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A plate-based single-cell ATAC-seq workflow for fast and robust profiling of chromatin accessibility

Nature Protocols volume 16, pages 4084–4107 (2021)Cite this article

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Abstract

Profiling chromatin accessibility at the single-cell level provides critical information about cell type composition and cell-to-cell variation within a complex tissue. Emerging techniques for the interrogation of chromatin accessibility in individual cells allow investigation of the fundamental mechanisms that lead to the variability of different cells. This protocol describes a fast and robust method for single-cell chromatin accessibility profiling based on the assay for transposase-accessible chromatin using sequencing (ATAC-seq). The method combines up-front bulk Tn5 tagging of chromatin with flow cytometry to isolate single nuclei or cells. Reagents required to generate sequencing libraries are added to the same well in the plate where cells are sorted. The protocol described here generates data of high complexity and excellent signal-to-noise ratio and can be combined with index sorting for in-depth characterization of cell types. The whole experimental procedure can be finished within 1 or 2 d with a throughput of hundreds to thousands of nuclei, and the data can be processed by the provided computational pipeline. The execution of the protocol only requires basic techniques and equipment in a molecular biology laboratory with flow cytometry support.

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Fig. 1: Workflow of the protocol.
Fig. 2: Removal of the supernatant.
Fig. 3: Single-nucleus/cell isolation using FACS.
Fig. 4: DNA purification after well barcode addition.
Fig. 5: Typical scATAC-seq library profiles.
Fig. 6: Overview of the Snakemake pipeline is used to process scATAC-seq data.
Fig. 7: Typical results from the data processing pipeline.
Fig. 8: Genome browser view of reads pileup from aggregated scATAC-seq data.
Fig. 9: Comparison of K562 scATAC-seq data obtained using different DNA stains.
Fig. 10: Comparison of PBMC scATAC-seq data from the 10x Genomics and the plate-based methods.

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Data availability

The scATAC-seq data from mouse splenocytes, mouse embryonic stem cells and human K562 cells are deposited in ArrayExpress, accession number E-MTAB-6714. The bulk ATAC-seq data from the ImmGen project are available in the European Nucleotide Archive (ENA), study accession number PRJNA392905. The data from K562 cells stained with different DNA staining methods and human PBMCs are deposited in the Genome Sequence Archive in BIG Data Center, under accession number PRJCA004353.

Code availability

The code used to process the data and the instructions to run the code are available in the Zenodo repository under https://doi.org/10.5281/zenodo.4734142 (ref. 58) and in the GitHub repository at https://github.com/dbrg77/scATAC_snakemake.

References

  1. Kornberg, R. D. & Lorch, Y. Twenty-five years of the nucleosome, fundamental particle of the eukaryote chromosome. Cell 98, 285–294 (1999).

    Article  CAS  PubMed  Google Scholar 

  2. Li, B., Carey, M. & Workman, J. L. The role of chromatin during transcription. Cell 128, 707–719 (2007).

    Article  CAS  PubMed  Google Scholar 

  3. Workman, J. & Kingston, R. Nucleosome core displacement in vitro via a metastable transcription factor-nucleosome complex. Science 258, 1780–1784 (1992).

    Article  CAS  PubMed  Google Scholar 

  4. Meuleman, W. et al. Index and biological spectrum of human DNase I hypersensitive sites. Nature 584, 244–251 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

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

    Article  CAS  PubMed  PubMed Central  Google Scholar 

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

    Article  CAS  PubMed  PubMed Central  Google Scholar 

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

    Article  CAS  PubMed  Google Scholar 

  8. Barski, A. et al. High-resolution profiling of histone methylations in the human genome. Cell 129, 823–837 (2007).

    Article  CAS  PubMed  Google Scholar 

  9. Giresi, P. G. & Lieb, J. D. Isolation of active regulatory elements from eukaryotic chromatin using FAIRE (Formaldehyde Assisted Isolation of Regulatory Elements). Methods 48, 233–239 (2009).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  10. 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).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

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

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  12. Ishii, H., Kadonaga, J. T. & Ren, B. MPE-seq, a new method for the genome-wide analysis of chromatin structure. Proc. Natl Acad. Sci. 112, E3457–E3465 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

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

    Article  CAS  PubMed  PubMed Central  Google Scholar 

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

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  15. Lake, B. B. et al. Integrative single-cell analysis of transcriptional and epigenetic states in the human adult brain. Nat. Biotechnol. 36, 70–80 (2018).

