Single-cell foundation models transfer across billion-year evolutionary distances, preprint says

[bioRxiv] A preprint study evaluated zero-shot embeddings from Geneformer, scGPT and UCE against PCA and scVI in developmental atlases of Dictyostelium discoideum and Caenorhabditis elegans, organisms separated from humans by 1.2 billion and 600 million years. According to the study, without retraining the models recovered developmental order and preserved organism-specific cell identities. UCE, which encodes genes by protein sequence, could use 97% of measured genes, whereas Geneformer and scGPT used under one-fifth after ortholog mapping. The authors said UCE performed most consistently, often exceeding species-native baselines; restricted to ortholog-mappable genes, it still scored above vocabulary-based models on five of six annotation tasks, some narrowly. The preprint concludes that foundation models can yield informative cell embeddings across more than a billion years of evolution, and that protein-sequence gene representations make these embeddings more complete and often more accurate.