Interferon-α (IFNα), a type I interferon, is expressed in the islets of type 1 diabetic individuals, and its expression and signaling are regulated by T1D genetic risk variants and viral infections associated with T1D. We presently characterize human beta cell responses to IFNα by combining ATAC-seq, RNA-seq and proteomics assays. The initial response to IFNα is characterized by chromatin remodeling, followed by changes in transcriptional and translational regulation. IFNα induces changes in alternative splicing (AS) and first exon usage, increasing the diversity of transcripts expressed by the beta cells. This, combined with changes observed on protein modification/degradation, ER stress and MHC class I, may expand antigens presented by beta cells to the immune system. Beta cells also up-regulate the checkpoint proteins PDL1 and HLA-E that may exert a protective role against the autoimmune assault. Data mining of the present multi-omics analysis identifies two compound classes that antagonize IFNα effects on human beta cells.

Biological tissues exhibit complex spatial heterogeneity that directs the functions of multicellular organisms. Quantifying protein expression is essential for elucidating processes within complex biological assemblies. Imaging mass spectrometry (IMS) is a powerful emerging tool for mapping the spatial distribution of metabolites and lipids across tissue surfaces, but technical challenges have limited the application of IMS to the analysis of proteomes. Methods for probing the spatial distribution of the proteome have generally relied on the use of labels and/or antibodies, which limits multiplexing and requires a priori knowledge of protein targets. Past efforts to make spatially resolved proteome measurements across tissues have had limited spatial resolution and proteome coverage and have relied on manual workflows. Here, we demonstrate an automated approach to imaging that utilizes label-free nanoproteomics to analyze tissue voxels, generating quantitative cell-type-specific images for >2000 proteins with 100-µm spatial resolution across mouse uterine tissue sections preparing for blastocyst implantation.

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Diagnosis at the edges of our knowledge calls upon clinicians to be data driven, cross-disciplinary, and collaborative in unprecedented ways. Exact disease recognition, an element of the concept of precision in medicine, requires new infrastructure that spans geography, institutional boundaries, and the divide between clinical care and research. The National Institutes of Health (NIH) Common Fund supports the Undiagnosed Diseases Network (UDN) as an exemplar of this model of precise diagnosis. Its goals are to forge a strategy to accelerate the diagnosis of rare or previously unrecognized diseases, to improve recommendations for clinical management, and to advance research, especially into disease mechanisms. The network will achieve these objectives by evaluating patients with undiagnosed diseases, fostering a breadth of expert collaborations, determining best practices for translating the strategy into medical centers nationwide, and sharing findings, data, specimens, and approaches with the scientific and medical communities. Building the UDN has already brought insights to human and medical geneticists. The initial focus has been on data sharing, establishing common protocols for institutional review boards and data sharing, creating protocols for referring and evaluating patients, and providing DNA sequencing, metabolomic analysis, and functional studies in model organisms. By extending this precision diagnostic model nationally, we strive to meld clinical and research objectives, improve patient outcomes, and contribute to medical science.

Highlights d Proteomics of cytokine-treated human islets identified GDF15 as a protective factor d GDF15 levels were reduced in cytokine-treated islets by translational blockade d GDF15 inhibited IL-1b+INF-g-induced apoptosis of human islets and a b-cell line d Administration of GDF15 prevented diabetes in non-obese diabetic mice