    Article  CAS  PubMed  Google Scholar 

  16. Preissl, S. et al. Single-nucleus analysis of accessible chromatin in developing mouse forebrain reveals cell-type-specific transcriptional regulation. Nat. Neurosci. 21, 432–439 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  17. Mezger, A. et al. High-throughput chromatin accessibility profiling at single-cell resolution. Nat. Commun. 9, 3647 (2018).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  18. Chen, X. et al. Joint single-cell DNA accessibility and protein epitope profiling reveals environmental regulation of epigenomic heterogeneity. Nat. Commun. 9, 4590 (2018).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  19. Chen, X., Miragaia, R. J., Natarajan, K. N. & Teichmann, S. A. A rapid and robust method for single cell chromatin accessibility profiling. Nat. Commun. 9, 5345 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  20. 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).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  21. Lareau, C. A. et al. Droplet-based combinatorial indexing for massive-scale single-cell chromatin accessibility. Nat. Biotechnol. 37, 916–924 (2019).

    Article  CAS  PubMed  Google Scholar 

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

    Article  CAS  PubMed  PubMed Central  Google Scholar 

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

    Article  CAS  PubMed  PubMed Central  Google Scholar 

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

    Article  CAS  PubMed  Google Scholar 

  25. 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).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  26. Amini, S. et al. Haplotype-resolved whole-genome sequencing by contiguity-preserving transposition and combinatorial indexing. Nat. Genet. 46, 1343–1349 (2014).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

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

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  28. 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  PubMed  PubMed Central  CAS  Google Scholar 

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

    Article  CAS  PubMed  Google Scholar 

  30. Goldenberger, D., Perschil, I., Ritzler, M. & Altwegg, M. A simple “universal” DNA extraction procedure using SDS and proteinase K is compatible with direct PCR amplification. Genome Res. 4, 368–370 (1995).

    Article  CAS  Google Scholar 

  31. Jia, G. et al. Single cell RNA-seq and ATAC-seq analysis of cardiac progenitor cell transition states and lineage settlement. Nat. Commun. 9, 4877 (2018).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  32. Ranzoni, A. M. et al. Integrative single-cell RNA-Seq and ATAC-Seq analysis of human developmental hematopoiesis. Cell Stem Cell https://doi.org/10.1016/j.stem.2020.11.015 (2020).

  33. Soon, M. S. F. et al. Transcriptome dynamics of CD4+ T cells during malaria maps gradual transit from effector to memory. Nat. Immunol. 21, 1597–1610 (2020).

    Article  CAS  PubMed  Google Scholar 

  34. Cusanovich, D. A. et al. A single-cell atlas of in vivo mammalian chromatin accessibility. Cell 174, 1309–1324.e18 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  35. Sinnamon, J. R. et al. The accessible chromatin landscape of the murine hippocampus at single-cell resolution. Genome Res. 29, 857–869 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  36. Cusanovich, D. A. et al. The cis-regulatory dynamics of embryonic development at single-cell resolution. Nature 555, 538–542 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  37. Pliner, H. A. et al. Cicero predicts cis-regulatory DNA interactions from single-cell chromatin accessibility data. Mol. Cell 71, 858–871.e8 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  38. Xing, Q. R. et al. Diversification of reprogramming trajectories revealed by parallel single-cell transcriptome and chromatin accessibility sequencing. Sci. Adv. 6, eaba1190 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  39. Rubin, A. J. et al. Coupled single-cell CRISPR screening and epigenomic profiling reveals causal gene regulatory networks. Cell 176, 361–376.e17 (2018).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  40. LaFave, L. M. et al. Epigenomic state transitions characterize tumor progression in mouse lung adenocarcinoma. Cancer Cell 38, 212–228.e13 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

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

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  42. Cooper, J., Ding, Y., Song, J. & Zhao, K. Genome-wide mapping of DNase I hypersensitive sites in rare cell populations using single-cell DNase sequencing. Nat. Protoc. 12, 2342–2354 (2017).

    Article  CAS  PubMed  Google Scholar 

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

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  44. 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).

    Article  CAS  PubMed  Google Scholar 

  45. Pott, S. Simultaneous measurement of chromatin accessibility, DNA methylation, and nucleosome phasing in single cells. eLife 6, e23203 (2017).

    Article  PubMed  PubMed Central  Google Scholar 

  46. Guo, F. et al. Single-cell multi-omics sequencing of mouse early embryos and embryonic stem cells. Cell Res. 27, 967–988 (2017).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  47. Clark, S. J. et al. scNMT-seq enables joint profiling of chromatin accessibility DNA methylation and transcription in single cells. Nat. Commun. 9, 781 (2018).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  48. Xiong, L. et al. SCALE method for single-cell ATAC-seq analysis via latent feature extraction. Nat. Commun. 10, 4576 (2019).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  49. Hashimshony, T. et al. CEL-Seq2: sensitive highly-multiplexed single-cell RNA-Seq. Genome Biol. 17, 77 (2016).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  50. Bagnoli, J. W. et al. Sensitive and powerful single-cell RNA sequencing using mcSCRB-seq. Nat. Commun. 9, 2937 (2018).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  51. Keren-Shaul, H. et al. MARS-seq2.0: an experimental and analytical pipeline for indexed sorting combined with single-cell RNA sequencing. Nat. Protoc. 14, 1841–1862 (2019).

