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Methods DOI:10.1016/j.ymeth.2015.12.002

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

Publication TypeJournal Article
Year of Publication2016
AuthorsLogan, DJ, Shan, J, Bhatia, SN, Carpenter, AE
Date Published2016 Mar 01
KeywordsAlgorithms, Animals, Cell Line, Cell Nucleus, Cell Survival, Coculture Techniques, Fibroblasts, Hepatocytes, High-Throughput Screening Assays, Humans, Image Processing, Computer-Assisted, Machine Learning, Mice, Pattern Recognition, Automated, Phenotype, Primary Cell Culture, Software

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.


Alternate JournalMethods
PubMed ID26687239
PubMed Central IDPMC4766037
Grant ListUH3 EB017103 / EB / NIBIB NIH HHS / United States
NIH UH3 EB017103 / EB / NIBIB NIH HHS / United States
/ / Howard Hughes Medical Institute / United States