We have developed a high dimensionality single cell proteomics database, which encompasses hundreds of samples from healthy individuals across the entire age (foetus to 90+ year old) and ethnicity gradients, which provides a reference standard. We have also developed a parallel dataset analysing with the same approach thousands of highly curated samples from diseases in which the immune system is relevant, including Autoimmunity, Cancer, Infectious Diseases, etc. We have developed AI-driven tools, which enable the depiction of the architecture of the immunome in health, across and within the entire age gradients. The same tool can identify how diseases, or therapy, affect the architecture of the immunome, and dissect mechanistically such effects. We will discuss examples of how this platform can: i) predict clinical fate, including onset of disease activity or responsiveness to immunotherapy; ii) identify and validate new targets and leads for immunotherapy; iii) contribute to a more efficient and agile clinical development of immunotherapies