NMJ-Analyser

NMJ-Analyser quantifies and classifies neuromuscular junction (NMJ) morphology to provide high-throughput, quantitative assessment of NMJ structure and innervation status in neuromuscular disease models.

Key Features:

• Automated Morphological Screening: Generates 29 biologically relevant features to quantitatively define healthy and aberrant neuromuscular synapses.
• Machine Learning Integration: Employs machine learning algorithms to diagnose NMJ degeneration, validated with 95% accuracy, 88% sensitivity, and 97% specificity.
• High-Throughput Image Processing: Processes input directories of jpg or png image files and requires NMJs to be separated by at least 20 micrometers to avoid analysis compromise.

Scientific Applications:

• Longitudinal and comparative studies: Validated in longitudinal studies of wildtype mice and four neuromuscular disease models, including three amyotrophic lateral sclerosis (ALS) models and one peripheral neuropathy model.
• NMJ pathology detection in ALS models: Detects structural changes at the NMJ, particularly in nerve terminals of mutant TDP43 and FUS ALS models, supporting analysis of early degeneration and disease progression.

Methodology:

Processes jpg/png NMJ images from input directories, extracts 29 morphological features and applies machine learning algorithms for classification; requires NMJs separated ≥20 micrometers.