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Updated: Sep 15, 2025

A Strategy for Sensitive, Large Scale Quantitative Metabolomics

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Published on: May 27, 2014

智能:在空间解析代谢学中准确的公式分配的方法

Yinghao Cao¹, Shengxi Li¹, Zhaoyi Liu¹

¹State Key Laboratory of Common Mechanism Research for Major Disease, Institute of Basic Medical Sciences, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing 100730, China.

Analytical chemistry

|July 15, 2025

概括

SMART是一个新的开源平台,用于在质谱成像中精确的公式分配. 它准确地注释了组织中的代谢物,改善了我们对代谢复杂性的理解.

科学领域:

代谢学代谢学代谢学
生物化学生物化学
分析化学分析化学

背景情况:

空间解析的代谢学对于理解组织代谢至关重要.
在质谱成像 (MSI) 中准确的代谢物注释仍然是一个挑战.
现有的方法很难跟上MSI产生的大量数据.

研究的目的:

推出SMART,一个开源平台,用于在质谱成像中精确的公式分配.
为了弥合MSI和LC-MS基于的方法之间的注释差距.
增强生物组织内的代谢产物系统注释.

主要方法:

智能利用KnownSet数据库,其中包含280万个公式,由ChemEdges和BioEdges连接在一起.
使用多重线性回归模型来提取公式网络.
候选公式的得分是基于链接的公式,数据库得分,化学/生物联系和ppm值.

主要成果:

在与参考数据集对比时,SMART的预测准确率高达95.8%.
该平台成功注释了使用MSI开发小鼠胚胎的986种公式.
在系统的公式注释中,SMART表现出强的表现.

结论:

在MSI中,SMART显著提高了代谢物公式分配的准确性和效率.
该平台增强了对复杂生物样本中代谢异质性的理解.
在空间解析代谢学中,SMART提供了一个强大的数据分析解决方案.

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