High-dimensional Analysis Research Articles

Amantadine modulates novel macrophage phenotypes to enhance neural repair following spinal cord injury

BackgroundSpinal cord injury (SCI) triggers a complex inflammatory response that impedes neural repair and functional recovery. The modulation of macrophage phenotypes is thus considered a promising therapeutic strategy to mitigate inflammation and promote regeneration.MethodsWe employed microarray and single-cell RNA sequencing (scRNA-seq) to investigate gene expression changes and immune cell dynamics in mice following crush injury at 3 and 7 days post-injury (dpi). High-dimensional gene co-expression network analysis (hdWGCNA) and slingshot trajectory analysis were employed to identify key gene modules and macrophage differentiation pathways. Subsequently, immunofluorescence staining, flow cytometry, and western blotting were performed to validate the identified effects of amantadine on macrophage differentiation and inflammation.ResultsTo elucidate the molecular mechanisms underlying the injury response at the transcriptional level, we performed a microarray analysis followed by gene set enrichment analysis (GSEA). The results revealed that pathways related to phagocytosis and macrophage activation are significantly involved post-injury, shedding light on the regulatory role of macrophages in SCI repair. To further investigate macrophage dynamics within the injured spinal cord, we conducted scRNA-Seq, identifying three distinct macrophage subtypes: border-associated macrophages (BAMs), inflammatory macrophages (IMs), and chemotaxis-inducing macrophages (CIMs). Trajectory analysis suggested a differentiation pathway from Il-1b+ IMs to Mrc1+ BAMs, and subsequently to Arg1+ CIMs, indicating a potential maturation process. Given the importance of these pathways in the injury response, we utilized molecular docking to hypothesize that amantadine might modulate this process. Subsequent in vitro and in vivo experiments demonstrated that amantadine reduces Il-1b+ IMs and facilitates the transition to Mrc1+ BAMs and Arg1+ CIMs, likely through modulation of the HIF-1α and NF-κB pathways. This modulation promotes neural regeneration and enhances functional recovery following SCI.ConclusionsAmantadine modulates macrophage phenotypes following SCI, reduces early inflammatory responses, and enhances neural function recovery. These findings highlight the therapeutic potential of amantadine as a treatment for SCI, and provide a foundation for future translational research into its clinical applications.

Characterizing Microglial Signaling Dynamics During Inflammation Using Single-Cell Mass Cytometry.

Microglia play a critical role in maintaining central nervous system (CNS) homeostasis and display remarkable plasticity in their response to inflammatory stimuli. However, the specific signaling profiles that microglia adopt during such challenges remain incompletely understood. Traditional transcriptomic approaches provide valuable insights, but fail to capture dynamic post-translational changes. In this study, we utilized time-resolved single-cell mass cytometry (CyTOF) to measure distinct signaling pathways activated in microglia upon exposure to bacterial and viral mimetics-lipopolysaccharide (LPS) and polyinosinic-polycytidylic acid (Poly(I:C)), respectively. Furthermore, we evaluated the immunomodulatory role of astrocytes on microglial signaling in mixed cultures. Microglia or mixed cultures derived from neonatal mice were treated with LPS or Poly(I:C) for 48 h. Cultures were stained with a panel of 33 metal-conjugated antibodies targeting signaling and identity markers. High-dimensional clustering analysis was used to identify emergent signaling modules. We found that LPS treatment led to more robust early activation of pp38, pERK, pRSK, and pCREB compared to Poly(I:C). Despite these differences, both LPS and Poly(I:C) upregulated the classical reactivity markers CD40 and CD86 at later time points. Strikingly, the presence of astrocytes significantly blunted microglial responses to both stimuli, particularly dampening CD40 upregulation. Our studies demonstrate that single-cell mass cytometry effectively captures the dynamic signaling landscape of microglia under pro-inflammatory conditions. This approach may pave the way for targeted therapeutic investigations of various neuroinflammatory disorders. Moreover, our findings underscore the necessity of considering cellular context, such as astrocyte presence, in interpreting microglial behavior during inflammation.

