Progress in High-resolution Mass Spectrometry‑based Approaches for Microbial Meta‑metabolomics

doi:10.13560/j.cnki.biotech.bull.1985.2026-0429

Abstract

Abstract:

Elucidating the regulatory mechanisms and ecological functions of microbial metabolism is crucial for understanding the carbon biogeochemical cycling, predicting carbon-climate feedbacks, and modulating carbon flows in engineered applications. Microbial meta-metabolomics conceptualizes organic molecules as a “molecular community”, enabling a holistic investigation into the composition and dynamics of metabolites at the community level. High-resolution mass spectrometry has emerged as a powerful approach for large-scale profiling of organic metabolites, and the resulting molecular fingerprints can be deeply explored by leveraging ecological analytical frameworks. However, challenges remain in linking metabolites to microbial functions and attributing metabolic processes. This review systematically summarizes recent methodological advances in meta-metabolomics, including the extended application of ecological mechanisms, the construction of molecular ecological networks, and multi-dimensional molecular profiling, while also highlighting current technical limitations. Looking forward, integrating multi-omics spatiotemporal analyses, identifying key metabolic roles, and extending ecological frameworks will be critical for establishing this field as a frontier in understanding ecosystem functions and carbon regulation.

Key words: meta?metabolomics, ecological processes, molecular ecological network, molecular feature

YANG Xing-sheng, Wu Shao-long, FENG Kai, WANG Shang, PENG Xi, ZHAO Bo, LIU Ming-qian, GU Song-song, HE Qing, LI Chun-ge, Hu Qiu-long, DENG Ye. Progress in High-resolution Mass Spectrometry‑based Approaches for Microbial Meta‑metabolomics[J]. Biotechnology Bulletin, 2026, 42(5): 5-15.

Figures/Tables 4

References 51

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研究体系 Research system	实验设计 Experimental design	核心数据 Core data	数据分析方法 Data analysis methods	参考文献 References
冻土	冻土融化梯度；3个深度采样	12T FTICR 负离子采集；100-1 200 m/z；<1 ppm；分子14432；宏基因组1402MAGs	微生物和代谢物在群落和个体水平的生态构建过程；共现网络	［31］
土壤	生物圈2号热带雨林；模拟干旱环境；同位素标记	12T FTICR 负离子采集；100-1 000 m/z；<1 ppm；分子数未报道；宏基因组和转录组	代谢功能注释和关联	［50］
土壤	施肥处理	9.4T FTICR 负离子采集；150-800 m/z；分子 7682	转化网络；应用机器学习基于分子特性预测转化潜力	［35］
热融湖塘	青藏高原1 100 km多年冻土带沿线10个热熔湖塘；阳光和微生物降解实验	15T FTICR 负离子采集；100-800 m/z；<1 ppm；分子数未报道；扩增子测序	对分子组成和分子的特征分析	［24］
基于湖泊水体的微宇宙实验	中国和挪威的2座山，5个不同海拔	15T FTICR 负离子采集；150-1 200 m/z；<1 ppm；分子19538；扩增子测序	代谢物的生态构建过程；分子持久性和转化活性分析	［18］
滩涂沉积物	沿海岸线采样	15T FTICR 负离子采集；200-800 m/z；<1 ppm；分子20980；扩增子测序	微生物和有机分子组成在生态模式上的比较	［25］
厌氧发酵罐	7个城市，6个时间序列	15T FTICR 负离子采集；100-800 m/z；<1 ppm；分子28925；宏基因组413MAGs；扩增子测序2960OTUs	加权分子特性；转化识别；共现网络	［29］

研究体系 Research system	实验设计 Experimental design	核心数据 Core data	数据分析方法 Data analysis methods	参考文献 References
冻土	冻土融化梯度；3个深度采样	12T FTICR 负离子采集；100-1 200 m/z；<1 ppm；分子14432；宏基因组1402MAGs	微生物和代谢物在群落和个体水平的生态构建过程；共现网络	［31］
土壤	生物圈2号热带雨林；模拟干旱环境；同位素标记	12T FTICR 负离子采集；100-1 000 m/z；<1 ppm；分子数未报道；宏基因组和转录组	代谢功能注释和关联	［50］
土壤	施肥处理	9.4T FTICR 负离子采集；150-800 m/z；分子 7682	转化网络；应用机器学习基于分子特性预测转化潜力	［35］
热融湖塘	青藏高原1 100 km多年冻土带沿线10个热熔湖塘；阳光和微生物降解实验	15T FTICR 负离子采集；100-800 m/z；<1 ppm；分子数未报道；扩增子测序	对分子组成和分子的特征分析	［24］
基于湖泊水体的微宇宙实验	中国和挪威的2座山，5个不同海拔	15T FTICR 负离子采集；150-1 200 m/z；<1 ppm；分子19538；扩增子测序	代谢物的生态构建过程；分子持久性和转化活性分析	［18］
滩涂沉积物	沿海岸线采样	15T FTICR 负离子采集；200-800 m/z；<1 ppm；分子20980；扩增子测序	微生物和有机分子组成在生态模式上的比较	［25］
厌氧发酵罐	7个城市，6个时间序列	15T FTICR 负离子采集；100-800 m/z；<1 ppm；分子28925；宏基因组413MAGs；扩增子测序2960OTUs	加权分子特性；转化识别；共现网络	［29］