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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-mesenchymal 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.