Evidence map›Paper›PMID 30763335›Full record

ArticlePloS one2019

A rapid methods development workflow for high-throughput quantitative proteomic applications.

Yan Chen, Jonathan Vu, Mitchell G Thompson, William A Sharpless, Leanne Jade G Chan, Jennifer W Gin, Jay D Keasling, Paul D Adams, Christopher J Petzold

Abstract read
In one paragraph

Article in PloS one, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.

0numbers the graph read from it
0cells of the map it votes in
4citing 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

4 citing papers in PubMed.

  1. Article
  2. Article
  3. Leveraging host metabolism for bisdemethoxycurcumin production inMetabolic engineering communications · 2020
    Article
  4. 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

9 authors.

Yan ChenJoint BioEnergy Institute, Lawrence Berkeley National Laboratory, Emeryville, CA, United States of America.
Jonathan VuJoint BioEnergy Institute, Lawrence Berkeley National Laboratory, Emeryville, CA, United States of America.ORCID 0000-0002-4950-7967
Mitchell G ThompsonJoint BioEnergy Institute, Lawrence Berkeley National Laboratory, Emeryville, CA, United States of America.
William A SharplessJoint BioEnergy Institute, Lawrence Berkeley National Laboratory, Emeryville, CA, United States of America.
Leanne Jade G ChanJoint BioEnergy Institute, Lawrence Berkeley National Laboratory, Emeryville, CA, United States of America.
Jennifer W GinJoint BioEnergy Institute, Lawrence Berkeley National Laboratory, Emeryville, CA, United States of America.
Jay D KeaslingJoint BioEnergy Institute, Lawrence Berkeley National Laboratory, Emeryville, CA, United States of America.
Paul D AdamsJoint BioEnergy Institute, Lawrence Berkeley National Laboratory, Emeryville, CA, United States of America.
Christopher J PetzoldJoint BioEnergy Institute, Lawrence Berkeley National Laboratory, Emeryville, CA, United States of America.ORCID 0000-0002-8270-5228

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Recent improvements in the speed and sensitivity of liquid chromatography-mass spectrometry systems have driven significant progress toward system-wide characterization of the proteome of many species. These efforts create large proteomic datasets that provide insight into biological processes and identify diagnostic proteins whose abundance changes significantly under different experimental conditions. Yet, these system-wide experiments are typically the starting point for hypothesis-driven, follow-up experiments to elucidate the extent of the phenomenon or the utility of the diagnostic marker, wherein many samples must be analyzed. Transitioning from a few discovery experiments to quantitative analyses on hundreds of samples requires significant resources both to develop sensitive and specific methods as well as analyze them in a high-throughput manner. To aid these efforts, we developed a workflow using data acquired from discovery proteomic experiments, retention time prediction, and standard-flow chromatography to rapidly develop targeted proteomic assays. We demonstrated this workflow by developing MRM assays to quantify proteins of multiple metabolic pathways from multiple microbes under different experimental conditions. With this workflow, one can also target peptides in scheduled/dynamic acquisition methods from a shotgun proteomic dataset downloaded from online repositories, validate with appropriate control samples or standard peptides, and begin analyzing hundreds of samples in only a few minutes.

Indexed as

WorkflowActinomycetalesAgrobacterium tumefaciensBasidiomycotaChromatography, LiquidEscherichia coliMass SpectrometryProteomicsPseudomonas putidaReproducibility of ResultsSaccharomyces cerevisiaeSoftware

Identifiers

PMID30763335
PMCPMC6375547

What OpenQuestion holds

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LicenceCC BY
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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.