TISMorph: A tool to quantify texture, irregularity and spreading of single cells
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journal.pone.0217346.pdf
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Univ Arizona, Dept Cellular & Mol MedIssue Date
2019-06-03
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PUBLIC LIBRARY SCIENCECitation
Alizadeh, E., Xu, W., Castle, J., Foss, J., & Prasad, A. (2019). TISMorph: A tool to quantify texture, irregularity and spreading of single cells. PloS one, 14(6), e0217346.Journal
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© 2019 Alizadeh et al. This is an open access article distributed under the terms of the Creative Commons Attribution License.Collection Information
This item from the UA Faculty Publications collection is made available by the University of Arizona with support from the University of Arizona Libraries. If you have questions, please contact us at repository@u.library.arizona.edu.Abstract
A number of recent studies have shown that cell shape and cytoskeletal texture can be used as sensitive readouts of the physiological state of the cell. However, utilization of this information requires the development of quantitative measures that can describe relevant aspects of cell shape. In this paper we develop a toolbox, TISMorph, that calculates a set of quantitative measures to address this need. Some of the measures introduced here have been used previously, while others are new and have desirable properties for shape and texture quantification of cells. These measures, broadly classifiable into the categories of textural, irregularity and spreading measures, are tested by using them to discriminate between osteosarcoma cell lines treated with different cytoskeletal drugs. We find that even though specific classification tasks often rely on a few measures, these are not the same between all classification tasks, thus requiring the use of the entire suite of measures for classification and discrimination. We provide detailed descriptions of the measures, as well as the TISMorph package to implement them. Quantitative morphological measures that capture different aspects of cell morphology will help enhance large-scale image-based quantitative analysis, which is emerging as a new field of biological data.Note
Open access journalISSN
1932-6203PubMed ID
31158241Version
Final published versionSponsors
National Science Foundation [PHY-1151454]ae974a485f413a2113503eed53cd6c53
10.1371/journal.pone.0217346
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Except where otherwise noted, this item's license is described as © 2019 Alizadeh et al. This is an open access article distributed under the terms of the Creative Commons Attribution License.
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