Integrated Cells and Collagen Fibers Spatial Image Analysis
Modern technologies designed for tissue structure visualization like brightfield microscopy, fluorescent microscopy, mass cytometry imaging (MCI) and mass spectrometry imaging (MSI) provide large amounts of quantitative and spatial information about cells and tissue structures like vessels, bronchio...
| Main Authors: | , , , , , , , |
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| Format: | Article |
| Language: | English |
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Frontiers Media S.A.
2021-11-01
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| Series: | Frontiers in Bioinformatics |
| Subjects: | |
| Online Access: | https://www.frontiersin.org/articles/10.3389/fbinf.2021.758775/full |
| _version_ | 1818407548009578496 |
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| author | Georgii Vasiukov Tatiana Novitskaya Maria-Fernanda Senosain Alex Camai Anna Menshikh Pierre Massion Andries Zijlstra Sergey Novitskiy |
| author_facet | Georgii Vasiukov Tatiana Novitskaya Maria-Fernanda Senosain Alex Camai Anna Menshikh Pierre Massion Andries Zijlstra Sergey Novitskiy |
| author_sort | Georgii Vasiukov |
| collection | DOAJ |
| description | Modern technologies designed for tissue structure visualization like brightfield microscopy, fluorescent microscopy, mass cytometry imaging (MCI) and mass spectrometry imaging (MSI) provide large amounts of quantitative and spatial information about cells and tissue structures like vessels, bronchioles etc. Many published reports have demonstrated that the structural features of cells and extracellular matrix (ECM) and their interactions strongly predict disease development and progression. Computational image analysis methods in combination with spatial analysis and machine learning can reveal novel structural patterns in normal and diseased tissue. Here, we have developed a Python package designed for integrated analysis of cells and ECM in a spatially dependent manner. The package performs segmentation, labeling and feature analysis of ECM fibers, combines this information with pre-generated single-cell based datasets and realizes cell-cell and cell-fiber spatial analysis. To demonstrate performance and compatibility of our computational tool, we integrated it with a pipeline designed for cell segmentation, classification, and feature analysis in the KNIME analytical platform. For validation, we used a set of mouse mammary gland tumors and human lung adenocarcinoma tissue samples stained for multiple cellular markers and collagen as the main ECM protein. The developed package provides sufficient performance and precision to be used as a novel method to investigate cell-ECM relationships in the tissue, as well as detect structural patterns correlated with specific disease outcomes. |
| first_indexed | 2024-12-14T09:29:35Z |
| format | Article |
| id | doaj.art-29041d8a47c14a7fbda334f76f3e2568 |
| institution | Directory Open Access Journal |
| issn | 2673-7647 |
| language | English |
| last_indexed | 2024-12-14T09:29:35Z |
| publishDate | 2021-11-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Bioinformatics |
| spelling | doaj.art-29041d8a47c14a7fbda334f76f3e25682022-12-21T23:08:06ZengFrontiers Media S.A.Frontiers in Bioinformatics2673-76472021-11-01110.3389/fbinf.2021.758775758775Integrated Cells and Collagen Fibers Spatial Image AnalysisGeorgii Vasiukov0Tatiana Novitskaya1Maria-Fernanda Senosain2Alex Camai3Anna Menshikh4Pierre Massion5Andries Zijlstra6Sergey Novitskiy7Department of Medicine, Division of Allergy, Pulmonary, Critical Care Medicine, Vanderbilt, University Medical Center, Nashville, TN, United StatesDepartment of Pathology, Microbiology, And Immunology, Vanderbilt University Medical Center, Nashville, TN, United StatesDepartment of Medicine, Division of Allergy, Pulmonary, Critical Care Medicine, Vanderbilt, University Medical Center, Nashville, TN, United StatesDepartment of Medicine, Division of Allergy, Pulmonary, Critical Care Medicine, Vanderbilt, University Medical Center, Nashville, TN, United StatesDepartment of Medicine, Division of Nephrology, Vanderbilt University Medical Center, Nashville, TN, United StatesDepartment of Medicine, Division of Allergy, Pulmonary, Critical Care Medicine, Vanderbilt, University Medical Center, Nashville, TN, United StatesDepartment of Pathology, Microbiology, And Immunology, Vanderbilt University Medical Center, Nashville, TN, United StatesDepartment of Medicine, Division of Allergy, Pulmonary, Critical Care Medicine, Vanderbilt, University Medical Center, Nashville, TN, United StatesModern technologies designed for tissue structure visualization like brightfield microscopy, fluorescent microscopy, mass cytometry imaging (MCI) and mass spectrometry imaging (MSI) provide large amounts of quantitative and spatial information about cells and tissue structures like vessels, bronchioles etc. Many published reports have demonstrated that the structural features of cells and extracellular matrix (ECM) and their interactions strongly predict disease development and progression. Computational image analysis methods in combination with spatial analysis and machine learning can reveal novel structural patterns in normal and diseased tissue. Here, we have developed a Python package designed for integrated analysis of cells and ECM in a spatially dependent manner. The package performs segmentation, labeling and feature analysis of ECM fibers, combines this information with pre-generated single-cell based datasets and realizes cell-cell and cell-fiber spatial analysis. To demonstrate performance and compatibility of our computational tool, we integrated it with a pipeline designed for cell segmentation, classification, and feature analysis in the KNIME analytical platform. For validation, we used a set of mouse mammary gland tumors and human lung adenocarcinoma tissue samples stained for multiple cellular markers and collagen as the main ECM protein. The developed package provides sufficient performance and precision to be used as a novel method to investigate cell-ECM relationships in the tissue, as well as detect structural patterns correlated with specific disease outcomes.https://www.frontiersin.org/articles/10.3389/fbinf.2021.758775/fullimage analysisECM–extracellular matrixspatial analysisfibersimage processingcollagen fiber (CF) |
| spellingShingle | Georgii Vasiukov Tatiana Novitskaya Maria-Fernanda Senosain Alex Camai Anna Menshikh Pierre Massion Andries Zijlstra Sergey Novitskiy Integrated Cells and Collagen Fibers Spatial Image Analysis Frontiers in Bioinformatics image analysis ECM–extracellular matrix spatial analysis fibers image processing collagen fiber (CF) |
| title | Integrated Cells and Collagen Fibers Spatial Image Analysis |
| title_full | Integrated Cells and Collagen Fibers Spatial Image Analysis |
| title_fullStr | Integrated Cells and Collagen Fibers Spatial Image Analysis |
| title_full_unstemmed | Integrated Cells and Collagen Fibers Spatial Image Analysis |
| title_short | Integrated Cells and Collagen Fibers Spatial Image Analysis |
| title_sort | integrated cells and collagen fibers spatial image analysis |
| topic | image analysis ECM–extracellular matrix spatial analysis fibers image processing collagen fiber (CF) |
| url | https://www.frontiersin.org/articles/10.3389/fbinf.2021.758775/full |
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