AMPK (5′-AMP-activated protein kinase) is an energetic sensor for metabolic regulation and integration. Here, we employed CRISPR/Cas9 to generate non-activatable Ampkα knock-in (KI) mice with a mutation of the threonine 172 phosphorylation site to alanine, circumventing the limitations of previous genetic interventions that disrupt the protein stoichiometry. KI mice of Ampkα2, but not Ampkα1, demonstrated phenotypic changes with increased fat-to-lean mass, impaired endurance exercise capacity, and diminished mitochondrial maximal respiration and conductance in skeletal muscle. Integrated temporal multi-omic analysis (proteomics/ phosphoproteomics/ metabolomics) in skeletal muscle at rest and during exercise establishes a pleiotropic yet imperative role of Ampkα2 T172 activation for glycolytic and oxidative metabolism, mitochondrial respiration, and contractile function. Importantly, there is a significant overlap of skeletal muscle proteomic changes in Ampkα2 T172A KI mice with those of type 2 diabetic patients. Our findings suggest that Ampkα2 T172 activation is critical for exercise performance and energy transduction in skeletal muscle and may serve as a therapeutic target for type 2 diabetes.

Aims/hypothesis: Progressive β-cell dysfunction precedes the onset of type 1 diabetes (T1D), yet the molecular mechanisms driving early pathogenesis remain poorly understood. Functional and multiplexed imaging studies have reported lobular heterogeneity in the pancreas with respect to immune and β-cell dysfunction signatures. Although single-cell transcriptomics has identified cellular changes, it provides limited insight into the heterogeneity of distinct islet microenvironments.  In this exploratory study, we employ single-islet proteomics to profile intra-donor islet heterogeneity across three multiple autoantibody-positive donors (mAAb+), representing the transition from Stage 1 to Stage 2 T1D, alongside matched non-diabetic controls, to resolve early T1D pathogenesis and identify cellular processes coupled to the islet immune response. Methods: Laser capture microdissection was used to isolate 439 individual pancreatic islets from presymptomatic mAAb+ (n = 3 donors, ~100 islets/donor) and non-diabetic control (n = 3 donors, ~50 islets/donor) organ donors obtained through the Network for Pancreatic Organ Donors with Diabetes. Islet identification and T-cell infiltration were evaluated using 3 multiplex immunohistochemistry assays for insulin, glucagon, and cell differentiation 3 proteins. Adjacent serial sections were used for islet laser capture microdissection and proteomic analysis using the Nanodroplet Processing in One pot for Trace Samples. Downstream proteomics analysis combined, weighted gene co-expression network analysis, random forest-based feature selection, linear modeling with empirical Bayes moderation, and gene set enrichment accounting for inter-gene correlation. Results: The single-islet proteomics workflow demonstrated high analytical reproducibility, with Pearson correlation coefficients exceeding 0.96 and an average of approximately 5,800 proteins quantified per donor. By combining weighted gene co-expression network analysis with random forest-based feature selection, we identified a 40-protein panel, defined as the Islet Immune Response Signature (IIRS), was identified that tracks activation and reflects a pseudo-temporal progression of the islet immune response. Additionally, functionally clustered protein modules across mAAb+ donors revealed significant intra-donor heterogeneity in β-cell-specific markers, pointing to β-cell dysfunction. Along the same axis, we established a panel of 42 proteins, defined as β-cell profile (BCP), with the highest correlation to insulin and Ectonucleoside triphosphate diphosphohydrolase 3, both β-cell markers. IIRS and BCP were found to correlate only weakly (Pearson r = 0.13), indicating that immune activation and β-cell function follow partially decoupled trajectories. Pathway analysis highlighted extracellular matrix remodeling associated with both the signature panels. Strong dysregulation of extracellular matrix organization in mAAb+ donors relative to their non-diabetic counterparts. Specifically, integrin-mediated signaling, cell-matrix adhesion, and collagen fibril organization show low association, while hyaluronan metabolic process shows strong association with IIRS. In contrast, ECM-modifying and ECM-degrading proteins (QSOX1, FBLN7, FAP, DPP4) consistently correlated negatively with BCP. Further, unique to β-cell function are pathways regarding mRNA processing and splicing, particularly in donors with insulin-depleted islets. Conclusion/interpretation: Our results reveal highly consistent proteomic patterns that reflect pseudo-time progression in the islet immune response and β-cell dysfunction. Pathways, including extracellular matrix remodeling and mRNA processing, were identified at the proteomic level as closely associated with progressive islet immune activation and loss of β-cell function. These findings provide evidence of early islet dysfunction, offer a valuable resource for investigating T1D pathogenesis, including novel candidates for functional studies, and underscore the utility of single-islet spatial proteomics for examining islet heterogeneity in T1D.

