ArticleRapid communications in mass spectrometry : RCM2024
A statistical approach to system suitability testing for mass spectrometry imaging.
Article in Rapid communications in mass spectrometry : RCM, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
What it found
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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.
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.
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Who cites it
4 citing papers in PubMed.
- MALDI mass spectrometry imaging across spatial scales in toxicology: tissue evidence, cellular heterogeneity, and emerging single-cell opportunities.Analytical and bioanalytical chemistry · 2026Review
- Computational Mass Spectrometry Imaging in the Era of AI.Chemical reviews · 2026Review
- Part A: Implementing an Analyte Panel and Sampling Protocol for Quality Control in Mass Spectrometry Imaging.Rapid communications in mass spectrometry : RCM · 2025Article
- Part B: SLICE-MSI-A Machine Learning Interface for System Suitability Testing of Mass Spectrometry Imaging Platforms.Rapid communications in mass spectrometry : RCM · 2025Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
7 authors.
Funding
Abstract
rationaleMass spectrometry imaging (MSI) elevates the power of conventional mass spectrometry (MS) to multidimensional space, elucidating both chemical composition and localization. However, the field lacks any robust quality control (QC) and/or system suitability testing (SST) protocols to monitor inconsistencies during data acquisition, both of which are integral to ensure the validity of experimental results. To satisfy this demand in the community, we propose an adaptable QC/SST approach with five analyte options amendable to various ionization MSI platforms (e.g., desorption electrospray ionization, matrix-assisted laser desorption/ionization [MALDI], MALDI-2, and infrared matrix-assisted laser desorption electrospray ionization [IR-MALDESI]).
methodsA novel QC mix was sprayed across glass slides to collect QC/SST regions-of-interest (ROIs). Data were collected under optimal conditions and on a compromised instrument to construct and refine the principal component analysis (PCA) model in R. Metrics, including mass measurement accuracy and spectral accuracy, were evaluated, yielding an individual suitability score for each compound. The average of these scores is utilized to inform if troubleshooting is necessary.
resultsThe PCA-based SST model was applied to data collected when the instrument was compromised. The resultant SST scores were used to determine a statistically significant threshold, which was defined as 0.93 for IR-MALDESI-MSI analyses. This minimizes the type-I error rate, where the QC/SST would report the platform to be in working condition when cleaning is actually necessary. Further, data scored after a partial cleaning demonstrate the importance of QC and frequent full instrument cleaning.
conclusionsThis study is the starting point for addressing an important issue and will undergo future development to improve the efficiency of the protocol. Ultimately, this work is the first of its kind and proposes this approach as a proof of concept to develop and implement universal QC/SST protocols for a variety of MSI platforms.
Identifiers
What Socratic holds
Registered trials
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 Socratic graph.