Bioengineershave found a way to radically increase the efficiency of single-cell RNA-sequencing, a powerful tool that can ‘read’ the genetic profile of an individual cell.

Because scRNA-seq captures the activity of all genes in the cell’s genome — thousands of genes at once — it has become the gold standard for defining cell states and phenotypes. This kind of data can reveal rare cell types within a cell population, even types never seen before.

Cost and efficiency

But scRNA-seq isn’t just a tool for basic cell biology; it has been widely adopted in medical and pharmacological research as it is capable of identifying which cells are actively dividing in a tissue, or which are reacting to a particular drug or treatment.

«These single-cell approaches have transformed our ability to resolve cellular properties across systems,» says Professor Bart Deplancke at EPFL’s School of Life Sciences. «The problem is that they are currently tailored toward large cell inputs.»

This isn’t a trivial problem, as scRNA-seq methods require over a thousand cells for a useful measurement. Dr Johannes Bues, a researcher in Deplancke’s group, adds: «This renders them inefficient and costly when processing small, individual samples such as small tissues or patient biopsies, which tends to be resolved by loading bulk samples, yielding confounded mosaic cell population read-outs.»

The DisCo solution

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Materials provided by Ecole Polytechnique Federale de Lausanne. Original written by Nik Papageorgiou. Note: Content may be edited for style and length.