Panoramic modelling of single-cell transcriptomes at scale with CellNet
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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BibTeX
@misc{lian2026panoramic,
title = {{Panoramic modelling of single-cell transcriptomes at scale with CellNet}},
author = {Xiaoqing Lian and Pei Liu and Jiahui Tan and Luoda Tan and Chaoyi Li and Quan Zou and Chen Lin and Xiangxiang Zeng},
howpublished = {arXiv},
year = {2026},
month = oct,
doi = {10.48550/arxiv.2610.21943},
eprint = {2610.21943},
archivePrefix = {arXiv},
url = {https://arxiv.org/abs/2610.21943},
}
RIS
TY - UNPB
TI - Panoramic modelling of single-cell transcriptomes at scale with CellNet
AU - Xiaoqing Lian
AU - Pei Liu
AU - Jiahui Tan
AU - Luoda Tan
AU - Chaoyi Li
AU - Quan Zou
AU - Chen Lin
AU - Xiangxiang Zeng
PY - 2026
DA - 2026/10/15
PB - arXiv
DO - 10.48550/arxiv.2610.21943
UR - https://arxiv.org/abs/2610.21943
AB - 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.
ER -