bioRxivImmunology
Single-cell transcriptomics of allo-reactive CD4+ T cells over time reveals divergent fates during gut GVHD
J. A. Engel, H. J. Lee, C. G. Williams, R. Kuns, S. Olver, L. I. M. Lansink, M. S. F. Soon, S. B. Andersen, J. E. Powell, V. Svensson, S. A. Teichmann, G. R. Hill, A. Varelias, M. Koyama, A. Haque
Abstract
Acute gastrointestinal Graft-versus-Host-Disease (GVHD) is a primary determinant of mortality after allogeneic hematopoietic stem-cell transplantation (alloSCT). It is mediated by alloreactive donor CD4+ T cells that differentiate into pathogenic subsets expressing IFN{gamma}, IL-17A or GM-CSF, and is regulated by subsets expressing IL-10 and/or Foxp3. Developmental relationships between T-helper states during priming in mesenteric lymph nodes (mLN) and effector function in the GI tract remain undefined at genome-scale. We used scRNA-seq and computational modelling to create an atlas of putative differentiation pathways during GVHD. Computational trajectory inference suggested emergence of pathogenic and regulatory states along a single developmental trajectory in mLN. Importantly, we identified an unexpected second trajectory, categorised by little proliferation or cytokine expression, reduced glycolysis, and high TCF1 expression. TCF1hi cells upregulated 4{beta}7 prior to gut migration and failed to express cytokines therein. Nevertheless, they demonstrated recall potential and plasticity following secondary transplantation, including cytokine or Foxp3 expression, but reduced TCF1. Thus, scRNA-seq revealed divergence of allo-reactive CD4+ T cells into quiescent and effector states during gut GVHD, reflecting putative heterogenous priming in vivo. These findings, the first at a single-cell level during GVHD over time, can now be used to interrogate T cell differentiation in patients undergoing alloSCT.
- organism: human
- assay: scRNA-seq
- research focus: cell type annotation
- research focus: trajectory inference
- research focus: differential expression
- approach: deep learning
- approach: statistical model
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