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Dual DNA and protein tagging of open chromatin unveils dynamics of epigenomic landscapes in leukemia

Nature Methods volume 18, pages 293–302 (2021)Cite this article

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Abstract

The architecture of chromatin regulates eukaryotic cell states by controlling transcription factor access to sites of gene regulation. Here we describe a dual transposase–peroxidase approach, integrative DNA and protein tagging (iDAPT), which detects both DNA (iDAPT-seq) and protein (iDAPT-MS) associated with accessible regions of chromatin. In addition to direct identification of bound transcription factors, iDAPT enables the inference of their gene regulatory networks, protein interactors and regulation of chromatin accessibility. We applied iDAPT to profile the epigenomic consequences of granulocytic differentiation of acute promyelocytic leukemia, yielding previously undescribed mechanistic insights. Our findings demonstrate the power of iDAPT as a platform for studying the dynamic epigenomic landscapes and their transcription factor components associated with biological phenomena and disease.

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Fig. 1: TP fusion probes tag DNA at regions of open chromatin.
Fig. 2: iDAPT-MS reveals the open chromatin-associated proteome.
Fig. 3: Integrative analysis of iDAPT-MS and iDAPT-seq classifies transcription factor activities on open chromatin at steady state.
Fig. 4: iDAPT profiling of the NB4 APL cell line on ATRA treatment reveals dynamics of transcription factor activity.

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Data availability

iDAPT-seq/ATAC-seq and CUT&RUN datasets are deposited in GEO (GSE158350). iDAPT-MS proteomics data are deposited to the ProteomeXchange Consortium via the PRIDE partner repository (PXD022252). Raw confocal image files (.czi) are deposited to the Dryad repository at https://doi.org/10.5061/dryad.4xgxd257p. Raw iDAPT-seq/ATAC-seq sequencing data (GSE158350) are associated with the following figures: Fig. 1b,c and Extended Data Fig. 2 (GM12878 ATAC-seq, iDAPT-seq); Figs. 2g,h and 3 and Extended Data Figs. 5, 7 and 8 (K562 iDAPT-seq); Fig. 4g, Extended Data Figs. 7 and 8 and Supplementary Figs 5–9 (NB4 iDAPT-seq). Raw CUT&RUN sequencing data (GSE158350) are associated with the following figures: Fig. 2c and Extended Data Fig. 5. Raw mass spectrometry data (PXD022252) are associated with the following figures: Figs. 2 and 3, Extended Data Figs. 3, 6 and 8 and Supplementary Figs. 3 and 4 (K562 iDAPT-MS); Fig. 4, Extended Data Figs. 4, 6 and 8–10 and Supplementary Figs. 4 and 6–10 (NB4 iDAPT-MS). Preprocessed mass spectrometry data are available as Supplementary Tables 1 and 2. Raw confocal microscopy image data https://doi.org/10.5061/dryad.4xgxd257p are associated with the following figures: Figs. 1d,e and 2d and Extended Data Fig. 6d,e. Publicly available sequencing datasets used are as follows: GM12878 ATAC-seq: https://www.ncbi.nlm.nih.gov//geo/query/acc.cgi?acc=GSE47753 (SRR891268, SRR891269, SRR891270, SRR891271), https://www.ncbi.nlm.nih.gov/bioproject/PRJNA482539 (SRR7586167, SRR7586168), https://www.ncbi.nlm.nih.gov/bioproject/PRJNA305986 (SRR2999312, SRR2999313, SRR2999314, SRR2999315), https://www.ncbi.nlm.nih.gov/bioproject/PRJNA380283 (SRR5427884, SRR5427885, SRR5427886, SRR5427887); ENCODE K562 ChIP–seq: https://www.encodeproject.org/, with unique identifiers listed in Supplementary Table 3; ENCODE K562 RNA-seq: https://www.encodeproject.org/files/ENCFF664LYH/@@download/ENCFF664LYH.tsv and https://www.encodeproject.org/files/ENCFF855OAF/@@download/ENCFF855OAF.tsv; NB4+/- ATRA RNA-seq: https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE53258 (GSM1288651, GSM1288652, GSM1288653, GSM1288654), https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE53259 (GSM1288659, GSM1288660, GSM1288661, GSM1288662), and https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE93877 (GSM2464389, GSM2464392). Publicly available proteome datasets used are as follows: whole cell proteome: https://gygi.med.harvard.edu/sites/gygi.med.harvard.edu/files/documents/protein_quant_current_normalized.csv.gz; nuclear proteome and differential salt fractionation: https://ars.els-cdn.com/content/image/1-s2.0-S2211124720301303-mmc2.xlsx, Alajem et al.: https://www.cell.com/cms/10.1016/j.celrep.2015.02.064/attachment/daebc867-0c82-45ef-837b-b408682c76cf/mmc2.xlsx; Torrente et al.: https://doi.org/10.1371/journal.pone.0024747.s004 and https://doi.org/10.1371/journal.pone.0024747.s006; Kulej et al.: https://www.mcponline.org/highwire/filestream/35613/field_highwire_adjunct_files/5/TABLE_S5_Host_chromatin_bound_proteome.xlsx. Additional public reference datasets are as follows: hg38 reference genome: ftp://ftp.ensembl.org/pub/release-94/fasta/homo_sapiens/dna/Homo_sapiens.GRCh38.dna.primary_assembly.fa.gz; hg38 blacklist regions: https://www.encodeproject.org/files/ENCFF356LFX/@@download/ENCFF356LFX.bed.gz; CORUM v3.0 complexes: http://mips.helmholtz-muenchen.de/corum/download/allComplexes.txt.zip; Human Protein Atlas v19: https://www.proteinatlas.org/download/subcellular_location.tsv.zip; BioGrid v3.5.178: https://downloads.thebiogrid.org/File/BioGRID/Release-Archive/BIOGRID-3.5.178/BIOGRID-MV-Physical-3.5.178.tab2.zip; Lambert et al. transcription factors: https://www.cell.com/cms/10.1016/j.cell.2018.01.029/attachment/ede37821-fd6f-41b7-9a0e-9d5410855ae6/mmc2.xlsx; HistoneDB 2.0: https://www.ncbi.nlm.nih.gov/research/HistoneDB2.0/HistoneDB/static/browse/dumps/seqs.txt; hRBPome: http://caps.ncbs.res.in/hrbpome/downloads/high_confidence_proteins.fasta; DepMap 19Q3: https://ndownloader.figshare.com/files/16757666. CisBP transcription factors (http://cisbp.ccbr.utoronto.ca/) were obtained via the command data(‘human_pwms_v2’) in R package ‘chromVARmotifs’: https://github.com/GreenleafLab/chromVARmotifs. ReactomeDB v.70 pathway annotations (https://reactome.org/) were obtained via the ‘reactomePathways’ command in R package ‘fgsea’: https://bioconductor.org/packages/release/bioc/html/fgsea.html. Gene Ontology (http://geneontology.org/) was queried from org.Hs.eg.db using the ‘select’ function from AnnotationDbi in R. UniProt IDs (https://www.uniprot.org/) were either downloaded from the UniProt website or collated via biomaRt in R (https://www.bioconductor.org/packages/release/bioc/html/biomaRt.html). Source data are provided with this paper.

