How do you generate reliable proteomics results where genomic data is limited?

I met Michelle Cilia, Kevin Howe and  Ted Thannhauser in Salt Lake City during ASMS last year and heard how they use Progenesis SameSpots to support proteomics research at the USDA-ARS. I learnt what they do to run well designed experiments then apply tags and correlation analysis to generate reliable results.  I’ll cover that in a separate post because there were more fundamental challenges to overcome before this.

The first was a lack of genomic resources for  an important species of crop pest, the Greenbug (Schizaphis graminum). This limits what LC-MS/MS can achieve, particularly using spectral counting.

So Michelle and colleagues used the recently sequenced genome of a different aphid species, A. pisum, and 2D-DIGE to see how LC-MS/MS could enhance their research. However, as Michelle was keen to point out, one spot in a 2D gel actually consists of multiple proteins. And LC-MS/MS often only reliably identifies the most abundant protein. This  may not actually be the one responsible for the difference in spot volumes.


In some years, greenbugs (Schizaphis graminum [Rondani]) cause more than $100 million in losses to U.S. crop growers. Image and fact courtesy of USDA-ARS.

Michelle and her colleagues have published the results from combining  2D-DIGE, LC-MS/MS and homology-based comparisons to measure the Schizaphis graminium proteome. The results1 show:

  • The benefit of optimising separation of extracts on 2D gels during pilot studies
  • Combining 2D gel image intensity values and LC-MS/MS data to reliably measure changes of the actual protein of interest within the spot
  • An increased number of identified proteins involved in virus transmission by Schizaphis graminium

2D-DIGE – pI and MW calculation

Michelle used Progenesis SameSpots to complement label-free LC-MS/MS analysis in two ways. Initial pilot studies maximised the number of proteins they could resolve2. So in the large-scale study, well-resolved differentially expressed proteins were picked for digestion and analysis by LC-MS/MS.

2D-DIGE analysis then provided MW, pI and quantitative information towards validating proteins identified  in Schizaphis graminium samples by comparisons made with the A. pisum genome. This included identifying protein isoforms based on MW/pI shifts compared with predictions of translated coding sequence from the different aphid species.

Unfortunately Progenesis LC-MS was not available to this group when the research was under way. So we are working with Michelle, Kevin and Ted to demonstrate how they could use it to further improve reliability and confidence in their results. Progenesis LC-MS quantifies using ion abundance, which has advantages over spectral counting.

Try it yourself

You can try Progenesis SameSpots yourself for quantifying differentially expressed proteins and measuring MW/pI of these interesting spots. Progenesis SpotCheck is also included, helping you to optimise 2D gel separation.

The challenge that not every spot is a single protein still remains, no matter what 2D image analysis software you have.  So try Progenesis LC-MS to see how a quantify-then-identify approach could help you to uncover the real proteins of interest.


1. Cilia M, Tamborindeguy C, Rolland M, Howe K, Thannhauser TW, Gray S. Tangible benefits of the aphid Acyrthosiphon pisum genome sequencing for aphid proteomics: Enhancements in protein identification and data validation for homology-based proteomics. J Insect Physiol. Jan;57(1):179-90. 2011.

2. Cilia M, Fish T, Yang X, Mclaughlin M, Thannhauser TW, Gray S. A Comparison of Protein Extraction Methods Suitable for Gel-Based Proteomic Studies of Aphid Proteins. J Biomol Tech. 2009 September; 20(4): 201–215.