Abstract:Gluten-specific CD4+ T cells are drivers of celiac disease (CeD). Previous studies of gluten-specific T-cell receptor (TCR) repertoires have found public TCRs shared across multiple individuals, biased usage of particular V-genes and conserved CDR3 motifs. The CDR3 motifs within the gluten-specific TCR repertoire, however, have not been systematically investigated. In the current study, we analyzed the largest TCR database of gluten-specific CD4+ T cells studied so far consisting of TCRs of 3122 clonotypes fro… Show more
“…In the future, our work could be extended to consider family-wise or false discovery error rates, as has been done for RNA sequencing [ 31 , 35 ]. Such an extension would allow for the quantification of detection power to entire sets of TCRs, which is especially relevant when considering ‘public’ TCR sets that are shared across many individuals [ 6 , 16 , 41 ]. Finally, our read count model could also be used to generate synthetic TCR sequencing repertoires.…”
T-cell receptor (TCR) sequencing has enabled the development of innovative diagnostic tests for cancers, autoimmune diseases and other applications. However, the rarity of many T-cell clonotypes presents a detection challenge, which may lead to misdiagnosis if diagnostically relevant TCRs remain undetected. To address this issue, we developed TCRpower, a novel computational pipeline for quantifying the statistical detection power of TCR sequencing methods. TCRpower calculates the probability of detecting a TCR sequence as a function of several key parameters: in-vivo TCR frequency, T-cell sample count, read sequencing depth and read cutoff. To calibrate TCRpower, we selected unique TCRs of 45 T-cell clones (TCCs) as spike-in TCRs. We sequenced the spike-in TCRs from TCCs, together with TCRs from peripheral blood, using a 5′ RACE protocol. The 45 spike-in TCRs covered a wide range of sample frequencies, ranging from 5 per 100 to 1 per 1 million. The resulting spike-in TCR read counts and ground truth frequencies allowed us to calibrate TCRpower. In our TCR sequencing data, we observed a consistent linear relationship between sample and sequencing read frequencies. We were also able to reliably detect spike-in TCRs with frequencies as low as one per million. By implementing an optimized read cutoff, we eliminated most of the falsely detected sequences in our data (TCR α-chain 99.0% and TCR β-chain 92.4%), thereby improving diagnostic specificity. TCRpower is publicly available and can be used to optimize future TCR sequencing experiments, and thereby enable reliable detection of disease-relevant TCRs for diagnostic applications.
“…In the future, our work could be extended to consider family-wise or false discovery error rates, as has been done for RNA sequencing [ 31 , 35 ]. Such an extension would allow for the quantification of detection power to entire sets of TCRs, which is especially relevant when considering ‘public’ TCR sets that are shared across many individuals [ 6 , 16 , 41 ]. Finally, our read count model could also be used to generate synthetic TCR sequencing repertoires.…”
T-cell receptor (TCR) sequencing has enabled the development of innovative diagnostic tests for cancers, autoimmune diseases and other applications. However, the rarity of many T-cell clonotypes presents a detection challenge, which may lead to misdiagnosis if diagnostically relevant TCRs remain undetected. To address this issue, we developed TCRpower, a novel computational pipeline for quantifying the statistical detection power of TCR sequencing methods. TCRpower calculates the probability of detecting a TCR sequence as a function of several key parameters: in-vivo TCR frequency, T-cell sample count, read sequencing depth and read cutoff. To calibrate TCRpower, we selected unique TCRs of 45 T-cell clones (TCCs) as spike-in TCRs. We sequenced the spike-in TCRs from TCCs, together with TCRs from peripheral blood, using a 5′ RACE protocol. The 45 spike-in TCRs covered a wide range of sample frequencies, ranging from 5 per 100 to 1 per 1 million. The resulting spike-in TCR read counts and ground truth frequencies allowed us to calibrate TCRpower. In our TCR sequencing data, we observed a consistent linear relationship between sample and sequencing read frequencies. We were also able to reliably detect spike-in TCRs with frequencies as low as one per million. By implementing an optimized read cutoff, we eliminated most of the falsely detected sequences in our data (TCR α-chain 99.0% and TCR β-chain 92.4%), thereby improving diagnostic specificity. TCRpower is publicly available and can be used to optimize future TCR sequencing experiments, and thereby enable reliable detection of disease-relevant TCRs for diagnostic applications.
