Devine, H. | Search bioRxiv

bioRxiv2025

Conserved Aberrant Developmental Trajectories of Human and Mouse SBMA Motor Neurons

Spinal bulbar muscular atrophy (SBMA) is a neuromuscular disease caused by a polyglutamine repeat expansion in the androgen receptor gene (AR). Lower motor neuron loss is a key feature of the disease, yet it remains poorly understood why these cells are affected. The transcriptional mechanisms underlying SBMA pathogenesis and how these evolve across developmental and disease stages remains incompletely defined. To elucidate the molecular mechanisms underlying motor neuron loss in SBMA, we first performed transcriptomic profiling of both induced pluripotent stem cell derived motor neurons (iPSC-MNs) generated from SBMA patients and laser-captured micro dissected motor neurons (LCM-MNs) from symptomatic AR100 SBMA mice. We compared differential gene expression between the two models to identify shared transcriptional programs. To address the temporal progression of molecular changes we conducted profiling at key stages of motor neurogenesis in the developing iPSC-MNs and at pre-symptomatic and end-stage disease in AR100 SBMA mice to elucidate the emergence of the transcriptional phenotype and the trajectory of the gene expression changes. We found significant transcriptional convergence between these two species. Notably, shared dysregulation was observed in pathways related to the spliceosome, the cell cycle and mitochondrial function. These transcriptional alterations emerged early in motor neurogenesis suggesting a developmental component to SBMA. Further in AR100 LCM-MNs we also observed disruption of mitochondrial and DNA damage repair pathways from pre-symptomatic to end stage disease. This study identifies conserved pathogenic mechanisms across two SBMA model systems and provides crucial insights into the molecular basis and temporal dynamics of SBMA progression which may help identify potential therapeutic targets for SBMA. O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=110 SRC="FIGDIR/small/674754v1_ufig1.gif" ALT="Figure 1"> View larger version (33K): org.highwire.dtl.DTLVardef@1577034org.highwire.dtl.DTLVardef@187824eorg.highwire.dtl.DTLVardef@17bf6a5org.highwire.dtl.DTLVardef@17e09f9_HPS_FORMAT_FIGEXP M_FIG C_FIG

neuroscience↗

bioRxiv2020

Automated and unbiased classification of motor neuron phenotypes with single cell resolution in ALS tissue.

Histopathological analysis of tissue sections is an invaluable resource in neurodegeneration research. Importantly, cell-to-cell variation in both the presence and severity of a given phenotype is however a key limitation of this approach, reducing the signal to noise ratio and leaving unresolved the potential of single-cell scoring for a given disease attribute. Here, we developed an image processing pipeline for automated identification and profiling of motor neurons (MNs) in amyotrophic lateral sclerosis (ALS) pathological tissue sections. This approach enabled unbiased analysis of hundreds of cells, from which hundreds of features were readily extracted. Next by testing different machine learning methods, we automated the identification of phenotypically distinct MN subpopulations in VCP- and SOD1-mutant transgenic mice, revealing common aberrant phenotypes in cellular shape. Additionally we established scoring metrics to rank cells and tissue samples for both disease probability and severity. Finally, by adapting this methodology to human post-mortem tissue analysis, we validated our core finding that morphological descriptors strongly discriminate ALS from control healthy tissue at the single cell level. In summary, we show that combining automated image processing with machine learning methods substantially improves the speed and reliability of identifying phenotypically diverse MN populations. Determining disease presence, severity and unbiased phenotypes at single cell resolution might prove transformational in our understanding of ALS and neurodegenerative diseases more broadly.

neuroscience↗