Mass Spectrometry to Identify Cellular Proteins
While the 2D gel method easily separates proteins, it doesn't identify them. If there are differences in spots between the proteins in a cancer cell and a normal cell, this method cannot determine the actual identity of the different proteins in the two cell types. To identify these proteins, individual spots are excised from 2D gels and then subjected to mass spectrometry, which separates charged particles, or ions, according to mass. First the molecules in the sample are ionized to produce a population of charged molecules. A mass analyzer then separates the sample's molecules based on their mass to charge ratio. A detector then produces a peak for each ion; this peak gives the mass and represents the amount of the ion. A computer program reads the complex spectral information from the mass spectrometry process. The program matches the information on the each peptide's mass against the mass of theoretical, predicted peptides, based on known proteins in databases. This is called peptide mass mapping. With many different peptides for each protein, the computer can match the sequence to one or more known proteins. Peptide mass mapping can only be used in situations where the genome has been sequenced and all predicted proteins for the genome are known.
Another application of mass spectrometry is protein fingerprinting. This technique has been used to identify unique sets of proteins in blood, which serve as markers for different forms of cancer. Interestingly, for this method to be useful, we do not need to know the actual identities of the particular proteins used as markers for a disease. Instead, this technique relies on pattern recognition software. Using training data from samples from individuals with and without cancer, the program searches for a particular pattern of peaks that correlates with cancer. This technique requires only a drop of blood and does not require any detailed genetic information; however, its accuracy in predicting some forms of cancer is limited because the number of marker peptides is not sufficiently large. As more samples are evaluated, the accuracy will likely increase because the software will be able to find more accurate peptide patterns correlating to cancer. Proteomic fingerprinting holds great promise as a diagnostic tool for a variety of diseases that produce distinctive patterns of proteins in blood.
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