PASTRI: Resolving Stage-Specific Cell-State Dynamics from Annotated Cell Lineage Trees
Yang, W.; Li, Z.; Yu, X.; Wu, P.; Zhang, X.; Ren, C.; Liu, K.; Chen, J.; Chen, F.; He, X.; Zhang, J.; Chen, X.; Yang, J.
Show abstract
Cellular transitions between phenotypic states are fundamental to development and disease, yet quantitative analysis of their dynamics remains challenging. Here we present PASTRI (Phylogenetic Adjacency-based State Transition Rate Inference), a computational framework that infers transition rates from cell lineages/phylogenies annotated with terminal phenotypic states, such as single-cell transcriptomes. We validate PASTRI using simulated lineages and the Caenorhabditis elegans embryonic lineage. Importantly, by leveraging cell pairs at varying phylogenetic distances, PASTRI accurately resolves stage-specific transition rates, circumventing the issue of developmental changes in dynamics. Applied to three cell phylogeny datasets from our lineage-tracing experiments spanning diverse developmental/disease models and tracing systems, PASTRI uncovers rate-limiting steps in the activation of hepatic stellate cells and the differentiation of primordial lung progenitors, as well as attractor states that support cancer cell proliferation. PASTRI thus opens up a venue for dissecting cell state transition dynamics from annotated cell lineage/phylogeny.
Matching journals
The top 5 journals account for 50% of the predicted probability mass.
Similar papers in this journal
- Inferring high-dimensional pathways of trait acquisition in evolution and disease 97%
- Single-cell morphodynamical trajectories enable prediction of gene expression accompanying cell state change 96%
- Learning multi-cellular representations of single-cell transcriptomics data enables characterization of patient-level disease states 96%
Similar papers in this journal
"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.