Automated local bright feature image analysis of nuclear proteindistribution identifies changes in tissue phenotype

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The organization of nuclear proteins is linked to cell and tissue phenotypes. When cells arrest proliferation, undergo apoptosis, or differentiate, the distribution of nuclear proteins changes. Conversely, forced alteration of the distribution of nuclear proteins modifies cell phenotype. Immunostaining and fluorescence microscopy have been critical for such findings. However, there is an increasing need for quantitative analysis of nuclear protein distribution to decipher epigenetic relationships between nuclear structure and cell phenotype, and to unravel the mechanisms linking nuclear structure and function. We have developed imaging methods to quantify the distribution of fluorescently-stained nuclear protein NuMA in different mammary phenotypes obtained ... continued below

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Knowles, David; Sudar, Damir; Bator, Carol & Bissell, Mina February 1, 2006.

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The organization of nuclear proteins is linked to cell and tissue phenotypes. When cells arrest proliferation, undergo apoptosis, or differentiate, the distribution of nuclear proteins changes. Conversely, forced alteration of the distribution of nuclear proteins modifies cell phenotype. Immunostaining and fluorescence microscopy have been critical for such findings. However, there is an increasing need for quantitative analysis of nuclear protein distribution to decipher epigenetic relationships between nuclear structure and cell phenotype, and to unravel the mechanisms linking nuclear structure and function. We have developed imaging methods to quantify the distribution of fluorescently-stained nuclear protein NuMA in different mammary phenotypes obtained using three-dimensional cell culture. Automated image segmentation of DAPI-stained nuclei was generated to isolate thousands of nuclei from three-dimensional confocal images. Prominent features of fluorescently-stained NuMA were detected using a novel local bright feature analysis technique, and their normalized spatial density calculated as a function of the distance from the nuclear perimeter to its center. The results revealed marked changes in the distribution of the density of NuMA bright features as non-neoplastic cells underwent phenotypically normal acinar morphogenesis. In contrast, we did not detect any reorganization of NuMA during the formation of tumor nodules by malignant cells. Importantly, the analysis also discriminated proliferating non-neoplastic cells from proliferating malignant cells, suggesting that these imaging methods are capable of identifying alterations linked not only to the proliferation status but also to the malignant character of cells. We believe that this quantitative analysis will have additional applications for classifying normal and pathological tissues.

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  • Journal Name: Proceedings of the National Academy of Science; Journal Volume: 103; Journal Issue: 12; Related Information: Journal Publication Date: 03/21/2006

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  • Report No.: LBNL--59219
  • Grant Number: DE-AC02-05CH11231
  • DOI: 10.1073/pnas.0509944102 | External Link
  • Office of Scientific & Technical Information Report Number: 900786
  • Archival Resource Key: ark:/67531/metadc890050

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  • February 1, 2006

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  • Sept. 22, 2016, 2:13 a.m.

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  • Sept. 29, 2016, 7:39 p.m.

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Knowles, David; Sudar, Damir; Bator, Carol & Bissell, Mina. Automated local bright feature image analysis of nuclear proteindistribution identifies changes in tissue phenotype, article, February 1, 2006; Berkeley, California. (digital.library.unt.edu/ark:/67531/metadc890050/: accessed August 22, 2017), University of North Texas Libraries, Digital Library, digital.library.unt.edu; crediting UNT Libraries Government Documents Department.