Collision cross section measurement and prediction methods in omics.

Area of Science:

• Analytical Chemistry
• Biochemistry
• Structural Biology

Background:

• Omics studies (metabolomics, lipidomics, proteomics) require precise compound identification.
• Structural isomers yield different biological roles, necessitating accurate structure elucidation.
• Liquid chromatography-mass spectrometry (LC-MS) is vital but has limitations in distinguishing isomers.

Purpose of the Study:

• To provide an overview of experimental and theoretical methods for estimating collision cross section (CCS) values.
• To discuss the limitations of current CCS estimation approaches.
• To explore potential mitigation strategies for these limitations.

Main Methods:

• Experimental CCS measurement (calibrant-dependent and independent approaches).
• Theoretical CCS prediction (computational and machine learning models).
• Review of existing literature on CCS estimation techniques.

Main Results:

• Ion mobility spectrometry offers a unique fingerprint (CCS) for compounds.
• Accurate CCS values aid in filtering potential analyte structures.
• Experimental CCS requires standards, which are limited; theoretical prediction is essential for untargeted analysis.

Conclusions:

• CCS is a powerful tool for compound identification and structure elucidation in omics.
• Both experimental and theoretical CCS estimation methods have limitations.
• Further development of theoretical models and experimental standardization is needed for broader application.