A Map of Human Type 1 Diabetes Progression by Imaging Mass Cytometry

The Extra-Islet Pancreas Supports Autoimmunity in Human Type 1 Diabetes

In autoimmune Type 1 diabetes (T1D), immune cells infiltrate and destroy the islets of Langerhans - islands of endocrine tissue dispersed throughout the pancreas. However, the contribution of cellular programs outside islets to insulitis is unclear. Here, using CO-Detection by indEXing (CODEX) tissue imaging and cadaveric pancreas samples, we simultaneously examine islet and extra-islet inflammation in human T1D. We identify four sub-states of inflamed islets characterized by the activation profiles of CD8 + T cells enriched in islets relative to the surrounding tissue. We further find that the extra-islet space of lobules with extensive islet-infiltration differs from the extra-islet space of less infiltrated areas within the same tissue section. Finally, we identify lymphoid structures away from islets enriched in CD45RA + T cells - a population also enriched in one of the inflamed islet sub-states. Together, these data help define the coordination between islets and the extra-islet pancreas in the pathogenesis of human T1D.

Network approach reveals preferential T-cell and macrophage association with α-linked β-cells in early stage of insulitis in NOD mice

One of the challenges in studying islet inflammation - insulitis - is that it is a transient phenomenon. Traditional reporting of the insulitis progression is based on cumulative, donor-averaged values of leucocyte density in the vicinity of pancreatic islets, that hinders intra- and inter-islet heterogeneity of disease progression. Here, we aimed to understand why insulitis is non-uniform, often with peri-insulitis lesions formed on one side of an islet. To achieve this, we demonstrated applicability of network theory in detangling intra-islet multi-cellular interactions during insulitis. Specifically, we asked the question "what is unique about regions of the islet which interact with immune cells first". This study utilized the non-obese diabetic mouse model of type one diabetes and examined the interplay among α-, β-, T-cells, myeloid cells, and macrophages in pancreatic islets during the progression of insulitis. Disease evolution was tracked based on T/β cell ratio in individual islets. In the early stage, we found that immune cells are preferentially interacting with α-cell-rich regions of an islet. At the islet periphery α-linked β-cells were found to be targeted significantly more compared to those without α-cell neighbors. Additionally, network analysis revealed increased T-myeloid, and T-macrophage interactions with all β-cells.

AnnoSpat annotates cell types and quantifies cellular arrangements from spatial proteomics

Cellular composition and anatomical organization influence normal and aberrant organ functions. Emerging spatial single-cell proteomic assays such as Image Mass Cytometry (IMC) and Co-Detection by Indexing (CODEX) have facilitated the study of cellular composition and organization by enabling high-throughput measurement of cells and their localization directly in intact tissues. However, annotation of cell types and quantification of their relative localization in tissues remain challenging. To address these unmet needs for atlas-scale datasets like Human Pancreas Analysis Program (HPAP), we develop AnnoSpat (Annotator and Spatial Pattern Finder) that uses neural network and point process algorithms to automatically identify cell types and quantify cell-cell proximity relationships. Our study of data from IMC and CODEX shows the higher performance of AnnoSpat in rapid and accurate annotation of cell types compared to alternative approaches. Moreover, the application of AnnoSpat to type 1 diabetic, non-diabetic autoantibody-positive, and non-diabetic organ donor cohorts recapitulates known islet pathobiology and shows differential dynamics of pancreatic polypeptide (PP) cell abundance and CD8+ T cells infiltration in islets during type 1 diabetes progression.

High-multiplex single-cell imaging analysis reveals tumor immune contexture associated with clinical outcomes after CAR T cell therapy

Chimeric antigen receptor (CAR) T cell therapy has made great progress in treating lymphoma, yet patient outcomes still vary greatly. The lymphoma microenvironment may be an important factor in the efficacy of CAR T therapy. In this study, we designed a highly multiplexed imaging mass cytometry (IMC) panel to simultaneously quantify 31 biomarkers from 13 patients with relapsed/refractory diffuse large B cell lymphoma (DLBCL) who received CAR19/22 T cell therapy. A total of 20 sections were sampled before CAR T cell infusion or after infusion when relapse occurred. A total of 35 cell clusters were identified, annotated, and subsequently redefined into 10 metaclusters. The CD4+ T cell fraction was positively associated with remission duration. Significantly higher Ki67, CD57, and TIM3 levels and lower CD69 levels in T cells, especially the CD8+/CD4+ Tem and Te cell subsets, were seen in patients with poor outcomes. Cellular neighborhood containing more immune cells was associated with longer remission. Fibroblasts and vascular endothelial cells resided much closer to tumor cells in patients with poor response and short remission after CAR T therapy. Our work comprehensively and systematically dissects the relationship between cell composition, state, and spatial arrangement in the DLBCL microenvironment and the outcomes of CAR T cell therapy, which is beneficial to predict CAR T therapy efficacy.

Spatially resolved immune exhaustion within the alloreactive microenvironment predicts liver transplant rejection

Allograft rejection is common following clinical organ transplantation, but defining specific immune subsets mediating alloimmunity has been elusive. Calcineurin inhibitor dose escalation, corticosteroids, and/or lymphocyte depleting antibodies have remained the primary options for treatment of clinical rejection episodes. Here, we developed a highly multiplexed imaging mass cytometry panel to study the immune response in archival biopsies from 79 liver transplant (LT) recipients with either no rejection (NR), acute T cell-mediated rejection (TCMR), or chronic rejection (CR). This approach generated a spatially resolved proteomic atlas of 461,816 cells (42 phenotypes) derived from 96 pathologist-selected regions of interest. Our analysis revealed that regulatory (HLADR+ Treg) and PD1+ T cell phenotypes (CD4+ and CD8+ subsets), combined with variations in M2 macrophage polarization, were a unique signature of active TCMR. These data provide insights into the alloimmune microenvironment in clinical LT, including identification of potential targets for focused immunotherapy during rejection episodes and suggestion of a substantial role for immune exhaustion in TCMR.

SpaceANOVA: Spatial Co-occurrence Analysis of Cell Types in Multiplex Imaging Data Using Point Process and Functional ANOVA

Multiplex imaging platforms have enabled the identification of the spatial organization of different types of cells in complex tissue or the tumor microenvironment. Exploring the potential variations in the spatial co-occurrence or colocalization of different cell types across distinct tissue or disease classes can provide significant pathological insights, paving the way for intervention strategies. However, the existing methods in this context either rely on stringent statistical assumptions or suffer from a lack of generalizability. We present a highly powerful method to study differential spatial co-occurrence of cell types across multiple tissue or disease groups, based on the theories of the Poisson point process and functional analysis of variance. Notably, the method accommodates multiple images per subject and addresses the problem of missing tissue regions, commonly encountered due to data-collection complexities. We demonstrate the superior statistical power and robustness of the method in comparison with existing approaches through realistic simulation studies. Furthermore, we apply the method to three real data sets on different diseases collected using different imaging platforms. In particular, one of these data sets reveals novel insights into the spatial characteristics of various types of colorectal adenoma.

Applicability of Spatial Technology in Cancer Research

This review explores spatial mapping technologies in cancer research, highlighting their crucial role in understanding the complexities of the tumor microenvironment (TME). The TME, which is an intricate ecosystem of diverse cell types, has a significant impact on tumor dynamics and treatment outcomes. This review closely examines cutting-edge spatial mapping technologies, categorizing them into capture-, imaging-, and antibody-based approaches. Each technology was scrutinized for its advantages and disadvantages, factoring in aspects such as spatial profiling area, multiplexing capabilities, and resolution. Additionally, we draw attention to the nuanced choices researchers face, with capture-based methods lending themselves to hypothesis generation, and imaging/antibody-based methods that fit neatly into hypothesis testing. Looking ahead, we anticipate a scenario in which multi-omics data are seamlessly integrated, artificial intelligence enhances data analysis, and spatiotemporal profiling opens up new dimensions.

Characterization of Vascular Niche in Systemic Sclerosis by Spatial Proteomics

BACKGROUND

Systemic sclerosis (SSc) is a connective tissue disease that can serve as a model to study vascular changes in response to inflammation, autoimmunity, and fibrotic remodeling. Although microvascular changes are the earliest histopathologic manifestation of SSc, the vascular pathophysiology remains poorly understood.

METHODS

We applied spatial proteomic approaches to deconvolute the heterogeneity of vascular cells at the single-cell level in situ and characterize cellular alterations of the vascular niches of patients with SSc. Skin biopsies of patients with SSc and control individuals were analyzed by imaging mass cytometry, yielding a total of 90 755 cells including 2987 endothelial cells and 4096 immune cells.

