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Metabolomic Profiling of Human Urine Samples Using LC-TIMS-QTOF Mass Spectrometry.

Cristina Di Poto1, Xiang Tian1, Xuejun Peng2

  • 1Dynamic Omics, Antibody Discovery, and Protein Engineering (ADPE), R&D, AstraZeneca, Gaithersburg, Maryland 20850, United States.

Journal of the American Society for Mass Spectrometry
|June 9, 2021
PubMed
Summary

This study introduces a new database of human urine metabolites with collision cross-section (CCS) values, improving metabolite identification in complex samples using trapped ion mobility spectrometry (TIMS). The method ensures reproducible and robust CCS measurements for urine metabolomics.

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Area of Science:

  • Analytical Chemistry
  • Biochemistry
  • Metabolomics

Background:

  • Metabolite identification in biological samples is challenging due to chemical diversity.
  • Urine metabolomics offers insights into disease and therapeutic responses but is complicated by numerous poorly characterized byproducts.
  • Ion mobility spectrometry (IMS) provides collision cross-section (CCS) data, an orthogonal descriptor to m/z and MS/MS, enhancing molecular identification.

Purpose of the Study:

  • To explore the utility of trapped ion mobility spectrometry (TIMS) with LC-PASEF for urine metabolomics.
  • To establish a human urine metabolite database with experimentally acquired CCS values.
  • To assess the reproducibility and robustness of CCS measurements in urine samples.

Main Methods:

  • Untargeted metabolomics was performed on 80 urine samples from healthy volunteers using HILIC and RP chromatography coupled with LC-PASEF TIMS-qTOF.
  • Targeted quantification of three analytes (Trp, Phe, Tyr) was also conducted.
  • Collision cross-section (CCS) values were measured and compared across different platforms.

Main Results:

  • A total of 362 urine metabolites were characterized with robust CCS measurements.
  • Both untargeted and targeted data demonstrated high reproducibility.
  • Inter-laboratory comparison showed minimal CCS value variation (<1.3% ΔCCS).

Conclusions:

  • LC-PASEF TIMS-qTOF is a powerful platform for urine metabolomics, providing robust and reproducible CCS measurements.
  • The developed human urine metabolite database with CCS values represents a significant resource for improved metabolite identification.
  • This approach enhances the accuracy and reliability of metabolite profiling in complex biological matrices like urine.