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

Han Zhang; Guo-Hua Yuan; Chaohao Yuan; Tingyang Xu; Tian Bian; Hong Cheng; Wenbing Huang; Deli Zhao

q-bio.QMMar 26, 2026

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,

Organizations: DAMO Academy, Alibaba Group

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 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.

Figures & tables

Figure 1: Overview of the Lingshu-Cell framework. a , Lingshu-Cell employs a masked discrete diffusion model to learn and generate single-cell transcriptomic data. In the forward process, gene expression values are progressively masked (from $t=0$ to $t=T$ ); in the reverse process, the model iteratively predicts masked values to generate realistic scRNA-seq expression profiles. b , Comparison of generative paradigms. Unlike autoregressive (AR) models that rely on a fixed sequential order and denoising diffusion probabilistic models (DDPMs) that corrupt all positions with continuous noise, Lingshu-Cell randomly masks and predicts gene expression values in an order-independent manner, which is inherently compatible with the orderless structure of gene expression data. c , Application scenarios of Lingshu-Cell, including unconditional generation across diverse human tissues and species, and conditional generation for genetic perturbation and cytokine perturbation response prediction.

Figure 2: Unconditional generation of cell states across diverse species and tissues by Lingshu-Cell. a , UMAP visualization of real and generated cells (10,000 each, randomly sampled) from the PARSE-PBMC dataset, colored by cell type annotation (left) and normalized expression (log1p) of canonical marker genes for each cell type. b , Comparison of cell type proportions between real and generated data. c , Quantitative benchmark comparing Lingshu-Cell, scDiffusion and scVI across five metrics (Pearson correlation, Spearman correlation, MMD, 1-WD and iLISI) on the PARSE-PBMC dataset. d , Unconditional generation results across human tissues, including neocortex, heart, lung and colon, with UMAP plots showing real (top) and generated (bottom) cells colored by cell type. e , Unconditional generation results across multiple species, including mouse, rhesus macaque, zebrafish and fly.

	Tissue	Pearson ( $\uparrow$ )	Spearman ( $\uparrow$ )	MMD ( $\downarrow$ )	iLISI ( $\uparrow$ )	1-WD ( $\downarrow$ )
Human tissues (CZ CELLxGENE)
	Neocortex	0.9995	0.9991	0.0128	0.9053	0.0105
	Heart	0.9992	0.9987	0.0196	0.8972	0.0096
	Lung	0.9967	0.9970	0.0314	0.8906	0.0159
	Colon	0.9966	0.9960	0.0376	0.8815	0.0152
Non-human species - Tissues

Figure 3: Unconditional generation performance of Lingshu-Cell across human tissues and non-human species.

Figure 4: Accurate prediction of single-cell transcriptomic responses to genetic perturbations in cell lines by Lingshu-Cell. a , Schematic of CRISPR-based genetic perturbation and the resulting transcriptomic changes. b , Conditional generation framework for perturbation prediction. Cell type and perturbation target are provided as conditioning inputs, and a masked diffusion model iteratively predicts gene expression values to generate perturbation-specific expression profiles. c , Three design components of Lingshu-Cell: classifier-free guidance (CFG), sequence compression, and biological prior injection (see Methods). d , Ablation study of CFG guidance weight. Bar plots show prediction performance across eight metrics (DES, PDS, MAE, Spearman #DEG, Spearman LFC, AUPRC, Pearson- $\Delta$ , and average score) on the H1 test set ( $n=100$ perturbation targets). e , Ablation study of sequence compression, comparing uncompressed input with patch sizes of 8 and 32. f , Ablation study of biological prior injection, comparing prediction performance with and without prior injection. In d–f , metrics highlighted in red denote improved performance relative to the corresponding baseline in each ablation setting.

