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Investigating the potential of ion mobility-mass spectrometry for microalgae biomass characterization Maíra Fasciotti, Gustavo H. M. F. Souza, Giuseppe Astarita, Ingrid Chastinet Ribeiro Costa, Thays V. C. Monteiro, Claudia M. L. L. Teixeira, Marcos Nogueira Eberlin, and Amarijt Singh Sarpal Anal. Chem., Just Accepted Manuscript • DOI: 10.1021/acs.analchem.9b02172 • Publication Date (Web): 31 May 2019 Downloaded from http://pubs.acs.org on June 3, 2019

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Analytical Chemistry

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Article

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Title: “Investigating the potential of ion mobility-mass spectrometry for microalgae

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biomass characterization”

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Authors: Maíra Fasciotti1,5*, Gustavo H. M. F. Souza2, Giuseppe Astarita3, Ingrid C.

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R. Costa1, Thays. V. C. Monteiro1, Claudia M. L. L. Teixeira4, Marcos N. Eberlin5,6,

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Amarijt S. Sarpal1

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1

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Chemical Metrology, Laboratory of organic analysis, 25250-020, Duque de Caxias, RJ,

National Institute of Metrology, Quality and Technology (INMETRO), Division of

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Brazil

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2

MS Applications & Development Laboratory, Waters Corporation, Barueri, SP, Brazil;

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3

Department of Biochemistry and Molecular & Cellular Biology, Georgetown

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University, Washington, DC, USA;

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4 Instituto

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5

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Campinas – UNICAMP, Campinas, SP, Brazil.

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6

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Brazil

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*corresponding author: M. Fasciotti, [email protected]

Nacional de Tecnologia (INT), Rio de Janeiro, Rio de Janeiro, Brazil;

ThoMSon Mass Spectrometry Laboratory, Institute of Chemistry, University of

Mackenzie Presbyterian University, School of Engineering, 01302-907 São Paulo, SP

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Analytical Chemistry 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60

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ABSTRACT

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Algae biomass is formed by an extremely complex set of metabolites and its molecular

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characterization has been very challenging. We report the characterization of

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microalgae extracts via traveling wave ion mobility – mass spectrometry (TWIM-MS)

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by two different analysis strategies. First, the extracts were analyzed by direct infusion

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electrospray ionization (ESI) with no previous chromatographic separation (DI-ESI-

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TWIM-MS). Second, the samples were screened for metabolites and lipids using an

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untargeted high throughput method that employs ultra-high performance liquid

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chromatography (UHPLC) using data independent analysis (DIA) – MSE (UHPLC-

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HDMSE). Sixteen different microalgae biomasses were evaluated by both strategies.

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DI-ESI-TWIM-MS was able, via distinct drift times, to set apart different classes of

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metabolites, with the differences in the profiles of each microalga readily evident. With

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the UHPLC-HDMSE approach, 1251 different compounds were putatively annotated

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across 16 samples with 210 classified as lipids. From the normalized abundance for

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each annotated compound category, a detailed profiling in terms of metabolites, lipids,

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and lipid classes of each sample was performed. The reported workflow represents a

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powerful tool to determine the most suitable biotechnological applications for a given

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alga type, and may allow for real-time monitoring of the algae composition distribution

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as function of growth conditions, feedstocks, and the like. The determination of collision

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cross section results in improved confidence in the identification of triacylglycerols in

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samples, highly applicable to biofuels production. The two analysis strategies explored

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in this work offer powerful tools for the biomass industry by aiding in the identification

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of ideal strains and culture conditions for a specific application, saving analysis time,

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and facilitating identification of large number of constituents at once.

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INTRODUCTION

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Algae are a diverse group of predominantly aquatic organisms that efficiently

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perform photosynthesis. They are responsible for close to 90% of the photosynthesis

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performed on the planet 1,2,3 and form a diversified polyphyletic group; that is, they in

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fact form a group of an expanded set of organisms belonging to different phylogenetic

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groups.4 Algae have been widely studied for decades within several fields of science.

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With the advent of modern biotechnology, algae have received even more attention,

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especially with the emergence of third- and fourth-generation fuels. 5,6,7 After it was

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realized that algae were able to provide better yields than processes with other

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biomasses, biofuels from algae have been framed as more promising 3rd-generation

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fuels.8 Intensive research followed in the quest of more efficient production of different

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types of biofuels and chemicals from algae. 9, 10 A very interesting advantage of algae

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as source of biomass is that they can be grown in waste and salty waters,11,12 acting

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simultaneously as a bioremediation strategy.13,14

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Currently, studies are focused on producing biohydrogen from algae via solar 15

energy and water,

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bioethanol.

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algae bioprocesses.

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produced via algae, since some oleaginous algae have 30-60% (w/w) of

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triacylglycerols on a dry basis.20,21 To obtain better yields to produce biodiesel

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precursor lipids – mainly triacylglycerols – the algae species have been cultivated in

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different culture media, which strongly influence biomass composition. 22 For instance,

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nitrogen limitation or nutrient imbalance increase lipid content in the microalgae cells,

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leading to high biodiesel potential production.

