Engineering the degradation of decellularized plant scaffolds for hard tissue engineering - PROJECT SUMMARY Despite recent advances in fabrication technology, synthetic scaffolds developed to address the chronic shortage of donor tissues for craniofacial reconstruction are constrained by their scalability, high-cost implications, and inability to replicate the structural complexities of natural extracellular matrices (ECMs) that provide structural cues for regulating cell-matrix interactions and tissue formation. Decellularized plant scaffolds, which have physical and structural similarities to natural ECMs, have emerged as viable platforms for cell proliferation and tissue formation, helping to circumvent the supply issues, high costs, and large-scale production constraints of decellularized tissue-derived ECMs. Although attractive, the primary challenge of using plant scaffolds lies in their limited degradation in the human tissue microenvironment due to the absence of cellulases to attack the β(1-4) linkages in plant structures. Consequently, designing plant scaffold degradation strategies has garnered intense interest as an essential paradigm to mediate temporo-spatial 3D tissue formation and remodeling. A key question remains whether plant scaffold degradation can be tuned to match cell growth and tissue regeneration to enhance the quality of regenerated tissues. In this project, we propose to engineer the controlled degradation of decellularized lucky bamboo (model plant scaffold) as a platform for bone tissue engineering (model tissue) based on (i) remote-controlled, ultrasound-triggered cellulase release from microbubbles to break down cellulose in the plant structure, and (ii) AI/machine learning-driven, image-guided monitoring of degradation profiles. This will be explored through three specific aims. In Aim 1, we will develop ultrasound-responsive, cellulase- encapsulated poly(lactic-co-glycolic acid) (PLGA) microbubbles that can be directly injected into scaffolds and remotely triggered using ultrasound to regulate cellulase release. Aim 2 will concentrate on measuring the effects of cellulase release on the degradation of decellularized bamboo scaffolds by quantifying changes in weight, compressive mechanical properties, and the amount of sugar fragments produced from cellulose breakdown. Additionally, we will implement machine learning (ML) algorithms to directly predict degradation levels using raw micro-computed tomography (micro-CT) and ultrasound images of the scaffolds, validated against the physico- chemical degradation metrics (scaffold weight loss, compressive properties, and sugar fragment degradation products). In Aim 3, we will quantify cell responses to the cellulase, decellularized scaffold, degradation products, and focused ultrasound waves. Ultimately, we anticipate that insights from this pilot study will facilitate a comprehensive follow-up project to develop our novel remote-controlled degradation technique in vivo, paving the way for utilizing decellularized plant scaffolds in craniofacial reconstruction.