Team	Avg Rank $\downarrow$	DES $\uparrow$	PDS $\uparrow$	MAE $\downarrow$	Sp. #DEG $\uparrow$	Sp. LFC $\uparrow$	AUPRC $\uparrow$	Pearson- $\Delta$ $\uparrow$
Lingshu-Cell	8.7	0.216	0.748	0.052	0.394	0.331	0.272	0.306
cleopatra	9.1	0.228	0.747	0.086	0.473	0.396	0.266	0.203
xBio	10.7	0.305	0.811	0.770	0.564	0.087	0.252	0.217
Cellock Holmes	10.9	0.356	0.679	0.239	0	0.238	0.576	0.125
Shippers	11.0	0.354	0.699	0.231	0	0.227	0.576	0.123
Mean Predictors	11.4	0.305	0.741	6.723	0.294	0.213	0.582	0.217

Figure 5: Genetic perturbation prediction on the VCC leaderboard ( Roohani et al., 2025 ) . Teams are ordered by final ranking. Avg Rank: average rank across the top 25 teams. Best per column in bold. See Table B1 for the full top-25 ranking.

Figure 6: Accurate prediction of single-cell transcriptomic responses to cytokine perturbations in PBMCs by Lingshu-Cell. a , Schematic of cytokine-induced transcriptomic perturbation. b , Conditional generation framework for cytokine perturbation prediction. Donor identity and cytokine condition are provided as conditioning inputs, and a masked diffusion process iteratively predicts gene expression values to generate perturbation-specific expression profiles. c , Prediction performance of Lingshu-Cell, PertMean, STATE, scGPT, and scVI on the PARSE 10M PBMC dataset, evaluated across eight metrics (as defined in Fig. 3d). Bar plots show performance on a test set comprising 4 of 12 donors, with 70% of cytokine conditions (63 of 90) held out for each donor. Bars indicate mean performance across donors; error bars, interquartile range (Q1–Q3); points, individual donor values. Metrics for which Lingshu-Cell achieves the best performance among all methods are highlighted in red.

Appendix figures & tables9 assets

Supplementary material from the paper’s appendix.

Appendix

		DES $\uparrow$		PDS $\uparrow$		MAE $\downarrow$		Sp. #DEG $\uparrow$		Sp. LFC $\uparrow$		AUPRC $\uparrow$		Pear.- $\Delta$ $\uparrow$
Team	Avg Rank $\downarrow$	val	rk	val	rk	val	rk	val	rk	val	rk	val	rk	val	rk
Lingshu-Cell	8.7	0.216	25	0.748	9	0.052	1	0.394	12	0.331	3	0.272	10	0.306	1
cleopatra	9.1	0.228	24	0.747	10	0.086	3	0.473	9	0.396	1	0.266	11	0.203	6
xBio	10.7	0.305	13	0.811	6	0.770	13	0.564	4	0.087	20	0.252	16	0.217	3
Cellock Holmes	10.9	0.356	3	0.679	26	0.239	7	0.000	19	0.238	6	0.576	4	0.125	11
Shippers	11.0	0.354	6	0.699	22	0.231	6	0.000	19	0.227	8	0.576	4	0.123	12

Appendix

Table B1 : Full VCC generalist track leaderboard. Bold values indicate the best performance for each metric. Lingshu-Cell was evaluated post-hoc and is separated by a horizontal rule. For each metric, “val” denotes the metric value and “rk” denotes the per-metric rank across all 26 entries. Abbreviated metric names: Sp. #DEG = Spearman #DEG; Sp. LFC = Spearman LFC; Pear.-

\Delta

= Pearson-

\Delta

. Metric definitions are given in Section B . Original leaderboard data are available at https://virtualcellchallenge.org/leaderboard .

Figure C1 : Model architecture of Lingshu-Cell. a , Overall model network architecture. Input tokens are first embedded and then passed through a compression module that reduces the gene token sequence length. The compressed sequence is processed by a stack of 13 Transformer blocks, each consisting of RMSNorm, bidirectional self-attention with rotary position embeddings (RoPE), and a SwiGLU feed-forward network, with residual connections around each sub-layer. A final RMSNorm is applied before the decompression module restores the original gene-level resolution, and a linear head produces per-token logits over the expression vocabulary. b , Compression module. Gene token embeddings are reordered with a fixed random permutation

\pi

, partitioned into consecutive groups, and linearly projected via

W_{\text{down}}

to produce a shorter sequence of compressed tokens. Condition prefix and tail tokens bypass this module. c , Decompression module. The inverse of compression: compressed tokens are linearly projected via

W_{\text{up}}

, ungrouped, and reordered with the inverse permutation

\pi^{-1}

to recover gene-level representations.