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as well as bioisoprene (2-methyl-1,3-butadiene)

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and

Butanol and isobutanol have also been directly produced from CO2 via 18,19

Biodiesel is perhaps the most promising biofuel to be

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Algae-based biofuels are indeed



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promising, hence major improvements in algae technology are being tested, especially

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for downstream and upstream processes optimization. 24, 25

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The characterization of the resulting algae biomass is also extremely important

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to ensure that the growth conditions are actually affecting the biomass composition as

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desired,26 and can guide best biotechnological practices in the production of biofuels,

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biogas, bio-based chemicals, bioremediation or even in the area of nutraceutical and

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food supplements. 27, 28

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The characterization of this extremely complex

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algae biomass has been

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performed using several analytical techniques, from the less expensive but less

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comprehensive classical physical-chemical methods such as elemental analysis,

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gravimetric determination of total lipid content or classical methods for total protein or

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carbohydrate measurements,

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consuming instrumental techniques such as NMR

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techniques 34 and chromatography. 35, 36

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to more complex but more training and time31,32,

FTIR,

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laser-based

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Mass spectrometry (MS) generally coupled to previous chromatographic

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separation has been established as a powerful and versatile analytical technique for

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both quantitation of target compounds as well as identification of a massive number of

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unknown compounds at once in a wide variety of matrices and samples. MS-based

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analytical methods have already been successfully applied for algae characterization,

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and a great variety of chemicals have been identified such as peptides,37 proteins,38

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toxins,39 isoflavones,

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hydrocarbons,45 aldehydes,46 and others. 47, 48, 49

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lipids,

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fatty acids,42 sterols,43 polysaccharides44

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With the introduction of commercially available instruments, ion mobility coupled

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with high resolution mass spectrometry (IM-HRMS), has recently become the

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technique of choice in omics sciences, especially in proteomics, lipidomics and 4 ACS Paragon Plus Environment

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50,51, 52, 53, 54

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metabolomics studies.

The technique is frequently coupled with liquid

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chromatography leading to multidimensional data, combining the compounds'

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parameters of retention times versus ions m/z ratios versus ions drift times, the former

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being achieved in a few minutes and the latter on the millisecond timescale.

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Ion mobility (IM) is a technique that separates gaseous ions while traveling

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through a buffer gas under the influence of an electrical field. 55,56 TWIM separation is

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based on shape/size of the ions as measured by their collision cross section (CCS),

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ion-dipole interactions of the ions with the neutral drift gas molecules.57

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Previous studies have demonstrated the excellent performance of the IM

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technique for isomers and isobars separation and characterization of complex

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mixtures,58,59,60,61 as well as for increasing the selectivity of lipidomics and

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metabolomics analyses.

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separated forming sets of characteristic tendencies, that is, drift time regions.

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In complex mixtures, different classes of compounds are

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To determine the best application for a specific biomass, and also to evaluate

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the process of algae production, a simple, fast but as comprehensive as possible

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molecular characterization, for instance of TAGs for biodiesel production would be of

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great help. This work aimed therefore to explore the potential of ion mobility coupled

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to high resolution mass spectrometry to comprehensively characterize different

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microalgae biomasses. Two different TWIM-MS analysis strategies were tested as

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detailed below: DI-ESI-TWIM-MS and UHPLC-HDMSE.

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EXPERIMENTAL SECTION

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Chemicals

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Methanol and chloroform (LC grade) were purchased from Tedia Brazil (Rio de

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Janeiro, Brazil). Analytical grade phosphoric acid (85% w/w), formic acid (98−100%),

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LC grade leucine enkephalin, poly-DL-alanine and ammonium formate were purchased

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from Sigma-Aldrich (Merck Sigma-Aldrich, KGaA, Darmstadt, Germany). Mobile

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phases acetonitrile and propan-2-ol (MS grade) were purchased from J. T. Backer

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(Sao Paulo, Brazil). Water was purified using a Millipore Milli-Q system (Merck

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Millipore, KGaA, Darmstadt, Germany).

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Samples

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In total, 16 microalgae biomasses were analyzed, including 5 from different

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genera (Tetraselmis aff. chuii, Spirulina platensis, Dunaliella salina, Chlorella vulgaris,

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Scenedesmus ecornis), and these obtained under different cultivation times and/or

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culture media composition or light source (Scheme 1). See SI for detailed cultivation

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conditions. For comparison with microalgae extracts, commercial samples of refined

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soybean, corn, rapeseed, sunflower and olive oils were purchased in the local market.


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SCHEME 1. Sixteen different microalgae biomasses analyzed in this work. Sample preparation Lyophilized biomasses of different algae were extracted using the Bligh and 62

with some modifications.

63

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Dyer like method

For Tetraselmis aff. chuii, Spirulina

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platensis, Chlorella vulgaris and Scenedesmus ecornis samples, 0.1 g of each

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lyophilized biomass was homogenized with 15 mL of chloroform:methanol:H2O

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(2:1:0.5, v/v) and placed in an ultrasonic bath (200W, 50 Hz) for one hour at 30 °C.

