Cell2Sentence-Scale 27B

Data-as-text and scaling behavior: scRNA-seq profiles are rank-ordered into gene-name sequences that preserve expression information and can be inverted with minimal loss. Pretraining spans multi-task prompts over 50M human and mouse transcriptomes, plus papers and metadata. Performance improves smoothly from 410M to 27B across annotation, tissue inference, and conditional generation.

Broad capabilities vs baselines: On classic single-cell tasks, C2S-Scale matches or beats scGPT and Geneformer. It also supports NL cluster captioning, dataset-level summarization, and QA, outperforming general LLMs like GPT-4o on these single-cell-grounded NL tasks.

Multi-cell and spatial reasoning: Without bespoke spatial modules, C2S-Scale predicts neighborhood structure from multi-cell context and improves further when prompted with receptor-ligand and PPI knowledge from CellPhoneDB and BioGRID.

Perturbation modeling and a new metric: A two-stage pipeline uses SFT to condition on perturbations, then GRPO to reward pathway-faithful predictions. The paper introduces scFID, an embedding-space analogue of image FID, yielding stable rankings of generated cell states. C2S-Scale leads on unseen cytokine combinations and lowers scFID after RL.

From virtual screen to biology A dual-context screen asked for drugs that raise antigen presentation only in low-IFN settings. The model nominated silmitasertib with a strong context split, and this was validated in two human cell models: silmitasertib alone had little effect, but with low-dose IFN increased HLA-A,B,C surface levels.