The Matrix within us
Concept art depicting the decoding of the composition of the extracellular matrix
At MatriChem, some of us are huge fans of The Matrix movies. In the years since the release of The Matrix Revolutions, we have seen numerous “trailers“ of upcoming sequels which have for a short period elevated our levels of dopamine, only to realize later that someone is taking advantage of the impulsiveness of System 1. This is why we were skeptical when we stumbled upon what seemed to be simply another collage of video clips posing as a trailer for an upcoming The Matrix movie. There is no describing how excited we were to be wrong! (Don’t be confused but in science, it is common to be excited for being wrong.) We are looking forward to enjoying the soon-to-be-released The Matrix Resurrections. But you know what we are even more excited about? We are passionate about the soon-to-be-decoded extracellular matrix (ECM), i.e. the matrix our cells live in!
MatriChem’s goal to create products best suited to mimic native human tissues means that we need to a gain deep understanding of the role and the function of the extracellular matrix. Why? The ECM, as the name suggests, is the matrix surrounding the cells in every tissue. It is the three-dimensional architectural scaffold that defines tissue boundaries, biomechanical properties, and cell polarity. The matrix consists of proteins, which as a whole are referred to as the matrisome, and proteoglycans, i.e. protein-polysaccharide complexes. The ECM proteins provide biochemical cues which are interpreted by receptors on the cell surface, regulate gene expression and initiate signaling cascades within the cell controlling its survival, proliferation, differentiation, and stem cell state. Thus, the ECM orchestrates cellular, tissue, and organ formation and functions. This makes it essential to grasp the mechanisms of its effects when devising tissue engineering strategies.
Artist depiction of the cell-ECM interaction and the ongoing effort to engineer a suitable in vitro matrix.
Tissue engineers have exploited, to varying degrees, aspects of ECM biology, in particular its biomechanical properties, to design novel scaffolds to support tissue regeneration. One approach to engineer a functional organ consists in using decellularized organ (an organ from which the cells have been stripped away) scaffolds comprising ECM and some (generally unknown) associated materials and repopulating the scaffold with stem cells. However, so far this approach has not yet succeeded in regenerating fully functional organs. This may be in part due to the fact that decellularization results in the loss of some crucial ECM components and associated growth factors. In contrast, MatriChem favors the approach which utilizes parsimoniously incorporated features of ECM proteins, e.g. RGD peptides or their mimics, in artificial scaffolds. However, proteomic studies have revealed that the ECMs of tissues are made of 150+ proteins and while we believe that reconstructing the entirety of this complexity is unnecessary, we pay close attention to the results of proteomics studies aimed at characterizing in vivo ECMs to guide the design of the scaffolds for in vitro tissue modeling.
In addition to its composition, the ECM’s tendency to be redesigned is also essential in tissue and organ development, as well as in disease progression. During development, the ECM plays vital roles in stem cell niches and in guiding migration, the polarity of cells, axonal projections, as well as in morphogenesis (the development of the shape of a tissue or an organ) and consistency of tissues. Furthermore, remodeling of the ECM is essential during angiogenesis and branching glands and in wound healing. At the same time, excessive deposition or, conversely, destruction of the ECM cause or accompany numerous pathologies such as fibrosis or osteoarthritis. ECM deposition (or desmoplasia) is a hallmark of tumor progression and has been used by pathologists as a marker of tumors with poor prognosis. Thus, the rates at which the ECM is destroyed and re-created are determining factors for the proper tissue and organ development while significant deviations from the norm can be used to detect a disease, including cancer.
The functional unit of the ECM is called matrisome. This is a small proteome encoded by 1027 genes in humans, 4% of the genome (according to the most recent survey published by Naba et al. back in 2012). However, a view has been established that in addition to the genes encoding the structural ECM components, the matrisome should include the genes encoding proteins that may interact with or remodel the ECM. (Ricard-Blum S. (eds) Extracellular Matrix Omics) Such proteins include ECM-affiliated proteins, ECM regulators, and secreted factors that may interact with core ECM proteins. In general, however, the matrisome is still underexplored due to challenges with the collection and analysis of multimeric and multidomain proteins which are often found in the ECM as insoluble and cross-linked supramolecular assemblies.
Characterization of ECM composition, metabolism, and biology can lead to the identification of novel prognostic and diagnostic markers and therapeutic opportunities and thus to faster biomedical breakthroughs. For instance, several ECM proteins, in particular members of the insulin-like-growth-factor-binding protein family, members of the CCN family have only been detected, so far, from tumor samples (colorectal or mammary carcinomas, melanomas) but not in those from normal tissues. This is why there are ongoing efforts to create a human ECM atlas containing the ECM composition of all tissues and tumor types. One such atlas (MatrisomeDB) already contains the composition of at least 14 tissues and tumors (Naba et al., 2016). A parallel effort has resulted in the creation of MatrixDB which is focused on interactions between ECM proteins, proteoglycans, and glycosaminoglycans. MatrixDB curates interactions established by monomeric ECM proteins, ECM oligomeric proteins, and by bioactive fragments released upon ECM remodeling. In addition to protein-protein interactions, MatrixDB also curates protein-glycosaminoglycan interactions, which are of critical importance for ECM assembly and functions. On the other hand, the Adhesome network curates the interactions and components found in focal adhesion complexes in mammalian cells. Proper cell-ECM interaction is critical to initiating ECM-dependent signal- and mechano-transduction which results in the modulation of gene expression and cellular phenotypes. Thus, for the development of novel prognostic and diagnostic methods, it is of utmost importance to decipher the molecular mechanisms involved in the cell-ECM interactions.
However, a human ECM atlas containing a snapshot of the ECM composition of various tissues and tumors can be misleading if the matrisome is not temporally and spatially resolved. This requires the utilization of quantitative proteomics to profile the dynamic changes in the composition of the extracellular matrix that occur during disease progression or during the course of treatment. This approach might allow the identification of novel prognostic or diagnostic ECM markers that could assist clinical decisions. Furthermore, high-throughput approaches will permit the identification of novel disease-specific ECM proteins and protein isoforms while the characterization of the molecular mechanisms downstream of these proteins will offer novel therapeutic opportunities. Therapeutic strategies could include (i) targeting ECM protein synthesis or post-translational modifications, (ii) targeting ECM remodeling (degradation or crosslinking) and (iii) targeting ECM/ECM receptor interactions as well as the antibody-mediated targeting. At the core of this effort, however, is the multi-omic approach, i.e. combining different omics (e.g. genomics, transcriptomics, proteomics, metabolomics and interactomics) which is a pre-requisite to fully understand complex biological systems. Understandably, however, there is resistance and skepticism among many researchers which doubt that such a comprehensive approach is required for all studies. Still, we expect that with the help of machine learning algorithms, the resulting mountain of data will provide us a four dimensional (space-time) human ECM atlas, essential for devising informed bottom-up tissue engineering strategies.