Quantitative mass spectrometry in proteomics: a critical review

By Marcus Bantscheff, M. Schirle, G. Sweetman, Jens M. Rick, B. Kuster. Analytical and Bioanalytical Chemistry, 2007

1,703 citations recorded by the source corpus.

AbstractThe quantification of differences between two or more physiological states of a biological system is among the most important but also most challenging technical tasks in proteomics. In addition to the classical methods of differential protein gel or blot staining by dyes and fluorophores, mass-spectrometry-based quantification methods have gained increasing popularity over the past five years. Most of these methods employ differential stable isotope labeling to create a specific mass tag that can be recognized by a mass spectrometer and at the same time provide the basis for quantification. These mass tags can be introduced into proteins or peptides (i) metabolically, (ii) by chemical means, (iii) enzymatically, or (iv) provided by spiked synthetic peptide standards. In contrast, label-free quantification approaches aim to correlate the mass spectrometric signal of intact proteolytic peptides or the number of peptide sequencing events with the relative or absolute protein quantity directly. In this review, we critically examine the more commonly used quantitative mass spectrometry methods for their individual merits and discuss challenges in arriving at meaningful interpretations of quantitative proteomic data. FigureCommon quantitative mass spectrometry workflows. Boxes in blue and yellow represent two experimental conditions. Horizontal lines indicate when samples are combined. Dashed lines indicate points at which experimental variation and thus quantification errors can occur. (adapted with permission from [11])

Subfields: Molecular Biology, Spectroscopy

View Paper: https://www.semanticscholar.org/paper/d00b252f5e99808e7cd1c2c8cd0cc8aa7370ac70

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Quantitative proteomics reveals subset-specific viral recognition in dendritic cells. (2010, background): “In this case, peptide intensities measured during individual liquid chromatography (LC) runs are compared across runs (Bantscheff et al., 2007).”
Applying mass spectrometry-based proteomics to genetics, genomics and network biology (2009, background): “The two approaches have different advantages, isotope-labelling methods are thought to measure protein abundance with higher accuracy, whereas label-free approaches have a greater dynamic range and achieve higher levels of proteome coverag”
Significance Analysis of Spectral Count Data in Label-free Shotgun Proteomics*S (2008, background): “6 and 7).”
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Less label, more free: Approaches in label‐free quantitative mass spectrometry (2011, methodology): “Other statistical tests generally used in the software and stand-alone data analysis protocols include t-test (triplicates or more), local pooled error test (two or more replicates), G-test (one replicate), Fisher’s exact test (one replicate), AC test (one replicate), and one-way ANOVA (one or more ”
A Guided Tour of the Trans-Proteomic Pipeline (2010, background): “In addition to the protein identities, quantitative information can also be obtained through a variety of stable isotope and isobaric tagging peptide labeling techniques [2].”
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iProphet: Multi-level Integrative Analysis of Shotgun Proteomic Data Improves Peptide and Protein Identification Rates and Error Estimates* (2011, methodology): “This includes the development of new and improved tandem MS (MS/MS) database search algorithms, as well as statistical data methods for estimating FDR and posterior peptide and protein probabilities (reviewed in (2, 6)).”
Accurate Proteome-wide Label-free Quantification by Delayed Normalization and Maximal Peptide Ratio Extraction, Termed MaxLFQ (2014, background): “However, the distance between the means of the 3:1 and 1:1 populations is near the correct value of log2(3).”