Category: Novel

  • Novel ‘living’ biomaterial aims to advance regenerative medicine

    Novel ‘living’ biomaterial aims to advance regenerative medicine

    A biomaterial that can mimic certain behaviors within biological tissues could advance regenerative medicine, disease modeling, soft robotics and more, according to researchers at Penn State.

    Materials created up to this point to mimic tissues and extracellular matrices (ECMs) — the body’s biological scaffolding of proteins and molecules that surrounds and supports tissues and cells — have all had limitations that hamper their practical applications, according to the team. To overcome some of those limitations, the researchers developed a bio-based, “living” material that encompasses self-healing properties and mimics the biological response of ECMs to mechanical stress.

    They published their results in Materials Horizons, where the research was also featured on the cover of the journal.

    “We developed a cell-free — or acellular — material that dynamically mimics the behavior of ECMs, which are key building blocks of mammalian tissues that are crucial for tissue structure and cell functions,” said corresponding author Amir Sheikhi, associate professor of chemical engineering and the Dorothy Foehr Huck and J. Lloyd Huck Early Career Chair in Biomaterials and Regenerative Engineering.

    According to the researchers, previous iterations of their material — a hydrogel, or water-rich polymer network — were synthetic and lacked the desired combination of mechanical responsiveness and biological mimicry of ECMs.

    “Specifically, these materials need to replicate nonlinear strain-stiffening, which is when ECM networks stiffen under strain caused by physical forces exerted by cells or external stimuli,” Sheikhi said, explaining nonlinear strain-stiffening is important for providing structural support and facilitating cell signaling. “The materials also need to replicate the self-healing properties necessary for tissue structure and survival. Prior synthetic hydrogels had difficulties in balancing material complexity, biocompatibility and mechanical mimicry of ECMs.”

    The team addressed these limitations by developing acellular nanocomposite living hydrogels (LivGels) made from “hairy” nanoparticles. The nanoparticles are composed of nanocrystals, or “nLinkers,” with disordered cellulose chains, or “hairs,” at the ends. These hairs introduce anisotropy, meaning the nLinkers have different properties depending on their directional orientation and allow dynamic bonding with biopolymer networks. In this case, the nanoparticles bonded with a biopolymeric matrix of modified alginate, which is a natural polysaccharide found in brown algae.

    “These nLinkers form dynamic bonds within the matrix that enable strain-stiffening behavior, that is, mimicking ECM’s response to mechanical stress; and self-healing properties, which restore integrity after damage,” Sheikhi said, noting that the researchers used rheological testing, which measures how material behaves under various stressors, to measure how rapidly the LivGels recovered their structure after high strain. “This design approach allowed fine-tuning of the material’s mechanical properties to match those of natural ECMs.”

    Critically, Sheikhi said, this material is entirely made of biological materials and avoids synthetic polymers with potential biocompatibility issues. Beyond mitigating the limitations of previously developed materials, LivGels achieve the dual traits of nonlinear mechanics and self-healing without sacrificing structural integrity. The nLinkers specifically facilitate dynamic interactions that allow precise control of stiffness and strain-stiffening properties. Taken together, the design approach converts bulk, static hydrogels to dynamic hydrogels that closely mimic ECMs.

    The potential applications include scaffolding for tissue repair and regeneration within regenerative medicine, simulating tissue behavior for drug testing and creating realistic environments for studying disease progression. The researchers said it could also be used for 3D bioprinting customizable hydrogels or for developing soft robotics with adaptable mechanical properties.

    “Our next steps include optimizing LivGels for specific tissue types, exploring in vivo applications for regenerative medicine, integrating LivGels with 3D bioprinting platforms and investigating potential in dynamic wearable or implantable devices,” Sheikhi said.

    Roya Koshani, a chemical engineering post-doctoral scholar at Penn State, and Sina Kheirabadi, a doctoral candidate in chemical engineering at Penn State, were co-authors on the paper. Sheikhi is also affiliated with the Departments of Biomedical Engineering, of Chemistry and of Neurosurgery, and with the Huck Institutes of the Life Sciences.

    Support for this research was provided by Penn State, including from: the Dorothy Foehr Huck and J. Lloyd Huck Early Career Chair; the Convergence Center for Living Multifunctional Material Systems and the Cluster of Excellence Living, Adaptive and Energy-autonomous Materials Systems Living Multifunctional Materials Collaborative Research Seed Grant Program; the Materials Research Institute; and the College of Engineering Materials Matter at the Human Level seed grants.

  • Novel software that combines gene activity and tissue location to decode disease mechanisms

    Novel software that combines gene activity and tissue location to decode disease mechanisms

    In disease research, it’s important to know gene expression and where in a tissue the expression is happening, but marrying the two sets of information can be challenging.

    “Single-cell technologies, especially in the emerging field of spatial transcriptomics, help scientists see where in a tissue the genes are turned on or off. It combines information about gene activity with the exact locations within the disease tissues,” explains Fan Zhang, PhD, assistant professor of medicine with a secondary appointment in the Department of Biomedical Informatics at the University of Colorado School of Medicine.

    “This is really valuable because it lets physicians and researchers see not just which genes are active, but also where they are active, which can give key insights into how different cells behave and interact in diseased conditions,” she continues.

    Effectively combining location and genetic information has been a tough obstacle for researchers — until now.

    Zhang and her lab developed a new computational machine learning method — called Spatial Transcriptomic multi-viEW, or “STew” for short — that enables the joint analysis of spatial variation and gene expression changes in a scalable way that can handle large amounts of cells.

    This new technology may help researchers learn more about the spatial biology behind many different diseases and lead them to better treatment therapies.

    A path toward an accurate target for effective treatment

    The new technology is accurate in finding significant patterns that show where specific cell activities happen, which is important for understanding how cells work and how clinical tissues are structured in diseases. Zhang’s lab has already successfully applied STew on human tissues, including human brains, skin with inflammation, and breast cancer tumors.

    For Zhang, who studies inflammatory diseases using computational AI tools and translational approaches, finding a good target for treatment is often a challenge, but STew could help change that.

    “With inflamed joints, for example, the genes causing inflammation could be closer to the blood vessel through interacting with mesenchymal structures, or they could be farther away, but knowing that exact location and cell-cell communication patterns helps us better understand the underlying mechanisms,” she says.

    By merging spatial biology and molecular diversity, STew gives researchers a new dimension in classifying patient heterogeneity.

    “If you only use gene expression to classify patients, you don’t have the full picture,” Zhang says. “Once you add in spatial information, you have a more comprehensive understanding.”

    “We expect STew to be effective in uncovering critical molecular and cellular signals in various clinical conditions, like different types of tumors and autoimmune disorders, opening new avenues for dysregulated immune pathways for therapeutic intervention for theses disease,” she continues.

    A novel software-driven route to empowering collaboration

    There’s another perk that comes with the development of STew: collaboration. Scientific discoveries often benefit from experts from different fields working together.

    Because STew has a wide application, Zhang says the software will bring researchers together in new and exciting ways that will ultimately benefit the field of medicine and offer promise to patients in need of treatments.

    “We want to encourage researchers across specialties, skillsets, and even departments, to collaborate in ways that they previously might not have been able to do,” Zhang says. “We can accomplish more together, so it’s important to boost data-driven and AI tool-motivated collaboration in a way that is meaningful.”