bioRxivGenomicsPreprint
Transcription factor collaboration enables precise T cell state engineering
Rachel E. SavageiD, Christian D. McRoberts AmadoriD, Conrad HockiD, Ruochi ZhangiD, Hung‐Che KuoiD, Aretha Gao, Max A. HorlbeckiD, Charles A. GersbachiD, Jason Daniel BuenrostroiD
Abstract
Transcription factors (TFs) collaborate to regulate gene expression programs that define cell fate. In CD8+ T cells, this coordinated regulation underlies exhaustion, a dysfunctional state that constrains immunity in chronic infection and cancer. Here, we screen for cell state-specific TFs by performing pooled overexpression screens of 3,548 TF and TF isoforms in primary T cells across multiple CD8+ T cell states. We identify 82 regulators that collaborate with exhaustion-specific programs and profile their effects using perturb-SHARE-seq, connecting perturbations to changes in chromatin accessibility and gene expression across 702,314 single cells. We identify 38 reproducible regulatory programs and construct a map of 12,616 TF-program connections that shape CD8+ T cell states, nominating KLF2 as predictive of positive response to CAR-T therapy. Using seq2PRINT, a deep learning framework that predicts functional TF interactions, we identify RUNX as a "master collaborator", a TF that broadly collaborates with other factors, and uncover a RUNX2:KLF2 interaction that specifies exhaustion-associated programs. Mutation of the RUNX2:KLF2 protein interface attenuates KLF2-mediated repression of exhaustion, while synthetic tethering of RUNX2 to KLF2 leads to an amplification of the phenotype. More broadly, we identify the collaborative action of RUNX as a driver in CD8+ T cell states, and show that tethering TFs enables the rational engineering of cell state identity for cell and gene therapies.
- organism: human
- assay: scRNA-seq
- research focus: gene regulatory network inference
- approach: deep learning
- Citations
- 3
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