This article has been reviewed according to Science X's editorial process and policies. Editors have highlighted the following attributes while ensuring the content's credibility: Researchers at Stanford Medicine have developed two new artificial intelligence models of the biological cell. The first, called universal cell embedding, paved the way for a second-generation model called TranscriptFormer, which has been trained on data from 112 million cells representing 12 species, ranging from single-celled yeast to humans.
The AI cell models make it dramatically easier to compare cells among species and offer possibilities for new insights into diseases and new cell treatments. "We're trying to open up a whole new way to think about cell biology," said Stephen Quake, Ph.D., a professor of bioengineering. He is co-lead author, along with Jure Leskovec, Ph.D., professor of computer science, and Theo Karaletsos, Ph.D., of the Chan Zuckerberg Initiative, of two scientific articles about the work, published recently in Nature and Science.
"These papers mark the beginning of, we hope, a whole new field and a decade of work," Quake said. The genome contains the instructions for life, but these days many biologists focus on gene expression—that is, which genes a particular cell is using. For example, a beta cell in the pancreas expresses the gene for insulin, plus genes related to storage and release of the hormone.
The white blood cells known as B cells produce antibodies to fight disease. A cell in your skin, on the other hand, might express genes to make hair or pigment. These differences in expression can be used to tell cell types apart.
Healthy and sick cells also have different gene expression patterns, as do cells from different species. In recent years, scientists have built various atlases, which are databases that define cells and tissues based on their gene expression patterns. The amount of data on tens of thousands of genes for hundreds of millions of cells—from many species—is staggering.
"How do we think about all these genes at once? The human brain can't," Quake said. "These models help us do that.
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