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Mathematical Framework for Quantifying Delocalization in MALDI-MSI via a Composite Scoring Approach.

Amin Jarrahi1, Allison Jones1, Weisheng Tang2

  • 1Department of Biomedical Engineering, Tickle College of Engineering, University of Tennessee, Knoxville, Tennessee 37996, United States.

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|February 23, 2026
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Summary
This summary is machine-generated.

Analyte delocalization in matrix-assisted laser desorption/ionization mass spectrometry imaging (MALDI-MSI) can affect results. Researchers developed a new delocalization score using area and mean distance to quantify this issue in mouse brain tissue.

Keywords:
MALDI-MSIcomposite scoringdelocalizationmass spectrometry imagingmolecular imagingpeak detectionspatial resolution

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Area of Science:

  • Biomedical imaging
  • Analytical chemistry
  • Molecular pathology

Background:

  • Matrix-assisted laser desorption/ionization mass spectrometry imaging (MALDI-MSI) is crucial for visualizing biomolecule distribution in tissues.
  • Analyte delocalization, the spreading of molecules beyond their original location, is a significant challenge impacting MALDI-MSI sensitivity and spatial resolution.
  • This phenomenon is often exacerbated by sample preparation techniques, including matrix application.

Purpose of the Study:

  • To develop and evaluate metrics for quantifying analyte delocalization in MALDI-MSI data.
  • To establish a robust delocalization score for assessing image quality and data reliability.
  • To investigate the correlation of delocalization patterns among different analytes within mouse brain sections.

Main Methods:

  • Utilized MALDI-MSI data from mouse brain sections to assess various delocalization metrics.
  • Evaluated metrics such as distances between centers of mass, off-tissue signal distances from the border, and background signal area.
  • Defined a novel delocalization score as a tunable linear combination of background area and mean off-tissue distance.

Main Results:

  • A linear combination of background area and mean distance effectively quantified analyte delocalization.
  • The proposed delocalization score demonstrated practical utility, with higher weighting on area proving effective.
  • Strong correlations in delocalization scores across different analytes were observed, indicating consistent delocalization behaviors.

Conclusions:

  • The developed delocalization score provides a quantitative method to assess and potentially mitigate issues in MALDI-MSI.
  • The score's tunability allows adaptation to specific experimental needs and applications.
  • Consistent delocalization patterns across analytes suggest shared underlying mechanisms or sensitivities to sample handling in MALDI-MSI.