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Automated classification and visualization of fluorescent live cell microscopy images

Urish, KL and Deasy, BM and Huard, J (2013) Automated classification and visualization of fluorescent live cell microscopy images. Journal of Microscopy, 249 (3). 206 - 214. ISSN 0022-2720

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Abstract

Robotic, high-throughput microscopy is a powerful tool for small molecule screening and classifying cell phenotype, proteomic and genomic data. An important hurdle in the field is the automated classification and visualization of results collected from a data set of tens of thousands of images. We present a method that approaches these problems from the perspective of flow cytometry with supporting open-source code. Image analysis software was created that allowed high-throughput microscopy data to be analysed in a similar manner as flow cytometry. Each cell on an image is considered an object and a series of gates similar to flow cytometry is used to classify and quantify the properties of cells including size and level of fluorescent intensity. This method is released with open-source software and code that demonstrates the method's implementation. Accuracy of the software was determined by measuring the levels of apoptosis in a primary murine myoblast cell line after exposure to staurosporine and comparing these results to flow cytometry. © 2013 Royal Microscopical Society.


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Details

Item Type: Article
Status: Published
Creators/Authors:
CreatorsEmailPitt UsernameORCID
Urish, KL
Deasy, BM
Huard, J
Centers: Other Centers, Institutes, or Units > Stem Cell Research Center
Date: 1 March 2013
Date Type: Publication
Journal or Publication Title: Journal of Microscopy
Volume: 249
Number: 3
Page Range: 206 - 214
DOI or Unique Handle: 10.1111/jmi.12010
Schools and Programs: School of Medicine > Orthopaedic Surgery
Swanson School of Engineering > Bioengineering
Refereed: Yes
ISSN: 0022-2720
PubMed ID: 23323688
Date Deposited: 31 Mar 2014 15:16
Last Modified: 13 Oct 2017 20:57
URI: http://d-scholarship.pitt.edu/id/eprint/20803

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