Alignment of Skeletal Muscle Cells Facilitates Acetylcholine Receptor Clustering and Neuromuscular Junction Formation with Co-Cultured Human iPSC-Derived Motor Neurons.

In vitro neuromuscular junction (NMJ) models are powerful tools for studying neuromuscular disorders. Although linearly patterned culture surfaces have been reported to be useful for the formation of in vitro NMJ models using mouse motor neuron (MNs) and skeletal muscle (SkM) myotubes, it is unclear how the linearly patterned culture surface increases acetylcholine receptor (AChR) clustering, one of the steps in the process of NMJ formation, and whether this increases the in vitro NMJ formation efficiency of co-cultured human MNs and SkM myotubes. In this study, we investigated the effects of a linearly patterned culture surface on AChR clustering in myotubes and examined the possible mechanism of the increase in AChR clustering using gene expression analysis, as well as the effects of the patterned surface on the efficiency of NMJ formation between co-cultured human SkM myotubes and human iPSC-derived MNs. Our results suggest that better differentiation of myotubes on the patterned surface, compared to the flat surface, induced gene expression of integrin α7 and AChR ε-subunit, thereby increasing AChR clustering. Furthermore, we found that the number of NMJs between human SkM cells and MNs increased upon co-culture on the linearly patterned surface, suggesting the usefulness of the patterned surface for creating in vitro human NMJ models.

Classification of forearm and finger motions using electromyogram and arm-shape-changes.

Robot arms for humanoid are widely developed for medical, welfare and education use. Surface electromyogram (sEMG) signals which are the electrical signals obtained from surface of human skin using electrodes have been mainly used for classification of hand motions. However, it is difficult to classify detailed motions such as finger motions and wrist pronation or supination. Moreover, Kinect is an integration sensor device which can capture human joints movement. It also has been widely used for recognition of body motions in many fields. However, it has some problems such as setting of camera and restriction of detection range. In this study, we propose an advanced method of motion classification by combining arm-shape-changes with sEMG to classify the detailed motions. Arm-shape-changes are forearm deformation caused by a bulge of muscle when subjects move an arm or a finger. Experimental results showed classification accuracies of 90% or more in wrist pronation and supination which are difficult to classify using only sEMG signals. As the result, our method could classify the detailed motions and contribute to expansion of classifiable hand motions.