Journal of Proteomics & Bioinformatics
Open Access

Abstract

Multidimensional Separation Using HILIC and SCX Pre-fractionation for RP LC-MS/MS Platform with Automated Exclusion List-based MS Data Acquisition with Increased Protein Quantification

Yu Zhou, Zhen Meng, Maria Edman-Woolcott, Sarah F Hamm-Alvarez and Ebrahim Zandi

Liquid chromatography–mass spectrometry (LC-MS) based proteomics is one of the most widely used analytical platforms for global protein discovery and quantification. One of the challenges is the difficulty of identifying low abundance biomarker proteins from limited biological samples. Extensive fractionation could expand proteomics dynamic range, however, at the cost of high sample and time consumption. Extensive fractionation would increase the sample need and the labeling cost. Also quantitative proteomics depending on high resolution MS have the limitation of spectral acquisition speed. Those practical problems hinder the in-depth quantitative proteomics analysis such as tandem mass tag (TMT) experiments. We found the joint use of hydrophilic interaction liquid chromatography (HILIC) and strong cation exchange Chromatography (SCX) prefractionation at medium level could improve MS/MS efficiency, increase proteome coverage, shorten analysis time and save valuable samples. In addition, we scripted a program, Exclusion List Convertor (ELC), which automates and streamlines data acquisition workflow using the precursor ion exclusion (PIE) method. PIE reduces redundancy of high abundance MS/MS analyses by running replicates of the sample. The precursor ions detected in the initial run(s) are excluded for MS/MS in the subsequent run. We compared PIE methods with standard data dependent acquisition (DDA) methods running replicates without PIE for their effectiveness in quantifying TMT-tagged peptides and proteins in mouse tears. We quantified a total of 845 proteins and 1401 peptides using the PIE workflow, while the DDA method only resulted in 347 proteins and 731 peptides. This represents a 144% increase of protein identifications as a result of PIE analysis.