Identification of a Small Molecule That Induces Mitotic Arrest Using a Simplified High-Content Screening Assay and Data Analysis Method

High content screening for drug discovery from traditional Chinese medicine

Traditional Chinese medicine (TCM) represents the crystallization of Chinese wisdom and civilization. It has been valued as the renewable source for the discovery of novel drugs, owing to its long-term proved efficacy in human diseases and abundant biologically active components pools. To dissect the mystery of TCM, modern technologies such as omics approaches (proteomics, genomics, metabolomics) and drug screening technologies (high through-put screening, high content screening and virtual screening) have been widely applied to either identify the drug target of TCM or identify the active component with certain bio-activity. The advent of high content screening technology has absolutely contributed to a breakthrough in compounds discovery and influenced the evolution of technology in screening field. The review introduces the concept and principle of high content screening, lists and compares the currently used HCS instruments, and summarizes the examples from ours and others research work which applied HCS in TCM-derived compounds screening. Meanwhile, this article also discusses the advantages and limitations of HSC technology in drug discovery from TCM libraries.

Toward Discovery of Novel Microtubule Targeting Agents: A SNAP-tag-Based High-Content Screening Assay for the Analysis of Microtubule Dynamics and Cell Cycle Progression

Microtubule targeting agents (MTAs) are used for the treatment of cancer. Novel MTAs could provide additional and beneficial therapeutic options. To improve the sensitivity and throughput of standard immunofluorescence assays for the characterization of MTAs, we used SNAP-tag technology to produce recombinant tubulin monomers. To visualize microtubule filaments, A549 cells transfected with SNAP-tubulin were stained with a membrane-permeable, SNAP-reactive dye. The treatment of SNAP-tubulin cells with stabilizing MTAs such as paclitaxel resulted in the formation of coarsely structured microtubule filaments, whereas depolymerizing MTAs such as nocodazole resulted in diffuse staining patterns in which the tubulin filaments were no longer distinguishable. By combining these components with automated microscopy and image analysis algorithms, we established a robust high-content screening assay for MTAs with a Z' factor of 0.7. Proof of principle was achieved by testing a panel of 10 substances, allowing us to identify MTAs and to distinguish between stabilizing and destabilizing modes of action. By extending the treatment of the cells from 2 to 20 h, our assay also detected abnormalities in cell cycle progression and in the formation of microtubule spindles, providing additional readouts for the discovery of new MTAs and facilitating their early identification during drug-screening campaigns.

Robust Classification of Small-Molecule Mechanism of Action Using a Minimalist High-Content Microscopy Screen and Multidimensional Phenotypic Trajectory Analysis

Phenotypic screening through high-content automated microscopy is a powerful tool for evaluating the mechanism of action of candidate therapeutics. Despite more than a decade of development, however, high content assays have yielded mixed results, identifying robust phenotypes in only a small subset of compound classes. This has led to a combinatorial explosion of assay techniques, analyzing cellular phenotypes across dozens of assays with hundreds of measurements. Here, using a minimalist three-stain assay and only 23 basic cellular measurements, we developed an analytical approach that leverages informative dimensions extracted by linear discriminant analysis to evaluate similarity between the phenotypic trajectories of different compounds in response to a range of doses. This method enabled us to visualize biologically-interpretable phenotypic tracks populated by compounds of similar mechanism of action, cluster compounds according to phenotypic similarity, and classify novel compounds by comparing them to phenotypically active exemplars. Hierarchical clustering applied to 154 compounds from over a dozen different mechanistic classes demonstrated tight agreement with published compound mechanism classification. Using 11 phenotypically active mechanism classes, classification was performed on all 154 compounds: 78% were correctly identified as belonging to one of the 11 exemplar classes or to a different unspecified class, with accuracy increasing to 89% when less phenotypically active compounds were excluded. Importantly, several apparent clustering and classification failures, including rigosertib and 5-fluoro-2’-deoxycytidine, instead revealed more complex mechanisms or off-target effects verified by more recent publications. These results show that a simple, easily replicated, minimalist high-content assay can reveal subtle variations in the cellular phenotype induced by compounds and can correctly predict mechanism of action, as long as the appropriate analytical tools are used.

