This study explores the inflammatory response observed in the pancreas and pancreatic lymph nodes (pLNs) during the natural history of type 1 diabetes (T1D). Using multicell-resolution spatial transcriptomics (ST), we profile individuals without diabetes (ND), at-risk autoantibody-positive (AAb+) individuals, and T1D donors. In the T1D pancreas, we observed global upregulation of inflammation-associated transcripts, including REG family genes, C3, SOD2, and OLFM4. In the T1D pLN, LTB was significantly upregulated within the lymphoid follicles. Using an orthogonal subcellular-resolution ST platform on an independent donor set, we identified follicular B cells as the primary source of LTB in the pLN and observed increased LTB expression in lymphocytes in insulitic lesions proximal to CCL19/CCL21-expressing endothelium. Collectively, these findings highlight lymphotoxin-β and downstream chemokine signatures in the pancreatic lymphatics as well as within the insulitic lesion, which can inform future therapeutic interventions.

OBJECTIVES:Gamma-aminobutyric acid (GABA) is produced in pancreatic beta cells and is implicated in modulating islet function, yet its precise physiological role remains uncertain. This study aimed to determine the function of endogenous beta cell-derived GABA on insulin secretion and islet calcium dynamics by developing a conditional beta cell-specific knockout of GABA-synthesizing enzymes (GAD65 and GAD67). METHODS:Conditional knockout mice (GadβKO) lacking both GAD65 and GAD67 specifically in pancreatic beta cells were generated. Glucose-stimulated insulin secretion was measured in isolated islets and in vivo and islet Ca2+ oscillations were recorded using calcium imaging. The effects of GABA and its receptor agonists/antagonists were tested under various glucose conditions. Additional analyses were performed in high-fat diet-fed mice and human islets from donors with and without type 2 diabetes (T2D). RESULTS:GadβKO islets were devoid of GABA and showed excessive insulin secretion in response to glucose without anatomical changes in islet composition. These islets had defective Ca2+ oscillations, with prolonged active phases and reduced amplitudes. GABA application suppressed Ca2+ oscillations, an effect mediated by GABAA and GABAB receptors. High-fat diet-fed and T2D human islets were unresponsive to GABA and exhibited impaired Ca2+ oscillations. CONCLUSIONS:This is the first study using a beta cell-specific GAD65/GAD67 knockout model. Endogenous beta cell-derived GABA is critical for modulating insulin secretion by maintaining proper Ca2+ oscillation dynamics. GABA signaling likely operates as a delayed negative feedback mechanism that reinforces oscillatory homeostasis in islets. The loss of GABA responsiveness, as seen in metabolic stress or T2D, may contribute to islet dysfunction. This work establishes GABA as a key regulator of islet rhythm and glucose responsiveness.

A high-definition description of pancreatic islets would prove beneficial for understanding the pathophysiology of type 1 diabetes (T1D), yet significant knowledge voids exist in terms of their size, endocrine cell composition, and number in both health and disease. Here, 3-dimensional (3D) analyses of pancreata from control persons without diabetes (ND) revealed heretofore underappreciated frequencies (approximately 50%) of insulin-positive (INS+) glucagon-negative (GCG-) islets. Non-diabetic individuals positive for a single Glutamic acid decarboxylase autoantibody (GADA+) yet at increased risk for disease consistently demonstrated endocrine features, including islet volume and cell composition, closely resembling the age-matched ND controls. In contrast, pancreata from individuals with short-duration T1D demonstrated significantly reduced islet density and a dramatic loss of INS+GCG- islets with preservation of large INS+GCG+ islets. The size and cellular composition of pancreatic islets may, therefore, represent influential factors that impact β-cell loss during T1D disease progression.

