Liquid MALDI MS Analysis of Complex Peptide and Proteome Samples

Area of Science:

• Analytical Chemistry
• Biochemistry
• Proteomics

Background:

• Matrix-assisted laser desorption/ionization time-of-flight mass spectrometry (MALDI-TOF MS) is a key technique for biological sample analysis.
• Conventional MALDI matrices can be limiting for complex sample analysis.
• Liquid support matrices (LSMs) offer a potential advancement over traditional MALDI matrices.

Purpose of the Study:

• To evaluate the efficacy of LSMs for identifying complex biological samples using MALDI-TOF MS.
• To compare the performance of liquid MALDI samples with solid MALDI samples for protein identification.
• To assess the suitability of LSMs for tandem mass spectrometry (MS/MS) analysis.

Main Methods:

• Utilized glycerol-based LSMs for sample preparation.
• Coupled nanoflow high-performance liquid chromatography (nanoHPLC) with an automated MALDI sample spotting device.
• Acquired MS and MS/MS data using TOF/TOF and Q-TOF instrumentation for protein identification via sequence database searching.
• Analyzed Lactobacillus proteome and bovine serum albumin (BSA) digest samples.

Main Results:

• Successfully acquired MS and MS/MS data at the femtomole level for both Lactobacillus and BSA samples.
• Liquid MALDI samples provided peptide mass fingerprints for BSA digest, leading to higher confidence in protein identification compared to solid MALDI samples.
• Post-source decay (PSD) MS/MS analysis revealed lower ion search scores for liquid MALDI samples compared to solid MALDI samples, indicating a need for optimization.

Conclusions:

• LSMs show promise for enhancing protein identification confidence in complex samples analyzed by MALDI-TOF MS.
• While LSMs improve peptide mass fingerprinting, further optimization is required for high-quality MS/MS fragmentation and ion detection in liquid MALDI-PSD analysis.
• The current differences in PSD parameters and achievable optimization limit the performance of liquid MALDI samples for detailed MS/MS-based protein identification.