Proteome profile of the MCF7 cancer cell line: a mass spectrometric evaluation.
The development of novel proteomic technologies that will enable the discovery of disease specific biomarkers is essential in the clinical setting to facilitate early diagnosis and increase survivability rates. We are reporting a shotgun two-dimensional (2D) strong cationic exchange/reversed-phase liquid chromatography/electrospray ionization tandem mass spectrometry (SCX/RPLC/ESI-MS/MS) protocol for the analysis of proteomic constituents in cancerous cells. The MCF7 breast cancer cell line was chosen as a model system. A series of optimization steps were performed to improve the LC/MS experimental setup, sample preparation, data acquisition and database search protocols, and a data filtering strategy was developed to enable confident identification of a large number of proteins and potential biomarkers. This research has resulted in the identification of >2000 proteins using multiple filtering and p-value sorting. Approximately 1600-1900 proteins had p < 0.001, and, of these, approximately 60% were matched by >or=2 unique peptides. Alternatively, >99% of the proteins identified by >or=2 unique peptides had p < 0.001. When searching the data against a reversed database of proteins, the rate of false positive identifications was 0.1% at the peptide level and 0.4% at the protein level. The typical reproducibility in detecting overlapping proteins across replicate runs exceeded 90% for proteins matched by >or=2 unique peptides. According to their biological function, approximately 200 proteins were involved in cancer-relevant cellular processes, and over 25 proteins were previously described in the literature as putative cancer biomarkers, as they were found to be differentially expressed between normal and cancerous cell states. Among these, biomarkers such PCNA, cathepsin D, E-cadherin, 14-3-3-sigma, antigen Ki-67, TP53RK, and calreticulin were identified. These data were generated by subjecting to MS analysis approximately 42 microg of sample, analyzing 16 SCX peptide fractions, and interpreting approximately 55,000 MS2 spectra. Total MS time required for analysis was 40 h.
Sarvaiya
Hetal A
HA
Virginia Bioinformatics Institute and Department of Biomedical Engineering, Virginia Polytechnic Institute and State University, Washington St. Bio II/283, Blacksburg, VA 24061, USA.
Yoon
Jung H
JH
Lazar
Iulia M
IM
D014408:Biomarkers, Tumor / Q000032:analysis*; D001943:Breast Neoplasms / Q000378:metabolism*; D045744:Cell Line, Tumor; D002851:Chromatography, High Pressure Liquid / Q000379:methods*; D020869:Gene Expression Profiling / Q000379:methods*; D006801:Humans; D009363:Neoplasm Proteins / Q000032:analysis*; D020543:Proteome / Q000032:analysis*; D021241:Spectrometry, Mass, Electrospray Ionization / Q000379:methods*
D014408:Biomarkers, Tumor; D009363:Neoplasm Proteins; D020543:Proteome