NDLI: Computer-assisted Large-scale Visualization and Quantification of Pancreatic Islet Mass, Size Distribution and Architecture

Content Provider	PubMed Central
Author	Kim, Abraham Kilimnik, German Guo, Charles Sung, Joshua Jo, Junghyo Vipul, Periwal Witkowski, Piotr Philip, Dilorio Hara, Manami
Copyright Year	2011
Abstract	The pancreatic islet is a unique micro-organ composed of several hormone secreting endocrine cells such as beta-cells (insulin), alpha-cells (glucagon), and delta-cells (somatostatin) that are embedded in the exocrine tissues and comprise 1-2% of the entire pancreas. There is a close correlation between body and pancreas weight. Total beta-cell mass also increases proportionately to compensate for the demand for insulin in the body. What escapes this proportionate expansion is the size distribution of islets. Large animals such as humans share similar islet size distributions with mice, suggesting that this micro-organ has a certain size limit to be functional. The inability of large animal pancreata to generate proportionately larger islets is compensated for by an increase in the number of islets and by an increase in the proportion of larger islets in their overall islet size distribution. Furthermore, islets exhibit a striking plasticity in cellular composition and architecture among different species and also within the same species under various pathophysiological conditions. In the present study, we describe novel approaches for the analysis of biological image data in order to facilitate the automation of analytic processes, which allow for the analysis of large and heterogeneous data collections in the study of such dynamic biological processes and complex structures. Such studies have been hampered due to technical difficulties of unbiased sampling and generating large-scale data sets to precisely capture the complexity of biological processes of islet biology. Here we show methods to collect unbiased "representative" data within the limited availability of samples (or to minimize the sample collection) and the standard experimental settings, and to precisely analyze the complex three-dimensional structure of the islet. Computer-assisted automation allows for the collection and analysis of large-scale data sets and also assures unbiased interpretation of the data. Furthermore, the precise quantification of islet size distribution and spatial coordinates (i.e. X, Y, Z-positions) not only leads to an accurate visualization of pancreatic islet structure and composition, but also allows us to identify patterns during development and adaptation to altering conditions through mathematical modeling. The methods developed in this study are applicable to studies of many other systems and organisms as well.
Related Links	http://dx.doi.org/10.3791/2471
Starting Page	2471
File Format	PDF
ISSN	1940087X
e-ISSN	1940087X
Journal	Journal of Visualized Experiments : JoVE
Issue Number	49
Language	English
Publisher	MyJove Corporation
Publisher Date	2011-03-01
Access Restriction	Open
Rights Holder	MyJove Corporation
Subject Keyword	Biochemistry, Genetics and Molecular Biology(all) Immunology and Microbiology(all) Chemical Engineering(all) Neuroscience(all) Medicine(all) Research in Higher Education
Content Type	Text
Resource Type	Article
Subject	Neuroscience Immunology and Microbiology Biochemistry, Genetics and Molecular Biology Chemical Engineering

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