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The dysfunction of α and β cells in pancreatic islets can lead to diabetes. Many questions remain on the subcellular organization of islet cells during the progression of disease. Existing three-dimensional cellular mapping approaches face challenges such as time-intensive sample sectioning and subjective cellular identification. To address these challenges, we have developed a subcellular feature-based classification approach, which allows us to identify α and β cells and quantify their subcellular structural characteristics using soft X-ray tomography (SXT). We observed significant differences in whole-cell morphological and organelle statistics between the two cell types. Additionally, we characterize subtle biophysical differences between individual insulin and glucagon vesicles by analyzing vesicle size and molecular density distributions, which were not previously possible using other methods. These sub-vesicular parameters enable us to predict cell types systematically using supervised machine learning. We also visualize distinct vesicle and cell subtypes using Uniform Manifold Approximation and Projection (UMAP) embeddings, which provides us with an innovative approach to explore structural heterogeneity in islet cells. This methodology presents an innovative approach for tracking biologically meaningful heterogeneity in cells that can be applied to any cellular system.

The molecular and functional heterogeneity of pancreatic β-cells is well recognized, but the underlying mechanisms remain unclear. Pancreatic islets harbor a subset of β-cells that co-express tyrosine hydroxylase (TH), an enzyme involved in synthesis of catecholamines that repress insulin secretion. Restriction of the TH+ β-cells within islets is essential for appropriate function in mice, such that a higher proportion of these cells corresponds to reduced insulin secretion. Here, we use these cells as a model to dissect the developmental control of β-cell heterogeneity. We define the specific molecular and metabolic characteristics of TH+ β-cells and show differences in their developmental restriction in mice and humans. We show that TH expression in β-cells is restricted by DNA methylation during β-cell differentiation. Ablation of de novo DNA methyltransferase Dnmt3a in the embryonic progenitors results in a dramatic increase in the proportion of TH+ β-cells, whereas β-cell-specific ablation of Dnmt3a does not. We demonstrate that maintenance of Th promoter methylation is essential for its continued restriction in postnatal β-cells. Loss of Th promoter methylation in response to chronic overnutrition increases the number of TH+ β-cells, corresponding to impaired β-cell function. These results reveal a regulatory role of DNA methylation in determining β-cell heterogeneity., (© 2023 by the American Diabetes Association.)

We set out to determine whether the C-terminus (amino acids 481-798) of peroxisome proliferator-activated receptor gamma coactivator-1 alpha (PGC-1α, UniProt Q9UBK2), a regulatory metabolic protein involved in mitochondrial biogenesis, and respiration, is an arginine methyltransferase substrate. Arginine methylation by protein arginine methyltransferases (PRMTs) alters protein function and thus contributes to various cellular processes. In addition to confirming methylation of the C-terminus by PRMT1 as described in the literature, we have identified methylation by another member of the PRMT family, PRMT7. We performed in vitro methylation reactions using recombinant mammalian PRMT7 and PRMT1 at 37, 30, 21, 18, and 4 °C. Various fragments of PGC-1α corresponding to the C-terminus were used as substrates, and the methylation reactions were analyzed by fluorography and mass spectrometry to determine the extent of methylation throughout the substrates, the location of the methylated PGC-1α arginine residues, and finally, whether temperature affects the deposition of methyl groups. We also employed two prediction programs, PRmePRed and MePred-RF, to search for putative methyltransferase sites. Methylation reactions show that arginine residues R548 and R753 in PGC-1α are methylated at or below 30 °C by PRMT7, while methylation by PRMT1 was detected at these same residues at 30 °C. Computational approaches yielded additional putative methylarginine sites, indicating that since PGC-1α is an intrinsically disordered protein, additional methylated arginine residues have yet to be experimentally verified. We conclude that temperature affects the extent of arginine methylation, with more methylation by PRMT7 occurring below physiological temperature, uncovering an additional control point for PGC-1α.