RNA-Seq and ATAC-Seq identify molecular signals that help control the development of interneurons

Inside our brains lives a myriad of cell types that support complex human thought — from our ability to make memories and decisions, to our capacity for smell, taste, movement, and communication. Scientists do not yet fully understand how this critical cellular diversity arises as the brain grows and develops.

Now, researchers at the Broad Institute of MIT and Harvard and the Flatiron Institute have shown how two key cell types in the brain’s cortex arise from a single progenitor in mice. Led by Kathryn Allaway, Orly Wapinski, and Gord Fishell of the Stanley Center for Psychiatric Research at Broad, as well as Mariano Gabitto and Richard Bonneau of the Flatiron Institute, the researchers have discovered genetic and molecular factors that allow the two populations of interneurons to develop different identities.

Fishell’s team knew from previous work that despite their differences, PV and SST cells arise from the same cell type. To determine what factors might influence how and when the cells diverge, the researchers used a combination of RNA sequencing, which provides information about how genes are expressed, and a technique called Assay for Transposase-Accessible Chromatin using sequencing, or ATAC-seq, to analyze the two cell types. ATAC-seq reveals which parts of chromatin — the tightly wound package of DNA and protein in the cell’s nucleus — are accessible to the cell’s protein-making machinery.

“When you bring these two data sets together, it’s a really rich data source for building amazing computational models for gene regulation,” said Kathryn Allaway, co-first author of the study and a graduate student in Fishell’s lab when the research took place.

Using these data, the researchers built maps showing how molecular regulators — such as complexes of DNA, RNA, and proteins — interconnect to control gene expression in mouse PV and SST cells. In collaboration with Richard Bonneau at the Flatiron Institute, Fishell’s team modeled these gene regulatory networks computationally.

Developmental characterization of embryonic E13 MGE cells surveyed using scRNA-seq, scATAC-seq, and multiomic methods

a, Analysis of Dlx6a−, Dlx6a+ and multiome scRNA-seq datasets collected from E13 MGE in Dlx6a−Cre;Ai9 mice and multiome dataset in E13 MGE wild type mice. b, Analysis of Dlx6a−, Dlx6a+ and multiome scATAC-seq datasets collected from E13 MGE in Dlx6a−Cre;Ai9 mice and multiome dataset in E13 MGE wild type mice. i, Dlx6+/− FAC-sorted and multiome cells. ii, Unbiased cluster annotation. iii, Dlx6+ and Dlx6− annotation. iv, Cell cycling phase annotation. v, Pseudotime annotation. vi, Mitotic and postmitotic cell annotation. In ii-vi, Annotations are performed on scRNA-seq datasets and transferred to scATAC-seq datasets through the multiome dataset. scRNA- and scATAC-seq low dimensional representation reflects UMAP embedding. vii, Average gene expression and promoter accessibility for unbiased clusters.

“In ten years, we’re going to be using incredibly powerful computation with incredibly powerful DNA and RNA work to model how genes affect the way our brain functions at a cellular level,” said Fishell. “That is a holy grail. We’re not there yet, but this really took a big step in the direction of predicting how gene loss affects brain function.”

Source – The Broad Institute