Skip to main content

Thank you for visiting nature.com. You are using a browser version with limited support for CSS. To obtain the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in Internet Explorer). In the meantime, to ensure continued support, we are displaying the site without styles and JavaScript.

Mitochondrial single-cell ATAC-seq for high-throughput multi-omic detection of mitochondrial genotypes and chromatin accessibility

Nature Protocols volume 18, pages 1416–1440 (2023)Cite this article

Subjects

Abstract

Natural sequence variation within mitochondrial DNA (mtDNA) contributes to human phenotypes and may serve as natural genetic markers in human cells for clonal and lineage tracing. We recently developed a single-cell multi-omic approach, called ‘mitochondrial single-cell assay for transposase-accessible chromatin with sequencing’ (mtscATAC-seq), enabling concomitant high-throughput mtDNA genotyping and accessible chromatin profiling. Specifically, our technique allows the mitochondrial genome-wide inference of mtDNA variant heteroplasmy along with information on cell state and accessible chromatin variation in individual cells. Leveraging somatic mtDNA mutations, our method further enables inference of clonal relationships among native ex vivo-derived human cells not amenable to genetic engineering-based clonal tracing approaches. Here, we provide a step-by-step protocol for the use of mtscATAC-seq, including various cell-processing and flow cytometry workflows, by using primary hematopoietic cells, subsequent single-cell genomic library preparation and sequencing that collectively take ~3–4 days to complete. We discuss experimental and computational data quality control metrics and considerations for the extension to other mammalian tissues. Overall, mtscATAC-seq provides a broadly applicable platform to map clonal relationships between cells in human tissues, investigate fundamental aspects of mitochondrial genetics and enable additional modes of multi-omic discovery.

This is a preview of subscription content, access via your institution

Access options

Rent or buy this article

Get just this article for as long as you need it

$39.95

Learn more

Prices may be subject to local taxes which are calculated during checkout

Fig. 1: Schematic of the mtscATAC-seq experimental workflow.
Fig. 2: Schematic of the computational mtscATAC-seq pipeline.
Fig. 3: Flow cytometry cell sorting strategies.
Fig. 4: Images of human PBMCs before and after lysis.
Fig. 5: mtscATAC-seq library size distribution of human blood and immune cells.
Fig. 6: Troubleshooting mtscATAC-seq library size distribution.
Fig. 7: Overview of mtscATAC-seq computational workflow and quality-control metrics.

Data availability

Raw mtscATAC-seq data for the demonstration of the analysis was downloaded from https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSM4472967 and processed with CellRanger v2 to the hg38 reference genome before being processed with mgatk v0.6.1 The comparison single-nucleus ATAC-seq dataset is available at https://www.10xgenomics.com/resources/datasets/5-k-peripheral-blood-mononuclear-cells-pbm-cs-from-a-healthy-donor-next-gem-v-1-1-1-1-standard-2-0-0.

Code availability

A code resource for performing mtscATAC-seq analyses and reproducing the analysis figures in this work is available at https://github.com/caleblareau/mtscatac_protocol. The mgatk package is made freely available and is actively maintained at https://github.com/caleblareau/mgatk. Additional support for interactive variant calling in the R environment has been incorporated in the CRAN Signac package for single-cell chromatin analyses49.

References

  1. Baron, C. S. & van Oudenaarden, A. Unravelling cellular relationships during development and regeneration using genetic lineage tracing. Nat. Rev. Mol. Cell Biol. 20, 753–765 (2019).

    Article  CAS  PubMed  Google Scholar 

  2. VanHorn, S. & Morris, S. A. Next-generation lineage tracing and fate mapping to interrogate development. Dev. Cell 56, 7–21 (2021).

    Article  CAS  PubMed  Google Scholar 

  3. Wagner, D. E. & Klein, A. M. Lineage tracing meets single-cell omics: opportunities and challenges. Nat. Rev. Genet. 21, 410–427 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  4. Woodworth, M. B., Girskis, K. M. & Walsh, C. A. Building a lineage from single cells: genetic techniques for cell lineage tracking. Nat. Rev. Genet. 18, 230–244 (2017).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  5. Biasco, L. et al. In vivo tracking of human hematopoiesis reveals patterns of clonal dynamics during early and steady-state reconstitution phases. Cell Stem Cell 19, 107–119 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  6. Ferrari, S. et al. Efficient gene editing of human long-term hematopoietic stem cells validated by clonal tracking. Nat. Biotechnol. 38, 1298–1308 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  7. Park, S. et al. Clonal dynamics in early human embryogenesis inferred from somatic mutation. Nature 597, 393–397 (2021).

    Article  CAS  PubMed  Google Scholar 

  8. Coorens, T. H. H. et al. Extensive phylogenies of human development inferred from somatic mutations. Nature 597, 387–392 (2021).

