Quantitative analysis of secretory organelles in pancreatic islet cells utilizing 2D and 3D approaches in serial block-face scanning electron microscopy

Authors

• BC Kuo
• GN Calco
• JD Hoyne
• MA Aronova
• G Zhang
• T Cai
• H Xu
• AL Notkins
• RD Leapman

Abstract

Using serial block-face scanning electron microscopy (SBF-SEM), we have imaged entire mouse pancreatic islets in 3D. At a subcellular level, this technique provides a high spatial resolution in the x-y plane (5-nm) and in the perpendicular z-direction (25-nm) that enables visualization of organelles throughout the volumes of individual beta cells within an islet. Given the large data sets that SBF-SEM generates, there is currently no generally applicable software available for automating the quantitative analysis of such large data sets in a short period of time. We have developed a hybrid approach for analyzing large 3D data sets that combines 3D volume segmentation with analyses of 2D planes randomly selected throughout the data set. This method enables quantification of the number of secretory organelles in beta cells, as well as estimation of the total insulin content of a beta cell. We have applied this method to compare the number of insulin secreting granules and insulin content from wild-type and 8-week and 20-week old non-obese diabetic mice.

Scientific Focus Area: Biomedical Engineering and Biophysics

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