Biology subjects

Schattgen, S.

Publications and source records attributed to Schattgen, S..

4 recordsLinked to original sources

bioRxiv2025

Diverse modes of T cell receptor sequence convergence define unique functional and cellular phenotypes

Single-cell techniques allow concurrent study of gene activity and T cell receptor (TCR) sequences, identifying connections between TCR structure and cell traits. Expanding on our CoNGA software, we present a "metaCoNGA" analysis of 6 million T cells from 91 diverse studies, mapping TCR sequence similarity across tissues and diseases. This approach exposes shared TCR features within specific T cell subsets, including those associated with infection, cancer, and autoimmunity. We introduce a method to identify T cell groups with similar gene expression and biased TCR amino acid composition, providing a systematic framework for classifying diverse unconventional T cells, including KIR+ CD8+ T cells, CD4+ regulatory T cells, and subsets of NKT and MAIT cells. A new TCR clustering approach identifies thousands of convergent TCR sequence clusters hypothesized to target shared antigens. These clusters show coherent gene expression, highlighting the role of antigen exposure in shaping T cell behavior. Finally, we provide a tool for users to merge new data with this resource and rapidly identify T cell features in their data sets. This resource empowers investigations into the complex relationship between TCR sequence and T cell function in human health.

immunology↗

bioRxiv2023

Spatiotemporal development of the human T follicular helper cell response to Influenza vaccination

We profiled blood and draining lymph node (LN) samples from human volunteers after influenza vaccination over two years to define evolution in the T follicular helper cell (TFH) response. We show LN TFH cells expanded in a clonal-manner during the first two weeks after vaccination and persisted within the LN for up to six months. LN and circulating TFH (cTFH) clonotypes overlapped but had distinct kinetics. LN TFH cell phenotypes were heterogeneous and mutable, first differentiating into pre-TFH during the month after vaccination before maturing into GC and IL-10+ TFH cells. TFH expansion, upregulation of glucose metabolism, and redifferentiation into GC TFH cells occurred with faster kinetics after re-vaccination in the second year. We identified several influenza-specific TFH clonal lineages, including multiple responses targeting internal influenza proteins, and show each TFH state is attainable within a lineage. This study demonstrates that human TFH cells form a durable and dynamic multi-tissue network.

immunology↗

bioRxiv2020

TCR meta-clonotypes for biomarker discovery with tcrdist3: quantification of public, HLA-restricted TCR biomarkers of SARS-CoV-2 infection

As the mechanistic basis of adaptive cellular antigen recognition, T cell receptors (TCRs) encode clinically valuable information that reflects prior antigen exposure and potential future response. However, despite advances in deep repertoire sequencing, enormous TCR diversity complicates the use of TCR clonotypes as clinical biomarkers. We propose a new framework that leverages antigen-enriched repertoires to form meta-clonotypes - groups of biochemically similar TCRs - that can be used to robustly identify and quantify functionally similar TCRs in bulk repertoires. We apply the framework to TCR data from COVID-19 patients, generating 1831 public TCR meta-clonotypes from the 17 SARS-CoV-2 antigen-enriched repertoires with the strongest evidence of HLA-restriction. Applied to independent cohorts, meta-clonotypes targeting these specific epitopes were more frequently detected in bulk repertoires compared to exact amino acid matches, and 59.7% (1093/1831) were more abundant among COVID-19 patients that expressed the putative restricting HLA allele (FDR < 0.01), demonstrating the potential utility of meta-clonotypes as antigen-specific features for biomarker development. To enable further applications, we developed an open-source software package, tcrdist3, that implements this framework and facilitates flexible workflows for distance-based TCR repertoire analysis.

immunology↗

bioRxiv2020

Linking T cell receptor sequence to transcriptional profiles with clonotype neighbor graph analysis (CoNGA)

Multi-modal single-cell technologies capable of simultaneously assaying gene expression and surface phenotype across large numbers of immune cells have described extensive heterogeneity within these complex populations, in healthy and diseased states. In the case of T cells, these technologies have made it possible to profile clonotype, defined by T cell receptor (TCR) sequence, and phenotype, as reflected in gene expression (GEX) profile, surface protein expression, and peptide:MHC (pMHC) binding, across large and diverse cell populations. These rich, high-dimensional datasets have the potential to reveal new relationships between TCR sequence and T cell phenotype that go beyond identification of features shared by clonally related cells. In order to uncover these connections in an unbiased way, we developed a graph-theoretic approach---clonotype neighbor-graph analysis or "CoNGA"---that identifies correlations between GEX profile and TCR sequence through statistical analysis of a pair of T cell similarity graphs, one in which cells are linked based on gene expression similarity and another in which cells are linked by similarity of TCR sequence. Applying CoNGA across diverse human and mouse T cell datasets uncovered known and novel associations between TCR sequence features and cellular phenotype including the classical invariant T cell subsets; a novel defined population of human blood CD8+ T cells expressing the transcription factors HOBIT and HELIOS, NK-associated receptors, and a biased TCR repertoire, representing a potential previously undescribed lineage of "natural lymphocytes"; a striking association between usage of a specific V-beta gene segment and expression of the EPHB6 gene that is conserved between mouse and human; and TCR sequence determinants of differentiation in developing thymocytes. As the size and scale of single-cell datasets continue to grow, we expect that CoNGA will prove to be a useful tool for deconvolving complex relationships between TCR sequence and cellular state in single-cell applications.

immunology↗