Research on charging patterns of electric taxis based on high-dimensional cluster analysis: a case study of Hangzhou, China

Research on charging patterns of electric taxis based on high-dimensional cluster analysis: a case study of Hangzhou, China

High-dimensional Subgroup Regression Analysis

High-dimensional Subgroup Regression Analysis

A high-dimensional reliability analysis method via subspace updating and Gaussian process

A high-dimensional reliability analysis method via subspace updating and Gaussian process

Safety of baricitinib in vaccinated patients with severe and critical COVID-19 sub study of the randomised Bari-SolidAct trial.

The Bari-SolidAct randomized controlled trial compared baricitinib with placebo in patients with severe COVID-19. A post hoc analysis revealed a higher incidence of serious adverse events (SAEs) among SARS-CoV-2-vaccinated participants who had received baricitinib. This sub-study aimed to investigate whether vaccination influences the safety profile of baricitinib in patients with severe COVID-19. Biobanked samples from 146 participants (55 vaccinated vs. 91 unvaccinated) were analysed longitudinally for inflammation markers, humoral responses, tissue viral loads, and plasma viral antigens on days 1, 3, and 8. High-dimensional analyses, including RNA sequencing and flow cytometry, were performed on available samples. Mediation analyses were used to assess relationships between SAEs, baseline-adjusted biomarkers, and treatment-vaccination status. Vaccinated participants were older, more frequently hospitalized, had more comorbidities, and exhibited higher nasopharyngeal viral loads. Baricitinib treatment did not affect antibody responses or viral clearance, but reduced markers of T-cell and monocyte activation compared to placebo (sCD25, sCD14, sCD163, sTIM-3). Age, baseline levels of plasma viral antigen, and several inflammatory markers, as well as IL-2, IL-6, Neopterin, CXCL16, sCD14, and suPAR on day 8 were associated with the occurrence of SAEs. However, mediation analyses of markers linked to SAEs, baricitinib treatment, or vaccination status did not reveal statistically significant interactions between vaccination status and SAEs. This sub-study did not identify any virus- or host-related biomarkers significantly associated with the interaction between SARS-CoV-2 vaccination status and the safety of baricitinib. However, caution should be exercised due to the moderate sample size. EU Horizon 2020 (grant number 101015736).

UniFORM: Towards Universal Immunofluorescence Normalization for Multiplex Tissue Imaging.

Multiplexed tissue imaging (MTI) technologies enable high-dimensional spatial analysis of tumor microenvironments but face challenges with technical variability in staining intensities. Existing normalization methods, including z-score, ComBat, and MxNorm, often fail to account for the heterogeneous, right-skewed expression patterns of MTI data, compromising signal alignment and downstream analyses. We present UniFORM, a non-parametric, Python-based pipeline for normalizing both feature- and pixel-level MTI data. UniFORM preserves marker intensity distribution shapes while maintaining positive population proportions without prior assumptions and uses automated normalization. Benchmarking across two distinct MTI platforms and datasets demonstrates that UniFORM outperforms existing methods in mitigating batch effects while maintaining biological signal fidelity. This is evidenced by improved marker distribution alignment, enhanced kBET scores, and improved downstream analyses such as UMAP visualizations and Leiden clustering. UniFORM also introduces a novel guided fine-tuning option for complex and heterogeneous datasets. Although optimized for fluorescence-based platforms, UniFORM provides a scalable and robust solution for MTI data normalization, enabling accurate and biologically meaningful interpretations.

CAFE: An Integrated Web App for High-Dimensional Analysis and Visualization in Spectral Flow Cytometry.

