Single-cell
RNA sequencing (
scRNA-seq) is a tool for studying gene expression at the single-cell level that has been widely used due to its unprecedented high resolution. In the present review, we outline the preparation process and sequencing platforms for the
scRNA-seq analysis of solid
tumor specimens and discuss the main steps and methods used during data analysis, including quality control, batch-effect correction, normalization, cell cycle phase assignment, clustering, cell trajectory and pseudo-time reconstruction, differential expression analysis and gene set enrichment analysis, as well as gene regulatory network inference. Traditional bulk
RNA sequencing does not address the heterogeneity within and between
tumors, and since the development of the first
scRNA-seq technique, this approach has been widely used in
cancer research to better understand
cancer cell biology and pathogenetic mechanisms.
ScRNA-seq has been of great significance for the development of targeted
therapy and
immunotherapy. In the second part of this review, we focus on the application of
scRNA-seq in solid
tumors, and summarize the findings and achievements in
tumor research afforded by its use.
ScRNA-seq holds promise for improving our understanding of the molecular characteristics of
cancer, and potentially contributing to improved diagnosis, prognosis, and
therapeutics.