Single-cell multi-omics analysis of human pancreatic islets reveals novel cellular states in type 1 diabetes

Bioengineering and omics approaches for Type 1 diabetes practical research: advancements and constraints

Insulin dependency arises from autoimmunity that targets the β cells of the pancreas, resulting in Type 1 diabetes (T1D). Despite the fact that T1D patients require insulin for survival, insulin does not provide a cure for this disease or prevent its complications. Despite extensive genetic, molecular, and cellular research on T1D over the years, the translation of this understanding into effective clinical therapies continues to pose a significant obstacle. It is therefore difficult to develop effective clinical treatment strategies without a thorough understanding of disease pathophysiology. Pancreatic tissue bioengineering models of human T1D offer a valuable approach to examining and controlling islet function while tackling various facets of the condition. And in recent years, due to advances in high-throughput omics analysis, the genotypic and molecular profiles of T1D have become finer tuned. The present article will examine recent progress in these areas, along with their utilization and constraints in the realm of T1D.

FOXM1 cooperates with ERα to regulate functional β-cell mass

The transcription factor forkhead box (FOX)M1 regulates β-cell proliferation and insulin secretion. Our previous work demonstrates that expressing a constitutively active form of FOXM1 (FOXM1*) in β-cells increases β-cell function, proliferation, and mass in male mice. However, in contrast to what is observed in males, we demonstrate here that in female mice expression of FOXM1* in β-cells does not affect β-cell proliferation or glucose tolerance. Similarly, FOXM1* transduction of male but not female human islets enhances insulin secretion in response to elevated glucose. We therefore examined the mechanism behind this sexual dimorphism. Estrogen contributes to diabetes susceptibility differences between males and females, and estrogen receptor (ER)α is the primary mediator of β-cell estrogen signaling. Moreover, in breast cancer cells, ERα and FOXM1 work together to drive gene expression. We therefore examined whether FOXM1 and ERα functionally interact in β-cells. FOXM1* rescued elevated fasting glucose, glucose intolerance, and homeostatic model assessment of β-cell function (HOMA-B) in female mice with a β-cell-specific ERα deletion. Furthermore, in the presence of estrogen, the FOXM1 and ERα cistromes exhibit significant overlap in βTC6 β-cells. In addition, FOXM1 and ERα binding sites frequently occur in complex enhancers co-occupied by other islet transcription factors. These data indicate that FOXM1 and nuclear ERα cooperate to regulate β-cell function and suggest a general mechanism contributing to the lower incidence of diabetes observed in women.NEW & NOTEWORTHY Here we investigate why the effects of increasing FOXM1 activity in β-cells observed in male mice are not seen in female mice. ERα likely collaborates with FOXM1 and other transcription factors to enhance gene expression related to β-cell function. Higher estrogen levels in females may contribute to their increased insulin secretion and the more severe consequences of losing transcription factors like FOXM1 in males. Overall, these findings shed light on sex differences in diabetes susceptibility.

Glucose-dependent insulinotropic polypeptide (GIP)

BACKGROUND

Glucose-dependent insulinotropic polypeptide (GIP) was the first incretin identified and plays an essential role in the maintenance of glucose tolerance in healthy humans. Until recently GIP had not been developed as a therapeutic and thus has been overshadowed by the other incretin, glucagon-like peptide 1 (GLP-1), which is the basis for several successful drugs to treat diabetes and obesity. However, there has been a rekindling of interest in GIP biology in recent years, in great part due to pharmacology demonstrating that both GIPR agonism and antagonism may be beneficial in treating obesity and diabetes. This apparent paradox has reinvigorated the field, led to new lines of investigation, and deeper understanding of GIP.

SCOPE OF REVIEW

In this review, we provide a detailed overview on the multifaceted nature of GIP biology and discuss the therapeutic implications of GIPR signal modification on various diseases.

MAJOR CONCLUSIONS

Following its classification as an incretin hormone, GIP has emerged as a pleiotropic hormone with a variety of metabolic effects outside the endocrine pancreas. The numerous beneficial effects of GIPR signal modification render the peptide an interesting candidate for the development of pharmacotherapies to treat obesity, diabetes, drug-induced nausea and both bone and neurodegenerative disorders.

Genetic protection from type 1 diabetes resulting from accelerated insulin mRNA decay

Insulin gene (INS) variation and beta-cell stress are associated with the risk of development of type 1 diabetes (T1D) and autoimmunity against insulin. The unfolded protein response alleviating endoplasmic reticulum (ER) stress involves activation of inositol-requiring enzyme 1α (IRE1α) that impedes translation by mRNA decay. We discover that the IRE1α digestion motif is present in insulin mRNA carrying SNP rs3842752 (G>A). This SNP in the 3' untranslated region of INS associates with protection from T1D (INSP). ER stress in beta cells with INSP led to accelerated insulin mRNA decay compared with the susceptible INS variant (INSS). Human islets with INSP showed improved vitality and function and reversed diabetes more rapidly when transplanted into diabetic mice than islets carrying INSS only. Surrogate beta cells with INSP expressed less ER stress and INS-DRiP neoantigen. This explanation for genetic protection from T1D may act instead of or in concert with the previously proposed mechanism attributed to INS promoter polymorphism.

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.

Single-cell RNA sequencing reveals important role of monocytes and macrophages during mucopolysaccharidosis treatment

Mucopolysaccharidosis (MPS) encompasses a heterogeneous group of lysosomal storage diseases resulting from mutations in genes encoding lysosomal enzymes responsible for the degradation of mucopolysaccharides, also known as glycosaminoglycans (GAGs). Current therapeutic strategies for MPS include hematopoietic stem cell transplantation (HSCT), enzyme replacement therapy (ERT), and symptomatic therapy. This study investigated dynamic changes in MPS type II (MPS-II) through genomic and single-cell sequencing in a patient undergoing ERT. Analysis of peripheral blood mononuclear cells (PBMCs) from one MPS-II patient of 10 year old at different disease stages through scRNA-seq identified various immune cell types, including natural killer (NK) cells, NKT cells, CD4 + and CD8 + T cells, CD14 + and CD16 + monocytes, and B cells. Monocytes and macrophages were significantly reduced during the severe stage of MPS-II but increased during the recovery stage following ERT. Notably, monocyte subtype mono3 was exclusively expressed in the severe stage, while mono1_2, a subtype of mono1, was absent during the severe stage and exhibited distinct biological functions. These findings suggest that monocytes and macrophages play critical roles in the pathogenesis of MPS-II and in the response to ERT. Pseudotime, Gene Ontology, and cell-communication analyses revealed unique functions for the different cellular subtypes. Notably, key molecules mediating cellular interactions during ERT in MPS-II included CXCR3, PF4, APP, and C5AR1 in macrophages, RPS19 in T cells, HLA-DPB1 in B cells, ADRB2 in NK cells, and IL1B, C5AR1, RPS19, and TNFSF13B in monocytes. Overall, integrative analysis delineated the expression dynamics of various cell types and identified mutations in MPS-II, providing a comprehensive atlas of transcriptional programs, cellular characterizations, and genomic variation profiles in MPS-II. This dataset, along with advanced integrative analysis, represents a valuable resource for the discovery of drug targets and the improvement of therapeutic strategies for MPS-II.

