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. 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. Here, we have developed a Python package designed for integrated analysis of cells and ECM in a spatially dependent manner. Computational image analysis methods in combination with spatial analysis and machine learning can reveal novel structural patterns in normal and diseased tissue. Many published reports have demonstrated that the structural features of cells and extracellular matrix (ECM) and their interactions strongly predict disease development and progression. 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. 3Department of Medicine, Division of Nephrology, Vanderbilt University Medical Center, Nashville, TN, United States.2Department of Pathology, Microbiology, And Immunology, Vanderbilt University Medical Center, Nashville, TN, United States.1Department of Medicine, Division of Allergy, Pulmonary, Critical Care Medicine, Vanderbilt, University Medical Center, Nashville, TN, United States.Georgii Vasiukov 1*, Tatiana Novitskaya 2, Maria-Fernanda Senosain 1, Alex Camai 1, Anna Menshikh 3, Pierre Massion 1, Andries Zijlstra 2 and Sergey Novitskiy 1
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