Single cell RNA sequencing is a set of techniques that measure gene expression in individual cells rather than in bulk tissue. This allows researchers to uncover cellular heterogeneity, identify rare cell types and reconstruct developmental and disease trajectories with high resolution.

The workflow typically begins with dissociation of tissue into single cells, followed by capture and barcoding. Methods such as microfluidic droplets, microwell plates and combinatorial indexing attach unique molecular identifiers and cell specific barcodes to each transcript. After reverse transcription and amplification, libraries are sequenced and computational pipelines align reads, correct for technical noise and generate gene by cell expression matrices.

These data enable clustering of cells into distinct populations, inference of cell type identities using known marker genes and detection of novel subtypes. Trajectory analysis algorithms order cells along pseudotime to infer differentiation paths and lineage relationships. Integration methods combine datasets across experiments, platforms or species, while batch correction reduces technical variation that could obscure biological signals.

Applications span developmental biology, immunology, oncology and neuroscience. In cancer, single cell RNA sequencing reveals tumor heterogeneity, cancer stem cell populations and interactions with immune and stromal cells, informing prognostic markers and therapeutic targets. In immunology, it maps complex immune cell states during infection, vaccination and autoimmunity. In neuroscience, it supports comprehensive cell atlases of the brain and dissection of neuronal circuits.

Technical challenges remain, including sparse data due to transcript dropouts, biases introduced during capture and amplification, and high computational demands. Ongoing research focuses on improved chemistries, multi omic integration, spatially resolved methods and standardized analysis frameworks to make single cell transcriptomics more robust and broadly accessible.