CoChat
Sign Up
Cited by 58

The DOI matches its Crossref record; 16 of the 16 references it cites were found in a registry or an index.

High-resolution, noninvasive single-cell lineage tracing in mice and humans based on DNA methylation epimutations

Chen, Mengyang, Fu, Ruijiang, Chen, Yiqian, Li, Li, Wang, Shou-Wen·2025·Nature Methods
DOI 10.1038/s41592-024-02567-1
Open this paper in CoChat — free account

Abstract

In vivo lineage tracing holds great potential to reveal fundamental principles of tissue development and homeostasis. However, current lineage tracing in humans relies on extremely rare somatic mutations, which has limited temporal resolution and lineage accuracy. Here, we developed a generic lineage-tracing tool based on frequent epimutations on DNA methylation, enabled by our computational method MethylTree. Using single-cell genome-wide DNA methylation datasets with known lineage and phenotypic labels, MethylTree reconstructed lineage histories at nearly 100% accuracy across different cell types, developmental stages, and species. We demonstrated the epimutation-based single-cell multi-omic lineage tracing in mouse and human blood, where MethylTree recapitulated the differentiation hierarchy in hematopoiesis. Applying MethylTree to human embryos, we revealed early fate commitment at the four-cell stage. In native mouse blood, we identified ~250 clones of hematopoietic stem cells. MethylTree opens the door for high-resolution, noninvasive and multi-omic lineage tracing in humans and beyond.

Overview

Integrity signals

Registry

Crossref · 10.1038/s41592-024-02567-1

Cites

16 of 16 references verified0 citation errors · 0 not checkable

Statistics

Recomputing p-values needs the paper’s full text.

Verification

Claims the paper makes

This paper’s full text isn’t openly available, so its claims and self-check weren’t run. The registry and citation checks still apply.

Claims other papers make about this one

Each is checked against this paper’s own text: does it say what the citing paper says it does?

