Targeted high-resolution and accurate mass analyses performed on fast sequencing mass spectrometers have opened new avenues for quantitative proteomics. More specifically, parallel reaction monitoring (PRM) implemented on quadrupole-orbitrap instruments exhibits exquisite selectivity to discriminate interferences from analytes. Furthermore, the instrument trapping capability enhances the sensitivity of the measurements. The PRM technique, applied to the analysis of limited peptide sets (typically 50 peptides or less) in a complex matrix, resulted in an improved detection and quantification performance as compared with the reference method of selected reaction monitoring performed on triple quadrupole instruments. However, the implementation of PRM for the analysis of large peptide numbers requires the adjustment of mass spectrometry acquisition parameters, which affects dramatically the quality of the generated data, and thus the overall output of an experiment. A newly designed data acquisition scheme enabled the analysis of moderate-to-large peptide numbers while retaining a Liquid chromatography (LC) 1 coupled to tandem mass spectrometry (MS/MS) approaches have been widely acknowledged as one of the most effective methods to study complex proteomes. In particular, their preclinical, and also clinical applications, have contributed to advances in biomedical sciences. A bottom-up proteomics workflow relies on the enzymatic digestion of the proteins constituting a proteome to generate thousands of peptides, which are subsequently separated by liquid chromatography and analyzed by tandem mass spectrometry. Two main MS-based strategies have emerged from this generic process, which differ in their objectives and acquisition schemes and are commonly referred to as discovery and targeted strategies, respectively; both presenting advantages and drawbacks to study specific biological and clinical questions.Discovery proteomics, relying on nonsupervised data dependent acquisition (DDA), is routinely used to effectively profile, with broad coverage, the proteome under investigation (1, 2). This strategy is focused on protein identification but has limitations with respect to quantitative applications. The stochastic nature of DDA sampling results in "missing values" in replicated experiments, directly affecting quantitative studies, whereas low abundance components remain largely undetected (3). By contrast, targeted proteomics has emerged to more systematically quantify peptides/proteins present in a wide range of concentrations in complex samples (4). The hypothesis-driven nature of targeted data acquisition (TDA), where the peptides used as surrogates for a preselected set of proteins are consistently measured across a multitude of