Researchers have developed a single-cell RNA sequencing (scRNA-seq) method that allows scientists to study the expression of genes without requiring large cell inputs and can instead be efficiently processed using samples with fewer cells.
scRNA-seq involves capturing the RNA of a single cell and—after multiple molecular conversion reactions—sequencing it. Since RNA is the intermediate step from gene to protein, it provides an overview about which genes in a cell are active and which are not.
Because single-cell RNA sequencing 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.
But scRNA-seq isn’t just a tool for basic cell biology; it has been widely adopted in medical and pharmacological research as it can identify 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.
“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,” says Johannes Bues, MSc, a researcher in Deplancke’s group.
Bues, with Marjan Biočanin and Joern Pezoldt, also in Deplancke’s group, have now developed a new method that allows scRNA-seq to efficiently process samples with fewer cells.
Published in Nature Methods, the method is dubbed DisCo, a shortening of the phrase “deterministic, mRNA-capture bead and cell co-encapsulation dropleting system”.
Unlike usual single-cell methods that rely on passive cell capture, DisCo uses machine-vision to actively detect cells and capture them in droplets of oil and beads. This approach allows for continuous operation, and also renders scaling and serial processing of cell samples highly cost efficient.
DisCo features precise particle and cell positioning, and controls droplet sorting through combined machine-vision and multilayer microfluidics. All this allows for continuous processing of low-input single cell suspensions at high capture efficiency at speeds that can reach 350 cells per hour.
“Our work demonstrates the unique ability of DisCo to provide high-resolution snapshots of cellular heterogeneity in small, individual tissues,” says Deplancke.
Featured image: Close-up of the microfluidics enabling deterministic co-encapsulation of single cells at outstanding efficiencies. Photo: Jörn Pezoldt (EPFL)