Leading the Way in Life Science Technologies

GEN Exclusives

More »


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

Cellular Imaging and Analysis Roundtable

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

  • GEN  What percentage of your work is live cell vs. fixed?

    Lam: Currently, live cells are used mainly by scientists looking in a microscope. As for using live cells in high-content screening, in our case it would be minimum. But we are planning to expand our stem cell research into the live-cell imaging area.

    Evans: Based on my past experiences at MIT and the Whitehead, there has been a growing movement toward live-cell assays. But it’s definitely still 10 percent or less of the total assays. Typically, some of the success stories have been in things like wound-healing assays and stem-cell differentiation, where people have done live-cell imaging over long periods.

    von Leoprechting: Many drug discovery companies would like to do more live-cell assays including the use of primary tissues, worms, or zebrafish for tox applications, but here the bottleneck is mainly in the upscaling and quality control. In contrast with the academic world in which researchers often look into kinetics over days and even longer, the drug discovery researchers are measuring kinetics predominantly by time points. Doing this in a standardized format for thousands of data points is something right now where we’re hitting the ceiling of what’s possible in terms of sample and data management.

    There are technical solutions emerging that enable integrated live-cell friendly environments for HCS in pharma. This trend is coming from a strong desire to move to more relevant live-cell applications.

    Evans: The allure of the kinetic analysis is that you can avoid doing those end-point kinetics, especially where you have a heterogeneous response, and especially where you’re doing phenotypic assays, it’s very hard to marry up those different time points and deal with what arises from the noise between those end points and see if you still have robust trends.

    If you have a kinetic analysis of the same cells over time, a lot of that noise and variation in cell states is connected and can be removed so you can actually follow a cell that changes from healthy to sick to dead and actually measure the transition in states more easily. There is a trade–off, and I believe it depends on where in the pipeline the assay is and while there are throughput requirements, when you start building models it is nice to be able to actually measure the state change, and the rates of state change, within your population.

Related content