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Chromatin accessibility profiling by ATAC-seq

Abstract

The assay for transposase-accessible chromatin using sequencing (ATAC-seq) provides a simple and scalable way to detect the unique chromatin landscape associated with a cell type and how it may be altered by perturbation or disease. ATAC-seq requires a relatively small number of input cells and does not require a priori knowledge of the epigenetic marks or transcription factors governing the dynamics of the system. Here we describe an updated and optimized protocol for ATAC-seq, called Omni-ATAC, that is applicable across a broad range of cell and tissue types. The ATAC-seq workflow has five main steps: sample preparation, transposition, library preparation, sequencing and data analysis. This protocol details the steps to generate and sequence ATAC-seq libraries, with recommendations for sample preparation and downstream bioinformatic analysis. ATAC-seq libraries for roughly 12 samples can be generated in 10 h by someone familiar with basic molecular biology, and downstream sequencing analysis can be implemented using benchmarked pipelines by someone with basic bioinformatics skills and with access to a high-performance computing environment.

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Fig. 1: Schematic of the ATAC-seq transposition reaction and library preparation.
Fig. 2: Schematic overview of ATAC-seq protocol.
Fig. 3: Assessing ATAC-seq library quality.
Fig. 4: Overview of the steps of ATAC-seq data analysis.
Fig. 5: Schematic of peak merging strategies and the resulting merged peak sets.

Data availability

The ATAC-seq datasets generated for the protocol optimizations detailed in Supplementary Figs. 1, 4 and 5 are available on the Gene Expression Omnibus under accession number GSE188797. The data used in Fig. 5 are taken from ref. 38. All analyses were performed using the hg38 human genome.

Code availability

The source code for the iterative overlap is freely available at https://github.com/corceslab/ATAC_IterativeOverlapPeakMerging88. All other ATAC-seq data analysis for the figures used in this protocol were generated using PEPATAC73 with Bulker (container version 1.0.8), available at http://pepatac.databio.org/en/latest/.

References

  1. Ernst, J. & Kellis, M. Discovery and characterization of chromatin states for systematic annotation of the human genome. Nat. Biotechnol. 28, 817–825 (2010).

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  2. Ernst, J. et al. Mapping and analysis of chromatin state dynamics in nine human cell types. Nature 473, 43–49 (2011).

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  3. Klemm, S. L., Shipony, Z. & Greenleaf, W. J. Chromatin accessibility and the regulatory epigenome. Nat. Rev. Genet. 20, 207–220 (2019).

    CAS  Article  PubMed  Google Scholar 

  4. Johnson, D. S., Mortazavi, A., Myers, R. M. & Wold, B. Genome-wide mapping of in vivo protein–DNA interactions. Science 316, 1497–1502 (2007).

    CAS  Article  PubMed  Google Scholar 

  5. Furey, T. S. ChIP–seq and beyond: new and improved methodologies to detect and characterize protein–DNA interactions. Nat. Rev. Genet. 13, 840–852 (2012).

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  6. Nakato, R. & Sakata, T. Methods for ChIP-seq analysis: a practical workflow and advanced applications. Methods 187, 44–53 (2021).

    CAS  Article  PubMed  Google Scholar 

  7. Schmid, M., Durussel, T. & Laemmli, U. K. ChIC and ChEC: genomic mapping of chromatin proteins. Mol. Cell 16, 147–157 (2004).

    CAS  PubMed  Google Scholar 

  8. Skene, P. J. & Henikoff, S. An efficient targeted nuclease strategy for high-resolution mapping of DNA binding sites. eLife 6, e21856 (2017).

    Article  PubMed  PubMed Central  Google Scholar 

  9. Kaya-Okur, H. S. et al. CUT&Tag for efficient epigenomic profiling of small samples and single cells. Nat. Commun. 10, 1930 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  10. Schmidl, C., Rendeiro, A. F., Sheffield, N. C. & Bock, C. ChIPmentation: fast, robust, low-input ChIP-seq for histones and transcription factors. Nat. Methods 12, 963–965 (2015).

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  11. Wang, Q. et al. CoBATCH for high-throughput single-cell epigenomic profiling. Mol. Cell 76, 206–216.e7 (2019).

    CAS  Article  PubMed  Google Scholar 

  12. Handa, T. et al. Chromatin integration labeling for mapping DNA-binding proteins and modifications with low input. Nat. Protoc. 15, 3334–3360 (2020).

