Inter-lab reproducibility with CV’s < 20% for top 30 differentially expressed proteins

Progenesis SameSpots helps you overcome the biggest challenge facing everyone in quantitative proteomics today, cross-lab reproducibility of results. If you can achieve this then you can be confident in measuring real biological effects that can make a difference, not just reporting artefacts of an experimental approach. Progenesis LC-MS applies a similar unique approach so we hope it can show the same benefits and cross-lab reproducibility with your quantitative MS proteomics analysis.

ProteoRed Reproducibility Study

A multi-laboratory study was performed by ProteoRed (Spanish network of proteomics facilities) and the results analysed by Progenesis SameSpots. Some of the data is represented below but you can see the full results from (#77) presented at the ABRF 2009 conference (883KB) and a poster with more details (223KB) presented by Francesc Canals for ProteoRed in 2008.

The image datasets from 8 laboratories were combined into one experiment and analysed using Progenesis SameSpots. A PCA biplot for the complete dataset (all 1179 spots shown in grey) demonstrates distinct clustering of the control gels (green circle) from the EGF treated gels (red circle). The corresponding colours indicate the location of gels from each laboratory within the clustered data.

Using the same combined data, within-group CV(%) were calculated for each spot to assess the technical reproducibility both within each lab and across all labs. In all cases the CV(%) for Control and Treated were calculated and the maximum value selected for each spot. The bar chart below shows the percentage of spots that achieved a CV(%) below the selected cut off values of 5, 10 and 20% respectively within each laboratory.

How does Progenesis achieve this?

The unique Progenesis workflow perfectly aligns data prior to analysis allowing all detected features to be 100% matched and compared directly across every run without the need to validate or edit each individual feature. This means you can quickly see the real expression differences without laborious, subjective editing and make valid conclusions using multivariate statistics applied to a complete data set.