Spectral flow cytometry provides greater insights into cellular heterogeneity by simultaneous measurement of up to 50 markers. However, analyzing such high-dimensional (HD) data is complex through traditional manual gating strategy. To address this gap, we developed CAFE as an open-source Python-based web application with a graphical user interface. Built with Streamlit, CAFE incorporates libraries such as Scanpy for single-cell analysis, Pandas and PyArrow for efficient data handling, and Matplotlib, Seaborn, Plotly for creating customizable figures. Its robust toolset includes density-based down-sampling, dimensionality reduction, batch correction, Leiden-based clustering, cluster merging and annotation. Using CAFE, we demonstrated analysis of a human PBMC dataset of 350,000 cells identifying 16 distinct cell clusters. CAFE can generate publication-ready figures in real time via interactive slider controls and dropdown menus, eliminating the need for coding expertise and making HD data analysis accessible to all. CAFE is licensed under MIT and is freely available at https://github.com/mhbsiam/cafe .

Partially factorized variational inference for high-dimensional mixed models

Abstract While generalized linear mixed models are a fundamental tool in applied statistics, many specifications, such as those involving categorical factors with many levels or interaction terms, can be computationally challenging to estimate due to the need to compute or approximate high-dimensional integrals. Variational inference is a popular way to perform such computations, especially in the Bayesian context. However, naive use of such methods can provide unreliable uncertainty quantification. We show that this is indeed the case for mixed models, proving that standard mean-field variational inference dramatically underestimates posterior uncertainty in high-dimensions. We then show how appropriately relaxing the mean-field assumption leads to methods whose uncertainty quantification does not deteriorate in high-dimensions, and whose total computational cost scales linearly with the number of parameters and observations. Our theoretical and numerical results focus on mixed models with Gaussian or binomial likelihoods, and rely on connections to random graph theory to obtain sharp high-dimensional asymptotic analysis. We also provide generic results, which are of independent interest, relating the accuracy of variational inference to the convergence rate of the corresponding coordinate ascent algorithm that is used to find it. Our proposed methodology is implemented in the R package vglmer. Numerical results with simulated and real data examples illustrate the favourable computation cost versus accuracy trade-off of our approach compared to various alternatives.

A novel deterministic sampling approach for the reliability analysis of high-dimensional structures

Overcoming the “curse of dimensionality” in high-dimensional reliability analysis is still an enduring challenge. This paper proposes an innovative deterministic sampling method designed to overcome this challenge. The approach starts with a two-dimensional uniform point set, generated using the good lattice point method. This set is then refined through the cutting method to produce a specific number of points. A novel generating vector is computed based on this method, enabling the generation of the targeted high-dimensional point set through a strategic dimension-by-dimension mapping. Notably, this method eliminates the need for complex congruence computation and primitive root optimization, enhancing its efficiency for high-dimensional sampling. The resulting point set is deterministic and uniform, greatly reducing variability in reliability analysis. Then, the proposed approach is integrated into the fractional exponential moment-based maximum entropy method with the Box–Cox transform. This integration efficiently recovers the probability distribution for the limit state function (LSF) with high-dimensional inputs, enabling precise assessment of the failure probability. The efficacy of the proposed method is demonstrated through three high-dimensional numerical examples, involving both explicit and implicit LSFs, highlighting its applicability for high-dimensional reliability analysis of structures.

Abstract B069: Spatial and single-cell proteomic landscaping of the hypoxic microenvironment in glioblastoma

