Single-cell omics in personalized medicine

Personalized medicine represents one of the major clinical outcomes of omics applications. Genomics and post-DNA omics allow an unprecedented characterization of the molecular landscape of each individual, endowing modern medicine with new predictive, preventive, personalized, and precision capabilities. Multiple omics techniques are available for all molecular families and over time a huge amount of molecular data has been produced in all the different biological and medical contexts. Their actionability requires a clinically meaningful rationalization, which with the use of bioinformatics has found a synthesis in the holistic approach of systems biology, where big data from omics can be represented as molecular networks of pathophysiological interest. Genomics, epigenomics, transcriptomics, proteomics, and metabolomics represent the major molecular layers of trans-omic networks that could allow Integrated Personal Omics Profiling (iPOP). However, given the intrinsic intercellular variability, bulk analysis can only provide average molecular signals from a multitude of different cells, each following its own fate and with its own epiphenomenal environment, thus limiting the potential of clinical applications of omics data in personalized medicine. Single-cell multiomics, thanks to its rapid development, is emerging as a strategy that can faithfully provide a real picture of the trans-omic relationships in multimolecular networks, highlighting pathogenetic cues that can be utilized in clinical practice. Single-cell multiomics applications are being developed, mostly in cancer, and some are proposing the technology for personalized approaches in other medical fields. Single-cell multiomics could also be exploited to overcome the cellular variability issue limiting the application of post-DNA omics, by applications to undifferentiated pluripotent stem cells, where the individual biological supersystem has yet to undergo the multitude of epiphenomenal events and the molecular pattern is closer to its original state. Dedifferentiation from somatic cells could provide induced pluripotent stem cells for improving the iPOP model in personalized medicine