Résumé
The extraction of accurate physiological parameters from clinical samples provides a unique perspective to understand disease etiology and evolution, including under therapy. We introduce a new methodologic framework to map patient proteome dynamics in vivo, either proteome-wide or in large targeted panels. We applied it to ventricular cerebrospinal fluid (CSF) and could determine the turnover parameters of almost 200 proteins, whereas a handful were known previously. We covered a large number of neuron biology- and immune system-related proteins, including many biomarkers and drug targets. This first large data set unraveled a significant relationship between turnover and protein origin that relates to our ability to investigate organ physiology with protein-labeling strategy specifics. Our data constitute the first draft of CSF proteome dynamics as well as a repertoire of peptides for the community to design new analyses. The disclosed methods apply to other fluids or tissues provided sequential sample collection can be performed. We show that the proposed mathematical modeling applies to other analytical methods in the field.