Abstract：
Amyotrophic lateral sclerosis (ALS) is a rapidly progressing, highly heterogeneous neurodegenerative disease, underscoring the importance of obtaining information to personalize clinical decisions quickly after diagnosis. Here, we investigated whether ALS-relevant signatures can be detected directly from biopsied patient fibroblasts. We profiled familial ALS (fALS) fibroblasts, representing a range of mutations in the fused in sarcoma (FUS) gene and ages of onset. To differentiate FUS fALS and healthy control fibroblasts, machine-learning classifiers were trained separately on high-content imaging and transcriptional profiles. \"Molecular ALS phenotype\" scores, derived from these classifiers, captured a spectrum from disease to health. Interestingly, these scores negatively correlated with age of onset, identified several pre-symptomatic individuals and sporadic ALS (sALS) patients with FUS-like fibroblasts, and quantified \"movement\" of FUS fALS and \"FUS-like\" sALS toward health upon FUS ASO treatment. Taken together, these findings provide evidence that non-neuronal patient fibroblasts can be used for rapid, personalized assessment in ALS.
摘要：
肌萎缩侧索硬化症（ALS）是一种快速发展，高度异质性的神经退行性疾病，强调在诊断后快速获取信息以个性化临床决策的重要性。这里,我们调查了ALS相关特征是否可以直接从活检患者成纤维细胞中检测到.我们分析了家族性ALS（fALS）成纤维细胞，代表融合肉瘤（FUS）基因的一系列突变和发病年龄。为了区分FUSfALS和健康对照成纤维细胞，机器学习分类器分别在高内容成像和转录谱上进行训练.“分子ALS表型”评分，从这些分类器中导出，捕捉到了从疾病到健康的光谱。有趣的是,这些分数与发病年龄呈负相关，确定了几个症状前个体和散发性ALS（SALS）患者的FUS样成纤维细胞，并对FUSASO治疗后FUSfALS和“类FUS”sALS的“运动”进行量化。一起来看,这些发现提供了非神经元患者成纤维细胞可用于快速，ALS的个性化评估。
