CellPolaris: Transfer Learning for Gene Regulatory Network Construction to Guide Cell State Transitions

doi:10.1002/advs.202508697

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	CellPolaris: Transfer Learning for Gene Regulatory Network Construction to Guide Cell State Transitions
	Feng, Guihai 1,2,3; Qin, Xin 4,5; Zhang, Jiahao 5,6; Huang, Wuliang 4,5; Zhang, Yiyang 6,9; Cui, Wentao 5,7; Chen, Yao 8; Li, Shirui 5,6; Liu, Wenhao 1,5; Tian, Yao 1,5; Liu, Yana 1,2,3; Dong, Jingxi 1,2,3; Xu, Ping 5,7; Man, Zhenpeng 5,6; Liu, Guole 5,10; Liang, Zhongming 5,6; Jiang, Xinlong 4,5; Yang, Xiaodong 4,5; Wang, Pengfei 5,7; Yang, Ge 5,10; Wang, Hongmei 1,2,3,5; Wang, Xuezhi 5,7; Tong, Ming-Han 5,8; Zhou, Yuanchun 5,7; Zhang, Shihua 5,6; Chen, Yiqiang 4,5; Wang, Yong 5,6; Li, Xin 1,2,3,5
	2026-01-07
发表期刊	ADVANCED SCIENCE
页码	18
摘要	Cell fate decisions are orchestrated by intricate gene regulatory networks (GRNs), which govern gene expression with precise spatiotemporal control. However, accurately capturing context-specific nature of gene regulation remains challenging, particularly when integrating multi-omics data at bulk and single-cell level across diverse cellular contexts.Here, we present CellPolaris, a unified computational framework designed to decode the roles of transcription factors (TFs) in developmental processes. CellPolaris performs TF-centered GRN construction, master TF identification, and TF perturbation simulation. By leveraging transfer learning, the framework generates tissue-specific or cell-type-specific GRNs using pre-constructed high-confidence GRNs of diverse contexts and requires only transcriptomic data as input. Using these learned GRNs, CellPolaris identifies underlying master TFs critical for cell fate transitions and simulates the effects of TF perturbations on developmental processes. Benchmarking tests demonstrate the robust performance of CellPolaris in GRN construction. The efficacy of CellPolaris is supported by the significant overlap between predicted top-ranked master regulators and known TF combinations experimentally validated in cell fate conversion experiments. Furthermore, CellPolaris accurately simulates the developmental consequences of Rfx2 knockout during round spermatid differentiation. In summary, we present CellPolaris, a comprehensive framework that enables GRN construction through transfer learning, identification of key TFs driving cell fate transitions, and simulation of TF perturbations. This tool allows us to further elucidate the regulatory mechanisms underlying developmental processes and cell state transitions.
关键词	cell fate gene regulatory network perturbation simulation transfer learning
DOI	10.1002/advs.202508697
收录类别	SCI
语种	英语
WOS研究方向	Chemistry ; Science & Technology - Other Topics ; Materials Science
WOS类目	Chemistry, Multidisciplinary ; Nanoscience & Nanotechnology ; Materials Science, Multidisciplinary
WOS记录号	WOS:001655495400001
出版者	WILEY
引用统计
文献类型	期刊论文
条目标识符	http://119.78.100.204/handle/2XEOYT63/42916
专题	中国科学院计算技术研究所
通讯作者	Feng, Guihai; Tong, Ming-Han; Zhou, Yuanchun; Zhang, Shihua; Chen, Yiqiang; Wang, Yong; Li, Xin
作者单位	1.Chinese Acad Sci, Inst Zool, State Key Lab Organ Regenerat & Reconstruct, Beijing, Peoples R China 2.Chinese Acad Sci, Inst Stem Cell & Regenerat Med, Beijing, Peoples R China 3.Beijing Inst Stem Cell & Regenerat Med, Beijing, Peoples R China 4.Chinese Acad Sci, Inst Comp Technol, Beijing, Peoples R China 5.Univ Chinese Acad Sci, Beijing, Peoples R China 6.Chinese Acad Sci, Acad Math & Syst Sci, State Key Lab Math Sci, Beijing, Peoples R China 7.Chinese Acad Sci, Comp Network Informat Ctr, Beijing, Peoples R China 8.Univ Chinese Acad Sci, Shanghai Inst Biochem & Cell Biol, Chinese Acad Sci,Shanghai Key Lab Mol Androl, Ctr Excellence Mol Cell Sci,State Key Lab Mol Biol, Shanghai, Peoples R China 9.Yunnan Univ, Sch Software, Kunming, Peoples R China 10.Chinese Acad Sci, Inst Automat, State Key Lab Multimodal Artificial Intelligence S, Beijing, Peoples R China
推荐引用方式 GB/T 7714	Feng, Guihai,Qin, Xin,Zhang, Jiahao,et al. CellPolaris: Transfer Learning for Gene Regulatory Network Construction to Guide Cell State Transitions[J]. ADVANCED SCIENCE,2026:18.
APA	Feng, Guihai.,Qin, Xin.,Zhang, Jiahao.,Huang, Wuliang.,Zhang, Yiyang.,...&Li, Xin.(2026).CellPolaris: Transfer Learning for Gene Regulatory Network Construction to Guide Cell State Transitions.ADVANCED SCIENCE,18.
MLA	Feng, Guihai,et al."CellPolaris: Transfer Learning for Gene Regulatory Network Construction to Guide Cell State Transitions".ADVANCED SCIENCE (2026):18.