Real-time cellular exometabolome analysis with a microfluidic-mass spectrometry platform

Area of Science:

• Systems Biology
• Analytical Chemistry
• Biochemistry

Background:

• Tracking signaling molecules and metabolites is crucial for understanding biological complexity.
• Existing analytical techniques face challenges in dynamic systems biology, particularly with sample volume and preparation time.
• High-resolution temporal analysis of the cellular microenvironment is needed.

Purpose of the Study:

• To develop and validate an integrated platform for high-temporal-resolution sampling and analysis of the cellular microenvironment.
• To overcome limitations of traditional methods in dynamic systems biology, such as sample dilution and lengthy preparation.
• To demonstrate the platform's utility in identifying metabolomic variations and exploring cellular memory.

Main Methods:

• Integration of microfluidics, online desalting, and mass spectrometry for automated sample collection and preparation.
• Surface passivation of polydimethylsiloxane to reduce non-specific adsorption.
• Real-time online mass spectrometry for near-instantaneous data acquisition.
• Comparison with ultra-performance liquid chromatography-electrospray ionization-ion mobility-mass spectrometry (UPLC-ESI-IM-MS) for validation.

Main Results:

• The platform successfully automated cellular stimulation, microenvironment control, and real-time sample analysis.
• Significant reduction in sample preparation and data collection time compared to traditional methods.
• Detection of metabolomic variations between naïve and experienced Jurkat T cells, including upregulation of benzoylecgonine in experienced cells.
• Demonstrated capability to reveal dynamics of the exometabolome over time and potential for metabolite discovery.

Conclusions:

• The developed platform offers a powerful tool for dynamic systems biology, enabling high-resolution analysis of cellular microenvironments.
• It overcomes key limitations of conventional methods, providing faster and more comprehensive data.
• The platform's ability to detect subtle metabolomic changes highlights its potential for discovering biomarkers and understanding cellular memory, as exemplified by T-cell responses to cocaine.