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.

  • Article
  • Published:

Single-cell multi-omics of mitochondrial DNA disorders reveals dynamics of purifying selection across human immune cells

Nature Genetics volume 55, pages 1198–1209 (2023)Cite this article

Subjects

Abstract

Pathogenic mutations in mitochondrial DNA (mtDNA) compromise cellular metabolism, contributing to cellular heterogeneity and disease. Diverse mutations are associated with diverse clinical phenotypes, suggesting distinct organ- and cell-type-specific metabolic vulnerabilities. Here we establish a multi-omics approach to quantify deletions in mtDNA alongside cell state features in single cells derived from six patients across the phenotypic spectrum of single large-scale mtDNA deletions (SLSMDs). By profiling 206,663 cells, we reveal the dynamics of pathogenic mtDNA deletion heteroplasmy consistent with purifying selection and distinct metabolic vulnerabilities across T-cell states in vivo and validate these observations in vitro. By extending analyses to hematopoietic and erythroid progenitors, we reveal mtDNA dynamics and cell-type-specific gene regulatory adaptations, demonstrating the context-dependence of perturbing mitochondrial genomic integrity. Collectively, we report pathogenic mtDNA heteroplasmy dynamics of individual blood and immune cells across lineages, demonstrating the power of single-cell multi-omics for revealing fundamental properties of mitochondrial genetics.

Access through your institution
Buy or subscribe

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

Access options

Access through your institution

Buy this article

  • Purchase on SpringerLink
  • Instant access to full article PDF
Buy now

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

Fig. 1: Identification and quantification of heteroplasmic pathogenic mtDNA deletions in single cells.
Fig. 2: Purifying selection against pathogenic mtDNA deletions in peripheral blood MAIT and CD8 T cells in PS.
Fig. 3: Altered CD8+ T cell expansion and purifying selection of pathogenic mtDNA deletions in vitro.
Fig. 4: Purifying selection and retention of pathogenic mtDNA heteroplasmy across the peripheral blood in adults with SLSMD.
Fig. 5: Myelodysplastic cells resolved by a chromosomal 7q deletion in CD34+ HSPCs in a case of PS.
Fig. 6: Multimodal characterization of PS BMMNCs with ASAP-seq.
Fig. 7: Altered erythroid differentiation and selection in PS.

Similar content being viewed by others

Data availability

Data associated with this work is available at GEO accession GSE173936.

Code availability

Software and documentation for mitochondrial DNA variant calling, including deletion calling and heteroplasmy estimation, is available via the mgatk package at http://github.com/caleblareau/mgatk as of version 0.6.3. All custom code to reproduce all analyses supporting this paper is available at https://github.com/caleblareau/pearson_syndrome. Code used in this paper is indexed in Zenodo (https://zenodo.org/record/7853604).

References

  1. Stewart, J. B. & Chinnery, P. F. Extreme heterogeneity of human mitochondrial DNA from organelles to populations. Nat. Rev. Genet. 22, 106–118 (2021).

    Article  CAS  PubMed  Google Scholar 

  2. 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 

  3. Wallace, D. C. & Chalkia, D. Mitochondrial DNA genetics and the heteroplasmy conundrum in evolution and disease. Cold Spring Harb. Perspect. Biol. 5, a021220 (2013).

    Article  PubMed  PubMed Central  Google Scholar 

  4. Gorelick G, A. N. et al. Respiratory complex and tissue lineage drive recurrent mutations in tumour mtDNA. Nat Metab. 3, 558–570 (2021).

    Article  Google Scholar 

  5. Smith, A. L. M. et al. Age-associated mitochondrial DNA mutations cause metabolic remodeling that contributes to accelerated intestinal tumorigenesis. Nat. Cancer 1, 976–989 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  6. Goldstein, A. & Falk, M. J. Mitochondrial DNA Deletion Syndromes (University of Washington, 2023).

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

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  8. 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 

  9. Lareau, C. A. et al. Massively parallel single-cell mitochondrial DNA genotyping and chromatin profiling. Nat. Biotechnol. 39, 451–461 (2020).

    Article  PubMed  PubMed Central  Google Scholar 

  10. 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 

  11. 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 

  12. Krishnan, K. J. et al. What causes mitochondrial DNA deletions in human cells? Nat. Genet. 40, 275–279 (2008).

    Article  CAS  PubMed  Google Scholar 

  13. Pitceathly, R. D. S., Rahman, S. & Hanna, M. G. Single deletions in mitochondrial DNA–molecular mechanisms and disease phenotypes in clinical practice. Neuromuscul. Disord. 22, 577–586 (2012).

