Perspectives in Omics 2022

developments of DDA-and DIA-based lipidomic workflows and listed available bioinformatic tools for the analysis of resulting spectral data. In the same field of application, Schwudke et al., in his minireview discussed about the considerations for interpreting membrane and storage lipid profile changes that are often the central focus of untargeted Lipidomics studies. Insights into how the most conserved molecular patterns are formed in eukaryotic systems and the consequences for the perturbation of lipid homeostasis were also addressed. The implications of lipid identification specificity and experimental variability on modeling membrane structure and systemic responses were discussed. Metabolomics aims to comprehensively analyze and identify small molecules to gain insights into the metabolic processes occurring within biological systems. Since metabolomics involves the characterization of multivariate metabolic phenotypes, such a field requires specific data analysis tools to fully exploit the relevant information considering the different factors, as well as their respective impact on metabolite levels. Boccard et al., in their perspective discussed about the challenges opened by the investigation of such a high-dimensional multifactorial data and pointed out the requirement of the development of innovative experimental strategies involving structured designs of experiments to assess cause-effect associations to reach deeper insight into relevant biological information. In a clinical point of view, Le Gouellec et al., in their mini review discussed about how metabolomics can be useful and implemented in clinical studies for the discovery of biomarkers, but also to propose the assays of biomarkers called "signatures," which can be composite biomarkers or fingerprints, which will ultimately guide the clinician. They exemplified their arguments by the illustration of trimethylamine Noxide found as a biomarker for cardiovascular diseases. Ultimately, Lazari et al., in their mini-review surveyed literature that have combined mass spectrometrybased omics techniques (proteomics, lipidomics, and metabolomics) and artificial intelligence to understand COVID-19 pathogenesis. They made an exhaustive bibliography studies pointed out the pros and the cons of used MS-based strategies as well as data analysis.