ProtCell foundation model for single-cell proteomics trained on about 380 million cells

[bioRxiv] Hu, Li, Jiang and co-authors present ProtCell, a foundation model for single-cell proteomics developed using approximately 380 million cells from 98 datasets for pretraining and evaluation, according to the bioRxiv preprint. The model combines protein-identity and continuous-abundance embeddings in a transformer encoder and learns cell- and protein-level representations through masked abundance modeling. According to the authors, ProtCell first connects fragmented profiles through transferable annotation and batch correction while preserving biological organization, then extends limited profiles through abundance reconstruction and inference of an unmeasured cytotoxic protein program enriched in natural killer cells and memory CD8+ T cells. The authors further say the learned protein dependencies guide selection of compact panels that retain information for profile reconstruction and biological classification. The preprint has not been peer-reviewed.