Abstract
Single-cell sequencing has revolutionized our understanding of cellular heterogeneity and responses to environmental stimuli. However, mapping transcriptomic changes across diverse cell types in response to various stimuli and elucidating underlying disease mechanisms remains challenging. Studies involving physical stimuli, such as radiotherapy, or chemical stimuli, like drug testing, demand labor-intensive experimentation, often hindering the rapid advancement of mechanistic insight and drug discovery. To address this, we present Squidiff, a diffusion model-based generative framework designed to predict transcriptomic changes across diverse cell types in response to a wide range of environmental changes. We demonstrate Squidiff’s robustness across various scenarios, including cell differentiation, gene perturbation, and drug response prediction. Through continuous denoising and semantic feature integration, Squidiff effectively learns transient cell states and predicts high-resolution transcriptomic landscapes over time and conditions. Furthermore, we applied Squidiff to model the development of blood vessel organoids and cellular responses to neutron irradiation and growth factors. Our results demonstrate that Squidiff enables in silico screening of cell molecular landscapes, facilitating rapid hypothesis generation and providing valuable insights for precision medicine.
Competing Interest Statement
The authors have declared no competing interest.
