Steering cell-state and phenotype transitions by causal disentanglement learning

doi:10.1101/2024.08.16.607277

	Steering cell-state and phenotype transitions by causal disentanglement learning
	Zhang, Chengming 1; Chen, Zexi 2; Miao, Yuanxiang2,3 ; Xue, Yun 2; Cai, Deyu2,3 ; Guo, Weifeng 4,5,6; Ji, Hongbin 2; Aihara, Kazuyuki 1; Chen, Luonan2,3,7,8
	2024-08-17
状态	已发表
摘要	Understanding and manipulating cell-state and phenotype transitions is essential for advancing biological research and therapeutic treatment. We introduce CauFinder, an advanced framework designed to accurately identify causal regulators of these transitions and further precisely steer such transitions by integrating causal disentanglement modelling with network control based solely on observed data. By leveraging do-calculus and optimizing information flow metrics, CauFinder can distinguish causal factors from spurious ones, ensuring precise control over desired state transitions. One significant advantage of CauFinder is its ability to identify those variables causally affecting the cell-state/phenotype transitions among all observed variables, both theoretically and computationally, leading to the identification of their master regulators when combined with network control. Consequently, by employing a counterfactual algorithm, CauFinder is able effectively to facilitate desirable state transitions or steer these transitional trajectories/paths by modulating these causal drivers. Beyond its theoretical advantages, CauFinder outperforms existing approaches computationally in both simulated and real-world settings. CauFinder is able to not only reveal natural biological transition processes such as (a) cell differentiation, (b) lung adenocarcinoma (LUAD) to lung squamous cell carcinoma (LUSC) transdifferentiation and (c) drug-sensitive to drug-resistant transitions but also identify the causal regulators of their reverse transition processes, such as (A) cell dedifferentiation, (B) LUSC to LUAD transdifferentiation and (C) drug-resistant to drug-sensitive transitions. These findings highlight its superior ability to causally uncover essential regulatory mechanisms and accurately steer cell-state/phenotype transitions, thus providing novel therapeutic strategies.
关键词	Causal disentanglement Information flow State transition System control Interpretive framework Deep learning
语种	英语
DOI	10.1101/2024.08.16.607277
相关网址	查看原文
出处	bioRxiv
收录类别	PPRN.PPRN
WOS记录号	PPRN:91413326
WOS类目	Computer Science, Interdisciplinary Applications
文献类型	预印本
条目标识符	https://kms.shanghaitech.edu.cn/handle/2MSLDSTB/414210
专题	生命科学与技术学院生命科学与技术学院_特聘教授组_陈洛南组生命科学与技术学院_博士生
通讯作者	Aihara, Kazuyuki; Chen, Luonan
作者单位	1.Univ Tokyo, Inst Adv Study, Int Res Ctr Neurointelligence, Tokyo 1130033, Japan 2.Chinese Acad Sci, Shanghai Inst Biochem & Cell Biol, Ctr Excellence Mol Cell Sci, Key Lab Syst Biol, Shanghai 200031, Peoples R China 3.ShanghaiTech Univ, Sch Life Sci & Technol, Shanghai 201210, Peoples R China 4.Zhengzhou Univ, Sch Elect & Informat Engn, Zhengzhou 450001, Peoples R China 5.State Key Lab Intelligent Agr Power Equipment, Luoyang, Peoples R China 6.State Key Lab Oncol South China, Guangzhou, Peoples R China 7.Univ Chinese Acad Sci, Chinese Acad Sci, Hangzhou Inst Adv Study, Sch Life Sci,Key Lab Syst Hlth Sci Zhejiang Prov, Hangzhou 310024, Peoples R China 8.Guangdong Inst Intelligence Sci & Technol, Zhuhai 519031, Guangdong, Peoples R China
推荐引用方式 GB/T 7714	Zhang, Chengming,Chen, Zexi,Miao, Yuanxiang,et al. Steering cell-state and phenotype transitions by causal disentanglement learning. 2024.