Abstract Glioblastoma (GBM) is a fatal adult solid tumour with median overall survival of 18-20 months post-diagnosis; contributing factors to therapeutic inefficacy include acquisition of genomic alterations post-therapy, immune evasion, deregulated hypervascularization, and tumor microenvironmental factors such as hypoxia. Combined with extensive inter- and intra-tumoral heterogeneity at bulk and single-cell level, hypoxia contributes to a gradient of molecular alterations that are specific to different cell populations that make up tumour bulk and reside in specific niches. Hitherto, high-dimensional histopathologic analyses of hypoxic regions within GBM tissue have not been performed. We took a combined spatial and single-cell proteomic profiling approach to investigate the histopathologic features of hypoxia by leveraging a unique clinical study wherein the exogenous hypoxia marker, pimonidazole (PIMO), was administered to GBM-patients preoperatively. Tissue specimens were subjected to imaging mass cytometry, high-resolution imaging, and serial immunohistochemistry using a panel of markers associated with cellular hallmarks of hypoxia, metabolism, proliferation, stemness, angiogenesis, and immune cell types. Our findings showed that PIMO staining is associated with histopathologic features of hypoxia and correlates with specific metabolic, immune, and stemness markers in GBM; specific lymphocyte populations were depleted from hypoxic regions alongside alterations in macrophagic and microglial landscape in a niche-specific manner; hypoxia reduced the proportion of proliferating glioma initiating cells and altered the proliferative, transcriptional, and translational capacity of different cell populations; microvessel density was reduced in hypoxic microenvironment. Cytometry by time-of-flight further validated the altered proportions of specific immune cell types enriched in hypoxic populations of GBM microenvironment. Our study is the first to report use of PIMO to interrogate spatial, single-cell, and phenotypic architecture associated with tissue hypoxia and altered expression of biomarkers associated with hypoxia/glycolysis, immune infiltration, proliferation, and stemness. Identification of targetable biomarkers and mediators of hypoxia-driven habitats in GBM may provide direction for future immunotherapeutic research. Citation Format: Shreya Gandhi, Sheila Mansouri, Olivia Singh, Mark Zaidi, Ronald Wu, Andrew Gao, Bradley Wouters, Gelareh Zadeh. Spatial and single-cell proteomic landscaping of the hypoxic microenvironment in glioblastoma [abstract]. In: Proceedings of the AACR Special Conference: Liquid Biopsy: From Discovery to Clinical Implementation; 2024 Nov 13-16; San Diego, CA. Philadelphia (PA): AACR; Clin Cancer Res 2024;30(21_Suppl):Abstract nr B069.

IMMU-60. MYELOID POPULATIONS MODULATE GD2 CAR T CELL ACTIVITY IN DIFFUSE MIDLINE GLIOMA

Abstract H3K27M-mutated diffuse midline gliomas (DMGs) are universally lethal cancers in children and young adults. Our team previously demonstrated efficacy of GD2-targeting chimeric antigen receptor T cells (GD2-CAR T-cells) in preclinical models of DMG of the pons (also called diffuse intrinsic pontine glioma (DIPG) and DMG of the spinal cord, and opened a Phase I clinical trial (NCT04196413) treating patients with first intravenous (IV) followed by repeated infusions of intracerebroventricular (ICV) GD2-CAR T-cells. We employed high-dimensional analyses to define immune states contributing to CAR-T activity in patients. Single cell RNA-sequencing (scRNAseq) was conducted on 555,406 single cells from 115 cerebrospinal fluid (CSF) samples of 13 patients before and after CAR-T treatment. This is the largest CSF CAR-T dataset in central nervous system (CNS) tumors and provides insights into the immune biology surrounding CAR-T treatment for CNS malignancies. Patient CSF samples were dominated by T cell and myeloid populations. After CAR-T infusion, patient CSF exhibited an increased fraction of regulatory T cells and myeloid populations from baseline. Myeloid cells in early timepoints after ICV administration demonstrated a unique pro-inflammatory signature, while CSF samples from IV and late ICV timepoints exhibited a suppressive signature. To further explore the immune biology of these myeloid contributors, we developed a patient-derived xenograft model of DMG relapse following low-dose ICV GD2 CAR-T treatment. Using a pharmacological CSF1R-inhibitor, we demonstrate that depletion of microglia/myeloid cells at a specific window following CAR-T administration enhances durability of tumor control. Together, these data display the power of in-depth correlative analyses to identify distinct immune populations that drive durability of response. Key findings from these data will allow for optimization of CAR-T therapies for H3K27M+ DMG patients, providing hope to shift the paradigm of this fatal disease.

High-dimensional analysis of NK cells in kidney transplantation uncovers subsets associated with antibody-independent graft dysfunction.