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The lipid fractions were filtered using filter paper and then dried under water vapor heat

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set to 60 °C. The dried lipid fractions were determined gravimetrically. For the 7 ACS Paragon Plus Environment


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Dunaliella salina samples (ABC and DEF culture media) the protocol was different due

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to the high concentration of salts in the dried biomass. The lipid extraction was then

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performed in two steps: the first consisted in the dry biomass extraction using pure

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chloroform (20 mL) under ultrasonic agitation also for 1h at 30 °C. The solid was

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decanted and the liquid phase was transferred to a beaker. Then, in the second step,

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a volume of 20 mL of methanol was used with 300 µL of H2O to extract the solid residue

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of the first step (with pure chloroform), under the same extraction conditions. The

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chloroform and methanol extracts were also evaporated under 60 °C and the dried

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extracts were gravimetrically determined. All the lipid extracts for all samples were then

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re-dissolved in pure methanol at an approximate concentration of 2 mg .mL-1. Then, 20

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µL of the lipid fraction in methanol were diluted in 1 mL of methanol with 0.1 % of formic

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acid for the direction infusion approach and in mobile phase B for the UHPLC-HDMSE

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analysis.

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METHODS

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DI-ESI-TWIM-MS approach

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Direct infusion (DI) approach was performed on a Traveling Wave Ion Mobility

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Mass Spectrometer (TWIM-MS, Synapt HDMS, Waters, UK). The samples were direct

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infused at a flow rate of 20 μL/min using an external syringe pump into the ESI source

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operated in positive mode (3.5 kV), cone and extractor cone were 40 and 5.0 V

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respectively. The source and desolvation temperatures were 80 and 150 °C, and a

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nitrogen nebulizer gas flow rate of 400 L/h. Parameters such as the drift gas pressure

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(N2, 1.05 mbar), height (30 V) and wave velocity (650 m/s) were optimized to obtain

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the same spectra as when only QTOF mode is used, avoiding any distortion or

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sensitivity loss, in a range of m/z 50 – 1,500. The mass spectrometer was calibrated in 8 ACS Paragon Plus Environment

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ESI(+) mode at 10,000 resolution (FWHM at m/z 882.7999) over acquisition mass

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range of m/z 50 − 2000, using a solution of phosphoric acid 0.1% (v/v) in

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acetonitrile:H2O (1:1). The most abundant ions in the mass range from m/z 800 to 1000

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(expected TAG region) had also their CCS values experimentally determined

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according to the procedure described by Paglia and Astarita in Nature Protocols (2017)

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50

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mg/L, in H2O: acetonitrile 50:50 + 0.1% of formic acid) as reference values for

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calculation. A spreadsheet with the CCS calculations for isobaric ions is available as

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Supporting Information.

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for performing the manual CCS calibration, using poly-DL-alanine oligomers (10

UHPLC-HDMS E

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UHPLC-HDMSE was performed in a Synapt G2-Si mass spectrometer with an

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ACQUITY™ UHPLC i-Class as chromatograph (both Waters, UK). The separation was

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carried out using an ACQUITY CSH™ C18, 2.1 mm x 100 mm column kept at 55 ˚C

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during analysis. Mobile phase consisted of solvent A (acetonitrile:H2O 60:40 + 10mM

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ammonium formate + 0.1% formic acid); and solvent B (propan-2-ol:acetonitrile 90:10

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+10 mM ammonium formate + 0.1% formic acid). The gradient elution program was as

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follows: 0 − 0.99 min from 60:40 A:B to 57:43 A:B; 0.99 - 1.24 min from 57:43 A:B to

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50:50 A:B; 1.24 – 5.39 min from 50:50 A:B to 1:99 A:B; 5.39 – 5.64 min from 1:99 A:B

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to 60:40 A:B, then remaining constant until 8 min. Flow rate was 0.7 mL.min-1 and

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sample injection volumes were 4 μL (FTN AutoSampler). Total run time was 8 min.

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The mass spectrometer was equipped with an ESI source, also in positive ion mode,

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operating under 3.5 kV of capillary and 40 V of cone. The source and the desolvation

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temperature were 110 and 450 °C, respectively, and the nitrogen nebulizer gas was

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used at flow rate of 900 L/h at a pressure of 6 bar. The mass spectrometer was 9 ACS Paragon Plus Environment


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operated in ion-mobility (HDMSE) mode. In this method, the mass analyzer operates

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in both low energy (for precursors ions) and high energy (for fragments) mode to

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promote collision induced dissociation (CID) of non-selected precursor ions, leading to

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spectra of product fragments aligned to the precursor ion. In this technique, the term

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“HD” comes from "high definition", because in this case the MSE acquisition technique

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is also coupled to the ion mobility. Trap collision energy (low) was 4 eV, and transfer

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collision energy (high) was a ramp from 25 to 65 eV, also using argon as collision gas.

