Improved LC−MS/MS Spectral Counting Statistics by Recovering Low-Scoring Spectra Matched to Confidently Identified Peptide Sequences

Abstract

Spectral counting has become a popular method for LC-MS/MS based proteome quantification; however, this methodology is often not reliable when proteins are identified by a small number of spectra. Here, we present a simple strategy to improve spectral counting based quantification for low-abundance proteins by recovering low-quality or low-scoring spectra for confidently identified peptides. In this approach, stringent data filtering criteria were initially applied to achieve confident peptide identifications with low false discovery rate (e.g., < 1% at peptide level) after LC-MS/MS analysis and database search by SEQUEST. Then, all low-scoring MS/MS spectra that matched to this set of confidently identified peptides were recovered, leading to more than 20% increase of total identified spectra. The validity of these recovered spectra was assessed by the parent ion mass measurement error distribution, retention time distribution, and by comparing the individual low score and high score spectra that correspond to the same peptides. The results support that the recovered low-scoring spectra have similar confidence levels in peptide identifications as the spectra passing the initial stringent filter. The application of this strategy of recovering low-scoring spectra significantly improved the spectral count quantification statistics for low-abundance proteins, as illustrated in the identification of mouse brain region specific proteins.