Working out how a cell is wired lets researchers find key regulators of behaviour.
When it comes to dissecting how a cell’s regulatory circuits are wired, some researchers turn to their pipettes. Emily Miraldi turns to her keyboard.
In what Bonneau, now at Genentech Research and Early Development in South San Francisco, California, calls a “tour de force” study, Miraldi and her colleagues used this updated Inferelator to trace networks comprising thousands of transcription factors in a class of white blood cells called type 17 T-helper cells. They found that the transcription factors STAT3 and FOXB1 in these cells are key regulators of genes that are implicated in inflammatory bowel disease.
Suppose, she says, that you perturb a single gene in a population of cells. By observing which genes are affected, you can model a regulatory circuit. But to confirm your hypothesis, you might need to disrupt dozens or even hundreds of other genes. That quickly becomes impractical, she says — but not at the single-cell level, where each cell is its own data set. “We thought that in single-cell genomics we would be able to do something that we were simply not able to do in bulk.
In a 2020 study, computer scientist T. M. Murali at Virginia Tech in Blacksburg and his team described a computational pipeline called BEELINE, which they used to test a dozen GRN-inference methods based on single-cell RNA sequencing against gold-standard and synthetic data sets. “Most methods do a relatively poor job of inference,” Murali says, at least when it comes to deducing interactions — performing about as well as a random predictor, he notes.
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