Single-Cell Transcriptomics

Abstract

The field of oncology has undergone significant change enabled by single-cell transcriptomics, allowing more precise analysis of tumor ecosystems which had previously been inaccessible using bulk profiling. This chapter outlines the steps involved in the transition from bulk RNA sequencing (bulk RNA-seq) to single-cell methodologies, detailing the evolution of experimental workflows and the computational frameworks that support them. Single-cell transcriptomics applications in oncology are discussed, particularly the way single-cell RNA sequencing (scRNA-seq) elucidates intratumoral heterogeneity and the complexity of the tumor microenvironment (TME), including its neoplastic, immune, and stromal constituents. These findings have, in turn, facilitated the discovery of predictive and prognostic biomarkers, treatment resistance mechanisms, and applicable therapeutic targets. The chapter will highlight advanced applications in precision medicine, therapeutic selection, and drug discovery. We detailed the gap of direct clinical application and highlighted the integration of multi-omics, artificial intelligence (AI)/machine learning (ML), and spatial transcriptomics (ST) as emerging future avenues. Single-cell methodologies promise more precise and personalized oncology by linking cellular heterogeneity to patient outcomes, representing a fundamental shift in the field.