Abstract
Recent advancements in single-cell DNA methylation (scDNAm) sequencing technologies have enabled the profiling of epigenetic landscapes at unprecedented resolution, offering insights into cellular heterogeneity, differentiation and evolution. Trajectory inference, which orders cells along pseudotime, allows researchers to track genomics changes across continuous cell states and identify key loci exhibiting differential methylation. However, no methods currently exist to model methylation changes along pseudotime in scDNAm data. Here, we present a hierarchical Bayesian framework for scDNAm data analysis. Our method, named mist (methylation inference for single-cell along trajectory), models stage-specific biological variations, identifies genomic features with significant methylation changes along pseudotime, and performs Differential Methylation (DM) analysis across phenotypical groups. Simulations demonstrate its superior accuracy in detecting DM genes along pseudotime compared to existing methods. Applied to multi-omics datasets of mouse embryonic development and developing human brain, mist identifies key developmental regulators, whose methylation patterns align with lineage transitions. mist is publicly available as an R/Bioconductor package.
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Data availability
The data for simulations and real-data analyses are available at https://doi.org/10.5281/zenodo.1845194067. The mouse embryos data is available under accession codes GSE121708 https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE12170829. The human brain data is available through https://cells.ucsc.edu/?ds=brain-epigenome49. Source data are provided with this paper.
Code availability
mist is publicly available as a R/Bioconductor package at: https://bioconductor.org/packages/mist/. The package includes detailed vignettes and example datasets to guide users through typical workflows. Source code is hosted at https://github.com/dxd429/mistand is released under the MIT License. The specific version of the code associated with this publication is archived in https://doi.org/10.5281/zenodo.1845194067.
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Acknowledgements
We thank Dr. Chongyuan Luo for insightful discussions and for publicly sharing the snm3C-seq data from his lab. This work was supported by grants from the National Institutes of Health awarded [NIGMS R35GM154862 to H.F.]. The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health. Funding to pay the Open Access publication charges for this article was provided by NIH [R35GM154862].
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L.Z. and H.F. conceived the study. D.D. and W.M. performed in-silico simulation studies and real data analysis. W.T. acquired and processed real scDNAm datasets. D.D., L.Z., and H.F. wrote the manuscript. H.W. supervised the research and led discussions. All authors helped edit the final manuscript.
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Duan, D., Ma, W., Tang, W. et al. mist: a hierarchical Bayesian framework for detecting differential DNA methylation dynamics in single-cell data. Nat Commun (2026). https://doi.org/10.1038/s41467-026-70523-y
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DOI: https://doi.org/10.1038/s41467-026-70523-y
