibility changes during reprogramming in oocytes•Genes with open promoters are preferentially activated during reprogramming•Transcription factors play a role in transcriptional reprogramming in oocytes•Closed chromatin is associated with reprogramming-resistant genes Miyamoto et al. show genome-wide changes in chromatin accessibility during transcriptional reprogramming in oocytes using the frog nuclear transfer system. They demonstrate that donor cell chromatin states affect transcriptional reprogramming and changes in open chromatin during reprogramming are associated with specific transcription factors. Transcriptional activation is pivotal for cell fate changes and is modulated by the access of chromatin- and transcription-related factors to gene regulatory regions such as promoters and enhancers. Chromatin accessibility at gene regulatory regions affects transcriptional outcome. Pronounced nucleosome-depleted regions are found around transcription start sites (TSSs) of active genes . In the In order to examine chromatin accessibility, DNase sequencing (DNase-seq) has been widely used, which identifies genomic regions that can be cut by the DNaseI enzyme, known as DNaseI-hypersensitive sites . RecentlUsing the genome-wide approaches, dynamic changes in chromatin accessibility have been investigated when transcription patterns of somatic cells are reprogrammed in induced pluripotent stem (iPS) cells. In the course of reprogramming toward iPS cells, nucleosome occupancy and open chromatin regions are changed in regulatory regions, especially at the binding sites for reprogramming transcription factors (TFs) . The binXenopus laevis oocytes , many reads were derived from mitochondria and the produced libraries showed characteristics of those with a low quality, such as duplicated reads and low library complexity. Hierarchical clustering of the mapped ATAC-seq reads around TSSs showed that the 1,000-cell and 50,000-cell samples clustered together while those created from 100, 10, and 1 cell did not cluster with the 1,000-cell and 50,000-cell samples C. For maGenomic locations of ATAC-seq peaks, representing open chromatin sites, were examined in C2C12 cells, and 52,697 unique sites were found. Open chromatin regions were overrepresented within 1 kb of TSSs by 15.9-fold relative to the whole genome (p < 1E−6) and by 13.1-fold in myotube enhancers (p < 1E−6) B. FurtheXenopus oocytes, in which hundreds of mouse somatic nuclei transplanted into the germinal vesicle (GV), a giant nucleus of a Xenopus oocyte, undergo extensive transcriptional reprogramming toward an oocyte-like state within 2 days, without cell divisions and DNA replication  super-enhancers C. The meXenopus NT embryos as somatic memory genes  had a higher level of open chromatin than non-expressed genes in donor cells, as expected, but strong open chromatin states were unexpectedly maintained in downregulated genes even after NT D. In fac oocytes E and S2F oocytes .Utf1 and Hoxc8, both of which have been shown to be reprogrammed after NT  and Utf1 have been shown to be regulated by RARs  at the TSS and is activated after NT. A RAR-α dominant-negative form (dnRAR) , whereas expression of Gapdh, a housekeeping gene, was not affected   was over (dnRAR) A. The ovaffected B. These We next asked whether an opening of somatic chromatin is efficiently carried out or not after NT to oocytes. Our previous study identified genes resistant to oocyte-mediated reprogramming in MEFs . These gOct4 activation , a histone deacetylase inhibitor, which can induce the accessible chromatin state in somatic nuclei . NT oocyectively B. These Xenopus oocytes. Transcriptional activation from the silenced genes in donor somatic cells can be accomplished from both pre-existing open chromatin and closed chromatin. In 56% of newly activated genes, pre-existing open chromatin peaks were found near TSSs before NT. These results indicate that primed genes are prone to transcriptional activation during oocyte-mediated reprogramming. Presumably, having open chromatin, H3K4me3, and poised RNA polymerase II (Pol II) at these genes helps ensure that these genes can be promptly and efficiently activated, compared with genes with closed chromatin. In contrast, only a few percentages of genes acquire obvious open chromatin sites after NT, implying that opening of closed chromatin is an inefficient process. Indeed, we found that a closed chromatin configuration at TSSs is a barrier to some genes successfully being activated during reprogramming. Chromatin opening related to transcriptional reprogramming is likely mediated by TFs such as RARs. Although oocytes possess many TFs and other machineries to relax chromatin  of Tricaine methanesulfonate (MS222). Subsequently, the frogs were slaughtered by exsanguination under anesthesia, followed by freezing for appropriate disposal.Our work using mature 2O). A total of 50 μL of the transposon reaction was used for 50,000 cells, while 1,000 or fewer cells were incubated in 5 μL of the transposon reaction. Moreover, diluted transposon  was used when cell numbers were 100, 10, and 1. The transposition reaction was performed at 37°C for 30 min. After the reaction, transposed DNA was purified using a Qiagen MinElute kit and used for subsequent PCR amplification. DNA from 1,000 or fewer cultured cells was directly subjected to PCR amplification without kit purification. Transposed DNA was amplified by two rounds of PCR using NEB Next High Fidelity Master Mix  and the Customized Nextera PCR primers in 50 μL . Further computational methods are described in the The number of biological replicates is shown as n. In transcriptional assays by qRT-PCR, the statistical difference was calculated by the two-tailed Student’s t test. Error bars were represented as the SEM. The levels of significance were set as


