Evidence map›Paper›PMID 28436239›Full record

ReviewExpert review of proteomics2017

Clinical applications of quantitative proteomics using targeted and untargeted data-independent acquisition techniques.

Jesse G Meyer, Birgit Schilling

Abstract readReview
In one paragraph

Review in Expert review of proteomics, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 86 papers.

0numbers the graph read from it
0cells of the map it votes in
86citing 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

86 citing papers in PubMed.

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26 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

2 authors.

Jesse G Meyera Mass Spectrometry Core , Buck Institute for Research on Aging , Novato , CA , USA.
Birgit Schillinga Mass Spectrometry Core , Buck Institute for Research on Aging , Novato , CA , USA.

Funding

REVERSIBLE MITOCHONDRIAL PROTEIN ACETYLATION AND METABOLIC REGULATIONR24DK085610 · NIDDK · J. DAVID GLADSTONE INSTITUTES · PI VERDIN, ERIC M. · 2010 to 2019
$16.0M
Training in Basic Research on Aging and Age-Related DiseaseT32AG000266 · NIA · UNIVERSITY OF CALIF-LAWRENC BERKELEY LAB · PI Lisa M Ellerby · 1998 to 2026
$13.8M
TripleTOF 5600 High Resolution Mass SpectrometerS10OD016281 · OD · BUCK INSTITUTE FOR RESEARCH ON AGING · PI GIBSON, BRADFORD WAYNE · 2013 to 2013
$597k
NIA NIH HHS T32 AG000266NIDDK NIH HHS R24 DK085610NIH HHS S10 OD016281
6 · The paper itself

Abstract

introductionWhile selected/multiple-reaction monitoring (SRM or MRM) is considered the gold standard for quantitative protein measurement, emerging data-independent acquisition (DIA) using high-resolution scans have opened a new dimension of high-throughput, comprehensive quantitative proteomics. These newer methodologies are particularly well suited for discovery of biomarker candidates from human disease samples, and for investigating and understanding human disease pathways. Areas covered: This article reviews the current state of targeted and untargeted DIA mass spectrometry-based proteomic workflows, including SRM, parallel-reaction monitoring (PRM) and untargeted DIA (e.g., SWATH). Corresponding bioinformatics strategies, as well as application in biological and clinical studies are presented. Expert commentary: Nascent application of highly-multiplexed untargeted DIA, such as SWATH, for accurate protein quantification from clinically relevant and disease-related samples shows great potential to comprehensively investigate biomarker candidates and understand disease.

Indexed as

BiomarkersHumansMolecular Diagnostic TechniquesProteomicsBiomarkersbiomarkerclinicData-independent acquisitionmass spectrometryPRMproteomicsquantificationSRMSWATH

Identifiers

PMID28436239
PMCPMC5671767

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.