Uncovering the complexity of the yeast lipidome by means of nLC/NSI-MS/MS
Analytica Chimica Acta, ISSN: 0003-2670, Vol: 1140, Page: 199-209
2020
- 16Citations
- 38Captures
Metric Options: Counts1 Year3 YearSelecting the 1-year or 3-year option will change the metrics count to percentiles, illustrating how an article or review compares to other articles or reviews within the selected time period in the same journal. Selecting the 1-year option compares the metrics against other articles/reviews that were also published in the same calendar year. Selecting the 3-year option compares the metrics against other articles/reviews that were also published in the same calendar year plus the two years prior.
Example: if you select the 1-year option for an article published in 2019 and a metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019. If you select the 3-year option for the same article published in 2019 and the metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019, 2018 and 2017.
Citation Benchmarking is provided by Scopus and SciVal and is different from the metrics context provided by PlumX Metrics.
Example: if you select the 1-year option for an article published in 2019 and a metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019. If you select the 3-year option for the same article published in 2019 and the metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019, 2018 and 2017.
Citation Benchmarking is provided by Scopus and SciVal and is different from the metrics context provided by PlumX Metrics.
Metrics Details
- Citations16
- Citation Indexes16
- 16
- CrossRef5
- Captures38
- Readers38
- 38
Article Description
Saccharomyces cerevisiae is a eukaryotic model organism widely used for the investigation of fundamental cellular processes and disease mechanisms. Consequently, the lipid landscape of yeast has been extensively investigated and up to this day the lipidome is considered as rather basic. Here, we used a nLC/NSI-MS/MS method combined with a semi-autonomous data analysis workflow for an in-depth evaluation of the steady state yeast lipidome. We identified close to 900 lipid species across 26 lipid classes, including glycerophospholipids, sphingolipids, glycerolipids and sterol lipids. Most lipid classes are dominated by few high abundant species, with a multitude of lower abundant lipids contributing to the overall complexity of the yeast lipidome. Contrary to previously published datasets, odd-chain and diunsaturated fatty acyl moieties were found to be commonly incorporated in multiple lipid classes. Careful data evaluation furthermore revealed the presence of putative new lipid species such as MMPSs (mono-methylated phosphatidylserine), not yet described in yeast. Overall, our analysis achieved a more than 4-fold increase in lipid identifications compared to previous approaches, underscoring the use of nLC/NSI-MS/MS methods for the in-depth investigation of lipidomes.
Bibliographic Details
http://www.sciencedirect.com/science/article/pii/S0003267020310242; http://dx.doi.org/10.1016/j.aca.2020.10.012; http://www.scopus.com/inward/record.url?partnerID=HzOxMe3b&scp=85093669604&origin=inward; http://www.ncbi.nlm.nih.gov/pubmed/33218482; https://linkinghub.elsevier.com/retrieve/pii/S0003267020310242; https://dx.doi.org/10.1016/j.aca.2020.10.012
Elsevier BV
Provide Feedback
Have ideas for a new metric? Would you like to see something else here?Let us know