Xiulan Chen ^1* Yanchang Li ^2* Daniela Braconi ^3*

• ¹ Institute of Biophysics, Chinese Academy of Sciences (CAS), Beijing, China
• ² State Key Laboratory of Medical Proteomics, Beijing Proteome Research Center, National Center for Protein Sciences (Beijing), Institute of Lifeomics, Beijing 102206, China, Beijing, China
• ³ Department of Biotechnology, Chemistry, and Pharmacy, University of Siena, Siena 53100, Italy, Siena, Italy

The growing prevalence of metabolic diseases (including type 2 diabetes, hypertension, hyperlipidemia, obesity, and non-alcoholic fatty liver disease) poses a significant economic burden to our society [1] . The pathogenesis of metabolic diseases is a multifactorial pathophysiological process arising from various systematic metabolic defects; therefore, a comprehensive, holistic approach is needed to deeply investigate the mechanisms associated with the development of metabolic diseases.In the last two decades, proteomics, the large-scale study of proteins, has emerged as a powerful and useful tool to investigate the roles of proteins in biological systems. The rapid development of MS technologies has enabled a system-wide, qualitative, and quantitative analysis of the proteome at the cell, tissue, or organism level [2] .Proteomic studies can be classified in three main categories, i.e., expression, functional and structural proteomics. Expression proteomics involves the qualitative and quantitative comparison of protein expression in the entire proteome of samples from different states. Different MS-based quantitative proteomic techniques, such as SILAC, dimethyl labeling, iTRAQ, TMT, label-free, and DIA, have been applied to highlight differentially expressed proteins in different states, such as health and disease states. However, these MS-based quantitation methods are characterized by different features, advantages, and shortcomings [3] . The choice of suitable quantitative methods is necessary for accurate and reliable results.Post-translational modifications (PTMs) of proteins, the covalent modifications involving the addition or removal of chemical/protein groups on proteins, are highly important for the regulation of protein function, localization, interaction, and activity both in physiological and disease states. MS technologies were implemented as important tools to characterize and discover novel PTMs [4] . PTMomics, which is the MS- Understanding the complexity of proteoforms, i.e., protein variants generated by mutations, alternate splicing, mRNA processing and PTMs, is crucial for the selection of biomarkers, therapeutic targets and drug candidates. By using commercial, highly purified serum albumin as a model, Woodland et al. show how the most common current analytical MS approaches (shotgun or bottom-up proteomics and mass spectrometryintensive top-down proteomics) both fail to fully and effectively identify proteoforms and/or provide their comprehensive analysis, proposing highresolution, quantitative integrated/integrative TDP as a better approach while raising interesting questions in readers' minds.To circumvent analytical challenges, several computational approaches have been developed to study PTMs, such as phosphorylation, glycosylation, S-nitrosylation, methylation, sumoylation, palmitoylation, and N-myristoylation [5] . These in silico bioinformatics tools could predict modified sites that can be then validated with experimental approaches, expanding the scope of PTM studies. In this research topic, Zhang et al. Given the complexity of biological systems, single omics analyses cannot provide a comprehensive understanding of molecular changes of complex diseases. Multi-omics, which combines two or more omics, such as genomics, transcriptomics, proteomics, metabolomics, could provide an integrated approach for deeper insights and discoveries and promote the understanding of human diseases [6] .