Leading the Way in Life Science Technologies

GEN Exclusives

More »

GEN News Highlights

More »
August 29, 2017

Single-Nucleus RNA-Seq Merges with Microfluidics

This family tree captures just a sampling of single-cell and single-nucleus RNA sequencing technologies that have burst onto the scene over the last eight years. [Susannah M. Hamilton and Tom Ulrich/Broad Communications]

  • Cell types, cell subtypes, cell sub-subtypes…how low can you go? When you’re dealing with complex tissues and sorting through transcriptome profiles, it can be hard to tell—and harder still if you happen to be working with tissues that won’t readily dissociate, such as brain tissues or frozen tissues. In these cases, splitting away single cells can so disturb them that they give skewed information about their RNA contents.

    Alternatively, one can opt to analyze single nuclei instead of single cells, thereby avoiding harsh enzymes. This shortcut to deeper transcriptomic analyses, however, can start to seem like the long way around. Single-nucleus RNA-sequencing (RNA-Seq) techniques are not always readily scalable. According to scientists based at the Broad Institute, single-nucleus RNA-Seq techniques often rely on flow cytometry and microfluidic technologies that cannot handle massively parallel operations.

    The Broad scientists, however, propose a solution: DroNc-Seq, a single-cell expression profiling technique that merges sNuc-Seq, a single-nucleus RNA sequencing method, with microfluidics, allowing massively parallel measurement of gene expression in structurally complicated tissues.

    Details appeared August 28 in the journal Nature Methods, in an article entitled, “Massively Parallel Single-Nucleus RNA-Seq with DroNc-Seq.” The article revisits the sNuc-Seq technique, which was developed last year by the Broad Insitute team. sNuc-Seq, the article’s authors admitted, 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 to efficiently study thousands of nuclei at a time, the Broad team turned to microfluidics. Their inspiration: 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.

    “DroNc-seq is a massively parallel sNuc-Seq method that is robust, cost effective, and easy to use,” explained the authors of the Nature Methods article. “Profiling of mouse and human frozen archived brain tissues successfully identified cell types and subtypes, rare cells, expression signatures, and activated pathways.

    “Classifications and signatures derived from DroNc-seq profiles were congruent with those from prior studies in human and mouse (despite the lower number of detected genes per nucleus) but were derived with considerably improved throughput and cost. Moreover, DroNc-seq readily identified rare cell types without the need for enrichment.”

    The Broad 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 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.

    In a related development, Dolomite Bio launched a new chip for DroNc-Seq. The single-nucleus RNA-Seq chip—based on the company’s established scRNA-Seq chip—is designed to produce smaller droplets, allowing straightforward, low-cost profiling of thousands of nuclei.

    According to Dolomite Bio, the transcriptomes acquired with the new single-nucleus technology are very similar to those from whole cells, albeit with a lower total transcript capture rate and greater representation of nuclear RNA and pre-mRNA.

Related content