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Life sciences · Journal article
Theoretical and Natural Science · October 20, 2026
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Diverse molecular and phenotypic variations can be observed across tumour-cell populations, manifested as inter-patient discrepancies, spatial intra-tumour diversity and phenotypic shifts triggered by anti-cancer therapies. Conventional analytical workflows including pathological sections, bulk RNA-seq and bulk-level proteomics generate population-averaged read-outs and fail to resolve distinct tumour-cell sub-clones. Single-cell RNA sequencing (scRNA-seq), supported by cell barcodes and Unique Molecular Identifiers, achieves high-resolution transcriptomic profiling for individual tumour cells, as demonstrated in exploratory clinical studies of gastric cancer. Nevertheless, transcriptomic profiles alone cannot reflect actual cellular function owing to extensive post-transcriptional regulatory events. Complementary single-cell proteomic tools, such as phospho-protein flow cytometry, mass-spectrometry-based pipelines and microfluidic single-cell western blotting (scWestern), have been applied to patient-derived leukaemia blasts and circulating tumour cells to map protein-level variation. Despite valuable biological outputs, these techniques are constrained by technical drawbacks, sample-processing-related artifacts, interpretive pitfalls and considerable translational barriers. Multi-omic integration of transcriptomic and proteomic datasets represents a practical research direction. Further technical refinement and validation in large independent clinical cohorts are required to obtain clinically actionable tumour biomarkers.