Source: Fotis Bobolas

A team from the Broad Institute of MIT and Harvard have unveiled a new technique for sequencing RNA from the nuclei of single cells, called DroNc-Seq. The technique, described in a paper in Nature Methods, overcomes the problems that the team’s previous technology, sNuc-Seq, had with large scale research.

Studying gene expression in complex tissues, such as the brain, has historically been very challenging. The traditional methods of cell isolation affect the RNA content of the cells and therefore make it very difficult to accurately map out the expression profile of different cell types. They also are largely ineffective tools when working with frozen, archived tissues. To combat these challenges, a team from the Broad Institute developed and released a technology called sNuc-Seq last year, which handled these problems by utilising RNA from a single nucleus.

However, sNuc-Seq is a low throughput technology, utilising 96- or 384-well plates. As a result, it is significantly limited in its applications, particularly in large genomic projects like the Human Cell Atlas. To improve on the technology, the authors of the paper examined how microfluidics could be used to increase throughput, using Drop-Seq as inspiration. Drop-Seq is another single cell RNA sequencing technology, which encapsulates DNA-barcoded beads with single cells in microdroplets to increase the speed and decrease the cost of sequencing.

Their research led the team to developing DroNc-Seq, combining sNuc-Seq with microfluidic technology that enables massively parallel measurements. When testing DroNc-Seq against similar technologies, including Drop-Seq and sNuc-Seq, they were able to demonstrate that the technology was able to identify gene expression signatures that were unique to different types of brain cell, such as neurons and glial cells. DroNc-Seq was also shown to be capable of accurately differentiating between closely related and highly similar cell subtypes.

Initial results indicate that DroNc-Seq is a robust and accurate way of sequencing RNA from single cell nuclei on a large scale.

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