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

High-Resolution Mass Spectrometry (HRMS)01:15

High-Resolution Mass Spectrometry (HRMS)

1.5K
The resolution of a mass spectrometer depends on the efficiency of separating ions with different ion masses. The mass of an atom is approximated to the sum of the masses of protons and neutrons inside, considering the masses of protons and neutrons as equal. However, the masses of the proton (1.6726 × 10−24 g) and neutron (1.6749 × 10−24 g) are not truly equal. There is a minor error in the expression of atomic masses relative to the simplest atom of hydrogen. For...
1.5K
2D NMR: Heteronuclear Single-Quantum Correlation Spectroscopy (HSQC)01:19

2D NMR: Heteronuclear Single-Quantum Correlation Spectroscopy (HSQC)

799
Heteronuclear single-quantum correlation spectroscopy (HSQC) is a 2D NMR technique that reveals one-bond correlations between hydrogen and a heteronucleus. The HSQC experiment is similar to the heteronuclear correlation experiment (HETCOR) but is more sensitive. In the HSQC spectrum, the proton chemical shift is plotted on the horizontal F2 axis, while the 13C chemical shift is plotted on the vertical F1 axis. The corresponding proton and 13C spectra are also shown. The HSQC contour plot does...
799
Mass Spectrometry: Complex Analysis01:21

Mass Spectrometry: Complex Analysis

843
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...
843

You might also read

Related Articles

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

Sort by
Same author

The dark side of the dot: How melanosomes dim T cell activity.

Science immunology·2026
Same author

Trend-Aligner: A Retention Time Modeling-Based Feature Alignment Method for Untargeted LC-MS Data Analysis.

Analytical chemistry·2026
Same author

Aird-MSI: A High Compression Rate and Decompression Speed Format for Mass Spectrometry Imaging Data.

Journal of proteome research·2025
Same author

How Much Storage Precision Can Be Lost: Guidance for Near-Lossless Compression of Untargeted Metabolomics Mass Spectrometry Data.

Journal of proteome research·2024
Same author

Ion entropy and accurate entropy-based FDR estimation in metabolomics.

Briefings in bioinformatics·2024
Same author

Aird: a computation-oriented mass spectrometry data format enables a higher compression ratio and less decoding time.

BMC bioinformatics·2022

Related Experiment Video

Updated: Aug 2, 2025

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

3D-MSNet: a point cloud-based deep learning model for untargeted feature detection and quantification in profile

Ruimin Wang1,2,3, Miaoshan Lu2,3,4, Shaowei An1,3,5

  • 1Fudan University, Shanghai 200433, China.

Bioinformatics (Oxford, England)
|April 18, 2023
PubMed
Summary

A new deep learning model, 3D-MSNet, accurately analyzes complex mass spectrometry data. This novel approach improves feature detection and quantification in untargeted metabolomics and proteomics research.

More Related Videos

Identifying Per- and Polyfluorinated Chemical Species with a Combined Targeted and Non-Targeted-Screening High-Resolution Mass Spectrometry Workflow
09:04

Identifying Per- and Polyfluorinated Chemical Species with a Combined Targeted and Non-Targeted-Screening High-Resolution Mass Spectrometry Workflow

Published on: April 18, 2019

12.5K
Liquid Chromatography Coupled to Refractive Index or Mass Spectrometric Detection for Metabolite Profiling in Lysate-based Cell-free Systems
14:42

Liquid Chromatography Coupled to Refractive Index or Mass Spectrometric Detection for Metabolite Profiling in Lysate-based Cell-free Systems

Published on: September 23, 2021

4.9K

Related Experiment Videos

Last Updated: Aug 2, 2025

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
Identifying Per- and Polyfluorinated Chemical Species with a Combined Targeted and Non-Targeted-Screening High-Resolution Mass Spectrometry Workflow
09:04

Identifying Per- and Polyfluorinated Chemical Species with a Combined Targeted and Non-Targeted-Screening High-Resolution Mass Spectrometry Workflow

Published on: April 18, 2019

12.5K
Liquid Chromatography Coupled to Refractive Index or Mass Spectrometric Detection for Metabolite Profiling in Lysate-based Cell-free Systems
14:42

Liquid Chromatography Coupled to Refractive Index or Mass Spectrometric Detection for Metabolite Profiling in Lysate-based Cell-free Systems

Published on: September 23, 2021

4.9K

Area of Science:

  • Analytical Chemistry
  • Bioinformatics
  • Computational Biology

Background:

  • High-resolution mass spectrometry (HRMS) is crucial for untargeted metabolomics, generating high-dimensional, complex data.
  • Current quantification methods often simplify data through dimensionality reduction or lossy transformations, leading to inaccuracies.
  • Existing software fails to perform direct 3D analysis on lossless profile MS signals, compromising feature detection and quantification.

Purpose of the Study:

  • To develop a novel deep learning model for direct 3D analysis of profile mass spectrometry data.
  • To improve feature extraction and quantification accuracy in untargeted metabolomics and proteomics.
  • To address the limitations of existing software in handling high-dimensional MS data.

Main Methods:

  • Proposed 3D-MSNet, a deep learning model for untargeted feature extraction from 3D MS point clouds.
  • Implemented 3D-MSNet as an instance segmentation task.
  • Trained the model on a self-annotated 3D feature dataset.

Main Results:

  • 3D-MSNet significantly outperformed nine popular software packages in feature detection and quantification accuracy across multiple datasets.
  • Demonstrated high robustness in feature extraction for profile MS data.
  • Showcased wide applicability to data from various high-resolution mass spectrometers.

Conclusions:

  • 3D-MSNet offers a robust and accurate solution for analyzing complex, high-dimensional mass spectrometry data.
  • The model's direct 3D analysis approach overcomes limitations of existing methods.
  • 3D-MSNet represents a significant advancement in untargeted metabolomics and proteomics data analysis.