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

Lipid Class Prediction from MS1 Data using Gaussian Graphical Models.

Analytical chemistry·2026
Same author

Decoding the Oxylipin Chemical Space Using Ion Identity Molecular Networking.

Analytical chemistry·2026
Same author

Early metabolic alterations in pediatric obesity: Depot-specific insights from untargeted adipose tissue metabolomics.

Journal of clinical lipidology·2026
Same author

Weaponizing nutrition: plants use a double strategy to fight herbivory, converting nutritionally essential fatty acids into defensive oxylipin signals.

The New phytologist·2026
Same author

Establishing an untargeted lipidomics workflow for cellular analysis: insights into endothelial cell function in anaphylaxis.

Frontiers in immunology·2026
Same author

Author Correction: Albumin orchestrates a natural host defence mechanism against mucormycosis.

Nature·2026
Same journal

A robust and validated method for the determination of 21 urinary metabolites of 15 plasticizers, including phthalates, DEHTP, and DINCH, by online SPE and liquid chromatography-tandem mass spectrometry.

Analytical and bioanalytical chemistry·2026
Same journal

A label-free membrane-based biosensor array with AuNP-modified PDMS for sensitive and specific detection of alpha-fetoprotein.

Analytical and bioanalytical chemistry·2026
Same journal

Smartphone-integrated one-step colorimetric glucose detection at physiological pH enabled by a haloperoxidase mimic.

Analytical and bioanalytical chemistry·2026
Same journal

Chemiluminescence functionalized magnetic nanoparticles-based biosensor for sensitive detection of glucose, uric acid, and cholesterol.

Analytical and bioanalytical chemistry·2026
Same journal

Single-cell mass spectrometry imaging: platform advances for multimodal spatial omics.

Analytical and bioanalytical chemistry·2026
Same journal

Advancing total uronic acid quantification using a stable isotope dilution approach: validation and application to plant- and algal-derived polysaccharides.

Analytical and bioanalytical chemistry·2026
See all related articles

Related Experiment Video

Updated: Dec 24, 2025

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

A Strategy for Sensitive, Large Scale Quantitative Metabolomics

Published on: May 27, 2014

21.5K

Data-dependent normalization strategies for untargeted metabolomics-a case study.

Paula Cuevas-Delgado1, Danuta Dudzik1,2, Verónica Miguel3

  • 1Centro de Metabolómica y Bioanálisis (CEMBIO), Facultad de Farmacia, Universidad San Pablo-CEU, CEU Universities, Urbanización Montepríncipe, 28660, Boadilla del Monte, Madrid, Spain.

Analytical and Bioanalytical Chemistry
|April 15, 2020
PubMed
Summary
This summary is machine-generated.

Data normalization is crucial for untargeted metabolomics in chronic kidney disease (CKD) research. Applying biological-model-driven strategies ensures robust and reliable data, preventing misleading conclusions in biomarker discovery.

Keywords:
Biomarker discoveryCapillary electrophoresis mass spectrometryData pre-treatmentNormalizationTissue samplesUnwanted variation

More Related Videos

An Integrated Workflow of Identification and Quantification on FDR Control-Based Untargeted Metabolome
05:35

An Integrated Workflow of Identification and Quantification on FDR Control-Based Untargeted Metabolome

Published on: September 20, 2022

4.1K
Identification and Quantification of Deranged Metabolites in Critically Ill Patients Using NMR-Based Metabolomics
11:02

Identification and Quantification of Deranged Metabolites in Critically Ill Patients Using NMR-Based Metabolomics

Published on: November 29, 2024

966

Related Experiment Videos

Last Updated: Dec 24, 2025

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

A Strategy for Sensitive, Large Scale Quantitative Metabolomics

Published on: May 27, 2014

21.5K
An Integrated Workflow of Identification and Quantification on FDR Control-Based Untargeted Metabolome
05:35

An Integrated Workflow of Identification and Quantification on FDR Control-Based Untargeted Metabolome

Published on: September 20, 2022

4.1K
Identification and Quantification of Deranged Metabolites in Critically Ill Patients Using NMR-Based Metabolomics
11:02

Identification and Quantification of Deranged Metabolites in Critically Ill Patients Using NMR-Based Metabolomics

Published on: November 29, 2024

966

Area of Science:

  • * Metabolomics
  • * Bioinformatics
  • * Renal disease research

Background:

  • * Untargeted metabolomics workflows are advancing, yet data treatment, particularly normalization, requires deeper biological insight.
  • * Chronic kidney disease (CKD) research benefits from untargeted metabolomics for biomarker discovery and understanding disease mechanisms.
  • * Renal fibrosis, a CKD consequence, significantly alters metabolite concentrations, necessitating effective normalization strategies.

Purpose of the Study:

  • * To evaluate various data normalization strategies for untargeted metabolomics in the context of CKD.
  • * To assess the impact of normalization on intragroup variation and differential analysis.
  • * To highlight the importance of biological-model-driven normalization for robust data.

Main Methods:

  • * Application of a multilevel normalization method to address biological variability in CKD models.
  • * Comprehensive evaluation of normalization strategy performance.
  • * Analysis of intragroup variation and impact on differential metabolite analysis.

Main Results:

  • * Normalization strategies significantly influence the reliability of metabolomic data in CKD studies.
  • * Biological-model-driven normalization is essential for reducing unwanted variation and focusing on relevant biological signals.
  • * Inappropriate data pre-treatment can lead to misleading conclusions in untargeted metabolomics.

Conclusions:

  • * Careful selection and application of data normalization methods are critical for valid untargeted metabolomics studies in CKD.
  • * Biological context must guide normalization choices to ensure data robustness and accurate biomarker identification.
  • * Standardized, biologically informed normalization protocols are needed to advance CKD research using metabolomics.