Direct Infusion Acoustic Droplet Ejection Mass Spectrometry: Enabling High-Throughput Shotgun Lipidomics

Area of Science:

• Analytical Chemistry
• Biochemistry
• Mass Spectrometry

Background:

• High-throughput lipidomics is crucial for large-scale studies and clinical applications.
• Existing shotgun lipidomics methods face challenges like carryover, ion suppression, and limited structural specificity.
• Acoustic droplet ejection mass spectrometry (ADE-MS) offers touchless, high-speed nanoliter sample introduction with precision.

Purpose of the Study:

• To develop and benchmark a scalable Acoustic droplet ejection-tandem mass spectrometry (ADE-MS/MS) workflow for high-throughput lipidomics.
• To optimize the ADE-MS/MS workflow for reproducible quantification of diverse lipid species.
• To evaluate the performance and applicability of the developed workflow for clinical and research lipidomic analyses.

Main Methods:

• Development of a 384-well format ADE-MS/MS workflow utilizing parallel reaction monitoring (PRM) on a ZenoTOF MS platform.
• Optimization of solvent composition, droplet parameters, and MS acquisition settings for enhanced performance.
• Application to NIST SRM 1950 plasma for method validation and comparison with hydrophilic interaction liquid chromatography (HILIC)-MS/MS.

Main Results:

• Reproducible quantification of over 1000 polar and nonpolar lipid species across 14 subclasses with minimal sample consumption (approx. 5 minutes per sample).
• Quantification of 731 lipid species in NIST SRM 1950 plasma, demonstrating robust linearity, precision, reproducibility, and recovery.
• Achieved >200 lipid identifications per minute, exceeding 280 samples daily, with strong agreement (R² > 0.80) compared to HILIC-MS/MS.
• Identified 656 differential lipid features linked to lipidomic dysregulation across body mass index categories.

Conclusions:

• The developed ADE-MS/MS workflow provides a highly scalable, precise, and rapid method for comprehensive lipidomics.
• This approach overcomes limitations of traditional methods, offering significantly enhanced throughput for large-scale lipidomic studies.
• The workflow is applicable to clinical research, enabling the identification of lipid biomarkers associated with metabolic dysregulation.