In type 1 diabetes, insulin deficiency results from immune-mediated destruction of beta cells. The majority of functional beta cell mass is typically lost within months to years of disease diagnosis, but the timing and nature of this loss, particularly in early disease stages, remain unclear. We developed a whole-slide scanned image (WSI) analysis pipeline for semi-automated quantitation of endocrine areas, islet frequencies, inter-islet distances, and endocrine object size distribution in 145 human pancreata from 60 non-diabetic (ND), 19 single autoantibody positive (sAAb+), 10 multiple autoantibody positive (mAAb+), 16 recent-onset (0-1 year duration), 23 medium-duration (1-7 years), and 17 long-duration type 1 diabetes (7+ years) donors. We observed age-related differences in endocrine composition and islet frequency in ND. Age-corrected data revealed decreased islet frequency and increased inter-islet distances in the type 1 diabetes pancreas. Insulin-positive (INS+) single cells (≤10µm), cell clusters (>10 to <35µm), small- and medium-sized islets (35-100µm and 100-200µm) were significantly lost at type 1 diabetes onset, while large INS+ islets (>200µm) were preserved. Moreover, changes in endocrine composition also occurred in mAAb+ donors, including a significant decrease in the INS+ islet fraction. These data suggest preferential loss of INS+ small endocrine objects early in type 1 diabetes development.

Introduction and Objective: The PD-1/PD-L1 immune checkpoint pathway plays a key role in preventing type 1 diabetes (T1D). Plasma EV data from autoantibody-positive (AAB+) patients suggests, PD-L1 level correlates with beta cell function and T1D progression. Thus, restoring PD-1/PD-L1 function is considered one potential strategy for treating T1D at different stages. Here, we outline the novel cytokine-induced PD-L1 regulation by the neuronal pentraxin (NPTX) proteins. NPTX protein functions are known in the brain, where they regulate synaptic plasticity by interacting with glutamate receptors. Recent studies have reported elevated levels of NPTX2 in a subtype beta cells population from AAB+ patients. However, their function in beta cell biology and T1D pathogenesis is little known. Methods: We have implemented molecular biology, mass spectrometry, and advanced high-throughput data analysis tools to outline NPTX protein function in EndoC-betaH1 human beta cells. Results: We observed that NPTXR knockdown significantly reduces glucose-stimulated insulin release by ~30% and critical insulin secretion machinery proteins, including glutamate receptor (GRIA4) and its binding partner, GRIP proteins. In cytokine-treated (IL1-beta and IFN-gamma) conditions, NPTXR is proapoptotic, and its knockdown further increases PD-L1 expression by ~64% while significantly suppressing ~88% of enriched pro-inflammatory pathways. NPTXR regulates PD-L1 expression via ERK/c-JUN signaling, an indicator of glutamine deprivation. Conclusion: Overall, our study elucidates a novel role for NPTXR in regulating beta cell insulin secretion and, in cases of inflammation, PD-L1 expression. Targeting NPTX signaling components holds potential as an early intervention target for T1D due to its association with AAB+ patients and its role in modulating beta cell fitness. S. Sarkar: None. W. Qian: None. R. Kulkarni: Advisory Panel; Novo Nordisk, Biomea Fusion, REDD Pharma, Inversago Pharma. Research Support; Inversago Pharma. Stock/Shareholder; Biomea Fusion. D.F. De Jesus: None. M. Campbell-Thompson: None. C.E. Mathews: None. M.A. Gritsenko: None. National Institutes of Health (5R01DK122160-04, R01DK067536, R01DK123329)

The loss of insulin secretory function associated with type 1 diabetes (T1D) is attributed to the immune-mediated destruction of beta cells. Yet, at onset of T1D, patients often retain a substantial beta cell mass, and T cell infiltration of pancreatic islets is typically sporadic. Here, we investigate the hypothesis that the remaining beta cells in T1D are dysfunctional, using live pancreas slices from organ donors recently diagnosed with T1D. Beta cells in slices from donors with T1D have significantly diminished Ca2+ mobilization and insulin secretion in response to glucose. Beta cell function is equally impaired in T-cell-infiltrated and non-infiltrated islets. Fixed tissue staining and gene expression profiling of laser-capture microdissected islets reveal significant reductions in proteins and genes involved in the glucose stimulus secretion coupling pathway. These findings support the notion that molecular changes evolve in beta cells during prediabetes and worsen to functional defects at human T1D diagnosis.

