2024/11/21 by Wenbo Guo, Xinqi Li, Dongfang Wang +6 · 1 voice
Biochemistry, Genetics and Molecular Biology · #Single-cell and spatial transcriptomics #Gene Regulatory Network Analysis #Cancer Genomics and Diagnostics
paper · doi:10.1186/s13059-024-03436-y
openalex publication_date 2024/11/21 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/23
Understanding tumor cell heterogeneity and plasticity is crucial for overcoming drug resistance. Single-cell technologies enable analyzing cell states at a given condition, but catenating static cell snapshots to characterize dynamic drug responses remains challenging. Here, we propose scStateDynamics, an algorithm to infer tumor cell state dynamics and identify common drug effects by modeling single-cell level gene expression changes. Its reliability is validated on both simulated and lineage tracing data. Application to real tumor drug treatment datasets identifies more subtle cell subclusters with different drug responses beyond static transcriptome similarity and disentangles drug action mechanisms from the cell-level expression changes.