A 32-WELL MICROFLUDIC PLATFORM WITH MEAS TO MODEL NEUROMUSCULAR JUNCTIONS ACROSS SMA SUBTYPES AND IDENTIFY SPECIFIC ELECTRICAL SIGNATURES USING A CUSTOM PIPELINE

Spinal Muscular Atrophy (SMA) presents various clinical forms from severe (type 0/I) to milder (type III/IV) phenotypes and lacks curative treatment. Understanding SMA mechanisms and identifying new therapeutic candidates is therefore crucial. To this end, we developed a 32-well microfluidic platform with MEAs to reconstruct human neuromuscular junctions (hNMJs) and monitor their electrophysiological activity, aiming to establish subtype-specific NMJ models and assess treatment response through electrophysiological profiling.
Using hiPSC-derived motor neuron and myotubes from SMA patients and healthy controls, hNMJs were reconstructed in separate microfluidic chambers connected by 4µm-wide, 500µm-long microchannels, mimicking in vivo MN-muscle distance. Grooves in the muscle chamber promote cell adhesion and polarization, while a subset of 2 electrodes in each chamber (128 total) enables separate study of muscle and MN activity.
Mature hNMJs were identified through α-bungarotoxin (AChR clusters), SMI‑32 (axonal marker), and alpha‑actinin staining for muscle maturation. Action potentials were detected in both compartments, further confirming maturation.
To identify electrophysiological signatures, we are developing a pipeline for artifact removal, spike detection and feature extraction (firing rate, inter-spike interval, burst rate, etc). Preliminary recordings from 16 healthy and 16 SMA type II samples indicate higher inter-spike variability and decreased firing rate, suggesting MN loss and dysfunction. From these features, we are building a database, used to train machine learning classifiers. Currently, the Random Forest classifier yields best accuracy and cross-validation score.
This platform aims to enable automated electrical defect detection and pharmacological screening to identify potential therapeutic candidates capable of restoring healthy-like electrophysiological function.