Literature DB >> 34011419

Immunofluorescence Image Feature Analysis and Phenotype Scoring Pipeline for Distinguishing Epithelial-Mesenchymal Transition.

Shreyas U Hirway1, Nadiah T Hassan2, Michael Sofroniou2, Christopher A Lemmon2, Seth H Weinberg1.   

Abstract

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-β1, 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.

Entities:  

Keywords:  cell signaling; feature extraction; image analysis; immunofluorescence; machine learning

Mesh:

Year:  2021        PMID: 34011419      PMCID: PMC8349798          DOI: 10.1017/S1431927621000428

Source DB:  PubMed          Journal:  Microsc Microanal        ISSN: 1431-9276            Impact factor:   4.127


  31 in total

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2.  Survival Outcomes in Cancer Patients Predicted by a Partial EMT Gene Expression Scoring Metric.

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4.  Morphological single cell profiling of the epithelial-mesenchymal transition.

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Authors:  N A Bhowmick; M Ghiassi; A Bakin; M Aakre; C A Lundquist; M E Engel; C L Arteaga; H L Moses
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Journal:  Eur Rev Med Pharmacol Sci       Date:  2016-07       Impact factor: 3.507

Review 8.  TGF-beta-induced epithelial to mesenchymal transition.

Authors:  Jian Xu; Samy Lamouille; Rik Derynck
Journal:  Cell Res       Date:  2009-02       Impact factor: 25.617

9.  Coupled reversible and irreversible bistable switches underlying TGFβ-induced epithelial to mesenchymal transition.

Authors:  Xiao-Jun Tian; Hang Zhang; Jianhua Xing
Journal:  Biophys J       Date:  2013-08-20       Impact factor: 4.033

10.  Combinatorial perturbation analysis reveals divergent regulations of mesenchymal genes during epithelial-to-mesenchymal transition.

Authors:  Kazuhide Watanabe; Nicholas Panchy; Shuhei Noguchi; Harukazu Suzuki; Tian Hong
Journal:  NPJ Syst Biol Appl       Date:  2019-06-14
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  3 in total

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2.  Interpretable, Scalable, and Transferrable Functional Projection of Large-Scale Transcriptome Data Using Constrained Matrix Decomposition.

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3.  Surgical Selection of T1 Stage Renal Tumor Resection Based on Imaging MAP Score under Smart Medical Care.

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  3 in total

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