RESULTS

We identified 7 different subpopulations of blood vascular endothelial cells (VECs), 2 subpopulations of lymphatic endothelial cells, and 3 subpopulations of pericytes. A novel population of CD34+;αSMA+ (α-smooth muscle actin);CD31+ VECs was more common in SSc, whereas endothelial precursor cells were decreased. Co-detection by indexing and tyramide signal amplification confirmed these findings. The microenvironment of CD34+;αSMA+;CD31+ VECs was enriched for immune cells and myofibroblasts, and CD34+;αSMA+;CD31+ VECs expressed markers of endothelial-to-mesenchymal transition. The density of CD34+;αSMA+;CD31+ VECs was associated with clinical progression of fibrosis in SSc.

CONCLUSIONS

Using spatial proteomics, we unraveled the heterogeneity of vascular cells in control individuals and patients with SSc. We identified CD34+;αSMA+;CD31+ VECs as a novel endothelial cell population that is increased in patients with SSc, expresses markers for endothelial-to-mesenchymal transition, and is located in close proximity to immune cells and myofibroblasts. CD34+;αSMA+;CD31+ VEC counts were associated with clinical outcomes of progressive fibrotic remodeling, thus providing a novel cellular correlate for the crosstalk of vasculopathy and fibrosis.

Multiplex protein imaging in tumour biology

Tissue imaging has become much more colourful in the past decade. Advances in both experimental and analytical methods now make it possible to image protein markers in tissue samples in high multiplex. The ability to routinely image 40-50 markers simultaneously, at single-cell or subcellular resolution, has opened up new vistas in the study of tumour biology. Cellular phenotypes, interaction, communication and spatial organization have become amenable to molecular-level analysis, and application to patient cohorts has identified clinically relevant cellular and tissue features in several cancer types. Here, we review the use of multiplex protein imaging methods to study tumour biology, discuss ongoing attempts to combine these approaches with other forms of spatial omics, and highlight challenges in the field.

Innate and adaptive immune cell interaction drives inflammasome activation and hepatocyte apoptosis in murine liver injury from immune checkpoint inhibitors

Immune checkpoints (CTLA4 & PD-1) are inhibitory pathways that block aberrant immune activity and maintain self-tolerance. Tumors co-opt these checkpoints to avoid immune destruction. Immune checkpoint inhibitors (ICIs) activate immune cells and restore their tumoricidal potential, making them highly efficacious cancer therapies. However, immunotolerant organs such as the liver depend on these tolerogenic mechanisms, and their disruption with ICI use can trigger the unintended side effect of hepatotoxicity termed immune-mediated liver injury from ICIs (ILICI). Learning how to uncouple ILICI from ICI anti-tumor activity is of paramount clinical importance. We developed a murine model to recapitulate human ILICI using CTLA4+/- mice treated with either combined anti-CTLA4 + anti-PDL1 or IgG1 + IgG2. We tested two forms of antisense oligonucleotides to knockdown caspase-3 in a total liver (parenchymal and non-parenchymal cells) or in a hepatocyte-specific manner. We also employed imaging mass cytometry (IMC), a powerful multiplex modality for immunophenotyping and cell interaction analysis in our model. ICI-treated mice had significant evidence of liver injury. We detected cleaved caspase-3 (cC3), indicating apoptosis was occurring, as well as Nod-like receptor protein 3 (NLRP3) inflammasome activation, but no necroptosis. Total liver knockdown of caspase-3 worsened liver injury, and induced further inflammasome activation, and Gasdermin-D-mediated pyroptosis. Hepatocyte-specific knockdown of caspase-3 reduced liver injury and NLRP3 inflammasome activation. IMC-generated single-cell data for 77,692 cells was used to identify 22 unique phenotypic clusters. Spatial analysis revealed that cC3+ hepatocytes had significantly closer interactions with macrophages, Kupffer cells, and NLRP3hi myeloid cells than other cell types. We also observed zones of three-way interaction between cC3+ hepatocytes, CD8 + T-cells, and macrophages. Our work is the first to identify hepatocyte apoptosis and NLRP3 inflammasome activation as drivers of ILICI. Furthermore, we report that the interplay between adaptive and innate immune cells is critical to hepatocyte apoptosis and ILICI.

Establishing evidence for immune surveillance of β-cell senescence

Cellular senescence is a programmed state of cell cycle arrest that involves a complex immunogenic secretome, eliciting immune surveillance and senescent cell clearance. Recent work has shown that a subpopulation of pancreatic β-cells becomes senescent in the context of diabetes; however, it is not known whether these cells are normally subject to immune surveillance. In this opinion article, we advance the hypothesis that immune surveillance of β-cells undergoing a senescence stress response normally limits their accumulation during aging and that the breakdown of these mechanisms is a driver of senescent β-cell accumulation in diabetes. Elucidation and therapeutic activation of immune surveillance mechanisms in the pancreas holds promise for the improvement of approaches to target stressed senescent β-cells in the treatment of diabetes.

The immunology of type 1 diabetes

Following the seminal discovery of insulin a century ago, treatment of individuals with type 1 diabetes (T1D) has been largely restricted to efforts to monitor and treat metabolic glucose dysregulation. The recent regulatory approval of the first immunotherapy that targets T cells as a means to delay the autoimmune destruction of pancreatic β-cells highlights the critical role of the immune system in disease pathogenesis and tends to pave the way for other immune-targeted interventions for T1D. Improving the efficacy of such interventions across the natural history of the disease will probably require a more detailed understanding of the immunobiology of T1D, as well as technologies to monitor residual β-cell mass and function. Here we provide an overview of the immune mechanisms that underpin the pathogenesis of T1D, with a particular emphasis on T cells.

Insulitis in human type 1 diabetes: lessons from an enigmatic lesion

Type 1 diabetes is caused by a deficiency of insulin secretion which has been considered traditionally as the outcome of a precipitous decline in the viability of β-cells in the islets of Langerhans, brought about by autoimmune-mediated attack. Consistent with this, various classes of lymphocyte, as well as cells of the innate immune system have been found in association with islets during disease progression. However, analysis of human pancreas from subjects with type 1 diabetes has revealed that insulitis is often less intense than in equivalent animal models of the disease and can affect many fewer islets than expected, at disease onset. This is especially true in subjects developing type 1 diabetes in, or beyond, their teenage years. Such studies imply that both the phenotype and the number of immune cells present within insulitic lesions can vary among individuals in an age-dependent manner. Additionally, the influent lymphocytes are often mainly arrayed peripherally around islets rather than gaining direct access to the endocrine cell core. Thus, insulitis remains an enigmatic phenomenon in human pancreas and this review seeks to explore the current understanding of its likely role in the progression of type 1 diabetes.

cytoviewer: an R/Bioconductor package for interactive visualization and exploration of highly multiplexed imaging data

BACKGROUND

Highly multiplexed imaging enables single-cell-resolved detection of numerous biological molecules in their spatial tissue context. Interactive visualization of multiplexed imaging data is crucial at any step of data analysis to facilitate quality control and the spatial exploration of single cell features. However, tools for interactive visualization of multiplexed imaging data are not available in the statistical programming language R.

RESULTS

Here, we describe cytoviewer, an R/Bioconductor package for interactive visualization and exploration of multi-channel images and segmentation masks. The cytoviewer package supports flexible generation of image composites, allows side-by-side visualization of single channels, and facilitates the spatial visualization of single-cell data in the form of segmentation masks. As such, cytoviewer improves image and segmentation quality control, the visualization of cell phenotyping results and qualitative validation of hypothesis at any step of data analysis. The package operates on standard data classes of the Bioconductor project and therefore integrates with an extensive framework for single-cell and image analysis. The graphical user interface allows intuitive navigation and little coding experience is required to use the package. We showcase the functionality and biological application of cytoviewer by analysis of an imaging mass cytometry dataset acquired from cancer samples.

CONCLUSIONS

The cytoviewer package offers a rich set of features for highly multiplexed imaging data visualization in R that seamlessly integrates with the workflow for image and single-cell data analysis. It can be installed from Bioconductor via https://www.bioconductor.org/packages/release/bioc/html/cytoviewer.html . The development version and further instructions can be found on GitHub at https://github.com/BodenmillerGroup/cytoviewer .