    CAS  Article  PubMed  Google Scholar 

  13. Harada, A. et al. A chromatin integration labelling method enables epigenomic profiling with lower input. Nat. Cell Biol. 21, 287–296 (2019).

    CAS  Article  PubMed  Google Scholar 

  14. Ku, W. L. et al. Single-cell chromatin immunocleavage sequencing (scChIC-Seq) to profile histone modification. Nat. Methods 16, 323–325 (2019).

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  15. Zheng, X.-Y. & Gehring, M. Low-input chromatin profiling in Arabidopsis endosperm using CUT&RUN. Plant Reprod. 32, 63–75 (2019).

    CAS  Article  PubMed  Google Scholar 

  16. Hainer, S. J., Bošković, A., McCannell, K. N., Rando, O. J. & Fazzio, T. G. Profiling of pluripotency factors in single cells and early embryos. Cell 177, 1319–1329 (2019).

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  17. Skene, P. J., Henikoff, J. G. & Henikoff, S. Targeted in situ genome-wide profiling with high efficiency for low cell numbers. Nat. Protoc. 13, 1006–1019 (2018).

    CAS  Article  PubMed  Google Scholar 

  18. Crawford, G. E. et al. Genome-wide mapping of DNase hypersensitive sites using massively parallel signature sequencing (MPSS). Genome Res. 16, 123–131 (2006).

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  19. Song, L. & Crawford, G. E. DNase-seq: a high-resolution technique for mapping active gene regulatory elements across the genome from mammalian cells. Cold Spring Harb. Protoc. 2010, pdb.prot5384 (2010).

    Article  PubMed  PubMed Central  Google Scholar 

  20. Boyle, A. P. et al. High-resolution mapping and characterization of open chromatin across the genome. Cell 132, 311–322 (2008).

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  21. Giresi, P. G., Kim, J., McDaniell, R. M., Iyer, V. R. & Lieb, J. D. FAIRE (Formaldehyde-Assisted Isolation of Regulatory Elements) isolates active regulatory elements from human chromatin. Genome Res. 17, 877–885 (2007).

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  22. Cui, K. & Zhao, K. in Chromatin Remodeling: Methods and Protocols (ed. Morse, R. H.) 413–419 (Humana, 2012).

  23. Schones, D. E. et al. Dynamic regulation of nucleosome positioning in the human genome. Cell 132, 887–898 (2008).

    CAS  Article  PubMed  Google Scholar 

  24. Kelly, T. K. et al. Genome-wide mapping of nucleosome positioning and DNA methylation within individual DNA molecules. Genome Res. 22, 2497–2506 (2012).

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  25. Minnoye, L. et al. Chromatin accessibility profiling methods. Nat. Rev. Methods Primer 1, 1–24 (2021).

    Article  CAS  Google Scholar 

  26. Weintraub, H. & Groudine, M. Chromosomal subunits in active genes have an altered conformation. Science 193, 848–856 (1976).

    CAS  Article  PubMed  Google Scholar 

  27. Galas, D. J. & Schmitz, A. DNAse footprinting: a simple method for the detection of protein–DNA binding specificity. Nucleic Acids Res. 5, 3157–3170 (1978).

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  28. He, H. H. et al. Refined DNase-seq protocol and data analysis reveals intrinsic bias in transcription factor footprint identification. Nat. Methods 11, 73–78 (2014).

    CAS  Article  PubMed  Google Scholar 

  29. Sung, M.-H., Baek, S. & Hager, G. L. Genome-wide footprinting: ready for prime time? Nat. Methods 13, 222–228 (2016).

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  30. Mieczkowski, J. et al. MNase titration reveals differences between nucleosome occupancy and chromatin accessibility. Nat. Commun. 7, 11485 (2016).

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  31. Chereji, R. V., Bryson, T. D. & Henikoff, S. Quantitative MNase-seq accurately maps nucleosome occupancy levels. Genome Biol. 20, 198 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  32. 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).

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  33. Goryshin, I. Y. & Reznikoff, W. S. Tn5 in vitro transposition. J. Biol. Chem. 273, 7367–7374 (1998).

    CAS  Article  PubMed  Google Scholar 

  34. Adey, A. et al. Rapid, low-input, low-bias construction of shotgun fragment libraries by high-density in vitro transposition. Genome Biol. 11, R119 (2010).