    Article  CAS  PubMed  Google Scholar 

  14. Lujan, S. A. et al. Ultrasensitive deletion detection links mitochondrial DNA replication, disease, and aging. Genome Biol. 21, 248 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  15. Hjelm, B. E. et al. Splice-Break: exploiting an RNA-seq splice junction algorithm to discover mitochondrial DNA deletion breakpoints and analyses of psychiatric disorders. Nucleic Acids Res. 47, e59 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  16. Hao, Y. et al. Dictionary learning for integrative, multimodal, and scalable single-cell analysis. Nat. Biotechnol. https://doi.org/10.1038/s41587-023-01767-y (2023).

  17. Battaglia, S. et al. Uridine and pyruvate protect T cells’ proliferative capacity from mitochondrial toxic antibiotics: a clinical pilot study. Sci. Rep. 11, 12841 (2021).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  18. King, M. P. & Attardi, G. Human cells lacking mtDNA: repopulation with exogenous mitochondria by complementation. Science 246, 500–503 (1989).

    Article  CAS  PubMed  Google Scholar 

  19. Basu, S. et al. Accurate mapping of mitochondrial DNA deletions and duplications using deep sequencing. PLoS Genet. 16, e1009242 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  20. Goudenège, D. et al. eKLIPse: a sensitive tool for the detection and quantification of mitochondrial DNA deletions from next-generation sequencing data. Genet. Med. 21, 1407–1416 (2019).

    Article  PubMed  Google Scholar 

  21. Kardos, G. et al. Refractory anemia in childhood: a retrospective analysis of 67 patients with particular reference to monosomy 7. Blood 102, 1997–2003 (2003).

    Article  CAS  PubMed  Google Scholar 

  22. Nishimura, A. et al. Acquisition of monosomy 7 and a RUNX1 mutation in Pearson syndrome. Pediatr. Blood Cancer 68, e28799 (2021).

    Article  CAS  PubMed  Google Scholar 

  23. Granja, J. M. et al. Single-cell multiomic analysis identifies regulatory programs in mixed-phenotype acute leukemia. Nat. Biotechnol. 37, 1458–1465 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  24. Buenrostro, J. D. et al. Integrated single-cell analysis maps the continuous regulatory landscape of human hematopoietic differentiation. Cell 173, 1535–1548 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  25. Chung, J.-W. et al. A combination of CD15/CD10, CD64/CD33, CD16/CD13 or CD11b flow cytometric granulocyte panels is sensitive and specific for diagnosis of myelodysplastic syndrome. Ann. Clin. Lab. Sci. 42, 271–280 (2012).

    PubMed  Google Scholar 

  26. Kussick, S. J. et al. Four-color flow cytometry shows strong concordance with bone marrow morphology and cytogenetics in the evaluation for myelodysplasia. Am. J. Clin. Pathol. 124, 170–181 (2005).

    Article  PubMed  Google Scholar 

  27. Pekalski, M. L. et al. Neonatal and adult recent thymic emigrants produce IL-8 and express complement receptors CR1 and CR2. JCI Insight 2, e93739 (2017).

    Article  PubMed  PubMed Central  Google Scholar 

  28. Farruggia, P., Di Marco, F. & Dufour, C. Pearson syndrome. Expert Rev. Hematol. 11, 239–246 (2018).

    Article  CAS  PubMed  Google Scholar 

  29. Gagne, K. E. et al. Pearson marrow pancreas syndrome in patients suspected to have Diamond-Blackfan anemia. Blood 124, 437–440 (2014).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  30. Cherry, A. B. C. et al. Induced pluripotent stem cells with a mitochondrial DNA deletion. Stem Cells 31, 1287–1297 (2013).

    Article  CAS  PubMed  Google Scholar 

  31. Ludwig, L. S. et al. Transcriptional states and chromatin accessibility underlying human erythropoiesis. Cell Rep. 27, 3228–3240 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  32. Bao, X. R. et al. Mitochondrial dysfunction remodels one-carbon metabolism in human cells. eLife 5, e10575 (2016).

    Article  PubMed  PubMed Central  Google Scholar 

  33. Yang, M. & Vousden, K. H. Serine and one-carbon metabolism in cancer. Nat. Rev. Cancer 16, 650–662 (2016).

    Article  CAS  PubMed  Google Scholar 

  34. Yang, L. et al. Serine catabolism feeds NADH when respiration is impaired. Cell Metab. 31, 809–821 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  35. Macaulay, I. C., Ponting, C. P. & Voet, T. Single-cell multiomics: multiple measurements from single cells. Trends Genet. 33, 155–168 (2017).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  36. Stuart, T. & Satija, R. Integrative single-cell analysis. Nat. Rev. Genet. 20, 257–272 (2019).

