Faculty, Staff and Student Publications

Language

English

Publication Date

3-12-2026

Journal

Nature Communications

DOI

10.1038/s41467-026-70523-y

PMID

41820383

PMCID

PMC13121613

PubMedCentral® Posted Date

3-12-2026

PubMedCentral® Full Text Version

Post-print

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.

Keywords

DNA Methylation, Bayes Theorem, Animals, Single-Cell Analysis, Humans, Mice, Epigenesis, Genetic, Brain, Embryonic Development, Sequence Analysis, DNA, Genomics, Statistical methods, Computational models, Epigenomics, DNA methylation

Published Open-Access

yes

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