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PIQED: automated identification and quantification of protein modifications from DIA-MS data

Nature Methods volume 14, pages 646–647 (2017)Cite this article

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When a pre-existing spectral library is not available, as is often the case with novel PTMs, samples are typically analyzed several times. One or more data-dependent acquisition (DDA) analyses are done to generate a spectral library, then DIA-MS is performed for quantification. This combined strategy is prohibitive when many biological replicates are required or sample quantity is limited. A recently developed software tool, DIA-Umpire, allows combined peptide identification and quantification from a single DIA analysis without a pre-existing spectral library2,3. However, DIA-Umpire was originally designed for protein-level quantification and lacks the capability to quantify post-translationally modified peptides.

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Figure 1: Automated identification and quantification of PTMs using PIQED.

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Acknowledgements

We thank B. MacLean for assistance with Skyline. This work was supported by the National Institute of Diabetes and Digestive and Kidney Diseases (R24DK085610 and R01DK090242, B.W.G.), the National Institute of General Medical Sciences (5R01GM094231 to A.I.N.), the National Institute of Allergy and Infectious Disease (R01AI099204 to H.S.), and the Thrasher Research Fund (to H.S.). J.G. Meyer was supported by a US National Institutes of Health (NIH) grant (T32G000266; J. Campisi, principal investigator). We acknowledge support from the NIH shared instrumentation grant for the TripleTOF system at the Buck Institute (1S10 OD016281, B.W.G.).

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Authors and Affiliations

  1. Buck Institute for Research on Aging, Novato, California, USA

    Jesse G Meyer, Sushanth Mukkamalla, Bradford W Gibson & Birgit Schilling

  2. Department of Pathology, Boston Children's Hospital and Harvard Medical School, Boston, Massachusetts, USA

    Hanno Steen

  3. Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, Michigan, USA

    Alexey I Nesvizhskii

  4. Department of Pathology, University of Michigan, Ann Arbor, Michigan, USA

    Alexey I Nesvizhskii

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  1. Jesse G Meyer
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  2. Sushanth Mukkamalla
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  3. Hanno Steen
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  4. Alexey I Nesvizhskii
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  5. Bradford W Gibson
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Correspondence to Birgit Schilling.

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Integrated supplementary information

Supplementary Figure 1 Details of PIQED automated qualitative and quantitative post-translational modification analysis workflow.

Modified peptides enriched from biological samples or peptides from proteome digestion without modification enrichment are analyzed by data-independent acquisition. PIQED supports data from SCIEX or Thermo instruments. PIQED automates all data analysis steps st arting from instrument .wiff or .raw files, including: (1) file conversion and pseudo-MS/MS spectra generation using DIA-Umpire, (2) database searching by MS-GF+, X! Tandem, and/or Comet followed by results refinement and combination using PeptideProphet/iProphet/PTMProphet, (3) automated spectral library generation and fragment area extraction using SkylineRunner, and finally, (4) Skyline report generation, filtering for only localized PTMs with certain iProphet and PTMProphet scores, option protein level correction, and table reformatting for significance testing with mapDIA. All files produced along the analysis are output by the pipeline, including the raw search outputs, the various refined pep.xml files, the Skyline file containing extracted peaks for manual review, and the final output from mapDIA, which includes site-level fold change for all desired comparisons.

Supplementary Figure 2 Screenshot of the open-source PIQED GUI written in Java, Windows Batch, Python, and R.

The software has several optional modules that can be run, where each subsequent module can automatically use the output from the previous module. These modules are broadly divided into three sections: (1) file conversions and DIAUmpire signal extraction, (2) database searches, and (3) post processing, quantification and significance testing. The leftmost section is used to specify parameters for file conversions and DIA-Umpire signal extraction module that generates pseudo-MS/MS spectra for database searches. The middle section is used to set the database search parameters for MS-GF+, X!Tandem, and Comet. The right section contains input for PeptideProphet, iProphet, PTMProphet, Skyline peak area report generation, and mapDIA significance testing. The program user can save and load up to four default parameter sets.

Supplementary Figure 3 Example of quantitative difference discovered using PIQED for the discovery of phosphorylation sites and changes using previously published Q-Exactive DIA data collected from urine samples without phospho-enrichment.

(A) Annotated spectra for the peptide TCVADEpSAENCDK, which contains phosphoserine pSer-82 from human serum albumin (HSA). (B) Example of extracted ion chromatograms from the precursor (MS1) and fragment (MS2) ions showing excellent coelution and high mass accuracy. (C) Plot of total MS1 and MS2 peak areas for all replicates measured among the three diagnosis groups: diagnosed urinary tract infections (UTI, n=11), diagnosed ovarian cyst (OC, n=11), or control (pain not diagnosed as UTI or OC, n=11). Group average of (D) raw site-level signal, (E) HSA protein-level, and (F) site-level corrected by protein level and local TIC. Error bars are standard error. Although the protein level of HSA is not significantly different between the diagnosis groups, we observed a statistically significant increase in abundance of pSer-82 comparing the UTI group with the undiagnosed pain group with and without the normalization options available in PIQED.

Supplementary Figure 4 Comparison of quantitative results for HSA and HSA pSer-82 using various normalization options in PIQED.

(A) Plot showing individual total site-level peak areas for the peptide TCVADEpSAENCDK containing phosphoserine pSer-82. (B) Protein-level areas computed with mapDIA using unmodified HSA peptides. (C) Site-level areas (from A) for the peptide TCVADEpSAENCDK containing phosphoserine pSer-82 corrected by the observed protein-level areas from (B). (D) Site-level areas for phosphoserine pSer-82 from the peptide in (A) corrected by local total ion chromatogram signal (TIC). (E) Site-level areas for phosphoserine pSer-82 from the peptide in (A) corrected by local total ion chromatogram signal (TIC) and by the protein-level area from (B). In all cases with or without normalizations, this phosphorylation site is determined to be statistically increased in urine from children diagnosed with UTI compared to the undiagnosed pain group.

Supplementary Figure 5 Examples of high-quality annotated pseudo-MS/MS spectra showing 4/34 phosphorylation sites in osteopontin identified using PIQED. Osteopontin is overexpressed in a variety of cancers.

(A) Annotated spectra for pSer-219 showing the presence of phosphate neutral loss on all y-ions and a prominent fragment ion corresponding to fragmentation n-terminal of proline. (B) Annotated spectra for a peptide containing pSer-310 from the Osteopontin C-terminal, which has a nearly-complete b-ion series. (C) Annotated spectra for a peptide containing pSer-308 and pSer-310 from the Osteopontin c-terminal, which also contains a nearly complete b-ion series and neutral losses of one or two phosphoric acids starting at b7 and b9, respectively. The spectra in (B) and (C) also contain unlabeled fragment ions that correspond to a neutral loss of water from one of the unmodified serine residues.

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Meyer, J., Mukkamalla, S., Steen, H. et al. PIQED: automated identification and quantification of protein modifications from DIA-MS data. Nat Methods 14, 646–647 (2017). https://doi.org/10.1038/nmeth.4334

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