AIMS/HYPOTHESIS:Earlier studies of pancreases from donors with type 1 diabetes demonstrated enteroviral capsid protein VP1 in beta cells. In the context of a multidisciplinary approach undertaken by the nPOD-Virus group, we assessed VP1 positivity in pancreas and other tissues (spleen, duodenum and pancreatic lymph nodes) from 188 organ donors, including donors with type 1 diabetes and donors expressing autoantibody risk markers. We also investigated whether VP1 positivity is linked to the hyperexpression of HLA class I (HLA-I) molecules in islet cells. METHODS:Organ donor tissues were collected by the Network for Pancreatic Organ Donors with Diabetes (nPOD) from donors without diabetes (ND, n=76), donors expressing a single or multiple diabetes-associated autoantibodies (AAb+, n=20; AAb++, n=9) and donors with type 1 diabetes with residual insulin-containing islets (T1D-ICIs, n=41) or only insulin-deficient islets (T1D-IDIs, n=42). VP1 was assessed using immunohistochemistry (IHC) and HLA-I using IHC and immunofluorescence, in two independent laboratories. We determined assay concordance across laboratories and overall occurrence of positive assays, on a case-by-case basis and between donor groups. RESULTS:Islet cell VP1 positivity was detected in most T1D-ICI donors (77.5%) vs only 38.2% of ND donors (p<0.001). VP1 positivity was associated with HLA-I hyperexpression. Of those donors assessed for HLA-I and VP1, 73.7% had both VP1 immunopositivity and HLA-I hyperexpression (p<0.001 vs ND). Moreover, VP1+ cells were detected at higher frequency in donors with HLA-I hyperexpression (p<0.001 vs normal HLA-I). Among VP1+ donors, the proportion with HLA-I hyperexpression was significantly higher in the AAb++ and T1D-ICI groups (94.9%, p<0.001 vs ND); this was not restricted to individuals with recent-onset diabetes. Critically, for all donor groups combined, HLA-I hyperexpression occurred more frequently in VP1+ compared with VP1- donors (45.8% vs 16%, p<0.001). CONCLUSIONS/INTERPRETATION:We report the most extensive analysis to date of VP1 and HLA-I in pancreases from donors with preclinical and diagnosed type 1 diabetes. We find an association of VP1 with residual beta cells after diagnosis and demonstrate VP1 positivity during the autoantibody-positive preclinical stage. For the first time, we show that VP1 positivity and HLA-I hyperexpression in islet cells are both present during the preclinical stage. While the study of tissues does not allow us to demonstrate causality, our data support the hypothesis that enterovirus infections may occur throughout the natural history of type 1 diabetes and may be one of multiple mechanisms driving islet cell HLA-I hyperexpression.

Progression to type 1 diabetes is associated with genetic factors, the presence of autoantibodies and a decline in beta cell insulin secretion in response to glucose. Very little is known regarding the molecular changes that occur in human insulin-secreting beta cells prior to the onset of type 1 diabetes. Herein, we applied an unbiased proteomics approach to identify changes in proteins and potential mechanisms of islet dysfunction in islet-autoantibody-positive organ donors with pre-symptomatic stage 1 type 1 diabetes (HbA1c ≤42 mmol/mol [6.0 https://massive.ucsd.edu/ProteoSAFe/static/massive.jsp ) with accession no. MSV000090212.

