The Role of Alpha Cells in the Self-Assembly of Bioengineered Islets

Beyond the loss of beta cells: a quantitative analysis of islet architecture in adults with and without type 1 diabetes

AIMS/HYPOTHESIS

The organisation and cellular architecture of islets of Langerhans are critical to the physiological regulation of hormone secretion but it is debated whether human islets adhere to the characteristic mantle-core (M-C) structure seen in rodents. It is also unclear whether inherent architectural changes contribute to islet dysfunction in type 1 diabetes, aside from the loss of beta cells. Therefore, we have exploited advances in immunostaining, spatial biology and machine learning to undertake a detailed, systematic analysis of adult human islet architecture in health and type 1 diabetes, by a quantitative analysis of a dataset of >250,000 endocrine cells in >3500 islets from ten individuals.

METHODS

Formalin-fixed paraffin-embedded pancreatic sections (4 μm) from organ donors without diabetes and living donors with recent-onset type 1 diabetes were stained for all five islet hormones and imaged prior to analysis, which employed a novel automated pipeline using QuPath software, capable of running on a standard laptop. Whole-slide image analysis involved segmentation classifiers, cell detection and phenotyping algorithms to identify islets, specific cell types and their locations as (x,y)-coordinates in regions of interest. Each endocrine cell was categorised into binary variables for cell type (i.e. beta or non-beta) and position (mantle or core). A χ2 test for independence of these properties was performed and the OR was considered to estimate the effect size of the potential association between position and cell type. A quantification of the M-C structure at islet level was performed by computing the probability, r, that the observed number of non-beta cells in the mantle is due to a random arrangement. The distribution of the r values for the islets in the study was contrasted against the r values of a digital population of equivalent randomly arranged islets, termed digital siblings. Both distributions of r values were compared using the earth mover's distance (EMD), a mathematical tool employed to describe differences in distribution patterns. The EMD was also used to contrast the distribution of islet size and beta cell fraction between type 1 diabetes and control islets.

RESULTS

The χ2 test supports the existence of a significant (p<0.001) relationship between cell position and type. The effect size was measured via the OR <0.8, showing that non-beta cells are more likely to be found at the mantle (and vice versa). At the islet level, the EMD between the distributions of r values of the observed islets and the digital siblings was emd-1d=0.10951 (0

CONCLUSIONS/INTERPRETATION

Using a novel analysis pipeline, statistical evidence supports the existence of an M-C structure in human adult islets, irrespective of type 1 diabetes status. The methods presented in the current study offer potential applications in spatial biology, islet immunopathology, transplantation and organoid research, and developmental research.

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Key Words

• Human
• Image analysis
• Immunopathology
• Islet architecture
• Machine learning
• Mantle–core
• Pancreas
• Quantitative
• Shareable tool
• Type 1 diabetes

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MESH Headings

• Humans
• Diabetes Mellitus, Type 1/pathology
• Diabetes Mellitus, Type 1/metabolism
• Adult
• Insulin-Secreting Cells/pathology
• Insulin-Secreting Cells/metabolism
• Islets of Langerhans/pathology
• Islets of Langerhans/metabolism
• Male
• Female
• Middle Aged

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Grants

• EPSRC/EP/V048716/1 Diabetes UK
• RD Lawrence Fellowship/22/0006378 Diabetes UK

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Affiliation(s)

Nicolás Verschueren van Rees Department of Mathematics and Statistics, University of Exeter, Exeter, UK. EPSRC Hub for Quantitative Modelling in Healthcare, University of Exeter, Exeter, UK. Living Systems Institute, University of Exeter, Exeter, UK.
Peter Ashwin Department of Mathematics and Statistics, University of Exeter, Exeter, UK EPSRC Hub for Quantitative Modelling in Healthcare, University of Exeter, Exeter, UK
Conor McMullan Exeter Centre of Excellence for Diabetes Research, Department of Clinical and Biomedical Science, University of Exeter Medical School, Exeter, UK
Lars Krogvold Division of Childhood and Adolescent Medicine, Oslo University Hospital, Oslo, Norway
Knut Dahl-Jørgensen Division of Childhood and Adolescent Medicine, Oslo University Hospital, Oslo, Norway Faculty of Medicine, University of Oslo, Oslo, Norway
Noel G Morgan Exeter Centre of Excellence for Diabetes Research, Department of Clinical and Biomedical Science, University of Exeter Medical School, Exeter, UK
Pia Leete Exeter Centre of Excellence for Diabetes Research, Department of Clinical and Biomedical Science, University of Exeter Medical School, Exeter, UK.
Kyle C A Wedgwood Department of Mathematics and Statistics, University of Exeter, Exeter, UK EPSRC Hub for Quantitative Modelling in Healthcare, University of Exeter, Exeter, UK Living Systems Institute, University of Exeter, Exeter, UK

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Scaffold-free endocrine tissue engineering: role of islet organization and implications in type 1 diabetes

