Introduction: Targeted proteomics often focuses on the selective identification and quantitation of a protein of interest from an array of proteins. In addition to the selection power of mass spectrometers, multi-step sample preparations (e.g. separations) are often needed for a highly complex sample prior to MS analysis. We have demonstrated here a methodology to identify and quantitate the differences in a low level (femtomole) protein, human growth hormone in human plasma, by a new multi-dimensional LC-MS system with new analysis software.
Methods: An aliquot of human plasma (5 mg/mL) containing human growth hormone in approximate low femtomole range were dissolved in 8M Urea, reduced and alkylated with either D0-ICAT or D8-ICAT (10-fold dilution from the D0-ICAT pool), buffer exchanged, then combined the two pools (D0 and D8-ICAT pools), and digested the combined pool with trypsin. The trypsin-digested solution was injected on a cation exchange column, and eluted onto a reversed phase column with sequential salt steps. The eluted peptides were on-line analyzed by a Finnigan LCQ Deca XP™ ion trap mass spectrometer. Protein identifications were made with Bioworks (SEQUEST®) software. The differential quantitation was made by the identification of the desired peptides (e.g. Cys bound with ICAT D0 and D8 peptides) using MS/MS spectra and then calculated the ratios of these peptides using MS spectra (Xpress imbedded in Bioworks software).
Preliminary Data: Preliminary data by this new multi-dimensional LC-MS system using 5 salt steps with 120 min RP gradients, over 100 proteins were identified with 2 or more peptides, and over 300 proteins were identified with 1 or more peptides. Using data dependent mass selection for fragmentation (e.g. select the parent masses of the targeted tryptic peptides), Human Growth Hormone was identified with 5 unique peptides plus the Cys bound with ICAT peptide. Thus, without using the additional cleaning and affinity steps, a low level (femtomole) protein, human growth hormone, was differentially quantitated and identified with high confidence by this approach.
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