Bioengineered 3D Living Fibers as In Vitro Human Tissue Models of Tendon Physiology and Pathology.
Isabel CalejoClaudia J Labrador-RachedManuel Gomez-FloritDenitsa DochevaRui L ReisRui M A DominguesManuela Estima GomesPublished in: Advanced healthcare materials (2022)
Clinically relevant in vitro models of human tissue's health and disease are urgently needed for a better understanding of biological mechanisms essential for the development of novel therapies. Herein, physiological (healthy) and pathological (disease) tendon states are bioengineered by coupling the biological signaling of platelet lysate components with controlled 3D architectures of electrospun microfibers to drive the fate of human tendon cells in different composite living fibers (CLFs). In the CLFs-healthy model, tendon cells adopt a high cytoskeleton alignment and elongation, express tendon-related markers (scleraxis, tenomodulin, and mohawk) and deposit a dense tenogenic matrix. In contrast, cell crowding with low preferential orientation, high matrix deposition, and phenotypic drift leading to increased expression of nontendon related and fibrotic markers, are characteristics of the CLFs-diseased model. This diseased-like profile, also reflected in the increase of COL3/COL1 ratio, is further evident by the imbalance between matrix remodeling and degradation effectors, characteristic of tendinopathy. In summary, microengineered 3D in vitro models of human tendon healthy and diseased states are successfully fabricated. Most importantly, these innovative and versatile microphysiological models offer major advantages over currently used systems, holding promise for drugs screening and development of new therapies.
Keyphrases
- endothelial cells
- rotator cuff
- anterior cruciate ligament reconstruction
- induced pluripotent stem cells
- induced apoptosis
- pluripotent stem cells
- healthcare
- cell cycle arrest
- public health
- machine learning
- stem cells
- bone marrow
- mesenchymal stem cells
- systemic sclerosis
- risk assessment
- big data
- deep learning
- electron transfer