Evidence map›Paper›PMID 38488849›Full record

ArticleJournal of mass spectrometry : JMS2024

Quantitative mass spectrometry imaging (qMSI): A tutorial.

Russell R Kibbe, David C Muddiman

Abstract read
In one paragraph

Article in Journal of mass spectrometry : JMS, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers.

0numbers the graph read from it
0cells of the map it votes in
15citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

15 citing papers in PubMed.

  1. Review
  2. Article
  3. Article
  4. Article
  5. Article
  6. Quantitative Measurement of Hexoses by Betaine Aldehyde Derivatisation.International journal of molecular sciences · 2026
    Article
  7. Article
  8. Article
  9. Review
  10. Article
  11. Article
  12. Article
  13. Mass Spectrometry Imaging for Spatial Toxicology Research.Journal of mass spectrometry : JMS · 2024
    Review
  14. Article
  15. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

2 authors.

Russell R KibbeDepartment of Chemistry, FTMS Laboratory for Human Health Research, North Carolina State University, Raleigh, North Carolina, USA.ORCID https://orcid.org/0000-0002-6606-5689
David C MuddimanDepartment of Chemistry, FTMS Laboratory for Human Health Research, North Carolina State University, Raleigh, North Carolina, USA.ORCID https://orcid.org/0000-0003-2216-499X

Funding

Development and Application of New Ionization Methods for Biological Mass SpectrometryR01GM087964 · NIGMS · NORTH CAROLINA STATE UNIVERSITY RALEIGH · PI MUDDIMAN, DAVID C. · 2010 to 2025
$4.7M
NIGMS NIH HHS R01 GM087964NIH HHS R01GM087964
6 · The paper itself

Abstract

Mass spectrometry imaging (MSI) is an analytical technique that enables the simultaneous detection of hundreds to thousands of chemical species while retaining their spatial information; usually, MSI is applied to biological tissues. Combining these elements can create ion images, which allows for the identification and localization of multiple chemical species within the sample. Being able to produce molecular images of biological tissues has already impacted the study of health and disease; however, the next logical step is being able to combine MSI with quantitative mass spectrometry methods to both quantify and determine the localization of disease progression or drug action. In this tutorial, we will detail the main factors to consider when designing a qMSI experiment and highlight the methods that have been developed to overcome these added complexities, specifically for those newer to the field of MSI.

Indexed as

Spectrometry, Mass, Matrix-Assisted Laser Desorption-Ionizationabsolute quantificationmass spectrometry imagingqMSIrelative quantification

Identifiers

PMID38488849
PMCPMC11608390

What OpenQuestion holds

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Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.