Code availability

R code used in this paper is deposited at https://github.com/jonathandlee12/iDAPT-MS.

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Acknowledgements

We thank J. Boehm, P. Cheung, J. Harper, J. Heo, P. Kharchenko and all members of the Pandolfi laboratory for their input. We are grateful to the Harvard Medical School Biopolymers Facility, Harvard Medical School Research Computing and BIDMC Confocal Imaging Core for their assistance and support. This work was supported in part by the Ludwig Center at Harvard, the Singapore Ministry of Health’s National Medical Research Council under its Singapore Translational Research (STaR) Investigator Award, the National Research Foundation Singapore and the Singapore Ministry of Education under its Research Centres of Excellence initiative, a Harvard Medical School Innovation Grant Program grant awarded to J.D.L. and National Institutes of Health (NIH) grants R01 GM132129 to J.A.P.; R35 CA232105 to F.J.S.; R35 CA197697 and P01 HL131477 to D.G.T.; GM67945 to S.P.G. and R35 CA197529 to P.P.P.

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Authors and Affiliations

  1. Cancer Research Institute, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, MA, USA

    Jonathan D. Lee, Ryan R. Posey, Vera Mugoni, Giulia Cheloni, Yu-Ru Lee, John G. Clohessy & Pier Paolo Pandolfi

  2. Department of Medicine and Pathology, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, MA, USA

    Jonathan D. Lee, Ryan R. Posey, Vera Mugoni, Giulia Cheloni, Yu-Ru Lee, John G. Clohessy & Pier Paolo Pandolfi

  3. Ludwig Center at Harvard, Harvard Medical School, Boston, MA, USA

    Jonathan D. Lee, Ryan R. Posey, Vera Mugoni, Giulia Cheloni, Yu-Ru Lee, John G. Clohessy & Pier Paolo Pandolfi

