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September 01, 2009 (Vol. 29, No. 15)

Cellular Imaging and Analysis Roundtable

Past, Present, and Future of Enabling Technology in Academia and Industry

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    Achim von Leoprechting, Ph.D.

    GEN  How has the emergence of phenotypic analysis impacted imaging?

    von Leoprechting: In pharma, drug discovery phenotypic assays have not historically been used for lead identification, as this has not been widely accepted by medicinal chemists. Today, this is changing, driven by the obvious advantages of phenotypic screens, for example, in RNAi campaigns or for re-evaluation of established drugs for new applications and therapies. With the advances in image analysis and data management, the benefits of phenotypic over biochemical screening methods are becoming more and more important  in the design of drug discovery campaigns. 

    Lam: I think phenotypic analysis when I think about high-content screening, because you are looking at the homogeneous populations within the heterogeneous population. So if you have 100 cells, 20 cells express a specific marker, therefore, you’re looking at one small population among the heterogeneous population. A phenotypic screen means that you’re dealing with a heterogeneous cell population, because in drug discovery we screen cell lines, which are a homogeneous population. In complex drug discovery paradigm, how would you screen a heterogeneous population? That’s where the phenotypic screen comes in.

    Evans: Classically, biologists and engineers tend to have the idea that cell lines should be homogeneous, but typically, there’s a lot of variation cell to cell. Just based on cell stain and the microenvironment, especially when you’re making subtle and complex measurements such as phenotypic measurements, your analyses can be quite difficult to interpret due to noise from the variation from cell to cell. This has been quite off-putting to chemists who, in many cases, want a single number per compound.

    The relatively high level of complexity in interpreting data from phenotypic screens is the major problem in getting their adoption into drug discovery. And part of that has been due to the lack of development of tools that really allow you to look at the variation within populations and compare treated and control populations in the way that a well-trained biologist or pathologist is able to do—filter out (in their heads) the noise.

    The problem in translating from pathology to drug discovery is real quantitation and scalability that you don’t get in the pathology lab, it’s very much dependent on the individual who’s doing the screening. So, coming up with a software package that gives you the expert-analysis that is scalable and can adapt to different biologies is something that’s a real challenge for the software developers.  

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