Active targeting of type 1 diabetes therapies to pancreatic beta cells using nanocarriers

Type 1 diabetes is an autoimmune disease characterised by the destruction of pancreatic beta cells, resulting in lifelong insulin dependence. Although exogenous insulin can maintain glycaemic control, this approach does not protect residual or replacement pancreatic beta cells from immune-mediated death. Current therapeutics designed to protect functional beta cell mass or promote beta cell proliferation and regeneration can have off-target effects, resulting in higher dose requirements and adverse side effects. Targeted drug delivery using nanocarriers has demonstrated potential for overcoming these limitations. The critical bottleneck limiting the development of beta cell-targeted therapies is a lack of highly specific beta cell markers. This review provides an overview of the use of nanocarriers for cell-targeted delivery and the current state of the field of beta cell targeting. Technologies such as systematic evolution of ligands by exponential enrichment (SELEX) aptamer selection, phage display screening, and omics datasets from human samples are highlighted as tools to identify novel beta cell-specific targets that can be combined with nanocarriers for targeted delivery of therapeutics. Ultimately, beta cell-targeted therapies using nanocarriers present a unique opportunity to develop tailored treatments for each stage of type 1 diabetes with the goal of providing individuals with treatment options that prevent further progression or reverse the course of the disease.

Tissue Resident and Infiltrating Immune Cells: Their Influence on the Demise of Beta Cells in Type 1 Diabetes

Type 1 diabetes (T1D) is an organ-specific autoimmune disease that results in the selective loss of pancreatic beta cells and an eventual deficit in insulin production to maintain glucose homeostasis. It is now increasingly accepted that this dynamic disease process is multifactorial; involves a variety of immune cells which contribute to an inflamed pancreatic microenvironment; and that the condition is heterogenous, resulting in variable rates of subsequent beta cell damage. In this review, we will explore the current understanding of the cellular interactions between both resident and infiltrating immune cells within the pancreatic environment, highlighting key mechanisms which may promote the beta cell destruction and islet damage associated with T1D.

Integrated histopathology of the human pancreas throughout stages of type 1 diabetes progression

Type 1 diabetes (T1D) is a progressive autoimmune condition that culminates in the loss of insulin-producing beta cells. Pancreatic histopathology provides essential insights into disease initiation and progression yet an integrated perspective of in situ pathogenic processes is lacking due to limited sample availability, the dispersed nature of anatomical lesions, and often restricted analytical dimensionality. Here, we combined multiplexed immunostaining, high-magnification whole-slide imaging, digital pathology, and semi-automated image analysis strategies to interrogate pancreatic tail and head regions obtained from organ donors across T1D stages including at-risk and at-onset cases. Deconvolution of architectural features, endocrine cell composition, immune cell burden, and spatial relations of ~25,000 islets revealed a series of novel histopathological correlates especially in the prodromal disease stage preceding clinical T1D. Altogether, our comprehensive "single-islet" analyses permit the reconstruction of a revised natural T1D history with implications for further histopathological investigations, considerations of pathogenetic modalities, and therapeutic interventions.

Application and Progression of Single-Cell RNA Sequencing in Diabetes Mellitus and Diabetes Complications

Diabetes is a systemic metabolic disorder primarily caused by insulin deficiency and insulin resistance, leading to chronic hyperglycemia. Prolonged diabetes can result in metabolic damage to multiple organs, including the heart, brain, liver, muscles, and adipose tissue, thereby causing various chronic fatal complications such as diabetic retinopathy, diabetic cardiomyopathy, and diabetic nephropathy. Single-cell RNA sequencing (scRNA-seq) has emerged as a valuable tool for investigating the cell diversity and pathogenesis of diabetes and identifying potential therapeutic targets in diabetes or diabetes complications. This review provides a comprehensive overview of recent applications of scRNA-seq in diabetes-related researches and highlights novel biomarkers and immunotherapy targets with cell-type information for diabetes and its associated complications.

Single-cell multiome and spatial profiling reveals pancreas cell type-specific gene regulatory programs driving type 1 diabetes progression

Cell type-specific regulatory programs that drive type 1 diabetes (T1D) in the pancreas are poorly understood. Here we performed single nucleus multiomics and spatial transcriptomics in up to 32 non-diabetic (ND), autoantibody-positive (AAB+), and T1D pancreas donors. Genomic profiles from 853,005 cells mapped to 12 pancreatic cell types, including multiple exocrine sub-types. Beta, acinar, and other cell types, and related cellular niches, had altered abundance and gene activity in T1D progression, including distinct pathways altered in AAB+ compared to T1D. We identified epigenomic drivers of gene activity in T1D and AAB+ which, combined with genetic association, revealed causal pathways of T1D risk including antigen presentation in beta cells. Finally, single cell and spatial profiles together revealed widespread changes in cell-cell signaling in T1D including signals affecting beta cell regulation. Overall, these results revealed drivers of T1D progression in the pancreas, which form the basis for therapeutic targets for disease prevention.

Single-cell RNA sequencing in autoimmune diseases: New insights and challenges

Autoimmune diseases involve a variety of cell types, yet the intricacies of their individual roles within molecular mechanisms and therapeutic strategies remain poorly understood. Single-cell RNA sequencing (scRNA-seq) offers detailed insights into transcriptional diversity at the single-cell level, significantly advancing research in autoimmune diseases. This article explores how scRNA-seq enhances the understanding of cellular heterogeneity and its potential applications in the etiology, diagnosis, treatment, and prognosis of autoimmune diseases. By revealing a comprehensive cellular landscape, scRNA-seq illuminates the functional regulation of different cell subtypes during disease progression. It aids in identifying diagnostic and prognostic markers, and analyzing cell communication networks to uncover potential therapeutic targets. Despite its valuable contributions, addressing the limitations of scRNA-seq is essential for making further advancements.

IFN-α Induces Heterogenous ROS Production in Human β-Cells

Type 1 diabetes (T1D) is a multifactorial disease involving genetic and environmental factors, including viral infection. We investigated the impact of interferon alpha (IFN-α), a cytokine produced during the immune response to viral infection or the presence of un-edited endogenous double-stranded RNAs, on human β-cell physiology. Intravital microscopy on transplanted human islets using a β-cell-selective reactive oxygen species (ROS) biosensor (RIP1-GRX1-roGFP2), revealed a subset of human β-cells that acutely produce ROS in response to IFN-α. Comparison to Integrated Islet Distribution Program (IIDP) phenotypic data revealed that healthier donors had more ROS accumulating cells. In vitro IFN-α treatment of human islets similarly elicited a heterogenous increase in superoxide production that originated in the mitochondria. To determine the unique molecular signature predisposing cells to IFN-α stimulated ROS production, we flow sorted human islets treated with IFN-α. RNA sequencing identified genes involved in inflammatory and immune response in the ROS-producing cells. Comparison with single cell RNA-Seq datasets available through the Human Pancreas Analysis Program (HPAP) showed that genes upregulated in ROS-producing cells are enriched in control β-cells rather than T1D donors. Combined, these data suggest that IFN-α stimulates mitochondrial ROS production in healthy human β-cells, potentially predicting a more efficient antiviral response.