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  35. Gangadharan, S., Mularoni, L., Fain-Thornton, J., Wheelan, S. J. & Craig, N. L. DNA transposon Hermes inserts into DNA in nucleosome-free regions in vivo. Proc. Natl Acad. Sci. USA 107, 21966–21972 (2010).

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  36. Corces, M. R. et al. An improved ATAC-seq protocol reduces background and enables interrogation of frozen tissues. Nat. Methods 14, 959–962 (2017).

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  37. Corces, M. R. et al. Lineage-specific and single-cell chromatin accessibility charts human hematopoiesis and leukemia evolution. Nat. Genet. 48, 1193–1203 (2016).

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  38. Corces, M. R. et al. The chromatin accessibility landscape of primary human cancers. Science 362, eaav1898 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  39. Calderon, D. et al. Landscape of stimulation-responsive chromatin across diverse human immune cells. Nat. Genet. 51, 1494–1505 (2019).

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  40. Naik, S. et al. Inflammatory memory sensitizes skin epithelial stem cells to tissue damage. Nature 550, 475–480 (2017).

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  41. Marco, A. et al. Mapping the epigenomic and transcriptomic interplay during memory formation and recall in the hippocampal engram ensemble. Nat. Neurosci. 23, 1606–1617 (2020).

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  42. Li, D. et al. Chromatin accessibility dynamics during iPSC reprogramming. Cell Stem Cell 21, 819–833 (2017).

    CAS  Article  PubMed  Google Scholar 

  43. Guo, J. et al. Chromatin and single-cell RNA-seq profiling reveal dynamic signaling and metabolic transitions during human spermatogonial stem cell development. Cell Stem Cell 21, 533–546 (2017).

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  44. Wu, J. et al. The landscape of accessible chromatin in mammalian preimplantation embryos. Nature 534, 652–657 (2016).

    CAS  Article  PubMed  Google Scholar 

  45. Daugherty, A. C. et al. Chromatin accessibility dynamics reveal novel functional enhancers in C. elegans. Genome Res. 27, 2096–2107 (2017).

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  46. Gury-BenAri, M. et al. The spectrum and regulatory landscape of intestinal innate lymphoid cells are shaped by the microbiome. Cell 166, 1231–1246 (2016).

    CAS  Article  PubMed  Google Scholar 

  47. Liu, Q. et al. Chromatin accessibility landscapes of skin cells in systemic sclerosis nominate dendritic cells in disease pathogenesis. Nat. Commun. 11, 5843 (2020).

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  48. Liu, Y. et al. Chromatin accessibility landscape of articular knee cartilage reveals aberrant enhancer regulation in osteoarthritis. Sci. Rep. 8, 15499 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  49. Greenwald, W. W. et al. Pancreatic islet chromatin accessibility and conformation reveals distal enhancer networks of type 2 diabetes risk. Nat. Commun. 10, 2078 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  50. Lee, J. et al. Activation of PDGF pathway links LMNA mutation to dilated cardiomyopathy. Nature 572, 335–340 (2019).

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  51. Schmidl, C. et al. Combined chemosensitivity and chromatin profiling prioritizes drug combinations in CLL. Nat. Chem. Biol. 15, 232–240 (2019).

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  52. Scharer, C. D. et al. Epigenetic programming underpins B cell dysfunction in human SLE. Nat. Immunol. 20, 1071–1082 (2019).

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  53. Corces, M. R. et al. Single-cell epigenomic analyses implicate candidate causal variants at inherited risk loci for Alzheimer’s and Parkinson’s diseases. Nat. Genet. 52, 1158–1168 (2020).

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  54. Banovich, N. E. et al. Impact of regulatory variation across human iPSCs and differentiated cells. Genome Res. 28, 122–131 (2018).

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  55. Forrest, M. P. et al. Open chromatin profiling in hiPSC-derived neurons prioritizes functional noncoding psychiatric risk variants and highlights neurodevelopmental loci. Cell Stem Cell 21, 305–318 (2017).

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  56. Chiou, J. et al. Interpreting type 1 diabetes risk with genetics and single-cell epigenomics. Nature 594, 398–402 (2021).

    CAS  Article  PubMed  Google Scholar 

  57. Nott, A. et al. Brain cell type-specific enhancer–promoter interactome maps and disease-risk association. Science 366, 1134–1139 (2019).

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  58. Liu, Q. et al. Genome-wide temporal profiling of transcriptome and open-chromatin of early cardiomyocyte differentiation derived from hiPSCs and hESCs. Circ. Res. 121, 376–391 (2017).

