Identifying and Quantifying Heterogeneity in High Content Analysis: Application of Heterogeneity Indices to Drug Discovery

Abstract

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.

Platform for Quantitative Evaluation of Spatial Intratumoral Heterogeneity in Multiplexed Fluorescence Images

“…are computed for each available biomarker, as are cell morphometric features (location, area and cell radius). These measurements can be used to calculate the Pittsburgh Indices (12) and other measures of cell-level phenotypic heterogeneity. Once each cell is described as a single point in a multivariate feature space, phenotypes can be identified through standard clustering techniques.…”

A Perspective on Implementing a Quantitative Systems Pharmacology Platform for Drug Discovery and the Advancement of Personalized Medicine

“…High-content profiling that includes analysis of heterogeneity of cellular responses can then help us determine the role of a particular pathway in the pathophysiology (step E). 56 However, drug-induced normalization of a single pathway may not be sufficient to rescue disease phenotype. Specific proteomic, transcriptomic, and metabolomic data 84,85 can then be analyzed and integrated using computational and pharmacodynamic systems analysis tools 86 to construct mathematical (computational) models of pathogenic pathways and their interrelationships (networks; Step J).…”

“…Several reviews reinforce the value of phenotypic screens and their success relative to target-focused approaches, particularly for complex disease. 1,2,5,6,56,57,92 In particular, high-content screening (HCS) is a powerful phenotypic screening tool for analyzing the temporal-spatial dynamics of multiple cellular function parameters in both cell-based 56,61,93 and experimental organism 94 models. As an example, HCS conducted on a genome-wide scale using RNAi or cDNA overexpression combined with in silico drug-target discovery strategies has been used to repurpose preclinical drugs for both common and rare diseases, such as alpha1-antitrypsin deficiency and acute megakaryocytic leukemia (AMKL).…”

“…82 In the future, iPSCs could be modulated with molecular and/or small-molecule perturbations to identify and quantify any heterogeneity in patient-specific disease models. 56 …”

Fluorescent protein biosensors applied to microphysiological systems

“…HCS was developed to create an automated platform to acquire image data and then analyze, display, database and report on the data from a large number of cells/tissues or even small experimental organisms (37–41). Significant statistical analyses on large datasets from HCS have demonstrated the critical role of heterogeneity in biological processes and the importance of measuring it in experimental studies (42, 43). Measuring and interpreting the temporal and spatial heterogeneity in the responses of the MPS disease and toxicity models will be a critical component of investigations using MPS.…”