Accelerating LC-MS analysis

For differential studies, improvements in LC and MS systems have allowed researchers to generate more proteomics data without the need for isotopic labeling, but this has come at the price of more complex data sets

Randall C Willis
UPPSALA, Sweden—For differential studies, improvements in LC and MS systems have allowed researchers to generate more proteomics data without the need for isotopic labeling, but this has come at the price of more complex data sets. Recently, however, researchers at GE Healthcare, Uppsala University, Karolinska Institutet, and The Informatics Factory developed an automated data analysis method to facilitate these studies.
 
As they presented in the Journal of Proteome Research, the researchers used the X! Tandem search engine and DeCyder MS to analyze data from LC-MS and LC-MS/MS experiments, looking both at absolute quantities of peptide as well as differential expression. Using spiked samples of a mixture of tryptic digests of eight proteins, they found that changes in relative ion intensities were proportional to the amount of peptide in the sample and that the analysis showed a linear response over three magnitudes (from 10 fmol/µL to 10 pmol/µL).
 
They then analyzed endogenous protein expression in brain samples from mice dosed with reserpine, a drug that induces a Parkinson's disease-like state. They found that of the 300 peptides automatically detected, 20 showed altered expression. They identified six of these peptides as arising from proenkephalin A precursor, a protein known to respond to changes in dopamine levels, the mode of action of reserpine.

Randall C Willis

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