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The IMS cell was filled with N2 under a pressure of 3.65 mbar. The IM cell was also

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calibrated using poly-DL-alanine (10 mg/L) in H2O: acetonitrile (50:50 + 0.1% of formic

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acid) to allow the calculation of the analytes CCS values. Both MS calibration and lock

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mass correction during analysis was performed using a leucine enkephalin (theoretical

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m/z 554.2620) solution in H2O:acetonitrile (50:50 + 0.1% formic acid) at a

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concentration of 100 pg/μL and infused at a flow rate of 0.010 mL/min. The acquisitions

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in HDMSE mode ranged from m/z 50 to 1500. Default IMS conditions were used for

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IMS screening (T-wave velocity ramp start of 1000 m/s and end of 300 m/s; and T-

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wave pulse height of 40 V).

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Data processing and statistics

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Both DI-ESI-TWIM-MS and UHPLC-HDMSE data were collected using the

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software MassLynx 4.1v (SCN 639 for Synapt HDMS and SCN 932 for Synapt G2-Si,

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Waters, UK). The DI-ESI-TWIM-MS spectral data were evaluated using the software

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DriftScope 2.7v and HDMS Compare 1.0v. The chemometric analysis for samples

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statistical comparison after data preprocessing (spectra normalization by height and

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Pareto scaling – default software parameters) was performed using the software

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EZinfo 2.0v (Umetrics, USA) appended into MarkerLynx (Waters, UK). 10 ACS Paragon Plus Environment

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For UHPLC-HDMSE data, processing and analysis was conducted by using

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Progenesis QI Informatics (Nonlinear Dynamics, Newcastle, U.K.) in a standard

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protocol 50 in which each UHPLC−MS run was imported as a raw file to obtain the ion-

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intensity map, including m/z and retention time. As default, these ion maps were then

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aligned in the retention-time direction. From the aligned runs, an aggregate run

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representing the compounds in all samples was used for peak picking. This aggregate

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was then compared with all runs, to ensure that the same ions are detected in every

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run. Isotope and adduct deconvolution were applied in order to reduce the number of

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features detected. Data were normalized according to total ion intensity. Metabolites

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were identified by searches against their accurate masses in publicly available

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databases, including the LIPID MAPS

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(HMDB), 65and MetaScope (CCS database provided in Progenesis QI software), as

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well as by fragmentation patterns, retention times, and CCS values, when available.

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The platform LipidCCS 66 was also used for additional CCS comparison.

64database,

the Human Metabolome database

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RESULTS AND DISCUSSION

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DI-ESI-TWIM-MS of microalgae extracts

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To determine the best application for a specific biomass, and also to evaluate

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the process of algae production, researchers and biorefineries must have robust

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analytical tools for the detailed analysis of these biomasses. We report two different

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TWIM-MS analysis strategies for performing a quite challenging molecular

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characterization of microalgae biomass extracts, focusing on triacylglycerols (TAGs),

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as they are the major transesterifiable lipids precursors of fatty acid methyl esters

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(FAMEs) for biodiesel production. 11 ACS Paragon Plus Environment


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Figure 1 shows the full scan mass spectrum obtained by DI-ESI-TWIM-MS of

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the microalgae extract Tetraselmis aff. chui “TG4”. It highlights the sample complexity,

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and some examples of expected lipid classes that could be present in specific m/z

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ranges. Figures S1-S16 show all other spectra. Quite different profiles are indeed

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observed which shows that the composition of the algal biomass is considerably

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altered as a function of the culture media and species or strains, and that these

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changes are easily detected via DI-ESI-TWIM-MS. Note that determining how specific

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classes of compounds vary more pronouncedly as a function of algae growth

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parameters is critical to optimize production conditions.

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FIGURE 1. DI- ESI(+)-TWIM-MS of the extract of Tetraselmis aff. chui cultivated in

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culture media G4 (Sample “TG4”).

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Data from all 16 DI-ESI-TWIM-MS spectra for each microalgae extract were

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statistically evaluated by chemometrics (Figure 2) using principal component analysis

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(PCA). Grouping occurred primarily by the microalgae genus, confirming that 12 ACS Paragon Plus Environment

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microalga metabolite profile is indeed a mainly characteristic feature of the genus.

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Therefore, it follows that the genus could be the initial variable to be considered in a

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study of alga prospecting for a specific biotechnological application. For example, after

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defining the best genus, the culture medium and cultivation conditions could be studied

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and optimized. This approach may also be suitable to compare with reference

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samples, that is, microalgae that have been already shown to be suitable for a

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particular application, or whose lipid profile has already been well characterized. It is a

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simple and fast approach and provides a good response for an initial profiling of a given

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set of samples.

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FIGURE 2. Principal component analysis (PCA) of the 16 algal extracts. Note the

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grouping by algae genus.

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The algae extracts were also evaluated by their 2D drift time versus mass

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spectra, considering the TWIM separation of the ionized lipids and metabolites in

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samples. Figure 3 shows representative examples of 2D drift time versus m/z spectra

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(detected ions with their m/z values) for some microalgae species. Figures S17-S32

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show the spectra for all samples.

