Improved performance and reliability of high throughput metabolomics by ion mobility - time of flight mass spectrometry

The success of a systems-wide quantitative approach to understand functional and dynamic aspects of biological processes and systems depends on the availability of accurate, robust, yet comprehensive data at various molecular levels, including the genome, epigenome, transcriptome, proteome, and the metabolome.

In order to investigate the wide range of organisms and diseases of interest, to reduce turn around time, as well as to increase data quality and comprehensiveness, the FGCZ expands its analytical infrastructure and methodologies by the addition of a latest generation mass spectrometry system, an Ion Mobility – Mass Spectrometry (IM-MS) System 2.0 IMS-HTOF from Tofwerk. The system features a cutting edge high-resolution ion mobility separation tube (resolution >=200) combined with a high resolution accurate time-of-flight mass spectrometer (resolution >=4000, <1ppm mass accuracy). This hybrid instrument allows for the measurement of two exact, computable, and orthogonal properties of a molecule, the exact mass (EM) and the collision cross section (CCS). The resulting analytical power and positive identification ability allows determining more than 1000 metabolites or lipids within less than 1 minute of measurement time. Based on comparative measurements on our existing IM-MS system (Waters Synapt G2 HDMS) and the new system, we could show that a true break-through in the improvement of the analytical performance is possible: run times are reduced by at least a factor of 20 (from 30 min to 3-1 min), mobility resolution increased by more than 5 fold (from 40 to more than 200), mass accuracy increased from typically +/- 10 mTh to +/-1 mTh. The overall gain in performance, despite a loss of MS resolution from 20'000 to 5000, is estimated to lead to a 20-fold increase in the productivity of the analytical workflow. This would be a very substantial increase in analytical performance, allowing for a significantly extended range of research questions to benefit from metabolomics data due to reduced resource constraints. In parallel, we also expect to achieve an improved reliability of metabolite identification and an increased metabolome coverage that will further increase the scientific value of the generated data.