Abstract
Gene expression is controlled by dynamic localization of thousands of regulatory proteins to precise genomic regions. Understanding this cell type-specific process has been a longstanding goal yet remains challenging because DNA–protein mapping methods generally study one protein at a time. Here, to address this, we developed chromatin immunoprecipitation done in parallel (ChIP-DIP) to generate genome-wide maps of hundreds of diverse regulatory proteins in a single experiment. ChIP-DIP produces highly accurate maps within large pools (>160 proteins) for all classes of DNA-associated proteins, including modified histones, chromatin regulators and transcription factors and across multiple conditions simultaneously. First, we used ChIP-DIP to measure temporal chromatin dynamics in primary dendritic cells following LPS stimulation. Next, we explored quantitative combinations of histone modifications that define distinct classes of regulatory elements and characterized their functional activity in human and mouse cell lines. Overall, ChIP-DIP generates context-specific protein localization maps at consortium scale within any molecular biology laboratory and experimental system.
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Data availability
All ChIP-DIP datasets generated in this study are available at GEO: GSE227773. Accession numbers for publicly available datasets used in this study are listed in Supplementary Methods.
Code availability
Publicly available software and packages were used in this study as indicated in Methods and Supplementary Methods. The original code for the ChIP-DIP pipeline is available on GitHub at https://github.com/GuttmanLab/chipdip-pipeline/tree/Paper (https://doi.org/10.5281/zenodo.13952458) (ref. 115).
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Acknowledgements
We thank S. Hiley for editing. We thank I.-M. Strazhnik and A. Koivula for illustrations and formatting the figures. This work was funded by grants from the NIH (R01 HG012216, R01 DA053178, U01 DK127420 to M.G.), the Chan Zuckerberg Initiative Ben Barres Early Career Acceleration Award, the NIH UCLA-Caltech Medical Scientist Training Program (T32GM008042, I.N.G. and B.T.Y.), NCI F30CA278005 (J.K.G.) and the University of Southern California MD/PhD program (J.K.G.). Sequencing was performed at the Millard and Muriel Jacobs Genetics and Genomics facility at Caltech with support from I. Antoshechkin and at the Broad Institute Genomics Platform.
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A.A.P., M.R.B. and M.G. conceived ChIP-DIP; A.A.P. and M.R.B. developed ChIP-DIP; A.A.P., I.N.G. and J.K.G. optimized ChIP-DIP; A.A.P. and I.N.G. generated the data presented in this paper; C.S.L., O.E. and A.B. cultured, collected and treated cells; I.N.G. developed the computational pipeline; B.T.Y. generated the GitHub repository for the pipeline; I.N.G. performed data analysis and visualization; A.A.P., I.N.G. and M.G. generated figures and wrote the paper.
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M.G., A.A.P., M.R.B., I.N.G. and J.K.G. are inventors of a submitted patent covering the ChIP-DIP method. The other authors declare no competing interests.
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Extended data
Extended Data Fig. 1 Potential sources of mixing in ChIP-DIP.
(a) Schematic of labeling strategy to generate Protein G beads coupled with a unique antibody-identifying oligonucleotide and a matched antibody. (i) Protein G beads are covalently modified with a biotin, (ii) oligonucleotides containing a 3’ biotin are conjugated to streptavidin, (iii) oligo-streptavidin complexes are mixed with biotinylated protein G beads and (iv) protein G beads are mixed with antibodies. This process is repeated for each unique oligonucleotide-antibody pair and then all bead-antibody conjugates are pooled together. (b) Schematic of three potential sources of dissociation of chromatin-antibody-bead-oligo conjugates that could lead to mixing during ChIP-DIP: dissociation 1) between oligo and bead, 2) between antibody and bead, or 3) between antibody and chromatin. (c) If oligos dissociate from their original beads and bind to distinct beads (oligo-bead dissociation), we would expect multiple distinct oligo types on the same bead. To quantify this, we computed the percent uniqueness of oligo-types within each split-pool cluster. The cumulative distribution of the uniqueness of antibody-ID oligos type (x-axis) within individual clusters is shown. (d) If antibodies dissociate from their original bead and reassociate with a different bead (antibody-bead dissociation), we expect that chromatin would associate with empty beads present in the experiment. We show a schematic of the experimental design to test for antibody movement between beads (top) and the quantification of reads per bead assigned to true targets (CTCF) or empty beads added during experimental processing steps (bottom). (e) If proteins (and their crosslinked chromatin) dissociate and reassociate to other beads containing the same epitope-specific antibodies (antibody-chromatin dissociation), we would expect that chromatin purified independently from human and mouse lysates would mix during the procedure. We show a schematic of the human-mouse mixing experimental design to test for chromatin movement (left) and quantification of species-specific reads assigned to human or mouse beads (right).
Extended Data Fig. 2 Mapping multiple components of the same regulator complex within a single experiment.
(a) Visualization of various components of the PRC1 (RING1B, CBX8) and PRC2 (EZH2, SUZ12, EED) complexes that were mapped within the same ChIP-DIP pool (K562 52 Antibody Pool) along a genomic region (hg38, chr4:500,000-5,500,000).
Extended Data Fig. 3 Histone modifications associated with five chromatin states.
