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Title: Phenotype clustering of breast epithelial cells in confocal images based on nuclear protein distribution analysis

Abstract

Background: The distribution of chromatin-associated proteins plays a key role in directing nuclear function. Previously, we developed an image-based method to quantify the nuclear distributions of proteins and showed that these distributions depended on the phenotype of human mammary epithelial cells. Here we describe a method that creates a hierarchical tree of the given cell phenotypes and calculates the statistical significance between them, based on the clustering analysis of nuclear protein distributions. Results: Nuclear distributions of nuclear mitotic apparatus protein were previously obtained for non-neoplastic S1 and malignant T4-2 human mammary epithelial cells cultured for up to 12 days. Cell phenotype was defined as S1 or T4-2 and the number of days in cultured. A probabilistic ensemble approach was used to define a set of consensus clusters from the results of multiple traditional cluster analysis techniques applied to the nuclear distribution data. Cluster histograms were constructed to show how cells in any one phenotype were distributed across the consensus clusters. Grouping various phenotypes allowed us to build phenotype trees and calculate the statistical difference between each group. The results showed that non-neoplastic S1 cells could be distinguished from malignant T4-2 cells with 94.19% accuracy; that proliferating S1 cells could bemore » distinguished from differentiated S1 cells with 92.86% accuracy; and showed no significant difference between the various phenotypes of T4-2 cells corresponding to increasing tumor sizes. Conclusion: This work presents a cluster analysis method that can identify significant cell phenotypes, based on the nuclear distribution of specific proteins, with high accuracy.« less

Authors:
 [1];  [2];  [3];  [4];  [3]
  1. Lawrence Berkeley National Lab. (LBNL), Berkeley, CA (United States). Life Sciences Division; Howard Hughes Medical Inst., Ashburn, VA (United States). Janelia Farm Research Campus
  2. Lawrence Berkeley National Lab. (LBNL), Berkeley, CA (United States). Genomics Division West; Howard Hughes Medical Inst., Ashburn, VA (United States). Janelia Farm Research Campus
  3. Lawrence Berkeley National Lab. (LBNL), Berkeley, CA (United States). Life Sciences Division
  4. Purdue Univ., West Lafayette, IN (United States). Dept. of Basic Medical Science
Publication Date:
Sponsoring Org.:
USDOE Office of Science (SC), Biological and Environmental Research (BER). Biological Systems Science Division
OSTI Identifier:
1626366
Grant/Contract Number:  
AC02-05CH11231
Resource Type:
Accepted Manuscript
Journal Name:
BMC Cell Biology
Additional Journal Information:
Journal Volume: 8; Journal Issue: Suppl 1; Journal ID: ISSN 1471-2121
Publisher:
BioMed Central
Country of Publication:
United States
Language:
English
Subject:
59 BASIC BIOLOGICAL SCIENCES; Cell Biology

Citation Formats

Long, Fuhui, Peng, Hanchuan, Sudar, Damir, Lelièvre, Sophie A., and Knowles, David W. Phenotype clustering of breast epithelial cells in confocal images based on nuclear protein distribution analysis. United States: N. p., 2007. Web. doi:10.1186/1471-2121-8-s1-s3.
Long, Fuhui, Peng, Hanchuan, Sudar, Damir, Lelièvre, Sophie A., & Knowles, David W. Phenotype clustering of breast epithelial cells in confocal images based on nuclear protein distribution analysis. United States. https://doi.org/10.1186/1471-2121-8-s1-s3
Long, Fuhui, Peng, Hanchuan, Sudar, Damir, Lelièvre, Sophie A., and Knowles, David W. Mon . "Phenotype clustering of breast epithelial cells in confocal images based on nuclear protein distribution analysis". United States. https://doi.org/10.1186/1471-2121-8-s1-s3. https://www.osti.gov/servlets/purl/1626366.
@article{osti_1626366,
title = {Phenotype clustering of breast epithelial cells in confocal images based on nuclear protein distribution analysis},
author = {Long, Fuhui and Peng, Hanchuan and Sudar, Damir and Lelièvre, Sophie A. and Knowles, David W.},
abstractNote = {Background: The distribution of chromatin-associated proteins plays a key role in directing nuclear function. Previously, we developed an image-based method to quantify the nuclear distributions of proteins and showed that these distributions depended on the phenotype of human mammary epithelial cells. Here we describe a method that creates a hierarchical tree of the given cell phenotypes and calculates the statistical significance between them, based on the clustering analysis of nuclear protein distributions. Results: Nuclear distributions of nuclear mitotic apparatus protein were previously obtained for non-neoplastic S1 and malignant T4-2 human mammary epithelial cells cultured for up to 12 days. Cell phenotype was defined as S1 or T4-2 and the number of days in cultured. A probabilistic ensemble approach was used to define a set of consensus clusters from the results of multiple traditional cluster analysis techniques applied to the nuclear distribution data. Cluster histograms were constructed to show how cells in any one phenotype were distributed across the consensus clusters. Grouping various phenotypes allowed us to build phenotype trees and calculate the statistical difference between each group. The results showed that non-neoplastic S1 cells could be distinguished from malignant T4-2 cells with 94.19% accuracy; that proliferating S1 cells could be distinguished from differentiated S1 cells with 92.86% accuracy; and showed no significant difference between the various phenotypes of T4-2 cells corresponding to increasing tumor sizes. Conclusion: This work presents a cluster analysis method that can identify significant cell phenotypes, based on the nuclear distribution of specific proteins, with high accuracy.},
doi = {10.1186/1471-2121-8-s1-s3},
journal = {BMC Cell Biology},
number = Suppl 1,
volume = 8,
place = {United States},
year = {2007},
month = {1}
}

Works referencing / citing this record:

Bioimage informatics: a new area of engineering biology
journal, July 2008


Quantification and its Applications in Fluorescent Microscopy Imaging
journal, August 2009