Evidence map›Paper›PMID 26867742›Full record

ArticleMethods in molecular biology (Clifton, N.J.)2016

A Method for Label-Free, Differential Top-Down Proteomics.

Ioanna Ntai, Timothy K Toby, Richard D LeDuc, Neil L Kelleher

Abstract read
In one paragraph

Article in Methods in molecular biology (Clifton, N.J.), 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 26 papers.

0numbers the graph read from it
0cells of the map it votes in
26citing 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

26 citing papers in PubMed.

  1. Article
  2. Article
  3. Article
  4. Article
  5. Article
  6. Article
  7. Article
  8. Article
  9. Article
  10. Review
  11. Article
  12. Quantitative Mass Spectrometry-Based Proteomics: An Overview.Methods in molecular biology (Clifton, N.J.) · 2021
    Review
  13. Article
  14. Article
  15. High-Throughput Quantitative Top-Down Proteomics: Histone H4.Journal of the American Society for Mass Spectrometry · 2019
    Article
  16. Article
  17. Review
  18. Article
  19. Review
  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

4 authors.

Ioanna NtaiDepartment of Chemistry, Northwestern University, Evanston, IL, USA.
Timothy K TobyDepartment of Chemistry, Northwestern University, Evanston, IL, USA.
Richard D LeDucDepartment of Chemistry, Northwestern University, Evanston, IL, USA.
Neil L KelleherDepartment of Chemistry, Northwestern University, Evanston, IL, USA. n-kelleher@northwestern.edu.

Funding

Tumor Environment and Metastasis (TEAM) Research ProgramP30CA060553 · NCI · NORTHWESTERN UNIVERSITY AT CHICAGO · PI Daniela E Matei · 1993 to 2026
$153.9M
TR&D 7: Cell Specific ProteomicsP41GM108569 · NIGMS · NORTHWESTERN UNIVERSITY · PI KELLEHER, NEIL L · 2015 to 2024
$13.6M
Chemistry of Life Processes Predoctoral Training ProgramT32GM105538 · NIGMS · NORTHWESTERN UNIVERSITY · PI KELLEHER, NEIL L · 2013 to 2022
$2.7M
NCI NIH HHS P30 CA060553NIGMS NIH HHS P41 GM108569NIGMS NIH HHS P41GM108569NIGMS NIH HHS T32 GM105538
6 · The paper itself

Abstract

Biomarker discovery in the translational research has heavily relied on labeled and label-free quantitative bottom-up proteomics. Here, we describe a new approach to biomarker studies that utilizes high-throughput top-down proteomics and is the first to offer whole protein characterization and relative quantitation within the same experiment. Using yeast as a model, we report procedures for a label-free approach to quantify the relative abundance of intact proteins ranging from 0 to 30 kDa in two different states. In this chapter, we describe the integrated methodology for the large-scale profiling and quantitation of the intact proteome by liquid chromatography-mass spectrometry (LC-MS) without the need for metabolic or chemical labeling. This recent advance for quantitative top-down proteomics is best implemented with a robust and highly controlled sample preparation workflow before data acquisition on a high-resolution mass spectrometer, and the application of a hierarchical linear statistical model to account for the multiple levels of variance contained in quantitative proteomic comparisons of samples for basic and clinical research.

Indexed as

Mass SpectrometryProteomeProteomicsProteomeDifferential expressionLabel-free quantitationProteoformQuantitative mass spectrometryTop-down proteomicsTop-down quantitation

Identifiers

PMID26867742
PMCPMC4777307

What OpenQuestion holds

Textmetadata
LicenceTDM
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.