It is often very important to integrate identification and quantitative information found in proteomic mass spectrometry data. This is why we have integrated a tool into PEAKS that provides peptide feature intensity information for identified peptides. By doing this, you can get an idea of the relative quantity of a peptide in your sample. Here you can see a graphical representation of a full proteomic LC-MS run. It is clear that there is a specific group of likely peptides represented by the high intensity peaks seen here. PEAKS then answer the question of: what is the identity of those high intensity peptide signals?
It does this using a concept already used in label free quantification algorithms called peptide feature detection. A peptide found in a LC-MS experiment will appear in a predictable way. It will have a visible and predictable isotopic distribution resulting from different carbon isotopes, and its intensity will follow a gamma distribution across the retention time range in which it illustrates. If the signal from the mass spec h has these characteristics we call it a peptide feature. PEAKS will automatically detect these peptide features and calculate the area under the retention time curve. It will include the area of all isotopes associated with the feature within 5% relative intensity of the most intense peak. These areas are then integrated into an XIC curve shown here. From this the area under the curve can be easily calculated.
We then have a group of peptide features. If that feature is selected as a precursor ion for MS/MS, and then the MS/MS is identified we can link the two together. This is how we’re able to match peptide feature intensity with an identified peptide.
Viewing this information in PEAKS is very intuitive. Once you click on the peptide tab, the associated peptide feature intensity is found in the area column. This can be sorted to see the peptides with the highest intensity signal. This information has been proven to be very informative. For example, in the publication shown here they reported the normalized area under the curve of peptide features associated with endogenous peptides. This gave the research group proof of the most abundant peptides eluted from their sample. We ran a subset of the data through PEAKS. What’s great is that was able to generate similar results with one click of a button! Sorting the peptide table by feature area gives you a clear idea of the most abundant peptides in the sample.
If you would like to validate the link between identified peptides and peptide features, it’s quite easy to do. Right click on a peptide in the peptide table and select ‘show spectrum in LC/MS’. It will bring you to the location in the LC/MS heatmap where the MS/MS event occurred. The identified MS/MS will be highlighted in red. This map gives a top down view of the signal coming out of the mass spec in terms of m/z, retention time, and intensity. Peptide features that are detected will be marked with a red circle. Scroll over the circle and a box will appear showing the detected range in which the peptide feature occurred. The area under the curve of the peptide feature will be displayed in the popup. This is the area we display in the peptide table.
You can even get a more intuitive, 3D view of the peptide feature by clicking on the 3D button in the top right hand corner of the pane. From this view, the peptide feature can be seen very clearly.
I hope this has helped you become familiar with peptide feature intensities in PEAKS. Thanks for listening.