Final Assay Replicates

Many researchers are not quite done once they have a "final-assay". Typically users want to perform a larger number of replicate injections with this candidate assay. A typical practice for assays that will be used for a large number of samples is to characterize the stability of the peptides and reproducibility of the LC/MS system using a 5x5 experiment. In this experiment, 5 different samples are digested and prepared, and each aliquoted into 5 vials, for 25 total. The vials are stored in the autosampler or a refridgerator at the same temperature, and each day for 5 days in a row, at least 5 replicates are acquired for each of the 5 vials. The inter-day, intra-day, and inter-sample variability can be assessed, and poorly performing peptides can be removed.

Here we will simply load some replicate data acquired with a final assay very similar to the one that we created in Step 2 of the walkthrough.

A Note about Spectral Libraries

An action that we could have included at the end of Step 2 is to create a final spectral library, once all the final peptides are refined and their retention times are known. In the future when replicates are imported, the retention time filtering for MS/MS IDs will be relative to the spectral library you have created, and not from the discovery runs, which can be useful. We can do this from the Step 2. Validation with Subsets\ecoli_subset_replicates_refined_cv.sky file.

Use File / Export / Spectral Library
You could use a name like ecoli_final_assay in the dialog that pops up. Note that when we exported a final method in the last step, the script actually created that library, so you don't have to overwrite it.
To use the new library, you go to Settings / Peptide Settings / Library, and Edit list.
Move the cursor to the bottom of the list, so the new library will be added there. Press Add.
Find the location of the library you made, and give it a name.
Then unselect the previous library, and select the new library.
Press Okay to exit. Now the new library is being used.
As it is, we'll use the library that comes with the file Step 3. Final Draft Replicates\ecoli_large_replicates.sky. Open this file.

Loading and Analyzing the Results

Use File / Import / Add single-injection replicates in files, and add the 8 files in Step 3. Final Draft Replicates\Raw.
After these load, you can arrange the Skyline document as before.
Save the Skyline file as ecoli_large_replicates_loaded.sky.

Use View / Retention Times / Regression / Run-to-Run. There are some peaks that are not consistently picked. Click on any of these outliers to see what they look like. Most are some kind of noise. Maybe they were "reproducible noise" in a previous step, or maybe there are interferences that get picked up over time with multiple replicates.

We could filter out the remaining peptides with poor CV's. This actually gets rid of all but a few of the retention time outliers. Additionally we could run PRM Conductor, and set some more stringent filtering settings, like in the figure below. We would unclick the Balance Load box to ensure that no precursors are filtered based on whether they fit in less than the cycle time, and press Send to Skyline.

Here we see the effect on the retention time outliers from run to run. Almost all the outliers are gone.

Make sure that Settings / Transitions Setttings / Filter / Auto-select all matching transitions is not selected, and then Save As ecoli_large_replicates_loaded_refined.sky. This assay is has removed a few precursors, and could be used going forward.

Filtering Retention Time Outliers in Another Way

Let's try another way of filtering the retention time outliers. Go back and open the file Step 2. Validation with Subsets\ecoli_subset_replicates_refined_cv.sky.

Open Settings / Peptide Settings / Prediction.
Select Add from the Retention time predictor drop down menu.
Select Add from the Calculator drop down menu.
In the iRT standards drop down, select Add.
In the Calibrate iRT Calculator, press Use Results, and enter a number like 100 in the Add Standard Peptides dialog that comes up, and press Okay.
Give the Calculator a name like E. Coli PRM Assay and press OKay in the Calibrate iRT Calculator.
In the Edit iRT Calculator, press Add Results, and then Press okay when asked to add these peptides, and if the standards should be updated.

Press Create on the iRT database button, and select Yes when ased to create a new database file. Save as Step 3. Final Draft Replicates/ecoli_irt_prm.irtdb.
In the Edit iRT Calculator dialog still, give it a name like E. Coli PRM and press Okay.
In the Edit Retention Time Predictor, set 2 minutes, and press Okay.
We are still in the ecoli_subset_replicates_refined_cv.sky file, so we don't need to use the calculator here. Set None in the Retention time predictor, press Cancel, and close this Skyline file.
Open up our ecoli_large_replicates_loaded.sky file, where we have all the retention time outliers.
Go to Settings / Peptide Settings / Prediction and the E. Coli PRM name should be there. If it wasn't, for some reason, you could add the calculator we created from the Edit List option. Select the E. Coli PRM calculator.
We were asked to add some standard peptides, because some of the ones that went into the calculator are not present. You can add them or not add them.

The background of the chromatogram plots is now beige, and there is a faint vertical line that says "Predicted" on the plots, like below.

Use View / Retention Times / Regression / Score-to-Run, and on the plot that comes up, right click and in the Calculator menu, select the E. Coli PRM. Make sure that you are in the right-click, Plot / Residuals mode. Some of the dots in the plot are pink just because our document here has some peptides that weren't in the calculator. That's fine for this walkthrough.

Use Refine / Advanced / Results and set Retention time outliers to 0.999 and press Okay. These peptides that were pink dots are now removed.
You can do the same thing by right clicking the plot and setting the threshold, and then right clicking again and selecting Remove outliers.

If we go back to the Run-to-Run regression, there are still a few outliers. Apparently we can't yet filter based on the experimental Run-to-Run deviations, but maybe in the future. This iRT filtering maybe wasn't as powerful in this example as just using the CV and PRM Conductor filtering, but it's another tool in our belt.

You've reached the end of this tutorial. Hopefully have a good idea of how to create an assay based on Stellar MS discovery results.