A multi-modal image fusion workflow incorporating MALDI imaging mass spectrometry and microscopy for the study of small pharmaceutical compounds.
A multi-modal image fusion workflow incorporating MALDI imaging mass spectrometry and microscopy for the study of small pharmaceutical compounds.
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结合 MALDI 成像质谱和显微镜的多模态图像融合工作流程，用于研究小药物化合物。
DOI：
10.1101/2024.03.12.584673
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发表时间：
2024
期刊：
影响因子：
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通讯作者：
Prentice,BooneM
中科院分区：
文献类型：
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作者：
Liang,Zhongling;Guo,Yingchan;Sharma,Abhisheak;McCurdy,ChristopherR;Prentice,BooneM
与单模态成像相比，给药组织样品的多模态成像分析可以提供对治疗活性化合物对靶组织的影响的更全面的见解。例如，药物化合物和内源性大分子受体的同时空间映射很难在单个成像实验中实现。在这里，我们提出了一个多模式的工作流程，结合成像质谱与免疫组织化学（IHC）荧光成像和明场显微镜成像。成像质谱能够直接绘制药物化合物和代谢物，IHC荧光成像可以可视化大蛋白质，明场显微镜成像提供组织形态信息。单细胞分辨率图像通常难以使用成像质谱法获得，但可以通过IHC荧光和明场显微镜成像容易地获得。质谱图像的空间锐化将因此允许与其他更高分辨率的显微镜图像更高保真的配准。成像质谱空间分辨率可以通过计算图像融合工作流预测到更精细的值，该计算图像融合工作流对质谱图像中的强度值与高空间分辨率显微图像的特征之间的关系进行建模。作为概念的证明，我们的多模式工作流程应用于从用kratom生物碱corynantheidine给药的Sprague-Dawley大鼠提取的脑组织。四个候选的数学模型，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），进行了测试。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。在本文中，图像融合使得预测的紫堇酰苯胺、GABA和谷氨酰胺的质谱图像能够达到约2.5 μm的空间分辨率，与以25 μm的空间分辨率获得的原始图像相比，这是一个显著的改进。然后将预测的质谱图像与μ-阿片受体的H&E图像和IHC荧光图像共配准，以评估紫穗槐定碱与脑细胞的共定位。我们的研究还提供了不同的评价参数，以考虑利用图像融合生物应用的见解。
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.