Igf2-H19 and Dlk1-Dio3 domains—and assessed the involvement of the insulator protein CTCF in mouse cells.Genomic imprinting is essential for mammalian development and provides a unique paradigm to explore intra-cellular differences in chromatin configuration. So far, the detailed allele-specific chromatin organization of imprinted gene domains has mostly been lacking. Here, we explored the chromatin structure of the two conserved imprinted domains controlled by paternal DNA methylation imprints—the Dlk1-Dio3 locus, using genome editing, results in perturbed sub-TAD organization and bi-allelic Dlk1 activation during differentiation.Both imprinted domains are located within overarching topologically associating domains (TADs) that are similar on both parental chromosomes. At each domain, a single differentially methylated region is bound by CTCF on the maternal chromosome only, in addition to multiple instances of bi-allelic CTCF binding. Combinations of allelic 4C-seq and DNA-FISH revealed that bi-allelic CTCF binding alone, on the paternal chromosome, correlates with a first level of sub-TAD structure. On the maternal chromosome, additional CTCF binding at the differentially methylated region adds a further layer of sub-TAD organization, which essentially hijacks the existing paternal-specific sub-TAD organization. Perturbation of maternal-specific CTCF binding site at the Igf2-H19 and the Dlk1-Dio3 domains adds an additional layer of sub-TAD organization, on top of an existing three-dimensional configuration and prior to imprinted activation of protein-coding genes. We speculate that this allele-specific sub-TAD organization provides an instructive or permissive context for imprinted gene activation during development.Maternal allele-specific CTCF binding at the imprinted  Genomic imprinting is an epigenetic mechanism through which parental origin dictates mono-allelic expression of about 200 mammalian genes . ImprintIgf2-H19 and Dlk1-Dio3), the ICR is methylated on the paternal allele. Uniquely, these “paternal ICRs” do not overlap promoters but are linked to allele-specific binding of the CTCF insulator protein on the maternal chromosome, either directly to the ICR itself (“H19 DMR” in the Igf2-H19 domain), or to a secondary DMR whose allelic methylation during pre-implantation development requires the presence of the nearby primary ICR (primary “IG-DMR” and secondary “Meg3 DMR” in the Dlk1-Dio3 domain) [Igf2-H19 ICR or mutations in its CTCF binding sites lead to the adoption of the paternal transcriptional program, indicating an essential role for allelic CTCF binding [Most ICRs are methylated on the maternally inherited chromosome, where they overlap promoters that are expressed only from the paternal chromosome. In contrast, only at two evolutionary conserved domains ( domain) –7. Loss  binding , 9.The CTCF insulator protein is essential for the organization of the genome into “Topologically Associating Domains” (TADs) –12. TADsIgf2-H19 and Dlk1-Dio3 domains urged us to investigate the chromatin structure of these domains within the context of TAD organization. Previously, non-comprehensive 3C (“Chromosome Conformation Capture”) studies at the Igf2-H19 domain reported various instances of allele-specific chromatin looping . In contrast, we identified three instances of putative allelic CTCF binding in the TAD on the maternal chromosome  or at a close-by secondary DMR (Meg3 DMR at the Dlk1-Dio3 domain).To investigate how the  in ESCs . This anIgf2-H19 and the Dlk1-Dio3 domain . Whereas both clusters looped towards the H19 DMR on the maternal chromosome, they formed chromatin loops on the paternal chromosome as well, but now with the more distal bi-allelic CTCF cluster located telomeric from the Igf2 gene  that acts as a maternal-specific Meg3 enhancer [Dlk1 gene and the CTCF binding site centromeric from the Dlk1 gene, both in hybrid and mono-parental ESCs , whose insulated nature coincides with an increased average spatial distance between the Begain, Dlk1, and Dio3 genes on the maternal chromosome F1 mESC  ESCs into neural progenitor cells (NPCs) with cortical identity. In this system, imprinted Dlk1 activation can be readily recapitulated [Meg3 remained strictly expressed from the maternal chromosome  from the maternal chromosome. Perturbation of maternal CTCF binding at the DMRs results in loss of imprinting of the paternally expressed protein-coding genes, as determined in this study (Meg3 DMR) or previously published (H19 DMR) [More generally, our study highlights striking mechanistic similarities between the two paternally imprinted domains conserved in mammals. At both the domains, sperm-derived DNA methylation imprints mediate, directly or indirectly, maternal allele-specific binding of CTCF to key regions. This, in turn, is accompanied by the presence of sub-TADs that prevent the developmental activation of essential protein-coding genes (H19 DMR) . AlthougH19 DMR)  to repreIgf2-H19 domain to previously reported non-comprehensive allele-specific 3C and CTCF-binding data provides both further support for our model and important nuance to the interpretation of these previous studies. Using 3C, a considerable number of allele-specific DNA loops between the regulatory elements  on both the maternal and paternal chromosomes were reported in a region spanning 150 kb around the H19 and Igf2 genes [H19 DMR, whereas on the paternal chromosome, contacts can also surpass the element  or sub-divided in two to three insulated sub-domains (4C-seq), the combination of both types of data suggests the presence of an intra-TAD 3D architecture where the Dlk1-Meg3 sub-TAD physically loops away from the other sub-domains. This is supported by our three-way DNA-FISH studies where distances involving the central Dlk1-Meg3 sub-TAD are longer than those between the flanking Begain and Mirg-Dio3 sub-domains . JoiIgf2-H19 domain, our DNA-FISH studies showed that average distances between loci on the parental chromosomes only differ for the H19 DMR and a CTCF cluster located at its centromeric side. This appears at odds with our 4C-seq data for the same viewpoint that reveals a highly different pattern of chromatin loops uniquely formed on the maternal chromosome. Interestingly though, for viewpoints at the H19 DMR and the 5′-located CTCF sites, we noticed a considerable enrichment of 4C-seq signal at the Igf2 gene on the maternal allele