At this year’s annual meeting of the Proteomics Methods Forum, Dr Duncan Smith of the Paterson Institute for Cancer Research gave a very impressive presentation. By making some simple changes to his analysis techniques, he has massively increased his proteome coverage, as well as sequence coverage, when compared to traditional methods. And Progenesis LC‑MS is a key to unlocking some of the benefits.
Gas phase fractionation
While Duncan’s presentation included a range of measures to optimise coverage, it’s his use of gas phase fractionation (GPF) on which I want to concentrate here.
Duncan’s experiments make use of technical replicates to provide robust quantitation. However, using the familiar DDA (data-dependent acquisition) mode of MS instruments gives little benefit for peptide identification in this situation; it tends to result in the same set of peptides being targeted for MS2 scans in each replicate run. Consequently, identifications are limited to those peptides with strong signals that the DDA is picking up.
To illustrate this, consider the following three (simplified) MS1 spectra, each collected at the same retention time in a different technical replicate:
As we can see, 5 peaks in each replicate have been selected for capture of MS2 data. However, because the DDA for each run is looking across the same m/z range each time, we pick up the same 5 peaks in each replicate. The lack of MS2 information for any other peaks means we’re quite limited in how many peptides we can identify; the use of replicates has added nothing to our ability to identify more peptides.
This is where Progenesis LC-MS and GPF come together to help. Remember that:
- GPF allows you to limit the capture of MS2 data to a specific range of m/z values
- In Progenesis, the alignment of peptide ions allows identifications from one run to be applied to the corresponding peptide ion in all runs
Traditionally, GPF has been used to limit both MS1 and MS2 capture. However, by limiting only the MS2 capture and capturing all MS1 data, we can retain all of the quantitative benefit of MS1 and still get identifications across the entire m/z range. Not only that, but by collecting the same number of MS2 traces in each fraction, we will be able to identify more of the low-intensity peptides.
In our example, we’ll create 3 fractions, collecting MS2 traces over different m/z ranges in each of our 3 replicates. The effect on overall coverage becomes clear:
Clearly, using GPF gives us much greater coverage than from DDA alone. To give an idea of how much more coverage, by increasing the number of fractions to 5, Duncan Smith was able to quote a 3- to 4-fold increase in the number of identified peptides. As I said at the start of this article, very impressive! And remember that this is being done without sacrificing any of the quantitative MS1 data and without increasing instrument time. Again, very impressive.
Choosing the m/z ranges for your fractions
Now that we’ve seen the benefits, how do you optimise your gas phase fractionation? That is, how do you decide on the m/z ranges for your fractions?
You could simply divide the normal range evenly (as seen above), but it’s better to have the same number of peptides in each fraction. For that, you’ll need to run a pilot sample. In his presentation, Duncan presented an example of how he did exactly that, once again with the help of Progenesis LC-MS.
After exporting the feature data, sorting by m/z value, splitting them into 5 fractions, and noting the boundary values, it allowed him to create the following visualisation of those boundaries on the run’s ion map:
As you can see, the greatest density of peptides is in the low-m/z end of the spectrum. By concentrating the fractions at that end, we’re not wasting MS2 scans on less-reliable, noisy peaks at the high-m/z end of the spectrum.
And some more good news: to make GPF even easier, we’re planning to add direct support for calculating these m/z ranges in the next release of Progenesis LC‑MS. The technique’s benefits are so clear, we want to make it as simple as possible for our users, so that more of you can benefit from it.
In the near future, we’re hoping to expand on Duncan’s techniques in a full application note. Keep watching the blog for news on this. In the meantime, I hope this has highlighted a simple technique you can use to increase proteome coverage in your own research.
If you’re not already using it, click here to download Progenesis LC-MS and try it out for yourself.