Citations

Cites

Show all 16 references

Research works printed but not linked: 61

  1. Kretzschmar, K. & Watt, F. M. Lineage tracing. Cell 148, 33–45 (2012). · DOI
  2. Sun, J. et al. Clonal dynamics of native haematopoiesis. Nature 514, 322–327 (2014). · DOI · Paper page
  3. McKenna, A. et al. Whole-organism lineage tracing by combinatorial and cumulative genome editing. Science 353, aaf7907 (2016). · DOI · Paper page
  4. Alemany, A., Florescu, M., Baron, C. S., Peterson-Maduro, J. & van Oudenaarden, A. Whole-organism clone tracing using single-cell sequencing. Nature 556, 108–112 (2018). · DOI
  5. Bowling, S. et al. An engineered CRISPR-Cas9 mouse line for simultaneous readout of lineage histories and gene expression profiles in single cells. Cell 181, 1410–1422.e27 (2020). · DOI · Paper page
  6. Xie, L. et al. Comprehensive spatiotemporal mapping of single-cell lineages in developing mouse brain by CRISPR-based barcoding. Nat. Methods https://doi.org/10.1038/s41592-023-01947-3 (2023). · DOI
  7. Liu, K. et al. Mapping single-cell-resolution cell phylogeny reveals cell population dynamics during organ development. Nat. Methods https://doi.org/10.1038/s41592-021-01325-x (2021). · DOI
  8. Choi, J. et al. A time-resolved, multi-symbol molecular recorder via sequential genome editing. Nature 608, 98–107 (2022). · DOI
  9. Wagner, D. E. & Klein, A. M. Lineage tracing meets single-cell omics: opportunities and challenges. Nat. Rev. Genet. https://doi.org/10.1038/s41576-020-0223-2 (2020). · DOI · Paper page
  10. Rodriguez-Fraticelli, A. E. et al. Single-cell lineage tracing unveils a role for TCF15 in haematopoiesis. Nature https://doi.org/10.1038/s41586-020-2503-6 (2020). · DOI · Paper page
  11. Patel, S. H. et al. Lifelong multilineage contribution by embryonic-born blood progenitors. Nature 606, 747–753 (2022). · DOI
  12. Jindal, K. et al. Single-cell lineage capture across genomic modalities with CellTag-multi reveals fate-specific gene regulatory changes. Nat. Biotechnol. https://doi.org/10.1038/s41587-023-01931-4 (2023). · DOI · Paper page
  13. Biddy, B. A. et al. Single-cell mapping of lineage and identity in direct reprogramming. Nature 564, 219–224 (2018). · DOI
  14. Yang, D. et al. Lineage tracing reveals the phylodynamics, plasticity, and paths of tumor evolution. Cell 185, 1905–1923.e25 (2022). · DOI
  15. Quinn, J. J. et al. Single-cell lineages reveal the rates, routes, and drivers of metastasis in cancer xenografts. Science 371, eabc1944 (2021). · DOI
  16. Goyal, Y. et al. Diverse clonal fates emerge upon drug treatment of homogeneous cancer cells. Nature https://doi.org/10.1038/s41586-023-06342-8 (2023). · DOI
  17. Abyzov, A. & Vaccarino, F. M. Cell lineage tracing and cellular diversity in humans. Annu. Rev. Genomics Hum. Genet. 21, 101–116 (2020). · DOI
  18. Mitchell, E. et al. Clonal dynamics of haematopoiesis across the human lifespan. Nature 606, 343–350 (2022). · DOI · Paper page
  19. Ludwig, L. S. et al. Lineage tracing in humans enabled by mitochondrial mutations and single-cell genomics. Cell 176, 1325–1339.e22 (2019). · DOI · Paper page
  20. Campbell, P. 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). · DOI · Paper page
  21. Wang, X. et al. Clonal expansion dictates the efficacy of mitochondrial lineage tracing in single cells. Preprint at bioRxiv https://doi.org/10.1101/2024.05.15.594338 (2024). · DOI
  22. Weng, C. et al. Deciphering cell states and genealogies of human hematopoiesis. Nature https://doi.org/10.1038/s41586-024-07066-z (2024). · DOI · Paper page
  23. Lynch, M. Evolution of the mutation rate. Trends Genet. 26, 345–352 (2010). · DOI
  24. Wang, Q. et al. Imprecise DNMT1 activity coupled with neighbor-guided correction enables robust yet flexible epigenetic inheritance. Nat. Genet. 52, 828–839 (2020). · DOI
  25. Xie, H. et al. Genome-wide quantitative assessment of variation in DNA methylation patterns. Nucleic Acids Res. 39, 4099–4108 (2011). · DOI
  26. Ushijima, T. et al. Fidelity of the methylation pattern and its variation in the genome. Genome Res. 13, 868–874 (2003). · DOI
  27. Brocks, D. et al. Intratumor DNA methylation heterogeneity reflects clonal evolution in aggressive prostate cancer. Cell Rep. 8, 798–806 (2014). · DOI
  28. Mazor, T. et al. DNA methylation and somatic mutations converge on the cell cycle and define similar evolutionary histories in brain tumors. Cancer Cell 28, 307–317 (2015). · DOI
  29. Bian, S. et al. Single-cell multiomics sequencing and analyses of human colorectal cancer. Science 362, 1060–1063 (2018). · DOI
  30. Bian, S. et al. Integrative single-cell multiomics analyses dissect molecular signatures of intratumoral heterogeneities and differentiation states of human gastric cancer. Natl Sci. Rev. 10, nwad094 (2023). · DOI
  31. Wang, Y. et al. Single-cell dissection of the multiomic landscape of high-grade serous ovarian cancer. Cancer Res. 82, 3903–3916 (2022). · DOI
  32. Yatabe, Y., Tavaré, S. & Shibata, D. Investigating stem cells in human colon by using methylation patterns. Proc. Natl Acad. Sci. USA 98, 10839–10844 (2001). · DOI
  33. Gabbutt, C. et al. Fluctuating methylation clocks for cell lineage tracing at high temporal resolution in human tissues. Nat. Biotechnol. 40, 720–730 (2022). · DOI · Paper page
  34. Chaligne, R. et al. Epigenetic encoding, heritability and plasticity of glioma transcriptional cell states. Nat. Genet. 53, 1469–1479 (2021). · DOI · Paper page
  35. Liu, Y. et al. Single-cell methylation sequencing data reveal succinct metastatic migration histories and tumor progression models. Genome Res. 33, 1089–1100 (2023).
