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Published on: August 19, 2025
IMFrag: A Tool to Recognize In-Source Fragmentation in Ion Mobility-Enabled Data-Independent Acquisition Workflows
Ryan Nguyen1, Anvitaa Anandkumar1, Griffin Rangel1
1Department of Medicinal Chemistry, University of Washington, Seattle, Washington 98195, United States.
Analytical Chemistry
|June 6, 2026
Summary
In untargeted metabolomics, in-source fragmentation (ISF) causes misidentification. IMFrag, using ion mobility-mass spectrometry (IM-MS), differentiates ISF from true fragments, improving compound identification in complex samples.
Area of Science:
- Analytical Chemistry
- Biochemistry
- Computational Chemistry
Background:
- High-resolution mass spectrometry is vital for untargeted metabolomics and exposomics.
- In-source fragmentation (ISF) poses a significant challenge, leading to compound misidentification.
- Current methods struggle to distinguish ISF from genuine MS/MS fragments, hindering accurate analysis.
Purpose of the Study:
- To develop a computational tool, IMFrag, for differentiating in-source fragments from true product ions using ion mobility-mass spectrometry (IM-MS).
- To enhance the accuracy of compound identification in untargeted metabolomics and exposomics studies.
- To leverage structural information from IM-MS to improve MS/MS spectral library matching.
Main Methods:
- Development of IMFrag, a Jupyter notebook-based tool integrating IM-MS data.
- Utilizing ion mobility signatures to distinguish fragments generated at different stages of the mass spectrometry process.
- Application of IM-enabled data-independent acquisition (IM-DIA) for analyzing l-tryptophan and diverse small molecules.
- Validation using complex biological matrices, including human plasma samples.
Main Results:
- IMFrag successfully differentiates ISF from collision-induced dissociation (CID) fragments based on distinct ion mobility signatures.
- Distinct mobility patterns were observed for identical fragment ions originating from different fragmentation events.
- IM-DIA provided additional structural insights, such as dimer formation and protonation sites, not evident in standard LC-MS.
- The tool demonstrated utility in resolving ambiguities in complex biological samples, like coeluting isobaric compounds.
Conclusions:
- IMFrag provides an accessible framework for analyzing MS¹ and post-IM MS² data in untargeted studies.
- The integration of IM-MS data significantly improves the reliability of compound identification by mitigating ISF.
- This approach enhances untargeted analysis pipelines and aids in the development of robust MS/MS spectral libraries.
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