Center for Mass Spectrometry and Optical Spectroscopy (CeMOS), Mannheim University of Applied Sciences, Paul-Wittsack Str. 10, Mannheim 68163, Germany.
KU Leuven-VIB Center for Brain & Disease Research, VIB, Leuven 3000, Belgium.
Anal Chem. 2024 Jun 18;96(24):9799-9807. doi: 10.1021/acs.analchem.3c05557. Epub 2024 Jun 3.
Cerebral accumulation of amyloid-β (Aβ) initiates molecular and cellular cascades that lead to Alzheimer's disease (AD). However, amyloid deposition does not invariably lead to dementia. Amyloid-positive but cognitively unaffected (AP-CU) individuals present widespread amyloid pathology, suggesting that molecular signatures more complex than the total amyloid burden are required to better differentiate AD from AP-CU cases. Motivated by the essential role of Aβ and the key lipid involvement in AD pathogenesis, we applied multimodal mass spectrometry imaging (MSI) and machine learning (ML) to investigate amyloid plaque heterogeneity, regarding Aβ and lipid composition, in AP-CU versus AD brain samples at the single-plaque level. Instead of focusing on a population mean, our analytical approach allowed the investigation of large populations of plaques at the single-plaque level. We found that different (sub)populations of amyloid plaques, differing in Aβ and lipid composition, coexist in the brain samples studied. The integration of MSI data with ML-based feature extraction further revealed that plaque-associated gangliosides GM2 and GM1, as well as Aβ, but not Aβ, are relevant differentiators between the investigated pathologies. The pinpointed differences may guide further fundamental research investigating the role of amyloid plaque heterogeneity in AD pathogenesis/progression and may provide molecular clues for further development of emerging immunotherapies to effectively target toxic amyloid assemblies in AD therapy. Our study exemplifies how an integrative analytical strategy facilitates the unraveling of complex biochemical phenomena, advancing our understanding of AD from an analytical perspective and offering potential avenues for the refinement of diagnostic tools.
淀粉样蛋白-β（Aβ）在大脑中的积累引发了导致阿尔茨海默病（AD）的分子和细胞级联反应。然而，淀粉样蛋白沉积并不一定会导致痴呆。淀粉样蛋白阳性但认知正常（AP-CU）个体表现出广泛的淀粉样蛋白病理学，这表明需要比总淀粉样蛋白负担更复杂的分子特征来更好地区分 AD 和 AP-CU 病例。鉴于 Aβ的重要作用和关键脂质在 AD 发病机制中的参与，我们应用多模态质谱成像（MSI）和机器学习（ML）技术，在单细胞水平上研究 AP-CU 与 AD 脑样本中淀粉样斑块异质性的 Aβ和脂质组成。我们的分析方法不是关注群体平均值，而是允许在单细胞水平上研究大量斑块。我们发现，在研究的脑样本中，不同（亚）种群的淀粉样斑块在 Aβ和脂质组成上存在差异。将 MSI 数据与基于 ML 的特征提取相结合，进一步表明与斑块相关的神经节苷脂 GM2 和 GM1 以及 Aβ，但不是 Aβ，是所研究病理之间的相关区分因素。所指出的差异可能指导进一步的基础研究，探讨淀粉样斑块异质性在 AD 发病机制/进展中的作用，并为进一步开发新兴免疫疗法以有效靶向 AD 治疗中的毒性淀粉样蛋白组装提供分子线索。我们的研究例证了综合分析策略如何促进复杂生化现象的揭示，从分析角度推进对 AD 的理解，并为诊断工具的改进提供潜在途径。
