Engineering a multicompartment in vitro model for dorsal root ganglia phenotypic assessment.

J Biomed Mater Res B Appl Biomater

Department of Biological Systems Engineering, University of Nebraska-Lincoln, Lincoln, Nebraska, USA.

Published: November 2023

Despite the significant global prevalence of chronic pain, current methods to identify pain therapeutics often fail translation to the clinic. Phenotypic screening platforms rely on modeling and assessing key pathologies relevant to chronic pain, improving predictive capability. Patients with chronic pain often present with sensitization of primary sensory neurons (that extend from dorsal root ganglia [DRG]). During neuronal sensitization, painful nociceptors display lowered stimulation thresholds. To model neuronal excitability, it is necessary to maintain three key anatomical features of DRGs to have a physiologically relevant platform: (1) isolation between DRG cell bodies and neurons, (2) 3D platform to preserve cell-cell and cell-matrix interactions, and (3) presence of native non-neuronal support cells, including Schwann cells and satellite glial cells. Currently, no culture platforms maintain the three anatomical features of DRGs. Herein, we demonstrate an engineered 3D multicompartment device that isolates DRG cell bodies and neurites and maintains native support cells. We observed neurite growth into isolated compartments from the DRG using two formulations of collagen, hyaluronic acid, and laminin-based hydrogels. Further, we characterized the rheological, gelation and diffusivity properties of the two hydrogel formulations and found the mechanical properties mimic native neuronal tissue. Importantly, we successfully limited fluidic diffusion between the DRG and neurite compartment for up to 72 h, suggesting physiological relevance. Lastly, we developed a platform with the capability of phenotypic assessment of neuronal excitability using calcium imaging. Ultimately, our culture platform can screen neuronal excitability, providing a more translational and predictive system to identify novel pain therapeutics to treat chronic pain.

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Source
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC10527728PMC
http://dx.doi.org/10.1002/jbm.b.35294DOI Listing

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