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CellNet models single-cell transcriptomes panoramically by integrating DNA-derived gene identities with measured RNA expression into a shared image representation, pretrained on 231 million cell profiles across 40 species. This approach significantly enhances recognition of rare cell populations and condition-associated states while maintaining competitive general cell representations. CellNet demonstrates cross-species prediction of gene expression, including novel targets, and recovers lineage-specific patterns from spatial data.
A single model now captures 40 species' cellular diversity by fusing DNA and RNA data, predicting novel gene expression and recovering lineage patterns.
Comprehensively characterizing cellular diversity is critical for understanding tissue development, identifying disease-associated populations and investigating gene programmes linked to specialized functions. Advances in single-cell profiling reveal additional cell-specific transcripts and molecular associations, yet many foundation models represent only a restricted portion of this increasingly rich molecular information. Here we present CellNet, which enables panoramic modelling of single-cell transcriptomes by integrating DNA-derived gene identities and measured RNA expression in a shared image representation. Pretrained on 231 million cell profiles spanning 40 species, CellNet improves rare-population and condition-associated state recognition in evaluated cohorts while retaining competitive general cell representations. It predicts withheld coding-gene expression and selected non-coding targets in total-RNA data, and recovers lineage-specific expression patterns in spatial measurements. Prediction also extends across species, including to targets without expression-pretraining exposure. Its gene representations retrieve known non-coding modules and developmental or neural programmes, including from sparsely detected RNA queries. Together, these results establish CellNet as a scalable framework for interpreting cellular diversity through increasingly comprehensive transcriptomic measurements.