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Achieving Absolute Molar Lipid Concentrations: A Phospholipidomics Cross-Validation Study.

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Summary

This study introduces traceable lipid class quantification to validate lipidomics workflows. A novel mass spectrometry approach achieves high sensitivity for accurate lipid quantification, addressing the lack of reference materials.

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Area of Science:

  • Lipidomics
  • Analytical Chemistry
  • Biochemistry

Background:

  • Standardization is crucial in lipidomics, but a lack of reference materials hinders accurate quantification.
  • Current validation methods for quantitative lipidomics workflows are limited.
  • Traceable quantification of lipid classes is proposed as a key validation layer.

Purpose of the Study:

  • To introduce and validate a novel method for traceable lipid class quantification.
  • To establish a robust validation scheme for quantitative lipidomics.
  • To demonstrate the utility of the proposed method using a real-world biological sample.

Main Methods:

  • Utilized 31P nuclear magnetic resonance (NMR) and inductively coupled plasma-mass spectrometry (ICP-MS) with certified standards for initial validation.
  • Developed a novel lipid class quantification strategy using lipid class separation and mass spectrometry with all-ion fragmentation (AIF).
  • Applied independent calibration strategies to assess the complete phospholipid sub-ome and individual lipid classes.

Main Results:

  • Achieved low absolute limits of detection in the femtomole (fmol) range using the AIF approach.
  • Demonstrated the ability to quantitatively assess the complete phospholipid sub-ome.
  • Successfully validated the lipidomics workflow using Komagataella phaffii, showing mass balances and supporting quantification at the lipid species level.

Conclusions:

  • Traceable lipid class quantification provides a vital layer for validating lipidomics workflows.
  • The novel AIF-based mass spectrometry approach offers high sensitivity and accuracy for lipid quantification.
  • This method supports robust lipidomics data by enabling comprehensive mass balance assessments and species-level quantification.