Natural killer (NK) cells respond to diseased and allogeneic cells through NKG2A/HLA-E or killer cell immunoglobulin-like receptor (KIR)/HLA-ABC interactions. Correlations between HLA/KIR disparities and kidney transplant pathology suggest an antibody-independent pathogenic role for NK cells in transplantation, but the mechanisms remain unclear. Using CyTOF to characterize recipient peripheral NK cell phenotypes and function, we observed diverse NK cell subsets among participants who responded heterogeneously to allo-stimulators. NKG2A+KIR+ NK cells responded more vigorously than other subsets, and this heightened response persisted after kidney transplantation despite immunosuppression. In test and validation sets from 2 clinical trials, pretransplant donor-induced release of cytotoxicity mediator Ksp37 by NKG2A+ NK cells correlated with reduced long-term allograft function. Separate analyses showed that Ksp37 gene expression in allograft biopsies lacking histological rejection correlated with death-censored graft loss. Our findings support an antibody-independent role for NK cells in transplant injury and support further testing of pretransplant, donor-reactive, NK cell-produced Ksp37 as a risk-assessing, transplantation biomarker.

Fast and universal single-molecule localization using multi-dimensional point spread functions

The recent development of single-molecule imaging techniques has enabled not only high accuracy spatial resolution imaging but also information rich functional imaging. Abundant information about single molecules can be encoded in its diffraction pattern and be extracted precisely (e.g. 3D position, wavelength, dipole orientation). However, sophisticated high dimensional point spread function (PSF) modeling and analyzing methods have greatly impeded the broad accessibility of these techniques. Here, we present a graphics processing unit (GPU) -based B-spline PSF modeling method that could flexibly model high dimensional PSFs with arbitrary shape without greatly increasing the model parameters. Our B-spline fitter achieves 100 times speed improvement and minimal uncertainty for each dimension, enabling efficient high dimensional single-molecule analysis. We demonstrated, both in simulations and experiments, the universality and flexibility of our B-spline fitter to accurately extract the abundant information from different types of high dimensional single-molecule data, including multicolor PSF (3D + color), multi-channel four-dimensional 4Pi-PSF (3D + interference phase) and five-dimensional vortex PSF (3D + dipole orientation).

Causal Mediation Analysis for Integrating Exposure, Genomic, and Phenotype Data

Causal mediation analysis provides an attractive framework for integrating diverse types of exposure, genomic, and phenotype data. Recently, this field has seen a surge of interest, largely driven by the increasing need for causal mediation analyses in health and social sciences. This article aims to provide a review of recent developments in mediation analysis, encompassing mediation analysis of a single mediator and a large number of mediators, as well as mediation analysis with multiple exposures and mediators. Our review focuses on the recent advancements in statistical inference for causal mediation analysis, especially in the context of high-dimensional mediation analysis. We delve into the complexities of testing mediation effects, especially addressing the challenge of testing a large number of composite null hypotheses. Through extensive simulation studies, we compare the existing methods across a range of scenarios. We also include an analysis of data from the Normative Aging Study, which examines DNA methylation CpG sites as potential mediators of the effect of smoking status on lung function. We discuss the pros and cons of these methods and future research directions.

Development of a Spectral Flow Cytometry Analysis Pipeline for High-dimensional Immune Cell Characterization.

Flow cytometry is used for immune cell analysis for cell composition and function. Spectral flow cytometry allows for high-dimensional analysis of immune cells, overcoming limitations of conventional flow cytometry. However, analyzing data from large Ab panels is challenging using traditional biaxial gating strategies. We present, to our knowledge, a novel analysis pipeline to improve analysis of spectral flow cytometry. We employ this method to identify rare T cell populations in aging. We isolated splenocytes from young (2-3 mo old) and aged (18-19 mo old) female C57BL/6N mice and then stained these with a panel of 20 fluorescently labeled Abs. We performed spectral flow cytometry and then data processing and analysis using Python within a Jupyter Notebook environment to perform dimensionality reduction, batch correction, unsupervised clustering, and differential expression analysis. Our analysis of 3,776,804 T cells from 11 spleens revealed 35 distinct T cell clusters identified by surface marker expression. We observed significant differences between young and aged mice, with clusters enriched in one age group over the other. Naive, effector memory, and central memory CD8+ and CD4+ T cell subsets exhibited age-associated changes in abundance and marker expression. We also demonstrate the utility of our pipeline in a human PBMC dataset that used a 50-fluorescent color panel. By leveraging high-dimensional analysis methods, we provide insights into the immune aging process. This approach offers a robust and easily implemented analysis pipeline for spectral flow cytometry data that may facilitate the discovery of novel therapeutic targets for age-related immune dysfunction.