    Article  CAS  PubMed  Google Scholar 

  37. Trifunovic, A. et al. Premature ageing in mice expressing defective mitochondrial DNA polymerase. Nature 429, 417–423 (2004).

    Article  CAS  PubMed  Google Scholar 

  38. Kujoth, G. C. et al. Mitochondrial DNA mutations, oxidative stress, and apoptosis in mammalian aging. Science 309, 481–484 (2005).

    Article  CAS  PubMed  Google Scholar 

  39. Vandiver, A. R. et al. Nanopore sequencing identifies a higher frequency and expanded spectrum of mitochondrial DNA deletion mutations in human aging. Aging Cell 22, e13842 (2022).

    Article  Google Scholar 

  40. Lehmann, D. et al. Understanding mitochondrial DNA maintenance disorders at the single muscle fibre level. Nucleic Acids Res. 47, 7430–7443 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  41. Nido, G. S. et al. Ultradeep mapping of neuronal mitochondrial deletions in Parkinson’s disease. Neurobiol. Aging 63, 120–127 (2018).

    Article  CAS  PubMed  Google Scholar 

  42. Lawless, C., Greaves, L., Reeve, A. K., Turnbull, D. M. & Vincent, A. E. The rise and rise of mitochondrial DNA mutations. Open Biol. 10, 200061 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  43. Morris, J. et al. Pervasive within-mitochondrion single-nucleotide variant heteroplasmy as revealed by single-mitochondrion sequencing. Cell Rep. 21, 2706–2713 (2017).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  44. Jones, N. et al. Metabolic adaptation of Human CD4+ and CD8+ T cells to T-cell receptor-mediated stimulation. Front. Immunol. 8, 1516 (2017).

    Article  PubMed  PubMed Central  Google Scholar 

  45. van der Windt, G. J. et al. Mitochondrial respiratory capacity is a critical regulator of CD8 T cell memory development. Immunity 36, 68–78 (2012).

    Article  CAS  PubMed  Google Scholar 

  46. Hinks, T. S. C. & Zhang, X.-W. MAIT cell activation and functions. Front. Immunol. 11, 1014 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  47. Lisci, M. et al. Mitochondrial translation is required for sustained killing by cytotoxic T cells. Science 374, eabe9977 (2021).

    Article  CAS  PubMed  Google Scholar 

  48. Korge, P., Calmettes, G. & Weiss, J. N. Increased reactive oxygen species production during reductive stress: The roles of mitochondrial glutathione and thioredoxin reductases. Biochim. Biophys. Acta 1847, 514–525 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  49. Sharma, R. et al. Circulating markers of NADH-reductive stress correlate with mitochondrial disease severity. J. Clin. Invest. 131, e136055 (2021).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  50. Enns, G. M. et al. Degree of glutathione deficiency and redox imbalance depend on subtype of mitochondrial disease and clinical status. PLoS ONE 9, e100001 (2014).

    Article  PubMed  PubMed Central  Google Scholar 

  51. De Franceschi, L. et al. Oxidative stress modulates heme synthesis and induces peroxiredoxin-2 as a novel cytoprotective response in β-thalassemic erythropoiesis. Haematologica 96, 1595–1604 (2011).

    Article  PubMed  PubMed Central  Google Scholar 

  52. Sankaran, V. G. & Weiss, M. J. Anemia: progress in molecular mechanisms and therapies. Nat. Med. 21, 221–230 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  53. Rosales, X. Q. et al. The North American mitochondrial disease registry. J. Transl. Genet Genom. 4, 81–90 (2020).

    PubMed  PubMed Central  Google Scholar 

  54. Hu, J. et al. Isolation and functional characterization of human erythroblasts at distinct stages: implications for understanding of normal and disordered erythropoiesis in vivo. Blood 121, 3246–3253 (2013).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  55. Giani, F. C. et al. Targeted application of human genetic variation can improve red blood cell production from stem cells. Cell Stem Cell 18, 73–78 (2016).

    Article  CAS  PubMed  Google Scholar 

  56. Al’Khafaji, A. M. et al. High-throughput RNA isoform sequencing using programmable cDNA concatenation. Nat. Biotechnol. https://doi.org/10.1038/s41587-023-01815-7 (2023).

