Enhancing detection coverage in untargeted metabolomics analysis by solid-phase extraction on-line coupled to...

Area of Science:

• Analytical Chemistry
• Metabolomics
• Biochemistry

Background:

• Untargeted metabolomics often suffers from low detection coverage with standard analytical techniques like NMR, LC-MS, and GC-MS.
• Improving metabolite detection is crucial for comprehensive biological and clinical insights.
• Current sample preparation methods can limit the depth of metabolome profiling.

Purpose of the Study:

• To evaluate the detection coverage of an automated solid-phase extraction (SPE) coupled to liquid chromatography-tandem mass spectrometry (LC-MS/MS) approach.
• To optimize SPE sorbent combinations for enhanced metabolite identification in human serum.
• To assess the sensitivity improvements offered by the SPE-LC-MS/MS strategy compared to conventional methods.

Main Methods:

• On-line coupling of solid-phase extraction (SPE) to LC-MS/MS was employed.
• Three types of sorbents were tested: alkyl bonded silica (e.g., C18), polymeric resins (polystyrene-divinylbenzene), and mixed-mode ionic resins.
• Dual cartridge SPE-LC-MS/MS configurations were designed and evaluated for human serum analysis.

Main Results:

• A combination of four sorbents (C18, polystyrene-divinylbenzene, and two mixed-mode ionic resins) achieved the highest detection coverage due to complementary retention mechanisms.
• Individual sorbents like C18 demonstrated high coverage, retaining up to 83% of detected entities.
• Optimized dual cartridge SPE-LC-MS/MS configurations increased detection coverage to 91% of all detected molecular features.
• The SPE-LC-MS/MS strategy yielded an average preconcentration factor of 10-75, significantly improving sensitivity over protein precipitation.

Conclusions:

• Automated SPE-LC-MS/MS, particularly with optimized sorbent combinations, substantially enhances metabolite detection coverage in human serum.
• Complementary retention mechanisms of different sorbents are key to maximizing the metabolome captured.
• This integrated approach offers a more sensitive and comprehensive alternative for untargeted metabolomics studies.