Dataset Information

Mass Spectrometry-Based Metabolomics Combined with Quantitative Analysis of the Microalgal Diatom (Chaetoceros calcitrans).

ABSTRACT: Although many metabolomics studies of higher land plant species have been conducted, similar studies of lower nonland plant species, which include microalgae, are still developing. The present study represents an attempt to characterize the metabolic profile of a microalgal diatom Chaetoceros calcitrans, by applying high-resolution mass spectrometry detection, via Q-ExactiveTM Plus Orbitrap mass spectrometry. The results showed that 54 metabolites of various classes were tentatively identified. Experimentally, the chloroform and acetone extracts were clearly distinguished from other solvent extracts in chemometric regression analysis using PLS, showing the differences in the C. calcitrans metabolome between the groups. In addition, specific metabolites were evaluated, which supported the finding of antioxidant and anti-inflammatory activities. This study also provides data on the quantitative analysis of four carotenoids based on the identification results. Therefore, these findings could serve as a reliable tool for identifying and quantifying the metabolome that could reflect the metabolic activities of C. calcitrans.

SUBMITTER: Azizan A

PROVIDER: S-EPMC7459737 | biostudies-literature | 2020 Jul

REPOSITORIES: biostudies-literature

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Mass Spectrometry-Based Metabolomics Combined with Quantitative Analysis of the Microalgal Diatom (<i>Chaetoceros calcitrans</i>).

Azizan Awanis A Maulidiani M M R Rudiyanto R Shaari Khozirah K Ismail Intan Safinar IS Nagao Norio N Abas Faridah F

Marine drugs 20200730 8

Although many metabolomics studies of higher land plant species have been conducted, similar studies of lower nonland plant species, which include microalgae, are still developing. The present study represents an attempt to characterize the metabolic profile of a microalgal diatom <i>Chaetoceros calcitrans</i>, by applying high-resolution mass spectrometry detection, via Q-ExactiveTM Plus Orbitrap mass spectrometry. The results showed that 54 metabolites of various classes were tentatively ident ...[more]

PMID: 32751412

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Project description:Unicellular phototrophic algae can form massive blooms with up to millions of individual cells per milliliter in freshwater and marine ecosystems. Despite the temporal dominance of bloom formers many algal species can co-exist and compete for nutrients and space, creating a complex and diverse community. While microscopy and single cell genomics can address the taxonomic inventory, the cellular metabolome has yet to be thoroughly explored to determine the physiological status of microalgae. This might, however, provide a key to understand the observed species diversity in the homogeneous environment. Here, we introduce an effective, rapid and versatile method to analyze living single cells from aqueous substrata with laser-desorption/ionization mass spectrometry (LDI-MS) using a simple and inexpensive matrix-free support. The cells deposited on a cultivation-medium wetted support are analyzed with minimal disturbance as they remain in their natural viable state until their disruption during LDI-MS. Metabolites desorbed from single cells are analyzed on High-Resolution Mass Spectrometry (HR-MS) using the Orbitrap FT-MS technology to fingerprint cellular chemistry. This live single-cell mass spectrometry (LSC-MS) allows assessing the physiological status and strain-specifics of different microalgae, including marine diatoms and freshwater chlorophytes, at the single-cell level. We further report a reliable and robust data treatment pipeline to perform multivariate statistics on the replicated LSC-MS data. Comparing single cell MS spectra from natural phytoplankton samples and from laboratory strains allows the identification and discrimination of inter and intra-specific metabolic variability and thereby has promising applications in addressing highly complex phytoplankton communities. Notably, the herein described matrix-free live-single-cell LDI-HR-MS approach enables monitoring dynamics of the plankton and might explain why key-players survive, thrive, avoid selective feeding or pathogenic virus and bacteria, while others are overcome and die.

Project description:BackgroundAssessing the reproducibility of measurements is an important first step for improving the reliability of downstream analyses of high-throughput metabolomics experiments. We define a metabolite to be reproducible when it demonstrates consistency across replicate experiments. Similarly, metabolites which are not consistent across replicates can be labeled as irreproducible. In this work, we introduce and evaluate the use (Ma)ximum (R)ank (R)eproducibility (MaRR) to examine reproducibility in mass spectrometry-based metabolomics experiments. We examine reproducibility across technical or biological samples in three different mass spectrometry metabolomics (MS-Metabolomics) data sets.ResultsWe apply MaRR, a nonparametric approach that detects the change from reproducible to irreproducible signals using a maximal rank statistic. The advantage of using MaRR over model-based methods that it does not make parametric assumptions on the underlying distributions or dependence structures of reproducible metabolites. Using three MS Metabolomics data sets generated in the multi-center Genetic Epidemiology of Chronic Obstructive Pulmonary Disease (COPD) study, we applied the MaRR procedure after data processing to explore reproducibility across technical or biological samples. Under realistic settings of MS-Metabolomics data, the MaRR procedure effectively controls the False Discovery Rate (FDR) when there was a gradual reduction in correlation between replicate pairs for less highly ranked signals. Simulation studies also show that the MaRR procedure tends to have high power for detecting reproducible metabolites in most situations except for smaller values of proportion of reproducible metabolites. Bias (i.e., the difference between the estimated and the true value of reproducible signal proportions) values for simulations are also close to zero. The results reported from the real data show a higher level of reproducibility for technical replicates compared to biological replicates across all the three different datasets. In summary, we demonstrate that the MaRR procedure application can be adapted to various experimental designs, and that the nonparametric approach performs consistently well.ConclusionsThis research was motivated by reproducibility, which has proven to be a major obstacle in the use of genomic findings to advance clinical practice. In this paper, we developed a data-driven approach to assess the reproducibility of MS-Metabolomics data sets. The methods described in this paper are implemented in the open-source R package marr, which is freely available from Bioconductor at http://bioconductor.org/packages/marr .

Dataset Information

Mass Spectrometry-Based Metabolomics Combined with Quantitative Analysis of the Microalgal Diatom (Chaetoceros calcitrans).

Publications

Mass Spectrometry-Based Metabolomics Combined with Quantitative Analysis of the Microalgal Diatom (<i>Chaetoceros calcitrans</i>).

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