Single-cell atlas of human pancreatic islet and acinar endothelial cells in health and diabetes

Characterization of the vascular heterogeneity within the pancreas has previously been lacking. Here, we develop strategies to enrich islet-specific endothelial cells (ISECs) and acinar-specific endothelial cells (ASECs) from three human pancreases and corroborate these findings with three published pancreatic datasets. Single-cell RNA sequencing reveals the unique molecular signatures of ISECs, including structural genes COL13A1, ESM1, PLVAP, UNC5B, and LAMA4, angiocrine genes KDR, THBS1, BMPs and CXCR4, and metabolic genes ACE, PASK and F2RL3. ASECs display distinct signatures including GPIHBP1, CCL14, CD74, AQP1, KLF4, and KLF2, which may manage the inflammatory and metabolic needs of the exocrine pancreas. Ligand-receptor analysis suggests ISECs and ASECs interact with LUM+ fibroblasts and RGS5+ pericytes and smooth muscle cells via VEGF-A:VEGFR2, CXCL12:CXCR4, and LIF:LIFR pathways. Comparative expression and immunohistochemistry indicate disruption of endothelial-expressed CD74, ESM1, PLVAP, THBD, VWA1, and VEGF-A cross-talk among vascular and other cell types in diabetes. Thus, our data provide a single-cell vascular atlas of human pancreas, enabling deeper understanding of pancreatic pathophysiology in health and disease.

Detection of Insulin in Insulin-Deficient Islets of Patients with Type 1 Diabetes

Type 1 diabetes (T1D) is related to the autoimmune destruction of β-cells, leading to their almost complete absence in patients with longstanding T1D. However, endogenous insulin secretion persists in such patients as evidenced by the measurement of plasma C-peptide. Recently, a low level of insulin has been found in non-β islet cells of patients with longstanding T1D, indicating that other islet cell types may contribute to persistent insulin secretion. The present study aimed to test the ability of various antibodies to detect insulin in insulin-deficient islets of T1D patients. Pancreatic autopsies from two children with recent-onset T1D, two adults with longstanding T1D, and three control subjects were examined using double immunofluorescent labeling with antibodies to insulin, glucagon and somatostatin. Immunoreactivity to insulin in glucagon+ cells of insulin-deficient islets was revealed using polyclonal antibodies and monoclonal antibodies simultaneously recognizing insulin and proinsulin. Along with this, immunoreactivity to insulin was observed in the majority of glucagon+ cells of insulin-containing islets of control subjects and children with recent-onset T1D. These results suggest that islet α-cells may contain insulin and/or other insulin-like proteins (proinsulin, C-peptide). Future studies are needed to evaluate the role of α-cells in insulin secretion and diabetes pathogenesis.

Identification of a pancreatic stellate cell gene signature and lncRNA interactions associated with type 2 diabetes progression

Background

Type 2 diabetes (T2D) has become a significant global health threat, yet its precise causes and mechanisms remain unclear. This study aims to identify gene expression patterns specific to T2D pancreatic islet cells and to explore the potential role of pancreatic stellate cells (PSCs) in T2D progression through regulatory networks involving lncRNA-mRNA interactions.

Methods

In this study, we screened for upregulated genes in T2D pancreatic islet samples using bulk sequencing (bulkseq) datasets and mapped these gene expression profiles onto three T2D single-cell RNA sequencing (scRNAseq) datasets. The identified T2D-specific gene features were further validated in an additional T2D scRNAseq dataset, a T1D scRNAseq dataset, and a T2D bulkseq dataset. To investigate regulatory networks, we analyzed the potential lncRNA-mRNA interactions within T2D peripheral blood mononuclear cell (PBMC) bulkseq data.

Results

Our analysis identified a specific gene panel-COL1A2, VCAN, and SULF1-that was consistently upregulated in T2D pancreatic islet samples. Expression of this gene panel was strongly associated with the activation of pancreatic stellate cells (PSCs), suggesting a unique T2D-specific signature characterized by COL1A2hi/VCANhi/SULF1hi PSCs. This signature was exclusive to T2D and was not observed in Type 1 diabetes (T1D) samples, indicating a distinct role for activated PSCs in T2D progression. Furthermore, we identified six long non-coding RNAs (lncRNAs) that potentially interact with the COL1A2hi/VCANhi/SULF1hi PSCs. These lncRNAs were mapped to a lncRNA-mRNA network, suggesting they may modulate immune responses and potentially reshape the immune microenvironment in T2D.

Discussion

Our findings highlight the potential immune-regulatory role of PSCs in T2D and suggest that PSC-related lncRNA-mRNA networks could serve as novel therapeutic targets for T2D treatment. This research provides insights into PSCs as a modulator in T2D progression, paving the way for innovative treatment strategies.

Multi-omics analysis of long-term cultured human islets

β-cell dysfunction in pancreatic islets, characterized as either the loss of β-cell mass or the resistance of β-cell to glucose, is the leading cause of progression to diabetes. Islet transplantation became a promising approach to replenish functional β-cell mass. However, not much known about changes in islets used for transplantation after isolation. We have subjected human islets into long-term in vitro culture (LTC) and characterized those survived islets. While most of the dysregulated genes were downregulated during LTC, specific groups of mRNA or miRNA were upregulated, and they are involved in specific pathways. In general, α-cells and β-cells of LTC-islets have elevated expressions of MAFB and MAFA genes, respectively. We also found that exocrine cells were eliminated faster than endocrine cells, and β-cells were lost at a higher rate than α-cells. Interestingly, one specific group of cells that have characteristics of immature α-cells or β-cells, were enriched in LTC-islets, revealing the possibility of transdifferentiation of α-cells to β-cells, or dedifferentiation of β-cells to α -cells, under in vitro culture. Our results suggest that there are intrinsic cellular and molecular mechanisms in pancreatic cells that are associated with their maturity and correlated with their survival ability under unfavorable living conditions.

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.

Exploring new frontiers in type 1 diabetes through advanced mass-spectrometry-based molecular measurements

Type 1 diabetes (T1D) is a devastating autoimmune disease for which advanced mass spectrometry (MS) methods are increasingly used to identify new biomarkers and better understand underlying mechanisms. For example, integration of MS analysis and machine learning has identified multimolecular biomarker panels. In mechanistic studies, MS has contributed to the discovery of neoepitopes, and pathways involved in disease development and identifying therapeutic targets. However, challenges remain in understanding the role of tissue microenvironments, spatial heterogeneity, and environmental factors in disease pathogenesis. Recent advancements in MS, such as ultra-fast ion-mobility separations, and single-cell and spatial omics, can play a central role in addressing these challenges. Here, we review recent advancements in MS-based molecular measurements and their role in understanding T1D.

Recovery of biological signals lost in single-cell batch integration with CellANOVA

Data integration to align cells across batches has become a cornerstone of single-cell data analysis, critically affecting downstream results. Currently, there are no guidelines for when the biological differences between samples are separable from batch effects. Here we show that current paradigms for single-cell data integration remove biologically meaningful variation and introduce distortion. We present a statistical model and computationally scalable algorithm, CellANOVA (cell state space analysis of variance), that harnesses experimental design to explicitly recover biological signals that are erased during single-cell data integration. CellANOVA uses a 'pool-of-controls' design concept, applicable across diverse settings, to separate unwanted variation from biological variation of interest and allow the recovery of subtle biological signals. We apply CellANOVA to diverse contexts and validate the recovered biological signals by orthogonal assays. In particular, we show that CellANOVA is effective in the challenging case of single-cell and single-nucleus data integration, where it recovers subtle biological signals that can be validated and replicated by external data.

Zinc-based Polyoxometalate Nanozyme Functionalized Hydrogels for optimizing the Hyperglycemic-Immune Microenvironment to Promote Diabetic Wound Regeneration

BACKGROUND

In diabetic wounds, hyperglycemia-induced cytotoxicity and impaired immune microenvironment plasticity directly hinder the wound healing process. Regulation of the hyperglycemic microenvironment and remodeling of the immune microenvironment are crucial.

