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Related Experiment Video

Updated: Jul 10, 2025

Dithranol as a Matrix for Matrix Assisted Laser Desorption/Ionization Imaging on a Fourier Transform Ion Cyclotron Resonance Mass Spectrometer

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Published on: November 26, 2013

On-tissue dataset-dependent MALDI-TIMS-MS² bioimaging.

Steffen Heuckeroth¹, Arne Behrens², Carina Wolf¹

¹Institute of Inorganic and Analytical Chemistry, University of Münster, Münster, Germany.

Nature Communications

|November 18, 2023

Summary

This summary is machine-generated.

Spatial ion mobility-scheduled exhaustive fragmentation (SIMSEF) enhances trapped ion mobility spectrometry-mass spectrometry imaging by adding crucial fragmentation spectra. This enables confident on-tissue compound annotation and mapping of the chemical space in spatial metabolomics.

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Area of Science:

Analytical Chemistry
Biochemistry
Metabolomics

Background:

Trapped ion mobility spectrometry (TIMS) coupled with mass spectrometry (MS) imaging offers advanced spatial metabolomics.
Lack of fragmentation spectra (MS2) in TIMS-MS imaging hinders confident compound identification.
On-tissue annotation remains a challenge in spatial metabolomics.

Purpose of the Study:

To introduce spatial ion mobility-scheduled exhaustive fragmentation (SIMSEF) for TIMS-MS imaging.
To augment TIMS-MS imaging datasets with essential MS2 spectra.
To improve on-tissue compound annotation and chemical space exploration.

Main Methods:

Developed SIMSEF, a dataset-dependent acquisition strategy for TIMS-MS imaging.
Systematically distributed fragmentation experiments with multiple collision energies.
Integrated SIMSEF into the open-source software MZmine for data processing and analysis.
Utilized matrix-assisted laser desorption/ionisation (MALDI)-TIMS-MS on rat brain tissue.

Main Results:

SIMSEF successfully augmented TIMS-MS imaging datasets with MS2 spectra.
Enabled on-tissue compound annotation via spectral library matching and rule-based lipid annotation in MZmine.
Facilitated mapping of known and unknown chemical space using molecular networking.
Demonstrated the workflow on rat brain tissue sections.

Conclusions:

SIMSEF significantly enhances the analytical capabilities of TIMS-MS imaging.
Provides a robust method for confident on-tissue compound annotation in spatial metabolomics.
The open-source algorithm and data processing pipelines offer a valuable community resource for chemical space exploration.