Single-cell RNA sequencing (scRNA-seq) measures the expression of thousands of genes, cell by cell. Where “bulk” RNA-seq averages the signal of millions of cells, scRNA-seq reveals the heterogeneity that no average can show: rare subpopulations, transient states, differentiation trajectories.
Principle and workflow
The most widespread method encapsulates each cell, in a droplet, with a bead carrying a unique barcode. After lysis, the messenger RNA is reverse-transcribed while tagging each transcript with its cell’s barcode and with a unique molecular identifier (UMI), which will allow molecules to be counted without amplification bias. The libraries are sequenced in bulk, then demultiplexed: each read is reassigned to its cell of origin to reconstruct, in silico, one transcriptome per cell.
Analysis is a discipline in its own right: quality control (removal of doublets and poor-quality cells), normalisation, dimensionality reduction, clustering and then annotation of the populations. Coupled with immune-receptor sequencing (BCR/TCR), scRNA-seq links a cell’s transcriptome to its clonal specificity — a major asset in immunology.
Variants and options
Droplet capture favours throughput (thousands of cells); plate-based approaches favour sensitivity and full-length coverage. Multi-omic approaches jointly measure RNA and surface proteins, or RNA and chromatin accessibility. Adding the BCR/TCR repertoire and integrating several datasets enrich interpretation. Upstream, cell sorting (FACS) enriches the populations of interest — for example antigen-specific cells — before encapsulation.
When and why this technique
scRNA-seq is warranted to dissect a heterogeneous population, discover unknown subtypes, follow a differentiation, or link transcriptional phenotype and clonality. It is a discovery approach, rich and free of prior assumptions.
The downside is technical. The method requires viable cells in suspension: dissociation stresses the cells and may under-represent fragile types. It loses spatial information — where each cell sits within the tissue — which is the domain of spatial omics and multiplexed immunofluorescence. Per-cell depth remains limited (undetected genes, or “drop-outs”), the cost and bioinformatic burden are high, and artefacts (doublets, dead cells) must be controlled. To quantify already-known populations, cytometry is faster and more economical; scRNA-seq comes into its own when transcriptional richness is the objective.
Inovarion’s expertise
Inovarion has deployed scRNA-seq in landmark studies of the human B-cell response. Its published work has profiled, at single-cell scale and coupled with the BCR repertoire, the maturation and persistence of the anti-SARS-CoV-2 memory B response, the durable imprinting of anti-smallpox memory B cells within the splenic niche (several thousand cells analysed, clustering and trajectories), and the B populations underlying immune thrombocytopenia relapses. These studies combine FACS sorting, single-cell transcriptomics and repertoire sequencing to link phenotype, transcriptome and clonality — from cell preparation to data analysis.
See also: Flow cytometry (upstream sorting) ; Bioinformatics (data analysis).
Key publications
- Sokal et al. Maturation and persistence of the anti-SARS-CoV-2 memory B cell response. Cell, 2021. Record → · PubMed
- Chappert et al. Human anti-smallpox long-lived memory B cells are defined by dynamic interactions in the splenic niche and long-lasting germinal center imprinting. Immunity, 2022. Record → · PubMed
- Crickx et al. Rituximab-resistant splenic memory B cells and newly engaged naive B cells fuel relapses in patients with immune thrombocytopenia. Science Translational Medicine, 2021. Record → · PubMed