RESULTS

Here, we developed a nanozymatic functionalized regenerative microenvironmental regulator (AHAMA/CS-GOx@Zn-POM) for the effective repair of diabetic wounds. This novel construct integrated an aldehyde and methacrylic anhydride-modified hyaluronic acid hydrogel (AHAMA) and chitosan nanoparticles (CS NPs) encapsulating zinc-based polymetallic oxonate nanozyme (Zn-POM) and glucose oxidase (GOx), facilitating a sustained release of release of both enzymes. The GOx catalyzed glucose to gluconic acid and (H₂O₂), thereby alleviating the effects of the hyperglycemic microenvironment on wound healing. Zn-POM exhibited catalase and superoxide dismutase activities to scavenge reactive oxygen species and H₂O₂, a by-product of glucose degradation. Additionally, Zn-POM induced M1 macrophage reprogramming to the M2 phenotype by inhibiting the MAPK/IL-17 signaling diminishing pro-inflammatory cytokines, and upregulating the expression of anti-inflammatory mediators, thus remodeling the immune microenvironment and enhancing angiogenesis and collagen regeneration within wounds. In a rat diabetic wound model, the application of AHAMA/CS-GOx@Zn-POM enhanced neovascularization and collagen deposition, accelerating the wound healing process.

CONCLUSIONS

Therefore, the regenerative microenvironment regulator AHAMA/CS-GOx@Zn-POM can achieve the effective conversion of a pathological microenvironment to regenerative microenvironment through integrated control of the hyperglycemic-immune microenvironment, offering a novel strategy for the treatment of diabetic wounds.

A precise microdissection strategy enabled spatial heterogeneity analysis on the targeted region of formalin-fixed paraffin-embedded tissues

In recent years, the development of spatial transcriptomic technologies has enabled us to gain an in-depth understanding of the spatial heterogeneity of gene expression in biological tissues. However, a simple and efficient tool is required to analyze multiple spatial targets, such as mRNAs, miRNAs, or genetic mutations, at high resolution in formalin-fixed paraffin-embedded (FFPE) tissue sections. In this study, we developed hydrogel pathological sectioning coupled with the previously reported Sampling Junior instrument (HPSJ) to assess the spatial heterogeneity of multiple targets in FFPE sections at a scale of 180 μm. The HPSJ platform was used to demonstrate the spatial heterogeneity of 9 ferroptosis-related genes (TFRC, NCOA4, FTH1, ACSL4, LPCAT3, ALOX12, SLC7A11, GLS2, and GPX4) and 2 miRNAs (miR-185-5p and miR522) in FFPE tissue samples from patients with triple-negative breast cancer (TNBC). The results validated the significant heterogeneity of ferroptosis-related mRNAs and miRNAs. In addition, HPSJ confirmed the spatial heterogeneity of the L858R mutation in 7 operation-sourced and 4 needle-biopsy-sourced FFPE samples from patients with lung adenocarcinoma (LUAD). The successful detection of clinical FFPE samples indicates that HPSJ is a precise, high-throughput, cost-effective, and universal platform for analyzing spatial heterogeneity, which is beneficial for elucidating the mechanisms underlying drug resistance and guiding the prescription of mutant-targeted drugs in patients with tumors.

Increased Inflammation as well as Decreased Endoplasmic Reticulum Stress and Translation Differentiate Pancreatic Islets of Pre-symptomatic Stage 1 Type 1 Diabetes and Non-diabetic Cases

Aims/hypothesis

Progression to type 1 diabetes (T1D) is associated with genetic factors, the presence of autoantibodies, and a decline in β cell insulin secretion in response to glucose. Very little is known regarding the molecular changes that occur in human insulin-secreting β-cells prior to the onset of T1D. Herein, we applied an unbiased proteomics approach to identify changes in proteins and potential mechanisms of islet dysfunction in islet autoantibody-positive organ donors with pre-symptomatic stage 1 T1D (HbA1c ≤ 6). We aimed to identify pathways in islets that are indicative of β-cell dysfunction.

Methods

Multiple islet sections were collected through laser microdissection of frozen pancreatic tissues of organ donors positive for islet autoantibodies (AAb+, n=5), compared to age/sex-matched nondiabetic controls (ND, n=5) obtained from the Network for Pancreatic Organ donors with Diabetes (nPOD). Islet sections were subjected to mass spectrometry-based proteomics and analyzed with label-free quantification followed by pathway and functional annotations.

Results

Analyses resulted in ~4,500 proteins identified with low false discovery rate (FDR) <1%, with 2,165 proteins reliably quantified in every islet sample. We observed large inter-donor variations that presented a challenge for statistical analysis of proteome changes between donor groups. We therefore focused on the three multiple AAb+ cases (mAAb+) with high genetic risk and their three matched controls for a final statistical analysis. Approximately 10% of the proteins (n=202) were significantly different between mAAb+ cases versus ND. The significant alterations clustered around major functions for upregulation in the immune response and glycolysis, and downregulation in endoplasmic reticulum (ER) stress response as well as protein translation and synthesis. The observed proteome changes were further supported by several independent published datasets, including proteomics dataset from in vitro proinflammatory cytokine-treated human islets and single cell RNA-seq data sets from AAb+ cases.

Conclusion/interpretation

In-situ human islet proteome alterations at the stage 1 of AAb+ T1D centered around several major functional categories, including an expected increase in immune response genes (elevated antigen presentation / HLA), with decreases in protein synthesis and ER stress response, as well as compensatory metabolic response. The dataset serves as a proteomics resource for future studies on β cell changes during T1D progression and pathogenesis.

Pancreatic Diseases: Genetics and Modeling Using Human Pluripotent Stem Cells

Pancreas serves endocrine and exocrine functions in the body; thus, their pathology can cause a broad range of irreparable consequences. Endocrine functions include the production of hormones such as insulin and glucagon, while exocrine functions involve the secretion of digestive enzymes. Disruption of these functions can lead to conditions like diabetes mellitus and exocrine pancreatic insufficiency. Also, the symptoms and causality of pancreatic cancer very greatly depends on their origin: pancreatic ductal adenocarcinoma is one of the most fatal cancer; however, most of tumor derived from endocrine part of pancreas are benign. Pancreatitis, an inflammation of the pancreatic tissues, is caused by excessive alcohol consumption, the bile duct obstruction by gallstones, and the premature activation of digestive enzymes in the pancreas. Hereditary pancreatic diseases, such as maturity-onset diabetes of the young and hereditary pancreatitis, can be a candidate for disease modeling using human pluripotent stem cells (hPSCs), due to their strong genetic influence. hPSC-derived pancreatic differentiation has been established for cell replacement therapy for diabetic patients and is robustly used for disease modeling. The disease modeling platform that allows interactions between immune cells and pancreatic cells is necessary to perform in-depth investigation of disease pathogenesis.

scMaui: a widely applicable deep learning framework for single-cell multiomics integration in the presence of batch effects and missing data

The recent advances in high-throughput single-cell sequencing have created an urgent demand for computational models which can address the high complexity of single-cell multiomics data. Meticulous single-cell multiomics integration models are required to avoid biases towards a specific modality and overcome sparsity. Batch effects obfuscating biological signals must also be taken into account. Here, we introduce a new single-cell multiomics integration model, Single-cell Multiomics Autoencoder Integration (scMaui) based on variational product-of-experts autoencoders and adversarial learning. scMaui calculates a joint representation of multiple marginal distributions based on a product-of-experts approach which is especially effective for missing values in the modalities. Furthermore, it overcomes limitations seen in previous VAE-based integration methods with regard to batch effect correction and restricted applicable assays. It handles multiple batch effects independently accepting both discrete and continuous values, as well as provides varied reconstruction loss functions to cover all possible assays and preprocessing pipelines. We demonstrate that scMaui achieves superior performance in many tasks compared to other methods. Further downstream analyses also demonstrate its potential in identifying relations between assays and discovering hidden subpopulations.

