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High-throughput Serum N-Glycomics: Method Comparison and Application to Study Rheumatoid Arthritis and Pregnancy-associated Changes* [S]

Overview of attention for article published in Molecular and Cellular Proteomics, September 2018
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  • In the top 25% of all research outputs scored by Altmetric
  • High Attention Score compared to outputs of the same age (89th percentile)
  • Good Attention Score compared to outputs of the same age and source (76th percentile)

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Title
High-throughput Serum N-Glycomics: Method Comparison and Application to Study Rheumatoid Arthritis and Pregnancy-associated Changes* [S]
Published in
Molecular and Cellular Proteomics, September 2018
DOI 10.1074/mcp.ra117.000454
Pubmed ID
Authors

Karli R Reiding, Albert Bondt, René Hennig, Richard A Gardner, Roisin O'Flaherty, Irena Trbojević-Akmačić, Archana Shubhakar, Johanna M W Hazes, Udo Reichl, Daryl L Fernandes, Maja Pučić-Baković, Erdmann Rapp, Daniel I R Spencer, Radboud J E M Dolhain, Pauline M Rudd, Gordan Lauc, Manfred Wuhrer

Abstract

N-Glycosylation is a fundamentally important protein modification with a major impact on glycoprotein characteristics such as serum half-life and receptor interaction. More than half of the proteins in human serum are glycosylated, and the relative abundances of protein glycoforms often reflect alterations in health and disease. Several analytical methods are currently capable of analyzing the total serum N-glycosylation in a high-throughput manner.Here we evaluate and compare the performance of three high-throughput released N-glycome analysis methods. Included were hydrophilic-interaction ultra-high-performance liquid chromatography with fluorescence detection (HILIC-UHPLC-FLD) with 2-aminobenzamide labeling of the glycans, multiplexed capillary gel electrophoresis with laser-induced fluorescence detection (xCGE-LIF) with 8-aminopyrene-1-3,6-trisulfonic acid labeling, and matrix-assisted laser desorption/ionization time-of-flight mass spectrometry (MALDI-TOF-MS) with linkage-specific sialic acid esterification. All methods assessed the same panel of serum samples, which were obtained at multiple time points during the pregnancies and postpartum periods of healthy women and patients with rheumatoid arthritis (RA). We compared the analytical methods on their technical performance as well as on their ability to describe serum protein N-glycosylation changes throughout pregnancy, with RA, and with RA disease activity.Overall, the methods proved to be similar in their detection and relative quantification of serum protein N-glycosylation. However, the non-MS methods showed superior repeatability over MALDI-TOF-MS, and allowed the best structural separation of low-complexity N-glycans. MALDI-TOF-MS achieved the highest throughput and provided compositional information on higher-complexity N-glycans. Consequentially, MALDI-TOF-MS could establish the linkage-specific sialylation differences within pregnancy and RA, whereas HILIC-UHPLC-FLD and xCGE-LIF demonstrated differences in α1,3- and α1,6-branch galactosylation. While the combination of methods proved to be the most beneficial for the analysis of total serum protein N-glycosylation, informed method choices can be made for the glycosylation analysis of single proteins or samples of varying complexity.

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X Demographics

The data shown below were collected from the profiles of 34 X users who shared this research output. Click here to find out more about how the information was compiled.
Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 99 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 99 100%

Demographic breakdown

Readers by professional status Count As %
Researcher 20 20%
Student > Ph. D. Student 19 19%
Student > Master 10 10%
Student > Postgraduate 6 6%
Student > Bachelor 5 5%
Other 14 14%
Unknown 25 25%
Readers by discipline Count As %
Biochemistry, Genetics and Molecular Biology 29 29%
Chemistry 9 9%
Medicine and Dentistry 8 8%
Agricultural and Biological Sciences 8 8%
Chemical Engineering 3 3%
Other 9 9%
Unknown 33 33%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 21. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 28 January 2019.
All research outputs
#1,772,193
of 25,420,980 outputs
Outputs from Molecular and Cellular Proteomics
#201
of 3,223 outputs
Outputs of similar age
#36,758
of 351,757 outputs
Outputs of similar age from Molecular and Cellular Proteomics
#11
of 46 outputs
Altmetric has tracked 25,420,980 research outputs across all sources so far. Compared to these this one has done particularly well and is in the 93rd percentile: it's in the top 10% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 3,223 research outputs from this source. They typically receive more attention than average, with a mean Attention Score of 7.6. This one has done particularly well, scoring higher than 93% of its peers.
Older research outputs will score higher simply because they've had more time to accumulate mentions. To account for age we can compare this Altmetric Attention Score to the 351,757 tracked outputs that were published within six weeks on either side of this one in any source. This one has done well, scoring higher than 89% of its contemporaries.
We're also able to compare this research output to 46 others from the same source and published within six weeks on either side of this one. This one has done well, scoring higher than 76% of its contemporaries.