    Article  CAS  PubMed  Google Scholar 

  9. Lodato, M. A. et al. Somatic mutation in single human neurons tracks developmental and transcriptional history. Science 350, 94–98 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  10. Lee-Six, H. et al. Population dynamics of normal human blood inferred from somatic mutations. Nature 561, 473–478 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  11. Brunner, S. F. et al. Somatic mutations and clonal dynamics in healthy and cirrhotic human liver. Nature 574, 538–542 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  12. Macaulay, I. C. et al. G&T-seq: parallel sequencing of single-cell genomes and transcriptomes. Nat. Methods 12, 519–522 (2015).

    Article  CAS  PubMed  Google Scholar 

  13. Ludwig, L. S. et al. Lineage tracing in humans enabled by mitochondrial mutations and single-cell genomics. Cell 176, 1325–1339.e22 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  14. Xu, J. et al. Single-cell lineage tracing by endogenous mutations enriched in transposase accessible mitochondrial DNA. Elife 8, e45105 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  15. Lareau, C. A. et al. Massively parallel single-cell mitochondrial DNA genotyping and chromatin profiling. Nat. Biotechnol. 39, 451–461 (2021).

    Article  CAS  PubMed  Google Scholar 

  16. Penter, L. et al. Longitudinal single-cell dynamics of chromatin accessibility and mitochondrial mutations in chronic lymphocytic leukemia mirror disease history. Cancer Discov. 11, 3048–3063 (2021).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  17. Taylor, R. W. et al. Mitochondrial DNA mutations in human colonic crypt stem cells. J. Clin. Invest. 112, 1351–1360 (2003).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  18. Teixeira, V. H. et al. Stochastic homeostasis in human airway epithelium is achieved by neutral competition of basal cell progenitors. Elife 2, e00966 (2013).

    Article  PubMed  PubMed Central  Google Scholar 

  19. Stewart, J. B. & Chinnery, P. F. The dynamics of mitochondrial DNA heteroplasmy: implications for human health and disease. Nat. Rev. Genet. 16, 530–542 (2015).

    Article  CAS  PubMed  Google Scholar 

  20. Kang, E. et al. Age-related accumulation of somatic mitochondrial DNA mutations in adult-derived human iPSCs. Cell Stem Cell 18, 625–636 (2016).

    Article  CAS  PubMed  Google Scholar 

  21. Walker, M. A. et al. Purifying selection against pathogenic mitochondrial DNA in human T cells. N. Engl. J. Med. 383, 1556–1563 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  22. 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 

  23. 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 

  24. Montefiori, L. et al. Reducing mitochondrial reads in ATAC-seq using CRISPR/Cas9. Sci. Rep. 7, 1–9 (2017).

    Article  CAS  Google Scholar 

  25. Fiskin, E. et al. Single-cell profiling of proteins and chromatin accessibility using PHAGE-ATAC. Nat. Biotechnol. 40, 374–381 (2022).

    Article  CAS  PubMed  Google Scholar 

  26. Mimitou, E. P. et al. Scalable, multimodal profiling of chromatin accessibility, gene expression and protein levels in single cells. Nat. Biotechnol. 39, 1246–1258 (2021).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  27. Santibanez-Koref, M. et al. Assessing mitochondrial heteroplasmy using next generation sequencing: a note of caution. Mitochondrion 46, 302–306 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  28. Laricchia, K. M. et al. Mitochondrial DNA variation across 56,434 individuals in gnomAD. Genome Res. 32, 569–582 (2022).

    Article  PubMed  PubMed Central  Google Scholar 

  29. Maude, H. et al. NUMT confounding biases mitochondrial heteroplasmy calls in favor of the reference allele. Front. Cell Dev. Biol. 7, 201 (2019).

    Article  PubMed  PubMed Central  Google Scholar 

  30. 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 

  31. Tang, Z. et al. A genetic bottleneck of mitochondrial DNA during human lymphocyte development. Mol. Biol. Evol. 39, msac090 (2022).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  32. 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 

  33. Domcke, S. et al. A human cell atlas of fetal chromatin accessibility. Science 370, eaba7612 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  34. 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 

  35. 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 

  36. Basu, U., Bostwick, A. M., Das, K., Dittenhafer-Reed, K. E. & Patel, S. S. Structure, mechanism, and regulation of mitochondrial DNA transcription initiation. J. Biol. Chem. 295, 18406–18425 (2020).

