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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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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