  4. Paulson School of Engineering and Applied Sciences, Harvard University, Cambridge, MA, USA

    Jonathan D. Lee

  5. Department of Cell Biology, Harvard Medical School, Boston, MA, USA

    Joao A. Paulo & Steven P. Gygi

  6. Harvard Stem Cell Institute, Boston, MA, USA

    Nikki R. Kong & Daniel G. Tenen

  7. Institute of Biomedical Sciences, Academia Sinica, Taipei, Taiwan

    Yu-Ru Lee

  8. HMS Initiative for RNA Medicine, Department of Pathology, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, MA, USA

    Frank J. Slack

  9. Cancer Science Institute of Singapore, Singapore, Singapore

    Daniel G. Tenen

  10. Preclinical Murine Pharmacogenetics Facility, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, MA, USA

    John G. Clohessy

  11. MBC, Department of Molecular Biotechnology and Health Sciences, University of Turin, Turin, Italy

    Pier Paolo Pandolfi

  12. Renown Institute for Cancer, Nevada System of Higher Education, Reno, NV, USA

    Pier Paolo Pandolfi

Authors
  1. Jonathan D. Lee
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  2. Joao A. Paulo
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  3. Ryan R. Posey
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  4. Vera Mugoni
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  5. Nikki R. Kong
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  6. Giulia Cheloni
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  7. Yu-Ru Lee
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  8. Frank J. Slack
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  9. Daniel G. Tenen
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  10. John G. Clohessy
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  11. Steven P. Gygi
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  12. Pier Paolo Pandolfi
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Contributions

J.D.L. conceived the project, supervised the study, designed and performed experiments, carried out computational analyses and wrote the manuscript. J.A.P. performed mass spectrometry analyses. R.R.P. designed and performed experiments and performed image analyses. V.M., N.R.K., G.C. and Y.-R.L. designed and performed experiments. F.J.S., D.G.T., J.G.C. and S.P.G. supervised the study. P.P.P. conceived the project, supervised the study and wrote the manuscript.

Corresponding authors

Correspondence to Jonathan D. Lee or Pier Paolo Pandolfi.

Ethics declarations

Competing interests

J.D.L., J.G.C. and P.P.P. have filed a patent describing iDAPT. All other authors declare no competing interests.

Additional information

Peer review information Lei Tang was the primary editor on this article and managed its editorial process and peer review in collaboration with the rest of the editorial team.

Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Extended data

Extended Data Fig. 1 Optimization of transposase/peroxidase fusion probes for transposase activity.

a, Schematic of recombinant fusion protein linear sequence. PT, peroxidase/transposase; TP, transposase/peroxidase; F, FLAG; L, linker. b, Sequences of protein linkers tested for fusion protein activity. c, Quantitative PCR assessment of pre-amplified GM12878 ATAC-seq libraries generated with the corresponding enzymes (n = 1 independent experiment). d, TapeStation DNA HS 5000 assessment of fragment size distributions of GM12878 ATAC-seq libraries. Nucleosomal fragmentation is marked inline. e,f, Gel shift assay (e) and DNA fragment distributions (f) of tagmentation reactions of linearized pSMART plasmid with the corresponding enzymes. Gel shift and DNA fragments were measured on a 1% agarose gel. Images are representative of two independent experiments. MEDS, Mosaic End double-stranded transposon.

Source data

Extended Data Fig. 2 Assessment of transposase activity on native chromatin.

a, Fragment size distributions of GM12878 ATAC-seq/iDAPT-seq libraries. b, Ratio of transposon insertions at Ensembl v94 transcription start sites (TSS) relative to background from in-house ATAC-seq/iDAPT-seq and published ATAC-seq libraries from refs. 8,18,19,20 generated from the GM12878 cell line (n = 1). c, Proportion of non-mitochondrial reads from GM12878 ATAC-seq/iDAPT-seq libraries. d, Heatmap of pairwise Pearson correlation coefficients of genome-wide transposon insertion frequencies for the indicated GM12878 ATAC-seq/iDAPT-seq libraries.