Improving drug discovery with high-content phenotypic screens by systematic selection of reporter cell lines

High-content, image-based screens enable the identification of compounds that induce cellular responses similar to those of known drugs but through different chemical structures or targets. A central challenge in designing phenotypic screens is choosing suitable imaging biomarkers. Here we present a method for systematically identifying optimal reporter cell lines for annotating compound libraries (ORACLs), whose phenotypic profiles most accurately classify a training set of known drugs. We generate a library of fluorescently tagged reporter cell lines, and let analytical criteria determine which among them--the ORACL--best classifies compounds into multiple, diverse drug classes. We demonstrate that an ORACL can functionally annotate large compound libraries across diverse drug classes in a single-pass screen and confirm high prediction accuracy by means of orthogonal, secondary validation assays. Our approach will increase the efficiency, scale and accuracy of phenotypic screens by maximizing their discriminatory power.

Development of an image-based screening system for inhibitors of the plastidial MEP pathway and of protein geranylgeranylation

In a preceding study we have recently established an in vivo visualization system for the geranylgeranylation of proteins in a stably transformed tobacco BY-2 cell line, which involves expressing a dexamethasone-inducible GFP fused to the prenylable, carboxy-terminal basic domain of the rice calmodulin CaM61, which naturally bears a CaaL geranylgeranylation motif (GFP-BD-CVIL). By using pathway-specific inhibitors it was there demonstrated that inhibition of the methylerythritol phosphate (MEP) pathway with oxoclomazone and fosmidomycin, as well as inhibition of protein geranylgeranyl transferase type 1 (PGGT-1), shifted the localization of the GFP-BD-CVIL protein from the membrane to the nucleus. In contrast, the inhibition of the mevalonate (MVA) pathway with mevinolin did not affect this localization. Furthermore, in this initial study complementation assays with pathway-specific intermediates confirmed that the precursors for the cytosolic isoprenylation of this fusion protein are predominantly provided by the MEP pathway. In order to optimize this visualization system from a more qualitative assay to a statistically trustable medium or a high-throughput screening system, we established now new conditions that permit culture and analysis in 96-well microtiter plates, followed by fluorescence microscopy. For further refinement, the existing GFP-BD-CVIL cell line was transformed with an estradiol-inducible vector driving the expression of a RFP protein, C-terminally fused to a nuclear localization signal (NLS-RFP). We are thus able to quantify the total number of viable cells versus the number of inhibited cells after various treatments. This approach also includes a semi-automatic counting system, based on the freely available image processing software. As a result, the time of image analysis as well as the risk of user-generated bias is reduced to a minimum. Moreover, there is no cross-induction of gene expression by dexamethasone and estradiol, which is an important prerequisite for this test system.

Distribution Analyzer, a methodology for identifying and clustering outlier conditions from single-cell distributions, and its application to a Nanog reporter RNAi screen

Background

Chemical or small interfering (si) RNA screens measure the effects of many independent experimental conditions, each applied to a population of cells (e.g., all of the cells in a well). High-content screens permit a readout (e.g., fluorescence, luminescence, cell morphology) from each cell in the population. Most analysis approaches compare the average effect on each population, precluding identification of outliers that affect the distribution of the reporter in the population but not its average. Other approaches only measure changes to the distribution with a single parameter, precluding accurate distinction and clustering of interesting outlier distributions.

Results

We describe a methodology to identify outlier conditions by considering the cell-level measurements from each condition as a sample of an underlying distribution. With appropriate selection of a distance metric, all effects can be embedded in a fixed-dimensionality Euclidean basis, facilitating identification and clustering of biologically interesting outliers. We demonstrate that measurement of distances with the Hellinger distance metric offers substantial computational efficiencies over alternative metrics. We validate this methodology using an RNA interference (RNAi) screen in mouse embryonic stem cells (ESC) with a Nanog reporter. The methodology clusters effects of multiple control siRNAs into their true identities better than conventional approaches describing the median cell fluorescence or the commonly used Kolmogorov-Smirnov distance between the observed fluorescence distribution and the null distribution. It identifies outlier genes with effects on the reporter distribution that would have been missed by other methods. Among them, siRNA targeting Chek1 leads to a wider Nanog reporter fluorescence distribution. Similarly, siRNA targeting Med14 or Med27 leads to a narrower Nanog reporter fluorescence distribution. We confirm the roles of these three genes in regulating pluripotency by mRNA expression and alkaline phosphatase staining using independent short hairpin (sh) RNAs.