Untangling the genetics of beta cell dysfunction and death in type 1 diabetes

BACKGROUND

Type 1 diabetes (T1D) is a complex multi-system disease which arises from both environmental and genetic factors, resulting in the destruction of insulin-producing pancreatic beta cells. Over the past two decades, human genetic studies have provided new insight into the etiology of T1D, including an appreciation for the role of beta cells in their own demise.

SCOPE OF REVIEW

Here, we outline models supported by human genetic data for the role of beta cell dysfunction and death in T1D. We highlight the importance of strong evidence linking T1D genetic associations to bona fide candidate genes for mechanistic and therapeutic consideration. To guide rigorous interpretation of genetic associations, we describe molecular profiling approaches, genomic resources, and disease models that may be used to construct variant-to-gene links and to investigate candidate genes and their role in T1D.

MAJOR CONCLUSIONS

We profile advances in understanding the genetic causes of beta cell dysfunction and death at individual T1D risk loci. We discuss how genetic risk prediction models can be used to address disease heterogeneity. Further, we present areas where investment will be critical for the future use of genetics to address open questions in the development of new treatment and prevention strategies for T1D.

ReCIDE: robust estimation of cell type proportions by integrating single-reference-based deconvolutions

In this study, we introduce Robust estimation of Cell type proportions by Integrating single-reference-based DEconvolutions (ReCIDE), an innovative framework for robust estimation of cell type proportions by integrating single-reference-based deconvolutions. ReCIDE outperforms existing approaches in benchmark and real datasets, particularly excelling in estimating rare cell type proportions. Through exploratory analysis on public bulk data of triple-negative breast cancer (TNBC) patients using ReCIDE, we demonstrate a significant correlation between the prognosis of TNBC patients and the proportions of both T cell and perivascular-like cell subtypes. Built upon this discovery, we develop a prognostic assessment model for TNBC patients. Our contribution presents a novel framework for enhancing deconvolution accuracy, showcasing its effectiveness in medical research.

Nano-enzyme functionalized hydrogels promote diabetic wound healing through immune microenvironment modulation

Non-healing diabetic wounds often culminate in amputation and mortality. The main pathophysiological features in diabetic wounds involve the accumulation of M1-type macrophages and excessive oxidative stress. In this study, we engineered a nano-enzyme functionalized hydrogel by incorporating a magnesium ion-doped molybdenum-based polymetallic oxide (Mg-POM), a novel bioactive nano-enzyme, into a GelMA hydrogel, to obtain the GelMA@Mg-POM system to enhance diabetic wound healing. GelMA@Mg-POM was crosslinked using UV light, yielding a hydrogel with a uniformly porous three-dimensional mesh structure. In vitro experiments showed that GelMA@Mg-POM extraction significantly enhanced human umbilical vein endothelial cell (HUVEC) migration, scavenged ROS, improved the inflammatory microenvironment, induced macrophage reprogramming towards M2, and promoted HUVEC regeneration of CD31 and fibroblast regeneration of type I collagen. In in vivo experiments, diabetic rat wounds treated with GelMA@Mg-POM displayed enhanced granulation tissue genesis and collagen production, as evidenced by HE and Masson staining. Immunohistochemistry demonstrated the ability of GelMA@Mg-POM to mitigate macrophage-associated inflammatory responses and promote angiogenesis. Overall, these findings suggest that GelMA@Mg-POM holds significant promise as a biomaterial for treating diabetic wounds.

Integrating multi-omics data to analyze the potential pathogenic mechanism of CTSH gene involved in type 1 diabetes in the exocrine pancreas

Type 1 diabetes (T1D) is an autoimmune disease caused by the destruction of insulin-producing pancreatic islet beta cells. Despite significant advancements, the precise pathogenesis of the disease remains unknown. This work integrated data from expression quantitative trait locus (eQTL) studies with Genome wide association study (GWAS) summary data of T1D and single-cell transcriptome data to investigate the potential pathogenic mechanisms of the CTSH gene involved in T1D in exocrine pancreas. Using the summary data-based Mendelian randomization (SMR) approach, we obtained four potential causative genes associated with T1D: BTN3A2, PGAP3, SMARCE1 and CTSH. To further investigate these genes'roles in T1D development, we validated them using a scRNA-seq dataset from pancreatic tissues of both T1D patients and healthy controls. The analysis showed a significantly high expression of the CTSH gene in T1D acinar cells, whereas the other three genes showed no significant changes in the scRNA-seq data. Moreover, single-cell WGCNA analysis revealed the strongest positive correlation between the module containing CTSH and T1D. In addition, we found cellular ligand-receptor interactions between the acinar cells and different cell types, especially ductal cells. Finally, based on functional enrichment analysis, we hypothesized that the CTSH gene in the exocrine pancreas enhances the antiviral response, leading to the overexpression of pro-inflammatory cytokines and the development of an inflammatory microenvironment. This process promotes β cells injury and ultimately the development of T1D. Our findings offer insights into the underlying pathogenic mechanisms of T1D.

Identification of unique cell type responses in pancreatic islets to stress

Diabetes involves the death or dysfunction of pancreatic β-cells. Analysis of bulk sequencing from human samples and studies using in vitro and in vivo models suggest that endoplasmic reticulum and inflammatory signaling play an important role in diabetes progression. To better characterize cell type-specific stress response, we perform multiplexed single-cell RNA sequencing to define the transcriptional signature of primary human islet cells exposed to endoplasmic reticulum and inflammatory stress. Through comprehensive pair-wise analysis of stress responses across pancreatic endocrine and exocrine cell types, we define changes in gene expression for each cell type under different diabetes-associated stressors. We find that β-, α-, and ductal cells have the greatest transcriptional response. We utilize stem cell-derived islets to study islet health through the candidate gene CIB1, which was upregulated under stress in primary human islets. Our findings provide insights into cell type-specific responses to diabetes-associated stress and establish a resource to identify targets for diabetes therapeutics.

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.

HumanIslets: An integrated platform for human islet data access and analysis

Comprehensive molecular and cellular phenotyping of human islets can enable deep mechanistic insights for diabetes research. We established the Human Islet Data Analysis and Sharing (HI-DAS) consortium to advance goals in accessibility, usability, and integration of data from human islets isolated from donors with and without diabetes at the Alberta Diabetes Institute (ADI) IsletCore. Here we introduce HumanIslets.com, an open resource for the research community. This platform, which presently includes data on 547 human islet donors, allows users to access linked datasets describing molecular profiles, islet function and donor phenotypes, and to perform various statistical and functional analyses at the donor, islet and single-cell levels. As an example of the analytic capacity of this resource we show a dissociation between cell culture effects on transcript and protein expression, and an approach to correct for exocrine contamination found in hand-picked islets. Finally, we provide an example workflow and visualization that highlights links between type 2 diabetes status, SERCA3b Ca2+-ATPase levels at the transcript and protein level, insulin secretion and islet cell phenotypes. HumanIslets.com provides a growing and adaptable set of resources and tools to support the metabolism and diabetes research community.

