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Mass Spectrometry: Complex Analysis

Mass Spectrometry: Complex Analysis

Mass spectrometry is an important technique for the identification of pure compounds. However, it has some limitations for the analysis of complex mixtures, often due to excessive fragmentation making the spectrum too complicated to decipher. Mass spectrometry can be combined with suitable separation methods in sequence, forming hyphenated methods, which are useful in the analysis of complex mixtures.
GC–MS is a powerful hyphenated method commonly used in forensics and environmental...

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Mass spectrometry is an analytical technique used to determine the molecular mass and molecular formula of a compound. The basic principle of mass spectrometry is to generate ions from the analyte molecule and measure these ion abundances against their molecular mass. One common type of ionization, known as electrospray ionization or EI, bombards the analyte molecules in the gas phase with high-energy electron beams. The electron beams displace an electron from the molecule and leave...

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Updated: May 22, 2025

Using a Cyclic Ion Mobility Spectrometer for Tandem Ion Mobility Experiments

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Published on: January 20, 2022

A Spatial Metabolomics Annotation Workflow Leveraging Cyclic Ion Mobility and Machine Learning-Predicted Collision

Dmitry Leontyev¹, Eric C Gier¹, Viraj A Master^2,3

¹School of Chemistry and Biochemistry, Georgia Institute of Technology, Atlanta, Georgia 30332, United States.

Journal of the American Society for Mass Spectrometry

|May 21, 2025

Summary

This summary is machine-generated.

Collision cross sections (CCS) improve metabolite identification in mass spectrometry imaging (MSI) of kidney cancer. High-accuracy CCS data enhances lipid annotation and helps identify unknown compounds, unlocking new biological insights from spatial metabolomics.

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Area of Science:

Spatial metabolomics
Mass spectrometry imaging (MSI)
Ion mobility spectrometry

Background:

Accurate metabolite annotation is vital for understanding biological roles and spatial patterns in nontargeted spatial metabolomics.
Incomplete MS2 mass spectrometry imaging (MSI) coverage leads to many unannotated features, representing lost biological information.
Collision cross sections (CCS) offer valuable data for confirming metabolite annotations, distinguishing isomers, and elucidating unknown structures.

Purpose of the Study:

To investigate how collision cross sections (CCS) measurements enhance MSI lipid annotation confidence.
To evaluate the combined use of machine learning CCS predictions and SIRIUS analysis with experimental CCS data.
To explore the utility of CCS in identifying unknown features in human renal cell carcinoma (RCC) tissues.

Main Methods:

Utilized desorption electrospray ionization cyclic ion mobility mass spectrometry imaging (DESI-cIM-MSI) on human RCC tissues.
Performed multipass ion mobility (IM) experiments to obtain high-accuracy CCS measurements (<0.4% accuracy).
Integrated experimental CCS data with machine learning CCS predictions and SIRIUS analysis of MS2 data for annotation.

Main Results:

High-accuracy multipass CCS measurements successfully annotated isobaric lipid database matches, even without MS2 data.
Experimental CCS data effectively filtered unlikely candidates from SIRIUS predictions.
Identified two unknown, spatially correlated features in RCC tissues as rocuronium, a previously unreported substance in MSI studies.

Conclusions:

High-accuracy CCS measurements significantly enhance metabolite annotation confidence in MSI.
The integration of experimental CCS with computational tools like SIRIUS and machine learning improves the analysis of complex MSI data.
This approach holds substantial potential for advancing spatial metabolomics by enabling the annotation of previously uncharacterized features.