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多模态图像融合工作流程,结合 MALDI 成像质谱和显微镜,用于研究小分子药物。

Multimodal Image Fusion Workflow Incorporating MALDI Imaging Mass Spectrometry and Microscopy for the Study of Small Pharmaceutical Compounds.

机构信息

Department of Chemistry, University of Florida, Gainesville, Florida 32611, United States.

Department of Pharmaceutics, College of Pharmacy, University of Florida, Gainesville, Florida 32610, United States.

出版信息

Anal Chem. 2024 Jul 23;96(29):11869-11880. doi: 10.1021/acs.analchem.4c01553. Epub 2024 Jul 10.

Abstract

Multimodal imaging analyses of dosed tissue samples can provide more comprehensive insights into the effects of a therapeutically active compound on a target tissue compared to single-modal imaging. For example, simultaneous spatial mapping of pharmaceutical compounds and endogenous macromolecule receptors is difficult to achieve in a single imaging experiment. Herein, we present a multimodal workflow combining imaging mass spectrometry with immunohistochemistry (IHC) fluorescence imaging and brightfield microscopy imaging. Imaging mass spectrometry enables direct mapping of pharmaceutical compounds and metabolites, IHC fluorescence imaging can visualize large proteins, and brightfield microscopy imaging provides tissue morphology information. Single-cell resolution images are generally difficult to acquire using imaging mass spectrometry but are readily acquired with IHC fluorescence and brightfield microscopy imaging. Spatial sharpening of mass spectrometry images would thus allow for higher fidelity coregistration with other higher-resolution microscopy images. Imaging mass spectrometry spatial resolution can be predicted to a finer value via a computational image fusion workflow, which models the relationship between the intensity values in the mass spectrometry image and the features of a high-spatial resolution microscopy image. As a proof of concept, our multimodal workflow was applied to brain tissue extracted from a Sprague-Dawley rat dosed with a kratom alkaloid, corynantheidine. Four candidate mathematical models, including linear regression, partial least-squares regression, random forest regression, and two-dimensional convolutional neural network (2-D CNN), were tested. The random forest and 2-D CNN models most accurately predicted the intensity values at each pixel as well as the overall patterns of the mass spectrometry images, while also providing the best spatial resolution enhancements. Herein, image fusion enabled predicted mass spectrometry images of corynantheidine, GABA, and glutamine to approximately 2.5 μm spatial resolutions, a significant improvement compared to the original images acquired at 25 μm spatial resolution. The predicted mass spectrometry images were then coregistered with an H&E image and IHC fluorescence image of the μ-opioid receptor to assess colocalization of corynantheidine with brain cells. Our study also provides insights into the different evaluation parameters to consider when utilizing image fusion for biological applications.

摘要

与单模态成像相比,对给药组织样本进行多模态成像分析可以更全面地了解治疗活性化合物对靶组织的影响。例如,在单个成像实验中,同时对药物化合物和内源性大分子受体进行空间映射是困难的。在这里,我们提出了一种将成像质谱与免疫组织化学(IHC)荧光成像和明场显微镜成像相结合的多模态工作流程。成像质谱能够直接映射药物化合物和代谢物,IHC 荧光成像可以可视化大蛋白,明场显微镜成像提供组织形态信息。虽然使用成像质谱一般难以获得单细胞分辨率的图像,但使用 IHC 荧光和明场显微镜成像很容易获得。因此,通过空间锐化质谱图像,可以与其他更高分辨率显微镜图像进行更高保真度的配准。通过计算图像融合工作流程,可以对成像质谱的空间分辨率进行更精细的预测,该工作流程模拟了质谱图像中的强度值与高空间分辨率显微镜图像的特征之间的关系。作为概念验证,我们的多模态工作流程应用于从给予咔哇生物碱 Corynantheidine 的 Sprague-Dawley 大鼠提取的脑组织。测试了四种候选数学模型,包括线性回归、偏最小二乘回归、随机森林回归和二维卷积神经网络(2-D CNN)。随机森林和 2-D CNN 模型最准确地预测了每个像素的强度值以及质谱图像的整体模式,同时还提供了最佳的空间分辨率增强。在这里,图像融合使 Corynantheidine、GABA 和谷氨酰胺的预测质谱图像能够达到约 2.5 μm 的空间分辨率,与原始的 25 μm 空间分辨率相比有了显著的提高。然后,将预测的质谱图像与 H&E 图像和 μ-阿片受体的 IHC 荧光图像进行配准,以评估 Corynantheidine 与脑细胞的共定位。我们的研究还为利用图像融合进行生物应用时需要考虑的不同评估参数提供了见解。

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