as well , possibly due to its relatively low level of CTCF binding  and Silver-Russell Syndrome (SRS) [Meg3 DMR is evolutionarily conserved in humans [MEG3 DMR have recently been linked to the developmental imprinting disorder Kagami-Ogata Syndrome (KOS14) [In conclusion, our study reveals how maternal-specific CTCF binding structures a further layer of sub-TAD organization that overrides the inherent paternal 3D chromatin organization and thus directly coincides with the implementation of the maternal transcriptional programs. Similarly as for the me (SRS) —maternaln humans . Micro-d (KOS14) , 45, indMeg3 transcript in all cell lines was periodically confirmed, as well as the absence of mycoplasma contamination.Hybrid ESC lines BJ1 ((C57BL/6 J × JF1)F1) and JB1 ((JF1 × C57BL/6 J)F1) are both male and were derived in serum-free (2i) medium as part of a previous study . These W5 cells per 10-cm dish in serum-free ESGRO Complete PLUS medium, and after 24 h, the medium was changed to DDM (DMEM/F12 + GlutaMAX (ThermoFisher)), supplemented with 1× N2 (ThermoFisher), B27 , 1 mM of sodium pyruvate, 500 μg/ml BSA, and 0.1 mM of 2-mercapto-ethanol for a total of 12 days. Cyclopamine  was added from day 2 to day 10 of differentiation. Media was changed every 2 days. After 12 days of differentiation, NPCs were dissociated using StemPro Accutase and were used for high-throughput 4C studies. Part of the cells were replated on poly-lysine (Sigma)/laminin (Sigma) for expression studies, and cultured in 1:1 mixture of DDM and Neurobasal/N27 media  and 2 mM GlutaMax) for further differentiation, till D21, or were re-plated onto coated coverslips to perform immunostainings and DNA-FISH studies 2 days later (D12+2).Differentiation of ESCs into cortical-identity neural progenitor cells (NPCs) was performed as described in detail before , 48. BriBbsI sticky ends: Meg3 DMR CTCF site 2—GTTGCACATAGAGACCGCTAG. It was cloned into the pUC57-sgRNA expression vector [Meg3 DMR CTCF site 2 was amplified  was designed using CRISPR Design tool (zlab.bio/guide-design-resources) and synthesized with n vector   using the SsoAdvanced Universal SYBR Green Supermix (BioRad). Duplicate qPCR experiments were performed on technical replicates with recovery in each cell type expressed versus a corresponding input sample .Indexed ChIP-seq libraries were constructed using the Next Ultra Library Prep Kit for Illumina (New England Biolabs) using the application note “Low input ChIP-seq.” Multiplexed sequencing was done using 86-bp single-end reads on the Next-Seq 500 system (Illumina) at the I2BC Next Generation Sequencing Core Facility. Data were mapped to ENSEMBL Mouse assembly GRCm38 (mm10) using BWA with default parameters . Reads fTotal RNA was extracted from AK2 and PR8 ESCs by lysing the cells on the culture dish with the addition of TRI reagent (Sigma-Aldrich). RNA samples were sequenced on the Illumina HiSeq-2000 system (Illumina) using the TruSeqTM SBS stranded mRNA sample kit (version 3). For both ES lines, RNA sequencing (2 × 100 bp) was performed in triplicate.R ≥ 0.99) indicating good reproducibility and reliability. Samples were normalized against each other using quantile normalization, followed by averaging of triplicate samples  using STAR . TranscrMeg3 and Dlk1 expression were quantified by RT-qPCR using SYBR Green I master mix (Roche) on a Lightcycler 480 instrument. Mean CT values were normalized with the mean of two housekeeping genes  and the ΔΔCt method [Total RNA was extracted from hybrid ESCs and NPCs using the miRNEasy Kit (Qiagen) and DNaseI treatment (Qiagen). cDNA was synthesized from 5 μg of RNA using random hexamers and SuperScript III (ThermoFisher) reverse transcriptase. t method . Primer Actb, Gapdh) amplified with SYBR Green Master Mix (Roche), as reported before [The Taqman mutation detection assay was used for allele-specific quantitation (ThermoFisher). Expression was quantified using Taqman Genotyping Master Mix (ThermoFisher) and a Lightcycler 480 instrument. The levels were normalized to two housekeeping genes  as the primary restriction enzyme and NlaIII (New England Biolabs) as the secondary restriction enzyme. For 4C-seq library preparation, 800 ng of 4C library was amplified using 16 individual PCR reactions with inverse primers including the Solexa or TruSeq adapter sequences  were detected with the FourCSeq package, using the smoothed 4C-seq replicate profiles (window size = 5) of the H19 DMR viewpoint and the following parameters: getZscores function with minCount = 10 and minDist = 20 kb [G test of independence.Chromatin fixation, cell lysis, and 4C library preparation were done as previously described  using 15 station . Distrib station , with un = 20 kb . Signifi = 20 kb , 60, by EcoRI, after which half of the reaction was further digested with the methylation-sensitive enzyme AciI. One nanogram of both the AciI-digested DNA and of the non-digested (EcoRI only) DNA was used for qPCR. Quantitative values were obtained using the standard curve method [AciI sites (Col1a2 and Col9a2). The ActB gene (unmethylated AciI site) and IAP retrotransposons (methylated AciI sites) were included as controls. Primer sequences are in Additional file DNA methylation was analyzed by digestion of genomic DNA samples with a methylation-sensitive restriction endonuclease, followed by qPCR amplification and Sanger sequencing of the PCR products . Brieflye method  with norMeg3  with Cy3-dUTP or Cy5-dUTP . Details on fosmids and BACs are provided in Additional file eg3 , using a 63×NA 1.4 Plan-Apochromat oil immersion objective. Optical sections separated by 0.4 μm steps were collected in the Z direction. Stacks were analyzed using Imaris software . FISH signals were segmented in 3D, and their centers of mass were defined. For double FISH experiments, the distances between closest neighbor’s centers of mass were calculated. Only FISH fluorescence signals within DAPI 3D-segmented object were considered for the analysis.t test.Measured distances between BAC probe FISH signals were normalized to the genomic distances and not to the radius of individual nuclei, because no differences in cell radius were observed between the three cell lines  (not shown). Significance of differences between distances from combined repeated experiments of DNA probes was calculated using the two-tailed unpaired Mann-Whitney Additional file 1. Supplementary Figures and Supplementary Tables.Additional file 2. CTCF binding peaks in the Dlk1-Dio3 TAD in mono-parental ESCs (ChIP-seq).Additional file 3. RNA transcript levels of genes in the Igf2-H19 and Dlk1-Dio3 TADs in mono-parental ESCs (RNA-seq).Additional file 4. Review history.


