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The emerging requests for handling complex samples in system biology studies highlighted the need to expand the metabolite coverage in metabolomics analysis and to take advantage of the quantitative and targeted assays. Here, we developed a novel workflow to integrate time staggered or mass staggered scan methods with globally optimized targeted-mass spectrometry (GOT-MS), to enable broad metabolites coverage with better stability, repeatability, and quantitative capability. To establish these methods, two scheduled selected reaction monitoring (SRM) methods, time staggered and mass staggered approaches, were configured to achieve optimal sensitivity and scan speed and were combined with the GOT-MS strategy. Both methods took advantage of the systematic selection and rearrangement of all detectable metabolic peaks from a GOT-MS peak list, based on either retention time or m/z of the precursor ions. The established methods were then applied to the metabolic profile-based differentiation of Staphylococcus aureus N315 and N315 ex, an isogenic pair of Methicillin-resistant and susceptible S. aureus (MRSA and MSSA). A total of 464 metabolite peaks was detected successfully from pooled MSSA and MRSA bacterial metabolite extract using the GOT-MS method, and ts/ms-GOT-MS demonstrated better sensitivity and repeatability than the GOT-MS and previously established targeted metabolomics method. The semi-quantitative analysis in a broader metabolome coverage was also achieved with ts/ms-GOT-MS methods. Multivariate statistical analyses were also performed to determine whether metabolic profiling approach could differentiate MSSA from MRSA. The comparison of these methods to GOT-MS and targeted metabolic profiling demonstrated that ts/ms-GOT-MS are significantly improved hybrid metabolomics methods and can be used as promising tools for future studies. • Development of two hybrid metabolic profiling methods • Novel methods combining broad metabolite coverage and quantitative capability • Demonstrated utility in differentiating MRSA and MSSA strains via metabolic profiling • Hybrid metabolomics approaches achieved excellent linearity in metabolic features detection. [ABSTRACT FROM AUTHOR]