Proteolabels FAQ

General Product Information

What types of experimental design are supported by Proteolabels?

Proteolabels supports approaches where chemical or in vivo labels have been introduced onto peptides, giving a fixed mass shift per residue or terminus, and where quantification is performed from MS1 signals (i.e. no current support for MS2 tag approaches). Currently supported styles of labelling experiment are:

  • duplex "SILAC style": unlabelled "light" channel and heavy labelled
  • triple "SILAC style": unlabelled "light" channel, medium labelled, heavy labelled
  • duplex "dimethyl style": light labelled, heavy labelled
  • triplex "dimethyl style": light labelled, medium labelled, heavy labelled

The styles are indicative of the common types of labelling strategy employed, rather than the actual reagent/labels types. For example, any other chemical or in vivo labelling approaching with a light, medium and heavy label will be detected as "triplex dimethyl style".

Proteolabels uses peptides data that has been quantified and identified via Progenesis QI for proteomics as a starting point.

Does Proteolabels support analysis of fractionated data?

Yes. If you wish to analyse fractionated samples, first use the Progenesis combine fraction mode. When fractions have been combined, then click "Export to Proteolabels".

Pre-processing in Progenesis

How should I process my data in Progenesis QI for proteomics before a Proteolabels analysis?

Follow the tutorials here up to the point of peptides having been confidently quantified and identified. You should then export data to Proteolabels (Identify Peptides panel). There are some sections that are less important or not needed for Proteolabels. For example, the experimental design is different in Proteolabels, and so you can enter any sensible design in here. Secondly, there is no need to continue to protein quantitation in Progenesis, as this stage will be performed by Proteolabels.

Important note: When performing peak picking, we strongly recommend that you select "Automatic" and "Maximum sensitivity". You may also wish to lower the filter for ions on import, as discussed in this post

My data does not align well in Progenesis QI for proteomics, should I continue with the analysis?

If replicate analyses of the same samples do not align well (i.e. much of the map is scored as "red" by Progenesis), we would not recommend analysing these files together further in Progenesis and Proteolabels, as this likely indicates a problem in sample preparation or LC-MS. If further analysis is performed, results should be interpreted with caution.

If you wish to analyse samples expected to have very different LC-MS profiles together in Proteolabels, we would recommend analysing these as independent fractions in Progenesis first.

How should I search my data in Mascot, prior to a Proteolabels analysis?

To ensure maximum sensitivity of identifications, and that data is passed correctly to Proteolabels for Auto-Detection, we recommend to use the Quantitation settings on the search menu e.g. "SILAC K+8 R+10 [MD]" or "SILAC K+6 R+6 multiplex". We do not recommend performing two searches in Mascot with and without SILAC reagents as fixed modifications - this will cause problems in the downstream analysis.

Import

How do I import data into Proteolabels?

The main way of importing data into Proteolabels is to process first your LC-MS data via Progenesis QI for proteomics, up to and including "Identify peptides". You may wish to continue via Progenesis, to "Refine Identifications" (see relevant Progenesis user guides for details), before returning to Identify Peptides for exporting.

To load data into Proteolabels, click on "Export to Proteolabels" from the Identify Peptides menu in Progenesis.

If you have a ".proteolabels" archive file analysed previously, these can also be imported in Proteolabels.

Identifications

Do I need to filter my identifications?

Depending on the search engine you have used, and the pre-processing in Progenesis, you may wish to disable peptides based on their identification score. If you have previously used the "Refine Identifications" mode in Progenesis to ensure only high quality peptide identifications are present, then there is no need to perform this step again in Proteolabels. If low-quality peptide identifications are still present in Proteolabels, you should remove any identifications below the recommended (e.g. 1% peptide FDR threshold) in the search engine used. You can visualise whether identifications have been correctly filtered via the "Identification Score Distribution" Chart.

