Due to the complexity of proteome samples, only a portion of peptides and thus proteins can be identified in a single LC-MS/MS analysis in current shotgun proteomics methodologies. It has been shown that replicate runs can be used to improve the comprehensiveness of the proteome analysis; however, high-intensity peptides tend to be analyzed repeatedly in different runs, thus reducing the chance of identifying low-intensity peptides. In contrast to commonly used online ESI-MS, offline MALDI decouples the separation from MS acquisition, thus allowing in-depth selection for specific precursor ions. Accordingly, we extended a strategy for offline LC-MALDI MS/MS analysis using a precursor ion exclusion list consisting of all identified peptides in preceding runs. The exclusion list eliminated redundant MS/MS acquisitions in subsequent runs, thus reducing MALDI sample depletion and allowing identification of a larger number of peptide identifications in the cumulative dataset. In the analysis of the digest of an Escherichia coli lysate, the exclusion list strategy resulted in a 25% increase in the number of unique peptide identifications in the second run, in contrast to simply pooling MS/MS data from two replicate runs. To reduce the increased LC analysis time for repeat runs, a four-column multiplexed LC system was developed to carry out separation simultaneously. The multiplexed LC-MALDI MS provides a high-throughput platform to utilize the exclusion list strategy in proteome analysis.
"Exclusion lists are often used for replicate analyses [6-11]: after each LC-MS/MS run the exclusion list is updated and contains the fragmented or identified signals of previous runs. In comparison to simple repetitions, Chen et al.  showed that the number of unique peptide identifications can be significantly increased. Bendall et al.  reached a higher number of proteins identifications. "
[Show abstract][Hide abstract] ABSTRACT: Background
Liquid chromatography mass spectrometry (LC-MS) maps in shotgun proteomics are often too complex to select every detected peptide signal for fragmentation by tandem mass spectrometry (MS/MS). Standard methods for precursor ion selection, commonly based on data dependent acquisition, select highly abundant peptide signals in each spectrum. However, these approaches produce redundant information and are biased towards high-abundance proteins.
We present two algorithms for inclusion list creation that formulate precursor ion selection as an optimization problem. Given an LC-MS map, the first approach maximizes the number of selected precursors given constraints such as a limited number of acquisitions per RT fraction. Second, we introduce a protein sequence-based inclusion list that can be used to monitor proteins of interest. Given only the protein sequences, we create an inclusion list that optimally covers the whole protein set. Additionally, we propose an iterative precursor ion selection that aims at reducing the redundancy obtained with data dependent LC-MS/MS. We overcome the risk of erroneous assignments by including methods for retention time and proteotypicity predictions. We show that our method identifies a set of proteins requiring fewer precursors than standard approaches. Thus, it is well suited for precursor ion selection in experiments with limited sample amount or analysis time.
We present three approaches to precursor ion selection with LC-MALDI MS/MS. Using a well-defined protein standard and a complex human cell lysate, we demonstrate that our methods outperform standard approaches. Our algorithms are implemented as part of OpenMS and are available under http://www.openms.de.
"A variety of methods have been proposed for improving upon the standard exclusion list by performing analyses interleaved between repeated rounds of MS experiments. Thus, peptides identified in an initial round are excluded from analysis in subsequent rounds     or precursor peaks identified in an MS analysis are used to select peaks for analysis in a subsequent MS/MS experiment     . Both types of approach yield a larger number of total identifications overall. "
[Show abstract][Hide abstract] ABSTRACT: Rationale: In a shotgun proteomics experiment with data-dependent
acquisition, real-time analysis of a precursor scan results in selection of a
handful of peaks for subsequent isolation, fragmentation and secondary
scanning. This peak selection protocol typically focuses on the most abundant
peaks in the precursor scan, while attempting to avoid re-sampling the same m/z
values in rapid succession. The protocol does not, however, incorporate
analysis of previous fragmentation scans into the peak selection procedure.
Methods: In this work, we investigate the feasibility and utility of
incorporating analysis of previous fragmentation scans into the peak selection
protocol. We demonstrate that real-time identification of fragmentation spectra
is feasible in principle, and we investigate, via simulations, several
strategies to make use of the resulting peptide identifications during peak
Results: Our simulations fail to provide evidence that peptide
identifications can provide a large improvement in the total number of peptides
identified by a shotgun proteomics experiment.
Conclusions: These results are significant because they point out the
feasibility of using peptide identifications during peak selection, and because
our experiments may provide a starting point for others working in this
"Essentially, PIE involves the reinjection of samples for re-analysis by LC-MS/MS with exclusion of the peptides identified previously. PIE has previously been tested for more comprehensive protein identification in samples less complex than plasma   . However, the effects of PIE on mapping the highly complex plasma proteome have not been comprehensively investigated. "
[Show abstract][Hide abstract] ABSTRACT: Our study aims to establish a plasma biomarker analysis workflow, with fewer fractionation steps, for enhanced identification of plasma biomarkers by precursor ion exclusion (PIE).
Plasma samples were depleted for highly abundant proteins, then further fractionated by molecular weight (MW), before trypsinization for LTQ-Orbitrap mass analysis. Data-dependent acquisition (DDA) was used for baseline analysis. PIE involves the re-injection of samples with exclusion of the previously identified peptides. We compared analyses using multiple PIE iterations, compared to DDA, for plasma interrogation
A higher percentage of unique plasma peptides was identified with PIE, compared to DDA. The first PIE iteration reveals an increase of 75-112% more peptides than the DDA method alone. PIE can interrogate complex plasma samples with the percentage of peptides identified successively increasing with even ≥4 iterations. The total number of peptides identified increases rapidly across the first three PIE iterations and then continues more slowly up to nine iterations.
Iterative injections with PIE resulted in many more peptide identifications in plasma samples of varying degrees of complexity, compared to re-injections using similar DDA parameters. PIE methods may therefore expand our ability to recover plasma peptides for plasma biomarker discovery.
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