High-Content Screening Assay for Activators of the Wnt/Fzd Pathway in Primary Human Cells

Borchert, Kristen M.; Galvin, Rachelle J. Sells; Frolik, Charles A.; Hale, Laura V.; Halladay, David L.; Gonyier, Robin J.; Trask, O. Joseph; Nickischer, Debra; Houck, Keith A.

doi:10.1089/adt.2005.3.133

Cited by 39 publications

(20 citation statements)

References 21 publications

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“…The analysis software usually contains a set of readymade solutions for standard applications of the high-content imager such as quantifying the translocation of proteins from the cytoplasm to the nucleus (Borchert et al, 2005;Dull et al, 2010;Granas et al, 2006;Kau et al, 2003;Link et al, 2009;Straschewski et al, 2010;Xu et al, 2008;Zanella et al, 2007;Zanella et al, 2008). The combination of high-content imagers and integrated analysis programs, has contributed to many applications of HCS in both basic biology and in drug discovery (reviewed by Dragunow, 2008;Bullen, 2008).…”

Section: Analysis Softwarementioning

confidence: 99%

Getting the whole picture: combining throughput with content in microscopy

Rimon

Schuldiner

2011

Journal of Cell Science

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Commentary 3743Introduction Technological developments in the past years have led to the emergence of high-throughput methods in cell biology (Box 1), where multiple perturbations of a system are automatically repeated in a controlled and identical fashion. These capabilities allow the acquisition of vast amounts of data on a biological system. Evaluating all aspects of such data using computational and mathematical tools can then lead to an unbiased systematic representation of the process studied (Ahn et al., 2006). Technical breakthroughs, often driven by the pharmaceutical industry, have pushed this field forwards and have led to major advances in drug screening (reviewed by Zanella et al., 2010) and numerous discoveries in basic biology.The need for systematic and unbiased approaches has always been at the core of scientific efforts. However, the technology that is required for such efforts often displays an inverse relationship between throughput (speed) and content (biological information). Sydney Brenner referred to this phenomenon by saying that highthroughput experiments are in danger of creating "low-input, high-throughput, no-output biology" (Brenner, 2008). Therefore, it remains a major goal to develop high-throughput science that will give all the advantages of being systematic, accurate, fast and unbiased without giving up the requirement to provide profound and highly informative data.Among the variety of high-throughput approaches available to date (Box 1), one of the methods that holds the promise to bridge the gap between these expectations is high-throughput microscopy, often also referred to as high-content screening (HCS). This is mainly owing to the ability of microscopy methods to focus on single cells at a subcellular resolution, in a time-dependent manner and to measure a large number of parameters in each frame. In this Commentary, we focus on the current frontiers of HCS by presenting the wide range of tools that are utilized and the biological questions that can be tackled using such approaches. We also discuss possible ways to increase content while maintaining throughput at all levels of the screen -from sample design and preparation through to the acquisition and analysis capabilities of the high-content imager system. The power of high-throughput microscopyHigh-throughput microscopy can be employed to address a wide spectrum of biological questions. At the basis of this capability lies the ever-growing variety of labeling methods (discussed below) that allow visualization of cellular architecture and function, as well as developmental or behavioral processes (Fig. 1). In addition, the technological advance of biological tools and microscopic platforms now enables screens to be performed in an array of genetic backgrounds, under different growth conditions and at various time points, as well as allowing the comparison of multiple tissues, cell lines or organisms. Combining the richness of visualization approaches with various experimental strategies creates nearly endless options ...

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Section: Analysis Softwarementioning

confidence: 99%

Getting the whole picture: combining throughput with content in microscopy

Rimon

Schuldiner

2011

Journal of Cell Science

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“…dose-response follow-up of single-point screen actives indicates that 1.6% to 10% of the pd 2 compounds tested have confirmed activity in at least one of the phenotypic screens. These confirmed actives demonstrated potencies with median iC 50 or EC 50 values ranging from 1.7 um (G2m) to 4.3 um (insulin), with high-potency compounds identified in the angio, Wnt, and G2m modules (EC 50 or iC 50 values <10 nm) and intermediate potency compounds in the apoE and insulin modules (EC 50 100 and 330 nm, respectively).…”

Section: Cell Cycle Inhibition G2/m Arrestmentioning

confidence: 69%

“…27 Test compound effects on nonspecific leakage of cytoplasmic proteins were monitored by release of lactate dehydrogenase (ldH) into the media. T-o901317 increased ldH release at concentrations 1000-fold greater than the EC 50 for induction of apoE secretion ( Fig. 2A, b).…”

Section: Apoe Secretionmentioning

confidence: 92%

“…The EC 50 or iC 50 of test compounds was determined by fitting the calculated percentage activation or inhibition values using a standard four-parameter logistic and nonlinear regression analysis.…”

Section: Methods Common To Assaysmentioning

confidence: 99%

“…Clustering of the resulting five compound potency data sets by Som analysis indicates the existence of several phenotypic classes. Each assay profile is a grid within the Som, which consists of the EC 50 or iC 50 of the assay parameter versus the five assay parameters, CycB-pH3, >2n, 2n, 4n, and dna condensation, respectively. Each activity profile is designated by a row (R1-4) and column (C1-4).…”

Section: Cell Cycle Inhibition G2/m Arrestmentioning

confidence: 99%

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Open Innovation for Phenotypic Drug Discovery: The PD2 Assay Panel

et al. 2011

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Phenotypic lead generation strategies seek to identify compounds that modulate complex, physiologically relevant systems, an approach that is complementary to traditional, target-directed strategies. Unlike gene-specific assays, phenotypic assays interrogate multiple molecular targets and signaling pathways in a target “agnostic” fashion, which may reveal novel functions for well-studied proteins and discover new pathways of therapeutic value. Significantly, existing compound libraries may not have sufficient chemical diversity to fully leverage a phenotypic strategy. To address this issue, Eli Lilly and Company launched the Phenotypic Drug Discovery Initiative (PD2), a model of open innovation whereby external research groups can submit compounds for testing in a panel of Lilly phenotypic assays. This communication describes the statistical validation, operations, and initial screening results from the first PD2 assay panel. Analysis of PD2 submissions indicates that chemical diversity from open source collaborations complements internal sources. Screening results for the first 4691 compounds submitted to PD2 have confirmed hit rates from 1.6% to 10%, with the majority of active compounds exhibiting acceptable potency and selectivity. Phenotypic lead generation strategies, in conjunction with novel chemical diversity obtained via open-source initiatives such as PD2, may provide a means to identify compounds that modulate biology by novel mechanisms and expand the innovation potential of drug discovery.

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