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arXiv · 2610.21943

Panoramic modelling of single-cell transcriptomes at scale with CellNet

Xiaoqing Lian·Pei Liu·Jiahui Tan·Luoda Tan·Chaoyi Li·Quan Zou·Chen Lin·Xiangxiang Zeng
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Abstract

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.

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Xiaoqing Lian, Pei Liu, Jiahui Tan, Luoda Tan, Chaoyi Li, Quan Zou, Chen Lin, Xiangxiang Zeng. 2026-10-15. Panoramic modelling of single-cell transcriptomes at scale with CellNet. https://arxiv.org/abs/2610.21943

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