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FIGURE 3. Representative two-dimensional drift time versus m/z of the MS detected

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ions for the samples extracts of a) TG3; b) SPIZK, c) TG4 and d) CHLOSPS.

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Different drift time tendencies, even those possessing overlap in their m/z

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values, indicate that distinct classes of compounds are potentially separable. The

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differences in the profiles of each algae extract becomes more evident upon such

13

visualization, and this approach allows for the profile screening of lipids. For example,

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to investigate the TAG content in a lipid extract, since this class of compounds will be

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distributed within a certain m/z range, (which, however, may be superimposed with

16

several other classes of lipids and metabolites, making the detection of this class 14 ACS Paragon Plus Environment

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difficult), and distributed in a tendency of characteristic drift times for a given

2

experimental condition. Therefore, this approach provides rapid turnaround with

3

respect to parameter changes and other decisions in algae culture studies, including

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continuation or termination of a biorefinery process depending on whether a desired

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product profile or other endpoint can be or has been met.

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Assessment of the lipid profile of algal samples can be confirmed by an

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additional approach, the binary comparison of drift time x m/z spectrum through the

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HDMS compare software. In this approach, the ion mobility spectra are superimposed,

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highlighting the classes of compounds that are more abundant in each sample,

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generating a map in which each sample is marked with a different color for each

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sample. Figure 4 shows the comparison of the spectrum of TG3 and a blend of

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commercial vegetable oils (a mixture of soybean, corn, rapeseed, sunflower and olive

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oils in even proportions that are knowingly composed by TAGs). Clearly, the TAG

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abundance in sample TG3 is superimposed with the TAGs of the vegetable oil blend

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(Figure 4). Figure S33 also shows another binary comparison of samples TG4 and

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SPIKZ, being evident the differences in their drift plots, indicating that the sample

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SPIKZ notably has a lower abundance of TAGs.

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FIGURE 4. Fusion map comparing the “DT versus m/z” spectrum of sample TG3 and

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a blend of commercial edible oils obtained with the DI-TWIM-MS approach. The insert

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highlights the superimposed drift time regions for GL_TAG class in both samples.

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The principal TAGs ions that show characteristic mobility tendencies can also

7

be further analyzed by their CCS values, manually determined via calibration with poly-

8

DL-alanine

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samples (N2 1.05 mbar, height 30 V and wave velocity 650 m/s). Each selected ion has

10

its CCS experimentally determined by calibration using substances whose CCS is

11

known, thereby increasing data selectivity.67 Adding one more intrinsic physical-

12

chemical ion property such as the CCS, significantly also increases the confidence of

13

metabolite/lipid identification. 68 One of the most common calibrants for CCS values is

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poly-DL-alanine, whose CCS values for its singly charged oligomers have been well

15

determined in N2 as drift gas.

standard solution infused in exactly the same TWIM conditions as the


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Figure 5 shows the experimentally determined CCS values (Ω in Å2)

2

(electronically available in Supp. Information) for the most abundant ions from m/z 800

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to 1000, exemplified for the sample TG4. Notably, a clear CCS separation exists for

4

TAGs, resulting in a cluster possessing slightly higher CCS values, versus other

5

isobaric ions likely probably from other metabolites classes. This highly interesting

6

finding could be useful in assessing a qualitative response regarding TAG abundance

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in algal samples, as this region of drift time x m/z would appear to be selective for the

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TAG class. This useful spectral characteristic of TAGs can be used to inform the

9

optimization of algae cultivation for biodiesel production.

10 11

FIGURE 5. Experimentally derived CCS values (Ω in Å2) for the most abundant ions in

12

the sample TG4 obtained by DI-TIWM-MS, with the GL_TAG region highlighted.



1

Page 18 of 41

High-throughput lipids and metabolites characterization by UHPLC-HDMSE

2 3

To ultimately perform a comprehensive characterization of algae extracts,

4

additional MS/MS spectra is crucial in allowing full annotation. However, the manual

5

interpretation of MS/MS data obtained by DI-ESI-TWIM-MS of the extracts would be

6

extremely laborious and time consuming, due to the complexity of the samples that

7

present different classes of lipids and other metabolites. We therefore applied the

8

much more comprehensive UHPLC-HDMSE technique as a second approach to

9

analyze the lipids and metabolites of the algae samples. In addition to the exact mass

10

and CCS values obtained for precursor ions, MS/MS spectra are collected, generating

11

information about fragments, that can be compared to spectral databases and lead to

12

a tentative annotation.

13

Each chromatographic run, together with the MS, MS/MS and IMS data

14

associated with it, was imported to Progenesis QI software, which via comparison with

15

the LIPID MAPS, HMDB, and a search engine with a CCS database provided by

16

Waters

17

comparison of the precursor and fragment exact masses, isotope distribution,

18

fragmentation profile (in silico fragmentation or known experimental fragmentation

19

pathways) and CCS values. The combination of all these identification parameters

20

results in an overall compound identification score for each identified compound.

