Aims/hypothesisKnowledge of number, size and content of insulin secretory granules is pivotal for understanding the physiology of pancreatic beta cells. Here we re-evaluated key structural features of rat beta cells, including insulin granule size, number and distribution as well as cell size.MethodsElectron micrographs of rat beta cells fixed either chemically or by high-pressure freezing were compared using a high-content analysis approach. These data were used to develop three-dimensional in silico beta cell models, the slicing of which would reproduce the experimental datasets.ResultsAs previously reported, chemically fixed insulin secretory granules appeared as hollow spheres with a mean diameter of ∼350 nm. Remarkably, most granules fixed by high-pressure freezing lacked the characteristic halo between the dense core and the limiting membrane and were smaller than their chemically fixed counterparts. Based on our analyses, we conclude that the mean diameter of rat insulin secretory granules is 243 nm, corresponding to a surface area of 0.19 μm2. Rat beta cells have a mean volume of 763 μm3 and contain 5,000–6,000 granules.Conclusions/interpretationA major reason for the lower mean granule number/rat beta cell relative to previous accounts is a reduced estimation of the mean beta cell volume. These findings imply that each granule contains about twofold more insulin, while its exocytosis increases membrane capacitance about twofold less than assumed previously. Our integrated approach defines new standards for quantitative image analysis of beta cells and could be applied to other cellular systems.Electronic supplementary materialThe online version of this article (doi:10.1007/s00125-011-2438-4) contains peer-reviewed but unedited supplementary material, which is available to authorised users.
High content screening (HCS) has established itself in the world of the pharmaceutical industry as an essential tool for drug discovery and drug development. HCS is currently starting to enter the academic world and might become a widely used technology. Given the diversity of problems tackled in academic research, HCS could experience some profound changes in the future, mainly with more imaging modalities and smart microscopes being developed. One of the limitations in the establishment of HCS in academia is flexibility and cost. Flexibility is important to be able to adapt the HCS setup to accommodate the multiple different assays typical of academia. Many cost factors cannot be avoided, but the costs of the software packages necessary to analyze large datasets can be reduced by using Open Source software. We present and discuss the Open Source software CellProfiler for image analysis and KNIME for data analysis and data mining that provide software solutions which increase flexibility and keep costs low.
The field of High Content Screening (HCS) has evolved from a technology used exclusively by the pharmaceutical industry for secondary drug screening, to a technology used for primary drug screening and basic research in academia. The size and the complexity of the screens have been steadily increasing. This is reflected in the fact that the major challenges facing the field at the present are data mining and data storage due to the large amount of data generated during HCS. On the one hand, technological progress of fully automated image acquisition platforms, and on the other hand advances in the field of automated image analysis have made this technology more powerful and more accessible to less specialized users. Image analysis solutions for many biological problems exist and more are being developed to increase both the quality and the quantity of data extracted from the images acquired during the screens. We highlight in this review some of the major challenges facing automatic high throughput image analysis and present some of the software solutions available on the market or from academic open source solutions.
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