LC–MS is used in proteomics as a method to detect and identify the components of a complex mixture. The bottom-up proteomics LC–MS approach generally involves protease digestion and denaturation using trypsin as a protease, urea to denature the tertiary structure, and iodoacetamide to modify the cysteine residues. After digestion, LC–MS is used for peptide mass fingerprinting, or LC–MS/MS (tandem MS) is used to derive the sequences of individual peptides. LC–MS/MS is most commonly used for proteomic analysis of complex samples where peptide masses may overlap even with a high-resolution mass spectrometry. Samples of complex biological (e.g., human serum) may be analyzed in modern LC–MS/MS systems, which can identify over 1000 proteins. However, this high level of protein identification is possible only after separating the sample by means of SDS-PAGE gel or HPLC-SCX. Recently, LC–MS/MS has been applied to search peptide biomarkers. Examples are the recent discovery and validation of peptide biomarkers for four major bacterial respiratory tract pathogens (Staphylococcus aureus, Moraxella catarrhalis; Haemophilus influenzae and Streptococcus pneumoniae) and the SARS-CoV-2 virus.
LC–MS has emerged as one of the most commonly used techniques in global metabolite profiling of biological tissue (e.g., blood plasma, serum, urine). LC–MS is also used for the analysis of natural products and the profiling of secondary metabolites in plants. In this regard, MS-based systems are useful to acquire more detailed information about the wide spectrum of compounds from a complex biological samples. LC–nuclear magnetic resonance (NMR) is also used in plant metabolomics, but this technique can only detect and quantify the most abundant metabolites. LC–MS has been useful to advance the field of plant metabolomics, which aims to study the plant system at molecular level providing a non-biased characterization of the plant metabolome in response to its environment. The first application of LC–MS in plant metabolomics was the detection of a wide range of highly polar metabolites, oligosaccharides, amino acids, amino sugars, and sugar nucleotides from Cucurbita maxima phloem tissues. Another example of LC–MS in plant metabolomics is the efficient separation and identification of glucose, sucrose, raffinose, stachyose, and verbascose from leaf extracts of Arabidopsis thaliana.