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  59. Wapinski, O. L. et al. Rapid chromatin switch in the direct reprogramming of fibroblasts to neurons. Cell Rep. 20, 3236–3247 (2017).

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  60. Denny, S. K. et al. Nfib promotes metastasis through a widespread increase in chromatin accessibility. Cell 166, 328–342 (2016).

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  61. Schep, A. N. et al. Structured nucleosome fingerprints enable high-resolution mapping of chromatin architecture within regulatory regions. Genome Res. 25, 1757–1770 (2015).

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  62. Kaya-Okur, H. S., Janssens, D. H., Henikoff, J. G., Ahmad, K. & Henikoff, S. Efficient low-cost chromatin profiling with CUT&Tag. Nat. Protoc. 15, 3264–3283 (2020).

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  63. Stark, R., Grzelak, M. & Hadfield, J. RNA sequencing: the teenage years. Nat. Rev. Genet. 20, 631–656 (2019).

    CAS  Article  PubMed  Google Scholar 

  64. Bartosovic, M., Kabbe, M. & Castelo-Branco, G. Single-cell CUT&Tag profiles histone modifications and transcription factors in complex tissues. Nat. Biotechnol. 39, 825–835 (2021).

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  65. Gao, W., Lai, B., Ni, B. & Zhao, K. Genome-wide profiling of nucleosome position and chromatin accessibility in single cells using scMNase-seq. Nat. Protoc. 15, 68–85 (2020).

    CAS  Article  PubMed  Google Scholar 

  66. Lai, B. et al. Principles of nucleosome organization revealed by single-cell micrococcal nuclease sequencing. Nature 562, 281–285 (2018).

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  67. Jin, W. et al. Genome-wide detection of DNase I hypersensitive sites in single cells and FFPE tissue samples. Nature 528, 142–146 (2015).

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  68. Takaku, M. et al. GATA3-dependent cellular reprogramming requires activation-domain dependent recruitment of a chromatin remodeler. Genome Biol. 17, 36 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  69. Fujiwara, S., Baek, S., Varticovski, L., Kim, S. & Hager, G. L. High quality ATAC-seq data recovered from cryopreserved breast cell lines and tissue. Sci. Rep. 9, 516 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  70. Mulqueen, R. M. et al. Improved single-cell ATAC-seq reveals chromatin dynamics of in vitro corticogenesis. Preprint at bioRxiv https://doi.org/10.1101/637256 (2019).

  71. nf-core/atacseq. (nf-core, 2021). https://doi.org/10.5281/zenodo.2634132

  72. ATAC-seq Data Standards and Processing Pipeline. ENCODE. https://www.encodeproject.org/atac-seq/

  73. Smith, J. P. et al. PEPATAC: an optimized pipeline for ATAC-seq data analysis with serial alignments. NAR Genomics Bioinform. 3, lqab101 (2021).

    Article  CAS  Google Scholar 

  74. Bajic, M., Maher, K. A. & Deal, R. B. Identification of open chromatin regions in plant genomes using ATAC-seq. Methods Mol. Biol. 1675, 183–201 (2018).

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  75. Deal, R. B. & Henikoff, S. The INTACT method for cell type-specific gene expression and chromatin profiling in Arabidopsis thaliana. Nat. Protoc. 6, 56–68 (2011).

    CAS  Article  PubMed  Google Scholar 

  76. Haines, J. E. & Eisen, M. B. Patterns of chromatin accessibility along the anterior-posterior axis in the early Drosophila embryo. PLOS Genet. 14, e1007367 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  77. Johnson, S., Nguyen, V. & Coder, D. Assessment of cell viability. Curr. Protoc. Cytom. 64, 9.2.1–9.2.26 (2013).

    Google Scholar 

  78. Chen, X. et al. ATAC-see reveals the accessible genome by transposase-mediated imaging and sequencing. Nat. Methods 13, 1013–1020 (2016).

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  79. Swanson, E. et al. Simultaneous trimodal single-cell measurement of transcripts, epitopes, and chromatin accessibility using TEA-seq. eLife 10, e63632 (2021).

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  80. Corces, R. Isolation of nuclei from frozen tissue for ATAC-seq and other epigenomic assays. https://doi.org/10.17504/protocols.io.6t8herw (2019).

