[Metabolic fingerprint analysis of RAW264.7 inflammatory cell model by using UPLC-Q-TOF/MS]

Area of Science:

• Metabolomics
• Cell Biology
• Biochemistry

Background:

• Inflammatory cells play a crucial role in immune responses.
• Understanding the metabolic changes in inflammation is vital for developing targeted therapies.
• RAW264.7 cell line is a widely used model for studying inflammation.

Purpose of the Study:

• To develop and optimize an ultra-high performance liquid chromatography coupled with quadrupole-time-of-flight mass spectrometry (UPLC-Q-TOF/MS)-based metabolomics protocol for polar metabolite extraction from RAW264.7 cells.
• To investigate the metabolic fingerprint of lipopolysaccharide (LPS)-induced inflammatory RAW264.7 cells.
• To identify key metabolites altered during inflammation.

Main Methods:

• Optimization of polar metabolite extraction using different solvent systems (MeOH-CHCl3-H2O).
• Metabolomic analysis using UPLC-Q-TOF/MS.
• Statistical analysis of metabolomic data using orthogonal partial least squares discriminant analysis (OPLS-DA).
• Identification of selected metabolites.

Main Results:

• The optimal extraction solvent was determined to be MeOH-CHCl3-H2O (8:1:1) for enhanced peak detection, extraction efficiency, and stability.
• A total of 17 metabolites were identified.
• LPS-induced inflammatory RAW264.7 cells showed significant metabolic alterations compared to normal cells.
• Key affected metabolic pathways include those related to protein, carbohydrate, nucleotide, and phospholipid metabolism.

Conclusions:

• A robust UPLC-Q-TOF/MS-based metabolomics protocol for polar metabolites in RAW264.7 cells was successfully developed.
• The study identified distinct metabolic profiles of inflammatory versus normal RAW264.7 cells.
• These findings provide valuable insights into the mechanisms of inflammation and potential targets for anti-inflammatory drug development.