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.
This is a preview of subscription content, access via your institution
Access Nature and 54 other Nature Portfolio journals
Get Nature+, our best-value online-access subscription
$29.99 per month
cancel any time
Subscribe to this journal
Receive 12 print issues and online access
$209.00 per year
only $17.42 per issue
Rent or buy this article
Get just this article for as long as you need it
Prices may be subject to local taxes which are calculated during checkout
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.
R code used in this paper is deposited at https://github.com/jonathandlee12/iDAPT-MS.
Kornberg, R. & Lorch, Y. Chromatin structure and transcription. Annu. Rev. Cell Dev. Biol. 8, 563–587 (1992).
Gerstein, M. B. et al. Architecture of the human regulatory network derived from ENCODE data. Nature 489, 91–100 (2012).
Lambert, S. A. et al. The human transcription factors. Cell 172, 650–665 (2018).
Klemm, S. L., Shipony, Z. & Greenleaf, W. J. Chromatin accessibility and the regulatory epigenome. Nat. Rev. Genet. 20, 207–220 (2019).
Allis, C. D. & Jenuwein, T. The molecular hallmarks of epigenetic control. Nat. Rev. Genet. 17, 487–500 (2016).
Thurman, R. E. et al. The accessible chromatin landscape of the human genome. Nature 489, 75–82 (2012).
Boyle, A. P. et al. High-resolution mapping and characterization of open chromatin across the genome. Cell 132, 311–322 (2008).
Buenrostro, J. D., Giresi, P. G., Zaba, L. C., Chang, H. Y. & Greenleaf, W. J. Transposition of native chromatin for fast and sensitive epigenomic profiling of open chromatin, DNA-binding proteins and nucleosome position. Nat. Methods 10, 1213–1218 (2013).
Sung, M.-H., Baek, S. & Hager, G. L. Genome-wide footprinting: ready for prime time? Nat. Methods 13, 222–228 (2016).
Baek, S., Goldstein, I. & Hager, G. L. Bivariate genomic footprinting detects changes in transcription factor activity. Cell Rep. 19, 1710–1722 (2017).
Wierer, M. & Mann, M. Proteomics to study DNA-bound and chromatin-associated gene regulatory complexes. Hum. Mol. Genet. 25, R106–R114 (2016).
Torrente, M. P. et al. Proteomic interrogation of human chromatin. PLoS ONE 6, e24747 (2011).
Alajem, A. et al. Differential association of chromatin proteins identifies BAF60a/SMARCD1 as a regulator of embryonic stem cell differentiation. Cell Rep. 10, 2019–2031 (2015).
Kulej, K. et al. Time-resolved global and chromatin proteomics during herpes simplex virus type 1 (HSV-1) infection. Mol. Cell. Proteom. 16, S92–S107 (2017).
Reznikoff, W. S. Transposon Tn5. Annu. Rev. Genet. 42, 269–286 (2008).
Lam, S. S. et al. Directed evolution of APEX2 for electron microscopy and proximity labeling. Nat. Methods 12, 51–54 (2014).
Paek, J. et al. Multidimensional tracking of GPCR signaling via peroxidase-catalyzed proximity labeling. Cell 169, 338–349.e11 (2017).
Chen, X. et al. ATAC-see reveals the accessible genome by transposase-mediated imaging and sequencing. Nat. Methods 13, 1013–1020 (2016).
Corces, M. R. et al. An improved ATAC-seq protocol reduces background and enables interrogation of frozen tissues. Nat. Methods 14, 959–962 (2017).
Johnston, A. D., Simões-Pires, C. A., Thompson, T. V., Suzuki, M. & Greally, J. M. Functional genetic variants can mediate their regulatory effects through alteration of transcription factor binding. Nat. Commun. 10, 3472 (2019).
Martell, J. D., Deerinck, T. J., Lam, S. S., Ellisman, M. H. & Ting, A. Y. Electron microscopy using the genetically encoded APEX2 tag in cultured mammalian cells. Nat. Protoc. 12, 1792–1816 (2017).
Mandelman, D., Li, H., Poulos, T. L. & Schwarz, F. P. The role of quaternary interactions on the stability and activity of ascorbate peroxidase. Protein Sci. 7, 2089–2098 (1998).
