Stable Isotope Labeling by Amino acid in Vivo (SILAV): a new method to explore protein metabolism.

RATIONALE:Intravenous administration of stable isotope labeled amino acid ((13)C6-leucine) to humans recently made it possible to study the metabolism of specific biomarkers in cerebrospinal fluid (CSF) using targeted mass spectrometry (MS). This labeling approach could be of great interest for monitoring many leucine-containing peptides in parallel, using high-resolution MS. This will make it possible to quantify the rates of synthesis and clearance of a large range of proteins in humans with a view to obtaining new insights into protein metabolism processes and the pathophysiology of diseases such as Alzheimer's disease. METHODS:Proteins from human lumbar and ventricular CSF samples collected at different times after intravenous (13)C6-leucine infusion were digested enzymatically with LysC/trypsin after being denatured, reduced and alkylated. Desalted tryptic peptides were fractionated using Strong Cation eXchange chromatography (SCX) and analyzed using nanoflow liquid chromatography (nano-LC) coupled to a QTOF Impact II (Bruker Daltonics) mass spectrometer. Data-dependent acquisition (DDA) mode was used to identify and quantify light and heavy (13)C6-leucine peptides. The ratios of (13)C6-leucine incorporation were calculated using the Skyline software program in order to determine the rates of appearance and clearance of proteins in the CSF. RESULTS:After SCX fractionation and quadrupole time-of-flight (QTOF) analysis, 4528 peptides containing leucine were identified in five fractions prepared from 40 μL of CSF. Upon analyzing one of these fractions, 66 peptides (2.7%) corresponding to 61 individual proteins had significant and reproducible rate of (13)C6-leucine incorporation at various time points. The plots of the light-to-heavy peptide ratios showed the existence of proteins with different patterns of appearance and clearance in the CSF. CONCLUSIONS:The Stable Isotope Labeling Amino acid in Vivo (SILAV) method presented here, which yields unprecedented information about protein metabolism in humans, constitutes a promising new approach which certainly holds great potential in the field of clinical proteomics.