  81. Polavarapu, V. K. et al. Profiling chromatin accessibility in formalin-fixed paraffin-embedded samples. Genome Res. https://doi.org/10.1101/gr.275269.121 (2021).

  82. Chin, H. G. et al. Universal NicE-seq for high-resolution accessible chromatin profiling for formaldehyde-fixed and FFPE tissues. Clin. Epigenetics 12, 143 (2020).

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  83. Orchard, P., Kyono, Y., Hensley, J., Kitzman, J. O. & Parker, S. C. J. Quantification, dynamic visualization, and validation of bias in ATAC-seq data with ataqv. Cell Syst. 10, 298–306 (2020).

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  84. Ewels, P. A. et al. The nf-core framework for community-curated bioinformatics pipelines. Nat. Biotechnol. 38, 276–278 (2020).

    CAS  Article  PubMed  Google Scholar 

  85. Koohy, H., Down, T. A. & Hubbard, T. J. Chromatin accessibility data sets show bias due to sequence specificity of the DNase I enzyme. PLoS ONE 8, e69853 (2013).

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  86. Satpathy, A. T. et al. Massively parallel single-cell chromatin landscapes of human immune cell development and intratumoral T cell exhaustion. Nat. Biotechnol. 37, 925–936 (2019).

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  87. Cusanovich, D. A. et al. Multiplex single cell profiling of chromatin accessibility by combinatorial cellular indexing. Science 348, 910–914 (2015).

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  88. Granja, J. M. & Corces, M. R. ATAC_IterativeOverlapPeakMerging https://doi.org/10.5281/zenodo.5903680 (2022).

  89. Picelli, S. et al. Tn5 transposase and tagmentation procedures for massively scaled sequencing projects. Genome Res. 24, 2033–2040 (2014).

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  90. Smith, J. P. & Sheffield, N. C. Analytical approaches for ATAC-seq data analysis. Curr. Protoc. Hum. Genet. 106, e101 (2020).

    CAS  PubMed  PubMed Central  Google Scholar 

  91. A Quality Control tool for High Throughput Sequence Data. Babraham Bioinformatics. https://www.bioinformatics.babraham.ac.uk/projects/fastqc/

  92. Zhang, Y. et al. Model-based analysis of ChIP-seq (MACS). Genome Biol. 9, R137 (2008).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  93. Gaspar, J. M. Genrich: Detecting Sites of Genomic Enrichment (2021).

  94. Tarbell, E. D. & Liu, T. HMMRATAC: a Hidden Markov ModeleR for ATAC-seq. Nucleic Acids Res. 47, e91 (2019).

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  95. 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).

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  96. Yu, G., Wang, L.-G. & He, Q.-Y. ChIPseeker: an R/Bioconductor package for ChIP peak annotation, comparison and visualization. Bioinformatics 31, 2382–2383 (2015).

    CAS  Article  PubMed  Google Scholar 

  97. Zhu, L. J. et al. ChIPpeakAnno: a Bioconductor package to annotate ChIP-seq and ChIP-chip data. BMC Bioinformatics 11, 237 (2010).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  98. Mumbach, M. R. et al. Enhancer connectome in primary human cells identifies target genes of disease-associated DNA elements. Nat. Genet. 49, 1602–1612 (2017).

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  99. Gontarz, P. et al. Comparison of differential accessibility analysis strategies for ATAC-seq data. Sci. Rep. 10, 10150 (2020).

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  100. Love, M. I., Huber, W. & Anders, S. Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2. Genome Biol. 15, 550 (2014).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  101. Robinson, M. D., McCarthy, D. J. & Smyth, G. K. edgeR: a Bioconductor package for differential expression analysis of digital gene expression data. Bioinformatics 26, 139–140 (2010).

    CAS  Article  PubMed  Google Scholar 

  102. Ritchie, M. E. et al. limma powers differential expression analyses for RNA-sequencing and microarray studies. Nucleic Acids Res. 43, e47 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  103. Yan, F., Powell, D. R., Curtis, D. J. & Wong, N. C. From reads to insight: a hitchhiker’s guide to ATAC-seq data analysis. Genome Biol. 21, 22 (2020).

    Article  PubMed  PubMed Central  Google Scholar 

  104. Risso, D., Ngai, J., Speed, T. P. & Dudoit, S. Normalization of RNA-seq data using factor analysis of control genes or samples. Nat. Biotechnol. 32, 896–902 (2014).

