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Natl. Acad. Sci. U.S.A."],"published-print":{"date-parts":[[2026,9,29]]},"abstract":"<jats:p>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 an in vivo metabolic labeling strategy with scRNA-seq to construct a comprehensive transcriptional kinetic landscape within tissues. This approach enables transcriptome-wide measurement of RNA abundance, turnover, synthesis, and degradation rates at single-cell resolution. By incorporating RNA kinetic parameters, Kinetics-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 screening tool to identify transcriptionally active gene subsets, revealing pathways such as estrogen response early, PI3K-AKT-mTOR signaling, and epithelial\u2013mesenchymal transition that display stronger temporal associations with tumor progression than abundance-based analyses. Collectively, Kinetics-seq provides a powerful tool for refining cellular taxonomy, elucidating RNA regulatory strategies, and identifying actively regulated genes that affect a tumor ecosystem, while also offering opportunities for mechanistic investigation, biomarker discovery, and therapeutic intervention.<\/jats:p>","DOI":"10.1073\/pnas.2534052123","type":"journal-article","created":{"date-parts":[[2026,9,22]],"date-time":"2026-09-22T16:12:31Z","timestamp":1790093551000},"update-policy":"https:\/\/doi.org\/10.1073\/pnas.cm10313","source":"Crossref","is-referenced-by-count":0,"title":["Kinetics-seq enables comprehensive profiling of single-cell RNA kinetics in vivo to reveal dynamic tumor heterogeneity"],"prefix":"10.1073","volume":"123","author":[{"ORCID":"https:\/\/orcid.org\/0009-0007-1859-5152","authenticated-orcid":false,"given":"Shanshan","family":"Zhang","sequence":"first","affiliation":[{"name":"State Key Laboratory of Physical Chemistry of Solid Surfaces, Innovation 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