Stanford researchers develop novel "scaffold-free" approach for treating damaged muscles

(Press-News.org) Traumatic muscle injury can be associated with volumetric muscle loss (VML), often leading to permanent functional loss. Until recently, experimental therapies to support muscle regeneration have faced several key limitations, including the challenge of delivering sufficient healing cells to the traumatized area and the inability of conventional tissue transplants to conform to the specific shape of a muscle defect.

A recent study, led by senior author Ngan F. Huang, PhD, Associate Professor of Cardiothoracic Surgery (Research) in the Stanford Department of Cardiothoracic Surgery, highlights a unique approach her research team has developed to address this problem and potentially treat VML. Unlike conventional methods, which use an artificial frame, or "scaffold," to hold cells in place, Dr. Huang’s efforts center on a “scaffold-free” technique. Using a simple, mold-based approach, researchers grew dense muscle tissue in customizable geometric shapes and sizes. This method allowed for more healing cells to be packed into the affected area where they then self-organized within the mold and jump-started the regeneration process.

Dr. Huang’s paper, "Geometrically Tunable Scaffold-Free Muscle Bioconstructs for Treating Volumetric Muscle Loss," was featured on the front cover of Advanced Healthcare Materials, published on March 10, 2026. 

A Custom "Muscle Patch" to Treat Injury

Within the body, muscles are held together by extracellular matrix proteins that they secrete. These proteins create a natural, 3D scaffolding structure that provides shape to the tissues, Dr. Huang said.

Many regenerative approaches focus on delivering cells to the traumatic site by building a “frame” or scaffold from biomaterials. As such, Dr. Huang explained, when researchers engineer tissues in the lab to treat muscle injury, they are mimicking the extracellular matrix using biomaterials.

But Dr. Huang and her team recognized a key drawback of this approach: the biomaterials occupied a significant volume. "We decided instead to omit external biomaterials so that we can deliver more healing cells into that volume," she said.

She noted that cells can still naturally secrete their own extracellular matrix even without any biomaterials. Since traumatic muscle loss involves the loss of a large number of cells within a defined muscle injury volume, the challenge, they realized, was the ability to replenish a sufficient number of cells. Dr. Huang believed that removing external biomaterial would free up more space for the cells to naturally create their own scaffolds and enable the delivery of more healing muscle cells.

This scaffold-free tissue engineering approach has been tested in previous studies, but Dr. Huang and her team advanced it by creating a custom "muscle patch" that can fit any unique injury.

"Our custom molding technology makes it easy to design any geometry for the tissue constructs, including shapes that form letters and words like 'Stanford,'" Dr. Huang said.

Key Findings in Treating Muscle Injury

Their study revealed several key findings for the potential treatment of massive muscle injuries. First, Dr. Huang pointed out, scaffold-free tissues allow cells to self-organize prior to implantation, leading to gene and protein expression that resembles a more robust muscle identity. This is in contrast to conventional cell therapy strategies, where the cells are detached from dishes as suspension cells and then injected into the body.

"We believe that the pre-formed cell-to-cell interactions afforded by these scaffold-free tissues allow the cells to communicate with one another, ultimately leading to more effective muscle cells," Dr. Huang said.

Another key finding showed that the scaffold-free constructs can be geometrically tuned and integrated with the injured area upon contact. This provides proof of principle that smaller, modular shapes can integrate into larger, more complex ones.

A Vision for the Future of Muscle Repair

Researchers in the Huang Lab are hopeful that the study will provide significant clinical benefits. They envision using this strategy to build a library of scaffold-free tissue shapes that can serve as building blocks.

In the future, Dr. Huang believes it will be feasible to combine this technology with robotic assistance, conventional clinical imaging modalities, and AI. With robotic assistance, the geometries of the muscle damage can be input, and a map could be designed to fill the defect region with customized shapes.

"This allows for generating more complex shapes," Dr. Huang said. "From a scalability point of view, the modular shapes form the building blocks to larger complex geometries that might be patient-personalized."

In this scenario, robotic assistance would enable the precise placement of the various shapes, so that a surgeon would not need to perform the precision placement. "Of course, the surgeon is still needed for overseeing the robot and intervening as needed," she said. 

Figure shows biofabrication of scaffold-free customizable modular shapes for tissue engineering.

Future Directions in Treating Volumetric Muscle Loss

As for the next steps and direction for this research, Dr. Huang and her team will focus on integrating the modular shapes to build more complex shapes. They will also add other tissue components: muscle is composed of numerous cell types, including blood vessels and nerves. They will also apply this technique to modular shapes that incorporate vessels, nerves, and muscle constructs, leading to more complex tissues.

Dr. Huang imagines this approach could be applied to other fields of muscle regeneration as well. "Although this strategy is shown here for muscle defects, we imagine this modular tissue-building approach to be applied to cardiovascular tissues such as the heart in the future," she said.

The publication was a collaboration with researchers and members from the Stanford Cardiovascular Institute and the VA Palo Alto Health Care System. Authors of the paper include Bugra Ayan, PhD; Gaoxian Chen, PhD; Ishita Jain, PhD; Sha Chen, MD, PhD; Gladys Chiang; Caroline Hu; Renato Reyes; and Beu P. Oropeza, PhD.

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