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Cell-derived Matrix Assays to Assess Extracellular Matrix Architecture and Track Cell Movement

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BIO-PROTOCOL
卷 12, 期 24, 页码 -

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BIO-PROTOCOL
DOI: 10.21769/BioProtoc.4570

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Cell-derived matrix; Cancer; Fibroblasts; Extracellular matrix; Biomechanics; Cell-matrix interactions; Second harmonic generation imaging

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The extracellular matrix (ECM) plays a crucial role in regulating cellular function and tissue tension, with even subtle changes impacting disease development. Cell-derived matrix (CDM) models are valuable for studying cell-ECM interactions, offering insights into cancer progression.
The extracellular matrix (ECM) is a non-cellular network of macromolecules, which provides cells and tissues with structural support and biomechanical feedback to regulate cellular function, tissue tension, and homeostasis. Even subtle changes to ECM abundance, architecture, and organization can affect downstream biological pathways, thereby influencing normal cell and tissue function and also driving disease conditions. For example, in cancer, the ECM is well known to provide both biophysical and biochemical cues that influence cancer initiation, progression, and metastasis, highlighting the need to better understand cell-ECM interactions in cancer and other ECM-enriched diseases. Initial cell-derived matrix (CDM) models were used as an in vitro system to mimic and assess the physiologically relevant three-dimensional (3D) cell-ECM interactions. Here, we describe an expansion to these initial CDM models generated by fibroblasts to assess the effect of genetic or pharmacological intervention on fibroblast-mediated matrix production and organization. Additionally, we highlight current methodologies to quantify changes in the ultrastructure and isotropy of the resulting ECM and also provide protocols for assessing cancer cell interaction with CDMs. Understanding the nature and influence of these complex and heterogeneous processes can offer insights into the biomechanical and biochemical mechanisms, which drive cancer development and metastasis, and how we can target them to improve cancer outcomes.

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