Single-cell RNA sequencing has generated massive datasets across tissues, experiments, and species, but integrating and analyzing them remains challenging due to batch effects, species differences, and the need for extensive annotations.

Researchers introduce UCE (Universal Cell Embedding), a foundation model trained self-supervised on 36 million cells. By representing cells as "bags of RNA" ordered by genomic location and leveraging protein language models (ESM2), UCE learns a unified latent space that captures biological variation while remaining robust to experimental noise.