If you visit our blog regularly, you will have noticed a recent theme around quality control (QC) for proteomics data and how our analysis solutions can support this. It’s one we have chosen to highlight for many reasons. Fundamentally, as my colleague Beth pointed out in this recent post, “…it’s a case of garbage in, garbage out”. This is especially true for the challenging experiments that proteomics demands. How to address QC in proteomics is becoming a hot topic, with talks at conferences and review articles citing QC as essential for translating quantitative proteomics discoveries into clinically relevant results.
I want to continue the theme and focus on where QC seems most mature in this respect, the analysis of 2D gels, by highlighting a recent publication.
The example comes from work published by Clémence Bièche et al at the French National Institute for Agricultural Research, as well as other organisations they collaborated with, who use Progenesis SameSpots v4.0. As well as applying QC at various stages in 2D gel analysis the conclusion of their experiments satisfied another hot topic in proteomics; putting results in the context of biological processes.
You can read the paper for full details of the study. Here, I just want to pick out the three key points in the analysis workflow that were used to check and maintain the quality of results they generated:
1. Image QC
Before any image processing the software applies image checks. In this case only those images which passed available QC checks were accepted including ensuring dynamic range was 85-96% and intensity levels of >96%.
2. Principle Components Analysis (PCA)
The experiment compared three different conditions - 0mins, 60mins and 120mins post-HP treatment – to a non-HP control, using a mixture of technical and biological replicates. PCA showed a clear separation of control gels and gels of 0mins. The gels at 0mins were also clearly seen as distinct from the 60 and 120mins post-treatment samples, which were clustered together based on 2D gel measurements. This indicated the gels could be used to define differences in the proteome as it recovered after treatment. Although the gels from 60mins post-treatment and 120mins post-treatment clustered together, the PCA plot showed that 120min samples had more similarity to the control than 60min samples. This indicates the samples measured at the longest time, post-treatment, have proteomes that were recovering enough to resemble the untreated, control samples.
3. p-values, q-values and power calculations
Some thresholds that were applied to these measures ensured only robust statistical differences were used to define the spots that characterised the proteome changing over time. In this case only normalised spots that met the criteria of p <0.05, q <0.05 and power >0.8 were validated and reported as being significantly different. We have FAQs on p-values, q-values and power analysis if you want to learn more.
These QC measures are automatically generated as part of the main analysis workflow in Progenesis SameSpots, so they provide a complementary series of checks as you head towards a final report of significant spots. As in this publication, the spots of interest can be selected for picking and identification by mass-spec. And with the latest version of SameSpots this protein identification information can be imported and linked to the spots in your experiment. This provides a complete picture based on high-quality results and allows you to put results into biological context.
So why not download Progenesis SameSpots and try these QC measure in the main analysis workflow as well as the SpotCheck workflow to quantify your gel running reproducibility? They help generate data you can publish and rely on.