ABSTRACT
As an extension of proteomics, peptidomics has been widely used in medical and biological researches. However, the effect of reproducibility of identification method on peptidomics is not yet clear. In this work, the urine sample of a healthy people was analyzed for seven times in parallel by nano-liquid chromatography-high-resolution tandem mass spectrometry. To illustrate the variability and stability among these experiments, the number of spectra, the utilization of total spectra, the number of identified peptides, the number of proteins, and the ionic strength and retention time of peptides were counted and compared. The average number of peptides was 208 and the standard deviation was 38. 7. After all of data were combined, 426 peptides belonging to 114 proteins were obtained, while only 109 peptides coming from 35 proteins were identified in each experiment, indicating that there were both an randomness and a relative stability for LC-MS analysis. Increasing the number of parallel experiments would expand the data set of peptidomics, but the rate of increase would decrease over 3 or more measurements. Compared with peptides, the results of peptidomics were more stable at protein level, indicating that proteins were more robustly peptidomics biomarker than the peptides.
ABSTRACT
As an extension of proteomics, peptidomics has been widely used in medical and biological researches. However, the effect of reproducibility of identification method on peptidomics is not yet clear. In this work, the urine sample of a healthy people was analyzed for seven times in parallel by nano-liquid chromatography-high-resolution tandem mass spectrometry. To illustrate the variability and stability among these experiments, the number of spectra, the utilization of total spectra, the number of identified peptides, the number of proteins, and the ionic strength and retention time of peptides were counted and compared. The average number of peptides was 208 and the standard deviation was 38. 7. After all of data were combined, 426 peptides belonging to 114 proteins were obtained, while only 109 peptides coming from 35 proteins were identified in each experiment, indicating that there were both an randomness and a relative stability for LC-MS analysis. Increasing the number of parallel experiments would expand the data set of peptidomics, but the rate of increase would decrease over 3 or more measurements. Compared with peptides, the results of peptidomics were more stable at protein level, indicating that proteins were more robustly peptidomics biomarker than the peptides.