Dynamic regulatory networks of T cell trajectory dissect transcriptional control of T cell state transition

• Main Authors: Min Yan (Author), Jing Hu (Author), Huating Yuan (Author), Liwen Xu (Author), Gaoming Liao (Author), Zedong Jiang (Author), Jiali Zhu (Author), Bo Pang (Author), Yanyan Ping (Author), Yunpeng Zhang (Author), Yun Xiao (Author), Xia Li (Author)
• Format: Book
• Published: Elsevier, 2021-12-01T00:00:00Z.
• Subjects: article
• Online Access: Connect to this object online.
• Notes: 2162-2531; 10.1016/j.omtn.2021.10.011
• Keywords: T cell dysfunction; state transition trajectory; dynamic regulatory network; pseudo-times; critical regulators; Therapeutics. Pharmacology; RM1-950
• Source: Molecular Therapy: Nucleic Acids, Vol 26, Iss , Pp 1115-1129 (2021)

T cells exhibit heterogeneous functional states, which correlate with responsiveness to immune checkpoint blockade and prognosis of tumor patients. However, the molecular regulatory mechanisms underlying the dynamic process of T cell state transition remain largely unknown. Based on single-cell transcriptome data of T cells in non-small cell lung cancer, we combined cell states and pseudo-times to propose a pipeline to construct dynamic regulatory networks for dissecting the process of T cell dysfunction. Candidate regulators at different stages were revealed in the process of tumor-infiltrating T cell dysfunction. Through comparing dynamic networks across the T cell state transition, we revealed frequent regulatory interaction rewiring and further refined critical regulators mediating each state transition. Several known regulators were identified, including TCF7, EOMES, ID2, and TOX. Notably, one of the critical regulators, TSC22D3, was frequently identified in the state transitions from the intermediate state to the pre-dysfunction and dysfunction state, exerting diverse roles in each state transition by regulatory interaction rewiring. Moreover, higher expression of TSC22D3 was associated with the clinical outcome of tumor patients. Our study embedded transcription factors (TFs) within the temporal dynamic networks, providing a comprehensive view of dynamic regulatory mechanisms controlling the process of T cell state transition.