TY  - JOUR
ID  - pittir22990
UR  - http://d-scholarship-dev.library.pitt.edu/22990/
IS  - 7
A1  - Gough, AH
A1  - Chen, N
A1  - Shun, TY
A1  - Lezon, TR
A1  - Boltz, RC
A1  - Reese, CE
A1  - Wagner, J
A1  - Vernetti, LA
A1  - Grandis, JR
A1  - Lee, AV
A1  - Stern, AM
A1  - Schurdak, ME
A1  - Taylor, DL
Y1  - 2014/07/18/
N2  - 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. © 2014 Gough et al.
JF  - PLoS ONE
VL  - 9
TI  - Identifying and quantifying heterogeneity in high content analysis: Application of heterogeneity indices to drug discovery
AV  - public
ER  -