Identification to species level of live single microalgal cells from plankton samples with matrix-free laser/desorption ionization mass spectrometry
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Published:2020-02-24
Issue:3
Volume:16
Page:
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ISSN:1573-3882
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Container-title:Metabolomics
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language:en
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Short-container-title:Metabolomics
Author:
Baumeister Tim U. H.ORCID, Vallet MarineORCID, Kaftan FilipORCID, Guillou LaureORCID, Svatoš AlešORCID, Pohnert GeorgORCID
Abstract
Abstract
Introduction
Marine 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.
Objective
Taxonomic 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.
Methods
Matrix-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.
Results
Several 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.
Conclusion
This is the first study to solve taxonomic identification of microalgae based on the metabolic fingerprints of the individual cell using an SPM approach.
Funder
Max-Planck-Gesellschaft
Publisher
Springer Science and Business Media LLC
Subject
Clinical Biochemistry,Biochemistry,Endocrinology, Diabetes and Metabolism
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