Defining the extracellular matrix using proteomics

Abstract

The cell microenvironment has a profound influence on the behaviour, growth and survival of cells. The extracellular matrix (ECM) provides not only mechanical and structural support to cells and tissues but also binds soluble ligands and transmembrane receptors to provide spatial coordination of signalling processes. The ability of cells to sense the chemical, mechanical and topographical features of the ECM enables them to integrate complex, multiparametric information into a coherent response to the surrounding microenvironment. Consequently, dysregulation or mutation of ECM components results in a broad range of pathological conditions. Characterization of the composition of ECM derived from various cells has begun to reveal insights into ECM structure and function, and mechanisms of disease. Proteomic methodologies permit the global analysis of subcellular systems, but extracellular and transmembrane proteins present analytical difficulties to proteomic strategies owing to the particular biochemical properties of these molecules. Here, we review advances in proteomic approaches that have been applied to furthering our understanding of the ECM microenvironment. We survey recent studies that have addressed challenges in the analysis of ECM and discuss major outcomes in the context of health and disease. In addition, we summarize efforts to progress towards a systems-level understanding of ECM biology.

Keywords: cell adhesion; extracellular matrix; mass spectrometry; proteomics.

Figure 1

Approaches for mass spectrometry (MS)–based proteomic analysis of the extracellular matrix (ECM). The schematic depicts multiple workflows for the isolation and proteomic analysis of extracellular molecules. For the derivation of ECM, tissue could be of human or animal origin from any part of healthy or diseased sources. Human tissue could be acquired from biopsies or biofluids. Cell culture models could be based on primary cells or cell lines or used as xenografts in animal models. Individual or groups of ECM molecules could be recombinantly expressed or purified from tissue or cells. Molecules that interact with purified ECM molecules could be affinity isolated from tissue or cells. Details of the MS-based proteomic pipeline are provided in Box 1.

Figure 2

Integration and modelling of extracellular matrix (ECM) proteomics data. The schematic depicts the integration of new experimental (context-specific) data with databases of curated interactions from multiple sources to enable the further analysis, modelling and interpretation of those data. For example, analysis of interaction networks, based on previously reported protein–protein interactions, can provide insights into the functional roles of the identified components and the general organizing principles of the molecular networks under study. The displayed hypothetical interaction network merges protein–protein interactions extracted from the MatrixDB database (release 2010-08-26) (Chautard et al. 2009) (ECM network, red) with a model of a fibronectin-induced adhesion complex (Humphries et al. 2009) (cell adhesion network, blue). Circles represent detected proteins, coloured according to the data set, and grey lines represent reported interactions between the detected proteins. Fibronectin (FN) and the fibronectin receptor integrin α5β1 are arranged at the cell–ECM network interface and are highlighted with black borders. Resulting interaction network models can be analysed using various computational methods to enable biological interpretation. For example, focussed subnetworks can be extracted to interrogate potential protein complexes. Proteins in subnetworks with many common interaction partners are more likely to function together. Annotation of network models with enriched protein functions or signalling pathways can reveal unpredicted roles for components of the networks. Protein–protein interactions can be predicted on the basis of protein domains or network structural parameters. Analysis of network topology can also afford insights into signalling hubs, which are key signalling control points, and subnetwork robustness, which can be tested by inhibiting or depleting a candidate protein in silico, in vitro or in vivo and assessing the resultant network perturbation. In the displayed hypothetical interaction network, the networks were visualized using the force-directed layout implemented in cytoscape (version 2.8.2) (Shannon et al. 2003), which clusters together proteins with common interaction partners, and circle diameter was sized proportionally to the number of interaction partners. The visualization reveals clusters of many proteins with few interaction partners (small circles) around relatively few proteins with many interaction partners (large circles), which suggests that these highly connected proteins may be important for cell–ECM network structure or function.