Generating high-quality chromatogram libraries for DIA-MS with empirically corrected peptide predictions.
Ontology highlight
ABSTRACT:
INSTRUMENT(S): Orbitrap Fusion, Q Exactive
ORGANISM(S): Plasmodium Falciparum (ncbitaxon:5833) Saccharomyces Cerevisiae (ncbitaxon:4932)
SUBMITTER: 
Brian C. Searle
PROVIDER: MSV000084000 | MassIVE | Wed Jun 19 18:20:00 BST 2019
SECONDARY ACCESSION(S): PXD017705
REPOSITORIES: MassIVE
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| S-EPMC7096433 | biostudies-literature
Project description:Targeted proteomics by selected/multiple reaction monitoring or, on a larger scale, by SWATH MS relies on spectral reference libraries for peptide identification. Quality and coverage of these libraries are therefore of critical importance. Here we present a detailed protocol that has been successfully used to build high-quality, extensive reference libraries supporting targeted proteomics by SWATH MS. We describe each step of the process, including data acquisition by discovery proteomics, assertion of peptide-spectrum matches, generation of consensus spectra and compilation of mass spectrometric coordinates that uniquely define each targeted peptide. Crucial steps of this process such as FDR control, retention time normalization and handling of post-translationally modified peptides are discussed in detail. Finally we show how to use the library to extract SWATH data with the open-source software Skyline. The protocol takes 2-3 days to complete, depending on the extent of the library and the computational resources available.
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Project description:Raw DDA and DIA data to support Searle et al. 2018. Comprehensive peptide quantification for data independent acquisition mass spectrometry using chromatogram libraries. bioRxiv doi:10.1101/277822
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Project description:For the complete description see the manuscript. Data dependent acquisition (DDA) is the method of choice for mass spectrometry based proteomics discovery experiments, data-independent acquisition (DIA) is steadily becoming more important. One of the most important requirement to perform a DIA analysis is the availability of spectral libraries for the peptide identification and quantification. Several researches were already conducted regarding the creation of spectral libraries from DDA analyses and obtaining identifications with these in DIA measurements. But so far only few experiments were conducted, to estimate the effect of these libraries on the quantitative level. In this work we created a spike-in gold standard dataset with known contents and ratios of proteins in a complex sample matrix. With this dataset, we first created spectral libraries using different sample preparation approaches with and without sample prefractionation on peptide and protein level. Two different search engines were used for protein identification. In total, five different spike-in states were compared with DIA analyses, comparing eight different spectral libraries generated by varying approaches and one library free method, as well as one default DDA analysis. Not only the number of identifications on peptide and protein level in the spectral libraries and the corresponding analyses was inspected, but also the number of expected and identified significant quantifications and their ratios were thoroughly examined.
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Project description:Data-independent mass spectrometry is the method of choice for deep, consistent and accurate single-shot profiling in bottom-up proteomics. While classic workflows required auxiliary DDA-MS analysis of subject samples to derive prior knowledge spectral libraries for targeted quantification from DIA-MS maps, library-free approaches based on in silico predicted libraries promise deep DIA-MS profiling with reduced experimental effort and cost. Coverage and sensitivity in such analyses, however, is limited, in part, by large library size and persistent deviations from experimental data. We present MSLibrarian, a workflow and tool to obtain optimized predicted spectral libraries by the integrated usage of spectrum-centric DIA data interpretation via the DIA-Umpire approach to inform and calibrate the in silico predicted library approach. Predicted-vs-observed comparisons enable optimization of intensity prediction parameters, calibration of retention time prediction for deviating chromatographic setups and optimization of library scope and sample representativeness. Benchmarking via a dedicated ground-truth-embedded species mixture experiment and quantitative ratio-validation confirms gains of up to 9 % on precursor and 7 % protein level at equivalent FDR control and validation criteria. MSLibrarian has been implemented as open-source R software package and, with step-by-step usage instructions, is availabe at https://github.com/MarcIsak/MSLibrarian.
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Project description:Targeted proteomics by selected/multiple reaction monitoring or, on a larger scale, by SWATH MS relies on spectral reference libraries for peptide identification. Quality and coverage of these libraries are therefore of critical importance. Here we present a detailed protocol that has been successfully used to build high-quality, extensive reference libraries supporting targeted proteomics by SWATH MS. We describe each step of the process, including data acquisition by discovery proteomics, assertion of peptide-spectrum matches, generation of consensus spectra and compilation of mass spectrometric coordinates that uniquely define each targeted peptide. Crucial steps of this process such as FDR control, retention time normalization and handling of post-translationally modified peptides are discussed in detail. Finally we show how to use the library to extract SWATH data with the open-source software Skyline. The protocol takes 2-3 days to complete, depending on the extent of the library and the computational resources available.
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Project description:Mass spectrometry is the state-of-the-art methodology for capturing the breadth and depth of the immunopeptidome across HLA allotypes and cell types. The majority of studies in the immunopeptidomics field are discovery-driven and data-dependent tandem mass spectrometry acquisition is commonly used, as it generates high quality references of peptide fingerprints. However, DDA suffers from the stochastic selection of abundant ions that leads to lower sensitivity and reproducibility. In contrast, in data-independent acquisition, the fragmentation and acquisition of all fragment ions from a predefined list of precursor isolation windows yields a comprehensive map for a given sample. Because often the amount of HLA peptides eluted from biological samples, is typically not sufficient for acquiring both meaningful DDA data necessary for generating comprehensive spectral libraries and DIA MS measurements, the implementation of DIA in the immunopeptidomics translational research domain has remained limited. We implemented a DIA immunopeptidomics workflow and assessed its sensitivity and accuracy by matching DIA data against libraries with growing complexity - from sample-specific libraries to libraries combining from two to forty different immunopeptidomics samples. Matching DIA immunopeptidomics data against complex pan-HLA spectral library resulted in a two-fold increase in peptide identification compared with sample-specific library and in a three-fold increase compared with DDA measurements, yet with no detrimental effect on the specificity. We concluded that a comprehensive pan-HLA library for DIA approach is highly advantageous for the clinical Immunopeptidomics where low amount of biological sample is available for immunopeptidomics.
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