科技科学

Artefacts in single-cell mtDNA analyses misinform phylogenies

Nature正文已收录本站

arising from: C. Weng et al. Nature https://doi.org/10.1038/s41586-024-07066-z (2024).

The identification of somatic mutations through sequencing has enabled the reconstruction of clonal relationships in native human tissues. In a recent study, Weng et al.1 introduced Regulatory Multiomics (transcriptomics and chromatin accessibility) with Deep Mitochondrial Mutation Profiling (ReDeeM), reporting unprecedented resolution of cellular phylogenies from single-cell mitochondrial DNA (mtDNA) variant data. However, we find that common sequencing artefacts substantially distort potential phylogenetic reconstructions, and removal of probable artificial variants yields markedly disparate phylogenetic inferences. Consequently, the study does not provide robust evidence that somatic mtDNA variation alone permits the confident inference of highly resolved cellular phylogenies.

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

Data availability

Data from this work were downloaded from GSE219015, which was previously published1.

Code availability

Custom analysis code used to reproduce all inferences in this response is available at: https://github.com/caleblareau/redeem-reanalysis-reproducibility.

References

  1. Weng, C. et al. Deciphering cell states and genealogies of human haematopoiesis. Nature 627, 389–398 (2024).

    Article  ADS  CAS  PubMed  PubMed Central  Google Scholar 

  2. 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 

  3. Chapman, M. et al. Mitochondrial mutation, drift and selection during human development and ageing. Preprint at Research Square https://doi.org/10.21203/rs.3.rs-3083262/v1 (2023).

  4. 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 

  5. Wang, X. et al. Clonal expansion dictates the efficacy of mitochondrial lineage tracing in single cells. Genome Biol. 26, 70 (2025).

  6. Roussou, R. et al. Real-time assessment of mitochondrial DNA heteroplasmy dynamics at the single-cell level. EMBO J. 43, 5340–5359 (2024).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  7. Mitchell, E. et al. Clonal dynamics of haematopoiesis across the human lifespan. Nature 606, 343–350 (2022).

    Article  ADS  CAS  PubMed  PubMed Central  Google Scholar 

  8. Chinnery, P. F. & Samuels, D. C. Relaxed replication of mtDNA: a model with implications for the expression of disease. Am. J. Hum. Genet. 64, 1158–1165 (1999).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  9. Kwok, A. W. C. et al. MQuad enables clonal substructure discovery using single cell mitochondrial variants. Nat. Commun. 13, 1205 (2022).

    Article  ADS  CAS  PubMed  PubMed Central  Google Scholar 

  10. Kim, M. et al. Single-cell mtDNA dynamics in tumors is driven by coregulation of nuclear and mitochondrial genomes. Nat. Genet. 56, 889–899 (2024).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  11. Pickrell, J. K., Gilad, Y. & Pritchard, J. K. Comment on ‘Widespread RNA and DNA sequence differences in the human transcriptome’. Science 335, 1302 (2012).

    Article  ADS  CAS  PubMed  PubMed Central  Google Scholar 

  12. Abascal, F. et al. Somatic mutation landscapes at single-molecule resolution. Nature 593, 405–410 (2021).

    Article  ADS  CAS  PubMed  Google Scholar 

  13. Valentine, C. C. 3rd et al. Direct quantification of in vivo mutagenesis and carcinogenesis using duplex sequencing. Proc. Natl Acad. Sci. USA 117, 33414–33425 (2020).

    Article  ADS  CAS  PubMed  PubMed Central  Google Scholar 

  14. Serrano, I. M. et al. Mitochondrial haplotype and mito-nuclear matching drive somatic mutation and selection throughout ageing. Nat. Ecol. Evol. 8, 1021–1034 (2024).

