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

Nature Methods volume 22, pages 488–498 (2025)Cite this article

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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.

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Fig. 1: Epimutation-based lineage inference in a homogeneous population.
Fig. 2: Lineage inference from DNA methylation profiles in a heterogeneous population.
Fig. 3: Single-cell multi-omic lineage tracing in in vitro blood differentiation from mouse.
Fig. 4: Lineage inference from in vitro differentiation of human CD34+ cells.
Fig. 5: Study of the first cell fate decision in human embryonic development.
Fig. 6: Estimation of HSC clone number in mice.

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Data availability

The sequencing data for human blood has been submitted to the Genome Sequence Archive database under the accession number HRA008624. Other sequencing data generated in this study have been submitted to NCBI GEO, with the accession number GSE262580. The methylation rate matrix associated with selected genomic regions for each analyzed dataset in our paper, along with each sample metadata and processed human blood dataset,is available via figshare at https://doi.org/10.6084/m9.figshare.27288630 (ref. 78). The accession number and analysis parameters for each analyzed dataset in this study are available in Supplementary Table 1.

Code availability

Scripts for data preprocessing are available at https://github.com/ShouWenWang-Lab/Preprocessing. MethylTree code is available at https://github.com/ShouWenWang-Lab/MethylTree. To reproduce our analysis, please check out our jupyter notebooks at https://github.com/ShouWenWang-Lab/MethylTree_notebooks. A web portal of MethylTree analysis is available at https://wangshouwen.lab.westlake.edu.cn/app/methylserver.

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Acknowledgements

We are grateful to D. Li, H. Yu and K. Zhang from Westlake University for their stimulating discussions. We thank W. Liu and D. Ruan from Zhejiang University for sharing LARRY lentivirus. We acknowledge H. Guo from Zhejiang University as well as W. Yue and H. Yan from Beijing Institute of Radiation Medicine for their help. We also thank other laboratory members for their input. L.L. is supported by National Key Research and Development Project of China (2024YFA1306600). We acknowledge support from NSFC (grant no. 32470700), Westlake High-Performance Computing Center, and ‘Pioneer’ and ‘Leading Goose’ R&D Programs of Zhejiang province (grant nos. 2024SSYS0034 and 2024SSYS0034).

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Author notes

  1. These authors contributed equally: Mengyang Chen, Ruijiang Fu.

Authors and Affiliations

  1. Westlake Laboratory of Life Sciences and Biomedicine, Hangzhou, China

    Mengyang Chen, Ruijiang Fu, Yiqian Chen, Li Li & Shou-Wen Wang

  2. School of Life Sciences, Westlake University, Hangzhou, China

    Mengyang Chen, Ruijiang Fu, Yiqian Chen, Li Li & Shou-Wen Wang

  3. School of Science, Westlake University, Hangzhou, China

    Ruijiang Fu & Shou-Wen Wang

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  1. Mengyang Chen
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Contributions

S.-W.W. and L.L. conceived the project. S.-W.W. acquired funding, developed MethylTree, designed experiments, carried out biological applications and wrote the manuscript with input from other authors. M.C. performed all experiments. R.F. carried out data analyses and generated figures. Y.C. developed the web portal. L.L. and S.-W.W. supervised M.C. to set up the experimental system and generate experimental datasets. S.-W.W. supervised the entire project.

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Correspondence to Li Li or Shou-Wen Wang.

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S.-W.W. is named inventor on a patent application for MethylTree (PCT/CN2024/095497). The other authors declare no competing interests.

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Nature Methods thanks Simon Anders and the other, anonymous, reviewer(s) for their contribution to the peer review of this work. Peer reviewer reports are available. Primary Handling Editor: Lei Tang, in collaboration with the Nature Methods team.

