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. © 2014 Gough et al

Defective HNF4alpha-dependent gene expression as a driver of hepatocellular failure in alcoholic hepatitis

Alcoholic hepatitis (AH) is a life-threatening condition characterized by profound hepatocellular dysfunction for which targeted treatments are urgently needed. Identification of molecular drivers is hampered by the lack of suitable animal models. By performing RNA sequencing in livers from patients with different phenotypes of alcohol-related liver disease (ALD), we show that development of AH is characterized by defective activity of liverenriched transcription factors (LETFs). TGFβ1 is a key upstream transcriptome regulator in AH and induces the use of HNF4α P2 promoter in hepatocytes, which results in defective metabolic and synthetic functions. Gene polymorphisms in LETFs including HNF4α are not associated with the development of AH. In contrast, epigenetic studies show that AH livers have profound changes in DNA methylation state and chromatin remodeling, affecting HNF4α-dependent gene expression. We conclude that targeting TGFβ1 and epigenetic drivers that modulate HNF4α-dependent gene expression could be beneficial to improve hepatocellular function in patients with AH

Organs-on-Chips as Bridges for Predictive Toxicology

The next generation of chemical toxicity testing will use organs-on-chips (OoCs)—3D cultures of heterotypic cells with appropriate extracellular matrices to better approximate the in vivo cellular microenvironment. Researchers are already working to validate whether OoCs are predictive of toxicity in humans. Here, we review two other key aspects of how OoCs may advance predictive toxicology—each taking advantage of OoCs as systems of intermediate complexity that remain experimentally accessible. First, the intermediate complexity of OoCs will help elucidate the scale(s) of organismal complexity that currently confound computational predictions of in vivo toxicity from in vitro data sets. Identifying the strongest confounding factors will help researchers improve the computational models underlying such predictions. Second, the experimental accessibility of OoCs will allow researchers to analyze chemical-exposure responses in OoCs using an array of high-content readouts—from fluorescent biosensors that report dynamic changes in specific cell signaling pathways to unbiased searches over broader biochemical space using technologies like ion mobility-mass spectrometry. Such high-content information on OoC responses will help determine the details of adverse outcome pathways. We note these possible uses of OoCs so that researchers and engineers can consider them in the design of next-generation OoC control, perfusion, and analysis platforms