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

    Article  PubMed  PubMed Central  Google Scholar 

  58. Lott, M. T. et al. mtDNA variation and analysis using mitomap and mitomaster. Curr. Protoc. Bioinforma. 44, 1.23.1–1.23.6 (2013).

    Google Scholar 

  59. Korsunsky, I. et al. Fast, sensitive and accurate integration of single-cell data with Harmony. Nat. Methods 16, 1289–1296 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  60. 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 

  61. Hao, Y. et al. Integrated analysis of multimodal single-cell data. Cell 184, 3573–3587 (2021).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  62. Wolock, S. L., Lopez, R. & Klein, A. M. Scrublet: computational identification of cell doublets in single-cell transcriptomic data. Cell Syst. 8, 281–291 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  63. 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).

    Article  CAS  PubMed  Google Scholar 

  64. Soneson, C. & Robinson, M. D. Bias, robustness and scalability in single-cell differential expression analysis. Nat. Methods 15, 255–261 (2018).

    Article  CAS  PubMed  Google Scholar 

  65. Liao, Y., Wang, J., Jaehnig, E. J., Shi, Z. & Zhang, B. WebGestalt 2019: gene set analysis toolkit with revamped UIs and APIs. Nucleic Acids Res. 47, W199–W205 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  66. GTEx Consortium. The GTEx Consortium atlas of genetic regulatory effects across human tissues. Science 369, 1318–1330 (2020).

    Article  Google Scholar 

  67. Bray, N., Pimentel, H., Melsted, P. & Pachter, L. Near-optimal RNA-seq quantification with kallisto. Nat. Biotechnol. 34, 525–527 (2016).

    Article  CAS  PubMed  Google Scholar 

  68. Müller, S., Cho, A., Liu, S. J., Lim, D. A. & Diaz, A. CONICS integrates scRNA-seq with DNA sequencing to map gene expression to tumor sub-clones. Bioinformatics 34, 3217–3219 (2018).

    Article  PubMed  PubMed Central  Google Scholar 

  69. 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 

  70. 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 

Download references

Acknowledgements

We are deeply grateful to the patients and families who made this work possible. Patient samples and data from adult donors with KSS and CPEO were provided by the North American Mitochondrial Disease Consortium (NAMDC), which is funded by a grant from NIH (U54NS078059). We thank A. Shimamura (Boston Children’s Hospital) and the Boston Children’s Hospital Bone Marrow Failure and Myelodysplastic Syndrome program for their support in this study (supported by NIH grant RC2DK122533). We thank the MDC/BIH Genomics Platform, Berlin (FacilityID = 1565, The CoreMarketplace: MDC&BIH Technology Platform Genomics) for technical support relating to flow cytometry and sequencing efforts and Claudia Quedenau for conducting the MAS-ISO-seq experiments. We acknowledge support from the Broad Institute and the Whitehead Institute Flow Cytometry core facilities. This work was supported by NIH K99 HG012579 (to C.A.L.), RC2 DK122533-01 (to M.D.F.), R01 DK103794 (to V.G.S.), R33 HL120791 (to V.G.S.), R01 DK107716 (to S.A.), R33 HL154133 (to S.A.) and UM1 HG012076 (to C.A.L., A.T.S. and L.S.L.). This research was supported by a Stanford Science Fellowship (to C.A.L.), BroadIgnite Award (to L.S.L.), an Emmy Noether fellowship by the German Research Foundation (D.F.G.; LU 2336/2-1 to L.S.L.), the Hector Fellow Academy (to Y.H.H., P.K., L.N. and L.S.L.), a Lloyd J. Old STAR Award from the Cancer Research Institute (A.T.S.), an ASH Scholar Award from the American Society of Hematology (to A.T.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.), the Howard Hughes Medical Institute and Klarman Cell Observatory (to A.R.), the Champ Foundation (to S.A.) and the Associazione Luigi Comini Onlus (to S.A.).

