The pathogenesis, natural history, and treatment of type 1 diabetes: time (thankfully) does not stand still

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.

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.

Cognitive Outcome After Islet Transplantation

Severe or repeated hypoglycemia events may favor memory complaints in type 1 diabetes (T1D). Pancreatic islet transplantation (IT) is an alternative option to exogenous insulin therapy in case of labile T1D, implying a maintenance immunosuppression regimen based on sirolimus or mycophenolate, associated with tacrolimus, that may also have neurological toxicity. The objective of this study was to compare a cognitive rating scale Mini-Mental State Examination (MMSE) between T1D patients with or without IT and to identify parameters influencing MMSE.

Methods

This retrospective cross-sectional study compared MMSE and cognitive function tests between islet-transplanted T1D patients and nontransplanted T1D controls who were transplant candidates. Patients were excluded if they refused.

Results

Forty-three T1D patients were included: 9 T1D patients before IT and 34 islet-transplanted patients (14 treated with mycophenolate and 20 treated with sirolimus). Neither MMSE score (P = 0.70) nor higher cognitive function differed between islet versus non-islet-transplanted patients, whatever the type of immunosuppression. In the whole population (N = 43), MMSE score was negatively correlated to glycated hemoglobin (r = -0.30; P = 0.048) and the time spent in hypoglycemia on the continuous glucose monitoring (r = -0.32; P = 0.041). MMSE score was not correlated to fasting C-peptide level, time spent in hyperglycemia, average blood glucose, time under immunosuppression, duration of diabetes, or beta-score (success score of IT).

Conclusions

This first study evaluating cognitive disorders in islet-transplanted T1D patients argues for the importance of glucose balance on cognitive function rather than of immunosuppressive treatment, with a favorable effect of glucose balance improvement on MMSE score after IT.

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, we developed 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 show the superior 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 recapitulated known islet pathobiology and showed differential dynamics of pancreatic polypeptide (PP) cell abundance and CD8+ T cells infiltration in islets during type 1 diabetes progression.

Computational workflow and interactive analysis of single-cell expression profiling of islets generated by the Human Pancreas Analysis Program

Type 1 and Type 2 diabetes are distinct genetic diseases of the pancreas which are defined by the abnormal level of blood glucose. Understanding the initial molecular perturbations that occur during the pathogenesis of diabetes is of critical importance in understanding these disorders. The inability to biopsy the human pancreas of living donors hampers insights into early detection, as the majority of diabetes studies have been performed on peripheral leukocytes from the blood, which is not the site of pathogenesis. Therefore, efforts have been made by various teams including the Human Pancreas Analysis Program (HPAP) to collect pancreatic tissues from deceased organ donors with different clinical phenotypes. HPAP is designed to define the molecular pathogenesis of islet dysfunction by generating detailed datasets of functional, cellular, and molecular information in pancreatic tissues of clinically well-defined organ donors with Type 1 and Type 2 diabetes. Moreover, data generated by HPAP continously become available through a centralized database, PANC-DB, thus enabling the diabetes research community to access these multi-dimensional data prepublication. Here, we present the computational workflow for single-cell RNA-seq data analysis of 258,379 high-quality cells from the pancreatic islets of 67 human donors generated by HPAP, the largest existing scRNA-seq dataset of human pancreatic tissues. We report various computational steps including preprocessing, doublet removal, clustering and cell type annotation across single-cell RNA-seq data from islets of four distintct classes of organ donors, i.e. non-diabetic control, autoantibody positive but normoglycemic, Type 1 diabetic, and Type 2 diabetic individuals. Moreover, we present an interactive tool, called CellxGene developed by the Chan Zuckerberg initiative, to navigate these high-dimensional datasets. Our data and interactive tools provide a reliable reference for singlecell pancreatic islet biology studies, especially diabetes-related conditions.

Stem cell-based multi-tissue platforms to model human autoimmune diabetes

BACKGROUND

Type 1 diabetes (T1D) is an autoimmune disease in which pancreatic insulin-producing β cells are specifically destroyed by the immune system. Understanding the initiation and progression of human T1D has been hampered by the lack of appropriate models that can reproduce the complexity and heterogeneity of the disease. The development of platforms combining multiple human pluripotent stem cell (hPSC) derived tissues to model distinct aspects of T1D has the potential to provide critical novel insights into the etiology and pathogenesis of the human disease.

