Automated assessment of β-cell area and density per islet and patient using TMEM27 and BACE2 immunofluorescence staining in human pancreatic β-cells

Automated image analysis method to detect and quantify fat cell infiltration in hematoxylin and eosin stained human pancreas histology images

Fatty infiltration in pancreas leading to steatosis is a major risk factor in pancreas transplantation. Hematoxylin and eosin (H and E) is one of the common histological staining techniques that provides information on the tissue cytoarchitecture. Adipose (fat) cells accumulation in pancreas has been shown to impact beta cell survival, its endocrine function and pancreatic steatosis and can cause non-alcoholic fatty pancreas disease (NAFPD). The current automated tools (E.g. Adiposoft) available for fat analysis are suited for white fat tissue which is homogeneous and easier to segment unlike heterogeneous tissues such as pancreas where fat cells continue to play critical physiopathological functions. The currently, available pancreas segmentation tool focuses on endocrine islet segmentation based on cell nuclei detection for diagnosis of pancreatic cancer. In the current study, we present a fat quantifying tool, Fatquant, which identifies fat cells in heterogeneous H and E tissue sections with reference to diameter of fat cell. Using histological images from a public database, we observed an intersection over union of 0.797 to 0.962 and 0.675 to 0.937 for manual versus Fatquant analysis of pancreas and liver, respectively.

BACE2 variant identified from HSCR patient causes AD-like phenotypes in hPSC-derived brain organoids

β-site APP-cleaving enzyme 2 (BACE2) is a homolog of BACE1, which is considered as the most promising therapeutic target for Alzheimer's disease (AD). However, the expression and functional role of BACE2 in central nervous system (CNS) remain obscured. Previously, we identified several BACE2 rare variants in Hirschsprung disease (HSCR) patients and proved that BACE2-mediated APP cleavage might represent a novel HSCR pathogenesis mechanism in enteric nervous system. Here, we validated that these HSCR-associated BACE2 variants were loss-of-function mutations. Using the human pluripotent stem cell (hPSC)-derived brain organoids (BOs), we further demonstrated that BACE2 was mainly expressed in the ventricular zone and cortical plate of BOs, and its expression level was gradually increased along with the BO maturation. Functionally, we found that the BOs carrying the BACE2 loss-of-function mutation (BACE2^G446R) showed greater apoptosis and increased levels of Aβ oligomers compared to the control BOs, resembling with the AD-associated phenotypes. All these phenotypes could be rescued via the removal of APP protein in BACE2^G446R BOs. Furthermore, rather than BACE2^G446R, BACE2^WT overexpression in BOs carrying the APP Swedish/Indiana mutations attenuated the AD-associated phenotypes, including Aβ accumulation and neuronal cell death. Taken together, our results unravel that BACE2 can protect the neuronal cell from apoptosis caused by Aβ accumulation, and the deficiency of BACE2-mediated APP cleavage may represent a common pathological mechanism for both HSCR and AD.

Machine Learning Algorithms, Applied to Intact Islets of Langerhans, Demonstrate Significantly Enhanced Insulin Staining at the Capillary Interface of Human Pancreatic β Cells

Pancreatic β cells secrete the hormone insulin into the bloodstream and are critical in the control of blood glucose concentrations. β cells are clustered in the micro-organs of the islets of Langerhans, which have a rich capillary network. Recent work has highlighted the intimate spatial connections between β cells and these capillaries, which lead to the targeting of insulin secretion to the region where the β cells contact the capillary basement membrane. In addition, β cells orientate with respect to the capillary contact point and many proteins are differentially distributed at the capillary interface compared with the rest of the cell. Here, we set out to develop an automated image analysis approach to identify individual β cells within intact islets and to determine if the distribution of insulin across the cells was polarised. Our results show that a U-Net machine learning algorithm correctly identified β cells and their orientation with respect to the capillaries. Using this information, we then quantified insulin distribution across the β cells to show enrichment at the capillary interface. We conclude that machine learning is a useful analytical tool to interrogate large image datasets and analyse sub-cellular organisation.

