Biology subjects
Publications and source records attributed to Trzupek, D..
Withdrawal statementThe authors have withdrawn their manuscript owing to failure to repeat some of the findings. Therefore, the authors do not wish this work to be cited as reference for the project. If you have any questions, please contact the corresponding author.
Type 1 diabetes (T1D) is a common autoimmune disorder characterized by the destruction of insulin-secreting pancreatic {beta} cells [1], in which polymorphism of the human leukocyte antigen (HLA) class II region is the major genetic risk factor [2, 3, 4]. However, how variation in class II molecules alters T1D risk remains a longstanding question. Here we show how T1D risk due to HLA class II haplotype combinations [5] correlates with the frequency of negatively charged sequences in the CDR3{beta} region of CD4+ T cell receptor (TCR) repertoires purified from peripheral blood. These sequences are known to be common in receptors that bind insulin B:9-23 [6], the primary autoantigen in T1D. We also show the same effect in circulating activated CD4+ T cells from newly-diagnosed T1D cases, and in islet-infiltrating T cells from patients with active T1D. Furthermore, we demonstrate that the proportion of insulin-reactive CD4+ T cells present in islets is predicted by the frequency of these negatively charged CDR3{beta} amino acid sequences. Our results suggest diagnostic uses of T cell repertoire profiling in early detection of insulin autoimmunity, and inform ongoing efforts to improve tolerance induction to insulin and prevention of T1D [7].
The transcriptomic and proteomic characterisation of CD4+ T cells at the single-cell level has been performed traditionally by two largely exclusive types of technologies: single cell RNA-sequencing (scRNA-seq) technologies and antibody-based cytometry. Here we demonstrate that the simultaneous targeted quantification of mRNA and protein expression in single-cells provides a high-resolution map of human primary CD4+ T cells, and identified precise trajectories of Th1, Th17 and regulatory T-cell (Treg) differentiation in blood and tissue. Furthermore, the sensitivity provided by this massively-parallel multi-omics approach revealed novel insight into the mechanism of expression of CD80 and CD86 on the surface of activated CD4+ Tregs and demonstrate their potential to identify recently activated T cells in circulation. This transcriptomic and proteomic hybrid technology provides a cost-effective solution to dissect the heterogeneity of immune cell populations, including more precise and detailed descriptions of the differentiation and activation of circulating and tissue-resident cells in response to therapies and in stratification of patients.