Evidence map›Paper›PMID 21637204›Full record

ArticleNature protocols2011

Monitoring protein expression in whole-cell extracts by targeted label- and standard-free LC-MS/MS.

Katharina Bluemlein, Markus Ralser

Abstract read
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In one paragraph

Article in Nature protocols, 2011. 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
3.7field-weighted citation impact, top 7% of its field
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, 58 citations in OpenAlex.

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  12. Deciphering Multifactorial Resistance Phenotypes inMolecular & cellular proteomics : MCP · 2018
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  17. Identification of aldolase A as a potential diagnostic biomarker for colorectal cancer based on proteomic analysis using formalin-fixed paraffin-embedded tissue.Tumour biology : the journal of the International Society for Oncodevelopmental Biology and Medicine · 2016
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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

2 authors at 2 institutions in 2 countries.

Katharina BluemleinMass Spectrometry Unit, Max Planck Institute for Molecular Genetics, Berlin, Germany.
Markus Ralser
Max Planck Institute for Molecular Genetics · DEUniversity of Cambridge · GB

Funding

Wellcome Trust
6 · The paper itself

Abstract

Targeted quantification of proteins is a daily task in biological research but often relies on techniques such as western blotting that are only barely quantitative. Here we present a broadly applicable workflow for protein quantification from unpurified whole-cell extracts that can be completed in less than 3 d. Without prefractionation or affinity enrichment, a whole-cell extract is trypsin-digested in an acetonitrile-containing ammonium carbonate buffer and high-molecular-weight compounds are removed by filtration. A normalization strategy, which involves endogenous reference proteins, facilitates the determination of relative changes in protein expression without requiring isotope labeling or standard addition. On a triple-quadrupole mass spectrometer, we demonstrate standard-free quantification of yeast proteins present over five orders of magnitude and present at ≥500 copies per cell. Liquid chromatography/multiple reaction monitoring (LC-MRM)-based proteomics is therefore a next-generation alternative to western blotting, as it allows simultaneous and reliable quantification of multiple endogenous proteins without the need for enrichment, isotope labeling or use of antibodies.

Indexed as

Protein BiosynthesisCell ExtractsChromatography, LiquidProteomicsSaccharomyces cerevisiaeSaccharomyces cerevisiae ProteinsTandem Mass SpectrometryCell ExtractsSaccharomyces cerevisiae Proteins

Identifiers

PMID21637204
OpenAlexW2084068802

What OpenQuestion holds

Textmetadata
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.