Back

Anisotropic Articular Cartilage Biofabrication based on Decellularized Extracellular Matrix

Puiggali-Jou, A.; Hui, I.; Baldi, L.; Frischknecht, R.; Asadikorayem, M.; Janiak, J.; Chansoria, P.; McCabe, M. C.; Stoddart, M. J.; Hansen, K.; Christman, K. L.; Zenobi, M.

2024-07-30 bioengineering
10.1101/2024.07.30.605411 bioRxiv
Show abstract

Tissue-engineered grafts that mimic articular cartilage show promise for treating cartilage injuries. However, engineering cartilage cell-based therapies to match zonal architecture and biochemical composition remains challenging. Decellularized articular cartilage extracellular matrix (dECM) has gained attention for its chondro-inductive properties, yet dECM-based bioinks have limitations in mechanical stability and printability. This study proposes a rapid light-based bioprinting method using a tyrosine-based crosslinking mechanism, which does not require chemical modifications of dECM and thereby preserves its structure and bioactivity. Combining this resin with Filamented Light (FLight) biofabrication enables the creation of cellular, porous, and anisotropic dECM scaffolds composed of aligned microfilaments. Specifically, we investigate the effects of various biopolymer compositions (i.e., hyaluronic acid, collagen I, and dECM) and inner architecture (i.e., bulk light vs FLight) on immune response and cell morphology, and we investigate their influence on nascent ECM production and long-term tissue maturation. Our findings highlight the importance of FLight scaffolds in directing collagen deposition resembling articular cartilage structure and promoting construct maturation, and they emphasize the superiority of biological-rich dECM over single-component materials for engineering articular cartilage, thereby offering new avenues for the development of effective cartilage tissue engineering strategies.

Matching journals

The top 6 journals account for 50% of the predicted probability mass.

50% of probability mass above

"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.