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Mass Spectrometers01:16

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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:
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Preparation of Homogeneous MALDI Samples for Quantitative Applications
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prepIMS: a robust data preprocessing workflow for ion mobility mass spectrometry imaging.

Linlin Wang1, Chengyi Xie2, Lei Guo3

  • 1Interdisciplinary Institute for Medical Engineering, Fuzhou University, Fuzhou, 350108, China; Department of Electronic Science, National Institute for Data Science in Health and Medicine, Xiamen University, Xiamen, 361005, China.

Analytica Chimica Acta
|January 9, 2026
PubMed
Summary
This summary is machine-generated.

A new preprocessing workflow, prepIMS, improves ion mobility-mass spectrometry imaging (IM-MSI) data analysis by effectively detecting biomolecular peaks from noisy signals. This enhances the separation of isomeric and isobaric ions for more reliable molecular profiling in complex biological tissues.

Keywords:
Data preprocessingDensity clusteringIon mobility-mass spectrometry imagingMolecular imagingSpatial segmentation

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Area of Science:

  • Biomolecular analysis
  • Mass spectrometry imaging
  • Computational biology

Background:

  • Ion mobility-mass spectrometry imaging (IM-MSI) provides spatial localization of biomolecules using mass-to-charge ratio (m/z) and collision cross-section (CCS).
  • IM-MSI is crucial for analyzing complex biological tissues, offering enhanced detection of low-abundance ions and separation of isobaric/isomeric species.
  • Effective preprocessing of IM-MSI data is essential for pixel-level feature extraction and molecular image reconstruction, but current methods struggle with noise-affected signals and low signal-to-noise ratios.

Purpose of the Study:

  • To develop a robust preprocessing workflow for IM-MSI data that overcomes limitations in peak detection from noisy ion mobility signals.
  • To improve the accuracy, sensitivity, and reliability of molecular feature extraction in IM-MSI data analysis.
  • To facilitate the separation of isomeric and isobaric ions and support downstream spatial segmentation for comprehensive molecular profiling.

Main Methods:

  • Development of prepIMS, a preprocessing workflow incorporating a density cluster-based peak detection strategy.
  • Evaluation of prepIMS using a simulation dataset to compare its performance against conventional local maxima-based methods.
  • Validation of prepIMS on whole-body mouse pup tissue datasets acquired with MALDI-TIMS (Matrix-Assisted Laser Desorption/Ionization - Time of Flight Mass Spectrometry).

Main Results:

  • The density cluster-based peak detection in prepIMS demonstrated superior performance over local maxima-based strategies on noise-affected data, showing significant improvements in accuracy, sensitivity, Matthews correlation coefficient, and F1-score.
  • prepIMS successfully detected and differentiated isomeric and isobaric ions within the same mass peak in MALDI-TIMS datasets.
  • Spatial segmentation analysis of preprocessed 4D IM-MSI data revealed prepIMS's ability to distinguish spatial distribution differences based on subtle variations in ion mobility.

Conclusions:

  • prepIMS offers a powerful and versatile solution for IM-MSI data preprocessing, effectively addressing challenges posed by noisy ion mobility signals.
  • The workflow enhances the separation of isomeric and isobaric ions and supports downstream spatial segmentation, leading to more reliable and in-depth IM-MSI data analysis.
  • prepIMS is expected to advance molecular profiling in complex biological systems by improving the reliability and depth of IM-MSI data interpretation.