Back

Single cell resolution analysis of multi-tissue derived human iNKT cells reveals novel transcriptional paradigms

Jayasinghe, R. G.; Hollingsworth, D.; Boonchalermvichian, C.; Gupta, B.; Yan, H.; Baker, J.; Dejene, B.; Weinberg, K. I.; Negrin, R. S.; Mavers, M.

2024-03-27 immunology
10.1101/2024.03.22.583992 bioRxiv
Show abstract

Invariant natural killer T (iNKT) cells are evolutionarily conserved innate lymphocytes important for host defense against pathogens. Further, they are increasingly recognized to play a role in tumor immune surveillance and in protection against graft versus host disease, and they are of particular importance as a universal donor for cellular therapies. Therefore, a thorough understanding of the biology of iNKT cells is critical. Murine studies have revealed the existence of transcriptionally and functionally distinct subsets, similar to T helper cell subsets. However, a comprehensive study of human iNKT cell heterogeneity is lacking. Herein, we define the transcriptomic heterogeneity of human iNKT cells derived from multiple immunologically relevant tissues, including peripheral blood, cord blood, bone marrow, and thymus, using single cell RNA-sequencing. We describe human iNKT cells with a naive/precursor transcriptional pattern, a Th2-like signature, and Th1/17/NK-like gene expression. This combined Th1/17 pattern of gene expression differs from previously described murine iNKT subsets in which Th1- and Th17- like iNKT cells are distinct populations. We also describe transcription factors regulating human iNKT cells with distinct gene expression patterns not previously described in mice. Further, we demonstrate a novel T effector memory RA+ (TEMRA)-like pattern of expression in some human iNKT cells. Additionally, we provide an in-depth transcriptional analysis of human CD8+ iNKT cells, revealing cells with two distinct expression patterns--one consistent with naive/precursor cells and one consistent with Th1/17/NK-like cells. Collectively, our data provide critical insights into the transcriptional heterogeneity of human iNKT cells, providing a platform to facilitate future functional studies and to inform the development of iNKT-based cellular therapies.

Matching journals

The top 8 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.