Panoramic modelling of single-cell transcriptomes at scale with CellNet
TL;DR
CellNet is presented, which enables panoramic modelling of single-cell transcriptomes at scale by integrating DNA-derived gene identities and measured RNA expression into a shared image representation and paves the way for AI-enabled cell-biology discovery at the whole-transcriptome scale.
Abstract
Comprehensively characterizing cellular landscapes to elucidate cell diversity is a central goal in biology. Recent advances in single-cell profiling have created unprecedented opportunities for this endeavor by capturing diverse transcriptomes with remarkable breadth. However, current foundation models generally struggle to leverage such holistic and valuable information to represent a cell. Here we present CellNet, which enables panoramic modelling of single-cell transcriptomes at scale by integrating DNA-derived gene identities and measured RNA expression into a shared image representation. Pretrained on 231 million image-based cell profiles across 40 species and covering over one million genes, CellNet improves the recognition of rare and condition-associated cellular states, enables the study of genes beyond experimentally measured panels, and uncovers associations between poorly characterized non-coding RNAs and known biological programmes. CellNet paves the way for AI-enabled cell-biology discovery at the whole-transcriptome scale. The CellNet website is available at https://www.cellnet.top.