1/13/2024 0 Comments Tcr repertoire imgt![]() Recent studies have used next-generation TCR sequencing to capture the diversity of TCR repertoires. Extrapolations of early sequencing results on a few hundred T cell receptors (TCRs) led to an estimate of approximately 10 6 different TCR β chains in human blood, each pairing, on average, with at least 25 different α chains ( 1). IntroductionĪ functional TCR repertoire requires great diversity to recognize a wide range of pathogens. Our results implicate positive selection for promiscuous TCRβ sequences that probably evade negative selection, given their low affinity for self-ligands, in the abundance of “public” human TCRβ sequences. Although previous studies suggested a role for recombination bias in producing “public” sequences in mice, our study is the first to our knowledge to demonstrate a role for thymic selection. Our data collectively implicate preferential positive selection for shared human CDR3βs that are highly cross-reactive. Single-cell TCR sequencing showed increased sharing of CDR3αs compared with CDR3βs between mice. Shared sequences were enriched for allo–cross-reactive CDR3βs and for type 1 diabetes–associated autoreactive CDR3βs. Sharing was similar between autologous and allogeneic thymi and occurred between different cell subsets. Whereas hydrophobicity analysis implicated self-peptides in positive selection of the overall repertoire, positive selection favored shorter shared sequences that had reduced hydrophobicity at positions 6 and 7 of CDR3βs, suggesting weaker interactions with self-peptides than were observed with unshared sequences, possibly allowing escape from negative selection. We observed repertoire narrowing and increased CDR3β sharing during thymocyte selection. Replicate humanized mice generated diverse and highly divergent repertoires. Tutorial is available here Ĭoming in the next releases: CDR3 amino acid physical and chemical properties assessment, mutation networks.We studied human T cell repertoire formation using high-throughput T cell receptor β (TCRβ) complementarity-determining region 3 (CDR3) sequencing in immunodeficient mice receiving human hematopoietic stem cells (HSCs) and human thymus grafts. K-mer distribution measures and statistics. Tracking of clonotypes across time points, widely used in vaccination and cancer immunology domains. Tutorial is available here ĭiversity evaluation (ecological diversity index, Gini index, inverse Simpson index, rarefaction analysis). Gene usage estimation (correlation, Jensen-Shannon Divergence, clustering). Repertoire overlap analysis (common indices including overlap coefficient, Jaccard index and Morisita’s overlap index). Most methods are incorporated in a couple of main functions with clear naming-no more remembering dozens and dozens of functions with obscure names. ![]() Works on any data source you are comfortable with: R data frames, data tables from data.table, databases like MonetDB, Apache Spark data frames via sparklyr īeginner-friendly. Supports all popular TCR and BCR analysis and post-analysis formats, including single-cell data: ImmunoSEQ, IMGT, MiTCR, MiXCR, MiGEC, MigMap, VDJtools, tcR, AIRR, 10XGenomics, ArcherDX. The package automatically detects the format of your files-no more guessing what format is that file, just pass them to the package Fast and easy manipulation of immune repertoire data: ![]()
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