The Hartford Engineering A Limb (HEAL) project is the brainchild of The Cato T. Laurencin Institute for Regenerative Engineering CEO Dr. Cato Laurencin, whose laboratory research successes include the growth of bone and knee ligaments. His method for limb replacement does not focus on mechanical or robotic limbs but rather on organic, flesh-and-blood ones that would grow on the person that receives the treatment. HEAL is aimed at helping wounded warriors as well as others who have lost limbs or experienced nerve damage. Other patients who could benefit from the future breakthroughs are those with amputations due to bone cancer, diabetes, dangerous infections, trauma accidents, or even children born with missing or impaired limbs.
The Cato T. Laurencin Institute for Regenerative Engineering has collaborated with top regenerative engineers who are dedicated to advancing their fields and developing future therapies for patients living with musculoskeletal defects or limb injury or loss. Senior HEAL Team investigators include Dr. Lakshmi Nair and Dr. Yusuf Khan of UConn Health, Dr. David M. Gardiner of the University of California Irvine, professors at Harvard University, Columbia University, and Sastra University in India. The HEAL project is further supported by 10 dedicated research fellows and a collaborative team of scientists and clinicians in biomedical engineering, stem cell sciences, molecular biology, orthopaedic surgery, plastic surgery, and rehabilitation medicine from across the UConn Health campus
Advanced Materials Science: Biodegradable polyesters have been traditionally used as the platform material to support tissue regeneration but come with challenges in terms of biocompatibility and biodegradation. The past 5 years have seen significant developments in bioinspired materials. A major focus is to build on these existing technologies and to design and develop unique tissue-inductive advanced biomaterials to develop integrated graft systems for complex tissue regeneration. Novel tunable biomaterial platforms will be developed to comprehensively study biomaterial-based cellular guidance for complex tissue regeneration. Considering the complexity of the approach, success will depend on a large integrated multi-institutional effort involving computational and combinatorial approaches.
Morphogenic Engineering: Current tissue engineering strategies in mammals use the functions of pattern following (e.g., using conductive matrices) and/or pattern enhancing (e.g., using morphogenetic proteins) methodologies to enhance tissue regeneration. However, the inability of current methodologies to support complex tissue regeneration calls for a paradigm shifting approach. Successfully addressing the challenges of mammalian organ and/or complex tissue regeneration lies in our ability to translate the unique regenerative processes preserved in poikilothermic animals such as salamanders. Encouraging results have come from some early studies indicating that several gene and growth factor signaling pathways involved in epimorphic regeneration are conserved in mammals. We believe this implies that mammals have a latent capability of complex tissue regeneration and under the proper settings, can be recapitulated. Here, we aim to study the positional associated signaling encoded in heparin sulfate as the first step towards understanding the possibility to unleash the regenerative potential in mammals.
Stem Cell Science: To identify the most appropriate source of progenitors, we will dissect the cellular and molecular mechanism underlying the transition from pluripotency and heterogeneity into lineage restricted differentiation. The studies will be focused on the utility of induced pluripotent stem cells as an alternative to somatic cells for limb regeneration; characterization of the heterogeneous population of satellite muscle cells to link stem cell functional properties with gene expression profiles to help establish these cells as a reliable and predictable cell source for muscle regeneration.
Biophysics and Mechanobiology: One major goal is to design and develop electrically and mechanically responsive 3D constructs that can be intelligent scaffold systems to guide tissue regeneration. These will build on our ongoing studies to develop electrically-conductive scaffolds to promote regeneration of muscle-tendon interface tissue regeneration, prevent fatty infiltration which inhibits muscle-tendon regeneration, mechanically and electrically stretchable substrates to support nerve tissue regeneration, and use of external electrical and mechanical forces to induce hard and soft musculoskeletal tissue regeneration.
Computational Regenerative Engineering: Computational regenerative engineering efforts will focus on modeling intra-cellular, inter-cellular, and tissue level dynamics for complex tissue regeneration to “guide” the regenerative engineering process via, for example, the manipulation of the microenvironment (e.g., nutrients, cofactors, key signaling molecules, etc. to achieve desired phenotypes). Simulated dynamics will be multi-omic and multi-scale spatially, temporally, and also at the level of biological organization (subcellular, cellular, multicellular/tissue). This effort would leverage biological knowledge of stem cells, differentiation processes, biological development at the cellular and multi-cellular/tissue level, biomolecular engineering, -omics technology, and machine learning/deep learning.learning/deep learning. It would also benefit from high performance computing/parallelization.
Developmental Biology: Understanding and engineering of pattern forming systems found in animals that have mastered complex regeneration are critical elements in developing novel convergent systems for complex regeneration.
Engineering Translation to Clinical Systems: One of the major goals of this effort is to design and develop novel mammalian regeneration models that reflect the clinical scenarios related to musculoskeletal regeneration with an emphasis on limb regeneration. The evaluation component addressed here is integral to the tissue regenerative team by providing timely information as to the level of success of a particular strategy. The efforts will focus on fine tuning existing animal models to answer the questions raised as well as to develop new unique models to address some of the emerging questions.
Big Data Techniques for Regenerative Engineering: Many functional properties of materials, such as mechanical, electrical, optical, and magnetic, can be obtained from the modeling of materials on a quantum-mechanical level. In general, such calculations are computationally demanding. The inverse problem—finding a material with a given set of desired properties—is even more challenging. In this project, we propose to develop novel big data and deep learning algorithms to address both properties prediction and materials synthesis problems to aid in regenerative engineering. These techniques are expected to guide the discovery of novel advanced materials bypassing computationally demanding first-principles calculations.