Atlas-Guided Discovery of Transcription Factors for T Cell Programming.

H Kay Chung, Cong Liu, Anamika Battu, Alexander N Jambor, Brandon M Pratt, Fucong Xie, Brian P Riesenberg, Eduardo Casillas, Ming Sun, Elisa Landoni, Yanpei Li, Qidang Ye, Daniel Joo, Jarred Green, Zaid Syed, Nolan J Brown, Mattew Smith, Shixin Ma, Brent Chick, Victoria Tripple, Shirong Tan, Z Audrey Wang, Jun Wang, Bryan Mcdonald, Peixiang He, Qiyuan Yang, Timothy Chen, Siva Karthik Varanasi, Michael LaPorte, Thomas H Mann, Dan Chen, Filipe Hoffmann, Josephine Ho, Jennifer Modliszewski, April Williams, Yusha Liu, Zhen Wang, Jieyuan Liu, Yiming Gao, Zhiting Hu, Ukrae H Cho, Longwei Liu, Yingxiao Wang, Diana C Hargreaves, Gianpietro Dotti, Barbara Savoldo, Jessica E Thaxton, J Justin Milner, Wei Wang, Susan M Kaech
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Abstract

CD8+ T cells differentiate into diverse states that shape immune outcomes in cancer and chronic infection. To systematically define the transcription factors (TFs) driving these states, we built a comprehensive atlas integrating transcriptional and epigenetic data across nine CD8+ T cell states and inferred TF activity profiles. Our analysis catalogued TF activity fingerprints, uncovering regulatory mechanisms governing selective cell state differentiation. Leveraging this platform, we focused on two transcriptionally similar but functionally opposing states critical in tumor and viral contexts: terminally exhausted T cells (TEXterm), which are dysfunctional, and tissue-resident memory T cells (TRM), which are protective. Global TF community analysis revealed distinct biological pathways and TF-driven networks underlying protective versus dysfunctional states. Through in vivo CRISPR screening integrated with single-cell RNA sequencing (in vivo Perturb-seq), we delineated that TFs selectively govern TEXterm. We identified HIC1 and GFI1 as shared regulators of TEXterm and TRM differentiation and KLF6 as a unique regulator of TRM. Importantly, we discovered novel TEXterm single-state TFs, including ZSCAN20 and JDP2 with no prior known function in T cells. Targeted deletion of these TFs enhanced tumor control and synergized with immune checkpoint blockade. Consistently, their depletion in human T cells reduces the expression of inhibitory receptors and improves effector function. By decoupling exhaustion-selective from protective TRM programs, our platform enables more precise engineering of T cell states, advancing rational design of effective immunotherapies.

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多组学图谱辅助发现转录因子,实现特定细胞状态编程。
同一类型的细胞可以呈现出不同的状态,具有不同的功能。有效的细胞疗法可以通过特异性驱动理想的细胞状态来实现,这需要阐明关键转录因子(TFs)。在这里,我们在系统水平上整合了表观基因组和转录组数据,以无偏见的方式确定了定义不同 CD8 + T 细胞状态的 TF。这些TF图谱可用于细胞状态编程,以最大限度地发挥T细胞的治疗潜力。例如,可以对 T 细胞进行编程,以避免终末衰竭状态(Tex Term),这是一种功能失调的 T 细胞状态,通常出现在肿瘤或慢性感染中。然而,Tex Term 与有益的组织驻留记忆 T 状态(T RM)在位置和转录特征方面表现出高度的相似性。我们的生物信息学分析预测,新型 TF Zscan20 在 Tex Term 中具有独特的活性。同样,敲除 Zscan20 会阻碍 Tex Term 在体内的分化,但不会影响 T RM 的分化。此外,扰乱 Zscan20 会使 T 细胞进入一种类似效应器的状态,这种状态会带来卓越的肿瘤和病毒控制能力,并与免疫检查点疗法产生协同作用。我们还发现 Jdp2 和 Nfil3 是强大的 Tex Term 驱动因子。一句话总结:多组学图谱能够系统鉴定细胞状态转录因子,用于治疗性细胞状态编程。
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