Introduction and Objective: Several challenges limit research on islet and CD3-cell quantifications for type 1 diabetes (T1D), including inherent heterogeneity and potentially biased manual analyses that rarely perform single-cell analyses. This study aimed to enhance understanding of T1D pathogenesis by including endocrine cell clusters, containing endocrine 2-6 cells, in the quantitative analysis of whole-slide images (WSI) from donors using our developed machine learning (ML)-assisted WSI analysis and images from the nPOD online pathology database. Methods: Our ML-assisted workflow using QuPath quantified clusters and islets for β-cells and α-cells area and CD3+ cell numbers from multiplex immunohistochemistry stained WSI. A total of 228 slides included 3 pancreas regions (head, body, tail) per donor from 32 controls, 19 autoantibody-positive donors (AAb+, 12 single and 7 multiple), and 25 T1D donors (6 recent-onset and 19 longstanding). Statistical analyses were performed using the Wilcoxon test. Results: When comparing recent-onset and longstanding T1D donors, endocrine cluster diameters decreased significantly (p<0.05), while islet diameters increased. Islet and cluster density increased, with β-cell areas decreasing and α-cells increasing as T1D progressed. Particularly, β-cell area decreased significantly in multi-AAb+, compared to single-AAb+ (p<0.05). The proportion of ≥ 7 CD3+ cells infiltrating islets within a 20µm boundary increased during disease progression to 1 year but declined when the disease lasted longer (p<0.05). Conclusion: Our ML quantification analysis, including small endocrine cell clusters, revealed notable differences based on T1D duration. While we identified differences between T1D donors, only one significant difference was found between single-AAb+ and multi-AAb+ donors. These findings from high-throughput ML analyses enhance understanding of endocrine cell changes and CD3+ infiltration during the T1D progression. S. Kang: None. N. Maya: None. M. Morillo: None. M. Outar: None. D. Lamb: None. M. Campbell-Thompson: None. S. Kim: None. Breakthrough T1D (3-SRA-2022-1157-S-B); National Institutes of Health (R01DK123329, U54DK127823)

Introduction and Objective: Type 1 diabetes (T1D) is a polygenic, autoimmune disease characterized by pancreatic β-cell dysfunction and loss. We have recently demonstrated that β-cell dysfunction in T1D is independent of T cell infiltration into islets. To define β-cell changes in T1D, we employed an islet-centric approach to identify differentially expressed genes (DEGs) that change during T1D pathogenesis in insulin (INS)+ CD3- islets of at-risk cases. We hypothesize these DEGs contribute to β-cell failure. Methods: To nominate genetic drivers, human islet gene expression data from single AAb+, multiple AAb+ and T1D cases were integrated with the Diversity Outbred (DO) mice. This enabled us to identify strong cis-eQTL associated with alleles from the T1D-prone NOD mouse, one of eight founder strains of the DO. Results: We filtered 827 human DEGs to identify 142 DEGs with strong NOD-driven cis-eQTL. From those, 88 DEGs showed associations with diabetes in human genome-wide association studies. Remarkably, 49 cis-eQTL mapped within mouse diabetes susceptibility loci. Those genes were associated with transcription-translation (Gatc, Creb3, Fam133b, H1f3, Niban1, Smad4), glucose metabolism (Pgm1), mitochondrial function (Suclg2, Atp5e, Atp5g1, Pdhb, Acly, Coa5), insulin-granule biogenesis and exocytosis (Pcsk1, Rab2a, Cadps2. Atp2a2), and amino acid and metal transport and homeostasis (Slc36a4, Slc39a8, Slc4a7, Fth1). Conclusion: By leveraging genetics of DO mice, our functional genomics approach has identified genetic elements as specific genetic drivers of human T1D (in transcription/translation, glycolysis, mitochondria, and insulin secretion). By restricting the analysis to islets without immune infiltration these loci promote loss of β-cell function in the absence of T cell infiltration. A.E. Cuaycal: None. M. Keller: None. E.A. Butterworth: None. J. Chen: None. M. Campbell-Thompson: None. P. Smadbeck: None. J. Flannick: None. I.C. Gerling: None. C.E. Mathews: None. JDRF, NIH (P01 AI42288, UC4 DK104194, UC4 DK104167, UC4 DK104155)

