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Reply to: Artefacts in single-cell mtDNA analyses misinform phylogenies

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replying to: C. A. Lareau et al. Nature https://doi.org/10.1038/s41586-026-10777-0 (2026).

In the accompanying Comment, Lareau et al.1 raise two concerns regarding our downstream analytical approach for lineage tracing from data generated using the single-cell Regulatory Multiomics (transcriptomics and chromatin accessibility) with Deep Mitochondrial Mutation Profiling (ReDeeM) workflow2: (1) that the variant-calling workflow in the ReDeeM framework identifies mutations that are detected in one molecule per cell—regarded as ‘low support’; and (2) that these variants found in excess on the edges of mitochondrial DNA (mtDNA) molecules could be artefacts that lead to low-mean and high-connectedness (LMHC) mutations and distort lineage inference. However, we disagree with these interpretations. Here we present multiple lines of evidence to address these concerns, reinforce the robustness of our core conclusions, and discuss limitations and future directions of mitochondrial-based lineage tracing.

Data availability

This study used the previously published ReDeeM haematopoiesis dataset (GEO accession GSE219015).

References

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Acknowledgements

We thank members of the Sankaran and Weissman laboratories for valuable comments. This work was supported by the Howard Hughes Medical Institute (V.G.S. and J.S.W.), the Mathers Foundation (V.G.S. and J.S.W.) and National Institutes of Health (NIH) grants R01DK103794, R01CA265726, R01CA292941, R33CA278393, and R01HL146500 (V.G.S.). C.W. is supported by an NIH Pathway to Independence Award (K99HG013991). J.S.W. and V.G.S. are investigators at the Howard Hughes Medical Institute.

Author information

Authors and Affiliations

  1. Division of Hematology/Oncology, Boston Children’s Hospital, Harvard Medical School, Boston, MA, USA

    Chen Weng & Vijay G. Sankaran

  2. Whitehead Institute for Biomedical Research, Cambridge, MA, USA

    Chen Weng & Jonathan S. Weissman

  3. Department of Pediatric Oncology, Dana-Farber Cancer Institute, Harvard Medical School, Boston, MA, USA

    Chen Weng & Vijay G. Sankaran

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

    Chen Weng & Vijay G. Sankaran

  5. Howard Hughes Medical Institute, Boston, MA, USA

    Chen Weng & Vijay G. Sankaran

  6. Department of Biology and Howard Hughes Medical Institute, Massachusetts Institute of Technology, Cambridge, MA, USA

    Jonathan S. Weissman

  7. Koch Institute For Integrative Cancer Research at MIT, MIT, Cambridge, MA, USA

    Jonathan S. Weissman

  8. Harvard Stem Cell Institute, Cambridge, MA, USA

    Vijay G. Sankaran

Authors

  1. Chen Weng
  2. Jonathan S. Weissman
  3. Vijay G. Sankaran

Contributions

C.W. helped conceive the project, performed experiments and data analysis and wrote the manuscript. J.S.W. and V.G.S. conceived the project, supervised and directed the studies and wrote the manuscript. The change in authorship reflects the focused scope of the Reply. The Comment concerns a narrow technical point related to the variant-filtering and analytical strategy used in the study, rather than the broader experimental design, data generation or major biological conclusions of the original Article. Accordingly, the Reply and associated revisions were prepared by the lead individuals who were directly involved in developing, implementing and interpreting this specific methodological aspect of the work. The authors not included on the revised Reply remain appropriately credited for their contributions to the original Article, but they did not make substantial contributions to the preparation or revision of this focused Reply.

Corresponding authors

Correspondence to Chen Weng, Jonathan S. Weissman or Vijay G. Sankaran.

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Competing interests

The authors declare no competing interests.

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Extended data figures and tables

Extended Data Fig. 1 Extended dual-lineage tracing analysis with additional ReDeeM filtering.

(a) Example mtDNA mutations that show lineage specificity. The 1+-molecule mutations are represented in triangles with colors. The mutations with more molecules per cell are represented in black dots. (b-d) Related to Fig. 1g,h. (b) Reanalysis of the agreement of closeness (AOC) between ReDeeM and CRISPR lineage inference using ReDeeM pipeline by cell-ranger (bwa) as aligner (without mitochondrial genome masking) is computed with or without edge trimming. (c) AOC distribution compared to random reshuffled for panel-b. (d) AOC distribution using all mtDNA mutations called by 4 varieties of ReDeeM pipelines, where two aligners: bowtie2, and cell-ranger (bwa) and two consensus calling thresholds are tested. (e-f) Extended example from a different tumor sample is shown, related to Fig. 1g,h, The agreement of closeness (AOC) between ReDeeM and CRISPR lineage inference is computed across ReDeeM filter1 and filter2. 4 panels are in the same order. The AOC is computed for (1) mtDNA mutations using filter1, including 1+-molecule mutations, (2) mtDNA mutations using filter1, excluding 1+-molecule mutations (3) mtDNA mutations using filter2, including 1+-molecule mutations (4) mtDNA mutations using filter2, excluding 1+-molecule mutations. The regions with enhanced AOC when including 1-molecule mutations are highlighted. AOC distribution across different mtDNA filtering strategies compared to random reshuffled is shown in panel f.

Extended Data Fig. 2 Robustness of ReDeeM mutation calling and connectivity using trim-edge and filter2 across samples.

(a) Impact of ReDeeM trim-edge and filter2 compared to original filter (filter1). Top two rows: total number of mutations detected across samples; Bottom: the number of cells with connections (at least share one mutation with other cells). Disconnected cells are labeled in grey. (b-c) Overlap analysis among different mutation calling strategies. The ReDeeM-only mutations (those identified by ReDeem trim-edge or ReDeeM filter2 but not by mgatk are further evaluated by mutational signature analysis. (d) comparison between filter2, trim-edge and filter1, including mutation and cell number, and various connectivity metrics.

Extended Data Fig. 3 Robustness of lineage tracing performance with additional filtering.

(a-d) Trim-edge filtering remains highly consistent with filter1, showing substantial phylogenetic concordance and strong correlation of cell–cell Jaccard distances across samples, whereas random reshuffling shows markedly reduced agreement. (e–h) Filter2 similarly preserves tree structure and cell–cell distances relative to filter1, supporting robustness of the refined filtering strategy. (i–j) k-nearest-neighbor analyses (k = 15, 30) show that local cell neighborhoods defined after trim-edge or filter2 remain close to those in filter1 and are far more concordant than random expectation. (k) A conceptual example illustrates that low MRCA concordance does not necessarily imply poor biological recovery: two hypothetical trees recover the same six ground-truth clones perfectly, yet differ in internal branching order within a larger clade, reducing MRCA agreement to 0.515. Thus, clone-level accuracy can remain high despite modest MRCA-based concordance.

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Weng, C., Weissman, J.S. & Sankaran, V.G. Reply to: Artefacts in single-cell mtDNA analyses misinform phylogenies. Nature 656, E32–E38 (2026). https://doi.org/10.1038/s41586-026-10776-1

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