Single cell RNA sequencing is a set of methods that measure gene activity in thousands to millions of individual cells at once. Instead of averaging signals across whole tissues, it captures the unique transcriptional profile of each cell, revealing cell types, states and rare populations that bulk RNA methods miss.

Modern workflows begin by isolating single cells, often using microfluidic droplets, microwell plates or cell sorting. Each cell’s RNA is tagged with a unique barcode, converted to complementary DNA and sequenced. Computational analysis then aligns reads to genes, counts transcripts per cell and uses dimensionality reduction and clustering to group cells with similar expression patterns. This generates atlases of cell populations and trajectories of differentiation or activation.

Applications span developmental biology, immunology, neuroscience, oncology and regenerative medicine. In cancer, single cell RNA sequencing uncovers intratumoral heterogeneity, identifies drug resistant subclones and profiles the tumor microenvironment. In the immune system, it maps diverse T cell and B cell states and responses to infection or vaccination. In the brain, it refines cell type taxonomies and links transcriptional states to function.

Recent advances include multiomic approaches that combine RNA with chromatin accessibility, surface proteins or spatial location. These methods connect gene expression to regulatory mechanisms and tissue architecture. Emerging techniques also increase throughput and sensitivity while reducing cost, making population scale studies feasible.

Challenges remain in sample preparation biases, data sparsity, batch effects and the need for standardized analytical pipelines, but single cell RNA sequencing has already transformed how cellular diversity and dynamics are studied in health and disease.