Advances in Mass Spectrometry-Based Proteomics and Its Application in Cancer Research
With the advent of high-resolution/high mass accuracy instrumentation, sophisticated informatic approaches, and advances in liquid chromatography, mass spectrometry-based proteomics has emerged as an indispensable and widely used tool for the identification, characterization, and quantification of proteins on a large scale. Deep proteome analyses can now sequence over 14,000 protein isoforms for a single human cell line rivaling the depth of next-generation RNA sequencing technology. Without additional enrichment steps, highly sensitive MS-based proteomic studies yield comprehensive identification of major post-translational modifications (PTMs). Isotopic labeling techniques enable the comparison of multiple samples in a single mass spectrometry experiment, while data-independent acquisition strategies provide comprehensive protein coverage and quantification against complex backgrounds.
KeywordsMass spectrometry Quantitative proteomics Post-translational modifications Cancer pathways Isotopic labeling
This work was supported by a New Investigator-Idea Development Award (W81XWH-13-1-0250) by the Congressionally Directed Medical Research Program in Prostate Cancer Research.
- Belczacka I et al (2018) Proteomics biomarkers for solid tumors: current status and future prospects. Mass Spectrom Rev 136:E359Google Scholar
- Breitkopf SB, Asara JM (2012) Determining in vivo phosphorylation sites using mass spectrometry. In: Ausubel FM et al (eds) Current protocols in molecular biology, Chapter 18(1), pp Unit18.19.1–27Google Scholar
- Franks A, Airoldi E, Slavov N (2017) Post-transcriptional regulation across human tissues (Vogel C (ed)). PLoS Comput Biol 13(5):e1005535Google Scholar
- Gevaert K et al (2008) Stable isotopic labeling in proteomics (Dunn MJ (ed)). Proteomics 8(23–24):4873–4885Google Scholar
- Huang Z et al (2017) Proteomic profiling of human plasma for cancer biomarker discovery (Pandey A (ed)). Proteomics 17(6):1600240Google Scholar
- Hüttenhain R et al (2013) Quantitative measurements of N-linked glycoproteins in human plasma by SWATH-MS (Figeys D (ed)). Proteomics 13(8):1247–1256Google Scholar
- Kusebauch U et al (2014) Using PeptideAtlas, SRMAtlas, and PASSEL: comprehensive resources for discovery and targeted proteomics (Baxevanis AD et al (ed)). Curr Protoc Bioinformatics 46(1):13.25.1–28Google Scholar
- Laine RA (1994) A calculation of all possible oligosaccharide isomers both branched and linear yields 1.05 × 10(12) structures for a reducing hexasaccharide: the Isomer Barrier to development of single-method saccharide sequencing or synthesis systems. Glycobiology 4(6):759–767PubMedCrossRefPubMedCentralGoogle Scholar
- Searle BC (2010) Scaffold: a bioinformatic tool for validating MS/MS-based proteomic studies (Martens L, Hermjakob H (eds)). Proteomics 10(6):1265–1269Google Scholar
- Veenstra TD (2013) Proteomic applications in cancer detection and discovery. Wiley, HobokenGoogle Scholar
- Vyatkina K et al (2017) De novo sequencing of peptides from high-resolution bottom-up tandem mass spectra using top-down intended methods (Mathivanan S (ed)). Proteomics 17(23–24):1600321Google Scholar
- Wichmann C et al (2018) MaxQuant.Live enables global targeting of more than 25,000 peptides. bioRxiv:1–15Google Scholar