MacLean - Baker IMS

Using Skyline to Analyze Data-Containing Liquid Chromatography, Ion Mobility Spectrometry, and Mass Spectrometry Dimensions. Journal of The American Society for Mass Spectrometry
ProteomeXchange: PXD010650
  • Organism: Bos taurus, Saccharomyces cerevisiae
  • Instrument: 6560 Q-TOF LC/MS
  • SpikeIn: Yes
  • Keywords: ion mobility spectrometry, Skyline, data independent acquisition, proteomics
  • Submitter: Brendan MacLean
Recent advances in ion mobility spectrometry (IMS) have illustrated its power in 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, so making better informatics tools are essential for assessing the resulting data. In this manuscript, Skyline was adapted to analyze LC-IMS-(CID)-MS data and determine the effect of adding the IMS dimension into the normal LC-MS molecular pipeline. For the evaluation, a tryptic digest of bovine serum albumin (BSA) was spiked into a yeast digest at 7 different concentrations, and calibration curves for both the precursor and all-ions fragments were analysesassessed with and without utilizing the IMS dimension. Skyline was able to rapidly analyze the MS and MS/MS data from 38 of the BSA peptides and in all cases the addition of the IMS dimension removed noise from interfering peptides resulting is in better calibration curves with higher correlation and lower limits of detection. This study presents an important informatics development since currently most LC-IMS-(CID)-MS data is studied manually and cannot be analyzed quickly. Since 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 multidimensional LC-IMS-(CID)-MS data and provide biological and environmental insights rapidly.
Experiment Description
To evaluate the effect of utilizing the IMS dimension in multidimensional analyses and determine the performance of Skyline in analyzing the LC-IMS-(CID)-MS data, tryptically digested BSA was spiked at seven concentrations (100 pM, 1 nM, 5 nM, 10 nM, 100 nM, 500 nM, and 1 µM) into a tryptic yeast digest with a final peptide concentration of 0.1 µg/µL. Yeast was picked as the matrix of interest since most of the peptide components have a similar concentration range, providing more interfering peaks than samples with a higher dynamic range (i.e. plasma). MS1 and MS/MS all-ions fragmentation spectra were alternated every other second during each 100 minute LC run. Skyline was then utilized to extract the ion chromatograms and calculate peak areas for 38 different tryptic BSA target peptides in four ways including: LC-MS precursor extraction, LC-IMS-MS precursor extraction; LC-MS fragment extraction and LC-IMS-MS fragment extraction.
Sample Description
Bovine serum albumin (BSA) was purchased from Sigma-Aldrich (St. Louis, MO) and a tryptically digested yeast protein extract was purchased from Promega (Madison, WI). The BSA was tryptically digested and brought up to a concentration of 0.5 µg/µL in water. The BSA was then spiked into the tryptically digested yeast extract at 7 different concentrations (100 pM, 1 nM, 5 nM, 10 nM, 50 nM 100 nM, 500 nM, and 1 µM), where a final concentration of 0.1 µg/µL was utilized for the yeast peptides in all samples. A sample of BS peptides in water was also prepared at 100 nM to develop the Skyline parameters for the target peptides. Datasets for each sample were acquired by LC-IMS-MS using a Waters NanoAcquity HPLC system and an Agilent 6560 IM-QTOF MS platform (Agilent Technologies, Santa Clara) [1, 2]. The HPLC system utilized reverse phase columns prepared in-house by slurry packing 3 µm Jupiter C18 (Phenomenex, Torrence, CA) into 40 cm x x 360 µm o.d. x 75 µm i.d. fused silica (Polymicro Technologies Inc., Phoenix, AZ) using a 1-cm sol-gel frit for media retention. Trapping columns were prepared similarly by slurry packing 5-µm Jupiter C18 into a 4-cm length of 150 µm i.d. fused silica and fritted on both ends. Sample injections (5 µL) were trapped and washed on the trapping columns at 3 µL/min for 20 min prior to alignment with analytical columns. Mobile phases consisted of 0.1% formic acid in water (A) and 0.1% formic acid acetonitrile (B) and were operated at 300 nL/min with a 100-min gradient profile as follows (min:%B); 0:5, 2:8, 20:12, 75:35, 97:60, 100: 85. The LC was directly connected to the Agilent NanoESI source. Upon entering the source of the Agilent 6560 IM-QTOF MS platform, ions were passed through the inlet glass capillary, focused by a high pressure ion funnel, and accumulated in an ion funnel trap. Ions were then pulsed into the 78.24 cm long IMS drift tube filled with ~ 3.95 torr of nitrogen gas, where they travelled under the influence of a weak electric field (10-20 V/cm). Ions exiting the drift tube were refocused by a rear ion funnel and the collision energy was alternated between 0 V and 29 V for acquisition of MS and all ions fragmentation spectra. The ions were then detected with the TOF MS and their arrival time (tA) were recorded for the 100-3200 m/z range.
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Here we present a data set used to assess the quantitative impact of filtering by drift time during chromatogram extraction from data files collected on an IMS enabled Agilent 6560 mass spectrometer. The five Skyline documents represent:

  1. The final processing with IMS filtering applied and all peak integration manually adjusted.
  2. The final processing without IMS filtering applied and all peak integration manually adjusted.
  3. Processing with IMS filtering applied but without any manual adjustment to peak integration.
  4. Processing without IMS filtering applied and without any manual adjustment to peak integration.
  5. The original template document before any data is imported into it and before drift time filter training.

Each of the first four documents contain imported results. However, only the first two were used to assess the dynamic range and linearity of a calibration curve in the 4 cases we explored:

  1. Peak areas from chromatograms extracted from MS1 with drift time filtering.
  2. Peak areas from chromatograms extracted from MS/MS with drift time filtering.
  3. Peak areas from chromatograms extracted from MS1 without drift time filtering.
  4. Peak areas from chromatograms extracted from MS/MS without drift time filtering.

The associated report files (in Excel CSV format) are attached below and were produced by exporting the "Precursor MS1 Areas" and "Precursor Fragment Areas" reports respectively from the two final processed documents.

The Skyline documents are data files used in developing this method can be found in the Method Dev folder.

  Attached Files  
 2017 Filtered Precursor Fragment Areas.csv
 2017 Unfiltered Precursor MS1 Areas.csv
 2017 Unfiltered Precursor Fragment Areas.csv
 2017 Filtered Precursor MS1 Areas.csv