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

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

Submitted to: Meeting Abstract
Publication Type: Abstract Only
Publication Acceptance Date: 5/30/2023
Publication Date: N/A
Citation: N/A

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: Introduction Measured fragment spectra (MS/MS) of chemical ions is commonly generated using tandem mass spectrometry (LC-MS/MS) to help identify compounds in untargeted metabolomics. Data-dependent acquisition (DDA) is of the most commonly employed method for the acquisition of MS/MS spectra. Multiple full scan and MS/MS parameters in a DDA require the user to define their values. The ability to select which of these parameters 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 parameters and the wide range of possible values for these parameters. We explored the effect of different MS and MS/MS parameters on the rate of metabolite identification in untargeted metabolomics. Methods NIST SRM 1950 reference human plasma was extracted using 80% methanol. Instrumental analysis was performed on a Vanquish UHPLC coupled to an Orbitrap Exploris 480 mass spectrometer (MS) equipped with heated-electrospray ionization (HESI) probe. Chromatographic separations were performed using Acquity Premier CSH C18 1.7 µm x 2.1 x 100 mm Column. MS operating parameters including resolution, radio frequency (RF) level, intensity threshold, mass isolation width, number of data dependent scans (TopN), dynamic exclusion, maximum injection time (MIT) and automatic gain control (AGC) were optimized using the one factor at a time (OFAT) approach. Optimum value was defined as the value that enabled the highest number of metabolite identification, greatest number of compounds with MS/MS information and improved MS/MS spectral quality. Preliminary Data For untargeted metabolomics to be successful, the LC and MS systems' performance and optimization are essential. Even though this is the case, little attention has been paid to the significance of evaluating and adjusting the MS instrument parameters when in DDA mode for untargeted metabolomics. Additionally, the majority of MS parameter improvements that have been reported in the open literature are either for specific target molecules or are attributed to the discipline of proteomics. NIST SRM 1950 was used in this study to examine the effects of mass spectrometric parameters, including mass resolution, RF lens, signal intensity threshold, number of MS/MS events, cycle time, collision energy, maximum ion injection time (MIT) for full MS and MS/MS, dynamic exclusion, and the automatic gain control (AGC) target value for MS and MS/MS scans on rate of identification. 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. Although the overall effect of collision energy on identification rate is minimal, two stepped normalized collision energy provided higher spectral quality than absolute collision energy values. Novel Aspect The findings of this work provides useful insights into understanding and optimizing important mass spectrometric parameters for untargeted metabolomics.