Author information

Author notes

  1. Eleni P. Mimitou

    Present address: Immunai, New York City, NY, USA

  2. Peter Smibert

    Present address: 10x Genomics, San Francisco, CA, USA

  3. Aviv Regev

    Present address: Genentech, San Francisco, CA, USA

  4. These authors contributed equally: Sonia M. Dubois, Frank A. Buquicchio, Yu-Hsin Hsieh.

Authors and Affiliations

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

    Caleb A. Lareau, Frank A. Buquicchio, Jacob C. Gutierrez, Yajie Yin & Ansuman T. Satpathy

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

    Caleb A. Lareau & Ansuman T. Satpathy

  3. Department of Genetics, Stanford University, Stanford, CA, USA

    Caleb A. Lareau & Anshul Kundaje

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

    Caleb A. Lareau, Kopal Garg, Jeffrey M. Verboon, Evgenij Fiskin, Wendy Luo, Christoph Muus, Rhea Malhotra, Aviv Regev, Vijay G. Sankaran & Leif S. Ludwig

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

    Caleb A. Lareau, Sonia M. Dubois, Kopal Garg, Jeffrey M. Verboon, Vijay G. Sankaran, Suneet Agarwal & Leif S. Ludwig

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

    Yu-Hsin Hsieh, Pauline Kautz, Lena Nitsch, Samantha D. Praktiknjo, Patrick Maschmeyer & Leif S. Ludwig