(MetaScope)

performs

the

identification

by

theoretical/experimental

21

From the 16 analyzed samples, all in the same conditions of analysis, 1251

22

different compounds were putatively annotated, within an m/z error of 10 ppm for both

23

precursor ion and fragments. Among these 1251 compounds: 1) only 158 could not be

24

identified by their fragments; 2) 30 of them were also identified by their CCS, assuming

25

a theoretical/experimental error lower than 4%; 3) the isotope similarities were from 18 ACS Paragon Plus Environment

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1

30.4 to 99.4, with an average of 82.1; 4) for the fragmented ions, the average

2

fragmentation score was 41.9 (ranging from 1 to 99.4); and finally, 5) the identification

3

score ranged from 30 to 59.1, with an average of 39.5. The identifications with the

4

highest scores were accepted and described in the accompanying spreadsheet with

5

the metabolomics and lipidomics description of this study, provided as electronic

6

Supporting Information. The number of compounds identified by their CCS values is

7

relatively low (30 compounds) due to limitation of the database used, as it is not as

8

broad as the LIPID MAPS or the HMDB. Among the 1251 compounds that were

9

putatively annotated, 210 were lipids, with the others categorized as “other

10

metabolites”. The lipids were also categorized into fatty acyls (FA), glycerolipids (GL),

11

glycerophospholipids (GP), sphingolipids (SP), sterol lipids (ST), prenol lipids (PR),

12

and saccharolipids (SL). Although TAG are also lipids (glycerolipids), this class was

13

categorized separately as “GL_TAG”, as we discuss and emphasize its importance for

14

biodiesel production.

15

From the normalized abundances of each deconvoluted annotated compound

16

per sample, several interpretations can be made. Figure 6 shows the lipidomics and

17

metabolomics results interpretations for all the samples. Figure 6a shows the sum of

18

the normalized abundance for each class of lipids and other metabolites, and Figure

19

6b shows only the normalized abundances for different lipid classes in the samples.

20

Figure 6c shows the percentage abundance of compounds identified as lipids and as

21

other metabolites, and Figure 6d shows the percentage abundance of lipid classes

22

distributed in each sample.

23



Page 20 of 41

1 2

FIGURE 6. Sum of the normalized abundances of a) each lipids classes and other

3

metabolites identified in the samples and b) each lipid class identified in the samples;

4

c) Relative abundance of lipids and metabolites for each sample; d) Relative

5

distribution of lipid classes in each sample. Legends: fatty acyls (FA), glycerolipids

6

(GL), glycerophospholipids (GP), triacylglycerol (GL_TAG), sphingolipids (SP), sterol

7

lipids (ST), prenol lipids (PR), saccharolipids (SL).

8 9

Figure 6 data are not quantitative, meaning that they cannot be considered as

10

a “weight by weight %” measurement in the sample extracts as not all the reported

11

compounds have the same ionization behavior, implying different response factors.

12

Note that lipidomics or metabolomics analysis are comparative methods and would not

13

cover 100% of the compounds that are indeed present in a given sample. It is expected

14

that several other compounds remained undetected and/or unidentified, but a

15

comprehensive profile is not essential for comparisons and predictions. Note also that,

16

as recently reported by Dorrestein and co-workers

49


in a recent metabolomics study

Page 21 of 41 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60


1

of algae and cyanobacteria, approximately 86% of the metabolomics signals detected

2

were not found in other available datasets, concluding that even using a very diverse

3

datasets, algae and cyanobacteria have unique features and families of metabolites

4

that are still uncharacterized. However, the normalized abundance of each class of

5

known compounds should serve as suitable screening for the alga biomass

6

composition and to understand how different processes may be affecting alga

7

composition.

8

Figures 6a and 6c point out that the abundance of compounds identified as

9

metabolites is higher than the compounds identified as lipids, as indeed expected, as

10

the number of potential metabolites is indeed much higher than the number of

11

compounds of the lipid class. The sample with the highest abundance of lipids was the

12

sample TG2 (Fig 6c), mainly distributed between GL (excepting GL_TAG – that is

13

highlighted separately) and GP. The sample with the highest % of GL_TAG was

14

DUNDEF_CHCl3 (Fig. 6d), but when comparing the sum of the normalized abundance

15

(Fig. 6b). It can be noted also that the samples DUNDEF_CHL3, TG3 and TG4 were

16

the ones presenting the highest abundances of compounds identified as TAGs. It

17

indicates that they would – at least in theory – provide better yields for the biodiesel

18

production, if it was a real case of a study of algae prospecting for biodiesel production.

19

The detection of some metabolites and ST compounds may have important

20

implications when alga biomass is used for nutraceutical products. When the

21

untargeted approach detects toxins or sterols compounds (i.e. testosterone), or other

22

classes of biologically active compounds, it is important to state at this point that further

23

studies must be conducted, and these compounds must be characterized using

24

targeted analysis to confirm the primary assumption of the untargeted analysis.



