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

MALDI-TOF Mass Spectrometry01:19

MALDI-TOF Mass Spectrometry

7.4K
Mass spectrometry is a powerful characterization technique that can identify and separate a wide variety of compounds ranging from chemical to biological entities, based on their mass-to-charge ratio (m/z). The instruments that allow this detection, known as mass spectrometers, have three components: an ion source, a mass analyzer, and a detector. These spectrometers differ based on the nature of their ion source and analyzers.Matrix-assisted laser desorption ionization (MALDI) is a commonly...
7.4K
Tandem Mass Spectrometry01:21

Tandem Mass Spectrometry

2.9K
Tandem mass spectrometry is a technique that uses multiple mass analyzers in series to obtain a higher selectivity and reduce chemical noise during analyte detection. Instruments with multiple analyzers separated by an interaction cell enable secondary fragmentation and selected study of the fragment ions.Secondary fragmentations occur in the interaction cell and can be induced by various factors. Fragmentation induced by collision with inert gases, such as N2, Ar, He, etc., is called...
2.9K
Mass Spectrometers01:16

Mass Spectrometers

10.3K
This lesson details the instrumentation of a mass spectrometer—a physical instrument to perform mass spectrometry on analyte molecules and record the characteristic mass spectra. This is achieved via three chief functions:
10.3K
Mass Spectrometry: Isotope Effect01:13

Mass Spectrometry: Isotope Effect

4.9K
Most elements exist in nature as a mixture of isotopes. The isotopes differ in weight due to their respective number of neutrons. The molecular weight of a molecule is different depending on the specific isotope of its elements involved. As a result, the mass spectrum of the molecule exhibits peaks from the same fragment at multiple positions. The positions of these mass signals depend on the mass differences between isotopes. Furthermore, the intensity of these signals is dependent on the...
4.9K
Mass Spectrum: Interpretation01:24

Mass Spectrum: Interpretation

3.9K
An unknown compound can be established by identifying the molecular ion peak in the mass spectrum. The molecular ion peak is often weak or absent due to the predominance of fragmentation in high-energy electron beams. In such cases, a soft-energy electron beam can be used to scan the spectrum to enhance the intensity of the molecular ion peak. Additionally, chemical ionization, field ionization, and desorption ionization spectra are used to obtain a relatively intense molecular ion peak.To...
3.9K
Mass Analyzers: Overview01:13

Mass Analyzers: Overview

2.0K
The mass analyzer is a crucial component of the mass spectrometer. In the ionization chamber, the vaporized sample is bombarded with a high-energy electron beam to generate a radical cation and further fragment into neutral molecules, radicals, and cations. A series of negatively charged accelerator plates accelerate the cations into the mass analyzer. The mass analyzer separates ions according to their mass-to-charge (m/z) ratios and then directs them to the detector. The common types of mass...
2.0K

You might also read

Related Articles

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

Sort by
Same author

Endocannabinoid cerebrospinal fluid levels in migraine and its relation to symptoms of depression.

Cephalalgia : an international journal of headache·2026
Same author

From bench to bedside: relevant animal models across the migraine attack phases.

The journal of headache and pain·2026
Same author

Impact of cenobamate on cortical responses to transcranial magnetic stimulation in people with drug-resistant focal epilepsy.

Epilepsia open·2026
Same author

Visually-induced hemodynamic response and white matter hyperintensities in middle-aged women with ischemic stroke or migraine with aura.

Cephalalgia : an international journal of headache·2026
Same author

Indirect crosstalk between signalling pathways activated by CGRP and Piezo1 in human iPSC-derived endothelial cells relevant to migraine.

Cephalalgia : an international journal of headache·2025
Same author

Hippocampal and cortical activities reflect early hyperexcitability in an Alzheimer's mouse model.

