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Using Skyline to Analyze Data-Containing Liquid Chromatography, Ion Mobility Spectrometry, and Mass Spectrometry Dimensions

  • Brendan X. MacLean
  • Brian S. Pratt
  • Jarrett D. Egertson
  • Michael J. MacCoss
  • Richard D. Smith
  • Erin S. Baker
Research Article

Abstract

Recent advances in ion mobility spectrometry (IMS) have illustrated its power in determining the structural characteristics of a molecule, especially when coupled with other separations dimensions such as liquid chromatography (LC) and mass spectrometry (MS). However, these three separation techniques together greatly complicate data analyses, making better informatics tools essential for assessing the resulting data. In this manuscript, Skyline was adapted to analyze LC-IMS-CID-MS data from numerous instrument vendor datasets and determine the effect of adding the IMS dimension into the normal LC-MS molecular pipeline. For the initial evaluation, a tryptic digest of bovine serum albumin (BSA) was spiked into a yeast protein digest at seven different concentrations, and Skyline was able to rapidly analyze the MS and CID-MS data for 38 of the BSA peptides. Calibration curves for the precursor and fragment ions were assessed with and without the IMS dimension. In all cases, addition of the IMS dimension removed noise from co-eluting peptides with close m/z values, resulting in calibration curves with greater linearity and lower detection limits. This study presents an important informatics development since to date LC-IMS-CID-MS data from the different instrument vendors is often assessed manually and cannot be analyzed quickly. Because these evaluations require days for the analysis of only a few target molecules in a limited number of samples, it is unfeasible to evaluate hundreds of targets in numerous samples. Thus, this study showcases Skyline’s ability to work with the multidimensional LC-IMS-CID-MS data and provide biological and environmental insights rapidly.

Graphical Abstract

Keywords

Ion mobility spectrometry Skyline Data independent acquisition Proteomics 

Notes

Acknowledgements

The authors would like to acknowledge John Fjeldsted for his help in editing the tutorial.

Funding Information

Portions of this research were supported by grants from the National Institute of Environmental Health Sciences of the NIH (R01 ES022190 and P42 ES027704), National Institute of General Medical Sciences (P41 GM103493, R01 GM103551, and R01 GM121696), National Cancer Institute (R21 CA192983), and the Laboratory Directed Research and Development Program at Pacific Northwest National Laboratory. This research used capabilities developed by the Pan-omics program (funded by the U.S. Department of Energy Office of Biological and Environmental Research Genome Sciences Program). This work was performed in the W. R. Wiley Environmental Molecular Sciences Laboratory (EMSL), a DOE national scientific user facility at the Pacific Northwest National Laboratory (PNNL). PNNL is operated by Battelle for the DOE under contract DE-AC05-76RL0 1830.

Compliance with Ethical Standards

Competing Interests

The authors declare that they have no competing interests.

Supplementary material

13361_2018_2028_MOESM1_ESM.docx (2.8 mb)
ESM 1 (DOCX 2904 kb)

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Copyright information

© American Society for Mass Spectrometry 2018

Authors and Affiliations

  1. 1.Department of Genome SciencesUniversity of WashingtonSeattleUSA
  2. 2.Biological Sciences Division, Pacific Northwest National LaboratoryRichlandUSA

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