“…On the other hand, the current knowledge about immunosenescence and TCR repertoire dynamics in autoimmune and T-cell mediated diseases is still quite fragmented. In RA ( 15 , 36 – 39 ), CD ( 49 – 53 ) and T1D ( 59 – 61 ) patients, different compartments (blood, synovia, gut, pancreatic draining lymph nodes) share TCRβ clones and show reduced TCR repertoire diversity. Furthermore, immunosenescence seems to be accelerated in RA patients ( 15 , 42 – 44 ), and T1D severity is aggravated in older patients ( 58 ); however, most of these studies are not centered on an age-related perspective, stressing for further insights.…”
Section: Discussionmentioning
confidence: 99%
“…HTS on single-cell gut IELs TCRγδ showed that CD patients, either under GFD or not, have a biased TRDV pattern compared to healthy controls, a private CDR3δ repertoire, whereas CDR3γ clones are shared between CD and controls ( 52 ). A recent investigation leveraging a large TCRαβ dataset of data from 63 CD patients ( 53 ) identified a number of 325 public TCRαβ clones in gluten-specific CD4+ T cells across patient; furthermore, they observed a biased V-gene usage and conserved CDR3α:CDR3β motifs across CD repertoires. Taken together, these findings indicate that TCR repertoire shares common features across CD patients that may be linked to disease pathogenesis and progression; however, the association between TCR repertoire dynamics, T-cell senescence and aging in CD is still not deeply investigated.…”
Section: Senescent T-cell Receptor Repertoire In Autoimmune Disorders: Progresses and Perspectivesmentioning
T-cell receptor (TCR) repertoire diversity is a determining factor for the immune system capability in fighting infections and preventing autoimmunity. During life, the TCR repertoire diversity progressively declines as a physiological aging progress. The investigation of TCR repertoire dynamics over life represents a powerful tool unraveling the impact of immunosenescence in health and disease. Multiple Sclerosis (MS) is a demyelinating, inflammatory, T-cell mediated autoimmune disease of the Central Nervous System in which age is crucial: it is the most widespread neurological disease among young adults and, furthermore, patients age may impact on MS progression and treatments outcome. Crossing knowledge on the TCR repertoire dynamics over MS patients’ life is fundamental to investigate disease mechanisms, and the advent of high- throughput sequencing (HTS) has significantly increased our knowledge on the topic. Here we report an overview of current literature about the impact of immunosenescence and age-related TCR dynamics variation in autoimmunity, including MS.
“…Two of these 12 shared sequences were found in the collection of public, gluten-specific TCR sequences. Six of the 12 shared TCR sequences were represented among the generated O, [25] ; 13 AV41_AVEGGSNYKLT_AJ53 O, [25] ; 10 BV7-2_ASSIRATDTQY_BJ2-3 R1 O, [25] ; 9 BV7-3_ASSIRSTDTQY_BJ2-3 NR1 O, [25] ; 14 BV20-1_SASRTSGRAGDEQF_BJ2-1 O, [26] The grey filling represents patients that express the indicated public TCR sequences. Some sequences were expressed by T cell clones (TCCs) that were tested for gluten reactivity.…”
The pathogenic immune response in celiac disease (CeD) is orchestrated by phenotypically distinct CD4 + T cells that recognize gluten epitopes in the context of disease-associated HLA-DQ allotypes. Cells with the same distinct phenotype, but with elusive specificities, are increased across multiple autoimmune conditions. Here, whether sorting of T cells based on their distinct phenotype (Tphe cells) yields gluten-reactive cells in CeD is tested. The method′s efficiency is benchmarked by parallel isolation of gluten-reactive T cells (Ttet cells), using HLA-DQ:gluten peptide tetramers. From gut biopsies of 12 untreated HLA-DQ2.5 + CeD patients, Ttet + /Tphe + , Ttet − /Tphe + , and Ttet − /Tphe − cells are sorted for single-cell T-cell receptor (TCR)-sequencing (n = 8) and T-cell clone (TCC)-generation (n = 5). The generated TCCs are TCR sequenced and tested for their reactivity against deamidated gluten. Gluten-reactivity is observed in 91.2% of Ttet + /Tphe + TCCs, 65.3% of Ttet − /Tphe + TCCs and 0% of Ttet − /Tphe − TCCs. TCR sequencing reveals clonal expansion and sequence sharing across patients, features reflecting antigen-driven responses. The feasibility to isolate antigen-specific CD4 + T cells by the sole use of phenotypic markers in CeD outlines a potential avenue for characterizing disease-driving CD4 + T cells in autoimmune conditions.
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