| Title |
Cell-Free Identification of S. cerevisiae Strains by Analysis of Supernatant Using LC-MS
|
|---|---|
| Published in |
Journal of the American Society for Mass Spectrometry, August 2018
|
| DOI | 10.1007/s13361-018-2046-3 |
| Pubmed ID | |
| Authors |
Cathy Muste, Kevin G. Owens |
| Abstract |
Current literature shows a gap for methods which can identify yeast sub-species (strains or serovars) in samples where there are no viable cells remaining. Presented here is a technique for the analysis of yeast supernatant, including solid phase extraction, data-dependent acquisition liquid chromatography/mass spectrometry (LC-MS), and two chemometric methods to identify and classify yeast strains. Five strains of Saccharomyces cerevisiae were successfully identified in various stages of growth. In addition, peptide/protein identification was performed, without the need for additional data acquisition. Graphical Abstract ᅟ. |
X Demographics
The data shown below were collected from the profiles of 2 X users who shared this research output. Click here to find out more about how the information was compiled.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| United States | 1 | 50% |
| Unknown | 1 | 50% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Scientists | 1 | 50% |
| Science communicators (journalists, bloggers, editors) | 1 | 50% |
Mendeley readers
The data shown below were compiled from readership statistics for 14 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Unknown | 14 | 100% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Student > Ph. D. Student | 5 | 36% |
| Student > Master | 2 | 14% |
| Professor > Associate Professor | 2 | 14% |
| Researcher | 1 | 7% |
| Other | 1 | 7% |
| Other | 0 | 0% |
| Unknown | 3 | 21% |
| Readers by discipline | Count | As % |
|---|---|---|
| Chemistry | 3 | 21% |
| Biochemistry, Genetics and Molecular Biology | 2 | 14% |
| Agricultural and Biological Sciences | 2 | 14% |
| Neuroscience | 1 | 7% |
| Unknown | 6 | 43% |
Attention Score in Context
This research output has an Altmetric Attention Score of 1. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 25 October 2018.
All research outputs
#20,663,600
of 25,385,509 outputs
Outputs from Journal of the American Society for Mass Spectrometry
#3,086
of 3,835 outputs
Outputs of similar age
#265,243
of 341,279 outputs
Outputs of similar age from Journal of the American Society for Mass Spectrometry
#35
of 56 outputs
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