Kinetics-seq enables comprehensive profiling of single-cell RNA kinetics in vivo to reveal dynamic tumor heterogeneity
Tumor tissue is a dynamic system governed by complex transcriptional kinetics. Although scRNA-seq has revolutionized cellular profiling, it captures static expression snapshots, lacking direct access to transcriptional dynamics. Here, we present Kinetics-seq, a time-resolved single-cell method that integrates in vivo metabolic labeling strategy with scRNA-seq to construct a comprehensive transcriptional kinetic landscape within tissues. This approach enables transcriptome-wide quantification of RNA abundance, turnover, synthesis, and degradation rates at single-cell resolution. By incorporating RNA kinetic parameters, Kinetic-seq introduces a dynamic dimension to cellular profiling, revealing heterogeneities not only across cell types but also within individual populations. Through joint modeling of RNA synthesis and degradation rates, Kinetics-seq uncovers gene-specific regulatory strategies and pronounced kinetic diversity among tumor cells. Moreover, RNA kinetics serves as a screen tool to identify transcriptionally active gene subsets, revealing pathways such as cholesterol homeostasis, myogenesis, and complement that display stronger temporal associations with tumor progression than abundance-based analyses. Collectively, KineMap-seq provides a powerful tool for refining cellular taxonomy, elucidating RNA regulatory strategies, and identifying actively regulated genes that affect tumor ecosystem, while also offering new opportunities for mechanistic investigation, biomarker discovery, and therapeutic intervention. Overall design: Expression profiling by high throughput sequencing