Journal
MICROSCOPY AND MICROANALYSIS
Volume 27, Issue 4, Pages 849-859Publisher
OXFORD UNIV PRESS
DOI: 10.1017/S1431927621000428
Keywords
cell signaling; feature extraction; image analysis; immunofluorescence; machine learning
Categories
Funding
- National Institutes of Health/National Institute of General Medical Sciences [R01GM122855, R01GM115678]
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The research developed an image analysis pipeline to distinguish between epithelial and mesenchymal cells in tissues, based on features extracted from immunofluorescence images. The pipeline successfully differentiated between control (epithelial) and high-dose (mesenchymal) groups, as well as demonstrated progression along the EMT process in intermediate dose groups. Validation using quantitative PCR showed a correlation between biomarker expression measurements and feature distance analysis.
Epithelial-mesenchymal transition (EMT) is an essential biological process, also implicated in pathological settings such as cancer metastasis, in which epithelial cells transdifferentiate into mesenchymal cells. We devised an image analysis pipeline to distinguish between tissues comprised of epithelial and mesenchymal cells, based on extracted features from immunofluorescence images of differing biochemical markers. Mammary epithelial cells were cultured with 0 (control), 2, 4, or 10 ng/mL TGF-beta 1, a well-established EMT-inducer. Cells were fixed, stained, and imaged for E-cadherin, actin, fibronectin, and nuclei via immunofluorescence microscopy. Feature selection was performed on different combinations of individual cell markers using a Bag-of-Features extraction. Control and high-dose images comprised the training data set, and the intermediate dose images comprised the testing data set. A feature distance analysis was performed to quantify differences between the treatment groups. The pipeline was successful in distinguishing between control (epithelial) and the high-dose (mesenchymal) groups, as well as demonstrating progress along the EMT process in the intermediate dose groups. Validation using quantitative PCR (qPCR) demonstrated that biomarker expression measurements were well-correlated with the feature distance analysis. Overall, we identified image pipeline characteristics for feature extraction and quantification of immunofluorescence images to distinguish progression of EMT.
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