Poster Presentation 23rd Annual Lorne Proteomics Symposium 2018

SWATH analysis using external spectral libraries: Evaluation of cross-instrument data for library construction and quantitation (#146)

Xiaomin Song 1 , Robert J. A. Goode 2 , Thiri Zaw 1 , Dana Pascovici 1 , Jemma X. Wu 1 , William Klare 3 , Stuart Cordwell 3 , Ralf B. Schittenhelm 2 , Mark P Molloy 1
  1. Australian Proteome Analysis Facility, Macquarie University, NSW, Australia
  2. Monash Biomedical Proteomics Facility, Monash University, Melbourne
  3. University of Sydney, Sydney

Data independent mass spectrometry such as SWATH optimally requires a reference peptide MS/MS spectral library to link peak areas to identified peptides. Because the library needs to be comprehensive for discovery purposes and extensive data acquisition using the same mass spectrometer is not always possible, it is desirable and sometimes necessary to use LC-MS/MS data obtained from different mass spectrometers. We have previously described the SwathXtend bioinformatics tool to accommodate this application [1, 2].

In this presentation, we demonstrate the use of SwathXtend to process SWATH data from a TripleTOF 6600 (SCIEX) by merging a seed library to external libraries generated from a QExactive orbitrap mass spectrometer (Thermo Scientific) obtained in a different laboratory. We used 5 strains of S. aureus with 6 replicates grown in 2 different experimental conditions (n=60).

The quality of the process of library extension was evaluated using the reliable SWATH workflow described in [2] and was found to be good in terms of retention time correlations and relative ion intensity correlations.  Additionally, the relative quantitation across the two platforms could be checked due to the controlled nature of the experiment in which the same samples were being run across the two instruments. This study demonstrated the feasibility for generating peptide MS/MS spectral libraries from two different MS architectures and using these for quantitation. The approach allowed quantitation of over 1300 proteins representing ~ 60% of the S. aureus predicted proteome.

[1] Wu JX et al., Molecular & Cellular Proteomics, 2016, 15, 2501-2514

[2] Wu JX et al., Proteomics 2017, 17, 1700174, DOI: 10.1002/pmic.201700174.