Extended Data Fig. 3 Assessment of iDAPT protein labeling in the K562 cell line.

a, Western blot of labeled nuclear lysates with negative (Tn5-F, APEX2-F) and fusion (TP1-5) probes. Images are representative of two independent experiments. Ratios, relative total streptavidin intensities normalized by corresponding PCNA intensities. b, Western blot of labeled nuclear lysates with either single enzymatic domains (T, Tn5-F; A, APEX2-F) or the TP3 fusion probe with or without either biotin-phenol or hydrogen peroxide (H2O2). Images are representative of two independent experiments. Ratios, relative total streptavidin intensities normalized by corresponding PCNA intensities. c, Heatmap of pairwise Pearson correlation coefficients of K562 iDAPT-MS profiles for the indicated probes. d, Venn diagram of significant proteins (log2 fold change > 0 and false discovery rate < 5%) identified by TP5 or TP3 versus negative control probes by iDAPT-MS.

Source data

Extended Data Fig. 4 Assessment of iDAPT protein labeling in the NB4 cell line.

a, Western blot of labeled nuclear lysates with Tn5-F or TP3 probes and with or without pre-transposition blocking of endogenous peroxidase activity with 0.1% sodium azide and 0.03% hydrogen peroxide. Images are of a single experiment. Ratios, relative total streptavidin intensities normalized by corresponding PCNA intensities. b, Schematic of iDAPT-MS experimental design and SL-TMT sample labeling for NB4 cell line profiling. c, Volcano plot of proteins enriched by fusion (TP3) versus negative control (Tn5-F and APEX2-F) probes in NB4 nuclei. Blue points, log2 fold change > 0 and false discovery rate (FDR)<5%; red points, CisBP sequence-specific transcription factors; black points, points with corresponding gene symbol labels. d, Heatmap of pairwise Pearson correlation coefficients of NB4 iDAPT-MS profiles for the indicated probes and treatment conditions.

Source data

Extended Data Fig. 5 Analysis of open chromatin protein localization by ChIP-seq and CUT&RUN.

a, Scatterplot of protein enrichment profiles by iDAPT-MS from both K562 and NB4 cell lines. b,c, CUT&RUN (top) and immunoprecipitation (bottom) enrichment of ERH (b) and WBP11 (c) in K562 cells relative to normal rabbit IgG antibody. Western blotting images are of a single experiment. Red lines, CUT&RUN enrichment of target epitopes across K562 iDAPT-seq peaks. Black lines, CUT&RUN enrichment of normal rabbit IgG antibody across K562 iDAPT-seq peaks. Solid and dashed lines, duplicate CUT&RUN analyses. d, Distribution of CUT&RUN peaks overlapping K562 iDAPT-seq peaks. CUT&RUN peaks were determined using a 1% false discovery rate cutoff from MACS2. e, Number of iDAPT-seq peaks overlapping ChIP-seq peaks in K562 cells. Listed proteins are profiled in K562 cells by the ENCODE consortium (Supplementary Table 3) and are enriched by K562 iDAPT-MS (5% FDR).

Source data

Extended Data Fig. 6 Analysis of subcellular enrichment by iDAPT-MS.

a,b, Subcellular enrichment of K562 (a) and NB4 (b) iDAPT-MS profiles, using annotations from the Human Protein Atlas. NES (normalized enrichment score) and FDR (false discovery rate), gene set enrichment analysis. c, Distribution of Pearson correlation coefficients between Tn5-F ATAC-see and co-immunostaining of the SC35 nuclear speckle marker or chromatin state markers (RNA Pol II S2P, H3K27Ac) per nucleus in three cancer cell lines. Numbers of nuclei assessed per marker are displayed inline, with images drawn from two independent experiments. Center line, median value; box limits, upper and lower quartiles; whiskers, 1.5x interquartile range; points, outliers. d,e, Representative images of co-immunofluorescence staining of the SC35 nuclear speckle marker with Tn5-F ATAC-see in the MDA-MB-231 (d) and the DU145 (e) cancer cell lines. Similar results were visually confirmed for more than ten nuclei for each cell line and are quantified in (c). Scale bars, 5 μm. f, Proportion of annotated proteins detected and significantly enriched (log2 fold change > 0 and FDR<0.05) by iDAPT-MS for the given protein families. n, total number of proteins annotated in each protein family. g, Distribution of iDAPT-MS log2 fold changes of detected histone and non-histone proteins. Center line, median value; box limits, upper and lower quartiles; whiskers, 1.5x interquartile range; black points, outliers. n, number of quantified proteins by iDAPT-MS per group. p-value, two-sided Wilcoxon rank-sum test with Bonferroni correction.

