Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Concept Videos

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by
Same author

Adiposity enhances the anti-metastatic efficacy of apatinib via PKA-mediated lipid metabolism.

Journal of gynecologic oncology·2026
Same author

High-Throughput In-Depth Single-Cell Lipidomics by Ozone-Induced Dissociation and Mass Cytometry.

Analytical chemistry·2026
Same author

A demethylation-activated fluorescent DNA aptamer strategy for visualising DNA alkylation repair in living cells.

Chemical science·2026
Same author

PROTAC-Based Proteomics Strategy Uncovers Arsenic-Binding Proteomes.

Analytical chemistry·2026
Same author

Photosystem-Driven Resilience of Green Microalga <i>Tetraselmis chuii</i> toward Acute Nonylphenol Stress.

Environmental science & technology·2026
Same author

Mass Spectrometric Analysis of Exercise-Induced Breath Metabolites, Lipids, and Proteins Using Wearable Cold-Mask Sampling.

Analytical chemistry·2026

Related Experiment Video

Updated: Aug 16, 2025

Large-Scale Multi-Omics Genome-Wide Association Studies Mo-GWAS: Guidelines for Sample Preparation and Normalization
08:27

Large-Scale Multi-Omics Genome-Wide Association Studies Mo-GWAS: Guidelines for Sample Preparation and Normalization

Published on: July 27, 2021

3.8K

Normalization Approach by a Reference Material to Improve LC-MS-Based Metabolomic Data Comparability of Multibatch

Yao Yao1, Hui Zhang2,3,4, Lanyin Tu1

  • 1Sate Key Laboratory of Biocontrol, School of Life Sciences, Sun Yat-Sen University, Guangzhou510275, China.

Analytical Chemistry
|December 20, 2022
PubMed
Summary

A novel reference material-based approach (Ref-M) corrects batch variation in large-scale metabolomic studies. This method enhances feature detection and improves biomarker discovery for diseases like lung cancer.

More Related Videos

A Strategy for Sensitive, Large Scale Quantitative Metabolomics
14:18

A Strategy for Sensitive, Large Scale Quantitative Metabolomics

Published on: May 27, 2014

21.0K
A Tandem Liquid Chromatography&#8211;Mass Spectrometry-based Approach for Metabolite Analysis of Staphylococcus aureus
08:03

A Tandem Liquid Chromatography–Mass Spectrometry-based Approach for Metabolite Analysis of Staphylococcus aureus

Published on: March 28, 2017

10.2K

Related Experiment Videos

Last Updated: Aug 16, 2025

Large-Scale Multi-Omics Genome-Wide Association Studies Mo-GWAS: Guidelines for Sample Preparation and Normalization
08:27

Large-Scale Multi-Omics Genome-Wide Association Studies Mo-GWAS: Guidelines for Sample Preparation and Normalization

Published on: July 27, 2021

3.8K
A Strategy for Sensitive, Large Scale Quantitative Metabolomics
14:18

A Strategy for Sensitive, Large Scale Quantitative Metabolomics

Published on: May 27, 2014

21.0K
A Tandem Liquid Chromatography&#8211;Mass Spectrometry-based Approach for Metabolite Analysis of Staphylococcus aureus
08:03

A Tandem Liquid Chromatography–Mass Spectrometry-based Approach for Metabolite Analysis of Staphylococcus aureus

Published on: March 28, 2017

10.2K

Area of Science:

  • Metabolomics
  • Biomarker Discovery
  • Analytical Chemistry

Background:

  • Large-scale metabolomic studies require extensive sample cohorts.
  • Eliminating systematic variation is crucial for accurate disease state characterization.
  • Batch effects can obscure true biological alterations in complex sample sets.

Purpose of the Study:

  • To introduce and validate a reference material-based approach (Ref-M) for data correction in liquid chromatography-mass spectrometry (LC-MS) based metabolomics.
  • To assess the effectiveness of Ref-M in reducing batch variation and improving data quality in multibatch human serum samples.
  • To evaluate the utility of Ref-M for enhancing biomarker discovery in lung cancer.

Main Methods:

  • Generation of a reference material by pooling healthy donor serum.
  • Distribution of reference material across extraction batches for normalization.
  • Application of pooled quality control samples and isotopic internal standards for data quality control.
  • Normalization of metabolite levels in subject samples against the reference material.
  • Analysis of 522 human serum samples from healthy individuals, benign pulmonary nodules, and lung cancer patients.

Main Results:

  • Ref-M significantly enhanced the number of detectable features.
  • Batch variation was effectively eliminated across the 522 serum samples.
  • Twenty differential metabolites were identified to distinguish lung cancer from healthy controls.
  • A discriminant model achieved an AUC of 0.853 in an independent dataset.
  • Further validation with Ref-M on 40 samples yielded an AUC of 0.843.

Conclusions:

  • The reference material-based approach (Ref-M) effectively corrects batch effects in large-scale metabolomic studies.
  • Ref-M improves data comparability and precision, crucial for reliable biomarker discovery.
  • This approach holds significant potential for advancing precision medicine and disease diagnostics through metabolomics.