Single cell RNA sequencing is a set of techniques that measure gene expression in thousands to millions of individual cells at once, instead of averaging signals across bulk tissue. By capturing and sequencing the messenger RNA of single cells, these methods reveal cellular heterogeneity, rare cell types and dynamic transitions that would otherwise be hidden.

Early plate based methods isolated single cells into wells and generated full length transcript information, but were relatively low throughput. Droplet based technologies then enabled massive parallelization. In these systems, individual cells are encapsulated in microfluidic droplets together with barcoded beads, so that transcripts from each cell receive a unique molecular label. This allows pooling of material, efficient sequencing and later computational assignment of reads back to single cells.

More recent developments push beyond simple expression profiling. Some protocols jointly measure RNA and surface proteins using oligonucleotide tagged antibodies, linking transcriptomes to immunophenotypes. Others integrate CRISPR based perturbations with single cell readout, so that the impact of specific genetic or regulatory changes on cell states can be traced across thousands of cells in one experiment. Multimodal approaches capture chromatin accessibility or spatial information alongside RNA, connecting gene regulation, location in tissue and transcriptional output.

Analytical advances accompany these experimental improvements. Dimension reduction, clustering, trajectory inference and RNA velocity methods reconstruct developmental lineages, activation paths and state transitions from snapshot data. Overall, single cell RNA sequencing has transformed the study of development, cancer, immunology and neuroscience by providing a detailed atlas of cell types and dynamic processes at unprecedented resolution.