Evelyn Rampler

RETURN TO ISSUEPREVReviewNEXTRecurrent Topics in Mass Spectrometry-Based Metabolomics and Lipidomics—Standardization, Coverage, and ThroughputEvelyn RamplerEvelyn RamplerDepartment of Analytical Chemistry, Faculty of Chemistry, University of Vienna, Währinger Str. 38, 1090 Vienna, AustriaVienna Metabolomics Center (VIME), University of Vienna, Althanstraße 14, 1090 Vienna, AustriaUniversity of Vienna, Althanstraße 14, 1090 Vienna, AustriaMore by Evelyn RamplerView Biographyhttp://orcid.org/0000-0002-9429-7663, Yasin El AbieadYasin El AbieadDepartment of Analytical Chemistry, Faculty of Chemistry, University of Vienna, Währinger Str. 38, 1090 Vienna, AustriaMore by Yasin El AbieadView Biography, Harald SchoenyHarald SchoenyDepartment of Analytical Chemistry, Faculty of Chemistry, University of Vienna, Währinger Str. 38, 1090 Vienna, AustriaMore by Harald SchoenyView Biographyhttp://orcid.org/0000-0001-8696-481X, Mate RuszMate RuszDepartment of Analytical Chemistry, Faculty of Chemistry, University of Vienna, Währinger Str. 38, 1090 Vienna, AustriaInstitute of Inorganic Chemistry, University of Vienna, Währinger Straße 42, 1090 Vienna, AustriaMore by Mate RuszView Biography, Felina HildebrandFelina HildebrandDepartment of Analytical Chemistry, Faculty of Chemistry, University of Vienna, Währinger Str. 38, 1090 Vienna, AustriaMore by Felina HildebrandView Biography, Veronika FitzVeronika FitzDepartment of Analytical Chemistry, Faculty of Chemistry, University of Vienna, Währinger Str. 38, 1090 Vienna, AustriaMore by Veronika FitzView Biography, and Gunda Koellensperger*Gunda KoellenspergerDepartment of Analytical Chemistry, Faculty of Chemistry, University of Vienna, Währinger Str. 38, 1090 Vienna, AustriaVienna Metabolomics Center (VIME), University of Vienna, Althanstraße 14, 1090 Vienna, AustriaUniversity of Vienna, Althanstraße 14, 1090 Vienna, Austria*Email: [email protected]. Phone: +43-1-4277-52303.More by Gunda KoellenspergerView Biographyhttp://orcid.org/0000-0002-1460-4919Cite this: Anal. Chem. 2021, 93, 1, 519–545Publication Date (Web):November 28, 2020Publication History Published online28 November 2020Published inissue 12 January 2021https://doi.org/10.1021/acs.analchem.0c04698Copyright © 2020 American Chemical SocietyRIGHTS & PERMISSIONSACS AuthorChoicewith CC-BYlicenseArticle Views7341Altmetric-Citations10LEARN ABOUT THESE METRICSArticle Views are the COUNTER-compliant sum of full text article downloads since November 2008 (both PDF and HTML) across all institutions and individuals. These metrics are regularly updated to reflect usage leading up to the last few days.Citations are the number of other articles citing this article, calculated by Crossref and updated daily. Find more information about Crossref citation counts.The Altmetric Attention Score is a quantitative measure of the attention that a research article has received online. Clicking on the donut icon will load a page at altmetric.com with additional details about the score and the social media presence for the given article. Find more information on the Altmetric Attention Score and how the score is calculated. Share Add toView InAdd Full Text with ReferenceAdd Description ExportRISCitationCitation and abstractCitation and referencesMore Options Share onFacebookTwitterWechatLinked InReddit PDF (5 MB) Get e-AlertsSUBJECTS:Lipids,Metabolomics,Metabolism,Fungi,Materials Get e-Alerts

