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Whole-body Mass Spectrometry Imaging by Infrared Matrix-assisted Laser Desorption Electrospray Ionization IR-MALDESI
Published on: March 24, 2016
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Evaluation of Data Analysis Platforms and Compatibility with MALDI-TOF Imaging Mass Spectrometry Data Sets
Gordon T Luu1, Alanna R Condren1, Lisa Juliane Kahl2
1Department of Pharmaceutical Sciences, University of Illinois at Chicago, Chicago, Illinois 60612, United States.
Journal of the American Society for Mass Spectrometry
|April 25, 2020
Summary
Imaging mass spectrometry (IMS) software, SCiLS and Cardinal, were evaluated for analyzing spatial distributions of metabolites and proteins in Pseudomonas aeruginosa. Both tools performed similarly in unsupervised segmentation of IMS data.
Area of Science:
- Biotechnology and Biomedical Engineering
- Analytical Chemistry
- Microbiology
Background:
- Imaging mass spectrometry (IMS) enables label-free, 3D chemical analysis of biological samples, revealing spatial distributions of metabolites and proteins.
- Increasing IMS data sizes necessitate advanced statistical tools for analyzing spatial relevance and deciphering complex biological information.
- Software packages like SCiLS and the R package Cardinal are developed for unbiased spectral grouping in IMS data analysis.
Purpose of the Study:
- To evaluate the compatibility and performance of SCiLS and Cardinal software with MALDI-TOF IMS data.
- To compare the unsupervised segmentation capabilities of SCiLS and Cardinal for analyzing the Gram-negative pathogen *Pseudomonas aeruginosa* PA14.
- To identify differences in feature identification and highlight optimization requirements for IMS data analysis.
Main Methods:
- MALDI-TOF IMS data acquisition from *Pseudomonas aeruginosa* PA14.
- Application of SCiLS software for unsupervised segmentation and analysis.
- Application of the open-source R package Cardinal for unsupervised segmentation and analysis.
Main Results:
- Both SCiLS and Cardinal demonstrated comparable performance in unsupervised segmentation of MALDI-TOF IMS data from *Pseudomonas aeruginosa*.
- Notable differences were observed in the identification of statistically significant features between the two software packages.
- Optimization of preprocessing steps, region of interest selection, and manual analysis were found to be crucial for accurate feature identification.
Conclusions:
- SCiLS and Cardinal are both viable tools for unsupervised segmentation of MALDI-TOF IMS data in microbiological studies.
- Careful optimization of analytical parameters and post-processing steps is essential for maximizing the utility of these software packages.
- Further investigation into specific differences may refine workflows for spatial metabolomics and proteomics in complex biological systems.
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