Proteomics with Enhanced In-Source Fragmentation/Annotation: Applying XCMS-EISA Informatics and Q-MRM High-Sensitivity Quantification

Journal of the American Society for Mass Spectrometry, vol. 32, pp. 2644–2654

Abstract

Enhanced in-source fragmentation/annotation (EISA) has recently been shown to produce fragment ions that match tandem mass spectrometry data across a wide range of small molecules. EISA has been developed to facilitate data-dependent acquisition (DDA), data-independent acquisiton (DIA), and multiple-reaction monitoring (MRM), enabling molecular identifications in untargeted metabolomics and targeted quantitative single-quadrupole MRM (Q-MRM) analyses. Here, EISA has been applied to peptide-based proteomic analysis using optimized in-source fragmentation to generate fragmentation patterns for a mixture of 38 peptides, which were comparable to the b- and y-type fragment ions typically observed in tandem MS experiments. The optimal in-source fragmentation conditions at which high-abundance peptide fragments and precursor ions coexist were compared with automated data-dependent acquisition (DDA) in the same quadrupole time-of-flight (QTOF–MS) mass spectrometer, generating a significantly higher fragment percentage of peptides from both singly and doubly charged b- and y-type fragment (b +, y +, b 2+, and y 2+ ) ions. Higher fragment percentages were also observed for these fragment ion series over linear ion trap instrumentation. An XCMS-EISA annotation/deconvolution program was developed, making use of the retention time and peak shape continuity between precursor fragment ions, to perform automated proteomic data analysis on the enhanced in-source fragments. Post-translational modification (PTM) characterization on peptides was demonstrated with EISA, producing fragment ions corresponding to a neutral loss of phosphoric acid with greater intensity than observed with DDA on a QTOF–MS. Moreover, Q-MRM demonstrated the ability to use EISA for peptide quantification. The availability of more sophisticated in-source fragmentation informatics, beyond XCMS-EISA, will further enable EISA for sensitive autonomous identification and Q-MRM quantitative analyses in proteomics.

Authors 5

1. Jingchuan Xue

   Scripps Research Institute · Guangdong University of Technology

   Affiliation as printed

   Key Laboratory for City Cluster Environmental Safety and Green Development of the Ministry of Education, Institute of Environmental and Ecological Engineering, Guangdong University of Technology, Guangzhou 510006, China

   Scripps Center for Metabolomics, The Scripps Research Institute, 10550 North Torrey Pines Road, La Jolla, California 92037, United States

2. Rico J. E. Derks Aachen Center for Proteomics and Metabolomics

   Leiden University Medical Center

   Affiliation as printed

   Leiden University Medical Center, Center for Proteomics and Metabolomics, Albinusdreef 2, 2333ZA Leiden, Netherlands

3. Linh T. Hoang

   Scripps Research Institute

   Affiliation as printed

   Scripps Center for Metabolomics, The Scripps Research Institute, 10550 North Torrey Pines Road, La Jolla, California 92037, United States

4. Martin A. Giera corresponding Aachen Center for Proteomics and Metabolomics

   Leiden University Medical Center

   Affiliation as printed

   Leiden University Medical Center, Center for Proteomics and Metabolomics, Albinusdreef 2, 2333ZA Leiden, Netherlands

5. Gary E. Siuzdak corresponding

   Scripps Research Institute

   Affiliation as printed

   Departments of Chemistry, Molecular, and Computational Biology, The Scripps Research Institute, 10550 North Torrey Pines Road, La Jolla, California 92037, United States

   Scripps Center for Metabolomics, The Scripps Research Institute, 10550 North Torrey Pines Road, La Jolla, California 92037, United States