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基于无标记形态学的脊髓性肌萎缩症肌肉细胞模型的表型分析

Label-free morphology-based phenotypic analysis of spinal and bulbar muscular atrophy muscle cell models.

作者信息

Sakakibara Kenji, Tanaka Kenjiro, Iida Madoka, Imai Yuta, Okada Mai, Sahashi Kentaro, Hirunagi Tomoki, Maeda Kentaro, Kato Ryuji, Katsuno Masahisa

机构信息

Department of Neurology, Nagoya University Graduate School of Medicine, Tokai National Higher Education and Research System, 65 Tsurumai-cho, Showa-ku, Nagoya, Aichi 466-8550, Japan.

Department of Basic Medicinal Sciences, Graduate School of Pharmaceutical Sciences, Nagoya University, Tokai National Higher Education and Research System, Furocho, Chikusa-ku, Nagoya, Aichi 464-8601, Japan.

出版信息

Dis Model Mech. 2025 Jun 1;18(6). doi: 10.1242/dmm.052220. Epub 2025 Jun 25.

DOI:10.1242/dmm.052220
PMID:40474744
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC12233066/
Abstract

Spinal and bulbar muscular atrophy (SBMA) is a neuromuscular disorder caused by CAG trinucleotide expansion in the androgen receptor (AR) gene. To improve the quality of in vitro cell-based assays for the evaluation of potential drug candidates for SBMA, we developed a morphology-based phenotypic analysis for a muscle cell model of SBMA that involves multiparametric morphological profiling to quantitatively assess the therapeutic effects of drugs on muscle cell phenotype. The analysis was validated using dihydrotestosterone and pioglitazone, which have been shown to exacerbate and ameliorate the pathophysiology of SBMA, respectively. Gene expression analysis revealed activation of the JNK pathway in the SBMA cells compared to the control cells. Phenotypic analysis revealed the effect of naratriptan, a JNK inhibitor, on the phenotypic changes of SBMA cells, and the results were confirmed by LDH assays. We then trained a predictive machine learning model to classify the drug responses, and it successfully discriminated between pioglitazone-type and naratriptan-type morphological profiles based on their morphological characteristics. Our morphology-based phenotypic analysis provides a noninvasive and efficient screening method to accelerate the development of therapeutics for SBMA.

摘要

脊髓延髓性肌萎缩症（SBMA）是一种由雄激素受体（AR）基因中CAG三核苷酸扩增引起的神经肌肉疾病。为了提高用于评估SBMA潜在候选药物的体外细胞检测质量，我们针对SBMA的肌肉细胞模型开发了一种基于形态学的表型分析方法，该方法涉及多参数形态学分析，以定量评估药物对肌肉细胞表型的治疗效果。使用二氢睾酮和吡格列酮对该分析进行了验证，结果表明这两种药物分别会加重和改善SBMA的病理生理过程。基因表达分析显示，与对照细胞相比，SBMA细胞中的JNK通路被激活。表型分析揭示了JNK抑制剂那拉曲普坦对SBMA细胞表型变化的影响，乳酸脱氢酶（LDH）检测证实了该结果。然后，我们训练了一个预测性机器学习模型来对药物反应进行分类，该模型基于形态学特征成功区分了吡格列酮型和那拉曲普坦型的形态学特征。我们基于形态学的表型分析提供了一种非侵入性的高效筛选方法，可加速SBMA治疗药物的开发。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/0ef9/12233066/47cd6479bbcd/dmm-18-052220-g6.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/0ef9/12233066/3795771cf6d8/dmm-18-052220-g1.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/0ef9/12233066/f27f0d613274/dmm-18-052220-g2.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/0ef9/12233066/4d191f41504a/dmm-18-052220-g3.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/0ef9/12233066/13a5def99659/dmm-18-052220-g4.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/0ef9/12233066/01ec3e1dd927/dmm-18-052220-g5.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/0ef9/12233066/47cd6479bbcd/dmm-18-052220-g6.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/0ef9/12233066/3795771cf6d8/dmm-18-052220-g1.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/0ef9/12233066/f27f0d613274/dmm-18-052220-g2.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/0ef9/12233066/4d191f41504a/dmm-18-052220-g3.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/0ef9/12233066/13a5def99659/dmm-18-052220-g4.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/0ef9/12233066/01ec3e1dd927/dmm-18-052220-g5.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/0ef9/12233066/47cd6479bbcd/dmm-18-052220-g6.jpg

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