Accurate peptide fragment mass analysis: multiplexed peptide identification and quantification

Abstract

Fourier transform-all reaction monitoring (FT-ARM) is a novel approach for the identification and quantification of peptides that relies upon the selectivity of high mass accuracy data and the specificity of peptide fragmentation patterns. An FT-ARM experiment involves continuous, data-independent, high mass accuracy MS/MS acquisition spanning a defined m/z range. Custom software was developed to search peptides against the multiplexed fragmentation spectra by comparing theoretical or empirical fragment ions against every fragmentation spectrum across the entire acquisition. A dot product score is calculated against each spectrum to generate a score chromatogram used for both identification and quantification. Chromatographic elution profile characteristics are not used to cluster precursor peptide signals to their respective fragment ions. FT-ARM identifications are demonstrated to be complementary to conventional data-dependent shotgun analysis, especially in cases where the data-dependent method fails because of fragmenting multiple overlapping precursors. The sensitivity, robustness, and specificity of FT-ARM quantification are shown to be analogous to selected reaction monitoring-based peptide quantification with the added benefit of minimal assay development. Thus, FT-ARM is demonstrated to be a novel and complementary data acquisition, identification, and quantification method for the large scale analysis of peptides.

Figure 1

Diagram illustrating FT-ARM strategy based on accurate peptide fragment mass measurements. All ions are fragmented in every scan during LC separation to produce the total ion chromatogram in A). B) Complex fragmentation spectrum of all ions. C) Hypothetical peptide fragmentation spectra. D) Dot product analysis result called a Score Chromatogram.

Figure 2

A) Experimental scan sequence. B) Correlation between data-dependent acquisition (DDA) identification of a yeast tryptic peptide (LGANAILGVSLAASR) from a whole cell lysate digest. Extracted ion chromatogram (EIC) for the m/z of this peptide shown in red (20 mDa window). The FT-ARM score chromatogram of the same sequence is shown in blue. C) Mascot annotated spectrum for the identification of the peptide.

Figure 3

A three dimensional plot presenting data from an FT-ARM simulation which allows for estimation of the number of potential false peptide matches in a fasta database. Empirical data presented in this manuscript has been collected in the regime in where this data suggests false matches are below 1%.

Figure 4

A) Experimental scan cycle for both DIA and DDA acquisitions B) Histogram of the identifications made using DDA mass spectrometry with Mascot versus DIA mass spectrometry with FT-ARM during equivalent acquisition periods. Inset shows identifications made when DIA acquisition window is reduced from 100 Da to 12 Da. Venn diagram inset showing the overlap between the identifications made with each approach at <5% estimated FDR. C) Histogram shows identifications made when DIA acquisition window is reduced from 100 Da to 12 Da. Venn diagram inset showing the overlap between the identifications made with each approach at <5% estimated FDR.

Figure 4

A) Experimental scan cycle for both DIA and DDA acquisitions B) Histogram of the identifications made using DDA mass spectrometry with Mascot versus DIA mass spectrometry with FT-ARM during equivalent acquisition periods. Inset shows identifications made when DIA acquisition window is reduced from 100 Da to 12 Da. Venn diagram inset showing the overlap between the identifications made with each approach at <5% estimated FDR. C) Histogram shows identifications made when DIA acquisition window is reduced from 100 Da to 12 Da. Venn diagram inset showing the overlap between the identifications made with each approach at <5% estimated FDR.

Figure 4

A) Experimental scan cycle for both DIA and DDA acquisitions B) Histogram of the identifications made using DDA mass spectrometry with Mascot versus DIA mass spectrometry with FT-ARM during equivalent acquisition periods. Inset shows identifications made when DIA acquisition window is reduced from 100 Da to 12 Da. Venn diagram inset showing the overlap between the identifications made with each approach at <5% estimated FDR. C) Histogram shows identifications made when DIA acquisition window is reduced from 100 Da to 12 Da. Venn diagram inset showing the overlap between the identifications made with each approach at <5% estimated FDR.

Figure 5

A) Spectrum for which an FT-ARM unique identification was found. The overlapping isotope distributions shown did not yield identification with DDA methods. B) A plot showing the effect of the reduction of the DIA window size on the identifications per amu.

Figure 5

A) Spectrum for which an FT-ARM unique identification was found. The overlapping isotope distributions shown did not yield identification with DDA methods. B) A plot showing the effect of the reduction of the DIA window size on the identifications per amu.

Figure 6

SRM and FT-ARM quantification of BSA digest spiked into yeast whole cell lysate digest (DAFLGSFLYEYSR and VPQVSTPTLVEVSR). A) Integrated area as function of concentration of BSA peptides using SRM. B) Integrated score area as function of concentration of BSA peptides using FT-ARM. C) Box and whisker plot for all peptides quantified from E. coli at <5% FDR normalized to 1. D) Random selection of peptides from the E. coli dataset in linear regression format of the average +/− σ at each dilution.

Figure 6

SRM and FT-ARM quantification of BSA digest spiked into yeast whole cell lysate digest (DAFLGSFLYEYSR and VPQVSTPTLVEVSR). A) Integrated area as function of concentration of BSA peptides using SRM. B) Integrated score area as function of concentration of BSA peptides using FT-ARM. C) Box and whisker plot for all peptides quantified from E. coli at <5% FDR normalized to 1. D) Random selection of peptides from the E. coli dataset in linear regression format of the average +/− σ at each dilution.

Figure 6

SRM and FT-ARM quantification of BSA digest spiked into yeast whole cell lysate digest (DAFLGSFLYEYSR and VPQVSTPTLVEVSR). A) Integrated area as function of concentration of BSA peptides using SRM. B) Integrated score area as function of concentration of BSA peptides using FT-ARM. C) Box and whisker plot for all peptides quantified from E. coli at <5% FDR normalized to 1. D) Random selection of peptides from the E. coli dataset in linear regression format of the average +/− σ at each dilution.

Figure 6

SRM and FT-ARM quantification of BSA digest spiked into yeast whole cell lysate digest (DAFLGSFLYEYSR and VPQVSTPTLVEVSR). A) Integrated area as function of concentration of BSA peptides using SRM. B) Integrated score area as function of concentration of BSA peptides using FT-ARM. C) Box and whisker plot for all peptides quantified from E. coli at <5% FDR normalized to 1. D) Random selection of peptides from the E. coli dataset in linear regression format of the average +/− σ at each dilution.