Identification of potential markers for type 2 diabetes mellitus via bioinformatics analysis

Type 2 diabetes mellitus (T2DM) is a multifactorial and multigenetic disease, and its pathogenesis is complex and largely unknown. In the present study, microarray data (GSE201966) of β-cell enriched tissue obtained by laser capture microdissection were downloaded, including 10 control and 10 type 2 diabetic subjects. A comprehensive bioinformatics analysis of microarray data in the context of protein-protein interaction (PPI) networks was employed, combined with subcellular location information to mine the potential candidate genes for T2DM and provide further insight on the possible mechanisms involved. First, differential analysis screened 108 differentially expressed genes. Then, 83 candidate genes were identified in the layered network in the context of PPI via network analysis, which were either directly or indirectly linked to T2DM. Of those genes obtained through literature retrieval analysis, 27 of 83 were involved with the development of T2DM; however, the rest of the 56 genes need to be verified by experiments. The functional analysis of candidate genes involved in a number of biological activities, demonstrated that 46 upregulated candidate genes were involved in ‘inflammatory response’ and ‘lipid metabolic process’, and 37 downregulated candidate genes were involved in ‘positive regulation of cell death’ and ‘positive regulation of cell proliferation’. These candidate genes were also involved in different signaling pathways associated with ‘PI3K/Akt signaling pathway’, ‘Rap1 signaling pathway’, ‘Ras signaling pathway’ and ‘MAPK signaling pathway’, which are highly associated with the development of T2DM. Furthermore, a microRNA (miR)-target gene regulatory network and a transcription factor-target gene regulatory network were constructed based on miRNet and NetworkAnalyst databases, respectively. Notably, hsa-miR-192-5p, hsa-miR-124-5p and hsa-miR-335-5p appeared to be involved in T2DM by potentially regulating the expression of various candidate genes, including procollagen C-endopeptidase enhancer 2, connective tissue growth factor and family with sequence similarity 105, member A, protein phosphatase 1 regulatory inhibitor subunit 1 A and C-C motif chemokine receptor 4. Smad5 and Bcl6, as transcription factors, are regulated by ankyrin repeat domain 23 and transmembrane protein 37, respectively, which might also be used in the molecular diagnosis and targeted therapy of T2DM. Taken together, the results of the present study may offer insight for future genomic-based individualized treatment of T2DM and help determine the underlying molecular mechanisms that lead to T2DM.

A Phenotypic Screen Identifies Calcium Overload as a Key Mechanism of β-Cell Glucolipotoxicity

Type 2 diabetes (T2D) is caused by loss of pancreatic β-cell mass and failure of the remaining β-cells to deliver sufficient insulin to meet demand. β-Cell glucolipotoxicity (GLT), which refers to combined, deleterious effects of elevated glucose and fatty acid levels on β-cell function and survival, contributes to T2D-associated β-cell failure. Drugs and mechanisms that protect β-cells from GLT stress could potentially improve metabolic control in patients with T2D. In a phenotypic screen seeking low-molecular-weight compounds that protected β-cells from GLT, we identified compound A that selectively blocked GLT-induced apoptosis in rat insulinoma cells. Compound A and its optimized analogs also improved viability and function in primary rat and human islets under GLT. We discovered that compound A analogs decreased GLT-induced cytosolic calcium influx in islet cells, and all measured β-cell-protective effects correlated with this activity. Further studies revealed that the active compound from this series largely reversed GLT-induced global transcriptional changes. Our results suggest that taming cytosolic calcium overload in pancreatic islets can improve β-cell survival and function under GLT stress and thus could be an effective strategy for T2D treatment.

A supervised learning framework for pancreatic islet segmentation with multi-scale color-texture features and rolling guidance filters

Islet cell quantification and function is important for developing novel therapeutic interventions for diabetes. Existing methods of pancreatic islet segmentation in histopathological images depend strongly on cell/nuclei detection, and thus are limited due to a wide variance in the appearance of pancreatic islets. In this paper, we propose a supervised learning pipeline to segment pancreatic islets in histopathological images, which does not require cell detection. The proposed framework firstly partitions images into superpixels, and then extracts multi-scale color-texture features from each superpixel and processes these features using rolling guidance filters, in order to simultaneously reduce inter-class ambiguity and intra-class variation. Finally, a linear support vector machine (SVM) is trained and applied to segment the testing images. A total of 23 hematoxylin-and-eosin-stained histopathological images with pancreatic islets are used for verifying the framework. With an average accuracy of 95%, training time of 20 min and testing time of 1 min per image, the proposed framework outperforms existing approaches with better segmentation performance and lower computational cost. © 2016 International Society for Advancement of Cytometry.