Aims/hypothesis: Progressive β-cell dysfunction precedes the onset of type 1 diabetes (T1D), yet the molecular mechanisms driving early pathogenesis remain poorly understood. Functional and multiplexed imaging studies have reported lobular heterogeneity in the pancreas with respect to immune and β-cell dysfunction signatures. Although single-cell transcriptomics has identified cellular changes, it provides limited insight into the heterogeneity of distinct islet microenvironments.  In this exploratory study, we employ single-islet proteomics to profile intra-donor islet heterogeneity across three multiple autoantibody-positive donors (mAAb+), representing the transition from Stage 1 to Stage 2 T1D, alongside matched non-diabetic controls, to resolve early T1D pathogenesis and identify cellular processes coupled to the islet immune response. Methods: Laser capture microdissection was used to isolate 439 individual pancreatic islets from presymptomatic mAAb+ (n = 3 donors, ~100 islets/donor) and non-diabetic control (n = 3 donors, ~50 islets/donor) organ donors obtained through the Network for Pancreatic Organ Donors with Diabetes. Islet identification and T-cell infiltration were evaluated using 3 multiplex immunohistochemistry assays for insulin, glucagon, and cell differentiation 3 proteins. Adjacent serial sections were used for islet laser capture microdissection and proteomic analysis using the Nanodroplet Processing in One pot for Trace Samples. Downstream proteomics analysis combined, weighted gene co-expression network analysis, random forest-based feature selection, linear modeling with empirical Bayes moderation, and gene set enrichment accounting for inter-gene correlation. Results: The single-islet proteomics workflow demonstrated high analytical reproducibility, with Pearson correlation coefficients exceeding 0.96 and an average of approximately 5,800 proteins quantified per donor. By combining weighted gene co-expression network analysis with random forest-based feature selection, we identified a 40-protein panel, defined as the Islet Immune Response Signature (IIRS), was identified that tracks activation and reflects a pseudo-temporal progression of the islet immune response. Additionally, functionally clustered protein modules across mAAb+ donors revealed significant intra-donor heterogeneity in β-cell-specific markers, pointing to β-cell dysfunction. Along the same axis, we established a panel of 42 proteins, defined as β-cell profile (BCP), with the highest correlation to insulin and Ectonucleoside triphosphate diphosphohydrolase 3, both β-cell markers. IIRS and BCP were found to correlate only weakly (Pearson r = 0.13), indicating that immune activation and β-cell function follow partially decoupled trajectories. Pathway analysis highlighted extracellular matrix remodeling associated with both the signature panels. Strong dysregulation of extracellular matrix organization in mAAb+ donors relative to their non-diabetic counterparts. Specifically, integrin-mediated signaling, cell-matrix adhesion, and collagen fibril organization show low association, while hyaluronan metabolic process shows strong association with IIRS. In contrast, ECM-modifying and ECM-degrading proteins (QSOX1, FBLN7, FAP, DPP4) consistently correlated negatively with BCP. Further, unique to β-cell function are pathways regarding mRNA processing and splicing, particularly in donors with insulin-depleted islets. Conclusion/interpretation: Our results reveal highly consistent proteomic patterns that reflect pseudo-time progression in the islet immune response and β-cell dysfunction. Pathways, including extracellular matrix remodeling and mRNA processing, were identified at the proteomic level as closely associated with progressive islet immune activation and loss of β-cell function. These findings provide evidence of early islet dysfunction, offer a valuable resource for investigating T1D pathogenesis, including novel candidates for functional studies, and underscore the utility of single-islet spatial proteomics for examining islet heterogeneity in T1D.

Additional file 2. Supplementary lipidomics data including experimental design, raw LC–MS intensities, processed inputs used for analysis using the lipidr package, and results of differential abundance analyses

Additional file 3. Supplementary redox, phospho, and global proteomics data merged with eggNOG annotations for bioinformatics analysis; also includes TMT plex designs

Additional file 3. Supplementary redox, phospho, and global proteomics data merged with eggNOG annotations for bioinformatics analysis; also includes TMT plex designs

Additional file 4. Supplementary data for percent cysteine thiol oxidation estimates and details regarding calculations

Additional file 4. Supplementary data for percent cysteine thiol oxidation estimates and details regarding calculations

Supplementary Material 2

Additional file 5. Select annotations for enzymes involved in nitrogen metabolism and autophagy as well as corresponding differential expression data (global and PTMs) and KEGG representative pathways