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Journal of Proteomics
Article . 2013 . Peer-reviewed
License: Elsevier TDM
Data sources: Crossref
Journal of Proteomics
Article . 2013 . Peer-reviewed
Data sources: Crossref
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Benchmarking stable isotope labeling based quantitative proteomics

Authors: A.F. Maarten Altelaar; Christian K. Frese; Christian Preisinger; Marco L. Hennrich; Andree W. Schram; H.Th. Marc Timmers; Albert J.R. Heck; +1 Authors

Benchmarking stable isotope labeling based quantitative proteomics

Abstract

Several quantitative mass spectrometry based technologies have recently evolved to interrogate the complexity, interconnectivity and dynamic nature of proteomes. Currently, the most popular methods use either metabolic or chemical isotope labeling with MS based quantification or chemical labeling using isobaric tags with MS/MS based quantification. Here, we assess the performance of three of the most popular approaches through systematic independent large scale quantitative proteomics experiments, comparing SILAC, dimethyl and TMT labeling strategies. Although all three methods have their strengths and weaknesses, our data indicate that all three can reach a similar depth in number of identified proteins using a classical (MS2 based) shotgun approach. TMT quantification using only MS2 is heavily affected by co-isolation leading to compromised precision and accuracy. This issue may be partly resolved by using an MS3 based acquisition; however, at the cost of a significant reduction in number of proteins quantified. Interestingly, SILAC and chemical labeling with MS based quantification produce almost indistinguishable results, independent of which database search algorithm used.

Countries
Netherlands, United Kingdom
Related Organizations
Keywords

Proteomics, Proteome, SILAC, Mass Spectrometry, Quantitation, Dimethyl labeling, Isotope Labeling, Isobaric labeling, Humans, Databases, Protein, Algorithms, HeLa Cells

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    selected citations
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    This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
    117
    popularity
    This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
    Top 10%
    influence
    This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
    Top 10%
    impulse
    This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
    Top 1%
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selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
117
Top 10%
Top 10%
Top 1%
Green