Spatial subsetting enables integrative modeling of oral squamous cell carcinoma multiplex imaging data

Oral squamous cell carcinoma (OSCC), a prevalent and aggressive neoplasm, poses a significant challenge due to poor prognosis and limited prognostic biomarkers. Leveraging highly multiplexed imaging mass cytometry, we investigated the tumor immune microenvironment (TIME) in OSCC biopsies, characterizing immune cell distribution and signaling activity at the tumor-invasive front. Our spatial subsetting approach standardized cellular populations by tissue zone, improving feature reproducibility and revealing TIME patterns accompanying loss-of-differentiation. Employing a machine-learning pipeline combining reliable feature selection with multivariable modeling, we achieved accurate histological grade classification (AUC = 0.88). Three model features correlated with clinical outcomes in an independent cohort: granulocyte MAPKAPK2 signaling at the tumor front, stromal CD4+ memory T cell size, and the distance of fibroblasts from the tumor border. This study establishes a robust modeling framework for distilling complex imaging data, uncovering sentinel characteristics of the OSCC TIME to facilitate prognostic biomarkers discovery for recurrence risk stratification and immunomodulatory therapy development.

Rarity: discovering rare cell populations from single-cell imaging data

MOTIVATION

Cell type identification plays an important role in the analysis and interpretation of single-cell data and can be carried out via supervised or unsupervised clustering approaches. Supervised methods are best suited where we can list all cell types and their respective marker genes a priori, while unsupervised clustering algorithms look for groups of cells with similar expression properties. This property permits the identification of both known and unknown cell populations, making unsupervised methods suitable for discovery. Success is dependent on the relative strength of the expression signature of each group as well as the number of cells. Rare cell types therefore present a particular challenge that is magnified when they are defined by differentially expressing a small number of genes.

RESULTS

Typical unsupervised approaches fail to identify such rare subpopulations, and these cells tend to be absorbed into more prevalent cell types. In order to balance these competing demands, we have developed a novel statistical framework for unsupervised clustering, named Rarity, that enables the discovery process for rare cell types to be more robust, consistent, and interpretable. We achieve this by devising a novel clustering method based on a Bayesian latent variable model in which we assign cells to inferred latent binary on/off expression profiles. This lets us achieve increased sensitivity to rare cell populations while also allowing us to control and interpret potential false positive discoveries. We systematically study the challenges associated with rare cell type identification and demonstrate the utility of Rarity on various IMC datasets.

AVAILABILITY AND IMPLEMENTATION

Implementation of Rarity together with examples is available from the Github repository (https://github.com/kasparmartens/rarity).

Type 1 diabetes in the pancreas: A histological perspective

AIMS

This review aims to introduce research in the pancreas to a broader audience. The pancreas is a heterocrine gland residing deep within our abdominal cavity. It is the home to our islets, which play a pivotal role in regulating metabolic homeostasis. Due to its structure and location, it is an impossible organ to study, in molecular detail, in living humans, and yet, understanding the pancreas is critical if we aim to characterise the immunopathology of type 1 diabetes (T1D) and one day prevent the triggering of the autoimmune attack associated with ß-cell demise.

METHODS

Over a 100 years ago, we began studying pancreatic histology using cadaveric samples and clever adaptations to microscopes. As histologists, some may say nothing much has changed. Nevertheless, our microscopes can now interrogate multiple proteins at molecular resolution. Images of pancreas sections are no longer constrained to a single field of view and can capture a thousands and thousands of cells. AI-image-analysis packages can analyse these massive data sets offering breakthrough findings.

CONCLUSION

This narrative review will provide an overview of pancreatic anatomy, and the importance of research focused on the pancreas in T1D. It will range from histological breakthroughs to briefly discussing the challenges associated with characterising the organ. I shall briefly introduce a selection of the available global biobanks and touch on the distinct pancreatic endotypes that differ immunologically and in ß-cell behaviour. Finally, I will introduce the idea of developing a collaborative tool aimed at developing a cohesive framework for characterising heterogeneity and stratifying endotypes in T1D more readily.

The beta cell-immune cell interface in type 1 diabetes (T1D)

BACKGROUND

T1D is an autoimmune disease in which pancreatic islets of Langerhans are infiltrated by immune cells resulting in the specific destruction of insulin-producing islet beta cells. Our understanding of the factors leading to islet infiltration and the interplay of the immune cells with target beta cells is incomplete, especially in human disease. While murine models of T1D have provided crucial information for both beta cell and autoimmune cell function, the translation of successful therapies in the murine model to human disease has been a challenge.

SCOPE OF REVIEW

Here, we discuss current state of the art and consider knowledge gaps concerning the interface of the islet beta cell with immune infiltrates, with a focus on T cells. We discuss pancreatic and immune cell phenotypes and their impact on cell function in health and disease, which we deem important to investigate further to attain a more comprehensive understanding of human T1D disease etiology.

MAJOR CONCLUSIONS

The last years have seen accelerated development of approaches that allow comprehensive study of human T1D. Critically, recent studies have contributed to our revised understanding that the pancreatic beta cell assumes an active role, rather than a passive position, during autoimmune disease progression. The T cell-beta cell interface is a critical axis that dictates beta cell fate and shapes autoimmune responses. This includes the state of the beta cell after processing internal and external cues (e.g., stress, inflammation, genetic risk) that that contributes to the breaking of tolerance by hyperexpression of human leukocyte antigen (HLA) class I with presentation of native and neoepitopes and secretion of chemotactic factors to attract immune cells. We anticipate that emerging insights about the molecular and cellular aspects of disease initiation and progression processes will catalyze the development of novel and innovative intervention points to provide additional therapies to individuals affected by T1D.

Mutual modulation of gut microbiota and the immune system in type 1 diabetes models

The transgenic 116C-NOD mouse strain exhibits a prevalent Th17 phenotype, and reduced type 1 diabetes (T1D) compared to non-obese diabetic (NOD) mice. A cohousing experiment between both models revealed lower T1D incidence in NOD mice cohoused with 116C-NOD, associated with gut microbiota changes, reduced intestinal permeability, shifts in T and B cell subsets, and a transition from Th1 to Th17 responses. Distinct gut bacterial signatures were linked to T1D in each group. Using a RAG-2-/- genetic background, we found that T cell alterations promoted segmented filamentous bacteria proliferation in young NOD and 116C-NOD, as well as in immunodeficient NOD.RAG-2-/- and 116C-NOD.RAG-2-/- mice across all ages. Bifidobacterium colonization depended on lymphocytes and thrived in a non-diabetogenic environment. Additionally, 116C-NOD B cells in 116C-NOD.RAG-2-/- mice enriched the gut microbiota in Adlercreutzia and reduced intestinal permeability. Collectively, these results indicate reciprocal modulation between gut microbiota and the immune system in rodent T1D models.

Footprint of pancreas infiltrating and circulating immune cells throughout type 1 diabetes development

Introduction

Type 1 diabetes (T1D) is defined by immune cell infiltration of the pancreas, in particular the islets of Langerhans, referred to as insulitis, which is especially prominent during the early disease stages in association with decreased beta cell mass. An in-depth understanding of the dynamics and phenotype of the immune cells infiltrating the pancreas and the accompanying changes in their profiles in peripheral blood during T1D development is critical to generate novel preventive and therapeutic approaches, as well as to find biomarkers for the disease process.

Methods

Using multi-parameter flow cytometry, we explored the dynamic changes of immune cells infiltrating the pancreas and the pancreatic draining lymph nodes (PLN), compared to those in peripheral blood in female and male non-obese diabetic (NOD) mice during T1D progression.

Results

The early stages of T1D development were characterized by an influx of innate dendritic cells and neutrophils in the pancreas. While dendritic cells seemed to move in and out (to the PLN), neutrophils accumulated during the pre-symptomatic phase and reached a maximum at 8 weeks of age, after which their numbers declined. During disease progression, CD4+ and CD8+ T cells appeared to continuously migrate from the PLN to the pancreas, which coincided with an increase in beta cell autoimmunity and insulitis severity, and a decline in insulin content. At 12 weeks of age, CD4+ and especially CD8+ T cells in the pancreas showed a dramatic shift from naïve to effector memory phenotype, in contrast to the PLN, where most of these cells remained naïve. A large proportion of pancreas infiltrating CD4+ T cells were naïve, indicating that antigenic stimulation was not necessary to traffic and invade the pancreas. Interestingly, a pre-effector-like T cell dominated the peripheral blood. These cells were intermediates between naïve and effector memory cells as identified by single cell RNA sequencing and might be a potential novel therapeutic target.