The Mediator complex regulates transcription by connecting enhancers to promoters. High Mediator binding density defines super enhancers, which regulate cell-identity genes and oncogenes. Protein interactions of Mediator may explain its role in these processes but have not been identified comprehensively. Here, we purify Mediator from neural stem cells (NSCs) and identify 75 protein-protein interaction partners. We identify super enhancers in NSCs and show that Mediator-interacting chromatin modifiers colocalize with Mediator at enhancers and super enhancers. Transcription factor families with high affinity for Mediator dominate enhancers and super enhancers and can explain genome-wide Mediator localization. We identify E-box transcription factor Tcf4 as a key regulator of NSCs. Tcf4 interacts with Mediator, colocalizes with Mediator at super enhancers and regulates neurogenic transcription factor genes with super enhancers and broad H3K4me3 domains. Our data suggest that high binding-affinity for Mediator is an important organizing feature in the transcriptional network that determines NSC identity. The Mediator complex regulates transcription by connecting enhancers to promoters. Here, the authors purify Mediator from neural stem cells (NSCs), identify 75 novel protein-protein interaction partners and characterize the Mediator-interacting network that regulates transcription and establishes NSC identity. The Mediator complex provides communication between active enhancers and promoters by interacting with proteins that bind to either of these two classes of regulatory DNA elements5. Accordingly, identified Mediator-interacting proteins include many transcription factors5, RNA polymerase II (RNApol2) and transcription elongation factors6. Recently, Mediator content was used to rank enhancers in embryonic stem cells (ESCs) and enhancers with the highest Mediator content were postulated as super enhancers (SEs)7, a class of enhancers that regulates key genes in cell identity and oncogenes9. Related enhancer types such as stretch enhancers and anti-pause enhancers were described independently11. There is debate on whether SEs act mechanistically different from typical enhancers12. Arguments in favor of the functional distinction of SEs is their ability to drive high levels of transcription and their selective sensitivity to inhibitors of Brd4, a chromatin-binding protein enriched at SEs13. Besides Mediator and Brd4, chromatin modifiers such as Ep300 and Kdm1a (LSD1 complex), chromatin remodelers such as Chd7, Brg1 (SWI-SNF complex) and Chd4 (NuRD complex) and Smc1a (Cohesin complex) were found to be enriched at SEs8. In a recently proposed model, the constituent enhancers of an SE and their regulated promoter(s) would group together to form a phase-separated assembly14. Such an assembly would rely on interactions between transcriptional and chromatin regulators14.The Mediator complex is a complex of ~30 subunits that is important for transcriptional regulation and is conserved from yeast to human8. However, evidence for interactions between master TFs and Mediator, which would underpin their role in recruiting Mediator to SEs, is scarce. For example, among SE-binding master TFs Oct4, Sox2 and Nanog (ESCs), Pu.1 (pro-B cells), MyoD (Myotubes) and C/EBPα (Macrophages)7, Mediator interactions were only detected in immunoprecipitations of Sox2 and C/EBPα and these were with single Mediator subunits16. Also our understanding of the recruitment of the above chromatin modifiers to enhancers and SEs and their subsequent maintenance at high levels at SEs is far from complete. Mediator was shown to interact with SE-enriched chromatin modifier Crebbp17 and the Cohesin complex18, suggesting that Mediator could, in principle, provide an anchoring role at enhancers, SEs and the proposed phase-separated assemblies.Cell-type specific master TFs colocalize with Mediator at SEsTo investigate the relevance of Mediator interactors in defining enhancers and SEs, here we describe the purification of the Mediator complex from neural stem cells (NSCs) and identify its protein–protein interaction partners by mass spectrometry. To prevent recording interactions that are mediated via DNA/chromatin, we purify Mediator from non-treated nuclear extracts, nuclear extracts treated with nuclease benzonase and nuclear extracts treated with ethidium bromide to disrupt protein-DNA interactions and only take interactions with the Mediator complex that are not affected by these treatments. Our resulting Mediator interactome contains 95 proteins of which 75 have not been, to the best of our knowledge, previously characterized as Mediator-interacting proteins. Subsequently, we perform Mediator ChIP-seq in NSCs and define SEs in NSCs by their Mediator content. Remarkably, we find that the three most frequent motifs in SEs are bound by multiple members of the small set of TFs that we identify as Mediator interactors in NSCs. We show that one of these TFs, Tcf4, regulates a set of key NSC transcription factor genes with SEs and broad H3K4me3 domain-containing promoters. High-Mediator affinity therefore appears an important characteristic of master TFs. Our Mediator interactome contains many known enhancer-binding chromatin modifiers and we show that Mediator-interacting chromatin modifiers Jmjd1c and Carm1 bind genome-wide to enhancers and SEs. Together this suggests that high-Mediator-binding affinity selects proteins that play important roles in establishing and maintaining enhancers and SEs to facilitate the regulation of cell identity.19 