Page 22 of 41

1

In fact, the DI-ESI-TWIM-MS data really converge with the CCS values

2

determined by UHPLC-HDMSE. Comparing the drift time versus m/z plot for a sample

3

obtained by the DI approach with the plot of all the experimental CCS values versus

4

m/z measured for all identified compounds, we can observe the same drift time

5

tendencies showing up in both cases. To illustrate that, Figure 7 compares the TG3

6

sample drift time plot, and the experimental CCS values determined for all the 1251

7

annotated compounds. We can highlight at least 4 different areas: I – a low abundance

8

very separated tendency, probably minor metabolites; II – the compounds identified as

9

TAGs, mainly from m/z 700 – 1000 and slightly higher CCS values; III – a very busy

10

region with several superimposed lipids and other metabolites; and IV – also a low

11

abundant separated tendency. We can also observe in what regions the lipid classes

12

tend to appear, which is helpful to detect misleading identifications if some of them

13

appear in a very distinct region. It can also help to choose classes of compounds and

14

take a closer look at their identification. The separation of distinct tendencies in the

15

sample TG3 are more evident because not all of the 1251 compounds are present in

16

sample TG3.

17

The use of more polarizable drift gasses such as CO2 has been proven to be an

18

alternative to increase the selectivity and classes separations in complex mixtures

19

such as crude oil.

20

calibrants (such as poly-DL-alanine) values in this gas are not yet available, as the

21

determination of this parameter in CO2 is much more complex. However, if only the

22

separation of classes of different compounds are needed, and no CCS values must be

23

determined, the use of CO2 can be a good alternative to better detect different classes

24

of lipids and metabolites in complex mixtures such as microalgae extracts.

59

On the other hand, databases of CCS values in CO2 or CCS


Page 23 of 41 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60


1 2

FIGURE 7. Comparison of the two-dimensional drift time versus m/z plot for sample

3

TG3 with the CCS experimentally determined by UHPLC-HDMSE for all the 1251

4

compounds, highlighting the areas I, II, III and IV that could be detected in both plots.

5 6

Additional TAGs confirmation was made by comparing the experimental data of

7

measured CCS values with the ones available in the recently released CCS database,

8

LipidsCCS,

9

were determined in N2, as drift gas, so they are comparable. Only two compounds

10

identified as TAG had no theoretical CCS value available in LipidCCS dataset, which

11

were TG(10:0_8:0_8:0) and TG(21:0_22:6_22:6). For the other TAGs, the

12

experimental versus theoretical CCS error was mostly bellow to 10%, with 6.7%

13

average. The experimental values tended to be around 20 Ǻ2 positively biased, that is,

14

20 Ǻ2 on average larger than the theoretical values, but this minor systematic error

15

probably comes from the Synapt G2-Si CCS calibration with poly-DL-alanine step. In

16

general, therefore, the CCS were mostly in accordance with LipidCCS data base,

17

which shows that this parameter is actually very useful and helps further identification

18

and confirmation of compounds. The calculated CCS values obtained with DI-TWIM-

66

(Table S1). Both the measured and the LipidsCCS theoretical values



Page 24 of 41

1

MS (Fig. 5) seem to be in accordance with theoretical data, being TAGs CCS values

2

mostly falling within 300 to 330 Å.

3

Another interesting approach that could be used for CCS determinations in both

4

DI-TWIM-MS and UHPLC-HDMSE is the use of a standard mixture of lipids of interest

5

with available theoretical CCS values.

6

CCS values could be determined more accurately, as the CCS calibrants and the

7

targeted lipid classes for a set of experimental conditions would exhibit the same gas

8

phase behavior.

66

In this way, it is expected that experimental

9

Finally, comparing the two analysis strategies presented in this work, it is

10

important to emphasize that the DI-TWIM-MS allows a rapid fingerprinting of

11

microalgae extracts, helping the visualization of different classes of lipids and

12

metabolites via mobility mass correlation curves such as the data presented in Fig. 7,

13

in which lipid classes such as TAGs can be detected in a specific m/z versus ion

14

mobility tendency. Moreover, from DI-TWIM-MS data, the binary comparison of the

15

two-dimensional drift time versus m/z spectrum of different samples can be applied,

16

for instance, to compare two cultivation conditions or two microalgae of different

17

species, highlighting the differences between them, and it can be very useful in the

18

biotechnology industry. However, a comprehensive molecular description with DI-

19

TWIM-MS is an extremely laborious task due to sample complexity, and not suitable

20

for routine analysis, as MS/MS data would have to be manually acquired. For that, if a

21

more detailed molecular characterization is required to understand what are the

22

metabolites or lipids that are affected by some specific cultivation condition, for

23

example, UHPLC-TWIM-HDMSE strategy allows annotation of metabolites and lipids in

24

samples considering also their CCS values as a specific molecular descriptor to

25

improve identification confidence. 24 ACS Paragon Plus Environment

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1


CONCLUSIONS

2

All the approaches demonstrated here have the potential to be applied as

3

methodologies for lipids and metabolites screening in real cases of microalgae

4

prospecting and can be implemented as routine both for research and in microalgae

5

biorefineries. Ion mobility mass spectrometry provided fast profile of microalgae

6

extracts, in which the new dimension of ion mobility separation reveals distinct classes

7

of compounds, especially triacylglycerols.