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Extended data

Extended Data Fig. 1 Systematic characterization of MethylTree performance in a homogeneous population.

a, b, Analysis on simulated single-cell expansion with more realistic features. a, The impact of division-free CpG mutations on lineage inference accuracy. After simulated clonal expansion as in Fig. 1d, we randomly mutated a given fraction of CpG sites in each of the 128 cells. b, Heatmap of lineage accuracy as a function of CpG coverage and the variation of epimutation rate controlled by the parameter \(\lambda\). Compared with Fig. 1f, we modeled epimutation on a diploid genome with a CpG-site specific epimutation rate sampled from a uniform distribution with a maximum value \(\lambda\). Each observed CpG status is obtained from sampling once on the same CpG site from either of the two DNA molecules. c–j, MethylTree analysis of a clonal expansion dataset of human HEK 293T cells. c, Heatmap of the similarity matrix computed with the cell-by-CpG matrix, without binning. d, Schematic of region selection. Non-overlapping 500-bp genomic bins with an intermediate methylation rate between \({m}_{0}\) and \({m}_{1}\) were selected. e, Merging neighboring bins after selection in d. This procedure was used in analyzing all datasets in this article. f, Heatmap of MethylTree lineage accuracies on the 293T dataset using ‘merged’ genomic regions selected at different thresholds according to e. The parameters indicated on this plot (m0 = 0.5, m1 = 0.9) were used to generate Fig. 1i–k. g, A scatter plot showing the number of genomic regions associated with each selection and the corresponding accuracy of MethylTree-inferred lineages, using the data from f. The selection parameters (m0, m1) for some data points are highlighted. h, Number of detected CpG sites per cell on the methylation embedding of 293T cells. i, Lineage accuracy using different metrics to compute the cell-cell similarity. With Euclidean distance matrix X, we converted it to a similarity with 1 − X/max(X), where \(\max (X)\) is the largest value in this matrix. j, Similarity heatmap ordered with the phylogenetic tree inferred from the neighbor-joining56 (NJ, left) or FastME57 (right) method.

Extended Data Fig. 2 Lineage inference from human embryonic stem cells and colorectal cancer.

a–c, Lineage analysis of clonal expansion of H9 human embryonic stem cells. a, Schematic of our experimental design, created using BioRender.com. There were five clones generated in this experiment. b, Heatmap of MethylTree lineage accuracies on the H9 dataset, similar with Extended Data Fig. 1f. c, Heatmap of the similarity matrix of the H9 dataset before (left) and after (right) correlation-bias correction. The color bar shows the actual clonal identify of each cell. d–f, Lineage inference from human colorectal cancer. Data is obtained from patient CRC11 in Bian et al.38. d, Schematic of tissue sampling and cell profiling, created using BioRender.com. e, Heatmap of the cell-cell similarity matrix computed from single-cell DNA methylation. Here, A1–A6 and B were inferred cancer lineages based on copy number variations (CNV) in the original analysis by Bian et al. NC marks the normal cells. f, Lineage phylogenetic tree inferred from the methylation matrix. Same color as e.

Extended Data Fig. 3 MethylTree analysis in a heterogeneous population.

a–c, Lineage inference from simulated differentiation. a, Heatmap of lineage accuracy as a function of CpG coverage and the variation of the epimutation rate controlled by \(\lambda\). Here, we simulated differentiation on a haploid genome with a site-specific epimutation rate sampled from a uniform distribution with a maximum value \(\lambda\). Here, the lineage-specific CpG fraction \(\alpha =0.5\). b, Inferred lineage accuracy from simulated differentiation with different fractions of cell-type-specific CpG sites \((1-\alpha )\). The cell-type signals are not removed. c, Lineage accuracy after removing cell-type signals, evaluated at different fractions of lineage-specific CpG sites \((\alpha )\). d, Heatmap of methylation similarity associated with fetus_2 from 17 weeks. e, Inferred lineage tree from d, colored by inferred methyl-clones. f, Methylation embedding colored by methyl-clone ID (top) or FGC sub-types (bottom). g, Heatmap of MethylTree lineage accuracies associated with Fig. 2l using different region choices. The selected regions associated with (0.3,0.6) were re-used to analyze other datasets in Fig. 2 and Extended Data Fig. 3. h, Similarity heatmaps of FGCs and somatic cells from two 7-week human embryos. Left panel: the raw similarity matrix; right panel: after removing cell-type-specific signals.

