Carnegie Mellon’s Feinberg lab has unveiled a groundbreaking FRESH 3D bioprinting technique that generates microphysiologic systems entirely from collagen, significantly advancing the study of diseases and tissue therapy.

This innovative method enables the creation of vascularized tissues with a single-step fabrication process, achieving unprecedented resolution and functional fidelity in collagen-based, perfusable microfluidic systems.

Recent research published in Science Advances highlights the production of complex vascularized tissues, including pancreatic-like constructs, which hold promise for treating Type 1 diabetes.

These new pancreatic tissue constructs are capable of glucose-stimulated insulin release, outperforming existing organoid methods, thereby enhancing therapeutic potential.

FluidForm Bio, a Carnegie Mellon spinout, is commercializing this technology and has successfully cured Type 1 diabetes in animal models, with plans to initiate human clinical trials soon.

The Feinberg lab team aims to integrate computational modeling and machine learning to optimize tissue designs for therapeutic applications.

Traditional tissue models have been limited by synthetic materials, which cannot fully replicate human biology; the FRESH technique allows for biologically relevant models that improve cell function.

Collagen, the most abundant protein in the body, is a vital component in bioprinting, providing essential structural support to tissues and organs.

To promote widespread adoption and innovation in tissue engineering, Feinberg and collaborators plan to release open-source designs and technologies.

Adam Feinberg emphasizes the importance of interdisciplinary collaboration in advancing bioprinting technology and its applications in medicine.

Daniel Shiwarski, a bioengineering professor at the University of Pittsburgh, notes that advancements in the single-step bioprinting process enhance design resolution and fidelity.

Future research will focus on determining which tissues to build using advanced fabrication capabilities combined with computational modeling and machine learning.