  7. Berlin Institute of Health at Charité—Universitätsmedizin Berlin, Berlin, Germany

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

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

    Pauline Kautz

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

    Lena Nitsch

  10. Technology Innovation Lab, New York Genome Center, New York City, NY, USA

    Eleni P. Mimitou & Peter Smibert

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

    Christoph Muus

  12. Center for Pediatric Neurosciences, Mitochondrial Medicine, Cleveland Clinic, Cleveland, OH, USA

    Sumit Parikh

  13. Department of Pathology, Boston Children’s Hospital, Harvard Medical School, Boston, MA, USA

    Mark D. Fleming

  14. Department of Pediatric Oncology, Charité-Universitätsmedizin Berlin, Campus Virchow Klinikum, Berlin, Germany

    Lena Oevermann, Johannes Schulte & Cornelia Eckert

  15. Department of Computer Science, Stanford University, Stanford, CA, USA

    Anshul Kundaje

  16. Memorial Sloan Kettering Cancer Center, New York, NY, USA

    Santosha A. Vardhana

  17. Department of Biology and Koch Institute, Massachusetts Institute of Technology, Cambridge, MA, USA

    Aviv Regev

Authors
  1. Caleb A. Lareau
    View author publications

    You can also search for this author inPubMed Google Scholar

  2. Sonia M. Dubois
    View author publications

    You can also search for this author inPubMed Google Scholar

  3. Frank A. Buquicchio
    View author publications

    You can also search for this author inPubMed Google Scholar

  4. Yu-Hsin Hsieh
    View author publications

    You can also search for this author inPubMed Google Scholar

  5. Kopal Garg
    View author publications

    You can also search for this author inPubMed Google Scholar

  6. Pauline Kautz
    View author publications

    You can also search for this author inPubMed Google Scholar

  7. Lena Nitsch
    View author publications

    You can also search for this author inPubMed Google Scholar

  8. Samantha D. Praktiknjo
    View author publications

    You can also search for this author inPubMed Google Scholar

  9. Patrick Maschmeyer
    View author publications

    You can also search for this author inPubMed Google Scholar

  10. Jeffrey M. Verboon
    View author publications

    You can also search for this author inPubMed Google Scholar

  11. Jacob C. Gutierrez
    View author publications

    You can also search for this author inPubMed Google Scholar

  12. Yajie Yin
    View author publications

    You can also search for this author inPubMed Google Scholar

  13. Evgenij Fiskin
    View author publications

    You can also search for this author inPubMed Google Scholar

  14. Wendy Luo
    View author publications

    You can also search for this author inPubMed Google Scholar

  15. Eleni P. Mimitou
    View author publications

    You can also search for this author inPubMed Google Scholar

  16. Christoph Muus
    View author publications

    You can also search for this author inPubMed Google Scholar

  17. Rhea Malhotra
    View author publications

    You can also search for this author inPubMed Google Scholar

  18. Sumit Parikh
    View author publications

    You can also search for this author inPubMed Google Scholar

  19. Mark D. Fleming
    View author publications

    You can also search for this author inPubMed Google Scholar

  20. Lena Oevermann
    View author publications

    You can also search for this author inPubMed Google Scholar

  21. Johannes Schulte
    View author publications

    You can also search for this author inPubMed Google Scholar

  22. Cornelia Eckert
    View author publications

    You can also search for this author inPubMed Google Scholar

  23. Anshul Kundaje
    View author publications

    You can also search for this author inPubMed Google Scholar

  24. Peter Smibert
    View author publications

    You can also search for this author inPubMed Google Scholar

  25. Santosha A. Vardhana
    View author publications

    You can also search for this author inPubMed Google Scholar

  26. Ansuman T. Satpathy
    View author publications

    You can also search for this author inPubMed Google Scholar

  27. Aviv Regev
    View author publications

    You can also search for this author inPubMed Google Scholar

  28. Vijay G. Sankaran
    View author publications

    You can also search for this author inPubMed Google Scholar

  29. Suneet Agarwal
    View author publications

    You can also search for this author inPubMed Google Scholar

  30. Leif S. Ludwig
    View author publications

    You can also search for this author inPubMed Google Scholar

Contributions

C.A.L., A.R., V.G.S., S.A. and L.S.L. conceived and designed the project. C.A.L. developed the mgatk-del software and led all analyses. L.S.L. led, designed and performed experiments with F.A.B., Y.H.H., P.K., L.N. and Y.Y., and assistance from S.M.D., W.L. and C.M. E.P.M. and P.S. contributed to reagents and advised on the ASAP-seq experiments. L.O., J.S. and C.E. contributed to healthy pediatric control samples and clinical perspectives. C.A.L., K.G. and J.M.V. developed the mtDNA simulation framework. F.B., Y.H.H., P.K., L.N., S.D.P., J.C.G., E.F., R.M., P.M., SuP, A.K., S.A. and L.S.L. contributed to data interpretation. S.M.D., S.D.P., M.D.F., A.K., S.A.V., A.T.S., A.R., V.G.S., S.A. and L.S.L. supervised various aspects of this work. C.A.L. and L.S.L. wrote the paper with input from all authors.

Corresponding authors

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

Ethics declarations

Competing interests

The Broad Institute has filed for a patent relating to the use of the technology described in this paper where C.A.L., L.S.L., C.M., A.R. and V.G.S. are named inventors (US provisional patent application 62/683,502). C.A.L. and L.S.L. are consultants to Cartography Biosciences. ATS is a founder of Immunai and Cartography Biosciences and receives research funding from Allogene Therapeutics and Merck Research Laboratories. A.R. is a founder and equity holder of Celsius Therapeutics, an equity holder in Immunitas Therapeutics, and until August 31, 2020, was an SAB member of Syros Pharmaceuticals, Neogene Therapeutics, Asimov and ThermoFisher Scientific. Since August 1, 2020, A.R. has been an employee of Genentech. V.G.S. serves as an advisor to and/or has equity in Branch Biosciences, Novartis, Forma, Cellarity and Ensoma. S.A.V. is an advisor to Immunai and has provided consulting services to Koch Disruptive Technologies and ADC Therapeutics. The remaining authors declare no competing interests.

Peer review

Peer review information

Nature Genetics thanks Na Cai, Dan Mishmar, and the other, anonymous, reviewer(s) for their contribution to the peer review of this work. Peer reviewer reports are available.

Additional information

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

Extended data

Extended Data Fig. 1 Deletion and heteroplasmy estimation using mgatk-del.

(a) Schematic of mgatk-del pipeline, which utilizes the outputs of CellRanger-ATAC. Two critical steps of base-resolution deletion calling (‘find’) and estimation of single-cell heteroplasmy (‘quantify’) are illustrated. (b) Output of mgatk-del ‘find’ for Pearson syndrome deletion 1 (PS1). The red vertical lines represent the called deletion breakpoints where the regions were joined (blue arc) via a secondary alignment (‘SA’ tag in.bam file). (c) Schematic of the simulation framework. Synthetic cells with known heteroplasmy were generated via mixtures of reference and PS mtDNA for all previously reported deletions. (d) Summary of results from a 50% mix showing the heteroplasmy as estimated from the ratio of clipped to unclipped reads. Parameters ‘inner-param’ and ‘outer-param’ define the number of bases that are discarded on the read when estimating the overall heteroplasmy per cell. (e) Results of exhaustive simulation for three mtDNA deletions used in the cell mixing experiment. The minimum value of the root mean squared error (RMSE) of the estimated and true heteroplasmy is noted with an asterisk over the grid search. (f) Difference in mean estimated heteroplasmy (RMSE) in optimal and default parameters across a variety of settings indicating the stability. (g) Decomposition of variance using a permuted model. Black shows the observed variance whereas green shows the spread under a permuted (null) model. The percent of the variance explained by this null model is shown. (h) Single-cell correlation of clipped (Fig. 1d) versus coverage-based (Fig. 1e) heteroplasmy estimates for valid deletions per indicated deletion/donor. The Pearson correlation for the three deletions is indicated. (i) % of cells with non-zero heteroplasmy for different deletions at different coverages using indicated methods. The left panel assesses sensitivity where the true proportion of cells with the deletion is 100%. The right panel assesses specificity where the true proportion of cells with the deletion is 0%. For both plots, detection of the deletion requires ≥1% heteroplasmy. (j) The mean absolute error in heteroplasmy at 50x coverage is indicated by the value shown on the graph for two methods of heteroplasmy estimation, as in (i).