Extended Data Fig. 4 Analysis of the single-cell multi-omic blood dataset from mouse.

a, Heatmap showing the expression of cell-type-specific marker genes (columns) in each annotated cell types (rows) in Fig. 3c. Expression values were column-wise normalized by the highest value in each column. b, Bar plot of cell counts of each cell type identified in this dataset. c, Histogram of LARRY clone sizes in this dataset. d, Heatmap of MethylTree lineage accuracies associated with different region choices on these blood cells. We highlight the parameters used to generate Fig. 3f. e, Lineage accuracy computed with non-overlapping bins at different sizes, with either correlation-bias correction or not. f, Box plot of lineage accuracies at different genomic coverages. At each coverage, results from all genomic choices are shown. See Fig. 1n for box plot description. g, Heatmap of clonal coupling scores computed from the observed LARRY lineage barcodes. h, Pseudobulk DNA methylation profiles on genomic regions not specifically related to hematopoiesis. Otherwise, same as Fig. 3m. i, Fraction of clone-specific CpG sites in different genomic contexts. These were differentially methylated CpG sites between the two largest clones in this dataset. WCGW: a solo CpG site franked by either A or T; CGI: CpG islands; Prom_CGI: CGI-enriched promoter region (within 2000 bp from transcription starting site); Prom_nonCGI: CGI-depleted promoter region; Genebody: gene body region; LINE: long interspersed nuclear elements; LTR: long terminal repeats. Results from randomly sampled CpG sites are also shown. **, one-sided p-value < 0.01, obtained from directly simulating the null distribution. See Methods.

Extended Data Fig. 5 Analysis of the single-cell multi-omic blood dataset from human.

a, Heatmap showing marker gene expression of each cell type. b, Similarity heatmap created the same as in Fig. 4e, but for all the cells passing methylation quality control. c, Heatmap of MethylTree lineage accuracies associated with different region choices.

Extended Data Fig. 6 MethylTree analysis on developing human and mouse embryos.

a, Heatmap of MethylTree lineage accuracies associated with different region choices on 4-cell-stage cells from mouse embryos. The selected regions associated with (0.4,0.6) were re-used to analyze mouse datasets from other stages in Fig. 5 and this figure. b, Methylation similarity heatmaps of mouse cells from other developmental stages, with the color bar indicating their embryonic origins. c, d, Same as a and b, but for cells from human embryos. The selected regions associated with (0.2,0.5) were re-used to analyze human datasets from other stages in Fig. 5 and this figure. e–g, Methylation similarity heatmaps and reconstructed lineages (with support values from bootstrap sampling) for additional three human embryos, in addition to those shown in Fig. 5h–k. These include E5 from day 5 (e), E7 from day 6 (f), and E8 from day 6 (g).

Extended Data Fig. 7 MethylTree analysis on mouse HSCs.

a, Heatmap of MethylTree lineage accuracies associated with different region choices on HSCs from mouse LL731. We highlight parameters used to generate Fig. 6b, and also the choice used in our previous study13. The same set of genomic regions was re-used in analyzing the remaining HSC datasets in Fig. 6 and this figure. b, Bar plot of adjusted rank index associated with each HSC dataset. c, Bar plot of the fraction of cells among the multi-cell methy-clones. d, HSC clone number inference on mouse LL653E1. From left to right: methylation similarity matrix, inferred lineage tree, distribution of putative clone sizes (same as Fig. 6e), and HSC clone number inference based on the observed singleton cell fraction (same as Fig. 6f). e, HSC clone number inference on mouse LL653E6. Otherwise, same as d.

Extended Data Fig. 8 Summary of all datasets analyzed in this study.

a, Schematic of the global methylation dynamics over the life time of an individual, created using BioRender.com. Our study analyzed datasets across all three key stages of methylation dynamics, including two global de-methylation waves before birth and a stable period after birth. b, Bar plot comparing lineage accuracies from raw similarity or corrected (cell-type-signal removed if needed) similarity across all datasets analyzed in this study. c, Bar plot comparing lineage accuracies from using all 500-bp bins, selected genomic regions, all single-CpG sites, or from a randomized cell ordering, across all datasets analyzed in this study. The accuracies from selected-region and single-CpG methods are significantly higher than those from randomization, each with a p-value < 0.0005.

Extended Data Fig. 9 Comparison between different lineage tracing methods in humans.

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Supplementary Table 1

Summary of all the datasets analyzed in this study.

Supplementary Table 2

Primers used in scBS-seq and Camellia-seq.

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Chen, M., Fu, R., Chen, Y. et al. High-resolution, noninvasive single-cell lineage tracing in mice and humans based on DNA methylation epimutations. Nat Methods 22, 488–498 (2025). https://doi.org/10.1038/s41592-024-02567-1

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