Mapping the tumor immune landscape: single-cell RNA sequencing in cancer immunotherapy

Abstract

Single-cell RNA sequencing (scRNA-seq) has emerged as a transformative technology for unraveling the cellular complexity and architecture of diseased tissues. By capturing the transcriptomic profiles of individual cells, scRNA-seq reveals intra-tumoral heterogeneity, distinct immune cell states, and dynamic intercellular interactions, making it particularly valuable in the study of the tumor immune microenvironment (TIME). In recent years, scRNA-seq has been widely adopted in both basic cancer research and clinical applications, advancing our understanding of tumor development, immune regulation, and therapy resistance. These advances have also driven the evolution of computational tools and analytical methods needed to interpret complex scRNA-seq datasets to support the development of precision diagnostics. This review systematically summarizes the core analytical workflows for scRNA-seq data, outlines best practices in data processing, and explores the emerging applications and challenges of this technology in TIME research and translational oncology.