In this work, simultaneous targeted metabolic profiling by isotope dilution and non-targeted fingerprinting is proposed for cancer cell studies. The novel streamlined metabolomics workflow was established using anion-exchange chromatography (IC) coupled to high-resolution mass spectrometry (MS). The separation time of strong anion-exchange (2 mm column, flow rate 380 μL min–1, injection volume 5 μL) could be decreased to 25 min for a target list comprising organic acids, sugars, sugar phosphates, and nucleotides. Internal standardization by fully 13C labeled Pichia pastoris extracts enabled absolute quantification of the primary metabolites in adherent cancer cell models. Limits of detection (LODs) in the low nanomolar range and excellent intermediate precisions of the isotopologue ratios (on average <5%, N = 5, over 40 h) were observed. As a result of internal standardization, linear dynamic ranges over 4 orders of magnitude (5 nM–50 μM, R2 > 0.99) were obtained. Experiments on drug-sensitive versus resistant SW480 cancer cells showed the feasibility of merging analytical tasks into one analytical run. Comparing fingerprinting with and without internal standard proved that the presence of the 13C labeled yeast extract required for absolute quantification was not detrimental to non-targeted data evaluation. Several interesting metabolites were discovered by accurate mass and comparing MS2 spectra (acquired in ddMS2 mode) with spectral libraries. Significant differences revealed distinct metabolic phenotypes of drug-sensitive and resistant SW480 cells.

microbeMASST, a taxonomically informed mass spectrometry (MS) search tool, tackles limited microbial metabolite annotation in untargeted metabolomics experiments. Leveraging a curated database of >60,000 microbial monocultures, users can search known and unknown MS/MS spectra and link them to their respective microbial producers via MS/MS fragmentation patterns. Identification of microbe-derived metabolites and relative producers without a priori knowledge will vastly enhance the understanding of microorganisms' role in ecology and human health.

Lipid identification and quantification are essential objectives in comprehensive lipidomics studies challenged by the high number of lipids, their chemical diversity, and their dynamic range. In this work, we developed a tailored method for profiling and quantification combining (1) isotope dilution, (2) enhanced isomer separation by C30 fused-core reversed-phase material, and (3) parallel Orbitrap and ion trap detection by the Orbitrap Fusion Lumos Tribid mass spectrometer. The combination of parallelizable ion analysis without time loss together with different fragmentation techniques (HCD/CID) and an inclusion list led to higher quality in lipid identifications exemplified in human plasma and yeast samples. Moreover, we used lipidome isotope-labeling of yeast (LILY)—a fast and efficient in vivo labeling strategy in Pichia pastoris—to produce (nonradioactive) isotopically labeled eukaryotic lipid standards in yeast. We integrated the 13C lipids in the LC-MS workflow to enable relative and absolute compound-specific quantification in yeast and human plasma samples by isotope dilution. Label-free and compound-specific quantification was validated by comparison against a recent international interlaboratory study on human plasma SRM 1950. In this way, we were able to prove that LILY enabled quantification leads to accurate results, even in complex matrices. Excellent analytical figures of merit with enhanced trueness, precision and linearity over 4–5 orders of magnitude were observed applying compound-specific quantification with 13C-labeled lipids. We strongly believe that lipidomics studies will benefit from incorporating isotope dilution and LC-MSn strategies.

We have applied a micro droplet generator (μDG) for sample introduction of single selenized yeast cells into a sector field ICP-MS, which was operated in a fast scanning mode with sampling rates of up to 10 kHz, to measure single cells time resolved with 100 μs integration time. Selenized yeast cells have been used as a model system for preliminary investigation. The single cells to be measured have been embedded into droplets and it will be shown that the time duration of a single cell event always is about 400 to 500 μs, and thus comparable to the time duration of a droplet without a cell. A fixed droplet generation rate of 50 Hz produced equidistant signals in time of each droplet event and was advantageous to separate contribution from background and blank from the analytical signal. Open vessel digestion and a multielement analysis were performed with washed yeast cells and absolute amounts per single cell were determined for Na (0.91 fg), Mg (9.4 fg), Fe (5.9 fg), Cu (0.54 fg), Zn (1.2 fg) and Se (72 fg). Signal intensities from single cells have been measured for the elements Cu, Zn and Se, and histograms were calculated for about 1000 cell events. The mean elemental sensitivities measured here range from 0.7 counts per ag (Se) to 10 counts per ag (Zn) with RSD's from 49% (Zn) to 69% (Se) for about 1000 cell events.