A ‘Rosetta Stone’ for neuroscience: new atlas helps define brain cell types

To deconstruct a thinking machine made of tens of billions of neurons, it helps to have a parts list—an inventory of the brain’s cell types. But neuroscientists have struggled to standardize a list across labs and experiments. Now, a network of more than 400 researchers has released the most comprehensive inventory to date: an analysis of millions of human, marmoset, and mouse cells extracted from a brain region involved in coordinating movement.

The results, described in 17 papers this week in Nature, collate genetic features of cells along with their shapes, locations, and electrical activity patterns to identify more than 100 cell types in the human brain. The catalog could help researchers define the types of cells affected by brain diseases, identify corresponding cells in animal models, and better target those cells with treatments. The cell atlas “is like the Rosetta Stone for neuroscience,” says Jens Hjerling-Leffler, a neuroscientist at the Karolinska Institute who was not involved in the project.

Categories to describe brain cells have emerged over decades as researchers teased apart differences in cells’ location, shape, and function. Do they sit in a superficial layer of the cortex, or a deeper one? What is the structure of their branches? Do the neurotransmitters they release excite other cells or inhibit them? But cells often defy tidy definitions. “We’ve been trying to bucket these cells in ways that are probably oversimplistic,” says Kristen Brennand, a stem cell biologist who studies brain disorders at Yale University and is not involved in the new project. “Biology doesn’t like black and white.”

The National Institutes of Health’s BRAIN Initiative Cell Census Network (BICCN), a $250 million program begun in 2017, promises to add some gray. Many studies it supports use genomic sequencing to delineate cell types. A cell’s RNA, for example, reveals the set of genes it has recently transcribed to make proteins—its transcriptome. Other sequencing methods describe a cell’s epigenome, the set of molecules studding its DNA that influence which genes get expressed.

“These are powerful approaches,” says BICCN investigator Xiaowei Zhuang, a biophysicist at Harvard University. But sequencing alone doesn’t tell the whole story, Zhuang adds. “We also need to know where [cells] are in the brain, what are their neighbor cells, how do they interact.”

To efficiently gather such information, BICCN collaborators agreed to focus on a single region: a strip of tissue across the top of the brain called the primary motor cortex that orchestrates muscle movements. Some techniques could capture multiple features at once. For example, a method developed in Zhuang’s lab allows researchers to image hundreds or thousands of RNA sequences in a slice of brain tissue, revealing both cells’ transcriptomes and their relative locations. Another method, Patch-seq, records electrical activity from cells, stains them to reveal their shapes, and then sucks out their innards to sequence their RNA.

In BICCN’s data, cells grouped according to their transcriptomes tend to share other features, such as location, shape, and electrical activity. That finding “provides strong validation to the molecularly defined cell types,” the BICCN authors write in a paper summarizing the work. For the human motor cortex, 127 such types emerged.

The exact number depends on the criteria used to group cells, says BICCN investigator Hongkui Zeng, director of the Allen Institute for Brain Science. “Is it 127? Is it 130? Is it 100? … It’s not clear cut like that.” What’s more important, she says, is the hierarchy of cell types that emerged, with a few basic cell types at the top and more nuanced divisions in lower branches. Cells in the same top-level class tend to be similar across various types of measurements—and across the three species studied. Further down, Zeng says, distinctions between cell types “become a lot fuzzier.”

The new census may shape the ways researchers model brain disease, says Mina Ryten, a clinical geneticist at University College London (UCL) who studies neurogenetic disorders. “It’s actually very hard to predict what a gene mutation is going to do to a person,” she says, partly “because you just don’t have a framework for understanding … which cell types [it’s expressed] in.”

The BICCN data could help scientists pin down which human cell types are most affected by specific mutations. Mouse and marmoset data could then help them identify and study comparable cell types in lab animals. The result, researchers hope, will be findings that translate better to humans. “We’ve cured a lot of diseases in mice,” says Michelle Gray, a neuroscientist at the University of Alabama, Birmingham. “It’s always a question when we’re trying to model disease using rodents: Are we really recapitulating the changes that are observed in humans?”

Having created a partial parts list, researchers still need to study how the cell types behave and interact in a functioning brain. UCL neuroscientist Kenneth Harris is among those imaging genetically labeled cells in living mice to relate gene expression to electrical firing patterns. “We’re going to have to learn what all these cell types are and try to figure out how they all work together,” he says. “It’s going to be difficult.” But with the new cell census, “we’re entering this stage where we know how much we don’t know—and that’s progress.”

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