An infrastructure for high-throughput microscopy: instrumentation, informatics, and integration.

High-throughput, image-based cell assays are rapidly emerging as valuable tools for the pharmaceutical industry and academic laboratories for use in both drug discovery and basic cell biology research. Access to commercially available assay reagents and automated microscope systems has made it relatively straightforward for a laboratory to begin running assays and collecting image-based cell assay data, but doing so on a large scale can be more challenging. Challenges include process bottlenecks with sample preparation, image acquisition, and data analysis as well as day-to-day assay consistency, managing unprecedented quantities of image data, and fully extracting useful information from the primary assay data. This chapter considers many of the decisions needed to build a robust infrastructure that addresses these challenges. Infrastructure components described include integrated laboratory automation systems for sample preparation and imaging, as well as an informatics infrastructure for multilevel image and data analysis. Throughout the chapter we describe a variety of strategies that emphasize building processes that are scaleable, highly efficient, and rigorously quality controlled.

Measuring drug action in the cellular context using protein-fragment complementation assays.

Cellular signal transduction occurs in the context of dynamic multiprotein complexes in highly ramified pathways. These complexes in turn interact with the cytoskeleton, protein scaffolds, membranes, lipid rafts, and specific subcellular organelles, contributing to the exquisitely tight regulation of their localization and activity. However, these realities of drug target biology are not addressed by currently available drug discovery platforms. In this article, we describe the use of protein-fragment complementation assays (PCAs) to assess drugs and drug targets in the context of their native environment. The PCA process allows for the detection of protein-protein complexes following the expression of full-length mammalian genes linked in-frame to polypeptide fragments of rationally dissected reporter genes. If cellular activity causes the association of two proteins linked to complementary reporter fragments, the interaction of the proteins of interest enables refolding of the fragments, which can then generate a quantifiable signal. Using a PCA based on a yellow fluorescent protein, we demonstrate that functional (p50/p65) complexes of the heterodimeric nuclear factor-kappaB transcription factor, as well as the transcription factor subunit p65 and its modulator IkappaBalpha, can be visualized and monitored in live cells. We observed similar responses of the PCA assays to the activities of the cognate endogenous proteins, including modulation by known agonists and antagonists. A proof-of-concept high throughput screen was carried out using the p50/p65 cell line, and potent inhibitors of this pathway were identified. These assays record the dynamic activity of signaling pathways in living cells and in real time, and validate the utility of PCA as a novel approach to drug discovery.

Identifying off-target effects and hidden phenotypes of drugs in human cells.

We present a strategy for identifying off-target effects and hidden phenotypes of drugs by directly probing biochemical pathways that underlie therapeutic or toxic mechanisms in intact, living cells. High-content protein-fragment complementation assays (PCAs) were constructed with synthetic fragments of a mutant fluorescent protein ('Venus', EYFP or both), allowing us to measure spatial and temporal changes in protein complexes in response to drugs that activate or inhibit particular pathways. One hundred and seven different drugs from six therapeutic areas were screened against 49 different PCA reporters for ten cellular processes. This strategy reproduced known structure-function relationships and also predicted 'hidden,' potent antiproliferative activities for four drugs with novel mechanisms of action, including disruption of mitochondrial membrane potential. A simple algorithm identified a 25-assay panel that was highly predictive of antiproliferative activity, and the predictive power of this approach was confirmed with cross-validation tests. This study suggests a strategy for therapeutic discovery that identifies novel, unpredicted mechanisms of drug action and thereby enhances the productivity of drug-discovery research.