8

The variability of the lipids in the algae was found to be incredibly high and

9

sensitive to the cultivation parameters, but the statistical classification among the

10

samples indicated that the main parameter that influences the composition of the

11

extracts is the genus of the algae. Genus seems therefore to be an initial variable to

12

be considered in a study of algae prospecting for a specific biotechnological

13

application. For example, after defining the best genus, the culture medium and

14

cultivation conditions could be studied and optimized.

15

Statistical or binary comparison of the samples drift time plots spectrum seems

16

necessary, especially for comparison with reference samples, that is, with a microalga

17

that has been already shown to be suitable for a particular application, or whose lipid

18

profile has already been characterized. DI-TIWIM-MS has been shown therefore to be

19

a simple and fast approach and provides a good response for an initial profiling of a

20

given set of samples, but performing comprehensive identification of all the ions of the

21

samples can be quite laborious.

22

The high level of complexity of the samples requires a more powerful

23

methodology to perform a more global and detailed sample screening. For this, the

24

UHPLC-HDMSE technique was used to putatively annotate over 1200 compounds,

25

collectively in the 16 samples, based on intrinsic parameters of the detected ions (exact 25 ACS Paragon Plus Environment


Page 26 of 41

1

mass, fragmentation pattern, isotope distribution and CCS). More than 200 were

2

annotated as lipids of several classes. Among the 16 samples evaluated in this study,

3

Tetraselmis aff. chuii, cultivated in F/2 medium plus fertilizers for 12 (TG3) and 16 days

4

(TG4), as well as biomass of Dunaliella salina produced in nitrogen-reduced Shaish

5

medium and extracted with CHCl3 (DUDEF_CHL3), were the ones with the highest

6

TAGs abundances, and are therefore more promising for biodiesel production within

7

the evaluated samples.

8

The methodologies explored in this work could be applied to microalgae

9

biorefineries to predict whether some specific algae will be viable for biodiesel

10

production or for other biotechnology applications, as well as help in studies of

11

microalgae cultivation improvements.

12 13

AKNOWLEDGEMENTS

14

We thank the State of São Paulo Research Foundation (FAPESP), State of Rio

15

de Janeiro Research Foundation (FAPERJ), the Brazilian National Council for

16

Scientific and Technological Development (CNPq) and the Financing Agency of

17

Studies and Projects (FINEP) for financial assistance. We gratefully acknowledge

18

Waters Brazil managers for opening their demo laboratory (Barueri, Brazil) to run the

19

UHPLC-HDMSE experiments. We also thank the National Institute of Technology for

20

microalgae biomasses donation and Dr. M. D. Bartberger for the scientific opinion in

21

the final work. Finally, we thank the referees of this work for their contribution to its

22

quality improvement.


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SCHEME 1. Sixteen different microalgae biomasses analyzed in this work. 135x144mm (96 x 96 DPI)

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FIGURE 1. DI- ESI(+)-TWIM-MS of the extract of Tetraselmis aff. chui cultivated in culture media G4 (Sample “TG4”). 390x211mm (96 x 96 DPI)



FIGURE 2. Principal component analysis (PCA) of the 16 algal extracts. Note the grouping by algae genus 314x227mm (96 x 96 DPI)


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FIGURE 3. Representative two-dimensional drift time versus m/z of the MS detected ions for the samples extracts of a) TG3; b) SPIZK, c) TG4 and d) CHLOSPS. 241x136mm (96 x 96 DPI)



FIGURE 4. Fusion map comparing the “DT versus m/z” spectrum of sample TG3 and a blend of commercial edible oils obtained with the DI-TWIM-MS approach. The insert highlights the superimposed drift time regions for GL_TAG class in both samples. 479x254mm (96 x 96 DPI)


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FIGURE 5. Experimentally derived CCS values (Ω in Å2) for the most abundant ions in the sample TG4 obtained by DI-TIWM-MS, with the GL_TAG region highlighted. 336x222mm (150 x 150 DPI)



FIGURE 6. Sum of the normalized abundances of a) each lipids classes and other metabolites identified in the samples and b) each lipid class identified in the samples; c) Relative abundance of lipids and metabolites for each sample; d) Relative distribution of lipid classes in each sample. Legends: fatty acyls (FA), glycerolipids (GL), glycerophospholipids (GP), triacylglycerol (GL_TAG), sphingolipids (SP), sterol lipids (ST), prenol lipids (PR), saccharolipids (SL). 352x212mm (150 x 150 DPI)


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FIGURE 7. Comparison of the two-dimensional drift time versus m/z plot for sample TG3 with the CCS experimentally determined by UHPLC-HDMSE for all the 1251 compounds, highlighting the areas I, II, III and IV that could be detected in both plots. 527x206mm (96 x 96 DPI)


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