Conclusion

These time- and tissue-dependent changes in the dynamics and functional states of CD4+ and CD8+ T cells are essential steps in our understanding of the disease process in NOD mice and need to be considered for the interpretation and design of disease-modifying therapies.

Chemical tagging mass spectrometry: an approach for single-cell omics

Single-cell (SC) analysis offers new insights into the study of fundamental biological phenomena and cellular heterogeneity. The superior sensitivity, high throughput, and rich chemical information provided by mass spectrometry (MS) allow MS to emerge as a leading technology for molecular profiling of SC omics, including the SC metabolome, lipidome, and proteome. However, issues such as ionization suppression, low concentration, and huge span of dynamic concentrations of SC components lead to poor MS response for certain types of molecules. It is noted that chemical tagging/derivatization has been adopted in SCMS analysis, and this strategy has been proven an effective solution to circumvent these issues in SCMS analysis. Herein, we review the basic principle and general strategies of chemical tagging/derivatization in SCMS analysis, along with recent applications of chemical derivatization to single-cell metabolomics and multiplexed proteomics, as well as SCMS imaging. Furthermore, the challenges and opportunities for the improvement of chemical derivatization strategies in SCMS analysis are discussed.

An end-to-end workflow for multiplexed image processing and analysis

Multiplexed imaging enables the simultaneous spatial profiling of dozens of biological molecules in tissues at single-cell resolution. Extracting biologically relevant information, such as the spatial distribution of cell phenotypes from multiplexed tissue imaging data, involves a number of computational tasks, including image segmentation, feature extraction and spatially resolved single-cell analysis. Here, we present an end-to-end workflow for multiplexed tissue image processing and analysis that integrates previously developed computational tools to enable these tasks in a user-friendly and customizable fashion. For data quality assessment, we highlight the utility of napari-imc for interactively inspecting raw imaging data and the cytomapper R/Bioconductor package for image visualization in R. Raw data preprocessing, image segmentation and feature extraction are performed using the steinbock toolkit. We showcase two alternative approaches for segmenting cells on the basis of supervised pixel classification and pretrained deep learning models. The extracted single-cell data are then read, processed and analyzed in R. The protocol describes the use of community-established data containers, facilitating the application of R/Bioconductor packages for dimensionality reduction, single-cell visualization and phenotyping. We provide instructions for performing spatially resolved single-cell analysis, including community analysis, cellular neighborhood detection and cell-cell interaction testing using the imcRtools R/Bioconductor package. The workflow has been previously applied to imaging mass cytometry data, but can be easily adapted to other highly multiplexed imaging technologies. This protocol can be implemented by researchers with basic bioinformatics training, and the analysis of the provided dataset can be completed within 5-6 h. An extended version is available at https://bodenmillergroup.github.io/IMCDataAnalysis/ .

The impact of similarity metrics on cell-type clustering in highly multiplexed in situ imaging cytometry data

Motivation

The advent of highly multiplexed in situ imaging cytometry assays has revolutionized the study of cellular systems, offering unparalleled detail in observing cellular activities and characteristics. These assays provide comprehensive insights by concurrently profiling the spatial distribution and molecular features of numerous cells. In navigating this complex data landscape, unsupervised machine learning techniques, particularly clustering algorithms, have become essential tools. They enable the identification and categorization of cell types and subsets based on their molecular characteristics. Despite their widespread adoption, most clustering algorithms in use were initially developed for cell suspension technologies, leading to a potential mismatch in application. There is a critical gap in the systematic evaluation of these methods, particularly in determining the properties that make them optimal for in situ imaging assays. Addressing this gap is vital for ensuring accurate, reliable analyses and fostering advancements in cellular biology research.

Results

In our extensive investigation, we evaluated a range of similarity metrics, which are crucial in determining the relationships between cells during the clustering process. Our findings reveal substantial variations in clustering performance, contingent on the similarity metric employed. These variations underscore the importance of selecting appropriate metrics to ensure accurate cell type and subset identification. In response to these challenges, we introduce FuseSOM, a novel ensemble clustering algorithm that integrates hierarchical multiview learning of similarity metrics with self-organizing maps. Through a rigorous stratified subsampling analysis framework, we demonstrate that FuseSOM outperforms existing best-practice clustering methods specifically tailored for in situ imaging cytometry data. Our work not only provides critical insights into the performance of clustering algorithms in this novel context but also offers a robust solution, paving the way for more accurate and reliable in situ imaging cytometry data analysis.

Availability and implementation

The FuseSOM R package is available on Bioconductor and is available under the GPL-3 license. All the codes for the analysis performed can be found at Github.

Global proteomics reveals insulin abundance as a marker of human islet homeostasis alterations

AIM

The variation in quality between the human islet samples represents a major problem for research, especially when used as control material. The assays assessing the quality of human islets used in research are non-standardized and limited, with many important parameters not being consistently assessed. High-throughput studies aimed at characterizing the diversity and segregation markers among apparently functionally healthy islet preps are thus a requirement. Here, we designed a pilot study to comprehensively identify the diversity of global proteome signatures and the deviation from normal homeostasis in randomly selected human-isolated islet samples.

METHODS

By using Tandem Mass Tag 16-plex proteomics, we focused on the recurrently observed disparity in the detected insulin abundance between the samples, used it as a segregating parameter, and analyzed the correlated changes in the proteome signature and homeostasis by pathway analysis.

RESULTS

In this pilot study, we showed that insulin protein abundance is a predictor of human islet homeostasis and quality. This parameter is independent of other quality predictors within their acceptable range, thus being able to further stratify islets samples of apparent good quality. Human islets with low amounts of insulin displayed changes in their metabolic and signaling profile, especially in regard to energy homeostasis and cell identity maintenance. We further showed that xenotransplantation into diabetic hosts is not expected to improve the pre-transplantation signature, as it has a negative effect on energy balance, antioxidant activity, and islet cell identity.

CONCLUSIONS

Insulin protein abundance predicts significant changes in human islet homeostasis among random samples of apparently good quality.

A Supportive Role of Mesenchymal Stem Cells on Insulin-Producing Langerhans Islets with a Specific Emphasis on The Secretome

Type 1 Diabetes (T1D) is a chronic autoimmune disease characterized by a gradual destruction of insulin-producing β-cells in the endocrine pancreas due to innate and specific immune responses, leading to impaired glucose homeostasis. T1D patients usually require regular insulin injections after meals to maintain normal serum glucose levels. In severe cases, pancreas or Langerhans islet transplantation can assist in reaching a sufficient β-mass to normalize glucose homeostasis. The latter procedure is limited because of low donor availability, high islet loss, and immune rejection. There is still a need to develop new technologies to improve islet survival and implantation and to keep the islets functional. Mesenchymal stem cells (MSCs) are multipotent non-hematopoietic progenitor cells with high plasticity that can support human pancreatic islet function both in vitro and in vivo and islet co-transplantation with MSCs is more effective than islet transplantation alone in attenuating diabetes progression. The beneficial effect of MSCs on islet function is due to a combined effect on angiogenesis, suppression of immune responses, and secretion of growth factors essential for islet survival and function. In this review, various aspects of MSCs related to islet function and diabetes are described.

S3-CIMA: Supervised spatial single-cell image analysis for identifying disease-associated cell-type compositions in tissue

The spatial organization of various cell types within the tissue microenvironment is a key element for the formation of physiological and pathological processes, including cancer and autoimmune diseases. Here, we present S3-CIMA, a weakly supervised convolutional neural network model that enables the detection of disease-specific microenvironment compositions from high-dimensional proteomic imaging data. We demonstrate the utility of this approach by determining cancer outcome- and cellular-signaling-specific spatial cell-state compositions in highly multiplexed fluorescence microscopy data of the tumor microenvironment in colorectal cancer. Moreover, we use S3-CIMA to identify disease-onset-specific changes of the pancreatic tissue microenvironment in type 1 diabetes using imaging mass-cytometry data. We evaluated S3-CIMA as a powerful tool to discover novel disease-associated spatial cellular interactions from currently available and future spatial biology datasets.

The pathogenic "symphony" in type 1 diabetes: A disorder of the immune system, β cells, and exocrine pancreas

Type 1 diabetes (T1D) is widely considered to result from the autoimmune destruction of insulin-producing β cells. This concept has been a central tenet for decades of attempts seeking to decipher the disorder's pathogenesis and prevent/reverse the disease. Recently, this and many other disease-related notions have come under increasing question, particularly given knowledge gained from analyses of human T1D pancreas. Perhaps most crucial are findings suggesting that a collective of cellular constituents-immune, endocrine, and exocrine in origin-mechanistically coalesce to facilitate T1D. This review considers these emerging concepts, from basic science to clinical research, and identifies several key remaining knowledge voids.

