Wehrli Patrick M, Ge Junyue, Michno Wojciech, Koutarapu Srinivas, Dreos Ambra, Jha Durga, Zetterberg Henrik, Blennow Kaj, Hanrieder Jörg
Department of Psychiatry and Neurochemistry, Institute of Neuroscience and Physiology, Sahlgrenska Academy, University of Gothenburg, Mölndal 431 80, Sweden.
Clinical Neurochemistry Laboratory, Sahlgrenska University Hospital Mölndal, Mölndal 431 80, Sweden.
JACS Au. 2023 Mar 7;3(3):762-774. doi: 10.1021/jacsau.2c00492. eCollection 2023 Mar 27.
We present a novel, correlative chemical imaging strategy based on multimodal matrix-assisted laser desorption/ionization (MALDI) mass spectrometry imaging (MSI), hyperspectral microscopy, and spatial chemometrics. Our workflow overcomes challenges associated with correlative MSI data acquisition and alignment by implementing 1 + 1-evolutionary image registration for precise geometric alignment of multimodal imaging data and their integration in a common, truly multimodal imaging data matrix with maintained MSI resolution (10 μm). This enabled multivariate statistical modeling of multimodal imaging data using a novel multiblock orthogonal component analysis approach to identify covariations of biochemical signatures between and within imaging modalities at MSI pixel resolution. We demonstrate the method's potential through its application toward delineating chemical traits of Alzheimer's disease (AD) pathology. Here, trimodal MALDI MSI of transgenic AD mouse brain delineates beta-amyloid (Aβ) plaque-associated co-localization of lipids and Aβ peptides. Finally, we establish an improved image fusion approach for correlative MSI and functional fluorescence microscopy. This allowed for high spatial resolution (300 nm) prediction of correlative, multimodal MSI signatures toward distinct amyloid structures within single plaque features critically implicated in Aβ pathogenicity.
我们提出了一种基于多模态基质辅助激光解吸/电离(MALDI)质谱成像(MSI)、高光谱显微镜和空间化学计量学的新型相关化学成像策略。我们的工作流程通过实施1 + 1进化图像配准来克服与相关MSI数据采集和对齐相关的挑战,以实现多模态成像数据的精确几何对齐,并将它们集成到一个具有保持的MSI分辨率(10μm)的通用、真正的多模态成像数据矩阵中。这使得能够使用一种新颖的多块正交分量分析方法对多模态成像数据进行多变量统计建模,以在MSI像素分辨率下识别成像模态之间和之内生化特征的协变关系。我们通过将该方法应用于描绘阿尔茨海默病(AD)病理学的化学特征来证明其潜力。在这里,转基因AD小鼠脑的三模态MALDI MSI描绘了与β-淀粉样蛋白(Aβ)斑块相关的脂质和Aβ肽的共定位。最后,我们为相关MSI和功能荧光显微镜建立了一种改进的图像融合方法。这允许对与单个斑块特征内不同淀粉样蛋白结构相关的多模态MSI特征进行高空间分辨率(300nm)预测,这些特征在Aβ致病性中至关重要。