一种使用质谱分子网络进行高通量、非靶向森林群落代谢组学的方案。

A protocol for high-throughput, untargeted forest community metabolomics using mass spectrometry molecular networks.

作者信息

Sedio Brian E, Boya P Cristopher A, Rojas Echeverri Juan Camilo

机构信息

Smithsonian Tropical Research Institute Apartado 0843-03092 Balboa, Ancón Republic of Panama.

Center for Biodiversity and Drug Discovery Instituto de Investigaciones Científicas y Servicios de Alta Tecnología Apartado 0843-01103 Ciudad del Saber Republic of Panama.

出版信息

Appl Plant Sci. 2018 Apr 2;6(3):e1033. doi: 10.1002/aps3.1033. eCollection 2018 Mar.

PREMISE OF THE STUDY

We describe a field collection, sample processing, and ultra-high-performance liquid chromatography-tandem mass spectrometry (UHPLC-MS/MS) instrumental and bioinformatics method developed for untargeted metabolomics of plant tissue and suitable for molecular networking applications.

METHODS AND RESULTS

A total of 613 leaf samples from 204 tree species was collected in the field and analyzed using UHPLC-MS/MS. Matching of molecular fragmentation spectra generated over 125,000 consensus spectra representing unique molecular structures, 26,410 of which were linked to at least one structurally similar compound.

CONCLUSIONS

Our workflow is able to generate molecular networks of hundreds of thousands of compounds representing broad classes of plant secondary chemistry and a wide range of molecular masses, from 100 to 2500 daltons, making possible large-scale comparative metabolomics, as well as studies of chemical community ecology and macroevolution in plants.