一种结合基质辅助激光解吸电离成像质谱和显微镜技术的多模态图像融合工作流程,用于研究小型药物化合物。

A multi-modal image fusion workflow incorporating MALDI imaging mass spectrometry and microscopy for the study of small pharmaceutical compounds.

作者信息

Liang Zhongling, Guo Yingchan, Sharma Abhisheak, McCurdy Christopher R, Prentice Boone M

机构信息

Department of Chemistry, University of Florida, Gainesville, FL 32611.

Department of Pharmaceutics, College of Pharmacy, University of Florida, Gainesville, FL 32610.

出版信息

bioRxiv. 2024 Mar 13:2024.03.12.584673. doi: 10.1101/2024.03.12.584673.

Abstract

Multi-modal imaging analyses of dosed tissue samples can provide more comprehensive insight 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 multi-modal 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 co-registration with 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 multi-modal 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 (PLS), 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 co-registered with an H&E image and IHC fluorescence image of the μ-opioid receptor to assess co-localization of corynantheidine with brain cells. Our study also provides insight 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洞察治疗活性化合物对靶组织的影响。例如,在单个成像实验中很难同时实现药物化合物和内源性大分子受体的空间映射。在此,我们展示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。成像质谱能够直接绘制药物化合物和代谢物的图谱,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。使用成像质谱通常很难获取单细胞分辨率图像,但使用IHC荧光和明场显微镜成像则很容易获得。因此,质谱图像的空间锐化将允许与更高分辨率的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,可以将成像质谱空间分辨率预测到更精细的值,该工作流程对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进行建模。作为概念验证,我们的多模态工作流程应用于从给予 kratom 生物碱可待因的 Sprague Dawley 大鼠提取的脑组织。测试了包括线性回归、偏最小二乘回归(PLS)、随机森林回归和二维卷积神经网络(2-D CNN)在内的四个候选数学模型。随机森林和2-D CNN模型最准确地预测了每个像素处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效果。在此,图像融合使可待因、GABA 和谷氨酰胺的预测质谱图像的空间分辨率达到约2.5μm,与以25μm空间分辨率获取的原始图像相比有显著提高。然后将预测的质谱图像与μ-阿片受体的苏木精-伊红(H&E)图像和IHC荧光图像进行配准,以评估可待因与脑细胞的共定位。我们的研究还深入探讨了在将图像融合用于生物学应用时需要考虑的不同评估参数。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/c826/10980041/19f29cf99717/nihpp-2024.03.12.584673v1-f0001.jpg

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