Diabetes is a chronic metabolic disorder affecting approximately 589 million adults worldwide; it is characterised by persistently elevated blood glucose levels due to impaired insulin production, utilisation, or sensitivity. Type 2 diabetes mellitus (T2DM) is the most prevalent form, and is defined by beta cell dysfunction and insulin resistance. Single-cell transcriptomics offers valuable insight into T2DM disease mechanisms at cell-type resolution; however, analysing these datasets typically requires considerable bioinformatics expertise. To lower this barrier, we developed T2DIAbetes, an intuitive, web-based application designed to accelerate research by making complex single-cell data accessible to a broader scientific community.

T2DIAbetes consolidates multiple publicly available single-cell RNA sequencing (scRNA-seq) datasets, each pre-processed to generate UMAP embeddings for interactive visualisation. Users can examine how T2DM affects distinct cell populations across tissues, with datasets further integrated into tissue-specific atlases to support targeted investigation. To explore disease mechanisms, the application enables comparison of gene expression levels and cell-type proportions between control and T2DM conditions. Key features include visualising the expression of user-selected genes, examining cell-type distribution shifts between conditions, and identifying differentially expressed genes across cell clusters.

Future development will extend the platform to support user-uploaded datasets, leveraging the integrated atlases to refine cell-type annotations, enable cross-sample comparisons, and map cell-to-cell signalling networks. Together, these capabilities will deepen our understanding of T2DM pathophysiology at single-cell resolution.

Co-authors: Cynthia Coleman, Pilib Ó Broin