Improving precursor quantitation results with ion mobility filtering

Mascot Distiller supports DDA raw data files from the Bruker timsTOF instrument. The timsTOF combines an ion mobility dimension with QTOF, allowing an additional separation dimension. The instrument can use this to help separate MS/MS spectra from otherwise isobaric peptides.

Mascot Distiller can use the precursor ion mobility values for identified peptides as a filter on the precursor scans during the XIC peak detection steps of any precursor-based quantitation method, such as SILAC or label-free methods (Replicate, Average). The aim is to clean up and improve the quality of the data in the target precursor regions, removing interfering signal, improving accuracy and giving more reliable quantitation results. For a given peptide match, an ion mobility range is calculated from the identified matches and the precursor ion mobility grouping tolerance defined in the processing options. Only MS1 signals from within the calculated ion mobility range are then used during XIC peak detection.

This page illustrates ion mobility filtering in Mascot Distiller. For the full Distiller quantitation help and tutorials, start Distiller and press F1, or open the menu About, Mascot Distiller Help. Please contact us for a 30-day trial.

Configuration

The effect of this precursor ion mobility filtering is shown in figure 1 below, which shows the precursor region for a peptide from one scan of a sample with and without filtering. As you can see, with ion mobility filtering enabled the signal in the precursor area is much cleaner, with far fewer noise peaks, despite this being a very low intensity precursor.

Figure 1: The precursor region for a low intensity 3+ precursor with precursor ion mobility filtering (A) disabled and (B) enabled.

The effect of filtering can be observed in both the fraction of the peak area in the precursor region accounted for by the expected precursor peaks and in the correlation coefficient between the predicted and observed precursor isotope distributions for this peptide. Both improve, increasing from 0.2978 to 0.9234 and 0.6723 to 0.9463, respectively.

In order to enable filtering, the data must be searched using Mascot Server 2.7 or later with the precursor ion mobility values exported in the MGF peak lists file(s). To enable export of precursor ion mobility values from Mascot Distiller, you must check the option on the “Peak List Format” tab of the Preferences dialog for the project, as illustrated in Figure 2 below:

Figure 2: The Peak List Format options in Mascot Distiller 2.8 with the option to export precursor ion mobility values in the peak lists highlighted.

If you are using Mascot Distiller as a data import filter for Mascot Daemon, you need to enable this using the following procedure:

• Log into Windows as a user with Administrator privileges
• Type ‘cmd’ into the Windows search bar
• Right click over ‘Command Prompt’ and click ‘Run as Administrator’
• Navigate to the directory containing MascotDistiller.exe (by default this will be C:\Program Files\Matrix Science\Mascot Distiller)
• Run the following command:
  

     MascotDistiller.exe -batch -daemonMGFIonMobilityParam 1

During quantitation, ion mobility filtering is automatically enabled for any search results with the precursor ion mobility values exported in the peak lists.

Example spiked in E.coli sample

This example uses four files representing two replicates from PRIDE project PXD010012. These are part of the dataset used in the original PASEF paper (Meier et al. Molecular & Cellular Proteomics 2018, 17(12), 2534-2545). The samples consist of a background of HeLa protein digest at a ratio of 1:1 with an E.coli protein digest spiked in. The authors reported the median ratio of E.coli spiked in proteins as 4.31.

The selected raw files were processed using Mascot Distiller using the default processing options for timsTOF data and searched twice using Mascot Server: once with precursor ion mobility values included in the peak lists, and once without.

All identified proteins in each search were then quantitated using Mascot Distiller and the peptide ratios normalised using the peptide matches to the 20 most intense identified human proteins. This gave two sets of results, one where precursor ion mobility filtering was used during quantitation, and one where it was not. Results are summarised in table 1 below:

	Ion mobility filtering disabled	Ion mobility filtering enabled
Time taken (hours)	36	10
Median human protein ratioa,b	0.979	1.017
Number of human proteins quantifieda	4458	4946
Median E.coli protein ratioa,c	4.21	4.30
Number of E.coli proteins quantifiera	718	821
Median peptide fraction value	0.487	0.686
Median peptide correlation value	0.912	0.959
Median protein ratio sample size	5	6

Table 1: Comparison of quantitation results with ion mobility filtering disabled and enabled. ^a Only protein ratios with valid peptide ratios from 2 or more distinct peptides were counted. ^b Expected ratio value 1¹. ^c Reported ratio 4.3 in original publication.

As you can see, using ion mobility filtering improves the results. Quantitation itself was significantly faster with filtering enabled, because with discrete ion mobility slices being taken, less data had to be processed. Median peptide fraction and correlation values also improved, showing the quality and signal to noise of the data are better with filtering enabled, allowing more peptide ratios to pass the quantitation quality thresholds. The median number of quantitated peptide sequences used to calculate the protein ratios increased from 5 to 6, and more proteins with at least distinct peptide sequences were quantified. The median protein ratios for both human and E.coli proteins are also slightly closer to the expected ratios.