Systematic Feature Filtering in Exploratory Metabolomics: Application toward Biomarker Discovery

Result code in IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216224%3A14310%2F21%3A00119354" target="_blank" >RIV/00216224:14310/21:00119354 - isvavai.cz</a>

Result on the web

<a href="https://pubs.acs.org/doi/10.1021/acs.analchem.1c00816" target="_blank" >https://pubs.acs.org/doi/10.1021/acs.analchem.1c00816</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1021/acs.analchem.1c00816" target="_blank" >10.1021/acs.analchem.1c00816</a>

Result language

angličtina

Original language name

Systematic Feature Filtering in Exploratory Metabolomics: Application toward Biomarker Discovery

Original language description

Exploratory mass spectrometry-based metabolomics generates a plethora of features in a single analysis. However, &gt;85% of detected features are typically false positives due to inefficient elimination of chimeric signals and chemical noise not relevant for biological and clinical data interpretation. The data processing is considered a bottleneck to unravel the translational potential in metabolomics. Here, we describe a systematic workflow to refine exploratory metabolomics data and reduce reported false positives. We applied the feature filtering workflow in a case/control study exploring common variable immunodeficiency (CVID). In the first stage, features were detected from raw liquid chromatography-mass spectrometry data by XCMS Online processing, blank subtraction, and reproducibility assessment. Detected features were annotated in metabolomics databases to produce a list of tentative identifications. We scrutinized tentative identifications' physicochemical properties, comparing predicted and experimental reversed-phase liquid chromatography (LC) retention time. A prediction model used a linear regression of 42 retention indices with the cLogP ranging from -6 to 11. The LC retention time probes the physicochemical properties and effectively reduces the number of tentatively identified metabolites, which are further submitted to statistical analysis. We applied the retention time-based analytical feature filtering workflow to datasets from the Metabolomics Workbench (www. metaboloinicsworkbench.org ), demonstrating the broad applicability. A subset of tentatively identified metabolites significantly different in CVID patients was validated by MS/MS acquisition to confirm potential CVID biomarkers' structures and virtually eliminate false positives. Our exploratory metabolomics data processing workflow effectively removes false positives caused by the chemical background and chimeric signals inherent to the analytical technique. It reduced the number of tentatively identified metabolites by 88%, from initially detected 6940 features in XCMS to 839 tentative identifications and streamlined consequent statistical analysis and data interpretation.

Czech name

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Czech description

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Type

J_imp - Article in a specialist periodical, which is included in the Web of Science database

CEP classification

—

OECD FORD branch

10406 - Analytical chemistry

Project

Result was created during the realization of more than one project. More information in the Projects tab.

Continuities

P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)<br>S - Specificky vyzkum na vysokych skolach<br>I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Publication year

2021

Confidentiality

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Name of the periodical

Analytical chemistry

ISSN

0003-2700

e-ISSN

—

Volume of the periodical

93

Issue of the periodical within the volume

26

Country of publishing house

US - UNITED STATES

Number of pages

8

Pages from-to

9103-9110

UT code for WoS article

000672115800013

EID of the result in the Scopus database

2-s2.0-85110105942