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Lingshu-Cell, a masked discrete diffusion model, learns transcriptomic state distributions directly from sparse single-cell data, avoiding pre-selection of genes. This allows for generative simulation and prediction of cellular responses to perturbations by jointly embedding cell type/donor identity with perturbation conditions. The model demonstrates strong performance in reproducing transcriptomic distributions, marker gene expression, and predicting perturbation responses, outperforming existing methods on benchmarks like the Virtual Cell Challenge.
Forget hand-picked genes – Lingshu-Cell models the entire transcriptome to predict cellular responses to perturbations, opening the door to in silico biological discovery.
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 distributions, marker-gene expression patterns and cell-subtype proportions, demonstrating its ability to capture complex cellular heterogeneity. Moreover, by jointly embedding cell type or donor identity with perturbation, Lingshu-Cell can predict whole-transcriptome expression changes for novel combinations of identity and perturbation. It achieves leading performance on the Virtual Cell Challenge H1 genetic perturbation benchmark and in predicting cytokine-induced responses in human PBMCs. Together, these results establish Lingshu-Cell as a flexible cellular world model for in silico simulation of cell states and perturbation responses, laying the foundation for a new paradigm in biological discovery and perturbation screening.
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