ArticleMetabolomics : Official journal of the Metabolomic Society2020
Identification to species level of live single microalgal cells from plankton samples with matrix-free laser/desorption ionization mass spectrometry.
Article in Metabolomics : Official journal of the Metabolomic Society, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
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Who cites it
3 citing papers in PubMed.
- Review
- A new glance at the chemosphere of macroalgal-bacterial interactions: In situ profiling of metabolites in symbiosis by mass spectrometry.Beilstein journal of organic chemistry · 2021Article
- Plant Single-Cell Metabolomics-Challenges and Perspectives.International journal of molecular sciences · 2020Review
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Authors and funding
6 authors.
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No grant is acknowledged in the PubMed record.
Abstract
introductionMarine planktonic communities are complex microbial consortia often dominated by microscopic algae. The taxonomic identification of individual phytoplankton cells usually relies on their morphology and demands expert knowledge. Recently, a live single-cell mass spectrometry (LSC-MS) pipeline was developed to generate metabolic profiles of microalgae.
objectiveTaxonomic identification of diverse microalgal single cells from collection strains and plankton samples based on the metabolic fingerprints analyzed with matrix-free laser desorption/ionization high-resolution mass spectrometry.
methodsMatrix-free atmospheric pressure laser-desorption ionization mass spectrometry was performed to acquire single-cell mass spectra from collection strains and prior identified environmental isolates. The computational identification of microalgal species was performed by spectral pattern matching (SPM). Three similarity scores and a bootstrap-derived confidence score were evaluated in terms of their classification performance. The effects of high and low-mass resolutions on the classification success were evaluated.
resultsSeveral hundred single-cell mass spectra from nine genera and nine species of marine microalgae were obtained. SPM enabled the identification of single cells at the genus and species level with high accuracies. The receiver operating characteristic (ROC) curves indicated a good performance of the similarity measures but were outperformed by the bootstrap-derived confidence scores.
conclusionThis is the first study to solve taxonomic identification of microalgae based on the metabolic fingerprints of the individual cell using an SPM approach.
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