High sensitivity and high-confidence compound identification with a flexible BoxCar acquisition method.

Area of Science:

• Analytical Chemistry
• Mass Spectrometry
• Biotechnology

Background:

• Liquid chromatography-mass spectrometry (LC-MS) is crucial for identifying and quantifying compounds in complex samples.
• Current methods struggle with highly sensitive and confident identification of low-abundance species, particularly in cell culture media.
• Understanding cell culture medium components is vital for optimizing recombinant antibody production.

Purpose of the Study:

• To develop a flexible BoxCar acquisition method for enhanced small molecule profiling in complex matrices.
• To integrate this method with iterative data-dependent acquisition (DDA) for improved identification.
• To apply the workflow to soy hydrolysate-based cell culture media for better characterization and support of antibody production.

Main Methods:

• Developed a flexible BoxCar acquisition method with increased segments in the low m/z range for small molecule analysis.
• Integrated flexible BoxCar with iterative data-dependent acquisition (DDA) using an inclusion list.
• Applied the method to analyze soy hydrolysate cell culture medium.

Main Results:

• Achieved 90% higher sensitivity for detecting low-abundance spiked substances at the MS1 level compared to full scan.
• Iterative DDA with an inclusion list generated higher quality MS2 spectra for confident identification.
• Increased MS2 coverage of low-concentration compounds by 50% compared to conventional DDA.

Conclusions:

• The developed flexible BoxCar acquisition method significantly improves sensitivity and confidence in small molecule profiling.
• This workflow is easily operable on Orbitrap mass spectrometers.
• The method provides substantial support for therapeutic monoclonal antibody production by enabling better characterization of cell culture media.