Self-supervised deep learning for highly efficient spatial immunophenotyping

BACKGROUND

Efficient biomarker discovery and clinical translation depend on the fast and accurate analytical output from crucial technologies such as multiplex imaging. However, reliable cell classification often requires extensive annotations. Label-efficient strategies are urgently needed to reveal diverse cell distribution and spatial interactions in large-scale multiplex datasets.

METHODS

This study proposed Self-supervised Learning for Antigen Detection (SANDI) for accurate cell phenotyping while mitigating the annotation burden. The model first learns intrinsic pairwise similarities in unlabelled cell images, followed by a classification step to map learnt features to cell labels using a small set of annotated references. We acquired four multiplex immunohistochemistry datasets and one imaging mass cytometry dataset, comprising 2825 to 15,258 single-cell images to train and test the model.

FINDINGS

With 1% annotations (18-114 cells), SANDI achieved weighted F1-scores ranging from 0.82 to 0.98 across the five datasets, which was comparable to the fully supervised classifier trained on 1828-11,459 annotated cells (-0.002 to -0.053 of averaged weighted F1-score, Wilcoxon rank-sum test, P = 0.31). Leveraging the immune checkpoint markers stained in ovarian cancer slides, SANDI-based cell identification reveals spatial expulsion between PD1-expressing T helper cells and T regulatory cells, suggesting an interplay between PD1 expression and T regulatory cell-mediated immunosuppression.

INTERPRETATION

By striking a fine balance between minimal expert guidance and the power of deep learning to learn similarity within abundant data, SANDI presents new opportunities for efficient, large-scale learning for histology multiplex imaging data.

FUNDING

This study was funded by the Royal Marsden/ICR National Institute of Health Research Biomedical Research Centre.

A platform-independent framework for phenotyping of multiplex tissue imaging data

Multiplex imaging is a powerful tool to analyze the structural and functional states of cells in their morphological and pathological contexts. However, hypothesis testing with multiplex imaging data is a challenging task due to the extent and complexity of the information obtained. Various computational pipelines have been developed and validated to extract knowledge from specific imaging platforms. A common problem with customized pipelines is their reduced applicability across different imaging platforms: Every multiplex imaging technique exhibits platform-specific characteristics in terms of signal-to-noise ratio and acquisition artifacts that need to be accounted for to yield reliable and reproducible results. We propose a pixel classifier-based image preprocessing step that aims to minimize platform-dependency for all multiplex image analysis pipelines. Signal detection and noise reduction as well as artifact removal can be posed as a pixel classification problem in which all pixels in multiplex images can be assigned to two general classes of either I) signal of interest or II) artifacts and noise. The resulting feature representation maps contain pixel-scale representations of the input data, but exhibit significantly increased signal-to-noise ratios with normalized pixel values as output data. We demonstrate the validity of our proposed image preprocessing approach by comparing the results of two well-accepted and widely-used image analysis pipelines.

Molecular architecture of proliferative lupus nephritis as elucidated using 50-plex imaging mass cytometry proteomics

Due to unique advantages that allow high-dimensional tissue profiling, we postulated imaging mass cytometry (IMC) may shed novel insights on the molecular makeup of proliferative lupus nephritis (LN). This study interrogates the spatial expression profiles of 50 target proteins in LN and control kidneys. Proliferative LN glomeruli are marked by podocyte loss with immune infiltration dominated by CD45RO+, HLA-DR+ memory CD4 and CD8 T-cells, and CD163+ macrophages, with similar changes in tubulointerstitial regions. Macrophages are the predominant HLA-DR expressing antigen presenting cells with little expression elsewhere, while macrophages and T-cells predominate cellular crescents. End-stage sclerotic glomeruli are encircled by an acellular fibro-epithelial Bowman's space surrounded by immune infiltrates, all enmeshed in fibronectin. Proliferative LN also shows signs indicative of epithelial to mesenchymal plasticity of tubular cells and parietal epithelial cells. IMC enabled proteomics is a powerful tool to delineate the spatial architecture of LN at the protein level.

Islet cells in human type 1 diabetes: from recent advances to novel therapies - a symposium-based roadmap for future research

There is a growing understanding that the early phases of type 1 diabetes (T1D) are characterised by a deleterious dialogue between the pancreatic beta cells and the immune system. This, combined with the urgent need to better translate this growing knowledge into novel therapies, provided the background for the JDRF-DiabetesUK-INNODIA-nPOD symposium entitled 'Islet cells in human T1D: from recent advances to novel therapies', which took place in Stockholm, Sweden, in September 2022. We provide in this article an overview of the main themes addressed in the symposium, pointing to both promising conclusions and key unmet needs that remain to be addressed in order to achieve better approaches to prevent or reverse T1D.

Acquired resistance to anti-PD1 therapy in patients with NSCLC associates with immunosuppressive T cell phenotype

Immune checkpoint inhibitor treatment has the potential to prolong survival in non-small cell lung cancer (NSCLC), however, some of the patients develop resistance following initial response. Here, we analyze the immune phenotype of matching tumor samples from a cohort of NSCLC patients showing good initial response to immune checkpoint inhibitors, followed by acquired resistance at later time points. By using imaging mass cytometry and whole exome and RNA sequencing, we detect two patterns of resistance¨: One group of patients is characterized by reduced numbers of tumor-infiltrating CD8+ T cells and reduced expression of PD-L1 after development of resistance, whereas the other group shows high CD8+ T cell infiltration and high expression of PD-L1 in addition to markedly elevated expression of other immune-inhibitory molecules. In two cases, we detect downregulation of type I and II IFN pathways following progression to resistance, which could lead to an impaired anti-tumor immune response. This study thus captures the development of immune checkpoint inhibitor resistance as it progresses and deepens our mechanistic understanding of immunotherapy response in NSCLC.

Computational Methods for Single-Cell Proteomics

Advances in single-cell proteomics technologies have resulted in high-dimensional datasets comprising millions of cells that are capable of answering key questions about biology and disease. The advent of these technologies has prompted the development of computational tools to process and visualize the complex data. In this review, we outline the steps of single-cell and spatial proteomics analysis pipelines. In addition to describing available methods, we highlight benchmarking studies that have identified advantages and pitfalls of the currently available computational toolkits. As these technologies continue to advance, robust analysis tools should be developed in tandem to take full advantage of the potential biological insights provided by these data.

Fecal microbiota transplantation plus anti-PD-1 immunotherapy in advanced melanoma: a phase I trial

Fecal microbiota transplantation (FMT) represents a potential strategy to overcome resistance to immune checkpoint inhibitors in patients with refractory melanoma; however, the role of FMT in first-line treatment settings has not been evaluated. We conducted a multicenter phase I trial combining healthy donor FMT with the PD-1 inhibitors nivolumab or pembrolizumab in 20 previously untreated patients with advanced melanoma. The primary end point was safety. No grade 3 adverse events were reported from FMT alone. Five patients (25%) experienced grade 3 immune-related adverse events from combination therapy. Key secondary end points were objective response rate, changes in gut microbiome composition and systemic immune and metabolomics analyses. The objective response rate was 65% (13 of 20), including four (20%) complete responses. Longitudinal microbiome profiling revealed that all patients engrafted strains from their respective donors; however, the acquired similarity between donor and patient microbiomes only increased over time in responders. Responders experienced an enrichment of immunogenic and a loss of deleterious bacteria following FMT. Avatar mouse models confirmed the role of healthy donor feces in increasing anti-PD-1 efficacy. Our results show that FMT from healthy donors is safe in the first-line setting and warrants further investigation in combination with immune checkpoint inhibitors. ClinicalTrials.gov identifier NCT03772899 .

Robust phenotyping of highly multiplexed tissue imaging data using pixel-level clustering

While technologies for multiplexed imaging have provided an unprecedented understanding of tissue composition in health and disease, interpreting this data remains a significant computational challenge. To understand the spatial organization of tissue and how it relates to disease processes, imaging studies typically focus on cell-level phenotypes. However, images can capture biologically important objects that are outside of cells, such as the extracellular matrix. Here, we describe a pipeline, Pixie, that achieves robust and quantitative annotation of pixel-level features using unsupervised clustering and show its application across a variety of biological contexts and multiplexed imaging platforms. Furthermore, current cell phenotyping strategies that rely on unsupervised clustering can be labor intensive and require large amounts of manual cluster adjustments. We demonstrate how pixel clusters that lie within cells can be used to improve cell annotations. We comprehensively evaluate pre-processing steps and parameter choices to optimize clustering performance and quantify the reproducibility of our method. Importantly, Pixie is open source and easily customizable through a user-friendly interface.

