Nature – by Jeffrey M. Perkel – Data from thousands of single cells can be tricky to analyse, but software advances are making it easier.

Single-cell biology is a hot topic these days. And at the cutting edge of the field is single-cell RNA sequencing (scRNA-seq).

Conventional ‘bulk’ methods of RNA sequencing (RNA-seq) process hundreds of thousands of cells at a time and average out the differences. But no two cells are exactly alike, and scRNA-seq can reveal the subtle changes that make each one unique. It can even reveal entirely new cell types.

For instance, after using scRNA-seq to probe some 2,400 immune-system cells, Aviv Regev of the Broad Institute in Cambridge, Massachusetts, and her colleagues came across some dendritic cells that had potent T-cell-stimulating activity (A.-C. Villani et al. Science 356, eaah4573; 2017). Regev says that a vaccine to stimulate these cells could potentially boost the immune system and protect against cancer.

But such discoveries are hard-won. It’s much more difficult to manipulate individual cells than large populations, and because each cell yields only a tiny amount of RNA, there’s no room for error. Another problem is analysing the enormous amounts of data that result — not least because the tools used can be unintuitive.

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