A Single-Cell Atlas of the Murine Pancreatic Ductal Tree Identifies Novel Cell Populations With Potential Implications in Pancreas Regeneration and Exocrine Pathogenesis

BACKGROUND & AIMS

Pancreatic ducts form an intricate network of tubules that secrete bicarbonate and drive acinar secretions into the duodenum. This network is formed by centroacinar cells, terminal, intercalated, intracalated ducts, and the main pancreatic duct. Ductal heterogeneity at the single-cell level has been poorly characterized; therefore, our understanding of the role of ductal cells in pancreas regeneration and exocrine pathogenesis has been hampered by the limited knowledge and unexplained diversity within the ductal network.

METHODS

We used single cell RNA sequencing to comprehensively characterize mouse ductal heterogeneity at single-cell resolution of the entire ductal epithelium from centroacinar cells to the main duct. Moreover, we used organoid cultures, injury models, and pancreatic tumor samples to interrogate the role of novel ductal populations in pancreas regeneration and exocrine pathogenesis.

RESULTS

We have identified the coexistence of 15 ductal populations within the healthy pancreas and characterized their organoid formation capacity and endocrine differentiation potential. Cluster isolation and subsequent culturing let us identify ductal cell populations with high organoid formation capacity and endocrine and exocrine differentiation potential in vitro, including a Wnt-responsive population, a ciliated population, and Flrt3+ cells. Moreover, we have characterized the location of these novel ductal populations in healthy pancreas, chronic pancreatitis, and tumor samples. The expression of Wnt-responsive, interferon-responsive, and epithelial-to-mesenchymal transition population markers increases in chronic pancreatitis and tumor samples.

CONCLUSIONS

In light of our discovery of previously unidentified ductal populations, we unmask potential roles of specific ductal populations in pancreas regeneration and exocrine pathogenesis. Thus, novel lineage-tracing models are needed to investigate ductal-specific populations in vivo.

Machine-learning-guided recognition of α and β cells from label-free infrared micrographs of living human islets of Langerhans

Human islets of Langerhans are composed mostly of glucagon-secreting α cells and insulin-secreting β cells closely intermingled one another. Current methods for identifying α and β cells involve either fixing islets and using immunostaining or disaggregating islets and employing flow cytometry for classifying α and β cells based on their size and autofluorescence. Neither approach, however, allows investigating the dynamic behavior of α and β cells in a living and intact islet. To tackle this issue, we present a machine-learning-based strategy for identification α and β cells in label-free infrared micrographs of living human islets without immunostaining. Intrinsic autofluorescence is stimulated by infrared light and collected both in intensity and lifetime in the visible range, dominated by NAD(P)H and lipofuscin signals. Descriptive parameters are derived from micrographs for ~ 103 cells. These parameters are used as input for a boosted decision-tree model (XGBoost) pre-trained with immunofluorescence-derived cell-type information. The model displays an optimized-metrics performance of 0.86 (i.e. area under a ROC curve), with an associated precision of 0.94 for the recognition of β cells and 0.75 for α cells. This tool promises to enable longitudinal studies on the dynamic behavior of individual cell types at single-cell resolution within the intact tissue.

Single-Cell Sequencing: High-Resolution Analysis of Cellular Heterogeneity in Autoimmune Diseases

Autoimmune diseases (AIDs) are complex in etiology and diverse in classification but clinically show similar symptoms such as joint pain and skin problems. As a result, the diagnosis is challenging, and usually, only broad treatments can be available. Consequently, the clinical responses in patients with different types of AIDs are unsatisfactory. Therefore, it is necessary to conduct more research to figure out the pathogenesis and therapeutic targets of AIDs. This requires research technologies with strong extraction and prediction capabilities. Single-cell sequencing technology analyses the genomic, epigenomic, or transcriptomic information at the single-cell level. It can define different cell types and states in greater detail, further revealing the molecular mechanisms that drive disease progression. These advantages enable cell biology research to achieve an unprecedented resolution and scale, bringing a whole new vision to life science research. In recent years, single-cell technology especially single-cell RNA sequencing (scRNA-seq) has been widely used in various disease research. In this paper, we present the innovations and applications of single-cell sequencing in the medical field and focus on the application contributing to the differential diagnosis and precise treatment of AIDs. Despite some limitations, single-cell sequencing has a wide range of applications in AIDs. We finally present a prospect for the development of single-cell sequencing. These ideas may provide some inspiration for subsequent research.

Modeling type 1 diabetes progression using machine learning and single-cell transcriptomic measurements in human islets

Type 1 diabetes (T1D) is a chronic condition in which beta cells are destroyed by immune cells. Despite progress in immunotherapies that could delay T1D onset, early detection of autoimmunity remains challenging. Here, we evaluate the utility of machine learning for early prediction of T1D using single-cell analysis of islets. Using gradient-boosting algorithms, we model changes in gene expression of single cells from pancreatic tissues in T1D and non-diabetic organ donors. We assess if mathematical modeling could predict the likelihood of T1D development in non-diabetic autoantibody-positive donors. While most autoantibody-positive donors are predicted to be non-diabetic, select donors with unique gene signatures are classified as T1D. Our strategy also reveals a shared gene signature in distinct T1D-associated models across cell types, suggesting a common effect of the disease on transcriptional outputs of these cells. Our study establishes a precedent for using machine learning in early detection of T1D.

CD80 on skin stem cells promotes local expansion of regulatory T cells upon injury to orchestrate repair within an inflammatory environment

Following tissue damage, epithelial stem cells (SCs) are mobilized to enter the wound, where they confront harsh inflammatory environments that can impede their ability to repair the injury. Here, we investigated the mechanisms that protect skin SCs within this inflammatory environment. Characterization of gene expression profiles of hair follicle SCs (HFSCs) that migrated into the wound site revealed activation of an immune-modulatory program, including expression of CD80, major histocompatibility complex class II (MHCII), and CXC motif chemokine ligand 5 (CXCL5). Deletion of CD80 in HFSCs impaired re-epithelialization, reduced accumulation of peripherally generated Treg (pTreg) cells, and increased infiltration of neutrophils in wounded skin. Importantly, similar wound healing defects were also observed in mice lacking pTreg cells. Our findings suggest that upon skin injury, HFSCs establish a temporary protective network by promoting local expansion of Treg cells, thereby enabling re-epithelialization while still kindling inflammation outside this niche until the barrier is restored.

Autoimmune CD8+ T cells in type 1 diabetes: from single-cell RNA sequencing to T-cell receptor redirection

Type 1 diabetes (T1D) is an organ-specific autoimmune disease caused by pancreatic β cell destruction and mediated primarily by autoreactive CD8+ T cells. It has been shown that only a small number of stem cell-like β cell-specific CD8+ T cells are needed to convert normal mice into T1D mice; thus, it is likely that T1D can be cured or significantly improved by modulating or altering self-reactive CD8+ T cells. However, stem cell-type, effector and exhausted CD8+ T cells play intricate and important roles in T1D. The highly diverse T-cell receptors (TCRs) also make precise and stable targeted therapy more difficult. Therefore, this review will investigate the mechanisms of autoimmune CD8+ T cells and TCRs in T1D, as well as the related single-cell RNA sequencing (ScRNA-Seq), CRISPR/Cas9, chimeric antigen receptor T-cell (CAR-T) and T-cell receptor-gene engineered T cells (TCR-T), for a detailed and clear overview. This review highlights that targeting CD8+ T cells and their TCRs may be a potential strategy for predicting or treating T1D.