and was extensively validated in the past for accuracy by independent immunoprecipitations of endogenous proteins20. F-Med15 NSCs and parental NSCs were grown to large scale and nuclear extracts prepared (see Methods). We were interested in proteins that can bind to the Mediator complex relying solely on protein–protein interactions and not being mediated via chromatin, which may co-purify with a chromatin-binding factor such as the Mediator complex. We reasoned that proteins interacting with Mediator by protein–protein interaction would not show a reduced interaction efficiency when treating the nuclear extract with the DNA–RNA digesting enzyme Benzonase or with ethidium bromide (EtBr), which intercalates in the DNA and disrupts protein-DNA interactions, as compared to untreated nuclear extracts  to enable the purification of the Mediator complex by our FLAG-affinity protocol, which combines high efficiency and low backgroundcts Fig. . The use18 was identified, whereas Cohesin subunit Smc3 and Cohesin loader Nipbl were observed in three out of four Mediator purifications and are therefore not part of the final Mediator interactor list  of which 14 are novel Mediator interactors ers Fig. . We finders Fig. . DNA moters Fig. . These m27, we found that binding sites of NFI and Sox2 highly overlap with Mediator-binding sites outside promoters, including at typical enhancers and SEs  and Sox2SEs Fig. . Using oSEs Fig. , consistSEs Fig. . The sumSEs Fig. . The com29 and indeed overlaps with Mediator at enhancers and SEs in NSCs  associated with cell-identity genes31 and tumour-suppressor genes32. Mechanistically, broad promoters have increased rates of transcription elongation and higher transcriptional consistency32 and show enhanced DNA looping interactions with SEs33, compared to their typical counterparts. We found that the complete sets of SE genes and broad promoter genes in NSCs both have Transcription Regulation as their lead Gene Ontology (GO) category  with Tcf4 expression in pre-natal development of the mouse brain (out of 1104 genes)40 and the second-best spatial-temporal correlation (0.90 Pearson coefficient) with TCF4 in pre-natal human brain development (out of 19700 genes)41, suggesting that a Tcf4-Nfib co-regulatory partnership could be widespread in mammalian brain development.Tcf4 showed the highest overlap with Mediator at enhancers and SEs of the tested Mediator-interacting TFs Fig.  promptinnes Fig.  and alsones Fig. . Genes wnes Fig. . This sunes Fig. . Tcf4-bones Fig.  of which228 Fig. . This al228 Fig.  where Tcene Fig. , the Salelf Fig. . Nfib ex44 and progressing high throughput interaction studies45, we find that 75 of our 95 identified Mediator interactions have not been, to the best of our knowledge, previously characterized.We have expanded the protein–protein interaction network of the Mediator complex with many proteins and complexes that reside at enhancers, super enhancers or promoters and thereby established the potential of the Mediator complex as a major interaction hub at enhancer-promoter assemblies. Mediator binds to enhancers and promoters in close proximity to many other proteins. We believed that chromatin-independent protein–protein interactions of purified Mediator complex, as identified by their detection by mass spectrometry, would be the best indicator of its recruitment capacity. Despite our stringent criteria, 20 years of research on the Mediator complex since its discovery by several labs46. Whereas Mediator-interacting transcription factors would be more specific for NSCs (see next paragraph), the Mediator-interacting chromatin modifiers and other proteins are mostly ubiquitously expressed and would have general relevance for transcriptional regulation. Supporting this suggestion, our Mediator interactor screen discovered two major enhancer-binding proteins. We observed and independently confirmed interactions between Mediator and arginine methylase Carm1 and putative H3K9 demethylase Jmjd1c. Carm1 is a highly studied enzyme and best known in transcriptional regulation as a co-activator of nuclear receptors and NF-kappaB and was shown to act at individual promoters48. We find that Carm1 is a genome-wide enhancer-binding protein in NSCs that closely colocalizes with Mediator. Jmjd1c was identified as a co-activator of the tumor-inducing fusion gene AML1-ETO and shown to be recruited by AML1-ETO to target gene promoters where it lowers the levels of the repressive mark H3K9me249. We show that Jmjd1c marks enhancers genome-wide in NSCs, together with Mediator, where it may perform a similar enzymatic role to maintain enhancer activity.Identified Mediator interactors can be broadly divided into DNA sequence-independent proteins, mostly chromatin modifiers, and sequence-specific transcription factors. The latter category of Mediator interactors would represent potential Mediator-recruitment factors. Indeed, NF-kappaB subunit RelA, one of the two known Mediator interactors among the 16 identified transcription factors, recruits Mediator to activate transcription8 showed that chromatin modifiers Brd4, Ep300, Crebbp, Chd7, SWI-SNF complex, LSD1 complex, Cohesin complex and NuRD complex colocalize with Mediator at enhancers and have an increased binding density at SEs, similar to the Mediator complex. With the exception of Brd4, we find all the above-mentioned chromatin modifiers as Mediator interactors, which may suggest that Mediator interaction aids in their recruitment to enhancers and SEs. The apparent correlation of having protein–protein interactions with Mediator and colocalising with Mediator on the genome would predict that other observed Mediator interactors of unknown genomic location also reside at enhancers or promoters. This remains to be tested.A recent analysis27. Nfia, Nfib, Sox2, Tcf4 and Tcf12, which can bind one of these motifs, are among the small set of 16 TFs that we identified as Mediator interactors. This shows a remarkable synchrony between Mediator-binding TFs and prominent enhancer motifs in NSCs. Our identified Mediator-binding TFs are not the highest expressed TFs in