Amyotrophic lateral sclerosis (ALS) is a multifactorial, multisystem motor neuron disease for which currently there is no effective treatment. There is an urgent need to identify biomarkers to tackle the disease’s complexity and help in early diagnosis, prognosis, and therapy. Extracellular vesicles (EVs) are nanostructures released by any cell type into body fluids. Their biophysical and biochemical characteristics vary with the parent cell’s physiological and pathological state and make them an attractive source of multidimensional data for patient classification and stratification. We analyzed plasma-derived EVs of ALS patients (n = 106) and controls (n = 96), and SOD1G93A and TDP-43Q331K mouse models of ALS. We purified plasma EVs by nickel-based isolation, characterized their EV size distribution and morphology respectively by nanotracking analysis and transmission electron microscopy, and analyzed EV markers and protein cargos by Western blot and proteomics. We used machine learning techniques to predict diagnosis and prognosis. Our procedure resulted in high-yield isolation of intact and polydisperse plasma EVs, with minimal lipoprotein contamination. EVs in the plasma of ALS patients and the two mouse models of ALS had a distinctive size distribution and lower HSP90 levels compared to the controls. In terms of disease progression, the levels of cyclophilin A with the EV size distribution distinguished fast and slow disease progressors, a possibly new means for patient stratification. Immuno-electron microscopy also suggested that phosphorylated TDP-43 is not an intravesicular cargo of plasma-derived EVs. Our analysis unmasked features in plasma EVs of ALS patients with potential straightforward clinical application. We conceived an innovative mathematical model based on machine learning which, by integrating EV size distribution data with protein cargoes, gave very high prediction rates for disease diagnosis and prognosis.

中文翻译：

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解码肌萎缩侧索硬化症中血浆细胞外囊泡的独特特征

肌萎缩侧索硬化症（ALS）是一种多因素、多系统的运动神经元疾病，目前尚无有效的治疗方法。迫切需要识别生物标志物来解决疾病的复杂性并帮助早期诊断、预后和治疗。细胞外囊泡 (EV) 是由任何细胞类型释放到体液中的纳米结构。它们的生物物理和生化特性随母细胞的生理和病理状态而变化，使它们成为患者分类和分层的多维数据的有吸引力的来源。我们分析了 ALS 患者 (n = 106) 和对照组 (n = 96) 的血浆衍生 EV，以及 ALS 的 SOD1G93A 和 TDP-43Q331K 小鼠模型。我们通过镍基隔离纯化了等离子 EV，通过纳米跟踪分析和透射电子显微镜分别表征了它们的 EV 大小分布和形态，并通过蛋白质印迹和蛋白质组学分析了 EV 标记和蛋白质货物。我们使用机器学习技术来预测诊断和预后。我们的程序导致完整和多分散血浆 EV 的高产率分离，脂蛋白污染最小。与对照组相比，ALS 患者血浆中的 EV 和两种 ALS 小鼠模型具有独特的大小分布和较低的 HSP90 水平。在疾病进展方面，EV 大小分布的亲环蛋白 A 水平区分快速和慢速疾病进展者，这可能是患者分层的新手段。免疫电子显微镜还表明磷酸化的 TDP-43 不是血浆衍生 EV 的囊内货物。我们的分析揭示了 ALS 患者血浆 EV 中具有潜在直接临床应用的特征。我们构思了一种基于机器学习的创新数学模型，通过将 EV 尺寸分布数据与蛋白质货物相结合，为疾病诊断和预后提供了非常高的预测率。