Multiplexed imaging analysis of human pancreatic islets from donors with and without type 2 diabetes

This dataset is part of the manuscript Genetic risk converges on regulatory networks mediating early type 2 diabetes (Walker, Saunders, & Rai et al., 2023), a body of work that includes tissue imaging, sorted islet cell transcriptomics, and islet functional analysis of donors with early-stage type 2 diabetes (T2D) and control donors. Raw imaging data was acquired from 4% PFA-fixed, cryopreserved human pancreas tissue using the CODEX system (now PhenoCycler Open; Akoya Biosciences) integrated with a BZ-X810 epifluorescence microscope (Keyence) with a CFI plan Apo I 20x/0.75 objective (Nikon). Image alignment, stitching, background subtraction, and deconvolution were performed using the CODEX Processor v1.7.0.6 (Akoya Biosciences). Tissue and islet areas were annotated by hand to exclude out-of-focus regions and poor tissue quality. Islets (estimated diameter ≥50 μm; mean 42 islets/donor) were annotated based on DAPI and CHGA channels. Cell segmentation and cell type annotations were performed using the HALO HighPlex FL v3.2.1 module (Indica Labs). For cell neighborhood (CN) analysis, two methods were applied in parallel to CODEX data from annotated islets: a community detection method, termed Dynamic CF-IDF, and a k-means approach. Processed imaging data is available via Pancreatlas (RRID:SCR_018567); packages used for cell neighborhood analyses are published in Github

Genetic risk converges on regulatory networks mediating early type 2 diabetes.

Type 2 diabetes mellitus (T2D), a major cause of worldwide morbidity and mortality, is characterized by dysfunction of insulin-producing pancreatic islet β cells1,2. T2D genome-wide association studies (GWAS) have identified hundreds of signals in non-coding and β cell regulatory genomic regions, but deciphering their biological mechanisms remains challenging3–5. Here, to identify early disease-driving events, we performed traditional and multiplexed pancreatic tissue imaging, sorted-islet cell transcriptomics and islet functional analysis of early-stage T2D and control donors. By integrating diverse modalities, we show that early-stage T2D is characterized by β cell-intrinsic defects that can be proportioned into gene regulatory modules with enrichment in signals of genetic risk. After identifying the β cell hub gene and transcription factor RFX6 within one such module, we demonstrated multiple layers of genetic risk that converge on an RFX6-mediated network to reduce insulin secretion by β cells. RFX6 perturbation in primary human islet cells alters β cell chromatin architecture at regions enriched for T2D GWAS signals, and population-scale genetic analyses causally link genetically predicted reduced RFX6 expression with increased T2D risk. Understanding the molecular mechanisms of complex, systemic diseases necessitates integration of signals from multiple molecules, cells, organs and individuals, and thus we anticipate that this approach will be a useful template to identify and validate key regulatory networks and master hub genes for other diseases or traits using GWAS data