NMJ-Analyser identifies subtle early changes in mouse models of neuromuscular disease

Abstract The neuromuscular junction (NMJ) is the peripheral synapse formed between a motor neuron axon terminal and a muscle fibre. NMJs are thought to be the primary site of peripheral pathology in many neuromuscular diseases, but innervation/denervation status is often assessed qualitatively with...

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Autores principales:	Alan Mejia Maza, Seth Jarvis, Weaverly Colleen Lee, Thomas J. Cunningham, Giampietro Schiavo, Maria Secrier, Pietro Fratta, James N. Sleigh, Elizabeth M. C. Fisher, Carole H. Sudre
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2021
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/3ff0e26ea9ca4cddb08257a5dd74ce5e
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spelling	oai:doaj.org-article:3ff0e26ea9ca4cddb08257a5dd74ce5e2021-12-02T17:47:18ZNMJ-Analyser identifies subtle early changes in mouse models of neuromuscular disease10.1038/s41598-021-91094-62045-2322https://doaj.org/article/3ff0e26ea9ca4cddb08257a5dd74ce5e2021-06-01T00:00:00Zhttps://doi.org/10.1038/s41598-021-91094-6https://doaj.org/toc/2045-2322Abstract The neuromuscular junction (NMJ) is the peripheral synapse formed between a motor neuron axon terminal and a muscle fibre. NMJs are thought to be the primary site of peripheral pathology in many neuromuscular diseases, but innervation/denervation status is often assessed qualitatively with poor systematic criteria across studies, and separately from 3D morphological structure. Here, we describe the development of ‘NMJ-Analyser’, to comprehensively screen the morphology of NMJs and their corresponding innervation status automatically. NMJ-Analyser generates 29 biologically relevant features to quantitatively define healthy and aberrant neuromuscular synapses and applies machine learning to diagnose NMJ degeneration. We validated this framework in longitudinal analyses of wildtype mice, as well as in four different neuromuscular disease models: three for amyotrophic lateral sclerosis (ALS) and one for peripheral neuropathy. We showed that structural changes at the NMJ initially occur in the nerve terminal of mutant TDP43 and FUS ALS models. Using a machine learning algorithm, healthy and aberrant neuromuscular synapses are identified with 95% accuracy, with 88% sensitivity and 97% specificity. Our results validate NMJ-Analyser as a robust platform for systematic and structural screening of NMJs, and pave the way for transferrable, and cross-comparison and high-throughput studies in neuromuscular diseases.Alan Mejia MazaSeth JarvisWeaverly Colleen LeeThomas J. CunninghamGiampietro SchiavoMaria SecrierPietro FrattaJames N. SleighElizabeth M. C. FisherCarole H. SudreNature PortfolioarticleMedicineRScienceQENScientific Reports, Vol 11, Iss 1, Pp 1-17 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Alan Mejia Maza Seth Jarvis Weaverly Colleen Lee Thomas J. Cunningham Giampietro Schiavo Maria Secrier Pietro Fratta James N. Sleigh Elizabeth M. C. Fisher Carole H. Sudre NMJ-Analyser identifies subtle early changes in mouse models of neuromuscular disease
description	Abstract The neuromuscular junction (NMJ) is the peripheral synapse formed between a motor neuron axon terminal and a muscle fibre. NMJs are thought to be the primary site of peripheral pathology in many neuromuscular diseases, but innervation/denervation status is often assessed qualitatively with poor systematic criteria across studies, and separately from 3D morphological structure. Here, we describe the development of ‘NMJ-Analyser’, to comprehensively screen the morphology of NMJs and their corresponding innervation status automatically. NMJ-Analyser generates 29 biologically relevant features to quantitatively define healthy and aberrant neuromuscular synapses and applies machine learning to diagnose NMJ degeneration. We validated this framework in longitudinal analyses of wildtype mice, as well as in four different neuromuscular disease models: three for amyotrophic lateral sclerosis (ALS) and one for peripheral neuropathy. We showed that structural changes at the NMJ initially occur in the nerve terminal of mutant TDP43 and FUS ALS models. Using a machine learning algorithm, healthy and aberrant neuromuscular synapses are identified with 95% accuracy, with 88% sensitivity and 97% specificity. Our results validate NMJ-Analyser as a robust platform for systematic and structural screening of NMJs, and pave the way for transferrable, and cross-comparison and high-throughput studies in neuromuscular diseases.
format	article
author	Alan Mejia Maza Seth Jarvis Weaverly Colleen Lee Thomas J. Cunningham Giampietro Schiavo Maria Secrier Pietro Fratta James N. Sleigh Elizabeth M. C. Fisher Carole H. Sudre
author_facet	Alan Mejia Maza Seth Jarvis Weaverly Colleen Lee Thomas J. Cunningham Giampietro Schiavo Maria Secrier Pietro Fratta James N. Sleigh Elizabeth M. C. Fisher Carole H. Sudre
author_sort	Alan Mejia Maza
title	NMJ-Analyser identifies subtle early changes in mouse models of neuromuscular disease
title_short	NMJ-Analyser identifies subtle early changes in mouse models of neuromuscular disease
title_full	NMJ-Analyser identifies subtle early changes in mouse models of neuromuscular disease
title_fullStr	NMJ-Analyser identifies subtle early changes in mouse models of neuromuscular disease
title_full_unstemmed	NMJ-Analyser identifies subtle early changes in mouse models of neuromuscular disease
title_sort	nmj-analyser identifies subtle early changes in mouse models of neuromuscular disease
publisher	Nature Portfolio
publishDate	2021
url	https://doaj.org/article/3ff0e26ea9ca4cddb08257a5dd74ce5e
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NMJ-Analyser identifies subtle early changes in mouse models of neuromuscular disease

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