Integrated Cells and Collagen Fibers Spatial Image Analysis

Abstract

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.

Integrated Multi-omics Analysis of Early Lung Adenocarcinoma Links Tumor Biological Features with Predicted Indolence or Aggressiveness

“…They analyzed the expression of HIF-1 alpha in NSCLC by IHC, defining as low cutoff the median staining (>5%) and as high cutoff >60%, and found that when using the latter an association with poor prognosis was significant. In a recent study of ours ( 67 ) using the same LUAD patient samples, we described in a previous study ( 20 ), we found, by multiplex immunofluorescence, that indolent and aggressive tumors did not show significant difference in neither the amount of collagen fibers nor the average fiber length. However, when we performed spatial analysis, we found that tumor cells from the indolent group were colocalized with an increased number of immune cells.…”

A spatial statistical framework for the parametric study of fiber networks: application to fibronectin deposition by normal and activated fibroblasts

“…As shown in Fig 8, the current method, compared to a naïve hard thresholding (threshold equal to 5), is better suited to detect only the abnormal elements, i.e. ellipses, thresholds equal to (5,10,15,20). A possible decision could jointly consider the two criteria, i.e.…”

“…Prognostic ECM-specific signatures have already been inferred in cancer-related studies, diabetes, wound healing, and nerve repair. There is also a growing interest in the integration of cell and ECM analyses in a spatially resolved manner to further understand the interactions between cells and their matrix microenvironment (15). Our study proposes a versatile pipeline for the analysis of ECM organization in human tissue, in which a future integration of staining for cellular components can be envisioned.…”

Cancer-associated fibroblasts in early-stage lung adenocarcinoma correlate with tumor aggressiveness

“…For patient stratification, we have used CANARY software as described previously 17 , 18 . The stratification was based on the SILA score—a computerized scoring system designed for predicting LUAD behavior and estimating patient survival.…”

“…Tumors were classified as “indolent” and “aggressive” using Computer-Aided Nodule Assessment and Risk Yield (CANARY) software as described previously 17 , 18 . The stratification strategy was based on the calculation of the Score Indicative of Lung Cancer Aggression (SILA).…”