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针对非向性NMR代谢物的潜在细胞代谢物的自动识别

Jiashang Chen¹, Angela Rao¹, Rajshree Ghosh Biswas¹

¹Departments of Radiology and Pathology, Massachusetts General Hospital, Harvard Medical School, Boston, Massachusetts, USA.

NMR in biomedicine

|September 1, 2025

概括

一个新的Python程序ROIAL-NMR可以使用核磁共振 (NMR) 光谱识别复杂的生物样本中的代谢物. 它成功地区分了肺癌患者和阿尔茨海默病及相关痴呆症患者.

科学领域:

代谢学
生物化学
生物信息学

背景情况:

代谢资料提供了对生物体生理学和病理学的见解.
核磁共振 (NMR) 光谱无创地监测生物样本中的代谢物变化.
复杂样本中的质子NMR光谱重叠阻碍了代谢物识别和监测.

研究的目的:

引入ROIAL-NMR,这是一个Python程序,用于从质子NMR光谱感兴趣区域 (ROI) 系统识别代谢物.
使用人类代谢数据库 (HMDB) 作为复杂生物样本中的代谢物识别的参考.
使用光谱强度变化来区分疾病状态 (例如肺癌,阿尔茨海默病和相关痴呆症).

主要方法:

开发一个Python程序,ROIAL-NMR,用于分析质子NMR光谱.
根据样本类型和疾病状况定义感兴趣的光谱区域 (ROI).
使用人类代谢数据库 (HMDB) 进行代谢物识别和比较.

主要成果:

通过ROIAL-NMR计划,成功地从复杂的生物样本中鉴定出88种潜在的代谢物.
该计划区分了66种代谢物,使肺癌患者与对照患者区分开来.
它确定了80种代谢物,使得患有阿尔茨海默病和相关痴呆症 (ADRD) 的LC患者与没有ADRD的患者有区别.

结论:

在复杂的生物样本中,ROIAL-NMR提供了一种系统的方法来识别使用NMR光谱的代谢物.
该计划有效地根据新陈代谢特征区分疾病状态.
这种工具为肺癌和ADRD等疾病的病理生理学提供了宝贵的见解.

关键词:

自动化代谢物识别核磁共振光谱学一个 python 程序

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