New Tool Combines scRNA-Seq and AI to Uncover Novel Neuronal Cell Types


Given the complexity of neuronal circuits, cell-type-specific tools are imperative to identify and functionally characterize the distinct cell types within. At present, these such tools exist for studying Drosophila—including the widely used GAL4 system—that rely on enhancer activity to label different subsets of cells. However, there are limitations: the enhancer-based GAL4 lines may not reflect the expression of nearby genes and the screening involved is both labor and time intensive.

Now, by feeding scRNa-seq data into an algorithm, researchers at New York University (NYU) systematically identified pairs of genes that are uniquely expressed in the majority of cell types in the fruit fly’s visual system at multiple stages of development.

One such gene pair led to the discovery of a brand-new cell type—MeSps. “Despite a long history of studying the fruit fly’s visual system, we had never seen this cell type before,” said Yu-Chieh David Chen, PhD, a postdoctoral associate in NYU’s Department of Biology.

This work is published in Proceedings of the National Academy of Sciences in the paper, “Using single-cell RNA sequencing to generate predictive cell-type-specific split-GAL4 reagents throughout development.”

Instead of the 86 billion neurons found in humans, Drosophila have about 100,000 neurons. The use of genetic tools that can distinguish different types of cells in Drosophila has revolutionized the study of neural circuits in the brain, allowing scientists to understand circuit development, function, and behavior in a precise manner.

“A hallmark of the central nervous system is the diversity of different cell types that are responsible for so many different functions,” said Claude Desplan, PhD, professor of biology and neural science at NYU.

Previous research in Desplan’s lab used single-cell sequencing to determine that there are approximately 200 cell types in the developing fly’s visual system. Scientists could identify roughly half of the 200 cell types in the developing fly’s visual system based on their gene expression and prior studies, but they lacked a way to easily study and label the other 100 cell types. Existing tools that allow precise manipulation of neural circuits of adult fruit flies often fail to label the same neurons during development, rendering these tools unfit to study cells in the developing brain.

“Moreover, the previous approach to identifying cell types involves laborious testing of numerous gene candidate combinations. We knew we needed a much more efficient approach to label specific cell types, and were able to tap into the growing amount of single-cell sequencing data that is available,” said Chen.

Chen and his colleagues created a tool that takes advantage of the extensive single-cell sequencing data for the developing fly visual system to identify genes—and combinations of genes—that are exclusively expressed in certain cell types. To identify a cell type, researchers typically look for genetic markers (single genes that are specific to a cell type.) But often a gene will be expressed in multiple cell types, making it difficult to use one gene to differentiate between them. The tool the NYU researchers developed uses a slightly different approach: finding two genes that overlap only in one cell type.

More specifically, the authors write that they used existing developmental scRNA-seq datasets “to select gene pairs for split-GAL4 and provide a highly efficient and predictive pipeline (scMarco) to generate cell-type-specific split-GAL4 lines at any time during development, based on the native gene regulatory elements.”

These gene-specific split-GAL4 lines can be generated from a large collection of coding intronic MiMIC/CRIMIC lines or by CRISPR knock-in, they adde.

The researchers note that their tools can also be used to study other systems beyond vision in the developing fly, as long as single-cell data are available. Moreover, the logic of finding marker gene pairs instead of one single marker gene can be applied in research in other species.

They write, “We use the developing Drosophila visual system as a model to demonstrate the high predictive power of scRNAseq-guided gene-specific split-GAL4 lines in targeting known cell types, annotating clusters in scRNAseq datasets as well as in identifying novel cell types. Lastly, the gene-specific split-GAL4 lines are broadly applicable to any other Drosophila tissue.”

“This pioneering and efficient approach provides exceptional tools for the field of neuroscience to investigate developmental questions with high precision,” said Desplan.