ArticlebioRxiv : the preprint server for biology2023
Primary Human Cell-Derived Extracellular Matrix from Decellularized Fibroblast Microtissues with Tissue-Dependent Composition and Microstructure.
Article in bioRxiv : the preprint server for biology, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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3 authors at 1 institution in 1 country.
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Abstract
Human extracellular matrix (ECM) exhibits complex protein composition and architecture depending on tissue and disease state, which remains challenging to reverse engineer. One promising approach is based on cell-secreted ECM from human fibroblasts, which can then be decellularized into an acellular biomaterial. However, fibroblasts initially seeded on rigid tissue culture plastic or biomaterial scaffolds experience aberrant mechanical cues that influence ECM deposition. Here, we show that engineered microtissues of primary human fibroblasts seeded in low-adhesion microwells can be decellularized to produce human, tissue-specific ECM. We investigate: 1) cardiac fibroblasts, as well as 2) lung fibroblasts from healthy, idiopathic fibrosis and chronic obstructive pulmonary disease donors. We demonstrate optimized culture and decellularization conditions, then characterize gene expression and protein composition. We further characterize ECM microstructure and mechanical properties. We envision that this method could be utilized for biomanufacturing of patient and tissue-specific ECM for organoid drug screening as well as implantable scaffolds. Impact: In this study, we demonstrate a method for preparing decellularized matrix using primary human fibroblasts with tissue and disease-specific features. We aggregate single cell dispersions into engineered tissues using low adhesion microwells and show culture conditions that promote ECM deposition. We demonstrate this approach for cardiac fibroblasts as well as lung fibroblasts (both normal and diseased). We systematically investigate tissue morphology, matrix architecture, and mechanical properties, along with transcriptomic and proteomic analysis. This approach should be widely applicable for generating personalized ECM with features of patient tissues and disease state, relevant for culturing patient cells ex vivo as well as implantation for therapeutic treatments.
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