Abstract
The global burden of cancer is rising, with treatment failures often due to the metastatic nature of late-stage malignancies. Circulating tumour cells (CTCs) are metastatic precursors shed from primary tumours, which survive in circulation, extravasate and colonise distant organs. The advent of high-throughput single-cell RNA sequencing (scRNA-seq) has revolutionised the investigation of transcriptomic landscape at single-cell resolution, enabling deep transcriptomic profiling, re-stratifying CTC subtypes and improving the detection of rare new subpopulations. Applications extend to understanding tumour microenvironments, characterising cellular heterogeneity, uncovering metastasis molecular mechanisms and improving prognosis and diagnostic strategies. A timeline of key milestones in CTC scRNA-seq research is also provided. Nevertheless, a knowledge gap remains due to unstandardised protocols and fragmented resources in CTC scRNA-seq research. We address this gap by proposing a 12-step CTC-specific scRNA-seq workflow to overcome methodological inconsistencies. This workflow spans the entire process from enrichment, single-cell sorting and sequencing to data pre-processing and downstream analyses, with a detailed compilation of data analysis tools. An in-depth discussion of the pros and cons of commonly used scRNA-seq tools is also included, specifically evaluating their suitability for CTC research. Additionally, emerging research frontiers, including the discovery of hybrid cells—fusion products of tumour and normal cells—and the integration of machine learning (ML) into scRNA-seq workflows, are explored. Future research should prioritise CTC scRNA-seq workflow standardisation, integrate ML-driven analysis and investigate rare and hybrid populations to advance metastasis research. This review supports these goals by guiding methods, informing tool selection and promoting data sharing for reproducibility.