Introduction: The prevalence of exocrine pancreatic insufficiency (EPI) in type 1 diabetes (T1D) is approximately 33%. We are investigating the timing of exocrine pancreatic loss in the pre-T1D period. We hypothesize that fecal elastase (FE-1) will be reduced prior to Stage 1 T1D and its rate of decline used to predict disease onset. Methods: From two parallel nested case control studies within the TEDDY study, we compare longitudinal FE-1 levels in subjects who develop T1D by age 4 (Cohort A) and who develop persistent multiple islet autoantibodies by age 4 but have not developed dysglycemia or T1D by age 6 (Cohort B) with their respective autoantibody negative euglycemic controls. We included subjects with available sample prior to key endpoints. We excluded subjects with prematurity, pancreatic disease, or medical conditions associated with EPI. Results: About1187 blinded samples from cases and controls have been evaluated. The median FE-1 level is 958 mcg/g with an interquartile range of 594-1480. 1.8% of samples meet criteria for EPI, defined by FE-1 less than 200 mcg/g. Conclusion: The low percentage of EPI found, along with the range of FE-1 values, agrees with previous literature (Penno et al). Our study is ongoing as there are about 4700 samples to be run. We hypothesize that future results will demonstrate the efficacy of using FE-1 as a predictive biomarker for T1D progression. Disclosure K. Morneault-Gill: None. M. Guyot: None. J. Geston: None. C. Georgas: None. M. Campbell-Thompson: None. M.J. Haller: Consultant; Sanofi. Advisory Panel; SAB Biotherapeutics, Inc. Consultant; MannKind Corporation. B.S. Bruggeman: Research Support; Tandem Diabetes Care, Inc. Funding National Institute of Health (NIH NIDDK R03DK129971 and NIH K23DK131363), Pediatric Endocrine Society Clinical Scholar Award

We developed Artificial Intelligence classification algorithms in QuPATH containing both supervised and unsupervised components to quantify and classify the pancreas cell types into their respective subtypes based on immunofluorescence (IF) data. The staining panel was performed in formalin-fixed paraffin embedded (FFPE) pancreatic tissue sections from a pancreas organ donor with no diabetes provided by nPOD. From the IF images, a training region was selected. This region was then segmented using a watershed algorithm and then an expert classified each cell in the region. In total, there were 6,899 cells, with an emphasis on the acinar and ductal cell types. This training data was used to train a Random Trees (RT), K Nearest Neighbor (KNN) and Artificial Neural Network (ANN) algorithm. Another region was selected for a validation data set, and once again, each cell was manually classified by an expert in the field. The accuracy of each algorithm was then assessed on the validation set. The accuracy of each algorithm is as follows: RT (89.53%), KNN (85.55%) and ANN (82.97%). In the future, we will include multiple no diabetes pancreases as well as pancreases from the different stages of T1D progression to ensure diversity of the training and validation data. With a more diverse training data set, we predict that ANN’s will become more accurate, and could potentially prove to be more useful than the RT algorithm. With a more diverse validation data set, we can look at the possibility of overfitting artificially inflating the accuracy of our algorithms.From this, we plan to extract relevant feature information such as cell size, number and density among others. After we have the architecture solidified, we hope to use it to analyze differences in control and diseased pancreases, specifically with regards to Type 1 Diabetes (T1D). We hope this will uncover previously overlooked information that may prove critical to the understanding of the mechanism of disease progression of T1D. Disclosure M. Guyot: None. M.D. Williams: None. B.M. Bumgarner: None. K.M. McGrail: None. M. Campbell-Thompson: None. M.J. Haller: Consultant; Sanofi. Advisory Panel; SAB Biotherapeutics, Inc. Consultant; MannKind Corporation. B.S. Bruggeman: Research Support; Tandem Diabetes Care, Inc.

Clinical trials seeking to delay or prevent the onset of type 1 diabetes (T1D) face a series of pragmatic challenges. Despite more than 100 years since the discovery of insulin, teplizumab remains the only FDA-approved therapy to delay progression from Stage 2 to Stage 3 T1D. To increase the efficiency of clinical trials seeking this goal, our project sought to inform T1D clinical trial designs by developing a disease progression model-based clinical trial simulation tool. Using individual-level data collected from the TrialNet Pathway to Prevention and The Environmental Determinants of Diabetes in the Young natural history studies, we previously developed a quantitative joint model to predict the time to T1D onset. We then applied trial-specific inclusion/exclusion criteria, sample sizes in treatment and placebo arms, trial duration, assessment interval, and dropout rate. We implemented a function for presumed drug effects. To increase the size of the population pool, we generated virtual populations using multivariate normal distribution and ctree machine learning algorithms. As an output, power was calculated, which summarizes the probability of success, showing a statistically significant difference in the time distribution until the T1D diagnosis between the two arms. Using this tool, power curves can also be generated through iterations. The web-based tool is publicly available: https://app.cop.ufl.edu/t1d/. Herein, we briefly describe the tool and provide instructions for simulating a planned clinical trial with two case studies. This tool will allow for improved clinical trial designs and accelerate efforts seeking to prevent or delay the onset of T1D.

