Label-Free Protein Quantitation Using Weighted Spectral Counting

  • Christine Vogel
  • Edward M. MarcotteEmail author
Part of the Methods in Molecular Biology book series (MIMB, volume 893)


Mass spectrometry (MS)-based shotgun proteomics allows protein identifications even in complex biological samples. Protein abundances can then be estimated from the counts of MS/MS spectra attributable to each protein, provided that one corrects for differential MS-detectability of the contributing peptides. We describe the use of a method, APEX, which calculates Absolute Protein EXpression levels based on learned correction factors, MS/MS spectral counts, and each protein’s probability of correct identification.

The APEX-based calculations consist of three parts: (1) Using training data, peptide sequences and their sequence properties, a model is built that can be used to estimate MS-detectability (O i) for any given protein. (2) Absolute abundances of proteins measured in an MS/MS experiment are calculated with information from spectral counts, identification probabilities and the learned O i-values. (3) Simple statistics allow for significance analysis of differential expression in two distinct biological samples, i.e., measuring relative protein abundances. APEX-based protein abundances span more than four orders of magnitude and are applicable to mixtures of hundreds to thousands of proteins from any type of organism.

Key words

Quantitative proteomics Protein expression Label-free mass spectrometry Spectral counting 



Absolute Protein EXpression


Mass spectrometry


Tandem mass spectrometry



C.V. acknowledges support by the International Human Frontier Science Program. We thank John Braisted and Srilatha Kuntumalla from JCVI for many useful discussions regarding the APEX calculations. This work was supported by grants from the Welch (F-1515) and Packard Foundations, the National Science Foundation, and National Institutes of Health (to E.M.M.).


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Copyright information

© Springer Science+Business Media, LLC 2012

Authors and Affiliations

  1. 1.Department of Biology, Center for Genomics and Systems BiologyNew York UniversityNew YorkUSA
  2. 2.Center for Systems and Synthetic Biology, Institute for Cellular and Molecular BiologyUniversity of Texas at AustinAustinUSA

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