Deep exploration of transcriptomic cellular identities over evolutionary time
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Whole-organism cell atlases have painted the cellular landscapes of individual species, however comparing cells across the tree of life remains challenging. Most cross-species analyses are restricted to orthologous genes, while the complexity of atlas data constitutes an access barrier for many researchers. We developed a computational strategy to accelerate the exploration of cellular identities at scale. We integrated 30 atlases detailing the expression of 861,013 genes by 2,645,508 animal and plant cells and trained a universal model of cellular transcriptomes to track cell type diversification over evolutionary times. A derived model could identify cell types of a new species within minutes on both mammals and a de novo constructed atlas of the insect Cryptocercus punctulatus . We then reduced the footprint of atlases by 100 times while retaining crucial information and developed interfaces to answer 26 query types within two seconds, accelerating atlas exploration by ∼50,000 times.
