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. 2025 Apr;51(2):e70014.
doi: 10.1111/nan.70014.

Computational Analysis of SOD1-G93A Mouse Muscle Biomarkers for Comprehensive Assessment of ALS Progression

Pedro Gómez-Gálvez  1   2   3   4 , Victoria Navarro  4   5 , Ana M Castro  3 , Carmen Paradas  4   5   6 , Luis M Escudero  3   4
Affiliations

Computational Analysis of SOD1-G93A Mouse Muscle Biomarkers for Comprehensive Assessment of ALS Progression

Pedro Gómez-Gálvez et al. Neuropathol Appl Neurobiol. 2025 Apr.

Abstract

Aims: To identify potential image biomarkers of neuromuscular disease by analysing morphological and network-derived features in skeletal muscle biopsies from a murine model of amyotrophic lateral sclerosis (ALS), the SOD1G93A mouse and wild-type (WT) controls at distinct stages of disease progression.

Methods: Using the NDICIA computational framework, we quantitatively evaluated histological differences between skeletal muscle biopsies from SOD1G93A and WT mice. The process involved the selection of a subset of features revealing these differences. A subset of discriminative features was selected to characterise these differences, and their temporal dynamics were assessed across disease stages.

Results: Our findings demonstrate that muscle pathology in the mutant model evolves from early alterations in muscle fibre arrangement, detectable at the presymptomatic stage through graph theory features, to the subsequent development of the typical morphological pattern of neurogenic atrophy at more advanced disease stages.

Conclusions: Our assay identifies a neurogenic signature in mutant muscle biopsies, even when the disease is phenotypically imperceptible.

Keywords: SOD1‐G93A; amyotrophic lateral sclerosis; graph theory; image analysis; image biomarker; motor neuron degeneration.

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