Identifying and quantifying heterogeneity in high content analysis: application of heterogeneity indices to drug discovery.

One of the greatest challenges in biomedical research, drug discovery and diagnostics is understanding how seemingly identical cells can respond differently to perturbagens including drugs for disease treatment. Although heterogeneity has become an accepted characteristic of a population of cells, in drug discovery it is not routinely evaluated or reported. The standard practice for cell-based, high content assays has been to assume a normal distribution and to report a well-to-well average value with a standard deviation. To address this important issue we sought to define a method that could be readily implemented to identify, quantify and characterize heterogeneity in cellular and small organism assays to guide decisions during drug discovery and experimental cell/tissue profiling. Our study revealed that heterogeneity can be effectively identified and quantified with three indices that indicate diversity, non-normality and percent outliers. The indices were evaluated using the induction and inhibition of STAT3 activation in five cell lines where the systems response including sample preparation and instrument performance were well characterized and controlled. These heterogeneity indices provide a standardized method that can easily be integrated into small and large scale screening or profiling projects to guide interpretation of the biology, as well as the development of therapeutics and diagnostics. Understanding the heterogeneity in the response to perturbagens will become a critical factor in designing strategies for the development of therapeutics including targeted polypharmacology.

Development of high-content imaging assays for lethal viral pathogens.

Filoviruses such as Ebola (EBOV) and Marburg (MARV) are single-stranded negative sense RNA viruses that cause acute hemorrhagic fever with high mortality rates. Currently, there are no licensed vaccines or therapeutics to counter filovirus infections in humans. The development of higher throughput/high-content primary screening assays followed by validation using the low-throughput traditional plaque or real-time PCR assays will greatly aid efforts toward the discovery of novel antiviral therapeutics. Specifically, high-content imaging technology is increasingly being applied for primary drug screening. In this study, the authors describe the challenges encountered when optimizing bioassays based on image acquisition and analyses for the highly pathogenic filoviruses Ebola and Marburg. A number of biological and imaging-related variables such as plating density, multiplicity of infection, the number of fields scanned per well, fluorescence intensity, and the cell number analyzed were evaluated during the development of these assays. Furthermore, the authors demonstrate the benefits related to the statistical analyses of single-cell data to account for heterogeneity in the subcellular localization and whole-cell integrated intensity of the viral antigen staining pattern. In conclusion, they show that image-based methods represent powerful screening tools for identifying antiviral compounds for highly pathogenic viruses.