Measuring anti-islet autoimmunity in mouse and human by profiling peripheral blood antigen specific CD4 T cells [TCR-Seq]
The endocrine pancreas is one of the most inaccessible organs of the human body. Its autoimmune attack leads to type 1 diabetes (T1D) in a genetically susceptible population and a lifelong need for exogenous insulin replacement. Monitoring disease progression by sampling peripheral blood would provide key insights into T1D immune-mediated mechanisms and potentially change preclinical diagnosis and the evaluation of therapeutic interventions. This effort has been limited to the measurement of circulating anti-islet antibodies, which despite a recognized diagnostic value, remain poorly predictive at the individual level for a fundamentally CD4 T cell-dependent disease. Here, peptide-major histocompatibility complex tetramers were used to profile blood anti-insulin CD4 T cells in mice and human. While percentages of these were not directly informative, the state of activation of anti-insulin T cells measured by RNA and protein profiling was able to distinguish normalcy versus disease progression. Activated anti-insulin CD4 T cell were detected at time of diagnosis but also in patients with established disease and in some at-risk individuals. These results support the concept that antigen specific CD4 T cells might be used to monitor autoimmunity in real time. This advance can inform our approach to T1D diagnosis and therapeutic interventions in the pre-clinical phase of anti-islet autoimmunity. Overall design: Human CD4+ T cells, isolated from blood of donors spanning four cohorts, were stained with either DQ8-INS1220, DQ8-INS1321, DQ8-CP59-70, or DQ8-CP65-75 ; all tetramers were labelled with the fluorscent moiety Phycoerythrin. Cells stained with either tetramer were single cell sorted into single wells of 96 well plates. Single cell cDNA libraries were made according to the Fluidigm BioMark "Two Step Single Cell Gene Expression Using EvaGreen" protocol. Then gene expression of 96 genes was assessed utilizing the StandardBiotools (formerly called Fluidigm) Dynamic Array 96x96 IFC and the "GE Fast 96x96 PCR+Melt v2.pcl" cycling conditions. Data was pre-processed via the SINGuLAR Analysis Toolset and then futhered analyzed using custom R scripts that incorporated UMAP, clustering via k.means and differential gene expression analysis. The protocol for single cell sorting, pre-amplification and analysis via the StandardBiotools Dynamic Array can be found using PMID 29224077 (Holt et al).