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Overview
We’re excited to be gathering in Prague for the 2025 Metabolomics Conference. Come connect with us at Booth #P6 during the exhibition hours to share ideas and see our full metabolomics solutions and newest technologies.
- Software demonstrations
- Lunchtime seminar
- Oral presentations
- Posters
- Solutions
Software demonstrations
Metabolomics analyses typically involve very large sample sets, resulting in the production of complex data outputs. To fully extrapolate meaningful biological information, large sample sets must be run to obtain statistical significance. Metabolomics data analysis typically consists of feature extraction, quantitation, statistical analysis and compound identification.
Learn more about our metabolomics data analysis software. Stop by our booth to make an appointment with our specialists in Compound Discoverer 3.4, Trace Finder or Mass Frontier Spectral Interpretation software.
Lunchtime seminar
You are cordially invited to join the Thermo Scientific lunchtime seminar on Tuesday, June 24, from 12:20 – 13:20 CEST in South Hall 1, during Metabolomics in Prague, Czech Republic.
Quantify what you know, discover what you don’t … SQUAD workflows powered by Orbitrap
Simultaneous Quantitation and Discovery (SQUAD) analysis workflows leverage the advantages of Thermo Scientific Orbitrap mass spectrometers. The Thermo Scientific Orbitrap Astral Mass Spectrometer unlocks additional insights into biology and disease mechanisms by utilizing two HRAM detectors, used in parallel, for efficient merging of both targeted and untargeted studies.
Join us for two informational lectures covering:
- The hardware design and functional impact of the Orbitrap Astral in your metabolomics and lipidomics research
Alexander Makarov, Sr. Director Global Research, LSMS, Thermo Fisher Scientific, Bremen, Germany - Orbitrap Astral SQUAD methodology applied to the study of healthy aging lipidome
Magali Sarafian, Manager D'Activité Métabolomique Et Protéomique, Bioaster, Lyon, France
Oral presentation
Tuesday, June 24 | 15:30 - 17:15 CEST
Orbitrap-based Metabolomics Workflows: Discover. Innovate. Exceed.
Presented by Susan Bird, Vertical Marketing Senior Manager, Thermo Fisher Scientific
Abstract: The field of Metabolomics has been advancing at an impressive rate, inspiring key analytical innovations designed to keep pace with the biological needs of a study. Despite these advances, analytical challenges remain around high-confidence detection and annotation of low abundance compounds in complex biological matrices. Thermo Fisher Scientific is committed to advancing the field of metabolomics through intelligence driven data acquisition, streamlined software processing strategies and key hardware innovations that keep at pace and push beyond the current analytical demands of the field. In this presentation, learn about the enhanced dynamic range (eDR) scanning mode of a novel modified Orbitrap Hybrid MS. eDR is designed to extend the dynamic range of MS full scan measurements by dividing the mass range into narrow windows, in an automated approach, which results in decreasing the saturating impact of high abundant species while improving the signal-to-noise ratios and increasing the quality of low low-abundance metabolites. With this approach, we can extend the range of the metabolites we detect and confidently identify leading to greater insights into important compounds previously undetected using full scan alone.
Posters
- Reducing false annotations in lipidomics using Compound Discoverer 3.4 software
- Simultaneous quantitation and discovery (SQUAD) of fecal bile acids and their conjugates in adults with autism spectrum disorder (ASD)
- Highly sensitive targeted method for single cell lipidomics on Stellar MS– a hybrid nominal mass instrument
- Improving selectivity and sensitivity of lipid mediator analyses by coupling nano-flow chromatography with the Stellar mass spectrometer
- SQUAD analysis of plasma using modified Orbitrap mass spectrometers: enhancing dynamic range, reducing unintentional MS1 fragmentation, and improving sensitivity and annotation rates in targeted and untargeted analysis
- Moving beyond ddMS2: improving annotation confidence in untargeted metabolomics using higher resolution MS and parallel ion trap experiments
- Advancing bile acid quantitation: high-specificity MS2 and MS3 analysis using the Stellar mass spectrometer
- Simultaneous quantitation and discovery (SQUAD) workflow using multiple fragmentation techniques for quantitation and characterization of trace-level nitrosamines in biological matrices
- Human milk lipidomics: from deep annotation to high-throughput lipid quantitation
- LC-MS workflows for diverse omics analysis of plasma samples in a mini cancer cohort using the Orbitrap Astral mass spectrometer
- Simultaneous quantitation and discovery (SQUAD) analysis of lipids in commercial vegetable oils using LC-HRAM-Tribrid platforms
- Increasing plant metabolome coverage using enhanced dynamic range (eDR) scan mode on a modified Orbitrap hybrid mass spectrometer
- SQUAD metabolomics: uniting targeted precision and untargeted discovery for in-depth fecal metabolite profiling on Orbitrap Astral MS
- High-resolution untargeted metabolomics of tea using a modified Orbitrap Astral mass spectrometer
- Enhanced identity spectrum search with AI/ML confidence scoring for HRAM data
- Automated tool for optimal quantitative analysis conditions using mzCloud spectral library
- A modified Orbitrap hybrid mass spectrometer for improved sensitivity and reduced false annotations in lipidomic applications
- Investigating cellular metabolism on a modified Orbitrap hybrid mass spectrometer by the simultaneous use of differently labeled elemental isotope substrates
- Comprehensive data acquisition workflow on a modified Orbitrap Astral MS to achieve deep lipidome coverage with high-confidence annotations
- Orbitrap-based metabolomics workflows: Discover. Innovate. Exceed.
- High-throughput and highly selective quantitative lipidomics approach with the Stellar mass spectrometer – a novel hybrid nominal mass instrument