Methodologies for Sample Multiplexing and Computational Deconvolution in Single-Cell Sequencing

Single-cell sequencing is revolutionizing biological research by enabling unprecedented cellular resolution, yet traditional multi-sample experiments are often constrained by high costs and batch effects. Sample multiplexing offers a critical solution by uniquely tagging individual cells from diverse samples for pooled sequencing, thereby dramatically boosting throughput and improving data reliability by minimizing technical variability. This review provides a comprehensive and integrated perspective on the rapidly evolving field of single-cell multiplexing. Major experimental strategies and the critical computational algorithms required for accurate sample deconvolution are surveyed, highlighting the crucial link between experimental design and computational accuracy. Furthermore, the diverse applications of these technologies in large-scale clinical cohorts, multi-omics integration, developmental biology, and high-throughput drug screening are summarized. This review serves as an essential guide for researchers, empowering them to select the most appropriate methods to accelerate discoveries in disease mechanisms, therapeutic responses, and developmental biology.