Tipping points in epithelial-mesenchymal lineages from single-cell transcriptomics data.

ABSTRACT: Understanding cell fate decision-making during complex biological processes is an open challenge that is now aided by high-resolution single-cell sequencing technologies. Specifically, it remains challenging to identify and characterize transition states corresponding to "tipping points" whereby cells commit to new cell states. Here, we present a computational method that takes advantage of single-cell transcriptomics data to infer the stability and gene regulatory networks (GRNs) along cell lineages. Our method uses the unspliced and spliced counts from single-cell RNA sequencing data and cell ordering along lineage trajectories to train an RNA splicing multivariate model, from which cell-state stability along the lineage is inferred based on spectral analysis of the model's Jacobian matr

SUBMITTER: Barcenas M 

PROVIDER: S-EPMC11393678 | biostudies-literature | 2024 Sep

REPOSITORIES: biostudies-literature