    Article  CAS  PubMed  Google Scholar 

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

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  53. Köster, J. & Rahmann, S. Snakemake—a scalable bioinformatics workflow engine. Bioinformatics 28, 2520–2522 (2012).

    Article  PubMed  CAS  Google Scholar 

  54. Stuart, T., Srivastava, A., Lareau, C. & Satija, R. Multimodal single-cell chromatin analysis with Signac. Preprint at bioRxiv https://doi.org/10.1101/2020.11.09.373613 (2020).

  55. Chen, H. et al. Assessment of computational methods for the analysis of single-cell ATAC-seq data. Genome Biol. 20, 241 (2019).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  56. Kent, W. J. et al. The human genome browser at UCSC. Genome Res. 12, 996–1006 (2002).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  57. Becht, E. et al. Dimensionality reduction for visualizing single-cell data using UMAP. Nat. Biotechnol. 37, 38–44 (2019).

    Article  CAS  Google Scholar 

  58. Chen, X. & Wen, Y., A plate-based single cell ATAC-seq workflow for fast and robust profiling of chromatin accessibility. Zenodo https://doi.org/10.5281/zenodo.4734142 (2021).

  59. Chen, S., Zhou, Y., Chen, Y. & Gu, J. fastp: an ultra-fast all-in-one FASTQ preprocessor. Bioinformatics 34, i884–i890 (2018).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  60. Kim, D., Paggi, J. M., Park, C., Bennett, C. & Salzberg, S. L. Graph-based genome alignment and genotyping with HISAT2 and HISAT-genotype. Nat. Biotechnol. 37, 907–915 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  61. Li, H. et al. The Sequence Alignment/Map format and SAMtools. Bioinformatics 25, 2078–2079 (2009).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

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

    Article  PubMed  PubMed Central  CAS  Google Scholar 

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

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  64. Mei, S. et al. Cistrome Data Browser: a data portal for ChIP-Seq and chromatin accessibility data in human and mouse. Nucleic Acids Res. 45, D658–D662 (2017).

    Article  CAS  PubMed  Google Scholar 

  65. Consortium, T. I. G. P. et al. The Immunological Genome Project: networks of gene expression in immune cells. Nat. Immunol. 9, 1091–1094 (2008).

    Article  CAS  Google Scholar 

  66. Waltman, L. & van Eck, N. J. A smart local moving algorithm for large-scale modularity-based community detection. Eur. Phys. J. B 86, 471 (2013).

    Article  CAS  Google Scholar 

  67. Renaud, G., Stenzel, U., Maricic, T., Wiebe, V. & Kelso, J. deML: robust demultiplexing of Illumina sequences using a likelihood-based approach. Bioinformatics 31, 770–772 (2015).

    Article  CAS  PubMed  Google Scholar 

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Acknowledgements

We thank all members from the Chen lab and Q. Wu for their helpful comments on the manuscript. We thank W. Chen for providing ideas during the revision. We thank C. Hou for sharing reagents. We thank the Flow Cytometry facility in the Department of Biology of Southern University of Science and Technology for the support of single-nucleus/cell sorting. This study was supported by National Key R&D Program of China (2018YFC1004500), National Natural Science Foundation of China (81872330) and Shenzhen Science And Technology Innovation Committee (ZDSYS20200811144002008). The computational work was supported by Center for Computational Science and Engineering at Southern University of Science and Technology.

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  1. Shenzhen Key Laboratory of Gene Regulation and Systems Biology, School of Life Sciences, Southern University of Science and Technology, Shenzhen, China

    Wei Xu, Yi Wen, Yingying Liang, Qiushi Xu, Xuefei Wang, Wenfei Jin & Xi Chen

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Contributions

X.C. conceived the project. W.X. and X.C. designed the protocol. W.X. performed the experiment with the help from Y.L., Q.X. and X.W.. Y.W. and X.C. designed the data processing pipeline and wrote the code. Y.W., W.X. and X.C. analyzed the data. X.C. and W.J. supervised the project. All authors contributed to the writing of the manuscript.

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Correspondence to Xi Chen.

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The authors declare no competing interests.

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Peer review information Nature Protocols thanks Hiroshi Kimura and the other, anonymous reviewer(s) for their contribution to the peer review of this work.

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Key references using this protocol

Chen, X. et al. Nat Commun. 9, 5345 (2018): https://doi.org/10.1038/s41467-018-07771-0

Jia, G. et al. Nat. Commun. 9, 4877 (2018): https://doi.org/10.1038/s41467-018-07307-6

Soon, M. et al. Nat. Immunol. 21, 1597–1610 (2020): https://doi.org/10.1038/s41590-020-0800-8

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Xu, W., Wen, Y., Liang, Y. et al. A plate-based single-cell ATAC-seq workflow for fast and robust profiling of chromatin accessibility. Nat Protoc 16, 4084–4107 (2021). https://doi.org/10.1038/s41596-021-00583-5

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