Evidence map›Paper›PMID 30088233›Full record

ArticleJournal of the American Society for Mass Spectrometry2018

Introducing a Cell-Free Approach for the Identification of Brewing Yeast (Saccharomyces cerevisiae) Strains Using MALDI-TOF MS.

Elsa Gorre, Cathy Muste, Kevin G Owens

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Article in Journal of the American Society for Mass Spectrometry, 2018. 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. Review
  4. Cell-Free Identification of S. cerevisiae Strains by Analysis of Supernatant Using LC-MS.Journal of the American Society for Mass Spectrometry · 2018
    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

3 authors.

Elsa GorreDepartment of Chemistry, Drexel University, 3141 Chestnut St, Philadelphia, PA, 19104-2875, USA.
Cathy MusteDepartment of Chemistry, Drexel University, 3141 Chestnut St, Philadelphia, PA, 19104-2875, USA.
Kevin G OwensDepartment of Chemistry, Drexel University, 3141 Chestnut St, Philadelphia, PA, 19104-2875, USA. kevin.owens@drexel.edu.ORCID http://orcid.org/0000-0002-2813-3169

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Matrix-assisted laser desorption ionization (MALDI) time-of-flight mass spectrometry (TOF MS) is now accepted as a quick, easy-to-use, cost-effective, and accurate technique for the identification of microorganisms. However, the successful identification of microorganisms is dependent upon careful attention to factors such as growth conditions, extraction methods, mass spectral data collection, and data analysis procedures. Currently, most microorganism identification has been limited to the species level, and only a limited number of publications have been successful in achieving strain-level identification. In this work, a "cell-free" approach is introduced where peptide analytes secreted by several Saccharomyces cerevisiae strains during their growth period are analyzed. The analysis of the cell supernatant generates mass spectral patterns that are specific to each strain. The patterns generated in combination with a robust data analysis workflow using the open-source programs MALDIquant and Mass-Up allows for strain-level identification of S. cerevisiae. The cell-free approach using the yeast supernatant to accurately identify yeast strains is presented here as a proof of concept. Graphical Abstract.

Indexed as

Cell-Free SystemPrincipal Component AnalysisSaccharomyces cerevisiaeSpectrometry, Mass, Matrix-Assisted Laser Desorption-IonizationCell-free approachMALDIquantMALDI-TOF MSMass-UpMicroorganism identificationS. cerevisiae strain identification

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Registered trials

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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.