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DKFZ Researchers Develop Infrared-Based Protein Arrays for Quantitative Proteomics


Ulrike Korf
German Cancer Research Center
Name: Ulrike Korf
Title: Scientist, German Cancer Research Center
Professional background: 2001-present, scientist, German Cancer Research Center, Heidelberg; 1999-2000, assistant to management board, German Cancer Research Center.
Education: 1993-1998, postdoc, University of Washington, Seattle; 1993 — PhD, organic chemistry, University of Hamburg, Germany; 1989 — MSc, chemistry and biochemistry, University of Münster, Germany.

Knee-deep in biomarkers and searching for an efficient protein screening tool, Ulrike Korf and the proteomics group in the division of molecular genome analysis at Deutschen Krebsforschungszentrum (DKFZ), or the German Cancer Research Center, set about tweaking existing lysate array technology to create a method to solve their major research obstacles.
Unlike protein-detection arrays, which require two different antibodies directed against the same protein, lysate arrays require only one antibody per analyte, a factor that Korf’s group determined was key for large-scale applications.
The group then scrapped the labeling step, and added new steps including a modified sample-preparation step, and an optional calibration step based on a serial dilution of specific recombinant proteins.
Dubbing the method “infrared-based protein arrays for quantitative proteomics,” or IPAQ, Korf’s group argued this month in Proteomics that the method has the potential to overcome the limitations of Western blotting for the quantitative analysis of protein abundance and protein modification in biological processes [Loebke C et al. Infrared-based protein detection arrays for quantitative proteomics. Proteomics. 2007 Feb 19;7(4):558-564].
To learn more about IPAQ, BioArray News spoke with Korf this week.
What are some of the main objectives of your research in Heidelberg, and how is the development of IPAQ related to your core research goals?
The majority of the focus of our work in the division of molecular genome analysis at the DKFZ Heidelberg became recently the validation of new cancer biomarkers on the protein level.
These candidate genes were originally identified to be up- or down-regulated in cancer by expression profiling. The list of differentially regulated genes is quite large and comprises typically hundreds of genes. Even if you cut down the list to 50 or a hundred candidates, the number of proteins that need to be investigated is still too large to be handled by a standard biochemistry tool.
For this reason we started to explore the potential of protein microarrays as tools for the quantification of proteins on a large scale. Initially we tried to establish the reverse phase arrays as described by NIH researchers a few years ago.
We introduced new steps to make the approach truly quantitative, and the outcome was very promising. Therefore we explored our approach further as a platform for quantitative biology. All of this development went hand-in-hand with the fact that over the past years our focus shifted towards identifying the role of target proteins in a particular signaling network.

Why is there a need for an approach like IPAQ?
Well, in the protein world there isn’t a tool that’s comparable to expression profiling in the DNA world. The capacity and the throughput of the biochemical approaches are much too low to deal with the high number of targets that are identified by expression profiling. Therefore, we had to look for a method that had a very high sample capacity and was also very sensitive.
How are current commercial arrays not capable of providing the kinds of results that IPAQ can?
Most of these arrays are antibody arrays, and in this case you need to label your sample. In our experience, labeling a sample always introduces some kind of artifact to the sample. For example, labeling might change the epitopes of the sample. The kinetics of parallel labeling reactions with different dyes might not be the same, and therefore the samples are no longer really comparable.
I just don’t trust those approaches because antibodies show always cross-reactivities with other proteins. Therefore proper controls and data interpretation are really a crucial point, especially if you run a protein array with a large number of antibodies. I think our approach is much cleaner. We are using very well characterized antibodies on our arrays that are validated by other means — such as Western blotting — for a particular type of biological or clinical sample.
What are the benefits of using the IPAQ approach?
With IPAQ you can investigate a sample in a labeling-free approach. IPAQ also includes proper controls so that you are getting a quantitative readout.
For example, we have introduced a serial dilution of BSA onto the microarray which serves for the calibration of the total protein content of the individual samples. We have also introduced an optional step for the calibration with a specific target protein. Both steps make IPAQ different from other approaches. In addition, the signal detection in the near-infrared range gives us an improved signal-to-noise ratio.

I saw you used some supplies from commercial partners like Whatman and PerkinElmer in your work on IPAQ. Does IPAQ work on all available slides for protein arrays, or do you recommend using certain slides for this work?



I would recommend using nitrocellulose slides because of their high binding capacity. These slides are also available from other companies. For example, we have been using nitrocellulose slides from Grace Biolabs and the outcome was very comparable.
How are you currently using IPAQ? Have you implemented it in your lab?
Currently IPAQ is used mostly for the quantification of RNA silencing experiments where we want to analyze the outcome of gene silencing on a complex signaling network. IPAQ is useful to quantify the knockdown experiment itself, and also the effect of the knockdown on the signaling network. Also it allows us to look at protein phosphorylation events that change as a consequence of the silencing of a single gene, or even of a multiple knock-down experiment.
We also collaborate with clinicians on the analysis of tumor biopsy samples. This is also developing nicely.
Are there any issues with automation? I know that there’s a lot of screening work you need to do. How do you overcome that obstacle?
We currently perform all post-printing steps manually. I think there is certainly a demand for automation especially for the incubation of slides with antibodies. In preliminary tests we found that automation of the incubation steps gives us better results for some antibodies, especially for weak antibodies. This is most likely due to the fact that you can wash the slides more efficiently in an automated set-up.

Are you exploring any licensing opportunities?
There’s no intellectual property issue connected to this, so everybody who has the proper equipment can set up this approach in their own lab.
What kind of researchers do you think would benefit the most from using this approach?
Any lab or company that’s interested in biomarker identification or quantitative biology could profit from this approach.
Are there any ways in which IPAQ could be made more valuable?
A key step in the next month will be to validate a higher number of antibodies for this approach, so that you can really get a more complete picture of signal transduction networks and how they are interlinked.
We also test an approach to perform sample delivery with a hand spotter.
Also, we are paying a lot of attention to testing phospho-specific antibodies, because this would give us information on how those signaling modules are activated in certain types of cancers. These are the issues we are focusing on currently.
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