Single-cell RNA sequencing (scRNA-seq)

Single-cell RNA sequencing measures which genes are active in each individual cell of a sample separately, instead of averaging gene expression across a bulk tissue sample the way older sequencing methods did.

Individual cells are isolated and tagged before sequencing so each resulting read can be traced back to its cell of origin, producing a large, sparse cell-by-gene expression matrix. Because that matrix is high-dimensional, analysis typically relies on dimensionality reduction and clustering to group cells into types or states before comparing expression between groups, building on the same sequence alignment step that underlies other sequencing analyses.

The technique reveals cell-to-cell heterogeneity that bulk sequencing hides entirely, for instance a rare cell population within what looked like a uniform tissue, and it has become central to immunology, cancer biology, and developmental biology. Pitfalls include technical dropout, where a gene that is truly expressed goes undetected in a given cell purely by chance, which can be mistaken for real biological absence, and batch effects between sequencing runs, which can masquerade as genuine biological differences between samples if not corrected for statistically.