Injectable bioorthogonal hydrogel (BIOGEL) accelerates tissue regeneration in degenerated intervertebral discs

Intervertebral disc(IVD)degeneration is a leading cause of back pain and precursor to more severe conditions,including disc herniation and spinal stenosis.While traditional growth factor therapies(e.g.,TGFβ)are effective at transiently reversing degenerated disc by stimulation of matrix synthesis,it is increasingly accepted that bioscaffolds are required for sustained,complete IVD regeneration.Current scaffolds(e.g.,metal/polymer composites,non-mammalian biopolymers)can be improved in one or more IVD regeneration demands:biodegradability,noninvasive injection,recapitulated healthy IVD biomechanics,predictable crosslinking,and matrix repair induction.To meet these demands,tetrazine-norbornene bioorthogonal ligation was combined with gelatin to create an injectable bioorthogonal hydrogel(BIOGEL).The liquid hydrogel precursors remain free-flowing across a wide range of temperatures and crosslink into a robust hydrogel after 5-10 min,allowing a human operator to easily inject the therapeutic constructs into degenerated IVD.Moreover,BIOGEL encapsulation of TGFβpotentiated histological repair(e.g.,tissue architecture and matrix synthesis)and functional recovery(e.g.,high water retention by promoting the matrix synthesis and reduced pain)in an in vivo rat IVD degeneration/nucleotomy model.This BIOGEL procedure readily integrates into existing nucleotomy procedures,indicating that clinical adoption should proceed with minimal difficulty.Since bioorthogonal crosslinking is essentially non-reactive towards biomolecules,our developed material platform can be extended to other payloads and degenerative injuries.

High-Content Screening and Analysis of Stem Cell-Derived Neural Interfaces Using a Combinatorial Nanotechnology and Machine Learning Approach

A systematic investigation of stem cell-derived neural interfaces can facilitate the discovery of the molecular mechanisms behind cell behavior in neurological disorders and accelerate the development of stem cell-based therapies.Nevertheless,high-throughput investigation of the cell-type-specific biophysical cues associated with stem cell-derived neural interfaces continues to be a significant obstacle to overcome.To this end,we developed a combinatorial nanoarray-based method for high-throughput investigation of neural interface micro-/nanostructures(physical cues comprising geometrical,topographical,and mechanical aspects)and the effects of these complex physical cues on stem cell fate decisions.Furthermore,by applying a machine learning(ML)-based analytical approach to a large number of stem cell-derived neural interfaces,we comprehensively mapped stem cell adhesion,differentiation,and proliferation,which allowed for the cell-type-specific design of biomaterials for neural interfacing,including both adult and human-induced pluripotent stem cells(hiPSCs)with varying genetic backgrounds.In short,we successfully demonstrated how an innovative combinatorial nanoarray and ML-based platform technology can aid with the rational design of stem cell-derived neural interfaces,potentially facilitating precision,and personalized tissue engineering applications.