SCOPE OF REVIEW

In this review, we summarize the state of hPSC differentiation approaches to generate cell types and tissues relevant to T1D, with a particular focus on pancreatic islet cells, T cells, and thymic epithelium. We present current applications as well as limitations of using these hPSC-derived cells for disease modeling and discuss efforts to optimize platforms combining multiple cell types to model human T1D. Finally, we outline remaining challenges and emphasize future improvements needed to accelerate progress in this emerging field of research.

MAJOR CONCLUSIONS

Recent advances in reprogramming approaches to create patient-specific induced pluripotent stem cell lines (iPSCs), genome engineering technologies to efficiently modify DNA of hPSCs, and protocols to direct their differentiation into mature cell types have empowered the use of stem cell derivatives to accurately model human disease. While challenges remain before complex interactions occurring in human T1D can be modeled with these derivatives, experiments combining hPSC-derived β cells and immune cells are already providing exciting insight into how these cells interact in the context of T1D, supporting the viability of this approach.

Emerging diabetes therapies: Bringing back the β-cells

BACKGROUND

Stem cell therapies are finally coming of age as a viable alternative to pancreatic islet transplantation for the treatment of insulin-dependent diabetes. Several clinical trials using human embryonic stem cell (hESC)-derived β-like cells are currently underway, with encouraging preliminary results. Remaining challenges notwithstanding, these strategies are widely expected to reduce our reliance on human isolated islets for transplantation procedures, making cell therapies available to millions of diabetic patients. At the same time, advances in our understanding of pancreatic cell plasticity and the molecular mechanisms behind β-cell replication and regeneration have spawned a multitude of translational efforts aimed at inducing β-cell replenishment in situ through pharmacological means, thus circumventing the need for transplantation.

SCOPE OF REVIEW

We discuss here the current state of the art in hESC transplantation, as well as the parallel quest to discover agents capable of either preserving the residual mass of β-cells or inducing their proliferation, transdifferentiation or differentiation from progenitor cells.

MAJOR CONCLUSIONS

Stem cell-based replacement therapies in the mold of islet transplantation are already around the corner, but a permanent cure for type 1 diabetes will likely require the endogenous regeneration of β-cells aided by interventions to restore the immune balance. The promise of current research avenues and a strong pipeline of clinical trials designed to tackle these challenges bode well for the realization of this goal.

Self-Antigens Targeted by Regulatory T Cells in Type 1 Diabetes

While progress has been made toward understanding mechanisms that lead to the development of autoimmunity, there is less knowledge regarding protective mechanisms from developing such diseases. For example, in type 1 diabetes (T1D), the immune-mediated form of diabetes, the role of pathogenic T cells in the destruction of pancreatic islets is well characterized, but immune-mediated mechanisms that contribute to T1D protection have not been fully elucidated. One potential protective mechanism includes the suppression of immune responses by regulatory CD4 T cells (Tregs) that recognize self-peptides from islets presented by human leukocyte antigen (HLA) class II molecules. In this review, we summarize what is known about the antigenic self-peptides recognized by Tregs in the context of T1D.

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

Type 1 diabetes (T1D) is an autoimmune disease in which immune cells destroy insulin-producing beta cells. The aetiology of this complex disease is dependent on the interplay of multiple heterogeneous cell types in the pancreatic environment. Here, we provide a single-cell atlas of pancreatic islets of 24 T1D, autoantibody-positive and nondiabetic organ donors across multiple quantitative modalities including ~80,000 cells using single-cell transcriptomics, ~7,000,000 cells using cytometry by time of flight and ~1,000,000 cells using in situ imaging mass cytometry. We develop an advanced integrative analytical strategy to assess pancreatic islets and identify canonical cell types. We show that a subset of exocrine ductal cells acquires a signature of tolerogenic dendritic cells in an apparent attempt at immune suppression in T1D donors. Our multimodal analyses delineate cell types and processes that may contribute to T1D immunopathogenesis and provide an integrative procedure for exploration and discovery of human pancreatic function.