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DroNc-Seq, A Technology That Merges Single-Nucleus RNA Sequencing with Microfluidics, Brings Massively Parallel Measurement to Gene Expression Studies in Complex Tissues

Last year, Broad Institute researchers described a single-nucleus RNA sequencing method called sNuc-Seq. This system enabled researchers to study the gene expression profiles of difficult-to-isolate cell types, as well as cells from archived tissues. Now, a Broad-led team has overcome a key stumbling block to sNuc-Seq's widespread use: i.e., scale. In a paper published online on August 28, 2017 in Nature Methods, postdoctoral fellows Dr. Naomi Habib, Dr. Inbal Avraham-Davidi, and Dr. Anindita Basu; core institute members Dr. Feng Zhang and Dr. Aviv Regev; and their colleagues reveal DroNc-Seq, a single-cell expression profiling technique that merges sNuc-Seq with microfluidics, allowing massively parallel measurement of gene expression in structurally-complicated tissues. The article is titled “Massively Parallel Single-Nucleus RNA-Seq with DroNc-Seq.” Researchers struggled in the past to study expression in neurons and other cells from complex tissues, like the brain, at the single-cell level. This was because the procedures for isolating the cells affected their RNA content and did not always accurately capture the true proportions of the cell types present in a sample. Moreover, the procedures did not work for frozen archived tissues. sNuc-Seq bypassed those problems by using individual nuclei extracted from cells as a starting point instead. sNuc-Seq, however, is a low-throughput technology, using 96- or 384-well plates to collect and run samples. To scale the method up to the level needed in order to efficiently study thousands of nuclei at a time, the team turned to microfluidics. Their inspiration was: Drop-Seq, a single-cell RNA-seq (scRNAseq) technique that encapsulates single cells together with DNA barcoded-beads in microdroplets to greatly accelerate expression-profiling experiments and reduce cost. To test the new method's accuracy and speed, the team successfully benchmarked DroNc-Seq against Drop-Seq, sNuc-Seq, and other lower-throughput scRNAseq methods using a mouse cell line and mouse brain tissue. They also applied it to human brain tissue collected by the Genotype-Tissue Expression (GTEx) Project, finding that they could a) identify expression signatures unique to neurons, glial cells, and other cell types in the brain (including rare types), and b) differentiate between closely related cell subtypes.DroNc-Seq's robustness and accuracy suggest that it could be a valuable addition to the stable of technologies being used as part of the Human Cell Atlas and other scRNAseq-based efforts.

[Press release] [Nature Methods abstract]