Advanced usage

The following instructions give you a more in-depth insight into PS:

Label filtering for pSilac experiments

pulsed SILAC experiments are based on introducing Medium and Heavy SILAC labels to cultures under different treatments for a short period of time and then focusing in de novo protein production and how this is different between the treatments for this very time period. All unlabeled proteins are considered to be “Light”-labelled, if a protein is found only as unlabelled, then it is of no interest, to procceed to this filtering choose Quantitation filtering form the advanced parameters and choose the “Light” label under Filtering based on which label?. As seen in this example.

Replication multiplexing for experiments representing replicates as different tags

In isobarically tagged experiments one can choose to represent their replicates as different tags, to minimize for example technical error, diverging from the standard experimental procedure described in [this][MS1link] or [this][MS2link] diagram. Let’s take for example the following case: we have 3 biological replicates treated with 2 different conditions that give us 6 different condition-replicate pairs. We decide to represent these as 6 different TMT 6-plex tags and produce a sample of tagged proteins that is fractionated to 12 different fractions. Each fraction is passed through a Mass Spectrometer to produce a rawfile. In the end we have 12 rawfiles and 6 tags of proteins as described in the following diagram: To define this experimental structure, upload the files from PD or MQ and in Step 2 right click on the experimental structure table and choose “Replication Multiplexing” as shown here. Then, choose how your replicates and conditions are represented in your experiment. In our case the biological replicates and conditions are in different tags and since we have no technical replicates we would select Raw Files as stated in the title (as in this screenshot). Then follow the instructions to fill the structure tables as you would do in Step 2, as seen here and here. After completing the aforementioned procedure, continue to Step 3 to define your experimental parameters as usual.

Label swap

Label-swap is a technique that is commonly used in order to correct experimental errors (as demonstrated in this experiment). Incomplete labeling for example is one of the most common sources of experimental error since by fault unlabelled proteins will be considered to be of wrong treatment. Swapping the labels in some replicates efficiently decreases this kind of error. To denote a label swap in some replicates, go to Step 3 > Advanced Parameters > Label Swap options… and choose the biological and technical numbers that denote the label swapped replicate - this will automatically give you the rawfiles that will be affected. Then, choose the labels that are swapped in this replicate and Add the Label Swap. Repeat the process to define all label swapped replicates. This process is described in this image. PS will automatically swap all ratios in label swapped replicates to provide you with accurate results - note that in the end a given ratio corresponds to the non swapped labels e.g. if treatment WT is labelled as Light and Mut as Heavy a ratio of Heavy/Light will always mean Mut/WT.

Label merging

Sometimes - mainly because of conditions and tag number mismatch - many tags might correspond to the same condition. If this is the case, you can choose the tags in Step 3 > Right click on them and choose “Same condition” as seen here, name the condition and hit the OK button. The merged condition will appear in bold.

Adjusting the p-value and other parameters

P value of statistical significance can be adjusted in Advanced parameters > Miscellaneous. This dialog box can also be used to adjust how many peptides should a protein be matched to and in how many biological replicates should it be quantified in in order to be considered valid. Usually proteins matched solely to one peptide and found in just once biological replicate are considered invalid for quantification.

Custom plot parameters using Plot_Generator.R

The results folder that is provided at the end of the analysis contains an R script called Plot_Generator.R that can be used to recreate all plots. This script can be tweaked using simple R and ggplot skills to customize the plots as needed. Simply unzip all data to a folder and execute your tweaked version of Plot_Generator.R.