Unveiling cell subpopulations in T1D mouse islets using single-cell RNA sequencing

Type 1 diabetes (T1D) is an autoimmune disease characterized by the destruction of beta cells by immune cells. The interactions among cells within the islets may be closely linked to the pathogenesis of T1D. In this study, we used single-cell RNA sequencing (scRNA-Seq) to analyze the cellular heterogeneity within the islets of a T1D mouse model. We established a T1D mouse model induced by streptozotocin and identified cell subpopulations using scRNA-Seq technology. Our results revealed 11 major cell types in the pancreatic islets of T1D mice, with heterogeneity observed in the alpha and beta cell subgroups, which may play a crucial role in the progression of T1D. Flow cytometry further confirmed a mature alpha and beta cell reduction in T1D mice. Overall, our scRNA-Seq analysis provided insights into the cellular heterogeneity of T1D islet tissue and highlighted the potential importance of alpha and beta cells in developing T1D.NEW & NOTEWORTHY In this study, we created a comprehensive single-cell atlas of pancreatic islets in a T1D mouse model using scRNA-Seq and identified 11 major cell types in the islets, highlighting the role of alpha and beta cells in T1D. This study revealed a significant reduction in the maturity alpha and beta cells in T1D mice through flow cytometry. It also demonstrated the heterogeneity of alpha and beta cells, potentially crucial for T1D progression. Overall, our scRNA-Seq analysis provided new insights for understanding and treating T1D by studying cell subtype changes and functions.

Single-cell transcriptomics reveals a role for pancreatic duct cells as potential mediators of inflammation in diabetes mellitus

Introduction

Inflammation of the pancreas contributes to the development of diabetes mellitus. Although it is well-accepted that local inflammation leads to a progressive loss of functional beta cell mass that eventually causes the onset of the disease, the development of islet inflammation remains unclear.

Methods

Here, we used single-cell RNA sequencing to explore the cell type-specific molecular response of primary human pancreatic cells exposed to an inflammatory environment.

Results

We identified a duct subpopulation presenting a unique proinflammatory signature among all pancreatic cell types.

Discussion

Overall, the findings of this study point towards a role for duct cells in the propagation of islet inflammation, and in immune cell recruitment and activation, which are key steps in the pathophysiology of diabetes mellitus.

Pancreatic β-Cell Identity Change through the Lens of Single-Cell Omics Research

The main hallmark in the development of both type 1 and type 2 diabetes is a decline in functional β-cell mass. This decline is predominantly attributed to β-cell death, although recent findings suggest that the loss of β-cell identity may also contribute to β-cell dysfunction. This phenomenon is characterized by a reduced expression of key markers associated with β-cell identity. This review delves into the insights gained from single-cell omics research specifically focused on β-cell identity. It highlights how single-cell omics based studies have uncovered an unexpected level of heterogeneity among β-cells and have facilitated the identification of distinct β-cell subpopulations through the discovery of cell surface markers, transcriptional regulators, the upregulation of stress-related genes, and alterations in chromatin activity. Furthermore, specific subsets of β-cells have been identified in diabetes, such as displaying an immature, dedifferentiated gene signature, expressing significantly lower insulin mRNA levels, and expressing increased β-cell precursor markers. Additionally, single-cell omics has increased insight into the detrimental effects of diabetes-associated conditions, including endoplasmic reticulum stress, oxidative stress, and inflammation, on β-cell identity. Lastly, this review outlines the factors that may influence the identification of β-cell subpopulations when designing and performing a single-cell omics experiment.

Extracellular metallothionein as a therapeutic target in the early progression of type 1 diabetes

Type 1 diabetes (T1D) is characterized by lymphocyte infiltration into the pancreatic islets of Langerhans, leading to the destruction of insulin-producing beta cells and uncontrolled hyperglycemia. In the nonobese diabetic (NOD) murine model of T1D, the onset of this infiltration starts several weeks before glucose dysregulation and overt diabetes. Recruitment of immune cells to the islets is mediated by several chemotactic cytokines, including CXCL10, while other cytokines, including SDF-1α, can confer protective effects. Global gene expression studies of the pancreas from prediabetic NOD mice and single-cell sequence analysis of human islets from prediabetic, autoantibody-positive patients showed an increased expression of metallothionein (MT), a small molecular weight, cysteine-rich metal-binding stress response protein. We have shown that beta cells can release MT into the extracellular environment, which can subsequently enhance the chemotactic response of Th1 cells to CXCL10 and interfere with the chemotactic response of Th2 cells to SDF-1α. These effects can be blocked in vitro with a monoclonal anti-MT antibody, clone UC1MT. When administered to NOD mice before the onset of diabetes, UC1MT significantly reduces the development of T1D. Manipulation of extracellular MT may be an important approach to preserving beta cell function and preventing the development of T1D.

Systematic immune cell dysregulation and molecular subtypes revealed by single-cell RNA-seq of subjects with type 1 diabetes

BACKGROUND

Type 1 diabetes mellitus (T1DM) is a prototypic endocrine autoimmune disease resulting from an immune-mediated destruction of pancreatic insulin-secreting β  cells. A comprehensive immune cell phenotype evaluation in T1DM has not been performed thus far at the single-cell level.

METHODS

In this cross-sectional analysis, we generated a single-cell transcriptomic dataset of peripheral blood mononuclear cells (PBMCs) from 46 manifest T1DM (stage 3) cases and 31 matched controls.

RESULTS

We surprisingly detected profound alterations in circulatory immune cells (1784 dysregulated genes in 13 immune cell types), far exceeding the count in the comparator systemic autoimmune disease SLE. Genes upregulated in T1DM were involved in WNT signaling, interferon signaling and migration of T/NK cells, antigen presentation by B cells, and monocyte activation. A significant fraction of these differentially expressed genes were also altered in T1DM pancreatic islets. We used the single-cell data to construct a T1DM metagene z-score (TMZ score) that distinguished cases and controls and classified patients into molecular subtypes. This score correlated with known prognostic immune markers of T1DM, as well as with drug response in clinical trials.

CONCLUSIONS

Our study reveals a surprisingly strong systemic dimension at the level of immune cell network in T1DM, defines disease-relevant molecular subtypes, and has the potential to guide non-invasive test development and patient stratification.

Mapping Cell Atlases at the Single-Cell Level

Recent advancements in single-cell technologies have led to rapid developments in the construction of cell atlases. These atlases have the potential to provide detailed information about every cell type in different organisms, enabling the characterization of cellular diversity at the single-cell level. Global efforts in developing comprehensive cell atlases have profound implications for both basic research and clinical applications. This review provides a broad overview of the cellular diversity and dynamics across various biological systems. In addition, the incorporation of machine learning techniques into cell atlas analyses opens up exciting prospects for the field of integrative biology.