Extended Data Fig. 2 Supporting information for PS PBMC mtscATAC-seq analyses.

(a) Result of mgatk-del hyperparameter optimization via a simulation framework. The minimum value of the root mean squared error (RMSE) of the estimated and true heteroplasmy is noted with an asterisk over the grid search. (b) Summary of % of cells with 0% heteroplasmy across all hematopoietic cells for the three PS donors. (c) Violin plots of each respective mtDNA deletion for all three patients in selected T cell populations. Black indicates the observed data. Gray represents heteroplasmy under a null model of one mean and the variation attributed to differences in coverage per cell. CD8.TEM and MAIT cells have a bimodal distribution (indicating purifying selection) whereas CD8+ naive cells have a distribution that is more consistent with a single mode of heteroplasmy. The percentage of cells with 0% heteroplasmy under observed and null settings are noted for each population below the violins. (d) UMAP visualization of MELAS bridge reference projection across three donors previously reported. (e) Summary of % of cells with 0% heteroplasmy across all hematopoietic cells for the three MELAS donors10 with refined cell type annotations from the bridge reference projection.

Extended Data Fig. 3 Supporting analyses for primary PS T cell cultures.

(a) Representative flow cytometry plot of CD4+ and CD8+ T cells. Cells from the four donors (columns) were assessed for CD45RA and CD45RO expression. The box indicates the proportion of CD45RAhi/CD45RO− cells summarized in Fig. 3d. (b) Summary of flow cytometry data from independent T cell culture comparing cells from a healthy pediatric donor to an adult donor. T cells were derived from both bone marrow mononuclear cells (BMMNCs) and peripheral blood mononuclear cells (PBMCs) for the pediatric donor. (c) Embedding of day 14T cells colored by antibody derived tags (ADTs) for CCR4 and IL2RA. (d) Dynamics of heteroplasmy for 76 heteroplasmic single nucleotide variants (SNVs) during T cell culture. m.12631T > C and m.4225A > G are highlighted. (e) Single-cell heteroplasmy of m.12631T > C and m.4225A > G, variants expanding during T cell culture, annotated in the day 14 embedding. Blue arrow indicates the subpopulation positive for the m.4225A > G variant. (f) Single-cell heteroplasmy of the PS deletion annotated on day 21 derived T cells profiled via mtscATAC-seq. (g) Single-cell heteroplasmy of m.12631T > C and m.4225A > G, variants expanding during T cell culture, annotated in the day 21 cell embedding. Blue arrow indicates the subpopulation positive for the m.4225A > G variant. (h) Day 21 embedding clustering, annotations, and per-cluster heteroplasmy quantified via a cumulative distribution function. (i) Independent validation of in vitro CD8+ T cell relative expansion (left) and overall T cell proliferation (right) defects from PT1 PBMCs. (j) Summary of cell clusters, heteroplasmy, and marker gene scores for PT1 T cells following culture. (k) Day 14 PT1 per-cluster heteroplasmy quantified via a cumulative distribution function for PT1 T cells after culture.

Extended Data Fig. 4 Supporting information for del7q calling and CD34+ mtscATAC-seq analyses.

(a) Flow cytometry gating strategy for the sorting of live CD66b−CD34+ bone marrow mononuclear cells. (b) Summary of 7q fragment abundances in healthy CD34+ and PBMC mtscATAC-seq samples9; compare to Fig. 4b with the same cutoff. (c) Result of Gaussian mixture model applied to indicated samples. The red trace indicates the first mixture component estimated (lower mean) whereas the blue trace represents the second component with a higher mean. The healthy PBMC sample does not contain a chromosome alteration. (d) Graphical density of cells from mixture model (y-axis) and from crude fragment abundance (x-axis; see Fig. 4b). The dotted line indicates the cutoff for wild type and del7q annotations. (e) Histograms of mtDNA deletion heteroplasmy proportions (%) stratified on del7q status. (f) Projection of a healthy control CD34+ mtscATAC-seq sample onto the reference embedding as shown in Fig. 5d. (g) Stacked bar plots of cell type proportions for projected cell types from PT3 with PS/MDS stratified by del7q status (MDS for positive and wild type for negative) and a healthy control donor. (h) Annotation of del7q status in PBMCs, which is primarily identified in myeloid, NK, and B-cell populations; see Fig. 2d for cluster annotations.

