Evidence map›Paper›PMID 27748581›Full record

ArticleAnalytical chemistry2016

Evaluation of Spectral Counting for Relative Quantitation of Proteoforms in Top-Down Proteomics.

Lucía Geis-Asteggiante, Suzanne Ostrand-Rosenberg, Catherine Fenselau, Nathan J Edwards

Abstract read
In one paragraph

Article in Analytical chemistry, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
15citing papers in PubMed, 1 pooled it
–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

15 citing papers in PubMed, 1 synthesis or guideline pooled it.

  1. Pooled it
  2. Article
  3. Article
  4. Article
  5. Article
  6. Article
  7. Review
  8. Article
  9. Article
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  12. Review
  13. Article
  14. Top-Down Proteomics: Ready for Prime Time?Analytical chemistry · 2018
    Review
  15. Protein-species quantitative venomics: looking through a crystal ball.The journal of venomous animals and toxins including tropical diseases · 2017
    Review
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

4 authors.

Lucía Geis-AsteggianteUniversity of Maryland , College Park, Maryland 20742, United States.
Suzanne Ostrand-RosenbergUniversity of Maryland Baltimore County , Baltimore, Maryland 21250, United States.
Catherine FenselauUniversity of Maryland , College Park, Maryland 20742, United States.
Nathan J EdwardsGeorgetown University Medical Center , Washington DC 20007, United States.

Funding

Ubiquitinated Proteins in Exosomes of Immune Suppressive Myeloid CellsR01GM021248 · NIGMS · UNIV OF MARYLAND, COLLEGE PARK · PI FENSELAU, CATHERINE C, FUSHMAN, DAVID · 1985 to 2017
$4.6M
Orbitrap Fusion Tribrid Mass SpectrometerS10OD019938 · OD · UNIV OF MARYLAND, COLLEGE PARK · PI FENSELAU, CATHERINE C · 2015 to 2015
$600k
APPLICATIONS OF MASS SPECTROMETRY IN PHARMACOLOGYR37GM021248 · NIGMS · UNIVERSITY OF MARYLAND COLLEGE PK CAMPUS · PI FENSELAU, CATHERINE C. · 1992 to 2001
$438k
NIGMS NIH HHS R01 GM021248NIGMS NIH HHS R37 GM021248NIH HHS S10 OD019938
6 · The paper itself

Abstract

Spectral counting is a straightforward label-free quantitation strategy used in bottom-up proteomics workflows. The application of spectral counting in label-free top-down proteomics workflows can be similarly straightforward but has not been applied as widely as quantitation by chromatographic peak areas or peak intensities. In this study, we evaluate spectral counting for quantitative comparisons in label-free top-down proteomics workflows by comparison with chromatographic peak areas and intensities. We tested these quantitation approaches by spiking standard proteins into a complex protein background and comparing relative quantitation by spectral counts with normalized chromatographic peak areas and peak intensities from deconvoluted extracted ion chromatograms of the spiked proteins. Ratio estimates and statistical significance of differential abundance from each quantitation technique are evaluated against the expected ratios and each other. In this experiment, spectral counting was able to detect differential abundance of spiked proteins for expected ratios ≥2, with comparable or higher sensitivity than normalized areas and intensities. We also found that while ratio estimates using peak areas and intensities are usually more accurate, the spectral-counting-based estimates are not substantially worse. Following the evaluation and comparison of these label-free top-down quantitation strategies using spiked proteins, spectral counting, along with normalized chromatographic peak areas and intensities, were used to analyze the complex protein cargo of exosomes shed by myeloid-derived suppressor cells collected under high and low conditions of inflammation, revealing statistically significant differences in abundance for several proteoforms, including the active pro-inflammatory proteins S100A8 and S100A9.

Indexed as

ProteomicsAnimalsCalgranulin ACalgranulin BCell Line, TumorChromatography, LiquidComputational BiologyMass SpectrometryMiceCalgranulin ACalgranulin BS100a8 protein, mouseS100A9 protein, mouse

Identifiers

PMID27748581
PMCPMC6178225

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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.