Decreased Expression of CD314 by NK Cells Correlates with Their Ability to Respond by Producing IFN-γ after BCG Moscow Vaccination and Is Associated with Distinct Early Immune Responses

The immune response to vaccines is complex and results in various outcomes. BCG vaccination induces innate and specific responses that can lead to protection against tuberculosis, and cross-protection against other infections. NK cells have been associated with BCG-induced protection. Therefore, we hypothesize that differences in NK cell status before BCG vaccination may have a role in the ability of BCG to activate the immune response. Participants of a clinical trial were evaluated after BCG vaccination. The participants were assigned to different groups according to variation in IFN-γ expression by NK cells between days 1 and 15 after BCG vaccination. Individuals that presented a higher increase in IFN-γ expression by NK cells presented reduced CD314 expression at day 1, and after vaccination an increase in inflammatory NK cells and CD4 T-cell expression of IL-17. A negative correlation between expression of CD314 at day 1 and that of IFN-γ by NK cells after BCG vaccination was observed. Participants with lower of IFN-γ expression by NK cells after BCG vaccination presented an increase in the cytotoxic NK subpopulation and CD4 T-cell expression of IL-17 and IFN-γ. In conclusion, the expression of CD314 by NK cells before BCG vaccination influences their IFN-γ responses, generation of NK subpopulations, and the specific T immune response at 15 days after vaccination.

How do parasitic worms prevent diabetes? An exploration of their influence on macrophage and β-cell crosstalk

Diabetes is the fastest growing chronic disease globally, with prevalence increasing at a faster rate than heart disease and cancer. While the disease presents clinically as chronic hyperglycaemia, two distinct subtypes have been recognised. Type 1 diabetes (T1D) is characterised as an autoimmune disease in which the insulin-producing pancreatic β-cells are destroyed, and type 2 diabetes (T2D) arises due to metabolic insufficiency, in which inadequate amounts of insulin are produced, and/or the actions of insulin are diminished. It is now apparent that pro-inflammatory responses cause a loss of functional β-cell mass, and this is the common underlying mechanism of both T1D and T2D. Macrophages are the central immune cells in the pathogenesis of both diseases and play a major role in the initiation and perpetuation of the proinflammatory responses that compromise β-cell function. Furthermore, it is the crosstalk between macrophages and β-cells that orchestrates the inflammatory response and ensuing β-cell dysfunction/destruction. Conversely, this crosstalk can induce immune tolerance and preservation of β-cell mass and function. Thus, specifically targeting the intercellular communication between macrophages and β-cells offers a unique strategy to prevent/halt the islet inflammatory events underpinning T1D and T2D. Due to their potent ability to regulate mammalian immune responses, parasitic worms (helminths), and their excretory/secretory products, have been examined for their potential as therapeutic agents for both T1D and T2D. This research has yielded positive results in disease prevention, both clinically and in animal models. However, the focus of research has been on the modulation of immune cells and their effectors. This approach has ignored the direct effects of helminths and their products on β-cells, and the modulation of signal exchange between macrophages and β-cells. This review explores how the alterations to macrophages induced by helminths, and their products, influence the crosstalk with β-cells to promote their function and survival. In addition, the evidence that parasite-derived products interact directly with endocrine cells to influence their communication with macrophages to prevent β-cell death and enhance function is discussed. This new paradigm of two-way metabolic conversations between endocrine cells and macrophages opens new avenues for the treatment of immune-mediated metabolic disease.

SpaceANOVA: Spatial co-occurrence analysis of cell types in multiplex imaging data using point process and functional ANOVA

Motivation

Multiplex imaging platforms have enabled the identification of the spatial organization of different types of cells in complex tissue or tumor microenvironment (TME). Exploring the potential variations in the spatial co-occurrence or co-localization of different cell types across distinct tissue or disease classes can provide significant pathological insights, paving the way for intervention strategies. However, the existing methods in this context either rely on stringent statistical assumptions or suffer from a lack of generalizability.

Results

We present a highly powerful method to study differential spatial co-occurrence of cell types across multiple tissue or disease groups, based on the theories of the Poisson point process (PPP) and functional analysis of variance (FANOVA). Notably, the method accommodates multiple images per subject and addresses the problem of missing tissue regions, commonly encountered in such a context due to the complex nature of the data-collection procedure. We demonstrate the superior statistical power and robustness of the method in comparison to existing approaches through realistic simulation studies. Furthermore, we apply the method to three real datasets on different diseases collected using different imaging platforms. In particular, one of these datasets reveals novel insights into the spatial characteristics of various types of precursor lesions associated with colorectal cancer.

Availability

The associated R package can be found here, https://github.com/sealx017/SpaceANOVA.

Why does the immune system destroy pancreatic β-cells but not α-cells in type 1 diabetes?

A perplexing feature of type 1 diabetes (T1D) is that the immune system destroys pancreatic β-cells but not neighbouring α-cells, even though both β-cells and α-cells are dysfunctional. Dysfunction, however, progresses to death only for β-cells. Recent findings indicate important differences between these two cell types. First, expression of BCL2L1, a key antiapoptotic gene, is higher in α-cells than in β-cells. Second, endoplasmic reticulum (ER) stress-related genes are differentially expressed, with higher expression levels of pro-apoptotic CHOP in β-cells than in α-cells and higher expression levels of HSPA5 (which encodes the protective chaperone BiP) in α-cells than in β-cells. Third, expression of viral recognition and innate immune response genes is higher in α-cells than in β-cells, contributing to the enhanced resistance of α-cells to coxsackievirus infection. Fourth, expression of the immune-inhibitory HLA-E molecule is higher in α-cells than in β-cells. Of note, α-cells are less immunogenic than β-cells, and the CD8+ T cells invading the islets in T1D are reactive to pre-proinsulin but not to glucagon. We suggest that this finding is a result of the enhanced capacity of the α-cell to endure viral infections and ER stress, which enables them to better survive early stressors that can cause cell death and consequently amplify antigen presentation to the immune system. Moreover, the processing of the pre-proglucagon precursor in enteroendocrine cells might favour immune tolerance towards this potential self-antigen compared to pre-proinsulin.

Spatial Multiplexed Protein Profiling of Cardiac Ischemia-Reperfusion Injury

BACKGROUND

Reperfusion therapy is critical to myocardial salvage in the event of a myocardial infarction but is complicated by ischemia-reperfusion injury (IRI). Limited understanding of the spatial organization of cardiac cells, which governs cellular interaction and function, has hindered the search for targeted interventions minimizing the deleterious effects of IRI.

METHODS

We used imaging mass cytometry to characterize the spatial distribution and dynamics of cell phenotypes and communities in the mouse left ventricle following IRI. Heart sections were collected from 12 cardiac segments (basal, mid-cavity, apical, and apex of the anterior, lateral, and inferior wall) and 8 time points (before ischemia [I-0H], and postreperfusion [R-0H, R-2H, R-6H, R-12H, R-1D, R-3D, R-7D]), and stained with 29 metal-isotope-tagged antibodies. Cell community analysis was performed on reconstructed images, and the most disease-relevant cell type and target protein were selected for intervention of IRI.

RESULTS

We obtained a total of 251 multiplexed images, and identified 197 063 single cells, which were grouped into 23 distinct cell communities based on the structure of cellular neighborhoods. The cellular architecture was heterogeneous throughout the ventricular wall and exhibited swift changes following IRI. Analysis of proteins with posttranslational modifications in single cells unveiled 13 posttranslational modification intensity clusters and highlighted increased H3K9me3 (tri-methylated lysine 9 of histone H3) as a key regulatory response in endothelial cells during the middle stage of IRI. Erasing H3K9 methylation, by silencing its methyltransferase Suv39h1 or overexpressing its demethylase Kdm4d in isolated endothelial cells, attenuated cardiac dysfunction and pathological remodeling following IRI. in vitro, H3K9me3 binding significantly increased at endothelial cell function-related genes upon hypoxia, suppressing tube formation, which was rescued by inhibiting H3K9me3.