Conclusions

Using our methodology, we describe each experimental condition by a probability distribution. Measuring distances between probability distributions permits a multivariate rather than univariate readout. Clustering points derived from these distances allows us to obtain greater biological insight than methods based solely on single parameters. We find several outliers from a mouse ESC RNAi screen that we confirm to be pluripotency regulators. Many of these outliers would have been missed by other analysis methods.

Electronic supplementary material

The online version of this article (doi:10.1186/s12859-015-0636-7) contains supplementary material, which is available to authorized users.

Structures of potent anticancer compounds bound to tubulin

Small molecules that bind to tubulin exert powerful effects on cell division and apoptosis (programmed cell death). Cell-based high-throughput screening combined with chemo/bioinformatic and biochemical analyses recently revealed a novel compound MI-181 as a potent mitotic inhibitor with heightened activity towards melanomas. MI-181 causes tubulin depolymerization, activates the spindle assembly checkpoint arresting cells in mitosis, and induces apoptotic cell death. C2 is an unrelated compound previously shown to have lethal effects on microtubules in tumorigenic cell lines. We report 2.60 Å and 3.75 Å resolution structures of MI-181 and C2, respectively, bound to a ternary complex of αβ-tubulin, the tubulin-binding protein stathmin, and tubulin tyrosine ligase. In the first of these structures, our crystallographic results reveal a unique binding mode for MI-181 extending unusually deep into the well-studied colchicine-binding site on β-tubulin. In the second structure the C2 compound occupies the colchicine-binding site on β-tubulin with two chemical moieties recapitulating contacts made by colchicine, in combination with another system of atomic contacts. These insights reveal the source of the observed effects of MI-181 and C2 on microtubules, mitosis, and cultured cancer cell lines. The structural details of the interaction between tubulin and the described compounds may guide the development of improved derivative compounds as therapeutic candidates or molecular probes to study cancer cell division.

Development of an image-based screening system for inhibitors of the plastidial MEP pathway and of protein geranylgeranylation

We have recently established an in vivo visualization system for the geranylgeranylation of proteins in a stably transformed tobacco BY-2 cell line, which involves expressing a dexamethasone-inducible GFP fused to the prenylable, carboxy-terminal basic domain of the rice calmodulin CaM61, which naturally bears a CaaL geranylgeranylation motif (GFP-BD-CVIL). By using pathway-specific inhibitors it was demonstrated that inhibition of the methylerythritol phosphate (MEP) pathway with oxoclomazone and fosmidomycin, as well as inhibition of protein geranylgeranyl transferase type 1 (PGGT-1), shifted the localization of the GFP-BD-CVIL protein from the membrane to the nucleus. In contrast, the inhibition of the mevalonate (MVA) pathway with mevinolin did not affect this localization. Furthermore, complementation assays with pathway-specific intermediates confirmed that the precursors for the cytosolic isoprenylation of this fusion protein are predominantly provided by the MEP pathway. In order to optimize this visualization system from a more qualitative assay to a statistically trustable medium or a high-throughput screening system, we established new conditions that permit culture and analysis in 96-well microtiter plates, followed by fluorescence microscopy. For further refinement, the existing GFP-BD-CVIL cell line was transformed with an estradiol-inducible vector driving the expression of a RFP protein, C-terminally fused to a nuclear localization signal (NLS-RFP). We are thus able to quantify the total number of viable cells versus the number of inhibited cells after various treatments. This approach also includes a semi-automatic counting system, based on the freely available image processing software. As a result, the time of image analysis as well as the risk of user-generated bias is reduced to a minimum. Moreover, there is no cross-induction of gene expression by dexamethasone and estradiol, which is an important prerequisite for this test system.

