HCS is a multiplexed, functional screening method based on extracting multiparametric fluorescence data from multiple targets in intact cells [2
]. By temporally and spatially resolving fluorescent readouts within individual cells, HCS yields an almost unlimited number of kinetic and morphometric outputs. HCS was developed to facilitate drug-target validation and lead optimization before costly animal testing [4
]. Today it is broadly used to catalog cellular, subcellular, and intercellular responses to multiple systematic perturbations and is applicable to basic science, translational research, and drug development. We distinguish HCS from high-content analysis (HCA). HCA refers to extracting information from image data. HCS is the automated, high-throughput application of HCA.
HCS can fill a gap in academic research. Our growing awareness of biological complexity underscores the need to examine more than one variable at a fixed point in time. Traditional low-throughput methods have severe limitations. For complex systems with many interacting genes, measuring any single perturbation is not very informative. For gain-of-function diseases, especially those with late onset, a toxic gain-of-function may not be related to a protein’s normal function. Unbiased screens therefore identify potential pathogenic mechanisms faster and more comprehensively, and the large datasets are less prone to sampling error when analyzing stochastic events.
HCS assays capture cell-system dynamics and exploit typically confounding cell-to-cell variability. For example, a recent study used simultaneous tracking of ~
1000 proteins in lung carcinoma cells after drug treatment to detect time-dependent proteomic changes that predicted individual cell fate [5
]. Hypotheses in HCS are used to design tracked variables and outputs that maximize the likelihood of meaningful results. We labeled mutant huntingtin and measured cell survival to determine the role of inclusion bodies in Huntington’s disease (HD)[6
], a question unanswered by 10 years of time-invariant, low-throughput approaches. HCS provides large datasets that unveil multiple, often nonintuitive, correlations that seed subsequent lines of thought. Thus, HCS accelerates the iterative process of classical hypothesis-driven research [7