Pathogenesis of interstitial lung disease in systemic sclerosis.

Interstitial lung disease (ILD) is a frequent important complication of systemic sclerosis (SSc). Factors relevant to aetiopathogenesis of SSc are also central to SSc-ILD. Severity of SSc-ILD is variable but it has a major impact on morbidity and mortality. Factors determining SSc-ILD susceptibility reflect the genetic architecture of SSc and are increasingly being defined. There are aspects linked to immunogenomics and non-immunological genetic factors that may be less conserved and underlie some of the geographical and racial diversity of SSc. These associations may also underlie important links between autoantibody subgroups and patient level risk of SSc-ILD. Examination of blood and tissue samples and observational clinical research together with integrated analysis of in vitro and in vivo preclinical models have elucidated pathogenic mechanisms of SSc-ILD. These have confirmed the potential importance of immune mechanisms in the innate and adaptive immune systemic as well as a significant role for profibrotic pathways especially transforming growth factor beta (TGFbeta) and its regulators and downstream mediators. Recent analysis of clinical trial cohorts as well as integrated and multilevel high dimensional analysis of bio-samples has shed further light on SSc-ILD. This is likely to underpin future advances in stratified and precision medicine for treatment of SSc.

Global Stochastic Comprehensive Sensitivity Analysis Based on Robustness Grouping and Improved Pelican Algorithm-Optimized Radial Basis Function Neural Network

Abstract Modeling analysis is one of the important means to analyze practical engineering, and as technology continues to evolve, various models are getting closer and closer to reality, while at the same time, there are more and more parameters in the models. It is important to analyse the impact of these parameters on the project to assist engineers in making plans or decisions. Sensitivity analysis (SA) can describe the effect of changes in these parameters on the model. However, complex models often have dozens or even hundreds of parameters, and most current SA methods struggle to deal reliably and effectively with these high-dimensional problems. In addition, it is difficult to obtain the sensitivity of continuous points in the parameter space with traditional SA methods. Therefore, this paper proposes a method that combines adaptive grouping and an Improved Pelican Optimization Algorithm for an optimal radial basis function (IPOA-RBF) agent model to solve these problems. Firstly, a clustering grouping method considering grouping robustness is established to obtain objective and stable parameter grouping results in high-dimensional sensitivity analysis. Secondly, a proxy model based on radial basis function neural network and an improved Pelican optimization algorithm are proposed to capture the logic of the proxy model to obtain the parameter sensitivity of continuous points in the parameter space. Finally, the superiority and applicability of this method is verified using an arch dam simulation model.

The potential mediating role of the gut microbiome and metabolites in the association between PFAS and kidney function in young adults: A proof-of-concept study

