The thymus contains a multitude of epithelial cell types that work in concert to educate immature T cells called thymocytes to distinguish self from non-self. A subset of thymic epithelial cells (TECs) express lineage defining transcription factors and differentiate into cell types -- such as muscle, neuroendocrine, and tuft cells -- that are typically found in peripheral tissues. However, the function and organization of these differentiated TECs especially in the human thymus are poorly understood. Specifically, much remains unknown about the gene regulation that enable their function, and their structural morphology and spatial localization in the thymus to ultimately orchestrate thymocyte education.
To resolve the diversity of TECs at a single cell resolution, we simultaneously profiled the gene expression and chromatin accessibility of human thymus samples. We integrated our data with all available thymic single cell datasets to establish concordant cell annotations across studies. We leveraged the CELLxGENE database to identify unique gene signatures in the differentiated thymic TECs compared to their peripheral counterparts in 31 tissues. Informed by the gene expression of maturing thymic muscle cells, we used quantitative microscopy to evaluate their cellular morphology and spatial localization in the thymus.
We show that differentiated TECs share substantial celltype restricted genes, yet take on a celltype specific identity, a phenomenon not shared by their peripheral counterparts. Narrowing in on the maturation of thymic muscle cells, we show that they have distinct cellular morphologies and thymic spatial localization as they differentiate.
Differentiated thymic celltypes, while sharing similarity to their peripheral counterparts, take on a thymus specific identity. Furthermore, thymic muscle cells have distinct cell morphology and micro-environmental niche as they mature.
NIH
Computational and Systems Immunology (COMP)
Rishvanth K. Prabakar, Sarah R. Chapin, Yong Lin et al.· Journal of Immunology· 0 citations
T cell receptor (TCR) repertoire diversity enables the orchestration of antigen-specific immune responses against the vast space of possible pathogenic peptides. Identifying TCR/antigen specificity from the large TCR repertoire and antigen space is crucial for biomedical research.
We introduce copepodTCR, an open-access tool for the design and interpretation of high-throughput experimental assays to determine TCR/antigen specificity. copepodTCR implements a combinatorial peptide pooling scheme for efficient experimental testing of T cell responses against large overlapping peptide libraries, useful for “deorphaning” TCRs of unknown specificity. The scheme detects experimental errors and, coupled with a hierarchical Bayesian model for unbiased results interpretation, identifies the response-eliciting peptide for a TCR of interest out of hundreds of peptides tested using a simple experimental set-up.
Using in silico simulation, we demonstrated the applicability of our design scheme and the sensitivity of our results evaluation across varied experimental layouts and range of TCR-peptide activation signals. We experimentally validated our approach on a library of 253 overlapping peptides covering the SARS-CoV-2 spike protein, split across 12 pools. A single stimulation with combinatorial pools identified the correct epitope of two TCRs with known specificity and then deorphanized two SARS-CoV-2 associated TCRs shared among a large cohort of COVID-19 patients.
In conclusion, copepodTCR enables efficient and accurate mapping of TCR—peptide specificities through optimized combinatorial peptide pooling coupled with Bayesian inference. Beyond deorphanizing TCRs from established cell lines, we anticipate that copepodTCR can facilitate primary T cells deorphanization using single-sequencing as a read out, due to optimization of the pooling scheme, rational assignment of peptides and robust error-correction.
Simons Center for Quantitative Biology at Cold Spring Harbor Laboratory; Starr Centennial Scholarship; US National Institutes of Health Grants U01AI150747, R01AI136514, S10OD028632-01, 1R01AI167862; Simons Pivot Fellowship; National Natural Science Foundation of China Grant 62331002, National Science Foundation grant PHY-2210452.
Computational and Systems Immunology (COMP)
V. Kovaleva, David J Pattinson, Guanchen He et al.· Journal of Immunology· 0 citations
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