While they were coaxing nuclei into 96-well plates, their partners in the Gábor Tamás lab at the University of Szeged in Hungary were analyzing live tissue samples from patients who had undergone brain surgery. By using traditional techniques like filling the cells with a special dye and then recording how they reacted to different electric stimuli, Tamás’s group spotted a group of hippy, well-connected neurons—whose molecular markers matched up almost perfectly with one of Lein’s cell types. When they went looking to see if a similar molecular profile existed for any cells in the mouse brain, they came up empty-handed.

“It’s too early to say that this is a completely unique cell type because we haven’t looked in other species yet,” adds Lein. “But it really highlights the fact that we need to be careful about assuming that the human brain is just a scaled-up version of a mouse.”

Because live human brain tissue is so difficult to get, the vast majority of work characterizing the electrophysiology and connectivity of neurons happens in mice. A transcriptomics approach, though, can be applied to frozen tissue. There’s plenty of that just sitting in biobanks all over the world.

“What will happen over the next five to 10 years or so is that these transcriptomic methods are going to speed ahead, because they’re much more high throughput than traditional approaches,” says Richard Scheuermann, a director at the Craig J. Venter Institute and immunologist at the University of California at San Diego. “So we’ll get this atlas based on the parts lists that cells are expressing, then as we learn more about their functions we can link that information back in.”

Scheuermann was one of the original architects of something called the Cell Ontology, a reference for how scientists represent different cell types. It’s more than just a common set of definitions. It also captures the relationships between cells—in time and space and function. Now that scientists are letting cells define themselves by the genes they turn on and off, he’s working to create a cell-ipedia for this new era.

That movement extends beyond neuroscience. In October 2016, hundreds of scientists across the globe banded together to launch the Human Cell Atlas—a massive project to compile transcriptomic data on all the cells in the human body to understand how they organize into tissues, how they talk to each other, how they age, and how things can go wrong. The Chan Zuckerberg Initiative has been one of the primary funders of the project. Scheuermann snagged one of the organization's grants to build software that can identify marker genes used to define different cell types. Another tool automatically translates the genes along with other data into a machine-readable classification system.

Lein’s brain cell data was the tool’s first test case, which the two groups published in March in Human Molecular Genetics. But they’re just getting started. They’ve already submitted another paper to Nature that defines 75 cell types by their transcriptome alone. Neuroscientists don’t agree how many cell types they might find, but it’s likely to be in the thousands if not tens of thousands. Santiago Ramón y Cajal might have defined the field of neuroscience, but these days, it’s algorithms that are doing the defining, with a little help from the neurons themselves.