Back

Imaging mass cytometry reveals early β-cell dysfunction and changes in immune signatures during type 1 diabetes progression in human pancreata

Steenbuck, N.; Damond, N.; Engler, S.; Kusmartseva, I.; Posgai, A. L.; Drotar, D. M.; Williams, M. D.; de Souza, N.; Brusko, T. M.; Brusko, M. A.; Wasserfall, C. H.; Atkinson, M. A.; Bodenmiller, B.

2025-03-11 immunology
10.1101/2025.03.05.641526 bioRxiv
Show abstract

The natural history and pathogenesis of type 1 diabetes, particularly during the autoantibody- positive stages preceding clinical onset, are not well understood, in part, due to limited availability of human pancreatic samples. Here, we studied 88 organ donors, including 28 single autoantibody-positive and 10 multiple autoantibody-positive donors, by imaging mass cytometry. Approximately 10,000 islets and 16 million single-cells were spatially analyzed using 79 antibodies revealing both {beta}-cell states and the islet-immune interface. We identified IAPP loss from {beta}-cells as an indicator of pre-clinical disease. Alterations in Interferon signatures and downregulation across lineage and functional markers, including markers of endoplasmic reticulum stress, were characteristic of recent-onset disease. Further, in single autoantibody- positive donors, we identified pro-inflammatory myeloid cells and PD1+ memory CD4+ T cells, and in multiple autoantibody-positive samples, found islet-specific and exhausted-like ebector CD8+ T cells. Multiple immune cell subtypes were associated with young age, disease severity and insulitis. This dataset is a major step toward creation of a multi-modal type 1 diabetes disease atlas that will be useful for identifying potential drug targets and association of disease features with clinical co-variates and trial outcomes.

Matching journals

The top 6 journals account for 50% of the predicted probability mass.

50% of probability mass above

"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.