    Article  CAS  PubMed  Google Scholar 

  37. Miller, T. E. et al. Mitochondrial variant enrichment from high-throughput single-cell RNA sequencing resolves clonal populations. Nat. Biotechnol. 40, 1030–1034 (2022).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  38. Mulqueen, R. M. et al. High-content single-cell combinatorial indexing. Nat. Biotechnol. 39, 1574–1580 (2021).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  39. Nam, A. S. et al. Somatic mutations and cell identity linked by Genotyping of Transcriptomes. Nature 571, 355–360 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  40. Rodriguez-Meira, A. et al. Unravelling intratumoral heterogeneity through high-sensitivity single-cell mutational analysis and parallel RNA sequencing. Mol. Cell 73, 1292–1305.e8 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  41. Stoeckius, M. et al. Simultaneous epitope and transcriptome measurement in single cells. Nat. Methods 14, 865–868 (2017).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  42. Triska, P. et al. Landscape of germline and somatic mitochondrial DNA mutations in pediatric malignancies. Cancer Res. 79, 1318–1330 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  43. Slyper, M. et al. A single-cell and single-nucleus RNA-Seq toolbox for fresh and frozen human tumors. Nat. Med. 26, 792–802 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  44. Qian, J. et al. A pan-cancer blueprint of the heterogeneous tumor microenvironment revealed by single-cell profiling. Cell Res. 30, 745–762 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  45. Wakiro, I. et al. HTAPP_Dissociation of human ovarian cancer resection to a single-cell suspension for single-cell RNA-seq. Available at https://www.protocols.io/view/htapp-dissociation-of-human-ovarian-cancer-resecti-bhbhj2j6 (2020).

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

    Article  CAS  PubMed  PubMed Central  Google Scholar 

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

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  48. Stoler, N. & Nekrutenko, A. Sequencing error profiles of Illumina sequencing instruments. NAR Genom. Bioinform. 3, lqab019 (2021).

    Article  PubMed  PubMed Central  Google Scholar 

  49. Stuart, T., Srivastava, A., Madad, S., Lareau, C. A. & Satija, R. Single-cell chromatin state analysis with Signac. Nat. Methods 18, 1333–1341 (2021).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  50. 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 

  51. Li, H. & Durbin, R. Fast and accurate long-read alignment with Burrows-Wheeler transform. Bioinformatics 26, 589–595 (2010).

    Article  PubMed  PubMed Central  Google Scholar 

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

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  53. Van der Auwera, G. A. et al. From FastQ data to high confidence variant calls: the Genome Analysis Toolkit best practices pipeline. Curr. Protoc. Bioinform. 43, 11.10.1–11.10.33 (2013).

    Article  Google Scholar 

Download references

Acknowledgements

We are grateful to members of the Satpathy, Regev, Sankaran and Ludwig laboratories for helpful discussions. This research was supported by a Stanford Science Fellowship (to C.A.L.) and a Parker Institute of Cancer Immunotherapy Scholarship (to C.A.L.), a Stanford Artificial Intelligence in Medicine and Imaging seed grant (to C.A.L.), a BroadIgnite Award (L.S.L.), R01 DK103794 (to V.G.S.), R33 HL120791 (to V.G.S.), a gift from the Lodish Family to Boston Children’s Hospital (to V.G.S.), the New York Stem Cell Foundation (NYSCF; to V.G.S.) and the Howard Hughes Medical Institute and Klarman Cell Observatory (to A.R.). V.G.S. is an NYSCF-Robertson Investigator. P.K. is an Associated Fellow of the Hector Fellow Academy. L.N. is funded by a fellowship from the MDC-NYU exchange program and is an Associated Fellow of the Hector Fellow Academy. L.S.L. is supported by the Berlin Institute of Health, an Emmy Noether fellowship by the German Research Foundation (DFG, LU 2336/2-1) and a Hector Research Career Development Award. A.T.S. is supported by the National Institutes of Health (NIH) U01CA260852, the Parker Institute for Cancer Immunotherapy, a Career Award for Medical Scientists from the Burroughs Wellcome Fund and a Pew-Stewart Scholars for Cancer Research Award. A.T.S., C.A.L. and L.S.L. are supported by NIH UM1HG012076.

Author information

Authors and Affiliations

  1. Department of Pathology, Stanford University, Stanford, CA, USA

    Caleb A. Lareau, Vincent Liu, Katalin Sandor, Yajie Yin, Jacob C. Gutierrez & Ansuman T. Satpathy

  2. Parker Institute for Cancer Immunotherapy, San Francisco, CA, USA

    Caleb A. Lareau & Ansuman T. Satpathy

  3. Broad Institute of MIT and Harvard, Cambridge, MA, USA

    Christoph Muus & Vijay G. Sankaran

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

    Christoph Muus

  5. Berlin Institute of Health at Charité–Universitätsmedizin Berlin, Berlin, Germany

    Samantha D. Praktiknjo, Lena Nitsch, Pauline Kautz & Leif S. Ludwig

  6. Max‐Delbrück‐Center for Molecular Medicine in the Helmholtz Association (MDC), Berlin Institute for Medical Systems Biology (BIMSB), Berlin, Germany

