Dr. Andy Feinberg discusses what technology advances are needed in the field of epigenetics. This interview was shot on campus at Johns Hopkins University.
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Single Cell Analysis
One of the areas I think is going to be really important is single-cell analysis. So especially from my perspective where we’re interested in this stochastic variation and heterogeneity in cell populations, it’s going to be very important to do the same kinds of analysis we’ve been doing on cell populations but on individual cells, and that’s going to require the development of even more sophisticated tools. There are a number of labs that are developing such methods and I think it’s a very important frontier area so we can achieve that kind of single-cell resolution and actually study cells in a population.
“When you think about how we decide if someone’s sick or not or what disease they have, we measure some very crude, mean level of some particular factor and we report that out as a number.”
That might be very important in understanding disease for that matter as well. When you think about how we decide if someone’s sick or not or what disease they have, we measure some very crude, mean level of some particular factor and we report that out as a number. But what if the diseases are related to a lack of normal responsiveness or the normal heterogeneity of responses that you’re supposed to see? Unless you have a way of measuring that or assaying that, I think you’re going to be stuck in asking some of these important questions.
Epigenetic Imaging Assays
Another area that I think would be wonderful, it’s not…it somewhat affects my own research, but I think it would have some very important clinical applications that we’re not pursuing ourselves, would be coming up with ways of doing epigenetic imaging. So obviously there’s some techniques for doing things like that in cells in culture, but trying to do that in an animal, trying to measure epigenetic changes in vivo, either in tissues or in whole living organisms or even in patients, would make potentially a major advance in understanding the pathophysiology and natural history of disease.
A third area I think is very important is integrating statistics, which is really modern probability theory, with modern epidemiology approaches, which also factor in things about genome organization and the complexities of epigenetic information, which of course are measured at many different levels, many different kinds of chromatin modification, but even DNA methylation itself is a quantitative measure, unlike the sequence, whether or not there’s a snip for example. And figuring out how to integrate that information in a way that can be used in a practical approach to study human populations, which is what we refer to as epidemiology. So major advances are being made in that area too, but I think it’s a very fruitful area of research and I’m hoping that epigenetics infects biostatistics departments the way it’s infected genetics and biochemistry departments too.