Lineage-aware stochastic modeling reveals gene-expression dynamics in development and disease

Xing, Jiawei, Staklinski, Stephen J, Liu, Zhihan, Nowak, Dawid, Siepel, Adam (June 2026) Lineage-aware stochastic modeling reveals gene-expression dynamics in development and disease. bioRxiv. ISSN 2692-8205 (Submitted)

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Abstract

Gene expression evolves dynamically along cell lineages, yet most analysis methods treat single-cell RNA-seq (scRNA-seq) data as static snapshots and fail to exploit phylogenetic relationships among cells. Recent advances in cell-lineage tracing now enable the reconstruction of high-resolution lineage phylogenies, providing a natural framework for identifying when and where transcriptional changes arise during development, differentiation, and disease progression. Some models of gene expression have begun to consider phylogenetic structure, but they generally rely on imprecise Gaussian assumptions, focus on endpoint-level comparisons, or fail to consider sparse and overdispersed scRNA-seq read counts. Here, we present LaVOUS ( L ineage- a ware V ariational O rnstein- U hlenbeck S ingle-cell RNA-seq analysis), a probabilistic framework that couples lineage-based models of latent dynamics derived from the Brownian motion and Ornstein-Uhlenbeck stochastic processes with negative-binomial observation models and scalable variational inference. LaVOUS enables likelihood-based tests for cellular heritability and branch-specific shifts in gene expression, as well as phylogenetic reconstruction of latent expression histories. In simulations, LaVOUS outperformed Gaussian method in detecting lineage-associated expression changes and produced accurate reconstructions of expression histories across expression levels. We additionally applied LaVOUS to paired single-cell lineage and transcriptomic data from metastatic lung cancer, class-switching B cells, and the developing brain. Across these settings, LaVOUS identified lineage-associated expression changes related to metastatic progression, B-cell isotype switching, and dopaminergic and glutamatergic neuron differentiation. By providing an expressive framework for modeling sparse count data on lineage trees, LaVOUS establishes a foundation for studying single-cell expression dynamics across developmental and disease contexts, with natural extensions to multi-gene regulation, lineage uncertainty, and multi-modal integration.

Item Type:	Paper
Subjects:	bioinformatics bioinformatics > quantitative biology
CSHL Authors:	Siepel, Adam Xing, Jiawei Staklinski, Stephen Liu, Zhihan
Communities:	CSHL labs > Kinney lab CSHL labs > Siepel lab CSHL Post Doctoral Fellows School of Biological Sciences > Publications
SWORD Depositor:	CSHL Elements
Depositing User:	CSHL Elements
Date:	28 June 2026
Date Deposited:	09 Jul 2026 11:57
Last Modified:	09 Jul 2026 11:57
PMCID:	PMC13320875
Related URLs:	Publisher
URI:	https://repository.cshl.edu/id/eprint/42266

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