Publication
Title
High content image cytometry in the context of subnuclear organization
Author
Abstract
The organization of proteins in space and time is essential to their function. 16 accurately quantify subcellular protein characteristics in a population of cells with regard for the stochasticity of events in a natural context, there is a fast-growing need for image-based cytometry. Simultaneously, the massive amount of data that is generated by image-cytometric analyses, calls for tools that enable pattern recognition and automated classification. In this article, we present a general approach for multivariate phenotypic profiling of individual cell nuclei and quantification of subnuclear spots using automated fluorescence mosaic microscopy, optimized image processing tools, and supervised classification. We demonstrate the efficiency of our analysis by determination of differential DNA damage repair patterns in response to genotoxic stress and radiation, and we show the potential of data mining in pinpointing specific phenotypes after transient transfection. The presented approach allowed for systematic analysis of subnuclear features in large image data sets and accurate classification of phenotypes at the level of the single cell. Consequently, this type of nuclear fingerprinting shows potential for high-throughput applications, Such as functional protein assays or drug compound screening. (C) 2009 International Society for Advancement of Cytometry
Language
English
Source (journal)
Cytometry: part A. - New York, 2003, currens
Publication
New York : 2010
ISSN
1552-4922 [print]
1552-4930 [online]
DOI
10.1002/CYTO.A.20807
Volume/pages
77A :1 (2010) , p. 64-75
ISI
000273384700009
Full text (Publisher's DOI)
Full text (publisher's version - intranet only)
UAntwerpen
Faculty/Department
Publication type
Subject
External links
Web of Science
Record
Identifier
Creation 11.12.2013
Last edited 28.01.2023
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