Single-cell RNA-seq analysis of longitudinal CD4+ T cell samples reveals cell-type-specific changes during early stages of type 1 diabetes
Bibliographic record
Abstract
BACKGROUND: T cells play a pivotal role in the autoimmune destruction of beta cells in type 1 diabetes. However, our understanding of the disease has been limited by lack of a comprehensive single-cell transcriptome analysis of T cells during its early stages. METHODS: T cell samples collected at an early age of 3-24 months from children who subsequently developed type 1 diabetes (N = 11) and their matched controls (N = 11). The samples analysed here were at or before the age of seroconversion, i.e., appearance of beta cell specific autoantibodies. These samples were obtained from the Trial to Reduce Insulin Dependent Diabetes Mellitus (IDDM) in Genetically at Risk (TRIGR) study (ClincalTrials.gov ID: NCT00179777). RESULTS: By phenotypically characterizing over 99,000 cells, we identified cell-type-specific gene expression patterns associated with disease progression. While the cell-type compositions were similar, several genes were differentially regulated in cases in different cell types. Besides pathways altered in cases in specific cell types, interferon related pathways and pathways related to viral response were altered in multiple cell types in cases. We also identified gene regulatory networks (regulon) that drives the transcriptional state of the cell types. Notably, we observed increased PRDM1 regulon activity in Th17 cells and diminished GATA3 regulon activity in naïve T cells, among other changes in the activity of different regulons in children progressing to disease. CONCLUSIONS: Our findings reveal early, cell-type-specific changes in transcription and gene regulatory networks in CD4⁺ T cells associated with type 1 diabetes progression, highlighting key pathways and transcriptional regulators. These insights provide a foundation for understanding early immune dysregulation in type 1 diabetes and basis for strategies to develop early diagnosis and intervention.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".