Chemical dissection of the cell cycle: probes for cell biology and anti-cancer drug development

Cancer cell proliferation relies on the ability of cancer cells to grow, transition through the cell cycle, and divide. To identify novel chemical probes for dissecting the mechanisms governing cell cycle progression and cell division, and for developing new anti-cancer therapeutics, we developed and performed a novel cancer cell-based high-throughput chemical screen for cell cycle modulators. This approach identified novel G1, S, G2, and M-phase specific inhibitors with drug-like properties and diverse chemotypes likely targeting a broad array of processes. We further characterized the M-phase inhibitors and highlight the most potent M-phase inhibitor MI-181, which targets tubulin, inhibits tubulin polymerization, activates the spindle assembly checkpoint, arrests cells in mitosis, and triggers a fast apoptotic cell death. Importantly, MI-181 has broad anti-cancer activity, especially against BRAF^V600E melanomas.

Increasing the Content of High-Content Screening: An Overview

Target-based high-throughput screening (HTS) has recently been critiqued for its relatively poor yield compared to phenotypic screening approaches. One type of phenotypic screening, image-based high-content screening (HCS), has been seen as particularly promising.

In this article, we assess whether HCS is as high content as it can be. We analyze HCS publications and find that although the number of HCS experiments published each year continues to grow steadily, the information content lags behind. We find that a majority of high-content screens published so far (60−80%) made use of only one or two image-based features measured from each sample and disregarded the distribution of those features among each cell population. We discuss several potential explanations, focusing on the hypothesis that data analysis traditions are to blame. This includes practical problems related to managing large and multidimensional HCS data sets as well as the adoption of assay quality statistics from HTS to HCS. Both may have led to the simplification or systematic rejection of assays carrying complex and valuable phenotypic information.

We predict that advanced data analysis methods that enable full multiparametric data to be harvested for entire cell populations will enable HCS to finally reach its potential.

Genome-wide single-cell-level screen for protein abundance and localization changes in response to DNA damage in S. cerevisiae

An effective response to DNA damaging agents involves modulating numerous facets of cellular homeostasis in addition to DNA repair and cell-cycle checkpoint pathways. Fluorescence microscopy-based imaging offers the opportunity to simultaneously interrogate changes in both protein level and subcellular localization in response to DNA damaging agents at the single-cell level. We report here results from screening the yeast Green Fluorescent Protein (GFP)-fusion library to investigate global cellular protein reorganization on exposure to the alkylating agent methyl methanesulfonate (MMS). Broad groups of induced, repressed, nucleus- and cytoplasm-enriched proteins were identified. Gene Ontology and interactome analyses revealed the underlying cellular processes. Transcription factor (TF) analysis identified principal regulators of the response, and targets of all major stress-responsive TFs were enriched amongst the induced proteins. An unexpected partitioning of biological function according to the number of TFs targeting individual genes was revealed. Finally, differential modulation of ribosomal proteins depending on methyl methanesulfonate dose was shown to correlate with cell growth and with the translocation of the Sfp1 TF. We conclude that cellular responses can navigate different routes according to the extent of damage, relying on both expression and localization changes of specific proteins.

Update on in vitro cytotoxicity assays for drug development

BACKGROUND

in vitro cytotoxicity testing provides a crucial means of ranking compounds for consideration in drug discovery. The choice of using a particular viability or cytotoxicity assay technology may be influenced by specific research goals.

OBJECTIVE

Although the high-throughput screening (HTS) utility is typically dependent upon sensitivity and scalability, it is also impacted by signal robustness and resiliency to assay interferences. Further consideration should be given to data quality, ease-of-use, reagent stability, and matters of cost-effectiveness.

METHODS

Here we focus on three main classes of assays that are at present the most popular, useful, and practical for HTS drug discovery efforts. These methods measure: i) viability by metabolism reductase activities; ii) viability by bioluminescent ATP assays; or iii) cytotoxicity by enzymes 'released' into culture medium. Multi-parametric technologies are also briefly discussed.

RESULTS/CONCLUSION

Each of these methods has its relative merits and detractions; however multi-parametric methods using both viability and cytotoxicity markers may mitigate the inherent shortcomings of single parameter measures.