CONCLUSIONS

We mapped the spatiotemporal heterogeneity of cellular phenotypes in the adult heart upon IRI, and uncovered H3K9me3 in endothelial cells as a potential therapeutic target for alleviating pathological remodeling of the heart following myocardial IRI.

Monitoring immunomodulation strategies in type 1 diabetes

Type 1 diabetes (T1D) is a T-cell mediated autoimmune disease. Short-term treatment with agents targeting T cells, B cells and inflammatory cytokines to modify the disease course resulted in a short-term pause in disease activity. Lessons learnt from these trials will be discussed in this review. It is expected that effective disease-modifying agents will become available for use in earlier stages of T1D. Progress has been made to analyze antigen-specific T cells with standardization of T cell assay and discovery of antigen epitopes but there are many challenges. High-dimensional profiling of gene, protein and TCR expression at single cell level with innovative computational tools should lead to novel biomarker discovery. With this, assays to detect, quantify and characterize the phenotype and function of antigen-specific T cells will continuously evolve. An improved understanding of T cell responses will help researchers and clinicians to better predict disease onset, and progression, and the therapeutic efficacy of interventions to prevent or arrest T1D.

cytoviewer: an R/Bioconductor package for interactive visualization and exploration of highly multiplexed imaging data

Highly multiplexed imaging enables single-cell-resolved detection of numerous biological molecules in their spatial tissue context. Interactive data visualization of multiplexed imaging data is necessary for quality control and hypothesis examination. Here, we describe cytoviewer, an R/Bioconductor package for interactive visualization and exploration of multi-channel images and segmentation masks. The cytoviewer package supports flexible generation of image composites, allows side-by-side visualization of single channels, and facilitates the spatial visualization of single-cell data in the form of segmentation masks. The package operates on SingleCellExperiment, SpatialExperiment and CytoImageList objects and therefore integrates with the Bioconductor framework for single-cell and image analysis. Users of cytoviewer need little coding expertise, and the graphical user interface allows user-friendly navigation. We showcase the functionality of cytoviewer by analysis of an imaging mass cytometry dataset of cancer patients.

The effects of hydroxytyrosol on Prdx6 and insulin expression in diabetic rat pancreases

Diabetes mellitus is a widespread endocrine disease worldwide, accompanying chronic hyperglycemia. In this study, we investigated the effect of hydroxytyrosol, which exerts an antioxidant effect, on the expressions of insulin and peroxiredoxin-6 (Prdx6), which protect cells against oxidative injury in diabetic rat pancreas. This experimental study had four groups with ten animals in each group: control (nondiabetic) group, hydroxytyrosol group [10 mg/kg/day intraperitoneal injection (ip) hydroxytyrosol for 30 days], streptozotocin group (single ip injection of 55 mg/kg streptozotocin), and streptozotocin + hydroxytyrosol group (single ip injection of streptozotocin and ip injection of 10 mg/kg/day hydroxytyrosol for 30 days). During the experiment, blood glucose levels were measured at regular intervals. Insulin expression was determined by immunohistochemistry and Prdx6 expression was determined by immunohistochemistry and western blot. Immunohistochemistry and western blot results were analyzed by one-way ANOVA with applied Holm-Sidak multiple comparison test, and blood glucose results were analyzed by two-way repeated measures ANOVA with applied Tukey's multiple comparison test. Blood glucose levels on days 21 and 28 were significantly lower in the streptozotocin + hydroxytyrosol group compared with the streptozotocin group (day 21, p = 0.049 and day 28, p = 0.003). Expression of both insulin and Prdx6 were lower in the streptozotocin and the streptozotocin + hydroxytyrosol groups compared with the control and hydroxytyrosol groups (p < 0.001). Insulin and Prdx6 expression in the streptozotocin + hydroxytyrosol group were higher compared with the streptozotocin group (p < 0.001). The immunohistochemical findings of Prdx6 and western blot were the same. In conclusion, hydroxytyrosol, which is an antioxidant compound, increased Prdx6 and insulin expression in diabetic rats. Insulin increased by hydroxytyrosol may have been effective in reducing blood glucose levels. Furthermore, hydroxytyrosol may exert its effect on insulin by increasing Prdx6 expression. Thus, hydroxytyrosol may decrease or prevent several hyperglycemia-dependent complications by increasing the expression of these proteins.

Spatial analysis with SPIAT and spaSim to characterize and simulate tissue microenvironments

Spatial proteomics technologies have revealed an underappreciated link between the location of cells in tissue microenvironments and the underlying biology and clinical features, but there is significant lag in the development of downstream analysis methods and benchmarking tools. Here we present SPIAT (spatial image analysis of tissues), a spatial-platform agnostic toolkit with a suite of spatial analysis algorithms, and spaSim (spatial simulator), a simulator of tissue spatial data. SPIAT includes multiple colocalization, neighborhood and spatial heterogeneity metrics to characterize the spatial patterns of cells. Ten spatial metrics of SPIAT are benchmarked using simulated data generated with spaSim. We show how SPIAT can uncover cancer immune subtypes correlated with prognosis in cancer and characterize cell dysfunction in diabetes. Our results suggest SPIAT and spaSim as useful tools for quantifying spatial patterns, identifying and validating correlates of clinical outcomes and supporting method development.

Single-cell atlas of the human neonatal small intestine affected by necrotizing enterocolitis

Necrotizing enterocolitis (NEC) is a gastrointestinal complication of premature infants with high rates of morbidity and mortality. A comprehensive view of the cellular changes and aberrant interactions that underlie NEC is lacking. This study aimed at filling in this gap. We combine single-cell RNA sequencing (scRNAseq), T-cell receptor beta (TCRβ) analysis, bulk transcriptomics, and imaging to characterize cell identities, interactions, and zonal changes in NEC. We find an abundance of proinflammatory macrophages, fibroblasts, endothelial cells as well as T cells that exhibit increased TCRβ clonal expansion. Villus tip epithelial cells are reduced in NEC and the remaining epithelial cells up-regulate proinflammatory genes. We establish a detailed map of aberrant epithelial-mesenchymal-immune interactions that are associated with inflammation in NEC mucosa. Our analyses highlight the cellular dysregulations of NEC-associated intestinal tissue and identify potential targets for biomarker discovery and therapeutics.

The role of endoplasmic reticulum aminopeptidases in type 1 diabetes mellitus

Type-I diabetes mellitus (T1DM) is generally considered as a chronic, T-cell mediated autoimmune disease. This notwithstanding, both the endogenous characteristics of β-cells, and their response to environmental factors and exogenous inflammatory stimuli are key events in disease progression and exacerbation. As such, T1DM is now recognized as a multifactorial condition, with its onset being influenced by both genetic predisposition and environmental factors, among which, viral infections represent major triggers. In this frame, endoplasmic reticulum aminopeptidase 1 (ERAP1) and 2 (ERAP2) hold center stage. ERAPs represent the main hydrolytic enzymes specialized in trimming of N-terminal antigen peptides to be bound by MHC class I molecules and presented to CD8+ T cells. Thus, abnormalities in ERAPs expression alter the peptide-MHC-I repertoire both quantitatively and qualitatively, fostering both autoimmune and infectious diseases. Although only a few studies succeeded in determining direct associations between ERAPs variants and T1DM susceptibility/outbreak, alterations of ERAPs do impinge on a plethora of biological events which might indeed contribute to the disease development/exacerbation. Beyond abnormal self-antigen peptide trimming, these include preproinsulin processing, nitric oxide (NO) production, ER stress, cytokine responsiveness, and immune cell recruitment/activity. The present review brings together direct and indirect evidence focused on the immunobiological role of ERAPs in T1DM onset and progression, covering both genetic and environmental aspects.

Genetic determinants of type 1 diabetes in individuals with weak evidence of islet autoimmunity at disease onset

AIMS/HYPOTHESIS

Islet autoantibodies (AAbs) are detected in >90% of individuals with clinically suspected type 1 diabetes at disease onset. A single AAb, sometimes at low titre, is often detected in some individuals, making their diagnosis uncertain. Type 1 diabetes genetic risk scores (GRS) are a useful tool for discriminating polygenic autoimmune type 1 diabetes from other types of diabetes, particularly the monogenic forms, but testing is not routinely performed in the clinic. Here, we used a type 1 diabetes GRS to screen for monogenic diabetes in individuals with weak evidence of autoimmunity, i.e. with a single AAb at disease onset.