Brain communications·2025

Related Experiment Video

Updated: Mar 21, 2026

Spatial Molecular Imaging of the Glycome Using Mass Spectrometry
08:52

Spatial Molecular Imaging of the Glycome Using Mass Spectrometry

Published on: November 28, 2025

715

Spatial Autocorrelation in Mass Spectrometry Imaging.

Alberto Cassese1, Shane R Ellis2, Nina Ogrinc Potočnik2

  • 1Department of Methodology and Statistics, Maastricht University , 6200 MD Maastricht, The Netherlands.

Analytical Chemistry
|May 17, 2016
PubMed
Summary

Spatial autocorrelation in mass spectrometry imaging (MSI) data can inflate false discoveries. This study introduces Conditional Autoregressive (CAR) models to accurately analyze MSI data, significantly reducing error rates in comparisons.

More Related Videos

Correlative Optical Spectroscopy and Mass Spectrometry Imaging Methodology to Visualise Drug Distribution in a Soft Tissue Section
07:05

Correlative Optical Spectroscopy and Mass Spectrometry Imaging Methodology to Visualise Drug Distribution in a Soft Tissue Section

Published on: June 20, 2025

1.5K
Confocal Microscopy Reveals Cell Surface Receptor Aggregation Through Image Correlation Spectroscopy
06:51

Confocal Microscopy Reveals Cell Surface Receptor Aggregation Through Image Correlation Spectroscopy

Published on: August 2, 2018

7.6K

Related Experiment Videos

Last Updated: Mar 21, 2026

Spatial Molecular Imaging of the Glycome Using Mass Spectrometry
08:52

Spatial Molecular Imaging of the Glycome Using Mass Spectrometry

Published on: November 28, 2025

715
Correlative Optical Spectroscopy and Mass Spectrometry Imaging Methodology to Visualise Drug Distribution in a Soft Tissue Section
07:05

Correlative Optical Spectroscopy and Mass Spectrometry Imaging Methodology to Visualise Drug Distribution in a Soft Tissue Section

Published on: June 20, 2025

1.5K
Confocal Microscopy Reveals Cell Surface Receptor Aggregation Through Image Correlation Spectroscopy
06:51

Confocal Microscopy Reveals Cell Surface Receptor Aggregation Through Image Correlation Spectroscopy

Published on: August 2, 2018

7.6K

Area of Science:

  • Molecular imaging
  • Mass spectrometry imaging (MSI)
  • Spatial statistics

Background:

  • Microprobe MSI generates images by analyzing individual spots on a surface.
  • Classical statistical tests are unsuitable for MSI data due to spatial autocorrelation, leading to increased false discovery rates.
  • Spatial autocorrelation, the dependence between nearby measurements, is a critical issue in MSI data analysis.

Purpose of the Study:

  • To investigate spatial autocorrelation in matrix-assisted laser desorption/ionization MSI data across different molecular classes and spatial resolutions.
  • To propose and validate a statistical method for accurate within-sample comparisons in MSI data.
  • To provide a workflow for addressing spatial autocorrelation in the analysis of large-scale MSI datasets.

Main Methods:

  • Analysis of three distinct MSI datasets (metabolites/drugs, lipids, proteins) with varying spatial resolutions (20-100 μm).
  • Investigation of spatial autocorrelation patterns and their relationship with pixel size.
  • Application and validation of Conditional Autoregressive (CAR) models for statistical testing in MSI data.

Main Results:

  • Significant spatial autocorrelation was detected in all investigated MSI datasets.
  • Spatial autocorrelation increased as pixel size decreased.
  • CAR models reduced discovery rates by 21%–69% by accounting for spatial autocorrelation.
  • Validation confirmed the reliability of the CAR model approach using control signals.

Conclusions:

  • Spatial autocorrelation is a pervasive issue in microprobe MSI data, particularly at higher resolutions.
  • Conditional Autoregressive (CAR) models offer a robust solution for accurate statistical comparisons within MSI datasets.
  • This approach is essential for reliable analysis of increasingly complex and large-scale MSI data, enabling more precise biological and clinical interpretations.