Hyperparameter	Value
Embedding dimension ( $D$ )	640
Transformer blocks ( $L$ )	13
Attention heads ( $n_{\mathrm{h}}$ )	10
Head dimension ( $d_{\mathrm{h}}$ )	64
Feed-forward dimension ( $d_{\mathrm{ff}}$ )	2,560
Position encoding	RoPE

Appendix

Table C2 : Architectural hyperparameters of Lingshu-Cell.

	Unconditional		Conditional
Setting	PARSE	Per-tissue	Genetic perturbation	Cytokine perturbation
Dataset	PARSE PBS	CZ CELLxGENE scBaseCount	VCC H1 + auxiliary	PARSE 10M
GPUs	$8$	$2$ – $4$	$16$	$8$
Peak LR	$1\times 10^{-4}$	$2\times 10^{-4}$	$2\times 10^{-4}$	$2\times 10^{-4}$
Warmup steps	100	100	1,000	1,000

Appendix

Table C3 : Training configurations for different experimental settings.

Task	Dataset	#Perturbed cells	#Original control cells	#Sub-sampled control cells
Genetic perturbation	H1	221,273	38,176	20,344
Genetic perturbation	External	323,913	583,377	35,990
Cytokine perturbation	PARSE 10M	6,499,077	629,701	629,701

Appendix

Table C4 : Training data composition for conditional generation tasks.

Figure C2 : Scaling unconditional generation to larger cell populations. a , UMAP visualization of real and generated cells from the PARSE 10M PBMC dataset, colored by cell type annotation (left) and normalized expression (log1p) of canonical marker genes. b , Comparison of major cell type proportions between real and generated data. c , UMAP visualization at higher resolution, showing that generated cells accurately recapitulate cell subtype structure of real data. d , Comparison of cell subtype proportions between real and generated data, demonstrating robust consistency at higher resolution.

Figure C3 : Quality control of human unconditional generation datasets. a , Summary of datasets used for unconditional generation, including data source, species, tissue type and cell number. b , c , Distribution of the number of detected genes ( b ) and total counts ( c ) across tissues. d , Density scatter plots of mitochondrial gene percentage versus total counts for each tissue. e , Density scatter plots of the number of detected genes versus total counts for each tissue.

Figure C4 : Unconditional generation results for additional human tissues. UMAP visualization of real (top) and generated (bottom) cells for thymus, kidney, breast and liver, colored by cell type annotation.

Figure C5 : Quality control of non-human species unconditional generation datasets. a , Summary of datasets used for unconditional generation across non-human species, including data source, species, tissue type and cell number. b , c , Distribution of the number of detected genes ( b ) and total counts ( c ) across species. d , Density scatter plots of mitochondrial gene percentage versus total counts for each species. The rhesus macaque lung dataset is not shown owing to the absence of annotated mitochondrial genes. e , Density scatter plots of the number of detected genes versus total counts for each species.

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VCLMU: Mechanism-Centric Virtual Cell World Modeling for Perturbation Response

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Predicting cellular responses to genetic perturbations is a central capability for virtual cells and a key step toward computational modeling of biological interventions. Most existing models directly map an unperturbed molecular profile and perturba- tion to the resulting observation without explicitly representing the latent cellular transition induced by the intervention. We introduce a mechanism-centric virtual cell world model that represents cellular state as a set of Latent Mechanism Units (LMUs) and treats genetic perturbations as actions on these latent states. Each LMU combines a reusable identity grounded in multimodal biological evidence with an observation-specific state, allowing a perturbation to induce mechanism- specific stochastic transitions before decoding the resulting transcriptional response. We train VCLMU through two-stage pretraining, first on around 200K pseudo-bulk perturbation profiles and then on gene-aligned single-cell perturbation data. Across six perturbation-disjoint benchmarks, VCLMU consistently improves perturbation- specific response recovery over strong baselines while maintaining competitive global response accuracy. We further analyze learned LMUs through enrichment between perturbation responses and LMU gene sets and show that they capture structured biological response programs. These results support mechanism-level latent state transition as a useful formulation for virtual cell models that aim to predict and interpret cellular responses to biological interventions.

Abstract

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