(a) UMAP embedding of 12 histone modifications measured in K562 correspond to five chromatin states. (b) Metaplot of signal distribution of H3K36me3, H3K79me1 and H3K79me2 across the gene body of protein coding genes in K562. (c) Correlation scatterplot of H3K9Ac and H3K4me3 signals at promoter sites in mESC. (d) Enrichment heatmap of H3K9me3 and H4K20me3 at various associated (ZNF genes, LTRs, LINES) and unassociated (SINES, TSS) genomic elements in K562. H3 is shown as reference. For A-D, see Methods for details on ChIP-DIP experiments used for each analysis.
Extended Data Fig. 4 Chromatin regulators co-localizing with known histone targets.
(a) Metaplots of read coverage for three H3K4me3-associated chromatin regulators (JARID1A, RBBP5, PHF8) and H3K4me3 at four promoter groups in mESC. Promoter groups were identified using k-means clustering of CR signal. (b) Metaplot showing colocalization of multiple PRC1 and PRC2 members and their respective histone modifications at RING1B sites in K562. (c) Genome-wide correlation matrix of multiple HP1 proteins versus heterochromatin and euchromatin markers in K562. For A-C, see Methods for details on ChIP-DIP experiments used for each analysis.
Extended Data Fig. 5 Simultaneous mapping of distinct RNA polymerases and their isoforms.
(a) Bar graph showing enrichment of gene class coverage (rRNA, mRNA, snRNA or tRNA) for RNAP I, II and III in mESC. For each RNAP, the bar of its associated class (or classes) is highlighted. (b) Visualization of RNAP II phosphorylation isoforms across the NUP214 gene in K562 (left). Metaplot of signal distribution of RNAP II phosphorylation isoforms across the gene body of protein coding genes in K562 (right).
Extended Data Fig. 6 Chromatin dynamics and the relationship to gene expression following LPS stimulation in mDCs.
(a) Heatmap of change in normalized coverage per 100 kb bin for various mapped factors. For each factor, only enriched bins are shown and bins are sorted left-to-right by magnitude of change. (b) Violin plot of gene expression fold change for 6hrs vs 0hrs (left) and 24hrs vs 0hrs (right) grouped by sets of genes corresponding to sets of regions from Fig. 5C(see Methods). Shown are Mann-Whitney U test p-values. (c) Track visualization of H3K27ac at 0hrs, 6hrs and 24hrs across a genomic region (mm10, chr5:29,838,000-30,024,000) upstream of the inflammatory gene IL6 and containing regions belonging to the ‘activated’ set from Fig. 5B. (d) Heatmap of spearman correlation coefficients between histone coverage change and gene expression change between time points. Change is defined as the ratio between the two time points. All genes were included in the correlation heatmap on the left; only genes with a fold change of >2 in gene expression were included in the correlation heatmap on the right (see Methods and Supplemental Methods).
Extended Data Fig. 7 Transcription levels of specific clusters of H3K4me3 enriched regions.
(a) Violin plot of the transcriptional levels, measured by the RNAP II occupancy, of the five major clusters of H3K4me3 regions identified in Fig. 7.
Extended Data Fig. 8 Histone acetylation marks are highly correlated genome-wide.
(a) Genome-wide pearson correlation coefficients of 15 different histone acetylation marks in mESC. Correlations are based on coverage computed in 10 kb windows. (b) Comparison of 15 different histone acetylation marks across a genomic region (mm10, chr1:55,048,000-55,148,000) in mESC.
Extended Data Fig. 9 Enrichment profiles for NMF generated combinations (C1-C5) of histone acetylation marks.
(a) RNAP II, TF and CR enrichment matrix for regions assigned to combinations (C1-C5) from NMF decomposition of highly acetylated regions using histone acetylation marks, shown in Fig. 8. (b) Heatmap of genome position enrichments relative to TSS for regions assigned to combinations. (c) Transcription factors of top 10 most significant sequence motifs for regions assigned to each combination are listed.
Extended Data Fig. 10 Profiles for high density regions of NANOG-OCT4-SOX2.
(a) Plot showing normalized region scores (x-axis) for peak regions of NANOG-OCT4-SOX2, ordered by rank (y-axis). High density regions are defined as regions past the point where the slope = 1. (b) Track visualization of NANOG-OCT4-SOX2 upstream of the gene for the pluripotency transcription factor KLF4 in mESC. A high density region is indicated with a red bar; low density regions are indicated with grey bars. (c) Visualization of NANOG-OCT4-SOX2 near the TET2 gene, a developmentally associated chromatin regulator, in mESC. A high density region internal to the gene is indicated with a red bar. (d) Coverage metaplots over low density regions (LDR) vs high density regions (HDR) for pluripotency transcription factors and other transcriptional-related factors. Metagenes are centered on the region and the lengths represent the approximate difference in mean lengths (500 bps for LDRs and 14,500 bps for HDRs). An additional 4 kb surrounding each region is shown. (e) Enrichment heatmap for GO terms of genes associated with HDRs or LDRs containing C4, C5 or neither C4/C5 chromatin signatures. (f) Enrichment heatmap for development-associated GO terms of genes associated with HDRs or LDRs containing C4, C5 or neither C4/C5 chromatin signatures.
Supplementary information
Supplementary Information
Supplementary Figs. 1–13, Notes 1–3, Methods and Tables 1–5.
Supplementary Data 1
Antibody ID oligonucleotide sequences.
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Perez, A.A., Goronzy, I.N., Blanco, M.R. et al. ChIP-DIP maps binding of hundreds of proteins to DNA simultaneously and identifies diverse gene regulatory elements. Nat Genet 56, 2827–2841 (2024). https://doi.org/10.1038/s41588-024-02000-5
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DOI: https://doi.org/10.1038/s41588-024-02000-5
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