Investigating the Potential of Ion Mobility-Mass Spectrometry for Microalgae Biomass Characterization.

Anal Chem

National Institute of Metrology, Quality and Technology (INMETRO), Division of Chemical and Thermal Metrology , Laboratory of Organic Analysis , 25250-020 , Duque de Caxias , Rio de Janeiro , Brazil.

Published: July 2019

AI Article Synopsis

  • Algae biomass characterization is complex due to its intricate metabolites, making analytical methods crucial for understanding it.
  • Two methods were utilized: DI-ESI-TWIM-MS for direct analysis without separation and UHPLC-HDMS for high-throughput screening, identifying 1251 compounds across 16 microalgae samples.
  • The findings enhance the ability to classify algae based on metabolite profiles, aiding in the optimization of strains and conditions for biotechnological applications, particularly in biofuels production.

Article Abstract

Algae biomass is formed by an extremely complex set of metabolites, and its molecular characterization has been very challenging. We report the characterization of microalgae extracts via traveling wave ion mobility-mass spectrometry (TWIM-MS) by two different analysis strategies. First, the extracts were analyzed by direct infusion electrospray ionization (ESI) with no previous chromatographic separation (DI-ESI-TWIM-MS). Second, the samples were screened for metabolites and lipids using an untargeted high-throughput method that employs ultrahigh-performance liquid chromatography (UHPLC) using data-independent analysis (DIA) - MS (UHPLC-HDMS). Sixteen different microalgae biomasses were evaluated by both strategies. DI-ESI-TWIM-MS was able, via distinct drift times, to set apart different classes of metabolites, with the differences in the profiles of each microalga readily evident. With the UHPLC-HDMS approach, 1251 different compounds were putatively annotated across 16 samples with 210 classified as lipids. From the normalized abundance for each annotated compound category, a detailed profiling in terms of metabolites, lipids, and lipid classes of each sample was performed. The reported workflow represents a powerful tool to determine the most suitable biotechnological applications for a given alga type and may allow for real-time monitoring of the algae composition distribution as a function of growth conditions, feedstocks, and the like. The determination of collision cross section results in improved confidence in the identification of triacylglycerols in samples, highly applicable to biofuels production. The two analysis strategies explored in this work offer powerful tools for the biomass industry by aiding in the identification of ideal strains and culture conditions for a specific application, saving analysis time and facilitating identification of a large number of constituents at once.

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http://dx.doi.org/10.1021/acs.analchem.9b02172DOI Listing

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