Extended Data Fig. 7 Assessment of TP3 iDAPT-seq from native chromatin versus naked genomic DNA.

a, Enrichment of CisBP sequence-specific transcription factors via NB4 iDAPT-MS. Normalized enrichment score (NES) and p-value, gene set enrichment analysis. b, Fragment size distributions of iDAPT-seq libraries generated from K562 and NB4 native chromatin and naked genomic DNA. c,d, Ratio of transposon insertions at Ensembl v94 transcription start sites (TSS) relative to background from K562 (c) and NB4 (d) iDAPT-seq datasets. e,f, Principal component analysis of genome-wide transposon insertion frequencies from K562 (e) and NB4 (f) iDAPT-seq libraries. g,h, Volcano plot of K562 (g) and NB4 (h) iDAPT-seq profiles analyzed with DESeq2. Peak statistics are listed below. FDR, false discovery rate; LFC, log2 fold change.

Extended Data Fig. 8 Classification of transcription factors by footprinting activity.

a,b, Classification scheme of transcription factor motifs by composite footprinting score from K562 (a) or NB4 (b) iDAPT-seq datasets. Separation of class A and B motifs was determined by a two-state Gaussian mixture model; separation of class B and C motifs was demarcated by either a false discovery rate > 5% or footprinting score < 0. c, Bivariate footprinting analysis of native chromatin versus naked genomic DNA from the NB4 cell line. Red, class A transcription factors; blue, class B transcription factors; gray, class C transcription factors. d, Tabulation of transcription factor footprinting classifications for those transcription factors significantly enriched by both K562 and NB4 iDAPT-MS. e, Number of significant CisBP transcription factors in each footprinting class as determined by iDAPT-MS or ENCODE ChIP-seq, with corresponding numbers of associated transcription factor motifs per class as determined by iDAPT-seq. f, Comparison of CisBP sequence-specific transcription factors enriched by fusion probe iDAPT-MS versus iDAPT-seq footprinting analysis in the NB4 cell line.

Extended Data Fig. 9 Analysis of NB4 iDAPT-MS profiles upon treatment with ATRA.

a, Representative gating strategy for flow cytometry analyses as in Fig. 4b. b, Western blotting analysis of the PML epitope from the NB4 cell line upon 48 hr ATRA treatment versus DMSO vehicle treatment (0.01%). Images are representative of two independent experiments. PCNA, loading control. c, NB4 cell counts after 48 hrs of treatment with either 1 μM ATRA or vehicle (0.01% DMSO), as measured by CellTiter-Glo (n = 5 independent wells). p-value, Welch two-tailed t-test. d, Volcano plot of proteins enriched by the TP3 fusion probe in NB4 nuclei treated with either ATRA or DMSO. Blue points, log2 fold change > 0 and false discovery rate (FDR) < 5%; red points, log2 fold change < 0 and false discovery rate (FDR) < 5%; black points, points with corresponding gene symbol labels. e, ReactomeDB pathway enrichment analysis from iDAPT-MS of NB4 ATRA versus DMSO treatment. FDR, gene set enrichment analysis false discovery rate.

Source data

Extended Data Fig. 10 Integrative analysis of iDAPT-MS and iDAPT-seq transcription factor abundance and activities.

a, Schematic outlining the nine classes emerging from the changes in transcription factor abundances and activities on open chromatin upon ATRA treatment. Concordant or discordant changes in abundance and activities suggest activating or repressive activities on chromatin, respectively. b, Distribution of log2 fold changes of transcription factor abundances as enriched by TP3 versus negative control iDAPT-MS profiles from untreated NB4 cells, separated by repressive (class I, increasing chromatin accessibility, decreasing protein abundance) or activating (class VII, decreasing chromatin accessibility, decreasing protein abundance) transcription factors as classified upon NB4 treatment with ATRA (mean ± s.e.m.). n, number of represented proteins from NB4 iDAPT-MS. p-value, two-sided Wilcoxon rank-sum test.

Supplementary information

Supplementary Information

Supplementary Figs. 1–12 and Protocol.

Reporting Summary

Supplementary Tables

Supplementary Tables 1–3

Source data

Source Data Extended Data Fig. 1

Unprocessed gels.

Source Data Extended Data Fig. 3

Unprocessed western blots.

Source Data Extended Data Fig. 4

Unprocessed western blots.

Source Data Extended Data Fig. 5

Unprocessed western blots.

Source Data Extended Data Fig. 9

Unprocessed western blots.

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Lee, J.D., Paulo, J.A., Posey, R.R. et al. Dual DNA and protein tagging of open chromatin unveils dynamics of epigenomic landscapes in leukemia. Nat Methods 18, 293–302 (2021). https://doi.org/10.1038/s41592-021-01077-8

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