SINGLE-CELL RNA SEQUENCING IN PRECISION MEDICINE: FROM CELLULAR HETEROGENEITY TO CLINICAL APPLICATION

Abstract

Bulk RNA sequencing measures average gene expression across an entire tissue sample, obscuring the substantial heterogeneity that exists between individual cells within that same tissue, a limitation that single-cell RNA sequencing was specifically developed to overcome by profiling transcriptomes at single-cell resolution. This capability has proven especially consequential in oncology, where intratumoral heterogeneity, the coexistence of genetically and transcriptionally distinct cell subpopulations within a single tumor, is now recognized as a major driver of treatment resistance and relapse, phenomena that bulk sequencing approaches are structurally unable to resolve [1]. Building on this capability, computational frameworks have recently been developed to integrate single-cell transcriptomic data directly with pharmacological response profiles, allowing identification of drug-sensitive and drug-resistant cellular subpopulations within a single tumor sample and, in principle, enabling selection of therapy tailored to the specific cellular composition of an individual patient's tumor rather than to the tumor's bulk-averaged molecular subtype alone [2].