Specter: linear deconvolution as a new paradigm for targeted analysis of data-independent acquisition mass spectrometry proteomics

Mass spectrometry with data-independent acquisition (DIA) has emerged as a promising method to greatly improve the comprehensiveness and reproducibility of targeted and discovery proteomics, in theory systematically measuring all peptide precursors within a biological sample. Despite the technical maturity of DIA, the analytical challenges involved in discriminating between peptides with similar sequences in convoluted spectra have limited its applicability in important cases, such as the detection of single-nucleotide polymorphisms and alternative site localizations in phosphoproteomics data. We have developed Specter, an open-source software tool that uses linear algebra to deconvolute DIA mixture spectra directly in terms of a spectral library, circumventing the problems associated with typical fragment correlation-based approaches. We validate the sensitivity of Specter and its performance relative to other methods by means of several complex datasets, and show that Specter is able to successfully analyze cases involving highly similar peptides that are typically challenging for DIA analysis methods.

[1]  Vladimir A. Likic,et al.  Extraction of pure components from overlapped signals in gas chromatography-mass spectrometry (GC-MS) , 2009, BioData Mining.

[2]  Oliver M. Bernhardt,et al.  Extending the Limits of Quantitative Proteome Profiling with Data-Independent Acquisition and Application to Acetaminophen-Treated Three-Dimensional Liver Microtissues* , 2015, Molecular & Cellular Proteomics.

[3]  Igor G. Zurbenko,et al.  Kolmogorov–Zurbenko filters , 2010 .

[4]  Chih-Chiang Tsou,et al.  DIA-Umpire: comprehensive computational framework for data-independent acquisition proteomics , 2015, Nature Methods.

[5]  Jian Wang,et al.  MSPLIT-DIA: sensitive peptide identification for data-independent acquisition , 2015, Nature Methods.

[6]  R. Foisner,et al.  Identification of Plectin as a Substrate of p34Kinase and Mapping of a Single Phosphorylation Site (*) , 1996, The Journal of Biological Chemistry.

[7]  R. Aebersold,et al.  Selected reaction monitoring–based proteomics: workflows, potential, pitfalls and future directions , 2012, Nature Methods.

[8]  B. Shastry.  SNPs in disease gene mapping, medicinal drug development and evolution , 2007, Journal of Human Genetics.

[9]  Knut Reinert,et al.  Statistical quality assessment and outlier detection for liquid chromatography-mass spectrometry experiments , 2009, BioData Mining.

[10]  G. L. Ritter,et al.  Factor analysis of the mass spectra of mixtures , 1976 .

[11]  Richard Sparling,et al.  Whole cell, label free protein quantitation with data independent acquisition: Quantitation at the MS2 level , 2015, Proteomics.

[12]  Aravind Subramanian,et al.  Reduced-representation Phosphosignatures Measured by Quantitative Targeted MS Capture Cellular States and Enable Large-scale Comparison of Drug-induced Phenotypes* , 2016, Molecular & Cellular Proteomics.

[13]  Ludovic C. Gillet,et al.  Targeted Data Extraction of the MS/MS Spectra Generated by Data-independent Acquisition: A New Concept for Consistent and Accurate Proteome Analysis* , 2012, Molecular & Cellular Proteomics.

[14]  Ruedi Aebersold,et al.  Artificial decoy spectral libraries for false discovery rate estimation in spectral library searching in proteomics. , 2010, Journal of proteome research.

[15]  G. Patti,et al.  An untargeted metabolomic workflow to improve structural characterization of metabolites. , 2013, Analytical chemistry.

[16]  William Stafford Noble,et al.  Analysis of peptide MS/MS spectra from large-scale proteomics experiments using spectrum libraries. , 2006, Analytical chemistry.

[17]  M. Mann,et al.  Protocol for micro-purification, enrichment, pre-fractionation and storage of peptides for proteomics using StageTips , 2007, Nature Protocols.

[18]  Frédérique Lisacek,et al.  Ranking Fragment Ions Based on Outlier Detection for Improved Label-Free Quantification in Data-Independent Acquisition LC-MS/MS. , 2015, Journal of proteome research.

[19]  Brendan MacLean,et al.  Bioinformatics Applications Note Gene Expression Skyline: an Open Source Document Editor for Creating and Analyzing Targeted Proteomics Experiments , 2022 .

[20]  Yasset Perez-Riverol,et al.  A multi-center study benchmarks software tools for label-free proteome quantification , 2016, Nature Biotechnology.

[21]  Xiuxia Du,et al.  Spectral Deconvolution for Gas Chromatography Mass Spectrometry-Based Metabolomics: Current Status and Future Perspectives , 2013, Computational and structural biotechnology journal.

[22]  Michael J MacCoss,et al.  Using BiblioSpec for Creating and Searching Tandem MS Peptide Libraries , 2007, Current protocols in bioinformatics.

[23]  Birgit Schilling,et al.  Repeatability and reproducibility in proteomic identifications by liquid chromatography-tandem mass spectrometry. , 2010, Journal of proteome research.

[24]  Wen-Lian Hsu,et al.  Spectrum-based method to generate good decoy libraries for spectral library searching in peptide identifications. , 2013, Journal of proteome research.

[25]  Matthew E Monroe,et al.  Linear discriminant analysis-based estimation of the false discovery rate for phosphopeptide identifications. , 2008, Journal of proteome research.

[26]  Ben C. Collins,et al.  OpenSWATH enables automated, targeted analysis of data-independent acquisition MS data , 2014, Nature Biotechnology.

[27]  Gennifer E. Merrihew,et al.  Deconvolution of mixture spectra from ion-trap data-independent-acquisition tandem mass spectrometry. , 2010, Analytical chemistry.

[28]  K. Green,et al.  Phosphorylation of serine 4642 in the C-terminus of plectin by MNK2 and PKA modulates its interaction with intermediate filaments , 2013, Journal of Cell Science.

[29]  Eric W. Deutsch,et al.  File Formats Commonly Used in Mass Spectrometry Proteomics* , 2012, Molecular & Cellular Proteomics.