The multidimensional perturbation value: a single metric to measure similarity and activity of treatments in high-throughput multidimensional screens

Screens using high-throughput, information-rich technologies such as microarrays, high-content screening (HCS), and next-generation sequencing (NGS) have become increasingly widespread. Compared with single-readout assays, these methods produce a more comprehensive picture of the effects of screened treatments. However, interpreting such multidimensional readouts is challenging. Univariate statistics such as t-tests and Z-factors cannot easily be applied to multidimensional profiles, leaving no obvious way to answer common screening questions such as "Is treatment X active in this assay?" and "Is treatment X different from (or equivalent to) treatment Y?" We have developed a simple, straightforward metric, the multidimensional perturbation value (mp-value), which can be used to answer these questions. Here, we demonstrate application of the mp-value to three data sets: a multiplexed gene expression screen of compounds and genomic reagents, a microarray-based gene expression screen of compounds, and an HCS compound screen. In all data sets, active treatments were successfully identified using the mp-value, and simulations and follow-up analyses supported the mp-value's statistical and biological validity. We believe the mp-value represents a promising way to simplify the analysis of multidimensional data while taking full advantage of its richness.

Open innovation for phenotypic drug discovery: The PD2 assay panel

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 (PD(2)), 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 PD(2) assay panel. Analysis of PD(2) submissions indicates that chemical diversity from open source collaborations complements internal sources. Screening results for the first 4691 compounds submitted to PD(2) 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 PD(2), may provide a means to identify compounds that modulate biology by novel mechanisms and expand the innovation potential of drug discovery.

Integration of Multiple Readouts into the Z' Factor for Assay Quality Assessment

Methods that monitor the quality of a biological assay (i.e., its ability to discriminate between positive and negative controls) are essential for the development of robust assays. In screening, the most commonly used parameter for monitoring assay quality is the Z' factor, which is based on 1 selected readout. However, biological assays are able to monitor multiple readouts. For example, novel multiparametric screening technologies such as high-content screening provide information-rich data sets with multiple readouts on a compound’s effect. Still, assay quality is commonly assessed by the Z' factor based on a single selected readout. This report suggests an extension of the Z' factor, which integrates multiple readouts for assay quality assessment. Using linear projections, multiple readouts are condensed to a single parameter, based on which the assay quality is monitored. The authors illustrate and evaluate this approach using simulated data and real-world data from a high-content screen. The suggested approach is applicable during assay development, to optimize the image analysis, as well as during screening to monitor assay robustness. Furthermore, data sets from high-content imaging assays and other state-of-the-art multiparametric screening technologies, such as flow cytometry or transcript analysis, could be analyzed.

Intensity calibration and automated cell cycle gating for high-throughput image-based siRNA screens of mammalian cells

High-content microscopic screening systems are powerful tools for extracting quantitative multiparameter measures from large number of cells under numerous conditions. These systems perform well in applications that monitor the presence of objects, but lack in their ability to accurately estimate object intensities and summarize these findings due to variations in background, aberrations in illumination, and variability in staining over the image and/or sample wells. We present effective and automated methods that are applicable to analyzing intensity-based cell cycle assays under high-throughput screening conditions. We characterize the system aberration response from images of calibration beads and then enhance the detection and segmentation accuracy of traditional algorithms by preprocessing images for local background variations. We also provide a rapid, adaptive, cell-cycle partitioning algorithm to characterize each sample well based on the estimated locally and globally corrected cell intensity measures of BrdU and DAPI incorporation. We demonstrated the utility and range of our cell ploidy and probe density measurement methods in a pilot screen using a siRNA library against 779 human protein kinases. With our method, multiple image-based quantitative phenotypes can be realized from a single high-throughput image-based microtiter-plate screen.

CellProfiler Analyst: data exploration and analysis software for complex image-based screens

Background

Image-based screens can produce hundreds of measured features for each of hundreds of millions of individual cells in a single experiment.

Results

Here, we describe CellProfiler Analyst, open-source software for the interactive exploration and analysis of multidimensional data, particularly data from high-throughput, image-based experiments.

Conclusion

The system enables interactive data exploration for image-based screens and automated scoring of complex phenotypes that require combinations of multiple measured features per cell.

