Lipids are regulators of insulitis and β-cell death in type 1 diabetes development, but the underlying mechanisms are poorly understood. Here, we investigated how the islet lipid composition and ...downstream signaling regulate β-cell death.
We performed lipidomics using three models of insulitis: human islets and EndoC-βH1 β cells treated with the pro-inflammatory cytokines interlukine-1β and interferon-γ, and islets from pre-diabetic non-obese mice. We also performed mass spectrometry and fluorescence imaging to determine the localization of lipids and enzyme in islets. RNAi, apoptotic assay, and qPCR were performed to determine the role of a specific factor in lipid-mediated cytokine signaling.
Across all three models, lipidomic analyses showed a consistent increase of lysophosphatidylcholine species and phosphatidylcholines with polyunsaturated fatty acids and a reduction of triacylglycerol species. Imaging assays showed that phosphatidylcholines with polyunsaturated fatty acids and their hydrolyzing enzyme phospholipase PLA2G6 are enriched in islets. In downstream signaling, omega-3 fatty acids reduce cytokine-induced β-cell death by improving the expression of ADP-ribosylhydrolase ARH3. The mechanism involves omega-3 fatty acid-mediated reduction of the histone methylation polycomb complex PRC2 component Suz12, upregulating the expression of Arh3, which in turn decreases cell apoptosis.
Our data provide insights into the change of lipidomics landscape in β cells during insulitis and identify a protective mechanism by omega-3 fatty acids. Video Abstract.
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IZUM, KILJ, NUK, PILJ, PNG, SAZU, UL, UM, UPUK
The need for a clinically accessible method with the ability to match protein activity within heterogeneous tissues is currently unmet by existing technologies. Our proteomics sample preparation ...platform, named microPOTS (Microdroplet Processing in One pot for Trace Samples), can be used to measure relative protein abundance in micron-scale samples alongside the spatial location of each measurement, thereby tying biologically interesting proteins and pathways to distinct regions. However, given the smaller pixel/voxel number and amount of tissue measured, standard mass spectrometric analysis pipelines have proven inadequate. Here we describe how existing computational approaches can be adapted to focus on the specific biological questions asked in spatial proteomics experiments. We apply this approach to present an unbiased characterization of the human islet microenvironment comprising the entire complex array of cell types involved while maintaining spatial information and the degree of the islet’s sphere of influence. We identify specific functional activity unique to the pancreatic islet cells and demonstrate how far their signature can be detected in the adjacent tissue. Our results show that we can distinguish pancreatic islet cells from the neighboring exocrine tissue environment, recapitulate known biological functions of islet cells, and identify a spatial gradient in the expression of RNA processing proteins within the islet microenvironment.
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•We used our microPOTS platform to carry out deep proteome imaging of the human pancreatic islet and its surrounding microenvironment with seven biological replicates.•Our analysis yielded the identification and quantification of over 6000 proteins from ∼50,000 μm2 tissue area samples.•Differential expression analysis identified biological pathways uniquely active in islet cells, and biological network interrogation identified additional relevant proteins.•Rank-based statistics enables the characterization of gradients that occur across the individual tissue images.
We applied our Microdroplet Processing in One pot for Trace Samples (microPOTS) TMT platform to study the pancreatic islet microenvironment in the human pancreas. With this approach, we were able to quantify over 6000 proteins from seven different regions within the same patient. Here we describe enhancements to existing computational approaches to extract functional hypotheses from this first-of-its-kind dataset. In addition to recapitulating known functions of the islet, we are also able to identify specific immune and RNA processing activities with spatial heterogeneity in expression.
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GEOZS, IJS, IMTLJ, KILJ, KISLJ, NLZOH, NUK, OILJ, PNG, SAZU, SBCE, SBJE, UILJ, UL, UM, UPCLJ, UPUK, ZAGLJ, ZRSKP
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