Researchers Build Inexpensive Open Source Bioprinter for 3D Printing Branching, Hydrogel-Based Vascular Constructs https://ift.tt/2LFbcCL While 3D bioprinting is not yet able to fabricate full human organs just yet, it can be used to manufacture several different kinds of human tissue, such as heart and bile duct. One of the main barriers of forming viable tissues for clinical and scientific use is the development of vasculature for engineered tissue constructs, mainly due to generating branching channels in hydrogel constructs that can later produce vessel-like structures after being seeded with endothelial cells. But thanks to 3D bioprinting, it’s now possible to 3D print complex structures on multiple length scales within a single construct. This enables the generation of branching, interconnected vessel systems of small, vein-like microvessels and larger macrovessels, which couldn’t be done with former tissue engineering methods. However, the best sacrificial material for fabricating branching vascular conduits in constructs based in hydrogel has yet to be determined. A team of researchers from the University of Toronto recently published a paper, titled “Generating vascular channels within hydrogel constructs using an economical open-source 3D bioprinter and thermoreversible gels,” in the Bioprinting journal. Co-authors of the paper include Ross EB Fitzsimmons, Mark S. Aquilino, Jasmine Quigley, Oleg Chebotarev, Farhang Tarlan, and Craig A. Simmons.
Existing 3D bioprinters have different technical advantages and deposition methods, which influence their prices and available applications. Extrusion-based 3D printers are good for tissue engineering, but the cost is usually too high for the field to experience significant growth. For this experiment, the researchers chose to create their own open source 3D bioprinter, which costs roughly $3,000 and can be used for lower resolution applications, such as 3D printing perfusable microvessels in tissue constructs. Both the chosen method and material have to meet a certain number of requirements to successfully 3D print complex branching vessel systems within hydrogel constructs. First, sacrificial materials, which need to be non-toxic and maintain a uniform filament diameter during printing, have to be deposited in the desired vascular design during printing, then flushed away once the construct is done. In addition, the 3D printer needs to have enough resolution to print all the channels – even those that will act as the small artery vessels of ~0.5–1 mm. It also needs to be able to deposit at least two materials, though more is better when it comes to creating heterogeneous tissues with different regions of varying cell and hydrogel composition. The team investigated formulations of gelatin and PF127 due to their potential advantages as sacrificial materials in hydrogel-based tissue constructs. Gelatin, which has been used in several biomedical applications, is a thermoreversible (the property of certain substances to be reversed when exposed to heat) biopolymer of several hydrolyzed collagen segments, and can be 3D printed at ~37 °C, which is a temperature compatible with cells. PF127 is a surfactant, meaning that it could have potential cytotoxic effects on embedded cells. But, it has inverse thermal gelation, which means it can be 3D printed at an ambient temperature, and then removed at ~4 °C to create void vascular channels.
The team’s modular 3D bioprinter includes extruding systems, 3D printed out of ABS on a MakerBot 3D printer, which were designed specifically to hold commercially-available, sterile 10 mL syringes, instead of custom-made reservoirs that would need to be specially made and repeatedly sterilized. An open-source Duet v0.6 controller board controls the system, and the print heads are isolated from the XYZ movements executed by the lower part of the chassis. For testing purposes, water droplets were 3D printed in a defined pattern with each extruder system, and the average distance between the droplets’ centers in the X and Y directions were measured; then, the mean distances were compared to the pre-defined CAD model distances.
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