3D Compartmentalised Human Pluripotent Stem Cell-derived Neuromuscular Co-cultures.

Human neuromuscular diseases represent a diverse group of disorders with unmet clinical need, ranging from muscular dystrophies, such as Duchenne muscular dystrophy (DMD), to neurodegenerative disorders, such as amyotrophic lateral sclerosis (ALS). In many of these conditions, axonal and neuromuscular synapse dysfunction have been implicated as crucial pathological events, highlighting the need for in vitro disease models that accurately recapitulate these aspects of human neuromuscular physiology. The protocol reported here describes the co-culture of neural spheroids composed of human pluripotent stem cell (PSC)-derived motor neurons and astrocytes, and human PSC-derived myofibers in 3D compartmentalised microdevices to generate functional human neuromuscular circuits in vitro. In this microphysiological model, motor axons project from a central nervous system (CNS)-like compartment along microchannels to innervate skeletal myofibers plated in a separate muscle compartment. This mimics the spatial organization of neuromuscular circuits in vivo. Optogenetics, particle image velocimetry (PIV) analysis, and immunocytochemistry are used to control, record, and quantify functional neuromuscular transmission, axonal outgrowth, and neuromuscular synapse number and morphology. This approach has been applied to study disease-specific phenotypes for DMD and ALS by incorporating patient-derived and CRISPR-corrected human PSC-derived motor neurons and skeletal myogenic progenitors into the model, as well as testing candidate drugs for rescuing pathological phenotypes. The main advantages of this approach are: i) its simple design; ii) the in vivo-like anatomical separation between CNS and peripheral muscle; and iii) the amenability of the approach to high power imaging. This opens up the possibility for carrying out live axonal transport and synaptic imaging assays in future studies, in addition to the applications reported in this study. Graphical abstract