Do I need to "Filter by abundance"

This option enables you to remove any peptides with particularly low abundance values from the analysis, as they can produce unreliable ratios. If you are using the default protein quantification method (weighted averaging), then low abundance peptides will be automatically down-weighted. As such, there is no need generally to remove low abundance peptides at this stage. You can view the distribution of Peptide abundance values via the Peptide Abundance Distribution chart.

Experiment

How does Proteolabels Auto-detect my experiment type and settings? Should I use this mode?

The auto-detect mode is usually able to figure out the experimental design automatically, by detecting some identified peptides carrying all the labels types with different mass shifts. Proteolabels then profiles the retention time, drift time (if available) and mass shift deltas of these peptides to optimise settings for detecting peptide pairs/triples where all the peptide features have not been identified.

The settings for m/z and retention time tolerance are usually auto-detected optimally, and do not need to be changed manually. If you manually change the settings to allow wider tolerances, you may increase the number of peptides quantified, but usually at a cost of some peptides being detected that are not reliable for quantification.

Why is Proteolabels unable to Auto-detect my Experiment type and settings?

The Auto-detect mode will fail if there are too few peptides identified with both light and heavy labels (for duplex SILAC). This often indicates an error in your search protocol, which should be fixed before continuing with a Proteolabels analysis.

Common quantification reagents include the following:

What does "Require only a single feature to be identified mean"?

For optimal sensitivity, we recommend to keep this feature switched on. This allows peptide pairs (or triples) to be quantified, even if only one of the peptide features has been confidently identified. The Proteolabels Peptide score can be used to assess whether these are reliable for quantification. If this feature is switched off, the number of peptides and proteins quantified will usually be significantly reduced.

What does "Allow missed feature in triple" mean?

For triplex experimental designs, you can choose whether you wish to quantify peptides even if a feature is only detected for two out of the three channels. By default, we recommend to leave this switched on, as the data can be reviewed on the Peptides panel if needed. If your experimental design requires that you only quantify from peptides where all of the three channels are quantified, then this setting can be switched off.

Experiment Design

How should I group my samples under Experiment Design?

The purpose of this step is only for performing statistical analysis (log ratio t-test) between paired samples with replicates. Replicate analysis of the same sample should be grouped together within the same condition.

Peptides

What is the Peptide score?

The peptide score ranges from 0 (lowest) to 100 (highest), assessing the reliability of quantification from that particular peptide, based on profiling the mass shift, the retention time shift, the match in chromatogram shape, and drift time shift (where ion mobility has been used). When using the default protein quantification method (weighted averaging), there is generally no need to remove peptides with low peptide scores, as they are down-weighted automatically. However, proteins quantified on the basis of only a small number (say 1 or 2) peptides with low scores should be treated with caution.

Why are some peptides disabled for quantitation?

These peptides are "conflicted" - meaning that they cannot easily be assigned to one- protein group. There is independent evidence that the peptide signal was derived from at least two different protein groups, and thus they are not reliable for quantitation.

What happens to unpaired peptides?

If there are identified peptides for which a paired peptide (i.e. with the alternative label) cannot be found, these are not used for quantification. If it appears that there are many identified peptides that do not end up being paired, this could indicate that peak picking was not sufficiently sensitive (see above), that the labelling efficiency was incomplete or that the Experiment settings were not right.

Protein Groups / Quantitation

How is protein quantitation performed in Proteolabels?

The default method for quantitation is weighted averaging. Ratios from different peptides matched to the same protein (group) are weighted by their overall abundance and the Peptide score, so that more trustworthy peptide pairs or triples contribute more to the final quantitation.

For some specialised applications, an alternative mode is available "best median", whereby the median ratio of peptides is used to give a protein ratio. If the number of peptides is even, instead of taking a mean of the two middle peptides, the peptide with the higher Peptide score is chosen.

Export

What formats can I export my data in?

At present, you can export data about peptides and proteins quantified to CSV format, which can be easily processed in a Spreadsheet package such as Excel, or statistical software such as SPSS or R. If you would like other export formats, please contact us with a feature request.