Drop-seq is a technology we developed for highly parallel analysis of RNA expression in thousands of individual cells. Drop-seq works by encapsulating individual cells into vast numbers of nanoliter-sized droplets, together with DNA-barcoded beads that uniquely identify the droplets. We described Drop-seq in a paper in 2015, and subsequent commercial technologies have been built on this approach. We have made Drop-seq open-source, and hundreds of labs have adopted it; detailed protocols and software are available on the Drop-seq web site.
The mammalian brain is composed of diverse, specialized cell populations. To systematically ascertain and learn from these cellular specializations, we used Drop-seq to profile RNA expression in 690,000 individual cells sampled from 9 regions of the adult mouse brain. We identified 565 transcriptionally distinct groups of cells using computational approaches developed to distinguish biological from technical signals. Cross-region analysis of these 565 cell populations revealed features of brain organization, including a gene-expression module for synthesizing axonal and presynaptic components, patterns in the co-deployment of voltage-gated ion channels, functional distinctions among the cells of the vasculature and specialization of glutamatergic neurons across cortical regions. Systematic neuronal classifications for two complex basal ganglia nuclei and the striatum revealed a rare population of spiny projection neurons. This adult mouse brain cell atlas, accessible through interactive online software (DropViz), serves as a reference for development, disease, and evolution.
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Seq-Well: portable, low-cost RNA sequencing of single cells at high throughput. Nature methods 14, pp 395–398, doi:10.1038/nmeth.4179 (2017).
Saunders A*, Macosko E.Z*, Wysoker A, Goldman M, Krienen, F, de Rivera H, Bien E, Baum M, Wang S, Bortolin L, Goeva A, Nemesh J, Kamitaki N, Brumbaugh S, Kulp D and McCarroll, S.A. 2018. Molecular Diversity and Specializations among the Cells of the Adult Mouse Brain. 2018. Cell. 174(4) P1015-1030.E16 (DOI)