Scientists Can Now Design Intricate Networks of Blood Vessels for 3D-Printed Organs
It's a crucial step toward the dream of printable organs on demand.
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Bioprinting holds the promise of engineering organs on demand. Now, researchers have solved one of the major bottlenecks—how to create the fine networks of blood vessels needed to keep organs alive.
Thanks to rapid advances in additive manufacturing and tissue engineering, it’s now possible to build biological structures out of living cells in much the same way you might 3D print a model plane. And there are hopes this approach could one day be used to print new organs for the more than 100,000 people in the US currently waiting for a donor.
However, reproducing the complex networks of ultra-fine blood vessels that keep living tissues alive has proven challenging. This has restricted bioprinting to smaller structures where essential nutrients and oxygen can simply diffuse into the tissue from the surrounding environment.
Now though, researchers from Stanford University have developed new software to rapidly design a blood-vessel, or vascular, network for a wide range of tissues. And in a paper in Science, they show that bioprinted tissues containing these networks significantly boosted cell survival.
“Our ability to produce human-scale biomanufactured organs is limited by inadequate vascularization,” write the authors. “This platform enables the rapid, scalable vascular model generation and fluid physics analysis for biomanufactured tissues that are necessary for future scale-up and production.”
To date, tissue engineers have mostly used simple lattice-shaped vascular networks to support the living structures they design. These work for tissues with a low density of cells but can’t meet the demands of denser structures that more closely mimic real tissues and organs.
Existing computational approaches can generate more realistic vascular networks. But they are extremely computationally expensive—often taking days to produce models for more complex tissues—and limited in the types of tissues they work with, says the Stanford team.
In contrast, their new approach generates organ-scale vascular network models for more than 200 engineered and natural tissue structures. Crucially, it was more than 230 times faster than the best previous methods. They did this by combining four algorithms, each responsible for solving a different problem.
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Typically, the algorithms used to create these kinds of structures recalculate key parameters across the entire network when each new section is added. Instead, the Stanford team used an algorithm that freezes and saves values for all the unchanged branches at each step, significantly reducing the computational workload.
They then added an algorithm that breaks the 3D structure into smaller, easier-to-model chunks, which made it simpler to work with awkward shapes. Finally, they combined this with a collision-avoidance algorithm to prevent branching vessels from crossing paths and another algorithm to ensure each vessel is always connected to another one to make sure the system is a closed loop.
The researchers used this approach to create efficient vascular networks for more than 200 models of real tissue structures. They also 3D printed models of some simpler networks to test their physical properties and even bioprinted one of these and showed it could dramatically improve the viability of living cells over a seven-day experiment.
“Democratizing virtual representation of vasculature networks could potentially transform biofabrication by allowing evaluation of perfusion efficiency prior to production rather than through a resource-intensive trial-and-error method,” wrote the authors of an accompanying perspective article in Science about the new approach.
But they also noted it’s a big leap from simulation to real life, and it will probably require a combination of computational approaches and experiments to create biologically feasible vascular trees. Still, the approach is a significant advance toward the dream of printable organs on demand.
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