There are many reasons for analyzing the gene expression profiles of individual cells rather than a pooled population. But scaling up single-cell transcriptomics to analyze multiple cells in parallel has been challenging. “One can only analyze a few, or maybe 100 cells at a time,” says Donald Zack of Johns Hopkins Hospital in Baltimore.
“There’s a need for techniques that are unlimited in terms of the number of cells,” says Marc Kirschner of Harvard University. In some instances you need increased statistical power, he says. “But also, it’s often the case that you’re looking for a needle in a haystack”—that is, extremely rare cells.
Kirschner’s laboratory and that of Steve McCarroll, also at Harvard, have now independently broken the scale barrier for single-cell analyses. And surprisingly, the teams achieved their feats in similar ways despite only learning of each other’s approach well along in development. Both groups used microfluidic devices ...
