Fractionation offers an attractive solution to capture reliable data on low molecular weight (MW), low abundant proteins or peptides, and push deeper into the proteome of highly complex samples. It is a solution that is easy to integrate into most labs and existing set-ups, for example off-line fractionation by 1D gel electrophoresis or in-line fraction by an orthogonal LC step, with well understood benefits:
- Concentration of specific parts of the proteome, not just seeing the same high abundance, high MW proteins
- Match sample complexity to MS instrument capability, including dynamic range detection limits
- Avoid ionisation suppression effects
- Fractionate at either the protein or peptide level
- Maximise protein and proteome coverage
The bioinformatics challenge is how to recombine the results from each individual fraction to produce a comprehensive view of protein expression. We have solved this challenge for you in Progenesis LC-MS, so you can report significant changes within the entire experiment.
Understanding the fractionated sample analysis approach
Analysis of fractionated samples in Progenesis LC‑MS is a 2-step process:
- Analyse each fraction. Create a series of experiments, each containing the sample runs from a single fraction. Once that is done, each experiment will contain its own set of identified proteins and peptides.
- Recombine fractions and generate a protein view. Create a multi-fraction experiment into which you import the single-fraction experiments from the first step. The software automatically collates the proteins for each sample.
I’ve illustrated these steps below using a typical control versus treated experiment to quantify and identify relative protein expression changes within a complex sample.
Analyse each fraction. In the graphic above we have four separate experiments made up of three replicate control and three replicate treated runs, for each of the four fractions. These are analysed in our non-fractionated sample workflow. Each experiment quantifies and then identifies the peptide ions, which are combined to produce protein abundance measures within each fraction for both control and treated samples.
Recombine fractions and generate a protein view: Now we effectively turn the data analysis ninety degrees as I’ve tried to illustrate above. To do this we combine the individual fraction analysis results, normalise between fractions, and look at the data analysis in terms of three whole replicate control samples compared to three whole replicate treated samples.
The result is an overall view of the original experiment at the protein level, including normalised abundances reported for each protein, so you can discover proteins that are significantly different between the two conditions. In the final graphic above, a selected protein of interest shows up-regulation in treated samples (in the blue box) relative to control samples (in the pink box).
How can this help my studies?
The summary tables produced at the end of the Progenesis LC-MS fractionated workflow shows all the proteins quantified and identified within the combined fractions. This includes important data such as Anova p-value, fold change and abundance, as well as ways to view all the individual peptide ion measurements underlying each protein. All this data can easily be exported to feed into your own wider bioinformatics studies or reported to support biological conclusions and publications.
Try it yourself
If you are trying a fractionation approach to dig deeper into the proteome of an already characterised sample or to overcome the challenge of looking for low MW, low abundance proteins and peptides in a highly complex proteomic sample such as serum, Progenesis LC-MS provides you with an analysis solution. Why not download it today to see how simple it is to get meaningful, reliable measures of protein expression using our fractionation workflow?