Enhanced MS/MS coverage for metabolite identification in LC-MS-based untargeted metabolomics by target-directed data dependent acquisition with time-staggered precursor ion list

Comparative Evaluation of Data Dependent and Data Independent Acquisition Workflows Implemented on an Orbitrap Fusion for Untargeted Metabolomics

“…As a consequence, some biologically relevant precursor ions may not be selected for fragmentation if they do not match the defined selection criteria or if they are coeluted with more intense species. These issues can be partially overcome by the use and careful optimization of additional MS-to-MS/MS triggering criteria (e.g., isotope pattern or mass defect) [8][9][10], by optimizing the use of exclusion or inclusion lists prior to further MS/MS acquisitions [11][12][13], or by implementing efficient software tools for assessing precursor ion purity of acquired data [14].Until very recently, the implementation of acquisition protocols combining MS and MS/MS acquisitions in one single run, especially those related to DIA, were rather limited to hybrid quadrupole-time-of-flight (Q-TOF) instruments due to their enhanced duty cycles [15,16]. Because of their lower acquisition speed, simultaneous MS and MS/MS data collection can rarely be performed at the highest mass resolution achievable on Orbitrap-based instruments, in order to keep enough data points per chromatographic peak, which is mandatory for further reliable peak integration and corresponding metabolite quantification from MS data [17].…”

“…Until very recently, the implementation of acquisition protocols combining MS and MS/MS acquisitions in one single run, especially those related to DIA, were rather limited to hybrid quadrupole-time-of-flight (Q-TOF) instruments due to their enhanced duty cycles [15,16]. Because of their lower acquisition speed, simultaneous MS and MS/MS data collection can rarely be performed at the highest mass resolution achievable on Orbitrap-based instruments, in order to keep enough data points per chromatographic peak, which is mandatory for further reliable peak integration and corresponding metabolite quantification from MS data [17].…”

Bridging Targeted and Untargeted Mass Spectrometry-Based Metabolomics via Hybrid Approaches

“…Meanwhile, it showed a higher selectivity and better reliability for quantitation in a broader metabolome coverage [ 27 ]. Researchers could use a variety of pooled biological samples for their hybrid method developments, such as rice seed [ 28 ], tobacco leaf [ 25 , 36 ], human serum/plasma [ 24 , 28 , 31 , 37 , 38 , 39 ], human urine [ 39 , 40 , 41 ], mouse serum/plasma [ 27 , 42 , 43 ], mouse brain and liver [ 44 ], cancer cell [ 44 ], and bacterial culture [ 29 , 45 ]. With the MS peak lists and ion pairs generated from the highly diverse set of small molecules within the studied biological matrix or reported database, hybrid methods in metabolomics permit the analysis of all metabolites in a given biological sample.…”

“…Unlike the conventional DDA that automatically fragments the most abundant ions, tDDA, or tsDDA highlighted the features of interest regardless of their peak abundance and reduced the initiation of unnecessary MS/MS events. In these studies, plasma samples were used to evaluate and compare the performance of the MS/MS acquisition using the conventional DDA, tDDA, and tsDDA, and 97.4% and 95.4% of the selected ions in the positive and negative ion mode of tsDDA analysis have an RSD value of less than 20%, respectively [ 42 ]. Compared to the conventional DDA, the ts-DDA also demonstrated superior performance in the high co-elution zones of plasma samples (especially for the metabolites of low abundance).…”

From Samples to Insights into Metabolism: Uncovering Biologically Relevant Information in LC-HRMS Metabolomics Data

“…To avoid the selection of the most highly abundant ions each time, across multiple scans, a preferred list of ions of interest can be defined and contaminant ions placed on the exclusion list. Data can also be acquired in a time-staggered fashion through a set of iterative injections (of pooled QC samples) with PC-driven exclusion (of ions on which the data has already been acquired in previous runs), which will significantly enhance the amount of acquired MS/MS data [63,64].…”