Evidence map›Paper›PMID 29924586›Full record

ArticleAnalytical chemistry2018

The Value of Activated Ion Electron Transfer Dissociation for High-Throughput Top-Down Characterization of Intact Proteins.

Nicholas M Riley, Jacek W Sikora, Henrique S Seckler, Joseph B Greer, Ryan T Fellers, Richard D LeDuc, Michael S Westphall, Paul M Thomas, Neil L Kelleher, Joshua J Coon

Abstract read
In one paragraph

Article in Analytical chemistry, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 27 papers.

0numbers the graph read from it
0cells of the map it votes in
27citing 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

27 citing papers in PubMed.

  1. Review
  2. Article
  3. Review
  4. Article
  5. Mobility-Assisted Pseudo-MSJournal of the American Society for Mass Spectrometry · 2024
    Article
  6. Article
  7. Article
  8. Article
  9. Review
  10. Proteoform-Selective Imaging of Tissues Using Mass Spectrometry.Angewandte Chemie (International ed. in English) · 2022
    Article
  11. Article
  12. Article
  13. Article
  14. Article
  15. Article
  16. Review
  17. Article
  18. Review
  19. Article
  20. Article
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

10 authors.

Jacek W SikoraNational Resource for Translational and Developmental Proteomics, Departments of Chemistry and Molecular Biosciences , Northwestern University , Evanston , Illinois 60208 , United States.
Henrique S SecklerNational Resource for Translational and Developmental Proteomics, Departments of Chemistry and Molecular Biosciences , Northwestern University , Evanston , Illinois 60208 , United States.
Joseph B GreerNational Resource for Translational and Developmental Proteomics, Departments of Chemistry and Molecular Biosciences , Northwestern University , Evanston , Illinois 60208 , United States.
Ryan T FellersNational Resource for Translational and Developmental Proteomics, Departments of Chemistry and Molecular Biosciences , Northwestern University , Evanston , Illinois 60208 , United States.
Richard D LeDucNational Resource for Translational and Developmental Proteomics, Departments of Chemistry and Molecular Biosciences , Northwestern University , Evanston , Illinois 60208 , United States.
Michael S Westphall
Paul M ThomasNational Resource for Translational and Developmental Proteomics, Departments of Chemistry and Molecular Biosciences , Northwestern University , Evanston , Illinois 60208 , United States.ORCID 0000-0003-2887-4765
Neil L KelleherNational Resource for Translational and Developmental Proteomics, Departments of Chemistry and Molecular Biosciences , Northwestern University , Evanston , Illinois 60208 , United States.ORCID 0000-0002-8815-3372
Joshua J CoonMorgridge Institute for Research , Madison , Wisconsin 53706 , United States.ORCID 0000-0002-0004-8253

Funding

TR&D 7: Cell Specific ProteomicsP41GM108569 · NIGMS · NORTHWESTERN UNIVERSITY · PI KELLEHER, NEIL L · 2015 to 2024
$13.6M
TR&D 2 Metabolic Labels for Ultraplexed Protein Quantification p. 453P41GM108538 · NIGMS · UNIVERSITY OF WISCONSIN-MADISON · PI COON, JOSHUA J · 2016 to 2025
$13.1M
Chemistry of Life Processes Predoctoral Training ProgramT32GM105538 · NIGMS · NORTHWESTERN UNIVERSITY · PI KELLEHER, NEIL L · 2013 to 2022
$2.7M
Uniting Mass Spectrometry and Glycoscience to Investigate Cancer BiologyF99CA212454 · NCI · UNIVERSITY OF WISCONSIN-MADISON · PI RILEY, NICHOLAS M · 2016 to 2017
$65k
NCI NIH HHS F99 CA212454NIGMS NIH HHS P41 GM108538NIGMS NIH HHS P41 GM108569NIGMS NIH HHS T32 GM105538
6 · The paper itself

Abstract

High-throughput top-down proteomic experiments directly identify proteoforms in complex mixtures, making high quality tandem mass spectra necessary to deeply characterize proteins with many sources of variation. Collision-based dissociation methods offer expedient data acquisition but often fail to extensively fragment proteoforms for thorough analysis. Electron-driven dissociation methods are a popular alternative approach, especially for precursor ions with high charge density. Combining infrared photoactivation concurrent with electron transfer dissociation (ETD) reactions, i.e., activated ion ETD (AI-ETD), can significantly improve ETD characterization of intact proteins, but benefits of AI-ETD have yet to be quantified in high-throughput top-down proteomics. Here, we report the first application of AI-ETD to LC-MS/MS characterization of intact proteins (<20 kDa), highlighting improved proteoform identification the method offers over higher energy-collisional dissociation (HCD), standard ETD, and ETD followed by supplemental HCD activation (EThcD). We identified 935 proteoforms from 295 proteins from human colorectal cancer cell line HCT116 using AI-ETD compared to 1014 proteoforms, 915 proteoforms, and 871 proteoforms with HCD, ETD, and EThcD, respectively. Importantly, AI-ETD outperformed each of the three other methods in MS/MS success rates and spectral quality metrics (e.g., sequence coverage achieved and proteoform characterization scores). In all, this four-method analysis offers the most extensive comparisons to date and demonstrates that AI-ETD both increases identifications over other ETD methods and improves proteoform characterization via higher sequence coverage, positioning it as a premier method for high-throughput top-down proteomics.

Indexed as

Amino Acid SequenceCell Line, TumorChromatography, LiquidColorectal NeoplasmsElectronsElectron TransportHigh-Throughput Screening AssaysHumansPhotochemical ProcessesProtein Processing, Post-TranslationalProteinsProteomicsTandem Mass SpectrometryProteins

Identifiers

PMID29924586
PMCPMC6050102

What OpenQuestion holds

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Read underepoch 390

Registered trials

None linked

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.