    Article  PubMed  PubMed Central  Google Scholar 

  15. DePristo, M. A. et al. A framework for variation discovery and genotyping using next-generation DNA sequencing data. Nat. Genet. 43, 491–498 (2011).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Computational and Systems Biology Program, Memorial Sloan Kettering Cancer Center, New York, NY, USA

    Caleb A. Lareau, Tal Nawy & Dana Pe’er

  2. Wellcome Sanger Institute, Hinxton, UK

    Michael Spencer Chapman

  3. Centre for Haemato-Oncology, Barts Cancer Institute, Queen Mary University of London, London, UK

    Michael Spencer Chapman

  4. Department of Hematology, Oncology, and Tumor Immunology, Campus Virchow Klinikum, Berlin, Charité–Universitätsmedizin Berlin, Freie Universität Berlin and Humboldt-Universität zu Berlin, Berlin, Germany

    Livius Penter

  5. Howard Hughes Medical Institute, New York, NY, USA

    Dana Pe’er

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

    Leif S. Ludwig

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

    Leif S. Ludwig

  8. Charité–Universitätsmedizin Berlin, Cluster of Excellence ImmunoPrecept, Berlin, Germany

    Leif S. Ludwig

Authors

  1. Caleb A. Lareau
  2. Michael Spencer Chapman
  3. Livius Penter
  4. Tal Nawy
  5. Dana Pe’er
  6. Leif S. Ludwig

Contributions

C.A.L. led bioinformatics analyses with critical input and guidance from M.S.C., L.P., T.N., D.P. and L.S.L. All authors participated in the drafting and revising of the manuscript.

Corresponding authors

Correspondence to Caleb A. Lareau or Leif S. Ludwig.

Ethics declarations

Competing interests

C.A.L. and L.S.L. are named inventors on a patent related to mitochondrial lineage tracing (PCT/US2019/036583). D.P. is on the scientific advisory board of Insitro.

Additional information

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

Extended data figures and tables

Extended Data Fig. 1 The majority of ReDeeM-called variants have low support, and many represent artificial variant calls.

(a) Summary of support per variant per cell, stratified by donor, represented as the percent of non-zero entries in the cell-by-variant ReDeeM matrix. The percent of variants with exactly 1, 2, or ≥3 molecules of support per cell appears above the corresponding bars. (b,c) Connectedness analyses, including key parameter adjustments (left) and the effects on mean connectivity (right). (d) The same analyses as in Fig. 1, but for the two other donors profiled by Weng et al., showing similar positional bias in LMHC variants. (e) Annotation of all variants called using the ReDeeM workflow, including variants called by MQuad (purple) for four donors (top). Meta pileup of the position of the mismatches for variants called by MQuad (bottom). The values above each plot represent the fold enrichment of mismatches at the edges of molecules. Panels b,c adapted with permission from ref. 2, Elsevier.

Extended Data Fig. 2 Mismatch position bias is present throughout ATAC-seq-derived molecules.

(a) Pile-up of LMHC variants for 9 additional donors from the ReDeeM hashing experiments. Values in each panel represent the fold enrichment of mismatches at the edges of molecules relative to the center. (b) Pileup of LMHC variants from Fig. 1c and Extended Data Fig. 1d from the multiome ATAC-seq library. Values in each panel represent the fold enrichment of mismatches at the edges of molecules. (c) Pileup of all positions containing a mismatch or reads mapping to the reverse strand or forward strand of chromosome 22, split by the sequencing read, from a publicly available single-nucleus peripheral blood mononuclear cell multiome experiment (Supplementary Methods). (d) Same as in (c) but for a bulk ATAC-seq experiment2 summarizing all nuclear chromosomes. Note that no variant calling was performed for panels (c) and (d), such that pileups reflect all mismatches from the reference observed in the library.

Extended Data Fig. 3 ReDeeM-based cell-cell connectivity is substantially reduced in both filter-1 and filter-2.

Schematic of key parameters indicating the number of shared mutations required to establish connectivity between two cells, as previously applied by ReDeeM (top). The mean number of connections per cell (y-axis) is shown with the relative differences between ReDeeM filters -1 and -2 being indicated in % across bone marrow mononuclear cells (BMMCs) from four profiled donors (bottom). Adapted with permission from ref. 2, Elsevier.

Supplementary information

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Lareau, C.A., Chapman, M.S., Penter, L. et al. Artefacts in single-cell mtDNA analyses misinform phylogenies. Nature 656, E25–E31 (2026). https://doi.org/10.1038/s41586-026-10777-0

Download citation

  • Received:

  • Accepted:

  • Published:

  • Version of record:

  • Issue date:

  • DOI: https://doi.org/10.1038/s41586-026-10777-0