    Samantha D. Praktiknjo, Lena Nitsch, Pauline Kautz & Leif S. Ludwig

  7. Department of Biology, Chemistry, Pharmacy, Freie Universität Berlin, Berlin, Germany

    Lena Nitsch

  8. Institute of Biotechnology, Technische Universität Berlin, Berlin, Germany

    Pauline Kautz

  9. Gladstone-UCSF Institute of Genomic Immunology, San Francisco, CA, USA

    Karin Pelka & Ansuman T. Satpathy

  10. Genentech, South San Francisco, CA, USA

    Aviv Regev

  11. Division of Hematology/Oncology, Boston Children’s Hospital and Department of Pediatric Oncology, Dana-Farber Cancer Institute, Harvard Medical School, Boston, MA, USA

    Vijay G. Sankaran

Authors
  1. Caleb A. Lareau
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Vincent Liu
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Christoph Muus
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Samantha D. Praktiknjo
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Lena Nitsch
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Pauline Kautz
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. Katalin Sandor
    View author publications

    You can also search for this author in PubMed Google Scholar

  8. Yajie Yin
    View author publications

    You can also search for this author in PubMed Google Scholar

  9. Jacob C. Gutierrez
    View author publications

    You can also search for this author in PubMed Google Scholar

  10. Karin Pelka
    View author publications

    You can also search for this author in PubMed Google Scholar

  11. Ansuman T. Satpathy
    View author publications

    You can also search for this author in PubMed Google Scholar

  12. Aviv Regev
    View author publications

    You can also search for this author in PubMed Google Scholar

  13. Vijay G. Sankaran
    View author publications

    You can also search for this author in PubMed Google Scholar

  14. Leif S. Ludwig
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

C.A.L., L.S.L. and C.M. conceived and designed the protocol with supervision from A.R. and V.G.S. K.S. designed and optimized the solid tissue dissociation protocol. V.L. and J.C.G. refined the computational workflow with supervision from C.A.L. and A.T.S. L.N., P.K., K.S., Y.Y. and K.P. independently executed the protocol and provided critical feedback on the manuscript. C.A.L., S.D.P., A.T.S., A.R., V.G.S. and L.S.L. supervised various aspects of this study. All authors contributed to the writing and editing of the protocol.

Corresponding authors

Correspondence to Caleb A. Lareau, Aviv Regev, Vijay G. Sankaran or Leif S. Ludwig.

Ethics declarations

Competing interests

The Broad Institute has filed a patent relating to the use of the technology described in this paper for which C.A.L., L.S.L., C.M., A.R. and V.G.S. are named as inventors (US Patent App. 17/251,451). A.R. is a founder and equity holder of Celsius Therapeutics, an equity holder in Immunitas Therapeutics and until 31 August 2020, was a scientific advisory board member of Syros Pharmaceuticals, Neogene Therapeutics, Asimov and Thermo Fisher Scientific. From 1 August 2020, A.R. has been an employee of Genentech. V.G.S. serves as an advisor to and/or has equity in Novartis, Forma, Cellarity, Ensoma and Polaris Partners. A.T.S. is a founder of Immunai and Cartography Biosciences and receives research funding from Merck Research Laboratories and Allogene Therapeutics.

Peer review

Peer review information

Nature Protocols thanks Judith Zaugg and the other, anonymous, reviewer(s) for their contribution to the peer review of this work.

Additional information

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

Related links

Key references using this protocol

Lareau, C. et al. Nat. Biotechnol. 39, 451–461 (2021): https://doi.org/10.1038/s41587-020-0645-6

Walker, M. et al. N. Engl. J. Med. 383, 1556–1563 (2020): https://doi.org/10.1056/NEJMoa2001265

Penter, L. et al. Cancer Discov. 11, 3048–3063 (2021): https://doi.org/10.1158/2159-8290.CD-21-0276

Rights and permissions

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and Permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Lareau, C.A., Liu, V., Muus, C. et al. Mitochondrial single-cell ATAC-seq for high-throughput multi-omic detection of mitochondrial genotypes and chromatin accessibility. Nat Protoc 18, 1416–1440 (2023). https://doi.org/10.1038/s41596-022-00795-3

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1038/s41596-022-00795-3

Comments

By submitting a comment you agree to abide by our Terms and Community Guidelines. If you find something abusive or that does not comply with our terms or guidelines please flag it as inappropriate.

Search

Advanced search

Quick links

Nature Briefing: Translational Research

Sign up for the Nature Briefing: Translational Research newsletter — top stories in biotechnology, drug discovery and pharma.

Get what matters in translational research, free to your inbox weekly. Sign up for Nature Briefing: Translational Research