A sequencing-based method that measures gene expression profiles at single-cell resolution by isolating individual cells or nuclei, reverse-transcribing their RNA to cDNA, adding cell-specific barcodes, sequencing the resulting libraries, and assigning reads to genes per cell to produce a cell-by-gene expression matrix.

Principle

Separate and tag RNA from individual cells so that sequence reads can be traced back to their cell of origin; counts (often UMI-corrected) approximate transcript abundance per cell after alignment and aggregation.

Demonstration

Profiling a dissociated tumor sample to identify malignant, stromal, and immune cell populations and to detect rare cell states such as exhausted T cells or cycling tumor cells.

Misapplication

Treating raw read or UMI counts as absolute molecule numbers without correcting for capture efficiency, library depth or dropout; clustering without controlling batch effects; interpreting technical zeros as biological absence.

Consequence

Enables discovery of cellular heterogeneity, identification of cell types and states, trajectory inference, and marker gene discovery when coupled with appropriate quality control, normalization, and downstream analysis.

Reversal

Bulk RNA sequencing, which measures average expression across many cells and therefore obscures cell-to-cell variability and rare populations.

Boundary

Covers sequencing-based single-cell transcriptomic assays; excludes protein-level measurements unless combined with CITE-seq or similar; limited by dissociation bias, capture efficiency, low-abundance transcript dropout, and loss of spatial information unless integrated with spatial methods.

Semantic Tension

Tension exists between 'single-cell' and 'single-nucleus' approaches, and between high-throughput 3'-end tag methods (shallow, many cells) and full-length protocols (deeper per cell); also between treating data as counts versus normalized continuous values.

Synthesis

Single-cell RNA sequencing is a family of sequencing protocols that assign transcript-derived reads to individual cells via barcoding, producing per-cell expression profiles that reveal heterogeneity and dynamic states, while requiring careful handling of technical noise, normalization, and interpretation limits.