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Nature Biotechnology
Article . 2009 . Peer-reviewed
License: Springer TDM
Data sources: Crossref
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Mass-spectrometric identification and relative quantification of N-linked cell surface glycoproteins

Authors: Bernd, Wollscheid; Damaris, Bausch-Fluck; Christine, Henderson; Robert, O'Brien; Miriam, Bibel; Ralph, Schiess; Ruedi, Aebersold; +1 Authors

Mass-spectrometric identification and relative quantification of N-linked cell surface glycoproteins

Abstract

Although the classification of cell types often relies on the identification of cell surface proteins as differentiation markers, flow cytometry requires suitable antibodies and currently permits detection of only up to a dozen differentiation markers in a single measurement. We use multiplexed mass-spectrometric identification of several hundred N-linked glycosylation sites specifically from cell surface-exposed glycoproteins to phenotype cells without antibodies in an unbiased fashion and without a priori knowledge. We apply our cell surface-capturing (CSC) technology, which covalently labels extracellular glycan moieties on live cells, to the detection and relative quantitative comparison of the cell surface N-glycoproteomes of T and B cells, as well as to monitor changes in the abundance of cell surface N-glycoprotein markers during T-cell activation and the controlled differentiation of embryonic stem cells into the neural lineage. A snapshot view of the cell surface N-glycoproteins will enable detection of panels of N-glycoproteins as potential differentiation markers that are currently not accessible by other means.

Keywords

Cell Membrane, Molecular Sequence Data, Amino Acid Sequence, Algorithms, Mass Spectrometry, Glycoproteins

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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!
514
Top 1%
Top 1%
Top 0.1%
bronze