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  105. UCSC Genome Browser Home. https://genome.ucsc.edu/index.html

  106. Robinson, J. T. et al. Integrative genomics viewer. Nat. Biotechnol. 29, 24–26 (2011).

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  107. WashU Epigenome Browser. http://epigenomegateway.wustl.edu/browser/

  108. Quinlan, A. R. & Hall, I. M. BEDTools: a flexible suite of utilities for comparing genomic features. Bioinformatics 26, 841–842 (2010).

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  109. genomecov. bedtools 2.30.0 documentation https://bedtools.readthedocs.io/en/latest/content/tools/genomecov.html

  110. bamCoverage. deepTools 3.5.0 documentation https://deeptools.readthedocs.io/en/develop/content/tools/bamCoverage.html

  111. Khan, A. et al. JASPAR 2018: update of the open-access database of transcription factor binding profiles and its web framework. Nucleic Acids Res. 46, D260–D266 (2018).

    CAS  Article  PubMed  Google Scholar 

  112. Weirauch, M. T. et al. Determination and inference of eukaryotic transcription factor sequence specificity. Cell 158, 1431–1443 (2014).

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  113. Kheradpour, P. & Kellis, M. Systematic discovery and characterization of regulatory motifs in ENCODE TF binding experiments. Nucleic Acids Res. 42, 2976–2987 (2014).

    CAS  Article  PubMed  Google Scholar 

  114. Tan, G. & Lenhard, B. TFBSTools: an R/bioconductor package for transcription factor binding site analysis. Bioinformatics 32, 1555–1556 (2016).

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  115. Fast Motif Matching in R. motifmatchr. https://greenleaflab.github.io/motifmatchr/index.html

  116. Bailey, T. L. et al. MEME Suite: tools for motif discovery and searching. Nucleic Acids Res. 37, W202–W208 (2009).

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  117. 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).

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  118. Berest, I. et al. Quantification of differential transcription factor activity and multiomics-based classification into activators and repressors: diffTF. Cell Rep. 29, 3147–3159 (2019).

    CAS  Article  PubMed  Google Scholar 

  119. Hesselberth, J. R. et al. Global mapping of protein–DNA interactions in vivo by digital genomic footprinting. Nat. Methods 6, 283–289 (2009).

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  120. Buenrostro, J. D. et al. Single-cell chromatin accessibility reveals principles of regulatory variation. Nature 523, 486–490 (2015).

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  121. Martins, A. L., Walavalkar, N. M., Anderson, W. D., Zang, C. & Guertin, M. J. Universal correction of enzymatic sequence bias reveals molecular signatures of protein/DNA interactions. Nucleic Acids Res. 46, e9 (2018).

    Article  CAS  PubMed  Google Scholar 

  122. Baek, S., Goldstein, I. & Hager, G. L. Bivariate genomic footprinting detects changes in transcription factor activity. Cell Rep. 19, 1710–1722 (2017).

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  123. Bentsen, M. et al. ATAC-seq footprinting unravels kinetics of transcription factor binding during zygotic genome activation. Nat. Commun. 11, 4267 (2020).

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  124. Singh, A. K. & Mueller-Planitz, F. Nucleosome positioning and spacing: from mechanism to function. J. Mol. Biol. 433, 166847 (2021).

    CAS  Article  PubMed  Google Scholar 

  125. Lai, W. K. M. & Pugh, B. F. Understanding nucleosome dynamics and their links to gene expression and DNA replication. Nat. Rev. Mol. Cell Biol. 18, 548–562 (2017).

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  126. Belton, J.-M. et al. Hi-C: a comprehensive technique to capture the conformation of genomes. Methods 58, 268–276 (2012).

    CAS  Article  PubMed  Google Scholar 

  127. Mumbach, M. R. et al. HiChIP: efficient and sensitive analysis of protein-directed genome architecture. Nat. Methods 13, 919–922 (2016).

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  128. Kumasaka, N., Knights, A. J. & Gaffney, D. J. Fine-mapping cellular QTLs with RASQUAL and ATAC-seq. Nat. Genet. 48, 206–213 (2016).

    CAS  Article  PubMed  Google Scholar 

  129. Gate, R. E. et al. Genetic determinants of co-accessible chromatin regions in activated T cells across humans. Nat. Genet. 50, 1140–1150 (2018).

