Quantifying co-cultured cell phenotypes in high-throughput using pixel-based classification

Biologists increasingly use co-culture systems in which two or more cell types are grown in cell culture together in order to better model cells' native microenvironments. Co-cultures are often required for cell survival or proliferation, or to maintain physiological functioning in vitro. Havin...

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Bibliographic Details
Main Authors: Logan, David J (Contributor), Shan, Jing (Contributor), Bhatia, Sangeeta N (Contributor), Van Dyk, Anne Carpenter (Contributor)
Other Authors: Massachusetts Institute of Technology. Institute for Medical Engineering & Science (Contributor), Broad Institute of MIT and Harvard (Contributor), Harvard University- (Contributor), Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science (Contributor), Koch Institute for Integrative Cancer Research at MIT (Contributor)
Format: Article
Language:English
Published: Elsevier, 2017-07-18T17:57:26Z.
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Online Access:Get fulltext
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042 |a dc 
100 1 0 |a Logan, David J  |e author 
100 1 0 |a Massachusetts Institute of Technology. Institute for Medical Engineering & Science  |e contributor 
100 1 0 |a Broad Institute of MIT and Harvard  |e contributor 
100 1 0 |a Harvard University-  |e contributor 
100 1 0 |a Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science  |e contributor 
100 1 0 |a Koch Institute for Integrative Cancer Research at MIT  |e contributor 
100 1 0 |a Logan, David J  |e contributor 
100 1 0 |a Shan, Jing  |e contributor 
100 1 0 |a Bhatia, Sangeeta N  |e contributor 
100 1 0 |a Van Dyk, Anne Carpenter  |e contributor 
700 1 0 |a Shan, Jing  |e author 
700 1 0 |a Bhatia, Sangeeta N  |e author 
700 1 0 |a Van Dyk, Anne Carpenter  |e author 
245 0 0 |a Quantifying co-cultured cell phenotypes in high-throughput using pixel-based classification 
260 |b Elsevier,   |c 2017-07-18T17:57:26Z. 
856 |z Get fulltext  |u http://hdl.handle.net/1721.1/110768 
520 |a Biologists increasingly use co-culture systems in which two or more cell types are grown in cell culture together in order to better model cells' native microenvironments. Co-cultures are often required for cell survival or proliferation, or to maintain physiological functioning in vitro. Having two cell types co-exist in culture, however, poses several challenges, including difficulties distinguishing the two populations during analysis using automated image analysis algorithms. We previously analyzed co-cultured primary human hepatocytes and mouse fibroblasts in a high-throughput image-based chemical screen, using a combination of segmentation, measurement, and subsequent machine learning to score each cell as hepatocyte or fibroblast. While this approach was successful in counting hepatocytes for primary screening, segmentation of the fibroblast nuclei was less accurate. Here, we present an improved approach that more accurately identifies both cell types. Pixel-based machine learning (using the software ilastik) is used to seed segmentation of each cell type individually (using the software CellProfiler). This streamlined and accurate workflow can be carried out using freely available and open source software. 
520 |a National Science Foundation (U.S.) (NSF CAREER DBI 1148823) 
520 |a National Institutes of Health (U.S.) (NIH UH3 EB017103) 
546 |a en_US 
655 7 |a Article 
773 |t Methods