Davies, D. R., Goryshin, I. Y., Reznikoff, W. S. & Rayment, I. Three-dimensional structure of the Tn5 synaptic complex transposition intermediate. Science 289, 77–85 (2000).
Navarrete-Perea, J., Yu, Q., Gygi, S. P. & Paulo, J. A. Streamlined tandem mass tag (SL-TMT) protocol: an efficient strategy for quantitative (phospho)proteome profiling using tandem mass tag-synchronous precursor selection-MS3. J. Proteome Res. 17, 2226–2236 (2018).
Encode Consortium. An integrated encyclopedia of DNA elements in the human genome. Nature 489, 57–74 (2012).
Skene, P. J. & Henikoff, S. An efficient targeted nuclease strategy for high-resolution mapping of DNA binding sites. eLife 6, e21856 (2017).
Thul, P. J. et al. A subcellular map of the human proteome. Science 356, eaal3321 (2017).
Xiao, R. et al. Pervasive chromatin-RNA binding protein interactions enable RNA-based regulation of transcription. Cell 178, 107–121.e18 (2019).
Guo, Y. E. et al. Pol II phosphorylation regulates a switch between transcriptional and splicing condensates. Nature 572, 543–548 (2019).
Allen, B. L. & Taatjes, D. J. The mediator complex: a central integrator of transcription. Nat. Rev. Mol. Cell Biol. 16, 155–166 (2015).
Kadoch, C. & Crabtree, G. R. Mammalian SWI/SNF chromatin remodeling complexes and cancer: mechanistic insights gained from human genomics. Sci. Adv. 1, 1–18 (2015).
Ruepp, A. et al. CORUM: the comprehensive resource of mammalian protein complexes. Nucleic Acids Res. 36, D646–D650 (2008).
Nusinow, D. P. et al. Quantitative proteomics of the Cancer Cell Line Encyclopedia. Cell 180, 387–402.e16 (2020).
Federation, A. J. et al. Highly parallel quantification and compartment localization of transcription factors and nuclear proteins. Cell Rep. 30, 2463–2471.e5 (2020).
Oughtred, R. et al. The BioGRID interaction database: 2019 update. Nucleic Acids Res. 47, D529–D541 (2019).
Weirauch, M. T. et al. Determination and inference of eukaryotic transcription factor sequence specificity. Cell 158, 1431–1443 (2014).
Nakahashi, H. et al. A genome-wide map of CTCF multivalency redefines the CTCF code. Cell Rep. 3, 1678–1689 (2013).
Bosisio, D. et al. A hyper-dynamic equilibrium between promoter-bound and nucleoplasmic dimers controls NF-kappaB-dependent gene activity. EMBO J. 25, 798–810 (2006).
Lanotte, M. et al. NB4, a maturation inducible cell line with t(15;17) marker isolated from a human acute promyelocytic leukemia (M3). Blood 77, 1080–1086 (1991).
Zhu, J. et al. Retinoic acid induces proteasome-dependent degradation of retinoic acid receptor alpha (RARalpha) and oncogenic RARalpha fusion proteins. Proc. Natl Acad. Sci. USA 96, 14807–14812 (1999).
de Thé, H. et al. The PML-RAR alpha fusion mRNA generated by the t(15;17) translocation in acute promyelocytic leukemia encodes a functionally altered RAR. Cell 66, 675–684 (1991).
Mueller, B. U. et al. ATRA resolves the differentiation block in t(15;17) acute myeloid leukemia by restoring PU.1 expression. Blood 107, 3330–3338 (2006).
Chih, D. Y., Chumakov, A. M., Park, D. J., Silla, A. G. & Koeffler, H. P. Modulation of mRNA expression of a novel human myeloid-selective CCAAT/enhancer binding protein gene (C/EBP epsilon). Blood 90, 2987–2994 (1997).
Duprez, E., Wagner, K., Koch, H. & Tenen, D. G. C/EBPbeta: a major PML-RARA-responsive gene in retinoic acid-induced differentiation of APL cells. EMBO J. 22, 5806–5816 (2003).
Martens, J. H. A. et al. PML-RARalpha/RXR alters the epigenetic landscape in acute promyelocytic leukemia. Cancer Cell 17, 173–185 (2010).
