A novel framework for segmentation of secretory granules in electron micrographs

Modulation of Unfolded Protein Response Restores Survival and Function of β-Cells Exposed to the Endocrine Disruptor Bisphenol A

Diabetes is a metabolic disease that currently affects nearly half a billion people worldwide. β-cells dysfunction is one of the main causes of diabetes. Exposure to endocrine-disrupting chemicals is correlated with increased diabetes incidence. We hypothesized that treatment with bisphenol A (BPA) induces endoplasmic reticulum (ER) stress that activates the unfolded protein response (UPR), leading to impaired function of the β-cells, which over time, can cause diabetes. In this study, we aimed to evaluate UPR pathways activation under BPA treatment in β-cells and possible recovery of ER homeostasis. MIN6 cells (mouse insulinoma cell line) and isolated pancreatic islets from NOR (non-obese diabetes resistant) mice were treated with BPA. We analyzed the impact of BPA on β-cell viability, the architecture of the early secretory pathway, the synthesis and processing of insulin and the activation of UPR sensors and effectors. We found that the addition of the chemical chaperone TUDCA rescues the deleterious effects of BPA, resulting in improved viability, morphology and function of the β-cells. In conclusion, we propose that modulators of UPR can be used as therapeutic interventions targeted towards regaining β-cells homeostasis.

State-of-the-art microscopy to understand islets of Langerhans: what to expect next?

The discovery of Langerhans and microscopic description of islets in the pancreas were crucial steps in the discovery of insulin. Over the past 150 years, many discoveries in islet biology and type 1 diabetes have been made using powerful microscopic techniques. In the past decade, combination of new probes, animal and tissue models, application of new biosensors and automation of light and electron microscopic methods and other (sub)cellular imaging modalities have proven their potential in understanding the beta cell under (patho)physiological conditions. The imaging evolution, from fluorescent jellyfish to real‐time intravital functional imaging, the revolution in automation and data handling and the increased resolving power of analytical imaging techniques are now converging. Here, we review innovative approaches that address islet biology from new angles by studying cells and molecules at high spatiotemporal resolution and in live models. Broad implementation of these cellular imaging techniques will shed new light on cause/consequence of (mal)function in islets of Langerhans in the years to come.

Deep-Learning-Based Segmentation of Small Extracellular Vesicles in Transmission Electron Microscopy Images

Small extracellular vesicles (sEVs) are cell-derived vesicles of nanoscale size (~30-200 nm) that function as conveyors of information between cells, reflecting the cell of their origin and its physiological condition in their content. Valuable information on the shape and even on the composition of individual sEVs can be recorded using transmission electron microscopy (TEM). Unfortunately, sample preparation for TEM image acquisition is a complex procedure, which often leads to noisy images and renders automatic quantification of sEVs an extremely difficult task. We present a completely deep-learning-based pipeline for the segmentation of sEVs in TEM images. Our method applies a residual convolutional neural network to obtain fine masks and use the Radon transform for splitting clustered sEVs. Using three manually annotated datasets that cover a natural variability typical for sEV studies, we show that the proposed method outperforms two different state-of-the-art approaches in terms of detection and segmentation performance. Furthermore, the diameter and roundness of the segmented vesicles are estimated with an error of less than 10%, which supports the high potential of our method in biological applications.