BackgroundChronic kidney disease (CKD) affects over 10 % of the global population and can lead to kidney failure and death. Exposure to per- and polyfluoroalkyl substances (PFAS) is associated with increased risk of CKD, yet studies examining the mechanisms linking PFAS and kidney function are lacking. In this exploratory study, we examined longitudinal associations of PFAS exposure with kidney function, and tested if associations were mediated by altered gut bacterial taxa or plasma metabolites using a multi-omics mediation analysis. MethodsSeventy-eight young adults from the Children's Health Study were included in this longitudinal cohort study. At baseline, seven plasma PFAS and untargeted plasma metabolomics were measured using liquid chromatography/mass-spectrometry. Baseline gut bacterial abundance was characterized using 16S rRNA sequencing and examined at the genus level. At follow-up, serum creatinine and cystatin-C concentrations were quantified to estimate glomerular filtration rate (eGFR). High-dimensional multi-omics analyses were conducted to assess the association between baseline PFAS exposure with follow-up eGFR, mediated by gut microbiome and circulating metabolite levels. ResultsPFAS burden score, a variable developed to estimate exposure to chemical mixtures, was associated with kidney function. Each standard deviation increase in baseline PFAS burden score was associated with a 2.4 % lower eGFR at follow-up (95 % CI:[0.1 %,4.8 %]). Following high-dimensional mediation analyses with the microbiome and circulating metabolites, a joint component (characterized by reduced Lachnospiraceae and 17b-estradiol and increased succinate, retinoate and dodecanoic acid) and a metabolite component (characterized by increased hypotaurine and decreased D-pinitol and ureidopropionate) mediated 38 % and 50 % of the effect between PFAS burden score and eGFR, respectively. ConclusionOur proof-of-concept analysis provides the first evidence that reduced short-chain fatty acid-producing bacteria and anti-inflammatory metabolites may link PFAS exposure with impaired kidney function. This study raises the possibility of future targeted interventions that can alter gut microbiome or circulating metabolite profiles to prevent PFAS induced kidney damage.

Quantitative analysis of islet prohormone convertase 1/3 expression in human pancreas donors with diabetes

Aims/hypothesisIslet prohormone-processing enzymes convert peptide hormone precursors to mature hormones. Defective beta cell prohormone processing and the release of incompletely processed peptide hormones are observed prior to the onset of diabetes, yet molecular mechanisms underlying impaired prohormone processing during the development of diabetes remains largely unknown. Previous studies have shown that prohormone convertase 1/3 (PC1/3) protein and mRNA expression levels are reduced in whole islets from donors with type 1 diabetes, although whether PC1/3-mediated prohormone processing in alpha and beta cells is disrupted in type 1 diabetes remained to be explored. Herein, we aimed to analyse the expression of PC1/3 in islets from non-diabetic donors, autoantibody-positive donors and donors diagnosed with type 1 diabetes or type 2 diabetes.MethodsImmunostaining and high-dimensional image analysis were performed on pancreatic sections from a cross-sectional cohort of 54 donors obtained from the Network for Pancreatic Organ Donors with Diabetes (nPOD) repository, to evaluate PC1/3 expression patterns in islet alpha, beta and delta cells at different stages of diabetes.ResultsAlpha and beta cell morphology were altered in donors with type 1 diabetes, including decreased alpha and beta cell size. As expected, the insulin-positive and PC1/3-positive areas in the islets were both reduced, and this was accompanied by a reduced percentage of PC1/3-positive and insulin-positive/PC1/3-positive cells in islets. PC1/3 and insulin co-localisation was also reduced. The glucagon-positive area, as well as the percentage of glucagon-positive and glucagon-positive/PC1/3-positive cells in islets, was increased. PC1/3 and glucagon co-localisation was also increased in donors with type 1 diabetes. The somatostatin-positive cell area and somatostatin staining intensity were elevated in islets from donors with recent-onset type 1 diabetes.Conclusions/interpretationOur high-resolution histomorphological analysis of human pancreatic islets from donors with and without diabetes has uncovered details of the cellular origin of islet prohormone peptide processing defects. Reduced beta cell PC1/3 and increased alpha cell PC1/3 in islets from donors with type 1 diabetes pinpointed the functional deterioration of beta cells and the concomitant potential increase in PC1/3 usage for prohormone processing in alpha cells during the pathogenesis of type 1 diabetes. Our finding of PC1/3 loss in beta cells may inform the discovery of new prohormone biomarkers as indicators of beta cell dysfunction, and the finding of elevated PC1/3 expression in alpha cells may encourage the design of therapeutic targets via leveraging alpha cell adaptation in diabetes.Graphical