Dynamic exclusion optimized for low-abundance proteins

When proteomic-scale samples are analyzed via spectral counting, a protein’s abundance is measured as the total number of tandem mass spectra matching its peptides. Less-abundant proteins can escape analysis by MS/MS because they can co-elute with more-abundant proteins and because quantitation is less reliable with fewer spectral counts. Utilization of the dynamic exclusion (DE) principle during data acquisition, which temporarily puts an m/z value onto an exclusion list after its MSⁿ spectrum has been acquired, allows for analysis of less-abundant peptides. However, DE must be used carefully to ensure data quality.

Now, Laurence Florens and colleagues at the Stowers Institute for Medical Research have performed a rigorous determination of optimal DE duration. They show that this approach can maximize the number of spectral counts as well as the total number of peptides, proteins, and peptides per protein.

In this study, the researchers set out to determine the optimal DE duration for multidimensional protein identification technology (known as MudPIT) analysis. They developed a mathematical model to analyze the effects of DE duration on spectral counts and found that for proteins of low (but not high) abundance, enabling DE at the optimal duration (90 seconds) led to higher peptide counts, higher normalized spectral abundance factors, and better reproducibility.

They also developed a method for determining the optimal DE time, which is proportional to the average chromatographic peak width at the base of the identified peptides. The method and analysis presented in this report, say the authors, will facilitate optimization of spectral-count-based quantitative proteomics, which will further improve the emerging statistical analysis of such data sets. (Anal. Chem. 2009, DOI 10.1021/ac9004887)