  36. Grady, W. M. & Carethers, J. M. Genomic and epigenetic instability in colorectal cancer pathogenesis. Gastroenterology 135, 1079–1099 (2008). · DOI
  37. Greenberg, M. V. C. & Bourc’his, D. The diverse roles of DNA methylation in mammalian development and disease. Nat. Rev. Mol. Cell Biol. 20, 590–607 (2019). · DOI
  38. Smallwood, S. A. et al. Single-cell genome-wide bisulfite sequencing for assessing epigenetic heterogeneity. Nat. Methods 11, 817–820 (2014). · DOI · Paper page
  39. Guo, H. et al. Single-cell methylome landscapes of mouse embryonic stem cells and early embryos analyzed using reduced representation bisulfite sequencing. Genome Res. 23, 2126–2135 (2013). · DOI · Paper page
  40. Liu, H. et al. Single-cell DNA methylome and 3D multi-omic atlas of the adult mouse brain. Nature 624, 366–377 (2023). · DOI · Paper page
  41. Bai, D. et al. Simultaneous single-cell analysis of 5mC and 5hmC with SIMPLE-seq. Nat. Biotechnol. https://doi.org/10.1038/s41587-024-02148-9 (2024). · DOI
  42. Adolph, S. C. & Hardin, J. S. Estimating phenotypic correlations: correcting for bias due to intraindividual variability. Funct. Ecol. 21, 178–184 (2007). · DOI
  43. Spearman, C. The proof and measurement of association between two things. Am. J. Psychol. 15, 72–101 (1904). · DOI · Paper page
  44. Saitou, N. & Nei, M. The neighbor-joining method: a new method for reconstructing phylogenetic trees. Mol. Biol. Evol. 4, 406–425 (1987).
  45. Lefort, V., Desper, R. & Gascuel, O. FastME 2.0: a comprehensive, accurate, and fast distance-based phylogeny inference program. Mol. Biol. Evol. 32, 2798–2800 (2015). · DOI
  46. Li, L. et al. Single-cell RNA-seq analysis maps development of human germline cells and gonadal niche interactions. Cell Stem Cell 20, 858–873.e4 (2017). · DOI
  47. Li, L. et al. Dissecting the epigenomic dynamics of human fetal germ cell development at single-cell resolution. Cell Res. https://doi.org/10.1038/s41422-020-00401-9 (2020). · DOI
  48. Wang, S.-W., Herriges, M. J., Hurley, K., Kotton, D. N. & Klein, A. M. CoSpar identifies early cell fate biases from single-cell transcriptomic and lineage information. Nat. Biotechnol. https://doi.org/10.1038/s41587-022-01209-1 (2022). · DOI
  49. Rossant, J. & Tam, P. P. L. Blastocyst lineage formation, early embryonic asymmetries and axis patterning in the mouse. Development 136, 701–713 (2009). · DOI
  50. Dietrich, J.-E. & Hiiragi, T. Stochastic patterning in the mouse pre-implantation embryo. Development 134, 4219–4231 (2007). · DOI
  51. Wang, J. et al. Asymmetric expression of LincGET biases cell fate in two-cell mouse embryos. Cell 175, 1887–1901.e18 (2018). · DOI
  52. Qi, S. et al. Single-cell multiomics analyses of spindle-transferred human embryos suggest a mostly normal embryonic development. PLoS Biol. 20, e3001741 (2022).
  53. Mikkola, H. K. A. & Orkin, S. H. The journey of developing hematopoietic stem cells. Development 133, 3733–3744 (2006). · DOI
  54. Rybtsov, S., Ivanovs, A., Zhao, S. & Medvinsky, A. Concealed expansion of immature precursors underpins acute burst of adult HSC activity in foetal liver. Development 143, 1284–1289 (2016). · DOI · Paper page
  55. Henninger, J. et al. Clonal fate mapping quantifies the number of haematopoietic stem cells that arise during development. Nat. Cell Biol. 19, 17–27 (2017). · DOI
  56. Kissa, K. & Herbomel, P. Blood stem cells emerge from aortic endothelium by a novel type of cell transition. Nature 464, 112–115 (2010). · DOI
  57. Boisset, J.-C. et al. In vivo imaging of haematopoietic cells emerging from the mouse aortic endothelium. Nature 464, 116–120 (2010). · DOI · Paper page
  58. Luo, C. et al. Single nucleus multi-omics identifies human cortical cell regulatory genome diversity. Cell Genom. 2, 100107 (2022).
  59. Cao, Y. et al. Single-cell bisulfite-free 5mC and 5hmC sequencing with high sensitivity and scalability. Proc. Natl Acad. Sci. USA 120, e2310367120 (2023).
  60. Pei, W. et al. Resolving fates and single-cell transcriptomes of hematopoietic stem cell clones by PolyloxExpress barcoding. Cell Stem Cell 27, 383–395.e8 (2020). · DOI · Paper page
  61. Islam, S. et al. Characterization of the single-cell transcriptional landscape by highly multiplex RNA-seq. Genome Res. 21, 1160–1167 (2011). · DOI · Paper page

Cited by

See all 58

Metadata

Authors
Chen, M., Fu, R., Chen, Y., Li, L., & Wang, S.-W.
Venue
Nature Methods
Publisher
Springer Science and Business Media LLC
Published
2025-01-16
Volume
22
Issue
3
Pages
488-498
ISSN
  • 1548-7091
  • 1548-7105
DOI
10.1038/s41592-024-02567-1
Cited by
58
References
16
Language
en
Licence

About these checks

Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.

Statistics and arithmetic are recomputed, not judged.

Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.

What counts as a claim ›How we measure confidence ›How we check this ›

Are you an author, or do you see a problem with this page? Request a review

How to cite

Cite CoChat for the checks on this page, and the paper for its own findings.

This page
CoChat. (2026). Citation check: High-resolution, noninvasive single-cell lineage tracing in mice and humans based on DNA methylation epimutations. https://app.cochat.ai/papers/10.1038/s41592-024-02567-1
The paper
Chen, M., Fu, R., Chen, Y., Li, L., & Wang, S.-W. (2025). High-resolution, noninvasive single-cell lineage tracing in mice and humans based on DNA methylation epimutations. Nature Methods. https://doi.org/10.1038/s41592-024-02567-1