Warrell, R. P., He, L. Z., Richon, V., Calleja, E. & Pandolfi, P. P. Therapeutic targeting of transcription in acute promyelocytic leukemia by use of an inhibitor of histone deacetylase. J. Natl Cancer Inst. 90, 1621–1625 (1998).
Schep, A. N., Wu, B., Buenrostro, J. D. & Greenleaf, W. J. chromVAR: inferring transcription-factor-associated accessibility from single-cell epigenomic data. Nat. Methods 14, 975–978 (2017).
Witzel, M. et al. Chromatin-remodeling factor SMARCD2 regulates transcriptional networks controlling differentiation of neutrophil granulocytes. Nat. Genet. 49, 742–752 (2017).
Orfali, N. et al. All-trans retinoic acid (ATRA)-induced TFEB expression is required for myeloid differentiation in acute promyelocytic leukemia (APL). Eur. J. Haematol. 104, 236–250 (2020).
Hu, Z. et al. RUNX1 regulates corepressor interactions of PU.1. Blood 117, 6498–6508 (2011).
Wang, K. et al. PML/RARalpha targets promoter regions containing PU.1 consensus and RARE half sites in acute promyelocytic leukemia. Cancer Cell 17, 186–197 (2010).
Liu, N. et al. Direct promoter repression by BCL11A controls the fetal to adult hemoglobin switch. Cell 173, 430–442.e17 (2018).
Li, B., Tournier, C., Davis, R. J. & Flavell, R. A. Regulation of IL-4 expression by the transcription factor JunB during T helper cell differentiation. EMBO J. 18, 420–432 (1999).
Schütte, J. et al. jun-B inhibits and c-fos stimulates the transforming and trans-activating activities of c-jun. Cell 59, 987–997 (1989).
Chiu, R., Angel, P. & Karin, M. Jun-B differs in its biological properties from, and is a negative regulator of, c-Jun. Cell 59, 979–986 (1989).
Descombes, P. & Schibler, U. A liver-enriched transcriptional activator protein, LAP, and a transcriptional inhibitory protein, LIP, are translated from the sam mRNA. Cell 67, 569–579 (1991).
Bedi, R., Du, J., Sharma, A. K., Gomes, I. & Ackerman, S. J. Human C/EBP-ϵ activator and repressor isoforms differentially reprogram myeloid lineage commitment and differentiation. Blood 113, 317–327 (2009).
Meyers, R. M. et al. Computational correction of copy number effect improves specificity of CRISPR–Cas9 essentiality screens in cancer cells. Nat. Genet. 49, 1779–1784 (2017).
Postigo, A. A. & Dean, D. C. Differential expression and function of members of the zfh-1 family of zinc finger/homeodomain repressors. Proc. Natl Acad. Sci. USA 97, 6391–6396 (2000).
Sleven, H. et al. De Novo Mutations in EBF3 cause a neurodevelopmental syndrome. Am. J. Hum. Genet. 100, 138–150 (2017).
Chao, H.-T. et al. A syndromic neurodevelopmental disorder caused by de novo variants in EBF3. Am. J. Hum. Genet. 100, 128–137 (2017).
Harms, F. L. et al. Mutations in EBF3 disturb transcriptional profiles and cause intellectual disability, ataxia, and facial dysmorphism. Am. J. Hum. Genet. 100, 117–127 (2017).
Pabst, T. et al. Dominant-negative mutations of CEBPA, encoding CCAAT/enhancer binding protein-α (C/EBPα), in acute myeloid leukemia. Nat. Genet. 27, 263–270 (2001).
Ji, X. et al. Chromatin proteomic profiling reveals novel proteins associated with histone-marked genomic regions. Proc. Natl Acad. Sci. USA 112, 3841–3846 (2015).
Villaseñor, R. et al. ChromID identifies the protein interactome at chromatin marks.Nat. Biotechnol. 38, 728–736 (2020).
Gauchier, M., van Mierlo, G., Vermeulen, M. & Déjardin, J. Purification and enrichment of specific chromatin loci. Nat. Methods 17, 380–389 (2020).
Chen, X., Zaro, J. L. & Shen, W.-C. Fusion protein linkers: property, design and functionality. Adv. Drug Deliv. Rev. 65, 1357–1369 (2013).
Picelli, S. et al. Tn5 transposase and tagmentation procedures for massively scaled sequencing projects. Genome Res. 24, 2033–2040 (2014).
