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

Mass Spectrometry: Complex Analysis01:21

Mass Spectrometry: Complex Analysis

874
Mass spectrometry is an important technique for the identification of pure compounds. However, it has some limitations for the analysis of complex mixtures, often due to excessive fragmentation making the spectrum too complicated to decipher. Mass spectrometry can be combined with suitable separation methods in sequence, forming hyphenated methods, which are useful in the analysis of complex mixtures.
GC–MS is a powerful hyphenated method commonly used in forensics and environmental...
874
Peptide Identification Using Tandem Mass Spectrometry01:33

Peptide Identification Using Tandem Mass Spectrometry

6.7K
Tandem mass spectrometry, also known as MS/MS or MS2, is an analytical technique that employs two mass analyzers. Essentially it is a series of mass spectrometers that helps isolate a particular biomolecule and then helps study its chemical properties.
This technique helps gather information regarding the protein from which the peptide was obtained and to study the peptides’ amino acid sequence. Identifying peptides from a complex mixture is an important component of the growing field of...
6.7K

You might also read

Related Articles

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

Sort by
Same author

Shaking culture improves physiological maintenance of primary rat kidney tissue slices.

Frontiers in toxicology·2026
Same author

Metabolomics of mouth-rinsed water for assessing psychophysiological stress in office workers.

Scientific reports·2026
Same author

Attachment Style and Perinatal Depressive Symptoms Across the Perinatal Period in Japan.

Children (Basel, Switzerland)·2026
Same author

Serum total apoptosis inhibitor of macrophage, metabolomic signatures, and incident dyslipidemia: A population-based prospective study.

Journal of clinical lipidology·2026
Same author

Salivary monoacetylated polyamines as noninvasive biomarkers for early detection and stratification of oral squamous cell carcinoma: A targeted metabolomics study.

Archives of oral biology·2026
Same author

Relationship Between Salivary Metabolites and Skeletal Muscle Index in Older Male Patients: A Retrospective Observational Pilot Study to Identify Potential Biomarkers.

Journal of aging research·2026

Related Experiment Video

Updated: Aug 27, 2025

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

A Strategy for Sensitive, Large Scale Quantitative Metabolomics

Published on: May 27, 2014

21.0K

Data Processing and Analysis in Liquid Chromatography-Mass Spectrometry-Based Targeted Metabolomics.

Masahiro Sugimoto1,2, Yumi Aizawa3, Atsumi Tomita3

  • 1Institute of Medical Science, Tokyo Medical University, Tokyo, Japan. mshrsgmt@tokyo-med.ac.jp.

Methods in Molecular Biology (Clifton, N.J.)
|September 24, 2022
PubMed
Summary

Mass spectrometry (MS)-based metabolomics generates complex data. This study details a data processing workflow, using intermediate results for quality control and introducing common statistical analyses for metabolomics.

Keywords:
Data processingMass spectrometryMultivariate analysisStatistical analysis

More Related Videos

Untargeted Metabolomics from Biological Sources Using Ultraperformance Liquid Chromatography-High Resolution Mass Spectrometry UPLC-HRMS
11:00

Untargeted Metabolomics from Biological Sources Using Ultraperformance Liquid Chromatography-High Resolution Mass Spectrometry UPLC-HRMS

Published on: May 20, 2013

22.6K
Large Scale Non-targeted Metabolomic Profiling of Serum by Ultra Performance Liquid Chromatography-Mass Spectrometry UPLC-MS
07:34

Large Scale Non-targeted Metabolomic Profiling of Serum by Ultra Performance Liquid Chromatography-Mass Spectrometry UPLC-MS

Published on: March 14, 2013

12.8K

Related Experiment Videos

Last Updated: Aug 27, 2025

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

A Strategy for Sensitive, Large Scale Quantitative Metabolomics

Published on: May 27, 2014

21.0K
Untargeted Metabolomics from Biological Sources Using Ultraperformance Liquid Chromatography-High Resolution Mass Spectrometry UPLC-HRMS
11:00

Untargeted Metabolomics from Biological Sources Using Ultraperformance Liquid Chromatography-High Resolution Mass Spectrometry UPLC-HRMS

Published on: May 20, 2013

22.6K
Large Scale Non-targeted Metabolomic Profiling of Serum by Ultra Performance Liquid Chromatography-Mass Spectrometry UPLC-MS
07:34

Large Scale Non-targeted Metabolomic Profiling of Serum by Ultra Performance Liquid Chromatography-Mass Spectrometry UPLC-MS

Published on: March 14, 2013

12.8K

Area of Science:

  • Biochemistry
  • Analytical Chemistry
  • Systems Biology

Background:

  • Mass spectrometry (MS)-based metabolomics yields high-dimensional datasets with numerous metabolite features.
  • Standard data analysis involves converting raw MS data into a metabolite × concentration matrix, followed by peak integration, data alignment, and metabolite identification.

Purpose of the Study:

  • To introduce a typical data processing procedure for MS-based metabolomics.
  • To present a method for utilizing intermediate data as a quality control measure.
  • To provide an overview of common statistical analysis methods for metabolomics data.

Main Methods:

  • Describes a standard data processing pipeline for mass spectrometry metabolomics.
  • Details the use of intermediate analytical results for quality assessment.
  • Introduces common statistical analysis techniques applicable to metabolomics datasets.

Main Results:

  • Demonstrates that intermediate data from processing steps can serve as effective quality control metrics.
  • Highlights the importance of data processing in assessing overall measurement quality.
  • Provides a framework for understanding and applying statistical analyses to metabolomics data.

Conclusions:

  • The described data processing workflow enhances the reliability of metabolomics studies.
  • Utilizing intermediate data for quality control is a practical approach to ensure data integrity.
  • Standard statistical methods are crucial for extracting meaningful biological insights from metabolomics data.