Evidence map›Paper›PMID 26795204›Full record

ArticleJournal of proteome research2016

Quantitation and Identification of Thousands of Human Proteoforms below 30 kDa.

Kenneth R Durbin, Luca Fornelli, Ryan T Fellers, Peter F Doubleday, Masashi Narita, Neil L Kelleher

Abstract read
In one paragraph

Article in Journal of proteome research, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 64 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
64citing 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

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

  1. Pooled it
  2. Deep Proteoform Sequencing with Top-Down Direct Mass Technology.bioRxiv : the preprint server for biology · 2026
    Article
  3. Article
  4. Article
  5. Article
  6. Top-down proteomics.Nature reviews. Methods primers · 2024
    Article
  7. Review
  8. Article
  9. Review
  10. Multidimensional Separations in Top-Down Proteomics.Analytical science advances · 2023
    Article
  11. Article
  12. Review
  13. Article
  14. Article
  15. Review
  16. Article
  17. Current Insights on the Impact of Proteomics in Respiratory Allergies.International journal of molecular sciences · 2022
    Review
  18. Article
  19. Article
  20. Online μSECAnalytical chemistry · 2021
    Article

4 more citing papers are in PubMed but not listed here.

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

6 authors.

Kenneth R DurbinDepartments of Chemistry and Molecular Biosciences, Northwestern University , 2170 Campus Drive, Evanston, Illinois 60208, United States.
Luca FornelliDepartments of Chemistry and Molecular Biosciences, Northwestern University , 2170 Campus Drive, Evanston, Illinois 60208, United States.
Ryan T FellersDepartments of Chemistry and Molecular Biosciences, Northwestern University , 2170 Campus Drive, Evanston, Illinois 60208, United States.
Peter F DoubledayDepartments of Chemistry and Molecular Biosciences, Northwestern University , 2170 Campus Drive, Evanston, Illinois 60208, United States.
Masashi NaritaCancer Research UK Cambridge Institute, Li Ka Shing Centre, University of Cambridge , Robinson Way, Cambridge CB2 0RE, U.K.
Neil L KelleherDepartments of Chemistry and Molecular Biosciences, Northwestern University , 2170 Campus Drive, Evanston, Illinois 60208, United States.

Funding

The UIUC Neuroproteomics Center on Cell-Cell SignalingP30DA018310 · NIDA · UNIVERSITY OF ILLINOIS URBANA-CHAMPAIGN · PI Elena V Romanova · 2004 to 2026
$24.9M
TR&D 7: Cell Specific ProteomicsP41GM108569 · NIGMS · NORTHWESTERN UNIVERSITY · PI KELLEHER, NEIL L · 2015 to 2024
$13.6M
Eukaryotic Post-Translational ModificationsR01GM067193 · NIGMS · UNIVERSITY OF ILLINOIS URBANA-CHAMPAIGN · PI KELLEHER, NEIL L · 2003 to 2015
$4.0M
Cancer Research UK 15890CCR NIH HHS HHSN261200800001CNCI NIH HHS HHSN261200800001ENIDA NIH HHS P30 DA018310NIGMS NIH HHS GM067193NIGMS NIH HHS GM108569NIGMS NIH HHS P41 GM108569NIGMS NIH HHS R01 GM067193
6 · The paper itself

Abstract

Top-down proteomics is capable of identifying and quantitating unique proteoforms through the analysis of intact proteins. We extended the coverage of the label-free technique, achieving differential analysis of whole proteins <30 kDa from the proteomes of growing and senescent human fibroblasts. By integrating improved control software with more instrument time allocated for quantitation of intact ions, we were able to collect protein data between the two cell states, confidently comparing 1577 proteoform levels. To then identify and characterize proteoforms, our advanced acquisition software, named Autopilot, employed enhanced identification efficiency in identifying 1180 unique Swiss-Prot accession numbers at 1% false-discovery rate. This coverage of the low mass proteome is equivalent to the largest previously reported but was accomplished in 23% of the total acquisition time. By maximizing both the number of quantified proteoforms and their identification rate in an integrated software environment, this work significantly advances proteoform-resolved analyses of complex systems.

Indexed as

Cells, CulturedCellular SenescenceChromatography, Reverse-PhaseHumansProtein IsoformsProteomeSoftwareTandem Mass SpectrometryProtein IsoformsProteomecellular senescencedifferential mass spectrometryFourier transform mass spectrometryGELFrEElabel-freeproteoformquantitative proteomicstop-down proteomics

Identifiers

PMID26795204
PMCPMC4794255

What OpenQuestion holds

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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.