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Specter: linear deconvolution for targeted analysis of data-independent acquisition mass spectrometry proteomics

Nature Methods volume 15, pages 371–378 (2018)Cite this article

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Abstract

Mass spectrometry with data-independent acquisition (DIA) is a promising method to improve the comprehensiveness and reproducibility of targeted and discovery proteomics, in theory by systematically measuring all peptide precursors in a biological sample. However, 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 (SNPs) and alternative site localizations in phosphoproteomics data. We report Specter (https://github.com/rpeckner-broad/Specter), an open-source software tool that uses linear algebra to deconvolute DIA mixture spectra directly through comparison to a spectral library, thus circumventing the problems associated with typical fragment-correlation-based approaches. We validate the sensitivity of Specter and its performance relative to that of other methods, and show that Specter is able to successfully analyze cases involving highly similar peptides that are typically challenging for DIA analysis methods.

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Figure 1: Specter uses linear algebra to formally deconvolute MS2 spectra derived from cofragmented precursors.
Figure 2: Total ion chromatograms calculated by Specter are as accurate as those from manual targeted analysis of DIA data in terms of both identification and quantification.
Figure 3: The false discovery rate of Specter is inherently <5%.
Figure 4: Specter chromatograms of groups of synthetic peptides with highly similar spectra.
Figure 5: Specter distinguished close positional isomers with overlapping chromatographic profiles in a phosphoproteomics data set.
Figure 6: Analysis of a public data set by Specter and five other software tools.

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Acknowledgements

This work was supported by the NIH (grant U54 HG008097 to J.D.J.). The authors thank S. Tenzer and L. Gillet for providing details of the data sets for the LFQbench study, and K. Clauser for many insightful discussions.

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Author notes

  1. Jennifer G Abelin

    Present address: Neon Therapeutics, Cambridge, Massachusetts, USA

Authors and Affiliations

  1. Proteomics Platform, Broad Institute of MIT and Harvard, Cambridge, Massachusetts, USA

    Ryan Peckner, Samuel A Myers, Alvaro Sebastian Vaca Jacome, Jennifer G Abelin, Steven A Carr & Jacob D Jaffe

  2. Department of Genome Sciences, University of Washington, Seattle, Seattle, Washington, USA

    Jarrett D Egertson & Michael J MacCoss

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  1. Ryan Peckner
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Contributions

R.P. conceived of the methodology, created the software, performed the analyses, and wrote the manuscript. S.A.M. conceived and carried out the experiments involving similar synthetic peptides and DIA-DDA comparison, and helped revise the manuscript. A.S.V.J. carried out the experiments and manual analysis of the synthetic phosphopeptide spike-in experiments. J.D.E. and J.G.A. developed the DIA acquisition method. M.J.M., S.A.C., and J.D.J. provided laboratory resources and guidance on the manuscript.

Corresponding author

Correspondence to Jacob D Jaffe.

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The authors declare no competing financial interests.

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Supplementary Figures 1–4, Supplementary Notes 1–6 and Supplementary Tables 1–6 (PDF 4934 kb)

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Peckner, R., Myers, S., Jacome, A. et al. Specter: linear deconvolution for targeted analysis of data-independent acquisition mass spectrometry proteomics. Nat Methods 15, 371–378 (2018). https://doi.org/10.1038/nmeth.4643

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