It has long been the dream of biologists to map gene expression at the
single-cell level. With such data one might track heterogeneous cell
sub-populations, and infer regulatory relationships between genes and
pathways. Recently, RNA sequencing has achieved single-cell resolution.
What is limiting is an effective way to routinely isolate and process
large numbers of individual cells for quantitative in-depth sequencing.
We have developed a high-throughput droplet-microfluidic approach for
barcoding the RNA from thousands of individual cells for subsequent
analysis by next-generation sequencing. The method shows a surprisingly
low noise profile and is readily adaptable to other sequencing-based
assays. We analyzed mouse embryonic stem cells, revealing in detail the
population structure and the heterogeneous onset of differentiation
after leukemia inhibitory factor (LIF) withdrawal. The reproducibility
of these high-throughput single-cell data allowed us to deconstruct cell
populations and infer gene expression relationships.