MSQuant: A Platform for Stable Isotope-Based Quantitative Proteomics

Part of the Methods in Molecular Biology book series (MIMB, volume 893)


Quantitative approaches in proteomics are emerging as a powerful tool to probe the dynamics of protein expression across biological conditions. Thereby, quantification helps to recognize proteins with potential biological relevance, which greatly aids in the design of follow-up experiments. Although multiple methods have been established that are based on stable-isotope labeling and label-free approaches, one of the remaining bottlenecks is the analysis and quantification of proteins in large datasets. MSQuant is a platform for protein quantification, capable of handling multiple labeling strategies and supporting several vendor data formats. Here, we report on the use and versatility of MSQuant.

Key words

Protein quantification Stable isotope labeling Quantitative proteomics Automation 


  1. 1.
    Gouw JW, Krijgsveld J, Heck AJ (2010) Quantitative proteomics by metabolic labeling of model organisms. Mol Cell Proteomics 9:11–24PubMedCrossRefGoogle Scholar
  2. 2.
    Mann M (2006) Functional and quantitative proteomics using SILAC. Nat Rev Mol Cell Biol 7:952–958PubMedCrossRefGoogle Scholar
  3. 3.
    Gouw JW, Pinkse MW, Vos HR et al (2009) In vivo stable isotope labeling of fruit flies reveals post-transcriptional regulation in the maternal-to-zygotic transition. Mol Cell Proteomics 8:1566–1578PubMedCrossRefGoogle Scholar
  4. 4.
    Choudhary C, Mann M (2010) Decoding signalling networks by mass spectrometry-based proteomics. Nat Rev Mol Cell Biol 11:427–439PubMedCrossRefGoogle Scholar
  5. 5.
    Mittler G, Butter F, Mann M (2009) A SILAC-based DNA protein interaction screen that identifies candidate binding proteins to ­functional DNA elements. Genome Res 19:284–293PubMedCrossRefGoogle Scholar
  6. 6.
    Gevaert K, Impens F, Ghesquiere B et al (2008) Stable isotopic labeling in proteomics. Proteomics 8:4873–4885PubMedCrossRefGoogle Scholar
  7. 7.
    Leitner A, Lindner W (2006) Chemistry meets proteomics: the use of chemical tagging reactions for MS-based proteomics. Proteomics 6:5418–5434PubMedCrossRefGoogle Scholar
  8. 8.
    Mueller LN, Brusniak MY, Mani DR, Aebersold R (2008) An assessment of software solutions for the analysis of mass spectrometry based quantitative proteomics data. J Proteome Res 7:51–61PubMedCrossRefGoogle Scholar
  9. 9.
    Cox J, Mann M (2008) MaxQuant enables high peptide identification rates, individualized p.p.b.-range mass accuracies and proteome-wide protein quantification. Nat Biotechnol 26:1367–1372PubMedCrossRefGoogle Scholar
  10. 10.
    Mortensen P, Gouw JW, Olsen JV et al (2010) MSQuant, an open source platform for mass spectrometry-based quantitative proteomics. J Proteome Res 9:393–403PubMedCrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media, LLC 2012

Authors and Affiliations

  1. 1.Department of Biochemistry and Molecular Biology, Centre for High-Throughput BiologyUniversity of British ColumbiaVancouverCanada
  2. 2.Genome Biology Unit & Proteomics Core FacilityEMBLHeidelbergGermany

Personalised recommendations