NSCs, suggesting that they have a higher binding affinity for Mediator than other TFs. The above set of TFs may therefore define enhancers and SEs in NSCs by having high affinity for Mediator and thereby being effective at recruiting Mediator and its interactors to its binding sites. Accordingly, we find that Tcf4 and Sox2 are required for optimal Mediator recruitment to some of the tested genomic sites where the three factors have overlapping binding. This would suggest Mediator affinity as an important organizing feature in establishing the enhancer landscape in a given cell type. Indeed, the sum of the binding sites of Tcf4, Sox2 and Nfi represents nearly all Mediator-binding sites at enhancers, and outside promoters in general, and can therefore explain genome-wide Mediator recruitment outside promoters in NSCs.We performed Mediator ChIP-seq to identify SEs in NSCs. We find that Mediator-defined SEs in NSCs have as their most frequent TF motifs E-box, NFI and SOX, similar to NSC enhancers in general32. Tcf4, Sox2 and Nfi show relatively weak occupancy at promoters in general. However, their binding is enhanced at broad promoters and Mediator follows closely their binding pattern in our genome-wide plots, as well as at individual broad promoters. We find that Mediator-interacting chromatin modifiers, such as Carm1 and Jmjd1c, also track Mediator binding at promoters. Relative enrichment of transcription factors at broad promoters was observed before in different cell types32. Our results suggest that broad promoters may act like proximal enhancers in recruiting TFs, which in turn can recruit Mediator and its interactors. The close resemblance of the Mediator genome-wide binding sites with the binding sites of its interacting TFs is highly suggestive of Mediator recruitment by these TFs.Relative promoter occupancy of Mediator has not been analysed genome-wide in higher eukaryotes, to our knowledge. We find that Mediator has higher and especially broader binding signals at promoters with a broad H3K4me3 signal, a class of promoters that was recently discovered51. Analogous to the ESC TF circuit, many of the TFs in our NSC circuit are essential for NSC self-renewal or their neuronal differentiation capacity. Together, this suggests that we have uncovered a TF circuit that would be central to the regulation of NSC identity. TCF4 heterozygosity in humans leads to Pitt Hopkins syndrome with severe intellectual disability53, whereas SNPs in the TCF4 locus are the most significant schizophrenia risk SNPs to date54. These genetic data suggest that TCF4 plays an important role in brain development and needs to be tightly regulated to prevent neurodevelopmental disease. Our TF circuit may facilitate this regulation.We find that Tcf4 preferentially regulates SE-containing genes in NSCs, including a set of neurogenic transcription factor genes that have SEs and broad promoters. Intriguingly, we find that a number of the TFs encoded by these genes have protein–protein interactions with the Tcf4 protein. Some of these Tcf4-interacting TFs colocalize with Tcf4 at SEs in this set of target genes, as well as on the Tcf4 gene itself, suggesting a feed-forward circuit that maintains the expression of these TFs in NSCs. Feed-forward circuits of key TFs in embryonic stem cells (ESCs), such as Oct4, Sox2, Nanog, Esrrb and Klf4, were shown to regulate pluripotency and follow the same above criteria7, which were subsequently shown to regulate cell-identity genes and oncogenes in many cell types9. More recently, promoters with a broad H3K4me3 domain were postulated to regulate cell-identity genes32. As was shown before in other cell types32, we find that SE genes and Broad genes partially overlap in NSCs. However, we show that the link to neurogenic transcriptional regulators in SE genes and Broad genes in NSCs is derived from neurogenic transcriptional regulator genes in the overlap of both categories; genes that have both SEs and broad promoters. This suggests that, at least in NSCs, SE + Broad genes represent a special category of genes that is strongly linked to cell identity. These SE + Broad genes have high recruitment of Mediator at their SEs (by definition) and we find that they also recruit high levels of Mediator to their promoters. Increased promoter levels of Mediator are also observed at broad promoters without surrounding SEs may therefore be recruited by Mediator-interacting TFs, which we also find enriched at broad promoters. SEs were recently shown to have increased 3D interactions with broad promoters, as compared to typical promoters33. We find that SE + Broad genes in NSCs are the category of genes with highest levels of RNApol2 and Integrator at their promoters. Integrator complex associates with RNApol2 and plays an important role in the transcription-initiation and pause-release of RNApol230. The efficient recruitment of RNApol2 and Integrator at SE + Broad genes thereby provides an explanation for our observation that this category of genes has the highest expression in NSCs.Mediator complex binding signal was used as one parameter to postulate SEs31. SE-broad promoter assemblies and our identified Mediator interactions could provide ideal building blocks for the phase-separated complexes that have been recently proposed to drive robust transcription of cell-identity genes in mammals14.All together this fits into a model Fig.  where Me55 and cultured on N2B27 medium (Stem Cell Sciences) supplemented with EGF and FGF (both from Peprotech)56 and regularly tested for mycoplasma contamination. Essentially all our NSCs express NSC markers Sox2 and Nestin . Eighty microlitre of anti-Flag M2 agarose beads (Sigma) equilibrated in buffer C-100 were added to 1.5 ml of nuclear extract and incubated for 3 h at 4 °C in the presence of Benzonase (Novagen). Beads were washed five times with buffer C-100 containing 0.02% NP-40 (C-100*) and bound proteins were subsequently eluted at 4 °C with buffer C-100* containing 0.2 mg/ml Flag-tripeptide (Sigma). Elutions were TCA precipitated, separated on a 10% NuPAGE Bis-Tris gel (Invitrogen) and stained with Colloidal