He Siyu, Zhu Yuefei, Tavakol Daniel Naveed, Ye Haotian, Lao Yeh-Hsing, Zhu Zixian, Xu Cong, Chauhan Sharadha, Garty Guy, Tomer Raju, Vunjak-Novakovic Gordana, Zou James, Azizi Elham, Leong Kam W
Department of Biomedical Engineering, Columbia University, NY.
Irving Institute for Cancer Dynamics, Columbia University, NY.
bioRxiv. 2025 Aug 26:2024.11.16.623974. doi: 10.1101/2024.11.16.623974.
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, hindering mechanistic insight and drug discovery. Here we present Squidiff, a diffusion model-based generative framework that predicts transcriptomic changes across diverse cell types in response to environmental changes. We demonstrate Squidiff's robustness across cell differentiation, gene perturbation, and drug response prediction. Through continuous denoising and semantic feature integration, Squidiff learns transient cell states and predicts high-resolution transcriptomic landscapes over time and conditions. Furthermore, we applied Squidiff to model blood vessel organoid development and cellular responses to neutron irradiation and growth factors. Our results demonstrate that Squidiff enables screening of molecular landscapes, facilitating rapid hypothesis generation and providing valuable insights for precision medicine.
单细胞测序彻底改变了我们对细胞异质性以及细胞对环境刺激反应的理解。然而，描绘不同细胞类型在各种刺激下的转录组变化并阐明潜在的疾病机制仍然具有挑战性。涉及物理刺激（如放射治疗）或化学刺激（如药物测试）的研究需要大量的实验工作，这阻碍了对机制的深入了解和药物发现。在此，我们展示了Squidiff，这是一个基于扩散模型的生成框架，可预测不同细胞类型在环境变化时的转录组变化。我们证明了Squidiff在细胞分化、基因扰动和药物反应预测方面的稳健性。通过持续去噪和语义特征整合，Squidiff学习瞬时细胞状态，并随时间和条件预测高分辨率的转录组图谱。此外，我们将Squidiff应用于模拟血管类器官发育以及细胞对中子辐射和生长因子的反应。我们的结果表明，Squidiff能够筛选分子图谱，促进快速提出假设，并为精准医学提供有价值的见解。
