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Max Planck s Michal Janitz on Cell Arrays for HT Functional Genomics

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At A Glance

Name: Michal Janitz

Position: Group leader, Department of Vertebrate Genomics, Max Planck Institute for Molecular Genetics, Berlin, Germany

Background: Postdoc, Max Planck Institute for Molecular Genetics — 2000

Postdoc, German Center for Rheumatology Research, Berlin — 1998-2000

MD, immunogenetics, Free University Berlin — 1995-1998

 

RNA interference has become an invaluable technology for performing functional genomics studies using live cells. RNAi also lends itself to higher-throughput methods, which is where multiplexed cell-based assays and high-content screening come in. Michal Janitz of the Max Planck Institute is using RNAi to conduct large-scale functional genomics studies, and recently co-authored a review article published in the Feb. 1 issue of Drug Discovery Today that discusses emerging high-throughput cell-based assay technologies to conduct such studies. Janitz took a few moments last week to talk with Inside Bioassays about this emerging field.

How does the use of RNA interference tie in with high-content or high-throughput cellular assays?

Max Planck is an academic institution, so we’re not primarily concentrating on drug discovery. However, since our department here is by definition focused on technology development in genomics, we obviously [are in] the same context as industry, and we are also biased toward application-driven science. RNA interference is a field that immediately caught our attention, and we started to think about how we could combine this technology with an array-based technology, which we are currently busy with. RNAi initially was more applied in single gene-based research, and high-throughput approaches using RNAi is just an emerging field, I think. In our case, we saw that we could use this technology in combination with a cellular array, which is described in the review article. Obviously this is a different application as compared with single gene-based research, or hypothesis-driven research, because there are different types of technological problems. First of all, there is the problem of which form we should use RNAi interference in arrays. For example, should it be chemically synthesized RNAs, or should they be vector-expressed RNAs that we introduce into the cells? But from our results, we can see that this is a very powerful screening tool at this moment.

Did you design these cellular arrays?

The cell arrays were developed at Whitehead Institute by David Sabatini, but since it was published, we immediately picked up on the idea because we found it is an excellent solution for the post-genomic era. We are dealing with — as are many other centers — thousands of open-reading frames that we haven’t sequenced. So the cell array was an obvious application. We picked it up and developed it further here. It is an array-based tool for functional genomics that is designed to screen for cDNAs or open-reading frames — up to several thousand in a single experiment. There are three parts to this technology. The first is array preparation, where you spot your cDNAs in the expression vector. Once you have an array ready, you cover it with a monolayer of cells, and then this DNA, which is in the form of a spot, gets transfected into cells so that only cells covering the spots will be transfected. The idea is that you are getting spatial separation of transfection events, and you can observe the effect of overexpression of different genes in a single experiment. So what we are observing is a cluster of cells, up to 50 cells, which are transfected with a plasmid construct. Then you can do a number of detection assays depending on what you are looking for. So you can either detect your protein expressed with a specific antibody, or you can use functional assays. At the moment we are working on apoptosis induction, where the overexpression of your genes could induce apoptosis in the cells, which is obviously very relative for cancer research. And another approach, which is actually more related to RNA interference, is you can spot siRNAs on the array, and you target a high number of genes in a single experiment. So this is quite an exciting new field where you can really perform a lot of functional studies in high throughput. I think this was not possible before.

Also, cell arrays are very cost-effective as compared with systems based on microwell plate approaches, which are very expensive in terms of reagent consumption. With cell arrays, you really only need picograms of your reagents, in terms of siRNAs, and also very small amounts of transfection reagents, all of which is obviously very important for a high-throughput approach. If you calculate the cost per sample, this is what people are always asking about first.

Would you use the same type of instrumentation platforms for well-based assays as you would cellular arrays?

That’s a very good question, because the instrumentation you need for cellular arrays is quite limited. You don’t need the sophisticated automation that you would need for microwell plates, where you need quite sophisticated machinery for liquid dispensing, and so on. Here, what you need is an arrayer to prepare the array. But this is a classical preparation of an array, as you would do for classical DNA microarrays for gene expression analysis. The difference is here that the DNA solution also contains gelatin. So by that approach, the DNA is not permanently fixed to the glass surface, so it can go into the cell, and that is actually the secret of it. Concerning downstream steps, like transfection and signal development, you can do this in a normal cell culture lab. What you need is automation at the level of signal detection. For certain applications you need automated microscopy, because you want to screen your data points — screen fluorescence, for example, from different cell classes. You can do this manually, but for thousands of trasnfections, you would like a scanning microscope. So this is a point where you would need some more sophisticated infrastructure. But again — because I think it’s a very common question, how competitive cell array approach is with microwell plates — I think it’s more cost-effective, because you generally don’t need as sophisticated an infrastructure. Especially for an academic center, this is a very good method to consider.

What types of instrumentation platforms is your lab using?

At the moment, concerning the preparation of the arrays, we either use solid pins — a classical way of doing customized cDNA arrays — or, we started just recently to use piezo spotting, which is non-contact spotting and probably a better idea because we get local concentrations on the glass surface.

Concerning the microscopy, we just use a Zeiss Axioplan microscope.

So have you automated the microscopy system yourself?

As an academic center, our budget is quite limited, so we’re trying to build this up. At the moment, it’s not fully automated — it’s more of a semi-automated platform. We don’t use the most sophisticated options available on the market at the moment.

Anne Carpenter, who was also in the Sabatini lab, is developing open-source software for analyzing cellular arrays such as these? Is this something you’ve followed?

I read about it, but I haven’t used it yet. At the moment, we’re still developing these detection assays, so we’re not a real, routine high-throughput facility. But obviously it’s something that we are carefully following, and at a certain point, we will probably consider solutions like that. But at the moment, it’s more manual work. We are working in a high-throughput field, but there’s a lot of stuff we’re still doing manually.

So what are the major studies your lab is conducting regarding the cellular arrays?

We have contact with many people that have tons of open reading frames and genes for which they do not know a function. So at the moment, we are developing the platform in three different areas. One is high-throughput protein localization studies, where we try to apply this platform to delivering very simple information — this to see where the protein you overexpress localizes. For many genes, where you know virtually nothing about the function, the localization of the protein is a great help. By using the cell arrays, we can already screen unknown genes for this type of information. A second area is screening protein-protein interactions. Although right know there are a lot of solutions for this — for example, high-throughput two-hybrid systems — we should remember that the cell arrays have been developed primarily and can be used in mammalian cell systems. This means that when you screen protein-protein interactions, you have a native environment in the cell, so to speak, which you cannot achieve in a bacterial or yeast system. In mammalian cells, you have all the post-translational modifications that you would like, so the window of detection is much broader. And then the third goes back to RNA interference. Many centers have also started to use cell arrays for screening of high-throughput silencing of genes. At the moment we use chemically synthesized siRNAs, which is probably not economically well-justified; however, these siRNAs are quite reliable, because we know what we spot. It’s a more controllable situation than with, say, easy RNAs, where it’s just a mixture of different RNAs. Obviously, the efficacy is better because you cover more targets, more sequence fragments. However, it’s a less-defined system. I’m not saying chemically synthesized RNAs are necessarily better; probably in the future, these easy RNAs will be dominant elements, because they’re simply cheaper. So we are open to that solution, as well.

One more area that’s just emerging is using cell arrays to screen promoter regions. Most people have concentrated on coding regions following the accomplishment of the Human Genome Project, but we also have the same number, if not greater, of upstream regions, which we also do not know anything about. So this is also a very exciting area as well, regulonomics, or regulatory genomics, whatever one wants to call it, where we see a great potential for cell array applications.

 

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