Coomassie (Biorad) according to manufacturer’s instructions. Gel lanes were cut and subjected to in-gel digestion with trypsin (Promega). Nano-LC-MS/MS was performed on an 11 series capillary LC system (Agilent Technologies) coupled to an LTQ mass spectrometer (Thermo). Peptide spectra from purified Mediator samples or control sample were searched against UniProt release 2012-11 for protein identification using MASCOT.NS-5 neural stem cells (NSCs) were derived from 46 C embryonic stem cellsMediator complex purifications were performed from nuclear extract with Benzonase (150 U per ml nuclear extract) added or Ethidium bromide (50 μg per ml) added at the start of the 3-h incubation period of the anti-FLAG antibody beads with the nuclear extract. Alternatively, Mediator complex purification was performed from untreated nuclear extract. In one experiment, Mediator complex purifications were performed from nuclear extracts treated with Benzonase, Ethidium bromide or untreated nuclear extract, together with a control purification from nuclear extract from control NSCs. In a second, independent, experiment, Mediator complex was purified from nuclear extract treated with Benzonase, together with a control purification. Control purifications were from nuclear extract treated with benzonase. All purifications are shown in Supplementary Data 19; (1) A minimal Mascot score of 50, (2) At least five-fold enrichment by emPAI score in the Mediator purified sample over the control sample. emPAI score is an estimate of the quantity of the identified protein in the purified protein sample, based on the number of peptide spectra identified by MS, normalized for the number of peptides that theoretically should be identifiable for that protein57. (3) At least three-fold enrichment by Mascot score in the mediator purified sample over the control sample. (4) Cytoskeletal and cytoplasmic proteins (Uniprot) were removed. Of note, of the 96 identified Mediator complex interactors, only 12 are also detected in any of the two control samples , 0.2 mg/ml chicken egg albumin (Sigma), 1% fish skin gelatin (Sigma) and added to 1.5 ml of nuclear extract with or without 225 units of Benzonase (Novagen) and rotated for 3 h at 4 °C in no-stick microcentrifuge tubes (Alpha Laboratories), washed five times with 1 ml of C-100* buffer  at 4 °C and proteins eluted from the beads by 5 min at 95 °C in 50 μl SDS-loading dye. Eluted proteins were separated by polyacrylamide gelelectrophoresis and analyzed by mass spectrometry, as described above. Inclusion criteria for endogenous Mediator interactors (Supplementary Data Immunoprecipitation of Med12 was performed from 1.5 ml of NSC nuclear extract using 15 μg Med12 antibody (Bethyl Laboratories #A300-774A) crosslinked by dimethyl pimelimidate (Sigma) to 50 μl (pellet volume) of protein G sepharose beads , as described60, Nfia61, Sox262, Nfib63, Sall331, Tcf1264, Znf2465, Rela66, Tcf468. TF mRNA levels in our NSCs are from our RNA-seq data on our wild-type NSCs28.References for function in neural development. Yy128. 1.5 × 108 NSCs were used per chromatin immunoprecipitation (ChIP). Cells were collected in 1xPBS and crosslinked first with 2 mM disuccinimidyl glutarate  solution for 45 min and then 1% formaldehyde solution for 15 min at room temperature. Cells were washed twice with 1X PBS and flash frozen in liquid nitrogen. Chromatin was prepared for sonication with 20 mM Tris-HCl pH8, 150 mM NaCl, 2 mM EDTA, 0.1% SDS, 1% Triton X-100. We used 15 cycles of 30 s ON, 30 s OFF on a Bioruptor Pico sonication device (Diagenode Cat# B01060001) to shear chromatin to 150–200 bp fragments. The resulting 300 µg of chromatin extract was incubated overnight at 4 °C with 100ul of Dynal Protein G magnetic beads that had been pre-incubated with 10 μg of the appropriate antibody. We used the following antibodies: Med1 (Bethyl Labs #A300-793A), Carm1 , Jmjd1c (Merck Millipore #17-10262), IgG . Beads were washed 1X with the sonication buffer, 1X with 20 mM Tris-HCl pH8, 500 mM NaCl, 2 mM EDTA, 0.1% SDS, 1%Triton X-100, 1X with 10 mM Tris-HCl pH8, 250 mM LiCl, 2 mM EDTA, 1% NP40 and 1X with TE containing 50 mM NaCl. Bound complexes were eluted from the beads in 50 mM Tris-HCl, pH 8.0, 10 mM EDTA and 1% SDS by heating at 65 °C for 1 hr with occasional vortexing and crosslinking was reversed by overnight incubation at 65 °C. ChIP-seq sample preparation and sequencing on Illumina GAII or HiSeq2500  platforms was performed at the Erasmus MC Center for Biomics, according to manufacturer’s instructions.We adapted protocols previously described28 into 3.5 × 106 NSCs  were performed using program A-33 on the Amaxa nucleofector I, kit Cell Line Nucleofector™ Kit V  and plated on 36 6-cm dishes. As control, pSuper-puro-Control-shRNA transfections were performed with the same set-up. Control (scrambled control sequence from Dharmacon) shRNA sequence: GGTGAGCTTCATGAGGATG. Selection was started 20 h after transfection with 2 µg/ml Puromycin and NSCs were collected after 28 h of selection (48 h after transfection). For Sox2 knock-down, 36 transfection of pSuper-puro-Sox2-shRNA#120 into NSCs were performed using the same set-up as for Tcf4. As control, pSuper-puro-Control-shRNA transfections were performed with the same set-up. Selection was started 18 h after transfection with 2 µg/ml Puromycin and cells were harvested after 28 h of selection (46 h after transfection). Before crosslinking the NSCs, one 6-cm dish for each shRNA construct was collected to verify the gene expression of Tcf4 and Sox2 by qRT-PCR. RNA was isolated using the GenElute™ Mammalian Total RNA Miniprep Kit  and reverted to cDNA using RevertAid First Strand cDNA Synthesis Kit .For Tcf4 knock-down, 36 transfections of each 3.5 μg of pSuper-puro-Tcf4-shRNA#1ChIP was performed as described above on 30 µg of chromatin per condition with 30 µl of Dyna Protein G beads pre-incubated with either 3 µg of Med12 antibody (Bethyl Laboratories #A300-774A) or 3 µg rabbit IgG (Santa Cruz #sc-2027) for control ChIPs. Tcf4 knock-down ChIPs, Sox2 knock-down ChIPs and their respective control ChIPs were performed in biological