Extended Data Fig. 5 Supporting analyses for PT3 bone marrow mononuclear cell ASAP-seq dataset.

(a) Projection of select protein-derived antibody tag abundances for indicated proteins. Select arrows indicate populations positive for the respective marker. UMAP coordinates same as Fig. 6c. (b) Projection of protein surface markers CD56 (NK cells and MDS-associated cells) and CD335 (only NK cells) with arrows indicating the two cell populations. (c) UMAP of ASAP-seq processed bone marrow mononuclear cells from a PS (top) and a healthy control11 (bottom) with hematopoietic stem and progenitor cells (‘progenitors’) indicated in the red boxes. (d) Projection of protein tags within the boxed progenitor populations as in (c), contrasting the presence of only CD71+ cells among CD34+/c-Kit+ cells in PS as compared to the healthy control. (e) Volcano plot showing differential gene activity scores for CD8 recent thymic emigrants (RTEs) compared to other CD8 naive T cells. Annotated genes in red represent known marker genes for RTEs. (f) Zoom (top) and mtDNA deletion heteroplasmy (bottom) in differentiated CD8 T and NK cells from the BMMNC populations. (g) Volcano plot illustrating the association between protein levels and mtDNA deletion heteroplasmy in single cells. P-values were computed from the default two-sided Seurat Wilcoxon test with Bonferroni p-value adjustment. (h) Projection of cell state surface markers (CD3, CD8) and top antibody tags (CD16, CD195) as determined in (g). (i,j) Reclustering and UMAP depiction of PT3 PBMC mtscATAC-seq data identify (i) low heteroplasmy and (j) recent thymic emigrants (RTEs). Cell type annotations as indicated. (k) Landscape of 69 heteroplasmic somatic mtDNA mutations identified in BMMNC. Statistical test: two-sided Fisher’s exact test. (l) Substitution rate of mgatk identified heteroplasmic mutations (y-axis) in each class of mononucleotide and trinucleotide change resolved by the heavy (H) and light (L) strands of the mitochondrial genome. (m) Scatter plot of 69 somatic mtDNA variants identified in panel (l) stratified based on cells annotated as del7q (x-axis) and wild type for chr7 (y-axis). (n) Projection of wild type (diploid chr7)-enriched somatic mtDNA mutations m.14476G > A (50%) and m.12242A > G (25%).

Extended Data Fig. 6 Supporting information for in vitro erythroid differentiation experiments.

(a) Reference embedding of bone marrow mononuclear cell CITE-seq reference dataset (left) with gene module scores for selected pathways annotated on the UMAP embedding. (b) Flow cytometry gating scheme used for sorting of in vitro differentiated healthy control and PS cells, related to Fig. 7. (c) Flow cytometry plots showing the distribution of CD71 and CD235a surface marker expression of in vitro differentiated healthy control and PS cells at indicated days of culture. (d) MayGrunwald Giemsa stained cytospins of in vitro differentiated healthy control and PS cells at day 8 of culture at 63x magnification. (e) UMAP of scRNA-seq data colored by predicted cell cycle state. Cluster annotations as in Fig. 7e–g. (f) Comparison of differential gene expression between PT3 donor cells with MDS (x-axis) and without MDS (y-axis) related to healthy control. The Pearson correlation between all genes is annotated (0.82). Genes from relevant pathways or genomic annotations are highlighted in specific colors. (g-i) Projection of gene expression of selected differentially expressed genes between PS and healthy control erythroblasts, including (g) PHGDH, (h) CPOX, and (i) HEBP2. Gene expression coloring is scaled for all plots between the first and 99th quantile per gene.

Supplementary information

Supplementary Information

Supplemental Methods and Supplementary Figs. 1–4.

Reporting Summary

Peer Review File

Supplementary Tables

Supplementary Table 1—Coordinates of mtDNA deletions investigated in this study. Supplementary Table 2—Summary of deletion heteroplasmy per donor and cell type in PBMCs. Supplementary Table 3—List of TotalSeq antibodies, clones and barcodes for ASAP-seq and DOGMA-seq.

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., Dubois, S.M., Buquicchio, F.A. et al. Single-cell multi-omics of mitochondrial DNA disorders reveals dynamics of purifying selection across human immune cells. Nat Genet 55, 1198–1209 (2023). https://doi.org/10.1038/s41588-023-01433-8

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1038/s41588-023-01433-8

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