Optimization of mass spectrometric parameters in data dependent acquisition (DDA) for untargeted metabolomics

Authors: ASSRESS, HAILEMARIAM - Arkansas Children'S Nutrition Research Center (ACNC); FERRUZZI, MARIO - Arkansas Children'S Nutrition Research Center (ACNC); LAN, RENNY - Arkansas Children'S Nutrition Research Center (ACNC)

Submitted to: Analytical Chemistry
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 6/21/2023
Publication Date: 7/7/2023
Citation: Assress, H., Ferruzzi, M., Lan, R. 2023. Optimization of mass spectrometric parameters in data dependent acquisition (DDA) for untargeted metabolomics. Analytical Chemistry. https://doi.org/10.1021/jasms.3c00084.
DOI: https://doi.org/10.1021/jasms.3c00084

Interpretive Summary: Metabolomics studies the set of metabolites present in a cell, tissue, bio fluid or organism. Mass spectrum of chemical ions is commonly generated using mass spectrometry instrument to help identify unknown metabolites in untargeted metabolomics. The majority of metabolomics datasets from mass spectrometer are generated using the data-dependent acquisition (DDA) technique, during which the instrument looks for the chemicals which are more abundant than others and breaks them down into smaller structures that are known as fragments. The analyzed metabolites can be properly identified by matching the spectra of the chemical and its fragment ions to existing spectral library. Multiple mass spectrometric instrument factors in DDA require the user to define their values. The ability to select which of these factors and value to be used is advantageous for the user, but in the same time also create difficulty in designing a DDA experiment due to the variety of factors and the wide range of possible values for these factors. This calls for the need to optimize the mass spectrometer systems to identify maximum possible number of metabolites using untargeted metabolomics. The impact of various mass spectrometric parameters on the rate of metabolite identification during untargeted metabolomics utilizing DDA was examined in this original work. For each mass spectrometric factor examined, the best experimental results are presented, followed by scientific justifications. The overall number of identified metabolites increased more than 1.5 fold as a result of the optimization. The findings of this work provides useful insights into understanding and optimizing important mass spectrometric factors for untargeted metabolomics.

Technical Abstract: Optimization of MS parameters for DDA experiment is demanding to increase the MS/MS coverage and hence increase identification rates in untargeted metabolomics. Majority of the MS parameter optimizations reported in the open literature are however either for selected target compounds or are attributed to the field of proteomics. In this work, the effect of mass spectrometric parameters namely mass resolution, RF lens, signal intensity threshold, the number of MS/MS events, cycle time, collision energy, the maximum ion injection time (MIT), dynamic exclusion and the automatic gain control (AGC) target value on metabolite identification rates was investigated on an Exploris 480-Orbitrap mass spectrometer using the NIST SRM 1950 human plasma. Higher metabolite identification rates were obtained by performing ten data dependent MS/MS scans with a mass isolation window of 2.0 m/z and a minimum signal intensity threshold of 1x105 at a mass resolution of 180,000 for MS and 30,000 for MS/MS, while maintaining the RF lens value at 70%,. Furthermore, optimal metabolite identification rates were obtained by combining an AGC target value of 5x106 and MIT of 100 ms for MS and an AGC target value of 1x105 and an MIT of 50 ms for MS/MS scans. A 40-second exclusion duration with a repeat count of one resulted in a higher identification rate than shorter dynamic exclusion durations. Two stepped normalized collision energy provided higher spectral quality than absolute collision energy values. The findings of this work provides useful insights into understanding and optimizing important mass spectrometric parameters for untargeted metabolomics.