Identifying tissue egressed TCF1-enriched stem-like CD4+ T Cell populations using a time-stamp Kaede system 2498

Abstract Description While tissue-resident memory T cells are well-characterized, the dynamics and transcriptional profiles of T cells egressing from inflamed tissues remain poorly understood. Here, we employed a photoconvertible Kaede system and adoptive transfer to track the spatiotemporal migration of CD4+ T helper 1 (Th1) cells during skin inflammation. OVA-specific Kaede+ OTII Th1 cells in the inflamed ear skin were photoconverted from Kaede-green to Kaede-red to timestamp tissue-localized T cells and follow their subsequent egress from the tissue to the draining lymph node (LN). Bulk-RNA sequencing identified significant transcriptional heterogeneity between tissue-retained and tissue-egressed populations. Notably, tissue-egressed Kaede-red Th1s had the highest TCF1 expression, a stem-cell marker for CD8 T cells. In addition, other stem-related genes including Cd27, Id3, Lef1, Slamf6, Tcf7, Sell were more highly expressed by Th1 cells that had exited the inflamed tissue. We carried out GSVA (gene set variation analysis) of TCF7-related gene sets, and the results showed that tissue-egressed Th1 cells had the highest stem-like signature score. Using tissue-clearing tools, these cells were found distributed throughout the LN within 24h of tissue exit. These findings suggest the existence of a stem-like CD4+ T cell population that preferentially exits inflamed tissues to LN, potentially contributing to long-term immune response in chronic inflammation, autoimmune disease, and cancer. Funding Sources NIH/NIAID R37 AI072690 Topic Categories Cellular Adhesion, Migration, and Inflammation (CAM)

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Publication Details

Journal
The Journal of Immunology
Published
2025-11-01
DOI
https://doi.org/10.1093/jimmun/vkaf283.408
Primary Topic
Single-cell and spatial transcriptomics
Type
article
Field-Weighted Citation Impact
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article

Identifying tissue egressed TCF1-enriched stem-like CD4+ T Cell populations using a time-stamp Kaede system 2498

Scott A. Leddon, Evan Moss Carter, Yang Xiang, Francesca Byun
The Journal of Immunology
Single-cell and spatial transcriptomics
article

Identifying tissue egressed TCF1-enriched stem-like CD4+ T Cell populations using a time-stamp Kaede system 2498

Scott A. Leddon, Evan Moss Carter, Yang Xiang, Francesca Byun
article en

Abstract

Abstract Description While tissue-resident memory T cells are well-characterized, the dynamics and transcriptional profiles of T cells egressing from inflamed tissues remain poorly understood. Here, we employed a photoconvertible Kaede system and adoptive transfer to track the spatiotemporal migration of CD4+ T helper 1 (Th1) cells during skin inflammation. OVA-specific Kaede+ OTII Th1 cells in the inflamed ear skin were photoconverted from Kaede-green to Kaede-red to timestamp tissue-localized T cells and follow their subsequent egress from the tissue to the draining lymph node (LN). Bulk-RNA sequencing identified significant transcriptional heterogeneity between tissue-retained and tissue-egressed populations. Notably, tissue-egressed Kaede-red Th1s had the highest TCF1 expression, a stem-cell marker for CD8 T cells. In addition, other stem-related genes including Cd27, Id3, Lef1, Slamf6, Tcf7, Sell were more highly expressed by Th1 cells that had exited the inflamed tissue. We carried out GSVA (gene set variation analysis) of TCF7-related gene sets, and the results showed that tissue-egressed Th1 cells had the highest stem-like signature score. Using tissue-clearing tools, these cells were found distributed throughout the LN within 24h of tissue exit. These findings suggest the existence of a stem-like CD4+ T cell population that preferentially exits inflamed tissues to LN, potentially contributing to long-term immune response in chronic inflammation, autoimmune disease, and cancer. Funding Sources NIH/NIAID R37 AI072690 Topic Categories Cellular Adhesion, Migration, and Inflammation (CAM)

The Journal of ImmunologyVol. 214(Supplement_1)
Cornell University (US), New York State College of Veterinary Medicine (US)
Openalex Percentile: Top 68%
Single-cell and spatial transcriptomics
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