Human islets display a high degree of heterogeneity in terms of size, number, architecture, and endocrine cell-type compositions. An ever-increasing number of immunohistochemistry-stained whole slide images (WSIs) are available through the online pathology database of the Network for Pancreatic Organ donors with Diabetes (nPOD) program at the University of Florida (UF). We aimed to develop an enhanced machine learning-assisted WSI analysis workflow to utilize the nPOD resource for analysis of endocrine cell heterogeneity in the natural history of type 1 diabetes (T1D) in comparison to donors without diabetes. To maximize usability, the user-friendly open-source software QuPath was selected for the main interface. The WSI data were analyzed with two pre-trained machine learning models (i.e., Segment Anything Model (SAM) and QuPath's pixel classifier), using the UF high-performance-computing cluster, HiPerGator. SAM was used to define precise endocrine cell and cell grouping boundaries (with an average quality score of 0.91 per slide), and the artificial neural network-based pixel classifier was applied to segment areas of insulin- or glucagon-stained cytoplasmic regions within each endocrine cell. An additional script was developed to automatically count CD3+ cells inside and within 20 μm of each islet perimeter to quantify the number of islets with inflammation (i.e., CD3+ T-cell infiltration). Proof-of-concept analysis was performed to test the developed workflow in 12 subjects using 24 slides. This open-source machine learning-assisted workflow enables rapid and high throughput determinations of endocrine cells, whether as single cells or within groups, across hundreds of slides. It is expected that use of this workflow will accelerate our understanding of endocrine cell and islet heterogeneity in the context of T1D endotypes and pathogenesis.

Introduction: Elevations in immune cell numbers are well documented in both the endocrine and exocrine pancreas in type 1 diabetes (T1D), with CD8+ T cells representing the predominant cell type in islet inflammation (insulitis). To expand on previous 2D characterizations, we used 3D light sheet fluorescent microscopy (LSFM) to quantify CD8+ cells within islets and surrounding exocrine regions. Methods: Pancreas samples from non-diabetic controls (ND, n = 4; age range 16-24 yr), non-diabetic GAD autoantibody-positive (GADA, n = 3; 18-24 yr), and recent-onset (duration <2 yr) T1D subjects (n = 4; 3-26 yr) were fixed, stained for insulin (INS), glucagon (GCG) and CD8a, cleared and imaged using LSFM. A mean tissue volume of 4.3x108 µm3 per donor was analyzed using Imaris. We generated 3D surfaces (INS+ and GCG+ signals) and 3D spots (CD8a+ cells), with analysis of 516 total islets (201 ND, 162 GADA, 153 T1D). Endocrine pancreas infiltration was quantified using CD8a spots contacting and inside islet surfaces. To assess exocrine infiltration, CD8a spots outside of the islets were quantified. Fold change and mean ± SEM values were analyzed by one-way ANOVA with Tukey's post-test. Results: T1D donors had significantly increased CD8a+ cells in exocrine (5.5-fold more than ND, 3.6-fold more than GADA, p<0.05) and endocrine pancreas (13-fold more than ND, 12-fold more than GADA, p≤0.02). The percentage of islets containing at least one CD8a+ cell was also increased in T1D (64 ± 0.02%, p<0.05) vs ND (28 ± 0.06%) and GADA (23 ± 0.08%). The maximum number of CD8a+ cells per islet was higher in T1D (133.5 ± 40.5, p≤0.02) vs ND (7.5 ± 2.3) and GADA (10 ± 2.3). Conclusion: This 3D analysis of the human pancreas provides a comprehensive view of immune cell distribution in T1D, enabling quantification of infiltration throughout the exocrine and endocrine compartments. Such efforts will advance our ability to identify the pathogenic mechanisms underlying pancreatic inflammation in T1D. Disclosure A. Rippa: None. A. Posgai: None. M.A. Hulme: None. C. Wasserfall: None. I. Kusmartseva: None. M. Campbell-Thompson: None. M.A. Atkinson: None.

