Single-cell gene expression analysis is a powerful technique that enables the accurate molecular characterization of single cells, the discovery of new sub-populations, and the dissection of new molecular pathways. Importantly, this technique addresses the potentially confounding effects of cell-to-cell variation within heterogeneous populations that exist within known and undefined tissues.
The data generated is medium-throughput, wherein a panel of tens to hundreds of genes is assayed across hundreds of single-cell samples. Analysis of the data as a whole, rather than at the single gene-of-interest level, is an informative and efficient way to identify patterns between genes and cells that define gene-expression signatures (“metagenes”) and classify sub-populations of cells.
We observed distinct migratory behaviour of T follicular helper (Tfh) cells in the germinal centre (GC) and follicular mantle (FM) during the primary and secondary response. To determine the molecular identity of these Tfh cell subpopulations, we optically marked Tfh cells based on their micro-anatomical location by two-photon microscopy, and isolated single cells for gene-expression analysis. Here we describe non-negative matrix factorization (NMF), a robust, unbiased computational biology method, that can be applied to the single-cell expression data, to identify metagenes that govern Tfh dynamics during the adaptive immune response and cluster Tfh cells based on their relative expression of these metagenes.