Evidence map›Paper›PMID 31065629›Full record

ArticleThe Analyst2019

Standardization of PGC-LC-MS-based glycomics for sample specific glycotyping.

Christopher Ashwood, Brian Pratt, Brendan X MacLean, Rebekah L Gundry, Nicolle H Packer

Abstract read
In one paragraph

Article in The Analyst, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 40 papers.

0numbers the graph read from it
0cells of the map it votes in
40citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

40 citing papers in PubMed.

  1. Review
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  9. Article
  10. Review
  11. ComprehensiveAnalytical chemistry · 2024
    Article
  12. Article
  13. Article
  14. Targeted Analysis of Permethylated N-Glycans Using MRM/PRM Approaches.Methods in molecular biology (Clifton, N.J.) · 2024
    Article
  15. Article
  16. Review
  17. Article
  18. Review
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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

5 authors.

Christopher AshwoodDepartment of Molecular Sciences, Macquarie University, Sydney, NSW, Australia. nicki.packer@mq.edu.au and ARC Centre of Excellence for Nanoscale Biophotonics, Macquarie University, Sydney, NSW, Australia and Department of Biochemistry, Medical College of Wisconsin, Milwaukee, WI, USA.ORCID http://orcid.org/0000-0001-5944-6179
Brian PrattDepartment of Genome Sciences, University of Washington, Seattle, WA, USA.
Brendan X MacLeanDepartment of Genome Sciences, University of Washington, Seattle, WA, USA.
Rebekah L GundryDepartment of Biochemistry, Medical College of Wisconsin, Milwaukee, WI, USA and Center for Biomedical Mass Spectrometry Research, Medical College of Wisconsin, Milwaukee, WI, USA.ORCID http://orcid.org/0000-0002-9263-833X
Nicolle H PackerDepartment of Molecular Sciences, Macquarie University, Sydney, NSW, Australia. nicki.packer@mq.edu.au and ARC Centre of Excellence for Nanoscale Biophotonics, Macquarie University, Sydney, NSW, Australia.ORCID http://orcid.org/0000-0002-7532-4021

Funding

Skyline Targeted Proteomics EnvironmentR01GM103551 · NIGMS · UNIVERSITY OF WASHINGTON · PI MACCOSS, MICHAEL · 2012 to 2020
$3.4M
Cell Surface Phenotyping Human Primary CellsR01HL134010 · NHLBI · UNIVERSITY OF NEBRASKA MEDICAL CENTER · PI GUNDRY, REBEKAH L. · 2016 to 2020
$1.9M
NHLBI NIH HHS R01 HL134010NIGMS NIH HHS R01 GM103551
6 · The paper itself

Abstract

Porous graphitized carbon (PGC) based chromatography achieves high-resolution separation of glycan structures released from glycoproteins. This approach is especially valuable when resolving structurally similar isomers and for discovery of novel and/or sample-specific glycan structures. However, the implementation of PGC-based separations in glycomics studies has been limited because system-independent retention values have not been established to normalize technical variation. To address this limitation, this study combined the use of hydrolyzed dextran as an internal standard and Skyline software for post-acquisition normalization to reduce retention time and peak area technical variation in PGC-based glycan analyses. This approach allowed assignment of system-independent retention values that are applicable to typical PGC-based glycan separations and supported the construction of a library containing >300 PGC-separated glycan structures with normalized glucose unit (GU) retention values. To enable the automation of this normalization method, a spectral MS/MS library was developed of the dextran ladder, achieving confident discrimination against isomeric glycans. The utility of this approach is demonstrated in two ways. First, to inform the search space for bioinformatically predicted but unobserved glycan structures, predictive models for two structural modifications, core-fucosylation and bisecting GlcNAc, were developed based on the GU library. Second, the applicability of this method for the analysis of complex biological samples is evidenced by the ability to discriminate between cell culture and tissue sample types by the normalized intensity of N-glycan structures alone. Overall, the methods and data described here are expected to support the future development of more automated approaches to glycan identification and quantitation.

Indexed as

AnimalsCell Line, TumorChromatography, LiquidGlycomicsGraphiteHEK293 CellsHumansIsomerismMaleMice, Inbred BALB CPolysaccharidesPorosityTandem Mass SpectrometryGraphitePolysaccharides

Identifiers

PMID31065629
PMCPMC6923133

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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.