Evidence map›Paper›PMID 34011419›Full record

ArticleMicroscopy and microanalysis : the official journal of Microscopy Society of America, Microbeam Analysis Society, Microscopical Society of Canada2021

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

Shreyas U Hirway, Nadiah T Hassan, Michael Sofroniou, Christopher A Lemmon, Seth H Weinberg

Open access · greenAbstract read
In one paragraph

Article in Microscopy and microanalysis : the official journal of Microscopy Society of America, Microbeam Analysis Society, Microscopical Society of Canada, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing papers in PubMed
0.5field-weighted citation impact, top 33% of its field
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

3 citing papers in PubMed, 6 citations in OpenAlex.

  1. Article
  2. Article
  3. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

5 authors at 2 institutions in 1 country.

Shreyas U HirwayBiomedical Engineering Department, The Ohio State University, Columbus, OH, USA.
Nadiah T HassanBiomedical Engineering Department, Virginia Commonwealth University, Richmond, VA, USA.
Michael SofroniouBiomedical Engineering Department, Virginia Commonwealth University, Richmond, VA, USA.
Christopher A LemmonBiomedical Engineering Department, Virginia Commonwealth University, Richmond, VA, USA.
Seth H WeinbergBiomedical Engineering Department, The Ohio State University, Columbus, OH, USA.ORCID 0000-0003-1170-0419
Virginia Commonwealth University · USThe Ohio State University · US

Funding

A Computational Model of Traction Force-Induced Fibronectin Fibril GrowthR01GM115678 · NIGMS · VIRGINIA COMMONWEALTH UNIVERSITY · PI LEMMON, CHRISTOPHER ANDREW, WEINBERG, SETH HOWARD · 2015 to 2019
$1.6M
Mechanochemical Signaling Dynamics in Epithelial-Mesenchymal TransitionR01GM122855 · NIGMS · VIRGINIA COMMONWEALTH UNIVERSITY · PI LEMMON, CHRISTOPHER ANDREW, WEINBERG, SETH HOWARD · 2018 to 2021
$1.2M
NIGMS NIH HHS R01 GM115678NIGMS NIH HHS R01 GM122855
6 · The paper itself

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.

Indexed as

Epithelial-Mesenchymal TransitionMesenchymal Stem CellsEpithelial CellsFluorescent Antibody TechniqueHumansPhenotypecell signalingfeature extractionimage analysisimmunofluorescencemachine learning

Identifiers

PMID34011419
PMCPMC8349798
OpenAlexW2940218840

What OpenQuestion holds

Textmetadata
LicenceTDM
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.