[2603.25240] Lingshu-Cell: A generative cellular world model for transcriptome modeling toward virtual cells

Quantitative Biology > Quantitative Methods arXiv:2603.25240 (q-bio) [Submitted on 26 Mar 2026] Title:Lingshu-Cell: A generative cellular world model for transcriptome modeling toward virtual cells Authors:Han Zhang, Guo-Hua Yuan, Chaohao Yuan, Tingyang Xu, Tian Bian, Hong Cheng, Wenbing Huang, Deli Zhao, Yu Rong View a PDF of the paper titled Lingshu-Cell: A generative cellular world model for transcriptome modeling toward virtual cells, by Han Zhang and 8 other authors View PDF HTML (experimental) Abstract:Modeling cellular states and predicting their responses to perturbations are central challenges in computational biology and the development of virtual cells. Existing foundation models for single-cell transcriptomics provide powerful static representations, but they do not explicitly model the distribution of cellular states for generative simulation. Here, we introduce Lingshu-Cell, a masked discrete diffusion model that learns transcriptomic state distributions and supports conditional simulation under perturbation. By operating directly in a discrete token space that is compatible with the sparse, non-sequential nature of single-cell transcriptomic data, Lingshu-Cell captures complex transcriptome-wide expression dependencies across approximately 18,000 genes without relying on prior gene selection, such as filtering by high variability or ranking by expression level. Across diverse tissues and species, Lingshu-Cell accurately reproduces transcriptomic distribution...