Back

Heterogeneity of CD8αα intraepithelial lymphocytes is transcriptionally conserved between TCRαβ and TCRγδ cell lineages.

Hioki, K. A.; Liang, X.; Lynch, A. C.; Ranjan, R.; Pobezinskaya, E. L.; Pobezinsky, L. A.

2025-05-25 immunology
10.1101/2025.05.20.655135 bioRxiv
Show abstract

Intestinal intraepithelial lymphocytes (IELs) are a versatile population of immune cells with both effector and regulatory roles in gut immunity. Although this functional diversity is thought to arise from distinct IEL subpopulations, the heterogeneity of TCR{beta}+ and TCR{gamma}{delta}+ IELs have not been well-characterized. Using scRNAseq, we identified CD8+ T cell subsets with memory-like (Tcf7) and effector-like (Prdm1) profiles in both TCR{beta}+ and TCR{gamma}{delta}+ IELs. Using CD160 and CD122 as markers of memory-like and effector-like cells, respectively, we found that while effector-like cells dominated the small intestine, memory-like IELs were more prevalent in the large intestine, suggesting a functional specialization of immune responses along the gut. Further transcriptional analysis revealed shared profiles between TCR{beta}+ and TCR{gamma}{delta}+ small intestinal IEL subsets, suggesting conserved functional roles across these populations. Finally, our analysis indicated that TCR{beta}+ memory-like IELs arise from Tcf7 double-negative (DN) precursors, and that effector-like IELs subsequently differentiate from the memory-like population. In contrast, TCR{gamma}{delta}+ IELs appear to originate from two distinct precursor populations, one expressing Tcf7 and the other Zeb2, indicating the presence of parallel developmental pathways within this lineage. Overall, our findings reveal that both TCR{beta}+ and TCR{gamma}{delta}+ cells contain memory-like and effector-like subsets, which may contribute to the functional heterogeneity of IELs.

Matching journals

The top 5 journals account for 50% of the predicted probability mass.

50% of probability mass above

"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.