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Diagnosis, based primarily on histopathological examination of duodenal biopsies, is confounded by poor concordance between pathologists and minimal histological abnormality if insufficient gluten is consumed. CeD pathogenesis involves both CD4<jats:sup>+<\/jats:sup> T\u2010cell\u2010mediated gluten recognition and CD8<jats:sup>+<\/jats:sup> and \u03b3\u03b4 T\u2010cell\u2010mediated inflammation, with a previous study demonstrating a permanent change in \u03b3\u03b4 T\u2010cell populations in CeD. We leveraged this understanding and explored the diagnostic utility of bulk T\u2010cell receptor (TCR) sequencing in assessing duodenal biopsies in CeD. Genomic DNA extracted from duodenal biopsies underwent sequencing for TCR\u2010\u03b4 (TRD) (CeD, <jats:italic>n<\/jats:italic>\u2009=\u200911; non\u2010CeD, <jats:italic>n<\/jats:italic>\u2009=\u200911) and TCR\u2010\u03b3 (TRG) (CeD, <jats:italic>n<\/jats:italic>\u2009=\u200933; non\u2010CeD, <jats:italic>n<\/jats:italic>\u2009=\u200921). We developed a novel machine learning\u2010based analysis of the TCR repertoire, clustering samples by diagnosis. Leave\u2010one\u2010out cross\u2010validation (LOOCV) was performed to validate the classification algorithm. Using TRD repertoire, 100% (22\/22) of duodenal biopsies were correctly classified, with a LOOCV accuracy of 91%. Using TCR\u2010\u03b3 (TRG) repertoire, 94.4% (51\/54) of duodenal biopsies were correctly classified, with LOOCV of 87%. Duodenal biopsy TRG repertoire analysis permitted accurate classification of biopsies from patients with CeD following a strict gluten\u2010free diet for at least 6 months, who would be misclassified by current tests. This result reflects permanent changes to the duodenal \u03b3\u03b4 TCR repertoire in CeD, even in the absence of gluten consumption. Our method could complement or replace histopathological diagnosis in CeD and might have particular clinical utility in the diagnostic testing of patients unable to tolerate dietary gluten, and for assessing duodenal biopsies with equivocal features. This approach is generalisable to any TCR\/BCR locus and any sequencing platform, with potential to predict diagnosis or prognosis in conditions mediated or modulated by the adaptive immune response. \u00a9 2020 The Authors. <jats:italic>The Journal of Pathology<\/jats:italic> published by John Wiley &amp; Sons, Ltd. on behalf of The Pathological Society of Great Britain and Ireland.<\/jats:p>","DOI":"10.1002\/path.5592","type":"journal-article","created":{"date-parts":[[2020,11,23]],"date-time":"2020-11-23T00:09:53Z","timestamp":1606090193000},"page":"279-291","update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":18,"title":["Classification of intestinal <scp>T<\/scp>\u2010cell receptor repertoires using machine learning methods can identify patients with coeliac disease regardless of dietary gluten status"],"prefix":"10.1002","volume":"253","author":[{"given":"Andrew 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