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  130. Örd, T. et al. Single-cell epigenomics and functional fine-mapping of atherosclerosis GWAS loci. Circ. Res. 129, 240–258 (2021).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  131. Kia, A. et al. Improved genome sequencing using an engineered transposase. BMC Biotechnol. 17, 6 (2017).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  132. Green, B., Bouchier, C., Fairhead, C., Craig, N. L. & Cormack, B. P. Insertion site preference of Mu, Tn5, and Tn7 transposons. Mob. DNA 3, 3 (2012).

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  133. Ason, B. & Reznikoff, W. S. DNA sequence bias during Tn5 transposition. J. Mol. Biol. 335, 1213–1225 (2004).

    CAS  Article  PubMed  Google Scholar 

  134. Lazarovici, A. et al. Probing DNA shape and methylation state on a genomic scale with DNase I. Proc. Natl Acad. Sci. USA 110, 6376–6381 (2013).

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  135. Dingwall, C., Lomonossoff, G. P. & Laskey, R. A. High sequence specificity of micrococcal nuclease. Nucleic Acids Res. 9, 2659–2673 (1981).

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  136. Hörz, W. & Altenburger, W. Sequence specific cleavage of DNA by micrococcal nuclease. Nucleic Acids Res. 9, 2643–2658 (1981).

    Article  PubMed  PubMed Central  Google Scholar 

  137. Wu, S. J. et al. Single-cell CUT&Tag analysis of chromatin modifications in differentiation and tumor progression. Nat. Biotechnol. 39, 819–824 (2021).

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  138. Patty, B. J. & Hainer, S. J. Transcription factor chromatin profiling genome-wide using uliCUT&RUN in single cells and individual blastocysts. Nat. Protoc. 16, 2633–2666 (2021).

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  139. Carter, B. et al. Mapping histone modifications in low cell number and single cells using antibody-guided chromatin tagmentation (ACT-seq). Nat. Commun. 10, 3747 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  140. Chang, P., Gohain, M., Yen, M.-R. & Chen, P.-Y. Computational methods for assessing chromatin hierarchy. Comput. Struct. Biotechnol. J. 16, 43–53 (2018).

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  141. Mo, A. et al. Epigenomic signatures of neuronal diversity in the mammalian brain. Neuron 86, 1369–1384 (2015).

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  142. Yang, A. C. et al. Dysregulation of brain and choroid plexus cell types in severe COVID-19. Nature 595, 565–571 (2021).

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  143. Drokhlyansky, E. et al. The human and mouse enteric nervous system at single-cell resolution. Cell. 182, 1606–1622 (2020).

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  144. Deal, R. B. & Henikoff, S. A simple method for gene expression and chromatin profiling of individual cell types within a tissue. Dev. Cell 18, 1030–1040 (2010).

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  145. Bhattacharyya, S., Sathe, A. A., Bhakta, M., Xing, C. & Munshi, N. V. PAN-INTACT enables direct isolation of lineage-specific nuclei from fibrous tissues. PLoS ONE 14, e0214677 (2019).

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  146. Nuclei Isolation for Single Cell ATAC Sequencing. https://support.10xgenomics.com/single-cell-atac/sample-prep/doc/demonstrated-protocol-nuclei-isolation-for-single-cell-atac-sequencing

  147. Lawler, A. J. et al. Cell type-specific oxidative stress genomic signatures in the globus pallidus of dopamine-depleted mice. J. Neurosci. 40, 9772–9783 (2020).

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  148. Kiseleva, E. et al. A protocol for isolation and visualization of yeast nuclei by scanning electron microscopy (SEM). Nat. Protoc. 2, 1943–1953 (2007).

    CAS  Article  PubMed  Google Scholar 

  149. Niepel, M., Farr, J. C., Rout, M. P. & Strambio-De-Castillia, C. Rapid isolation of functionally intact nuclei from the yeast Saccharomyces. Preprint at bioRxiv https://doi.org/10.1101/162388 (2017).

  150. Nott, A., Schlachetzki, J. C. M., Fixsen, B. R. & Glass, C. K. Nuclei isolation of multiple brain cell types for omics interrogation. Nat. Protoc. 16, 1629–1646 (2021).

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  151. Fullard, J. F. et al. An atlas of chromatin accessibility in the adult human brain. Genome Res. 28, 1243–1252 (2018).

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  152. Hauberg, M. E. et al. Common schizophrenia risk variants are enriched in open chromatin regions of human glutamatergic neurons. Nat. Commun. 11, 5581 (2020).

