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Updated: Sep 15, 2025

Real-Time Fluorescent Measurement of Synaptic Functions in Models of Amyotrophic Lateral Sclerosis
Published on: July 16, 2021
Transcriptional modulation unique to vulnerable motor neurons predicts ALS across species and SOD1 mutations
Irene Mei1, Susanne Nichterwitz1,2, Melanie Leboeuf1,3
1Department of Biochemistry and Biophysics, Stockholm University, 106 91 Stockholm, Sweden.
Abstract:
Amyotrophic lateral sclerosis (ALS) is characterized by the progressive loss of motor neurons (MNs) that innervate skeletal muscles. However, certain MN groups including ocular MNs, are relatively resilient. To reveal key drivers of resilience versus vulnerability in ALS, we investigate the transcriptional dynamics of four distinct MN populations in SOD1G93A ALS mice using LCM-seq and single-molecule fluorescent in situ hybridization. We find that resilient ocular MNs regulate few genes in response to disease. Instead, they exhibit high baseline gene expression of neuroprotective factors, including En1, Pvalb, Cd63, and Gal, some of which vulnerable MNs upregulate during disease. Vulnerable MN groups upregulate both detrimental and regenerative responses to ALS and share pathway activation, indicating that breakdown occurs through similar mechanisms across vulnerable neurons, albeit with distinct timing. Meta-analysis across four rodent mutant Sod1 MN transcriptome data sets identify a shared vulnerability code of 39 genes, including Atf4, Nupr1, Ddit3, and Penk, involved in apoptosis, as well as a proregenerative and antiapoptotic signature consisting of Atf3, Vgf, Ina, Sprr1a, Fgf21, Gap43, Adcyap1, and Mt1 Machine learning using genes upregulated in SOD1G93A spinal MN predicts disease in human stem cell-derived SOD1E100G MNs and shows that dysregulation of VGF, INA, and PENK is a strong disease predictor across species and SOD1 mutations. Our study reveals MN population-specific gene expression and temporal disease-induced regulation that together provide a basis to explain ALS selective vulnerability and resilience and that can be used to predict disease.

