Integrated Cells and Collagen Fibers Spatial Image Analysis

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

Due to the complex architectural diversity of biological networks, there is an increasing need to complement statistical analyses with a qualitative and local description of their spatial properties. One such network is the extracellular matrix (ECM), a biological scaffold for which changes in its spatial organization significantly impact tissue functions in health and disease. Quantifying variations in the fibrillar architecture of major ECM proteins should considerably advance our understanding of the link between tissue structure and function. Inspired by the analysis of functional magnetic resonance imaging (fMRI) images, we propose a novel statistical analysis approach embedded into a machine learning paradigm, to measure and detect local variations of meaningful ECM parameters. We show that parametric maps representing fiber length and pore directionality can be analyzed within the proposed framework to differentiate among various tissue states. The parametric maps are derived from graph-based representations that reflect the network architecture of fibronectin (FN) fibers in a normal, or disease-mimicking in vitro setting. Such tools can potentially lead to a better characterization of dynamic matrix networks within fibrotic tumor microenvironments and contribute to the development of better imaging modalities for monitoring their remodeling and normalization following therapeutic intervention.

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

Lung adenocarcinoma (LUAD) is the predominant type of lung cancer in the U.S. and exhibits a broad variety of behaviors ranging from indolent to aggressive. Identification of the biological determinants of LUAD behavior at early stages can improve existing diagnostic and treatment strategies. Extracellular matrix (ECM) remodeling and cancer-associated fibroblasts play a crucial role in the regulation of cancer aggressiveness and there is a growing need to investigate their role in the determination of LUAD behavior at early stages. We analyzed tissue samples isolated from patients with LUAD at early stages and used imaging-based biomarkers to predict LUAD behavior. Single-cell RNA sequencing and histological assessment showed that aggressive LUADs are characterized by a decreased number of ADH1B+ CAFs in comparison to indolent tumors. ADH1B+ CAF enrichment is associated with distinct ECM and immune cell signatures in early-stage LUADs. Also, we found a positive correlation between the gene expression of ADH1B+ CAF markers in early-stage LUADs and better survival. We performed TCGA dataset analysis to validate our findings. Identified associations can be used for the development of the predictive model of LUAD aggressiveness and novel therapeutic approaches.