Wang, Y. et al. Reversed-phase chromatography with multiple fraction concatenation strategy for proteome profiling of human MCF10A cells. Proteomics 11, 2019–2026 (2011).
Paulo, J. A. et al. Quantitative mass spectrometry-based multiplexing compares the abundance of 5000S. cerevisiae proteins across 10 carbon sources. J. Proteom. 148, 85–93 (2016).
Ting, L., Rad, R., Gygi, S. P. & Haas, W. MS3 eliminates ratio distortion in isobaric multiplexed quantitative proteomics. Nat. Methods 8, 937–940 (2011).
McAlister, G. C. et al. MultiNotch MS3 enables accurate, sensitive, and multiplexed detection of differential expression across cancer cell line proteomes. Anal. Chem. 86, 7150–7158 (2014).
Paulo, J. A., O’Connell, J. D. & Gygi, S. P. A Triple Knockout (TKO) proteomics standard for diagnosing ion interference in isobaric labeling experiments. J. Am. Soc. Mass. Spectrom. 27, 1620–1625 (2016).
Nowinski, S. M. et al. Mitochondrial fatty acid synthesis coordinates oxidative metabolism in mammalian mitochondria. eLife 9, e58041 (2020).
Huttlin, E. L. et al. A tissue-specific atlas of mouse protein phosphorylation and expression. Cell 143, 1174–1189 (2010).
Paulo, J. A. & Gygi, S. P. Isobaric tag-based protein profiling of a nicotine-treated Alpha7 nicotinic receptor-null human haploid cell line. Proteomics 18, e1700475 (2018).
Elias, J. E. & Gygi, S. P. Target-decoy search strategy for mass spectrometry-based proteomics. Methods Mol. Biol. 604, 55–71 (2010).
Elias, J. E. & Gygi, S. P. Target-decoy search strategy for increased confidence in large-scale protein identifications by mass spectrometry. Nat. Methods 4, 207–214 (2007).
McAlister, G. C. et al. Increasing the multiplexing capacity of TMTs using reporter ion isotopologues with isobaric masses. Anal. Chem. 84, 7469–7478 (2012).
Ritchie, M. E. et al. limma powers differential expression analyses for RNA-sequencing and microarray studies. Nucleic Acids Res. 43, e47 (2015).
Fabregat, A. et al. The reactome pathway knowledgebase. Nucleic Acids Res. 46, D649–D655 (2018).
Draizen, E. J. et al. HistoneDB 2.0: a histone database with variants—an integrated resource to explore histones and their variants. Database 2016, baw014 (2016).
The Uniprot Consortium. UniProt: a worldwide hub of protein knowledge. Nucleic Acids Res. 47, D506–D515 (2019).
The Gene Ontology Consortium. The gene ontology resource: 20 years and still GOing strong. Nucleic Acids Res. 47, D330–D338 (2019).
Ghosh, P., Murugavel, P. & Sowdhamini, R. hRBPome: a central repository of all known human RNA-binding proteins. Preprint at bioRxiv https://doi.org/10.1101/269043 (2018).
Meers, M. P., Bryson, T. D., Henikoff, J. G. & Henikoff, S. Improved CUT&RUN chromatin profiling tools. eLife 8, e46314 (2019).
Corces, M. R. et al. The chromatin accessibility landscape of primary human cancers. Science 362, eaav1898 (2018).
Heinz, S. et al. Simple combinations of lineage-determining transcription factors prime cis-regulatory elements required for macrophage and B cell identities. Mol. Cell 38, 576–589 (2010).
Fornes, O. et al. JASPAR 2020: update of the open-access database of transcription factor binding profiles.Nucleic Acids Res. 48, 87–92 (2019).
R Core Team. R: A Language and Environment for Statistical Computing (R Foundation for Statistical Computing, 2014); https://www.R-project.org/
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.
J.D.L., J.G.C. and P.P.P. have filed a patent describing iDAPT. All other authors declare no competing interests.
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 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.
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.
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.
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).
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.
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 Figs. 1–12 and Protocol.
Supplementary Tables 1–3
Source Data Extended Data Fig. 1
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.
Rights and permissions
About this article
Cite this article
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
This article is cited by
Characterizing accessible chromatin with iDAPT
Nature Reviews Genetics (2021)