Development of photoreactive demineralized bone matrix 3D printing colloidal inks for bone tissue engineering.

Demineralized bone matrix (DBM) has been widely used clinically for dental, craniofacial and skeletal bone repair, as an osteoinductive and osteoconductive material. 3D printing (3DP) enables the creation of bone tissue engineering scaffolds with complex geometries and porosity. Photoreactive methacryloylated gelatin nanoparticles (GNP-MAs) 3DP inks have been developed, which display gel-like behavior for high print fidelity and are capable of post-printing photocrosslinking for control of scaffold swelling and degradation. Here, novel DBM nanoparticles (DBM-NPs, ∼400 nm) were fabricated and characterized prior to incorporation in 3DP inks. The objectives of this study were to determine how these DBM-NPs would influence the printability of composite colloidal 3DP inks, assess the impact of ultraviolet (UV) crosslinking on 3DP scaffold swelling and degradation and evaluate the osteogenic potential of DBM-NP-containing composite colloidal scaffolds. The addition of methacryloylated DBM-NPs (DBM-NP-MAs) to composite colloidal inks (100:0, 95:5 and 75:25 GNP-MA:DBM-NP-MA) did not significantly impact the rheological properties associated with printability, such as viscosity and shear recovery or photocrosslinking. UV crosslinking with a UV dosage of 3 J/cm2 directly impacted the rate of 3DP scaffold swelling for all GNP-MA:DBM-NP-MA ratios with an ∼40% greater increase in scaffold area and pore area in uncrosslinked versus photocrosslinked scaffolds over 21 days in phosphate-buffered saline (PBS). Likewise, degradation (hydrolytic and enzymatic) over 21 days for all DBM-NP-MA content groups was significantly decreased, ∼45% less in PBS and collagenase-containing PBS, in UV-crosslinked versus uncrosslinked groups. The incorporation of DBM-NP-MAs into scaffolds decreased mass loss compared to GNP-MA-only scaffolds during collagenase degradation. An in vitro osteogenic study with bone marrow-derived mesenchymal stem cells demonstrated osteoconductive properties of 3DP scaffolds for the DBM-NP-MA contents examined. The creation of photoreactive DBM-NP-MAs and their application in 3DP provide a platform for the development of ECM-derived colloidal materials and tailored control of biochemical cue presentation with broad tissue engineering applications.

Extracellular matrix component-derived nanoparticles for drug delivery and tissue engineering.

The extracellular matrix (ECM) consists of a complex combination of proteins, proteoglycans, and other biomolecules. ECM-based materials have been demonstrated to have high biocompatibility and bioactivity, which may be harnessed for drug delivery and tissue engineering applications. Herein, nanoparticles incorporating ECM-based materials and their applications in drug delivery and tissue engineering are reviewed. Proteins such as gelatin, collagen, and fibrin as well as glycosaminoglycans including hyaluronic acid, chondroitin sulfate, and heparin have been employed for cancer therapeutic delivery, gene delivery, and wound healing and regenerative medicine. Strategies for modifying and functionalizing these materials with synthetic and natural polymers or to enable stimuli-responsive degradation and drug release have increased the efficacy of these materials and nano-systems. The incorporation and modification of ECM-based materials may be used to drive drug targeting and increase tissue-specific cell differentiation more effectively.

Aspects of a Suspended Bioprinting System Affect Cell Viability and Support Bath Properties.

Suspended hydrogel printing is a growing method for fabricating bioprinted hydrogel constructs, largely due to how it enables nonviscous hydrogel inks to be used in extrusion printing. In this work, a previously developed poly(N-isopropylacrylamide)-based thermogelling suspended bioprinting system was examined in the context of chondrocyte-laden printing. Material factors such as ink concentration and cell concentration were found to have a significant effect on printed chondrocyte viability. In addition, the heated poloxamer support bath was able to maintain chondrocyte viability for up to 6 h of residence within the bath. The relationship between the ink and support bath was also assessed by measuring the rheological properties of the bath before and after printing. Bath storage modulus and yield stress decreased during printing as nozzle size was reduced, indicating the likelihood that dilution occurs over time through osmotic exchange with the ink. Altogether this work demonstrates the promise for printing high-resolution cell-encapsulating tissue engineering constructs, while also elucidating complex relationships between the ink and bath, which must be taken into consideration when designing suspended printing systems.

Bioinspired electrospun decellularized extracellular matrix scaffolds promote muscle regeneration in a rat skeletal muscle defect model.

Volumetric muscle loss is a debilitating injury that can leave patients with long-lasting or permanent structural and functional deficits. With clinical treatments failing to address these shortcomings, there is a great need for tissue-engineered therapies to promote skeletal muscle regeneration. In this study, we aim to assess the potential for electrospun decellularized skeletal muscle extracellular matrix (dECM) to promote skeletal muscle regeneration in a rat partial thickness tibialis anterior defect model. Aligned electrospun scaffolds with varying degrees of crosslinking density were implanted into the defect site and compared to an empty defect control. After 8 weeks, muscles were harvested, weighed, and cellular and morphological analyses were performed via histology and immunohistochemistry. Cell infiltration, angiogenesis, and myogenesis were observed in the defect site in both dECM groups. However, favorable mechanical properties and slower degradation kinetics resulted in greater support of tissue remodeling in the more crosslinked scaffolds and preservation of existing myofiber area in both dECM groups compared to the empty defect control. More sustained release of pro-regenerative degradation products also promoted greater myofiber formation in the defect site. This study allowed for a greater understanding of how electrospun skeletal muscle scaffolds interact with existing skeletal muscle and can inform their potential as a therapy in a wide variety of soft tissue applications.

Machine Learning-Guided Three-Dimensional Printing of Tissue Engineering Scaffolds.

Various material compositions have been successfully used in 3D printing with promising applications as scaffolds in tissue engineering. However, identifying suitable printing conditions for new materials requires extensive experimentation in a time and resource-demanding process. This study investigates the use of Machine Learning (ML) for distinguishing between printing configurations that are likely to result in low-quality prints and printing configurations that are more promising as a first step toward the development of a recommendation system for identifying suitable printing conditions. The ML-based framework takes as input the printing conditions regarding the material composition and the printing parameters and predicts the quality of the resulting print as either "low" or "high." We investigate two ML-based approaches: a direct classification-based approach that trains a classifier to distinguish between low- and high-quality prints and an indirect approach that uses a regression ML model that approximates the values of a printing quality metric. Both modes are built upon Random Forests. We trained and evaluated the models on a dataset that was generated in a previous study, which investigated fabrication of porous polymer scaffolds by means of extrusion-based 3D printing with a full-factorial design. Our results show that both models were able to correctly label the majority of the tested configurations while a simpler linear ML model was not effective. Additionally, our analysis showed that a full factorial design for data collection can lead to redundancies in the data, in the context of ML, and we propose a more efficient data collection strategy.