Single-Cell RNA Sequencing Reveals Expanded Clones of Islet Antigen-Reactive CD4+ T Cells in Peripheral Blood of Subjects with Type 1 Diabetes

Abstract

The significance of islet antigen-reactive T cells found in peripheral blood of type 1 diabetes (T1D) subjects is unclear, partly because similar cells are also found in healthy control (HC) subjects. We hypothesized that key disease-associated cells would show evidence of prior antigen exposure, inferred from expanded T cell receptor (TCR) clonotypes, and essential phenotypic properties in their transcriptomes. To test this, we developed single-cell RNA sequencing (RNA-seq) procedures for identifying TCR clonotypes and transcript phenotypes in individual T cells. We applied these procedures to analysis of islet- antigen reactive CD4+ memory T cells from the blood of T1D and HC individuals following activation with pooled immunodominant islet peptides. We found extensive TCR clonotype sharing in antigen-activated cells, especially from individual T1D subjects, consistent with in vivo T cell expansion during disease progression. The expanded clonotype from one T1D subject was detected at repeat visits spanning more than 15 months, demonstrating clonotype stability. Notably, we found no clonotype sharing between subjects, indicating a predominance of “private” TCR specificities. Expanded clones from two T1D subjects recognized distinct IGRP peptides, implicating this molecule as a trigger for CD4+ T cell expansion. While overall transcript profiles of cells from HC and T1D subjects were similar, profiles from the most expanded clones were distinctive. Our findings demonstrate that islet- antigen reactive CD4+ memory T cells with unique antigen specificities and phenotypes are expanded during disease progression and can be detected by single-cell analysis of peripheral blood.

Benchmarking principal component analysis for large-scale single-cell RNA-sequencing

“…PCA is widely used for data visualization [39][40][41], data QC [42], feature selection [13,[43][44][45][46][47][48][49], de-noising [50,51], imputation [52][53][54], confirmation and removal of batch effects [55][56][57], confirmation and estimation of cell-cycle effects [58], rare cell type detection [59,60], cell type and cell state similarity search [61], pseudotime inference [13,[62][63][64][65][66], and spatial reconstruction [9].…”

Benchmarking principal component analysis for large-scale single-cell RNA-sequencing

“…PCA is widely used for data visualization [39][40][41], data QC [42], feature selection [13,[43][44][45][46][47][48][49], de-noising [50,51], imputation [52][53][54], confirmation and removal of batch effects [55][56][57], confirmation and estimation of cell-cycle effects [58], rare cell type detection [59,60], cell type and cell state similarity search [61], pseudotime inference [13,[62][63][64][65][66], and spatial reconstruction [9].…”

“…As is the case with the eigenvectors, even for loading vectors, downsampling, orthiter/gd/sgd (OnlinePCA.jl), and PCA (dask-ml [115]) become inaccurate as the dimensionality of the PC increases. Because the genes with large absolute values for loading vectors are used as feature values in some studies [43][44][45][46][47][48], inaccurate PCA implementations may lower the accuracy of such an approach.…”

Type 1 Diabetes: Disease Stratification

“…Changes in islet antibody titers do not directly correlate with active disease [26] . Subject-specific, expanded clones of islet antigen-reactive CD4+ memory T cells can be detected in the peripheral blood of individuals with T1D [106] , and may prove to be an approach applicable to detecting active presymptomatic T1D in an individual. Circulating CXCR5+PD-1+ICOS+-activated circulating follicular T helper cells increase in the periphery in stage 2 T1D that has advanced close to Stage 3 T1D and may prove to be a useful biomarker of active disease [107] .…”