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  153. Haines, J. ATAC-seq on nuclei from frozen, sliced, Drosophila melanogaster embryo halves. https://doi.org/10.17504/protocols.io.kj5cuq6 (2017).

  154. Steiner, F. A., Talbert, P. B., Kasinathan, S., Deal, R. B. & Henikoff, S. Cell-type-specific nuclei purification from whole animals for genome-wide expression and chromatin profiling. Genome Res. 22, 766–777 (2012).

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  155. Han, M., Wei, G., McManus, C. E., Hillier, L. W. & Reinke, V. Isolated C. elegans germ nuclei exhibit distinct genomic profiles of histone modification and gene expression. BMC Genomics 20, 500 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  156. Martin, M. Cutadapt removes adapter sequences from high-throughput sequencing reads. EMBnet.journal 17, 10–12 (2011).

    Article  Google Scholar 

  157. Bolger, A. M., Lohse, M. & Usadel, B. Trimmomatic: a flexible trimmer for Illumina sequence data. Bioinformatics 30, 2114–2120 (2014).

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  158. Jiang, H., Lei, R., Ding, S.-W. & Zhu, S. Skewer: a fast and accurate adapter trimmer for next-generation sequencing paired-end reads. BMC Bioinformatics 15, 182 (2014).

    Article  PubMed  PubMed Central  Google Scholar 

  159. Krueger, F. Trim Galore. (2021).

  160. Langmead, B. & Salzberg, S. L. Fast gapped-read alignment with Bowtie 2. Nat. Methods 9, 357–359 (2012).

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  161. Li, H. & Durbin, R. Fast and accurate short read alignment with Burrows–Wheeler transform. Bioinformatics 25, 1754–1760 (2009).

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  162. broadinstitute/picard (Broad Institute, 2021) https://github.com/broadinstitute/picard

  163. Faust, G. G. & Hall, I. M. SAMBLASTER: fast duplicate marking and structural variant read extraction. Bioinformatics 30, 2503–2505 (2014).

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  164. Boyle, A. P., Guinney, J., Crawford, G. E. & Furey, T. S. F-Seq: a feature density estimator for high-throughput sequence tags. Bioinformatics 24, 2537–2538 (2008).

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  165. Granja, J. M. et al. ArchR is a scalable software package for integrative single-cell chromatin accessibility analysis. Nat. Genet. 53, 403–411 (2021).

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  166. Buenrostro, J. D., Wu, B., Chang, H. Y. & Greenleaf, W. J. ATAC-seq: a method for assaying chromatin accessibility genome-wide. Curr. Protoc. Mol. Biol. 109, 21.29.1–21.29.9 (2015).

    Article  Google Scholar 

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Acknowledgements

This work was supported by NIH R00-AG059918, U01-AG072573, P01-AG073082, UM1-HG012076 and a gift from the Ray and Dagmar Dolby Family Fund (to the Gladstone Institutes). F.C.G. is an Alan Kaganov Scholar. M.R.C. is additionally supported by the Farmer Family Foundation Parkinson’s Research Initiative and an American Society of Hematology Scholar Award.

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Contributions

All authors contributed to developing this protocol. All experiments were performed by H.M., F.C.G. and L.K. with supervision from M.R.C. The manuscript was written by F.C.G., L.K. and H.M. with input from all authors.

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Correspondence to M. Ryan Corces.

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Nature Protocols thanks the anonymous reviewers for their contribution to the peer review of this work.

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Key references using this protocol

Corces, M. R. et al. Nat. Methods 14, 959–962 (2017): https://doi.org/10.1038/nmeth.4396

Corces, M. R. et al. Science 362, 6413 (2018): https://doi.org/10.1126/science.aav1898

Corces, M. R. et al. Nat. Genet. 52, 1158–1168 (2020): https://doi.org/10.1038/s41588-020-00721-x

Supplementary information

Supplementary Information

Supplementary Figs. 1–6, Supplementary Notes 1–4, Supplementary Protocol 1 (Nuclei Isolation), Supplementary Methods and Supplementary References.

Supplementary Tables 1 and 2

Alignment statistics for different ATAC-seq library read lengths. ATAC-seq dual-indexing adapters and barcode sequences.

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Grandi, F.C., Modi, H., Kampman, L. et al. Chromatin accessibility profiling by ATAC-seq. Nat Protoc 17, 1518–1552 (2022). https://doi.org/10.1038/s41596-022-00692-9

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