Repression of latent NF-κB enhancers by PDX1 regulates β cell functional heterogeneity

Interactions between lineage-determining and activity-dependent transcription factors determine single-cell identity and function within multicellular tissues through incompletely known mechanisms. By assembling a single-cell atlas of chromatin state within human islets, we identified β cell subtypes governed by either high or low activity of the lineage-determining factor pancreatic duodenal homeobox-1 (PDX1). β cells with reduced PDX1 activity displayed increased chromatin accessibility at latent nuclear factor κB (NF-κB) enhancers. Pdx1 hypomorphic mice exhibited de-repression of NF-κB and impaired glucose tolerance at night. Three-dimensional analyses in tandem with chromatin immunoprecipitation (ChIP) sequencing revealed that PDX1 silences NF-κB at circadian and inflammatory enhancers through long-range chromatin contacts involving SIN3A. Conversely, Bmal1 ablation in β cells disrupted genome-wide PDX1 and NF-κB DNA binding. Finally, antagonizing the interleukin (IL)-1β receptor, an NF-κB target, improved insulin secretion in Pdx1 hypomorphic islets. Our studies reveal functional subtypes of single β cells defined by a gradient in PDX1 activity and identify NF-κB as a target for insulinotropic therapy.

Trans-ethnic genomic informed risk assessment for Alzheimer's disease: An International Hundred K+ Cohorts Consortium study

BACKGROUND

As a collaboration model between the International HundredK+ Cohorts Consortium (IHCC) and the Davos Alzheimer's Collaborative (DAC), our aim was to develop a trans-ethnic genomic informed risk assessment (GIRA) algorithm for Alzheimer's disease (AD).

METHODS

The GIRA model was created to include polygenic risk score calculated from the AD genome-wide association study loci, the apolipoprotein E haplotypes, and non-genetic covariates including age, sex, and the first three principal components of population substructure.

RESULTS

We validated the performance of the GIRA model in different populations. The proteomic study in the participant sites identified proteins related to female infertility and autoimmune thyroiditis and associated with the risk scores of AD.

CONCLUSIONS

As the initial effort by the IHCC to leverage existing large-scale datasets in a collaborative setting with DAC, we developed a trans-ethnic GIRA for AD with the potential of identifying individuals at high risk of developing AD for future clinical applications.

Protection of β cells against pro-inflammatory cytokine stress by the GDF15-ERBB2 signaling

Aim/hypothesis

Growth/differentiation factor 15 (GDF15) is a therapeutic target for a variety of metabolic diseases, including type 1 diabetes (T1D). However, the nausea caused by GDF15 is a challenging point for therapeutic development. In addition, it is unknown why the endogenous GDF15 fails to protect from T1D development. Here, we investigate the GDF15 signaling in pancreatic islets towards opening possibilities for therapeutic targeting in β cells and to understand why this protection fails to occur naturally.

Methods

GDF15 signaling in islets was determined by proximity-ligation assay, untargeted proteomics, pathway analysis, and treatment of cells with specific inhibitors. To determine if GDF15 levels would increase prior to disease onset, plasma levels of GDF15 were measured in a longitudinal prospective study of children during T1D development (n=132 cases vs. n=40 controls) and in children with islet autoimmunity but normoglycemia (n=47 cases vs. n=40 controls) using targeted mass spectrometry. We also investigated the regulation of GDF15 production in islets by fluorescence microscopy and western blot analysis.

Results

The proximity-ligation assay identified ERBB2 as the GDF15 receptor in islets, which was confirmed using its specific antagonist, tucatinib. The untargeted proteomics analysis and caspase assay showed that ERBB2 activation by GDF15 reduces β cell apoptosis by downregulating caspase 8. In plasma, GDF15 levels were higher (p=0.0024) during T1D development compared to controls, but not in islet autoimmunity with normoglycemia. However, in the pancreatic islets GDF15 was depleted via sequestration of its mRNA into stress granules, resulting in translation halting.

Conclusions/interpretation

GDF15 protects against T1D via ERBB2-mediated decrease of caspase 8 expression in pancreatic islets. Circulating levels of GDF15 increases pre-T1D onset, which is insufficient to promote protection due to its localized depletion in the islets. These findings open opportunities for targeting GDF15 downstream signaling for pancreatic β cell protection in T1D and help to explain the lack of natural protection by the endogenous protein.

Dynamic scRNA-seq of live human pancreatic slices reveals functional endocrine cell neogenesis through an intermediate ducto-acinar stage

Human pancreatic plasticity is implied from multiple single-cell RNA sequencing (scRNA-seq) studies. However, these have been invariably based on static datasets from which fate trajectories can only be inferred using pseudotemporal estimations. Furthermore, the analysis of isolated islets has resulted in a drastic underrepresentation of other cell types, hindering our ability to interrogate exocrine-endocrine interactions. The long-term culture of human pancreatic slices (HPSs) has presented the field with an opportunity to dynamically track tissue plasticity at the single-cell level. Combining datasets from same-donor HPSs at different time points, with or without a known regenerative stimulus (BMP signaling), led to integrated single-cell datasets storing true temporal or treatment-dependent information. This integration revealed population shifts consistent with ductal progenitor activation, blurring of ductal/acinar boundaries, formation of ducto-acinar-endocrine differentiation axes, and detection of transitional insulin-producing cells. This study provides the first longitudinal scRNA-seq analysis of whole human pancreatic tissue, confirming its plasticity in a dynamic fashion.

RANKL/RANK is required for cytokine-induced beta cell death; osteoprotegerin, a RANKL inhibitor, reverses rodent type 1 diabetes

Treatment for type 1 diabetes (T1D) requires stimulation of functional β cell regeneration and survival under stress. Previously, we showed that inhibition of the RANKL/RANK [receptor activator of nuclear factor kappa Β (NF-κB) ligand] pathway, by osteoprotegerin and the anti-osteoporotic drug denosumab, induces rodent and human β cell proliferation. We demonstrate that the RANK pathway mediates cytokine-induced rodent and human β cell death through RANK-TRAF6 interaction and induction of NF-κB activation. Osteoprotegerin and denosumab protected β cells against this cytotoxicity. In human immune cells, osteoprotegerin and denosumab reduce proinflammatory cytokines in activated T-cells by inhibiting RANKL-induced activation of monocytes. In vivo, osteoprotegerin reversed recent-onset T1D in nonobese diabetic/Ltj mice, reduced insulitis, improved glucose homeostasis, and increased plasma insulin, β cell proliferation, and mass in these mice. Serum from T1D subjects induced human β cell death and dysfunction, but not α cell death. Osteoprotegerin and denosumab reduced T1D serum-induced β cell cytotoxicity and dysfunction. Inhibiting RANKL/RANK could have therapeutic potential.

An Integrated Map of Cell Type-Specific Gene Expression in Pancreatic Islets

Pancreatic islets consist of multiple cell types that produce hormones required for glucose homeostasis, and islet dysfunction is a major factor in type 1 and type 2 diabetes. Numerous studies have assessed transcription across individual cell types using single-cell assays; however, there is no canonical reference of gene expression in islet cell types that is also easily accessible for researchers to query and use in bioinformatics pipelines. Here we present an integrated map of islet cell type-specific gene expression from 192,203 cells from single-cell RNA sequencing of 65 donors without diabetes, donors who were type 1 diabetes autoantibody positive, donors with type 1 diabetes, and donors with type 2 diabetes from the Human Pancreas Analysis Program. We identified 10 distinct cell types, annotated subpopulations of several cell types, and defined cell type-specific marker genes. We tested differential expression within each cell type across disease states and identified 1,701 genes with significant changes in expression, with most changes observed in β-cells from donors with type 1 diabetes. To facilitate user interaction, we provide several single-cell visualization and reference mapping tools, as well as the open-access analytical pipelines used to create this reference. The results will serve as a valuable resource to investigators studying islet biology.

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