Issue 5, 2021

Enhanced detection and annotation of small molecules in metabolomics using molecular-network-oriented parameter optimization

Abstract

Metabolomics, especially the large-scale untargeted metabolomics, generates massive amounts of data on a regular basis, which often needs to be filtered, screened, analyzed and annotated via a variety of approaches. Data-dependent-acquisition (DDA) mode including inclusion and exclusion rules for tandem mass spectrometry (MS) is routinely used to perform such analyses. While the parameters of data acquisition are important in these processes, there is a lack of systematic studies on these parameters that can be used in data collection to generate metabolic features for molecular-network (MN) analysis on the Global Natural Products Social Molecular Networking (GNPS) platform. To explore the key parameters that impact the formation and quality of MNs, several data-acquisition parameters for metabolomic studies were proposed in this study. The influences of MS1 resolution, normalized collision energy (NCE), intensity threshold, and exclusion time on GNPS analyses were demonstrated. Moreover, an optimization workflow dedicated to Thermo Scientific QE Hybrid Orbitrap instruments is described, and a comparison of phytochemical contents from two forms of black raspberry extract was performed based on the GNPS MN results. Overall, we expect this study to provide additional thoughts on developing a natural-product-analysis workflow using the GNPS network, and to shed some light on future analyses that utilize similar instrumental setups.

Graphical abstract: Enhanced detection and annotation of small molecules in metabolomics using molecular-network-oriented parameter optimization

Supplementary files

Article information

Article type
Research Article
Submitted
08 янв 2021
Accepted
09 июл 2021
First published
09 июл 2021

Mol. Omics, 2021,17, 665-676

Enhanced detection and annotation of small molecules in metabolomics using molecular-network-oriented parameter optimization

R. Xu, J. Lee, L. Chen and J. Zhu, Mol. Omics, 2021, 17, 665 DOI: 10.1039/D1MO00005E

To request permission to reproduce material from this article, please go to the Copyright Clearance Center request page.

If you are an author contributing to an RSC publication, you do not need to request permission provided correct acknowledgement is given.

If you are the author of this article, you do not need to request permission to reproduce figures and diagrams provided correct acknowledgement is given. If you want to reproduce the whole article in a third-party publication (excluding your thesis/dissertation for which permission is not required) please go to the Copyright Clearance Center request page.

Read more about how to correctly acknowledge RSC content.

Social activity

Spotlight

Advertisements