Deciphering lung cancer at high resolution: a narrative review of applications of single-cell and spatial transcriptomics sequencing

Background and Objective: Lung cancer remains the leading cause of cancer-related death worldwide. Many patients are still facing limited treatment benefit or therapeutic resistance. Single-cell RNA sequencing (scRNA-seq) enables high-resolution characterization of cellular states, whereas spatial transcriptomics (ST) preserves tissue architecture and maps gene expression within the tumor microenvironment (TME). This review summarizes recent advances in scRNA-seq and ST in lung cancer and discusses how these technologies have improved our understanding of tumor biology and therapeutic response.

Methods: A literature search was conducted in PubMed/MEDLINE for English-language studies published between January 2020 and 2025 using terms related to lung cancer, single-cell sequencing, single-nucleus sequencing, and spatial transcriptomics.

Key Content and Findings: Recent studies have used scRNA-seq and ST to characterize immune and stromal compartments, tumor cell heterogeneity, early cancer evolution, metastasis, and treatment-associated remodeling. These findings suggest that lung cancer progression is shaped not only by genetic alterations but also by transcriptional plasticity, spatially organized TME niches, and dynamic interactions between tumor, immune, and stromal cells.

Conclusions: scRNA-seq and ST have shifted lung cancer research from bulk tumor profiling toward cellular and spatial ecosystem analysis. However, many proposed biomarkers and therapeutic targets remain exploratory. Further spatial validation, functional experiments, and clinically annotated cohorts are needed before these findings can be translated into routine precision medicine.