Post analysis data acquisition for the iterative MS/MS sampling of proteomics mixtures

Abstract

The identification of peptides by microcapillary liquid chromatography-tandem mass spectrometry (microLC-MS/MS) has become routine because of the development of fast scanning mass spectrometers, data-dependent acquisition, and database searching algorithms. However, many peptides within the detection limit of the mass spectrometer remain unidentified because of limitations in MS/MS sampling speed despite the dynamic range and peak capacity of the instrument. We have developed an automated approach that uses the mass spectra from high resolution microLC-MS data to define the molecular species present in the mixture and directs the acquisition of MS/MS spectra to precursors that were missed in prior analyses. This approach increases the coverage of the molecular species sampled by MS/MS and consequently the number of peptides and proteins identified during the acquisition of technical or biological replicates using a simple one-dimensional chromatographic separation. The combination of a unique workflow and custom software contribute to the improved identification of molecular features detected in proteomics experiments of complex protein mixtures.

Figure 1

Schematic illustration of PAnDA versus DDA analysis. The experiment began with an initial data-dependent acquisition step. The data obtained in this initial step were analyzed to identify PPIDs, which were stored in the PAnDA database. PPIDs which were not subjected to MS/MS fragmentation were put into an inclusion list and prioritized in PAnDA replicate analyses. After each PAnDA replicate, the database was updated to mark PPIDs that now contained fragmentation spectra and to include new PPIDs not detected in previous µLCMS analyses. Interspersed between each PAnDA replicate is a standard DDA replicate to minimize artifactual differences when comparing the two data sets.

Figure 2

Hardklör detected persistent peptide isotope distributions (PPIDs) plotted by m/z and retention time. The height and color of each bar represents the intensity of each PPIDs. (A) The upper figure shows the total PPIDs from the initial analysis. (B) The lower figure shows only the PPIDs remaining after removing the features that were isolated for data-dependent MS/MS. In general, only the most intense features were selected for fragmentation by data-dependent acquisition. There are still numerous signals that were never selected for fragmentation and these signals are used to target MS/MS in subsequent analyses by PAnDA.

Figure 3

Comparison of the sampled persistent peptide isotope distributions (PPIDs) with and without PAnDA. A) The upper figure compares the fraction of the detected PPIDs sampled in replicate analyses by MS/MS. B) The lower figure compares signal intensity of the PPIDs sampled by MS/MS with and without PAnDA.

Figure 4

The cumulative number of peptides (A) and proteins (B) identified by PAnDA or standard DDA in replicate µLC-MS/MS analyses. All identifications were based on a constant q-value ≤ 0.01 applied on the peptide spectrum match level.

Figure 5

Venn diagram comparing the overlap in protein identifications with (red) and without (blue) PAnDA. A total of 1220 unique proteins were identified, of which 86.8% were identified by PAnDA.