Type 1 diabetes (T1D) is a chronic hyperglycemia disorder emerging from beta-cell (insulin secreting cells of the pancreas) targeted autoimmunity. As the blood glucose levels significantly increase and the insulin secretion is gradually lost, the entire body suffers from the complications. Although various advances in the insulin analogs, blood glucose monitoring and insulin application practices have been achieved in the last few decades, a cure for the disease is not obtained. Alternatively, pancreas/islet transplantation is an attractive therapeutic approach based on the patient prognosis, yet this treatment is also limited mainly by donor shortage, life-long use of immunosuppressive drugs and risk of disease transmission. In research and clinics, such drawbacks are addressed by the endocrine tissue engineering of the pancreas. One arm of this engineering is scaffold-free models which often utilize highly developed cell-cell junctions, soluble factors and 3D arrangement of islets with the cellular heterogeneity to prepare the transplant formulations. In this review, taking T1D as a model autoimmune disease, techniques to produce so-called pseudoislets and their applications are studied in detail with the aim of understanding the role of mimicry and pointing out the promising efforts which can be translated from benchside to bedside to achieve exogenous insulin-free patient treatment. Likewise, these developments in the pseudoislet formation are tools for the research to elucidate underlying mechanisms in pancreas (patho)biology, as platforms to screen drugs and to introduce immunoisolation barrier-based hybrid strategies.

Clinically compliant enrichment of human pluripotent stem cell-derived islets

Human pluripotent stem cell-derived islet (SC-islet) transplantation is a promising β cell replacement therapy for patients with type 1 diabetes, offering a potential unlimited cell supply. Yet, the heterogeneity of the final cell product containing non-target cell types has relevant implications for SC-islet function, transplant volume, and cell product safety. Here, we present a clinically compliant, full three-dimensional differentiation protocol that includes a purification step of endocrine cell-rich clusters, relying on the principle of isopycnic centrifugation (density gradient separation). Enriched SC-islets displayed signs of functionality in vitro and in vivo. In contrast with antibody-based single-cell sorting approaches, this method does not destroy the islet cytoarchitecture associated with alterations of islet function and cell loss. Furthermore, it is fast, is easily scalable to large cell volumes, and can be applied during cell manufacturing. This method may also contribute to the generation of improved cell-based therapies for regenerative medicine purposes beyond the SC-islet field.

Methodological approaches in aggregate formation and microscopic analysis to assess pseudoislet morphology and cellular interactions

Microscopy has revolutionised our view on biology and has been vital for many discoveries since its invention around 200 years ago. Recent developments in cell biology have led to a strong interest in generating spheroids and organoids that better represent tissue. However, the current challenge faced by many researchers is the culture and analysis of these three-dimensional (3D) cell cultures. With the technological improvements in reconstructing volumetric datasets by optical sections, it is possible to quantify cells, their spatial arrangement, and the protein distribution without destroying the physical organization. We assessed three different microwell culture plates and four analysis tools for 3D imaging data for their applicability for the analysis of 3D cultures. A key advantage of microwell plates is their potential to perform high-throughput experiments in which cell cultures are generated and analysed in one single system. However, it was shown that this potential could be impacted by the material composition and microwell structure. For example, antibody staining was not possible in a hydrogel microwell, and truncated pyramid-structured microwells had increased background fluorescence due to their structure. Regarding analysis tools, four different software, namely CellProfiler, Fiji/ImageJ, Nikon GA3 and Imaris, were compared for their accuracy and applicability in analysing datasets from 3D cultures. The results showed that the open-access software, CellProfiler and Fiji, could quantify nuclei and cells, yet with varying results compared to manual counting, and may require post-processing optimisation. On the other hand, the GA3 and Imaris software packages showed excellent versatility in usage and accuracy in the quantification of nuclei and cells, and could classify cell localisation. Together these results provide critical considerations for microscopic imaging and analysis of 3D cell cultures.

The Role of Pancreatic Alpha Cells and Endothelial Cells in the Reduction of Oxidative Stress in Pseudoislets

Pancreatic beta cells have inadequate levels of antioxidant enzymes, and the damage induced by oxidative stress poses a challenge for their use in a therapy for patients with type 1 diabetes. It is known that the interaction of the pancreatic endocrine cells with support cells can improve their survival and lead to less vulnerability to oxidative stress. Here we investigated alpha (alpha TC-1), beta (INS1E) and endothelial (HUVEC) cells assembled into aggregates known as pseudoislets as a model of the pancreatic islets of Langerhans. We hypothesised that the coculture of alpha, beta and endothelial cells would be protective against oxidative stress. First, we showed that adding endothelial cells decreased the percentage of oxidative stress-positive cells. We then asked if the number of endothelial cells or the size (number of cells) of the pseudoislet could increase the protection against oxidative stress. However, no additional benefit was observed by those changes. On the other hand, we identified a potential supportive effect of the alpha cells in reducing oxidative stress in beta and endothelial cells. We were able to link this to the incretin glucagon-like peptide-1 (GLP-1) by showing that the absence of alpha cells in the pseudoislet caused increased oxidative stress, but the addition of GLP-1 could restore this. Together, these results provide important insights into the roles of alpha and endothelial cells in protecting against oxidative stress.