Biology subjects

Descombes, X.

Publications and source records attributed to Descombes, X..

3 recordsLinked to original sources

bioRxiv2025

Human mammary 3D spheroid models uncover the role of filopodia in breaching the basement membrane to facilitate invasion

Basement membranes (BM) are thin, nanoporous sheets of specialized extracellular matrix (ECM) that line epithelial tissues. They are dynamic structures that serve multiple key functions, as evidenced by numerous diseases, including cancer progression, that are associated with their alterations. Our understanding of the BM and its communication with adjoining epithelial cells remains highly fragmented due to the BMs complex molecular architecture, the lack of molecular tools, limitations in utilizing high-resolution imaging techniques to BMs assembled on tissues, and the difficulty of assessing their functional contributions in vivo. Here, by combining multiple -omics analyses and advanced microscopy methodologies, we characterized the BM from two normal human mammary epithelial cell lines, MCF10 and HMLE, grown as spheroids in 3D matrices. Our findings indicate that the spheroids autonomously assemble a BM exhibiting all the molecular, structural, and biophysical characteristics of physiological BM. Using these minimalist model systems, we show for the first time that the laminins, perlecan, and the hemidesmosomes are all arranged along a shared porous lattice defined by the collagen IV molecular network. Next, we demonstrate that the invasion-promoting PSD4/EFA6B knockout, found in patients with breast cancer, decreases the expression of BM components and their assembly on the spheroid surface. We then show that invasive spheroids develop enlarged pores in the BM via filopodia-like plasma membrane extensions, which further expand in a protease-dependent manner, thereby facilitating the passage of invasive cells. Significance StatementThe basement membrane (BM) directs epithelial tissue architecture and behavior. How tumor cells breach this barrier during invasion remains poorly understood. Using minimalist 3D spheroid models mimicking physiological BM assembly, we reveal a novel infiltration mechanism. Filopodia-like protrusions perforate and widen BM pores. This facilitates cell dissemination through sequential protease-independent and -dependent steps.

cell biology↗

bioRxiv2025

Computational detection, characterization, and clustering of microglial cells in a mouse model of fat-induced postprandial hypothalamic inflammation

Obesity is associated with brain inflammation, glial reactivity, and immune cells infiltration. Studies in rodents have shown that glial reactivity occurs within 24 hour of high-fat diet (HFD) consumption, long before obesity development, and takes place mainly in the hypothalamus (HT), a crucial brain structure for controlling body weight. Understanding more precisely the kinetics of glial activation of two major brain cells (astrocytes and microglia) and their impact on eating behavior could prevent obesity and offer new prospects for therapeutic treatments. To understand the mechanisms pertaining to obesity-related neuroinflammation, we developed a fully automated algorithm, NutriMorph. Although some algorithms were developed in the past decade to detect and segment cells, they are highly specific, not fully automatic, and do not provide the desired morphological analysis. Our algorithm cope with these issues and performs the analysis of cells images (here, microglia of the hypothalamic arcuate nucleus), and the morphological clustering of these cells through statistical analysis and machine learning. Using the k-Means algorithm, it clusters the microglia of the control condition (healthy mice) and the different states of neuroinflammation induced by high-fat diets (obese mice) into subpopulations. This paper is an extension and re-analysis of a first published paper showing that microglial reactivity can already be seen after few hours of high-fat diet (Cansell et al., 2021). Thanks to NutriMorph algorithm, we unravel the presence of different hypothalamic microglial subpopulations (based on morphology) subject to proportion changes in response to already few hours of high-fat diet in mice.

neuroscience↗

bioRxiv2022

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

Changes in the spatial landscape of the extracellular matrix (ECM) in health and disease significantly impact the surrounding tissues. Quantifying the spatial variations in the fibrillar architecture of major ECM proteins could enable a profound understanding of the link between tissue structure and function. We propose a method to capture relevant ECM features using graph networks for fiber representation in normal and tumor-like states of 4 alternatively spliced isoforms of fibronectin (FN) associated with embryonic development and disease. Then, we construct graph-derived statistical parametric maps, to study the differences across variants in normal and tumor-like architectures. This novel statistical analysis approach, inspired from the analysis of functional magnetic resonance imaging (fMRI) images, provides an appropriate framework for measuring and detecting local variations of meaningful matrix parameters. We show that parametric maps representing fiber length and pore orientation isotropy can be studied within the proposed framework to differentiate among various tissue states. Such tools can potentially lead to a better understanding of dynamic matrix networks within the tumor microenvironment and contribute to the development of better imaging modalities for monitoring their remodeling and normalization following therapeutic intervention. Author SummaryDue to the complex architectural diversity of biological networks, there is an increasing need to complement statistical analyses with a qualitative and local description. The extracellular matrix (ECM) is one such network for which fiber arrangement has a major impact on tissue structure and function. Thus, a flexible numerical representation of fibrillar networks is needed for accurate analysis and meaningful statistical comparison of ECM in healthy and diseased tissue. First, we propose a versatile computational pipeline to study fiber-specific features of the ECM with graph networks. Then, we introduce a novel framework for the statistical analysis of graph-derived parametric maps, inspired from the statistical analysis of fMRI parametric maps. This analysis is useful for the quantitative/qualitative comparison of ECM fiber networks observed in normal and tumor-like, or fibrotic states. These methods are applied to study networks of fibronectin (FN), a provisional ECM component that dictates the organization of matrix structure. From 2D confocal images we analyzed architectural variations among 4 alternatively spliced isoforms of FN, termed oncofetal FN, that are prevalent in diseased tissue. We show how our approach can be used for the computation and statistical comparison of heterogeneous parametric maps representing FN variant-specific topological/geometrical features. These methods may be further developed and implemented into tumor tissue ECM profiling to decipher the specific roles of ECM landscapes and their remodeling in disease.

cancer biology↗