title: Quantifying co-cultured cell phenotypes in high-throughput using pixel-based classification.
abstract: 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.
authors.affiliation: The Broad Institute of MIT and Harvard, 415 Main Street, Cambridge, MA 02142, United States. Electronic address: dlogan@broadinstitute.org.
authors.forename: David J
authors.initials: DJ
authors.lastname: Logan
authors.affiliation: Harvard-MIT Division of Health Sciences and Technology, MIT, E25-518, 77 Massachusetts Ave, Cambridge, MA 02139, United States. Electronic address: js8686@mit.edu.
authors.forename: Jing
authors.initials: J
authors.lastname: Shan
authors.affiliation: The Broad Institute of MIT and Harvard, 415 Main Street, Cambridge, MA 02142, United States; Harvard-MIT Division of Health Sciences and Technology, MIT, E25-518, 77 Massachusetts Ave, Cambridge, MA 02139, United States; Department of Medicine, Brigham and Women's Hospital, 75 Francis St, Boston, MA 02115, United States; Institute for Medical Engineering and Science, MIT, E25-330, 77 Massachusetts Ave, Cambridge, MA 02139, United States; Department of Electrical Engineering and Computer Science, MIT, 38-401, 77 Massachusetts Ave, Cambridge, MA 02139, United States; David H. Koch Institute for Integrative Cancer Research, MIT, 76-158, 77 Massachusetts Avenue, Cambridge, MA 02139, United States; Howard Hughes Medical Institute, 4000 Jones Bridge Road, Chevy Chase, MD 20815-6789, United States. Electronic address: sbhatia@mit.edu.
authors.forename: Sangeeta N
authors.initials: SN
authors.lastname: Bhatia
authors.affiliation: The Broad Institute of MIT and Harvard, 415 Main Street, Cambridge, MA 02142, United States. Electronic address: anne@broadinstitute.org.
authors.forename: Anne E
authors.initials: AE
authors.lastname: Carpenter
mesh_terms: D000465:Algorithms; D000818:Animals; D002460:Cell Line; D002467:Cell Nucleus / Q000648:ultrastructure; D002470:Cell Survival; D018920:Coculture Techniques; D005347:Fibroblasts / Q000648:ultrastructure*; D022781:Hepatocytes / Q000648:ultrastructure*; D057166:High-Throughput Screening Assays; D006801:Humans; D007091:Image Processing, Computer-Assisted; D000069550:Machine Learning; D051379:Mice; D010363:Pattern Recognition, Automated; D010641:Phenotype; D061251:Primary Cell Culture; D012984:Software
keywords: Assay development; Co-culture; Hepatocytes; High content screening; Image analysis; Open-source software