Introduction: Type 1 diabetes (T1D) is characterized by a period of β cell dysfunction prior to β cell death. During this time, β cells lack a first-phase insulin response and exhibit an intolerance to glucose. However, the underlying mechanisms of this dysfunction remain unclear. Here, we use live human pancreas tissue slices (LPTS) to investigate the possible mechanisms underlying β cell dysfunction in T1D. Methods: LPTS from organ donors without diabetes (ND, n=13), donors without T1D and positive for one (sAAb+, n=6) or multiple T1D-relavent autoantibodies (mAAb+, n=2), and individuals with short-duration T1D (T1D+, n=9) were studied. Simultaneous time-lapse Ca2+ imaging and CD3+ T-cell tracking were conducted to assess alterations in β-cell function. Additionally, fixed pancreas sections (ND, n=8; sAAb+, n=7; mAAb+, n=7; T1D+, n=6) were probed with a panel of 22 antibodies to investigate mechanisms underlying β-cell dysfunction including insulin, CD3, ATP5B (a mitochondrial ATP synthase subunit), and ATPIF1 (ATP synthase inhibitor). Results: Islets within LPTS from ND and AAb+ donors exhibited typical Ca2+ mobilization in response to high glucose (HG) and potassium chloride (KCl) stimulations. Islets from T1D+ donors had significantly diminished Ca2+ responses to HG compared to ND donors (p<0.05, one-way ANOVA). Low glucose and KCl stimulations between ND, AAb+, and T1D+donors were similar, indicating that the dysfunction occurs in the glucose metabolism pathway. Additionally, there were no significant differences in HG responses in insulitic and non-insulitic islets from T1D+ LPTS with both being dysfunctional. Fixed tissue staining revealed a significant (p<0.0001, two-way ANOVA) decrease in mitochondria in β cells from T1D+ donors. Conclusion: The loss of HG responses in T1D+ β cells independent of the local T cell infiltration, coupled with the decrease in mitochondria, indicates that the dysfunction likely originates in the β cell and contributes to T1D pathogenesis. Disclosure M. Huber: None. A. Widener: None. D. Smurlick: None. H. Hiller: None. M. Beery: None. E. Verney: None. I. Kusmartseva: None. M. Campbell-Thompson: None. M.A. Atkinson: None. C.E. Mathews: None. E. Phelps: Research Support; Immunocore, Ltd, MESO SCALE DIAGNOSTICS, LLC. Funding T32DK108736F31DK130607P01; AI42288 R01DK132387

Multiplexed bimolecular profiling of tissue microenvironment, or spatial omics, can provide deep insight into cellular compositions and interactions in healthy and diseased tissues. Proteome-scale tissue mapping, which aims to unbiasedly visualize all the proteins in a whole tissue section or region of interest, has attracted significant interest because it holds great potential to directly reveal diagnostic biomarkers and therapeutic targets. While many approaches are available, however, proteome mapping still exhibits significant technical challenges in both protein coverage and analytical throughput. Since many of these existing challenges are associated with mass spectrometry-based protein identification and quantification, we performed a detailed benchmarking study of three protein quantification methods for spatial proteome mapping, including label-free, TMT-MS2, and TMT-MS3. Our study indicates label-free method provided the deepest coverages of ∼3500 proteins at a spatial resolution of 50 μm and the highest quantification dynamic range, while TMT-MS2 method holds great benefit in mapping throughput at >125 pixels per day. The evaluation also indicates both label-free and TMT-MS2 provides robust protein quantifications in identifying differentially abundant proteins and spatially covariable clusters. In the study of pancreatic islet microenvironment, we demonstrated deep proteome mapping not only enables the identification of protein markers specific to different cell types, but more importantly, it also reveals unknown or hidden protein patterns by spatial coexpression analysis.