Kinetic Analysis of Label-Free Microscale Collagen Gel Contraction Using Machine Learning-Aided Image Analysis

Kinetic Analysis of Label-Free Microscale Collagen Gel Contraction Using Machine Learning-Aided Image Analysis

Pulmonary fibrosis is a deadly lung disease, wherein normal lung tissue is progressively replaced with fibrotic scar tissue. An aspect of this process can be recreated in vitro by embedding fibroblasts into a collagen matrix and providing a fibrotic stimulus. This work expands upon a previously desc...

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Bibliographic Details
Main Authors: Cameron Yamanishi, Eric Parigoris, Shuichi Takayama
Format: Article
Language:English
Published: Frontiers Media S.A. 2020-09-01
Series:Frontiers in Bioengineering and Biotechnology
Subjects:
pulmonary fibrosis
collagen contraction
fibroblasts
phenotypic assay
aqueous two-phase systems
machine learning
Online Access:https://www.frontiersin.org/article/10.3389/fbioe.2020.582602/full
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spelling doaj-95a2962516604e9baa48bac4939612a02020-11-25T03:23:24ZengFrontiers Media S.A.Frontiers in Bioengineering and Biotechnology2296-41852020-09-01810.3389/fbioe.2020.582602582602Kinetic Analysis of Label-Free Microscale Collagen Gel Contraction Using Machine Learning-Aided Image AnalysisCameron Yamanishi0Cameron Yamanishi1Eric Parigoris2Eric Parigoris3Shuichi Takayama4Shuichi Takayama5Wallace H. Coulter Department of Biomedical Engineering, Georgia Institute of Technology, Atlanta, GA, United StatesThe Parker H. Petit Institute of Bioengineering and Bioscience, Georgia Institute of Technology, Atlanta, GA, United StatesWallace H. Coulter Department of Biomedical Engineering, Georgia Institute of Technology, Atlanta, GA, United StatesThe Parker H. Petit Institute of Bioengineering and Bioscience, Georgia Institute of Technology, Atlanta, GA, United StatesWallace H. Coulter Department of Biomedical Engineering, Georgia Institute of Technology, Atlanta, GA, United StatesThe Parker H. Petit Institute of Bioengineering and Bioscience, Georgia Institute of Technology, Atlanta, GA, United StatesPulmonary fibrosis is a deadly lung disease, wherein normal lung tissue is progressively replaced with fibrotic scar tissue. An aspect of this process can be recreated in vitro by embedding fibroblasts into a collagen matrix and providing a fibrotic stimulus. This work expands upon a previously described method to print microscale cell-laden collagen gels and combines it with live cell imaging and automated image analysis to enable high-throughput analysis of the kinetics of cell-mediated contraction of this collagen matrix. The image analysis method utilizes a plugin for FIJI, built around Waikato Environment for Knowledge Analysis (WEKA) Segmentation. After cross-validation of this automated image analysis with manual shape tracing, the assay was applied to primary human lung fibroblasts including cells isolated from idiopathic pulmonary fibrosis patients. In the absence of any exogenous stimuli, the analysis showed significantly faster and more extensive contraction of the diseased cells compared to the healthy ones. Upon stimulation with transforming growth factor beta 1 (TGF-β1), fibroblasts from the healthy donor showed significantly more contraction throughout the observation period while differences in the response of diseased cells was subtle and could only be detected during a smaller window of time. Finally, dose-response curves for the inhibition of collagen gel contraction were determined for 3 small molecules including the only 2 FDA-approved drugs for idiopathic pulmonary fibrosis.https://www.frontiersin.org/article/10.3389/fbioe.2020.582602/fullpulmonary fibrosiscollagen contractionfibroblastsphenotypic assayaqueous two-phase systemsmachine learning
collection DOAJ
language English
format Article
sources DOAJ
author Cameron Yamanishi
Cameron Yamanishi
Eric Parigoris
Eric Parigoris
Shuichi Takayama
Shuichi Takayama
spellingShingle Cameron Yamanishi
Cameron Yamanishi
Eric Parigoris
Eric Parigoris
Shuichi Takayama
Shuichi Takayama
Kinetic Analysis of Label-Free Microscale Collagen Gel Contraction Using Machine Learning-Aided Image Analysis
Frontiers in Bioengineering and Biotechnology
pulmonary fibrosis
collagen contraction
fibroblasts
phenotypic assay
aqueous two-phase systems
machine learning
author_facet Cameron Yamanishi
Cameron Yamanishi
Eric Parigoris
Eric Parigoris
Shuichi Takayama
Shuichi Takayama
author_sort Cameron Yamanishi
title Kinetic Analysis of Label-Free Microscale Collagen Gel Contraction Using Machine Learning-Aided Image Analysis
title_short Kinetic Analysis of Label-Free Microscale Collagen Gel Contraction Using Machine Learning-Aided Image Analysis
title_full Kinetic Analysis of Label-Free Microscale Collagen Gel Contraction Using Machine Learning-Aided Image Analysis
title_fullStr Kinetic Analysis of Label-Free Microscale Collagen Gel Contraction Using Machine Learning-Aided Image Analysis
title_full_unstemmed Kinetic Analysis of Label-Free Microscale Collagen Gel Contraction Using Machine Learning-Aided Image Analysis
title_sort kinetic analysis of label-free microscale collagen gel contraction using machine learning-aided image analysis
publisher Frontiers Media S.A.
series Frontiers in Bioengineering and Biotechnology
issn 2296-4185
publishDate 2020-09-01
description Pulmonary fibrosis is a deadly lung disease, wherein normal lung tissue is progressively replaced with fibrotic scar tissue. An aspect of this process can be recreated in vitro by embedding fibroblasts into a collagen matrix and providing a fibrotic stimulus. This work expands upon a previously described method to print microscale cell-laden collagen gels and combines it with live cell imaging and automated image analysis to enable high-throughput analysis of the kinetics of cell-mediated contraction of this collagen matrix. The image analysis method utilizes a plugin for FIJI, built around Waikato Environment for Knowledge Analysis (WEKA) Segmentation. After cross-validation of this automated image analysis with manual shape tracing, the assay was applied to primary human lung fibroblasts including cells isolated from idiopathic pulmonary fibrosis patients. In the absence of any exogenous stimuli, the analysis showed significantly faster and more extensive contraction of the diseased cells compared to the healthy ones. Upon stimulation with transforming growth factor beta 1 (TGF-β1), fibroblasts from the healthy donor showed significantly more contraction throughout the observation period while differences in the response of diseased cells was subtle and could only be detected during a smaller window of time. Finally, dose-response curves for the inhibition of collagen gel contraction were determined for 3 small molecules including the only 2 FDA-approved drugs for idiopathic pulmonary fibrosis.
topic pulmonary fibrosis
collagen contraction
fibroblasts
phenotypic assay
aqueous two-phase systems
machine learning
url https://www.frontiersin.org/article/10.3389/fbioe.2020.582602/full
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