METHODS

In a pilot study, we genetically screened 142 individuals with suspected type 1 diabetes, 42 of whom were AAb-negative, 27 of whom had a single AAb (single AAb-positive) and 73 of whom had multiple AAbs (multiple AAb-positive) at disease onset. Next-generation sequencing (NGS) was performed in 41 AAb-negative participants, 26 single AAb-positive participants and 60 multiple AAb-positive participants using an analysis pipeline of more than 200 diabetes-associated genes.

RESULTS

The type 1 diabetes GRS was significantly lower in AAb-negative individuals than in those with a single and multiple AAbs. Pathogenetic class 4/5 variants in MODY or monogenic diabetes genes were identified in 15/41 (36.6%) AAb-negative individuals, while class 3 variants of unknown significance were identified in 17/41 (41.5%). Residual C-peptide levels at diagnosis were higher in individuals with mutations compared to those without pathogenetic variants. Class 3 variants of unknown significance were found in 11/26 (42.3%) single AAb-positive individuals, and pathogenetic class 4/5 variants were present in 2/26 (7.7%) single AAb-positive individuals. No pathogenetic class 4/5 variants were identified in multiple AAb-positive individuals, but class 3 variants of unknown significance were identified in 19/60 (31.7%) patients. Several patients across the three groups had more than one class 3 variant.

CONCLUSIONS/INTERPRETATION

These findings provide insights into the genetic makeup of patients who show weak evidence of autoimmunity at disease onset. Absence of islet AAbs or the presence of a single AAb together with a low type 1 diabetes GRS may be indicative of a monogenic form of diabetes, and use of NGS may improve the accuracy of diagnosis.

IMC-Denoise: a content aware denoising pipeline to enhance Imaging Mass Cytometry

Imaging Mass Cytometry (IMC) is an emerging multiplexed imaging technology for analyzing complex microenvironments using more than 40 molecularly-specific channels. However, this modality has unique data processing requirements, particularly for patient tissue specimens where signal-to-noise ratios for markers can be low, despite optimization, and pixel intensity artifacts can deteriorate image quality and downstream analysis. Here we demonstrate an automated content-aware pipeline, IMC-Denoise, to restore IMC images deploying a differential intensity map-based restoration (DIMR) algorithm for removing hot pixels and a self-supervised deep learning algorithm for shot noise image filtering (DeepSNiF). IMC-Denoise outperforms existing methods for adaptive hot pixel and background noise removal, with significant image quality improvement in modeled data and datasets from multiple pathologies. This includes in technically challenging human bone marrow; we achieve noise level reduction of 87% for a 5.6-fold higher contrast-to-noise ratio, and more accurate background noise removal with approximately 2 × improved F1 score. Our approach enhances manual gating and automated phenotyping with cell-scale downstream analyses. Verified by manual annotations, spatial and density analysis for targeted cell groups reveal subtle but significant differences of cell populations in diseased bone marrow. We anticipate that IMC-Denoise will provide similar benefits across mass cytometric applications to more deeply characterize complex tissue microenvironments.

The extent and magnitude of islet T cell infiltration as powerful tools to define the progression to type 1 diabetes

AIMS/HYPOTHESIS

Insulitis is not present in all islets, and it is elusive in humans. Although earlier studies focused on islets that fulfilled certain criteria (e.g. ≥15 CD45⁺ cells or ≥6 CD3⁺ cells), there is a fundamental lack of understanding of the infiltration dynamics in terms of its magnitude (i.e. how much) and extent (i.e. where). Here, we aimed to perform an in-depth characterisation of T cell infiltration by investigating islets with moderate (1-5 CD3⁺ cells) and high (≥6 CD3⁺ cells) infiltration in individuals with and without type 1 diabetes.

METHODS

Pancreatic tissue sections from 15 non-diabetic, eight double autoantibody-positive and ten type 1 diabetic (0-2 years of disease duration) organ donors were obtained from the Network for Pancreatic Organ Donors with Diabetes, and stained for insulin, glucagon, CD3 and CD8 by immunofluorescence. T cell infiltration was quantified in a total of 8661 islets using the software QuPath. The percentage of infiltrated islets and islet T cell density were calculated. To help standardise the analysis of T cell infiltration, we used cell density data to develop a new T cell density threshold capable of differentiating non-diabetic and type 1 diabetic donors.

RESULTS

Our analysis revealed that 17.1% of islets in non-diabetic donors, 33% of islets in autoantibody-positive and 32.5% of islets in type 1 diabetic donors were infiltrated by 1 to 5 CD3⁺ cells. Islets infiltrated by ≥6 CD3⁺ cells were rare in non-diabetic donors (0.4%) but could be found in autoantibody-positive (4.5%) and type 1 diabetic donors (8.2%). CD8⁺ and CD8^- populations followed similar patterns. Likewise, T cell density was significantly higher in the islets of autoantibody-positive donors (55.4 CD3⁺ cells/mm²) and type 1 diabetic donors (74.8 CD3⁺ cells/mm²) compared with non-diabetic individuals (17.3 CD3⁺ cells/mm²), which was accompanied by higher exocrine T cell density in type 1 diabetic individuals. Furthermore, we showed that the analysis of a minimum of 30 islets and the use of a reference mean value for T cell density of 30 CD3⁺ cells/mm² (the 30-30 rule) can differentiate between non-diabetic and type 1 diabetic donors with high specificity and sensitivity. In addition, it can classify autoantibody-positive individuals as non-diabetic or type 1 diabetic-like.

CONCLUSIONS/INTERPRETATION

Our data indicates that the proportion of infiltrated islets and T cell density change dramatically during the course of type 1 diabetes, and these changes can be already observed in double autoantibody-positive individuals. This suggests that, as disease progresses, T cell infiltration extends throughout the pancreas, reaching the islets and exocrine compartment. While it predominantly targets insulin-containing islets, large accumulations of cells are rare. Our study fulfils the need to further understand T cell infiltration, not only after diagnosis but also in individuals with diabetes-related autoantibodies. Furthermore, the development and application of new analytical tools based on T cell infiltration, like the 30-30 rule, will allow us to correlate islet infiltration with demographic and clinical variables with the aim of identifying individuals at the very early stages of the disease.

Human pancreatic capillaries and nerve fibers persist in type 1 diabetes despite beta cell loss

The autonomic nervous system regulates pancreatic function. Islet capillaries are essential for the extension of axonal projections into islets, and both of these structures are important for appropriate islet hormone secretion. Because beta cells provide important paracrine cues for islet glucagon secretion and neurovascular development, we postulated that beta cell loss in type 1 diabetes (T1D) would lead to a decline in intraislet capillaries and reduction of islet innervation, possibly contributing to abnormal glucagon secretion. To define morphological characteristics of capillaries and nerve fibers in islets and acinar tissue compartments, we analyzed neurovascular assembly across the largest cohort of T1D and normal individuals studied thus far. Because innervation has been studied extensively in rodent models of T1D, we also compared the neurovascular architecture between mouse and human pancreas and assembled transcriptomic profiles of molecules guiding islet angiogenesis and neuronal development. We found striking interspecies differences in islet neurovascular assembly but relatively modest differences at transcriptome level, suggesting that posttranscriptional regulation may be involved in this process. To determine whether islet neurovascular arrangement is altered after beta cell loss in T1D, we compared pancreatic tissues from non-diabetic, recent-onset T1D (<10-yr duration), and longstanding T1D (>10-yr duration) donors. Recent-onset T1D showed greater islet and acinar capillary density compared to non-diabetic and longstanding T1D donors. Both recent-onset and longstanding T1D had greater islet nerve fiber density compared to non-diabetic donors. We did not detect changes in sympathetic axons in either T1D cohort. Additionally, nerve fibers overlapped with extracellular matrix (ECM), supporting its role in the formation and function of axonal processes. These results indicate that pancreatic capillaries and nerve fibers persist in T1D despite beta cell loss, suggesting that alpha cell secretory changes may be decoupled from neurovascular components.NEW & NOTEWORTHY Defining the neurovascular architecture in the pancreas of individuals with type 1 diabetes (T1D) is crucial to understanding the mechanisms of dysregulated glucagon secretion. In the largest T1D cohort of biobanked tissues analyzed to date, we found that pancreatic capillaries and nerve fibers persist in human T1D despite beta cell loss, suggesting that alpha cell secretory changes may be decoupled from neurovascular components. Because innervation has been studied extensively in rodent T1D models, our studies also provide the first rigorous direct comparisons of neurovascular assembly in mouse and human, indicating dramatic interspecies differences.