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

质谱成像 质谱法 马尔迪成像 显微镜 化学 人工智能 图像分辨率 荧光寿命成像显微镜 计算机科学 基质辅助激光解吸/电离 病理 光学 荧光 物理 色谱法 医学 吸附 解吸 有机化学
作者
Zhongling Liang,Yingchan Guo,Abhisheak Sharma,Christopher R. McCurdy,Boone M. Prentice
出处
期刊:Analytical Chemistry [American Chemical Society]
卷期号:96 (29): 11869-11880
标识
DOI:10.1021/acs.analchem.4c01553
摘要

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.

科研通智能强力驱动
Strongly Powered by AbleSci AI
科研通是完全免费的文献互助平台,具备全网最快的应助速度,最高的求助完成率。 对每一个文献求助,科研通都将尽心尽力,给求助人一个满意的交代。
实时播报
天真千凡发布了新的文献求助10
刚刚
bin发布了新的文献求助10
1秒前
小鱼完成签到,获得积分10
1秒前
byby完成签到,获得积分10
1秒前
车车完成签到,获得积分10
1秒前
飞翔的企鹅完成签到,获得积分10
1秒前
平常的雁凡完成签到,获得积分20
1秒前
Shan完成签到 ,获得积分10
2秒前
Faith完成签到,获得积分10
2秒前
朱洪帆发布了新的文献求助10
2秒前
3秒前
4秒前
怡然的岱周完成签到,获得积分10
4秒前
hj123完成签到,获得积分10
4秒前
Sandy完成签到 ,获得积分10
4秒前
雪雨夜心完成签到,获得积分10
4秒前
4秒前
4秒前
小蘑菇应助benny279采纳,获得10
5秒前
认真的可冥完成签到,获得积分10
5秒前
iitj发布了新的文献求助10
5秒前
张阳阳完成签到,获得积分10
6秒前
长颈鹿完成签到 ,获得积分10
6秒前
6秒前
7秒前
7秒前
7秒前
卷卷完成签到,获得积分10
7秒前
8秒前
Wuu完成签到,获得积分10
8秒前
高高从霜完成签到 ,获得积分10
8秒前
8秒前
9秒前
bqk发布了新的文献求助10
9秒前
浩天完成签到,获得积分10
9秒前
Bob完成签到 ,获得积分10
9秒前
雪儿完成签到,获得积分10
10秒前
义气尔芙完成签到,获得积分10
10秒前
情怀应助专一的幻莲采纳,获得10
10秒前
ggjy完成签到,获得积分10
10秒前
高分求助中
Malcolm Fraser : a biography 680
Signals, Systems, and Signal Processing 610
天津市智库成果选编 600
Climate change and sports: Statistics report on climate change and sports 500
Forced degradation and stability indicating LC method for Letrozole: A stress testing guide 500
Organic Reactions Volume 118 400
A Foreign Missionary on the Long March: The Unpublished Memoirs of Arnolis Hayman of the China Inland Mission 400
热门求助领域 (近24小时)
化学 材料科学 医学 生物 纳米技术 工程类 有机化学 化学工程 生物化学 计算机科学 物理 内科学 复合材料 催化作用 物理化学 光电子学 电极 细胞生物学 基因 无机化学
热门帖子
关注 科研通微信公众号,转发送积分 6459319
求助须知:如何正确求助?哪些是违规求助? 8268445
关于积分的说明 17622079
捐赠科研通 5528578
什么是DOI,文献DOI怎么找? 2905911
邀请新用户注册赠送积分活动 1882638
关于科研通互助平台的介绍 1727808