duplicate. RT-PCR was performed on genomic targets indicated with their distance from the TSS of the nearest gene with—indicating upstream of the TSS and +indicating downstream of the TSS. Supplementary Table Neural stem cells were grown on poly-D-lysine  coated glass coverslips and fixed in 4% Paraformaldehyde for 10 min at room temperature. After fixation, the cells were washed with PBS+ (0.5% Bovine Serum Albumin and 0.15% Glycine in PBS) and permeabilized with PBS-0.1% Triton X-100. Subsequently, slides were incubated with the primary antibodies  diluted in PBS+ for 2 h at room temperature. After subsequent washes with PBS-0.1% Triton X-100 and with PBS+, the coverslips were incubated with secondary antibodies (Donkey anti-Rabbit Alexafluor 488 and Donkey anti-Goat Alexafluor 594) diluted in PBS+ and washed with PBS-0.1% Triton X-100 and PBS+. The samples were mounted with MOWIOL  and nuclei were stained with DAPI (Vector laboratories). Images were captured using a Leica DM4000 B fluorescent microscope and image processing was performed using FIJI (ImageJ). Sox2-positive NSCs or Nestin-positive NSCs were counted.69, where we used a seed length of 36 in which we allowed a maximum of two mismatches. If a read had multiple alignments only the best matching read was reported. ChIP-seq datasets with multiple replicates were merged. Duplicated reads were removed. MACS46 v1.4.2 was used for peak calling using default settings, using IgG ChIP-seq as background control for our Med1, Carm1, Jmjd1c, Tcf4, Olig2 and Chd7 ChIP-seq data. For external ChIP-seq datasets either IgG ChIP-seq or sequenced chromatin input was used as background control. For histone modifications we used HOMER findPeaks70 using -region -size 1000 -minDist 2500 parameters. Genomic datasets that are generated and/or used in this study are summarized in Supplementary Table All ChIP-seq datasets were mapped to the mouse mm9 reference genome using Bowtie v0.12.771 to generate overlaps between Ep300 peaks and H3K27ac peaks. SEs were identified using the ROSE algorithm7. ROSE stitches together enhancers that are within 12.5 kb of each other and do not overlap with a window of 1 kb on either side of a TSS and ranks such combined enhancers by their total Med1 ChIP-seq signal7. Four hundred forty-five super enhancers were identified and the rest were assigned as typical enhancers. Plotting was performed using hockey function in R We used the already described list of mouse NSCs broad H3K4me3 promoters31.Enhancers in mouse NSCs were defined by recalling Ep300 and H3K27ac peaks using HOMER, function REGION, and using Bedtools28 consisting of three replicates to calculate the mean mRNA expression levels. Super enhancer (SE) genes and typical enhancer genes are defined as the closest active gene, RKPM > 0.5 in our NSC RNA-seq data28, to an SE or a typical enhancer, respectively.For mRNA levels in our mouse NSCs, we used our published RNA sequencing dataset70 and selecting the most frequent motifs found at Med1 binding sites at SE constituents and typical enhancers.Motif analyses were performed using HOMERFor genome-wide binding site overlaps, we used the 5000 most significant binding sites for each factor to determine the percentage of overlap between two factors. Two binding sites were considered overlapping if their summits were within 200 bp. Promoters were defined as the regions within 1.5 kb of a transcription start site (TSS). Top 5000 peaks from Mediator and its interactors were separated in the TSS, non TSS, typical enhancer and super enhancer categories and the percentage of overlap recalculated for each subset.Generation of histograms documenting ChIP-seq signal density at specific sets of promoters in the NSC genome was performed by HOMER annotatePeaks with 10 bp bins and 12,000 bp around the TSS. By default, HOMER normalizes the output histogram such that the resulting units are per bp per peak, on top of the standard total mapped tag normalization of 10 million tags. For each promoter, directionality was extracted from TSS annotation and each subset was split between plus or minus strand. Subsequently, split lists were then remerged taking into account directionality to finally calculate the ChIP-signal density values. Enhancer-annotated expressed genes not present on the super enhancer gene list or the broad H3K4me3 promoter gene list were used as typical genes.72 using default categories. SE genes are defined as the closest active gene, RKPM > 0.5 in our NSC RNA-seq data28, to an SE. Broad H3K4me3 genes are defined as having the top 5% broadest H3K4me3 domains31. Additionally, we performed GO ontology biological process analysis of the transcription regulators found in each subset. Benjamini-corrected p-value was used for ranking Gene Ontology terms.Gene Ontology analyses on the different gene categories were performed using DAVID version 6.731Top 100 SE + Broad genes are genes with a broad promoter and from that category the 100 genes that are the nearest gene to the highest SEs (i.e. one gene per SE), as ranked by Mediator signal. Top 100 SE-Broad genes are genes without a broad promoter and from that category the 100 genes that are the nearest gene to the highest SEs, as ranked by Mediator signal. Top 100 Broad-SE genes are genes that not the nearest gene to an SE and from that category the 100 genes ranked by the broadest H3K4me3 signal at their promoter, from an already described list of mouse NSC broad H3K4me3 promoters28. Gene expression values with significant triplicates were assigned to the different subsets. The effect of Tcf4 knock-down was indicated by plotting the mean fold change vs the scrambled shRNA condition for each subset.Tcf4-regulated genes were derived from an RNA-seq. experiment performed in triplicate 48 h after Tcf4-shRNA-transfection or control shRNA-transfection in mouse NSCsFurther information on research design is available in the Supplementary InformationPeer Review FileDescription of Additional Supplementary FilesSupplementary Data 1Supplementary Data 2Supplementary Data 3Supplementary Data 4Supplementary Data 5Reporting SummarySource Data