Format

Send to

Choose Destination
Methods. 2016 Mar 1;96:6-11. doi: 10.1016/j.ymeth.2015.12.002. Epub 2015 Dec 11.

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

Author information

1
The Broad Institute of MIT and Harvard, 415 Main Street, Cambridge, MA 02142, United States. Electronic address: dlogan@broadinstitute.org.
2
Harvard-MIT Division of Health Sciences and Technology, MIT, E25-518, 77 Massachusetts Ave, Cambridge, MA 02139, United States. Electronic address: js8686@mit.edu.
3
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.
4
The Broad Institute of MIT and Harvard, 415 Main Street, Cambridge, MA 02142, United States. Electronic address: anne@broadinstitute.org.

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.

KEYWORDS:

Assay development; Co-culture; Hepatocytes; High content screening; Image analysis; Open-source software

PMID:
26687239
PMCID:
PMC4766037
[Available on 2017-03-01]
DOI:
10.1016/j.ymeth.2015.12.002
[Indexed for MEDLINE]
Free PMC Article

Supplemental Content

Full text links

Icon for Elsevier Science Icon for PubMed Central
Loading ...
Support Center