scRNAseq

scRNASeq, or single-cell RNA sequencing, is a set of methods for measuring gene expression in individual cells. By profiling thousands to millions of cells, it reveals cellular heterogeneity within tissues and identifies rare cell populations masked in bulk RNA-seq.

Most workflows isolate single cells and capture mRNA with barcoded beads or wells that assign reads to cells and transcripts. cDNA is amplified and sequenced. Common approaches include droplet-based platforms (such as Drop-seq and 10x Genomics Chromium) and plate-based methods (e.g., Smart-seq2/Smart-seq3). Some protocols profile 3' ends, others capture full-length transcripts.

Data analysis starts with quality control and normalization, followed by transcript counting. Dimensionality reduction and clustering identify cell types or states. Differential expression, gene signatures, pseudotime, and trajectory analyses explore dynamics and lineages. Integration across batches or modalities is an active area.

Applications include creating cell atlases, studying development, immunology, neuroscience, and cancer, and mapping tissue organization. The approach helps characterize cellular hierarchies and responses to perturbations.

Limitations include technical noise and dropout, biases in capture efficiency, reliance on viable cells or nuclei, cost, and substantial computational requirements. Annotation can be challenging without reference datasets, and results are sensitive to analysis choices.

Related technologies include single-nucleus RNA sequencing (snRNA-seq) and multi-omics methods such as CITE-seq (protein and RNA) or scATAC-seq (chromatin accessibility). Spatial transcriptomics adds location information, enabling mapping of cell types within tissue context.