Image-based chemical screening

Technological advances have made it feasible to conduct high-throughput small-molecule screens based on visual phenotypes of individual cells, using automated imaging and analysis. These screens are rapidly moving from being small, proof-of-principle tests to robust and widespread screens of hundreds of thousands of compounds. Automated imaging screens maximize the information obtained in an initial screen and improve the ability to select high-quality leads. In this Perspective, I highlight the key steps necessary for conducting a high-throughput image-based chemical compound screen.

Plasma membrane assays and three-compartment image cytometry for high content screening

High throughput image cytometers analyze individual cells in digital photomicrographs by first assigning pixels within each image to plasma membrane, cytoplasm, nucleus, or other regions. In this study, we report on a novel algorithm that: 1) identifies plasma membrane regions to measure changes in plasma membrane-associated proteins (protein kinase C [PKC] alpha, N-cadherin, E-cadherin, vascular endothelium [VE]-cadherin, and pan-cadherin) that regulate cell division, migration, and adhesion and 2) delineates the cell for generalized three-compartment image cytometry. Validation assays were performed for these proteins on cells cultured in 96-well plates and also for tissue sections obtained from transgenic and chemical carcinogenic models of skin cancer. The algorithm successfully quantified phorbol 12-myristate 13-acetate (PMA)-induced plasma membrane localization of PKCalpha in HeLa cells (Z' of 0.88). Additionally, PMA activated translocation to the plasma membrane at P < .01 of N-cadherin (in HeLa cells), E-cadherin (in A431 cells), and VE-cadherin (in human dermal microvascular endothelial cells), suggesting a relationship between PKCalpha activity and cadherin localization. For VE-cadherin, a Z' of 0.52 was obtained between serum-free medium, which increased VE-cadherin, and EGTA, which diminished VE-cadherin at the plasma membrane. For sections obtained from the transgenic skin cancer model, analysis of images with the plasma membrane algorithm revealed that tumor cells exhibited cadherin expression that was just 34% of that expressed by surrounding normal tissue; furthermore, tumor cells expressed elevated DNA content, consistent with development of aneuploidy. In contrast, increased DNA content did not occur for tumor cells produced by chemical carcinogenesis. The results demonstrate that this new algorithm for plasma membrane image cytometry enables statistically significant analyses in a variety of applications in both cultured cells and tissue sections.

A novel platform for accelerated pharmacodynamic profiling for lead optimization of anticancer drug candidates

Oncology drug discovery is, by definition, a target-rich enterprise. High-throughput screening (HTS) laboratories have supported a wide array of molecularly targeted and chemical genomic approaches for anticancer lead generation, and the number of hits emerging from such campaigns has increased dramatically. Although automation of HTS processes has eliminated primary screening as a bottleneck, the demands on secondary screening in appropriate cell-based assays have increased concomitantly with the numbers of hits delivered to therapeutic area laboratories. The authors describe herein the implementation of a novel platform using off-the-shelf solutions that have allowed them to efficiently characterize hundreds of HTS hits using a palette of Western blot-based pharmacodynamic assays. The platform employs a combination of a flatbed bufferless SDS-PAGE system, a dry ultra-rapid electroblotting apparatus, and a highly sensitive and quantitative infrared imaging system. Cumulatively, this platform has significantly reduced the cycle time for HTS hit evaluation. In addition, the routine use of this platform has resulted in higher quality data that have allowed the development of structure-activity databases that have tangibly improved lead optimization. The authors describe in detail the application of this platform, designated the Accelerated Pharmaco-Dynamic Profiler (APDP), to the annotation of inhibitors of 2 attractive oncology targets, BRAF kinase and Hsp90.

Toxicity assays in nanodrops combining bioassay and morphometric endpoints

BACKGROUND

Improved chemical hazard management such as REACH policy objective as well as drug ADMETOX prediction, while limiting the extent of animal testing, requires the development of increasingly high throughput as well as highly pertinent in vitro toxicity assays.

METHODOLOGY

This report describes a new in vitro method for toxicity testing, combining cell-based assays in nanodrop Cell-on-Chip format with the use of a genetically engineered stress sensitive hepatic cell line. We tested the behavior of a stress inducible fluorescent HepG2 model in which Heat Shock Protein promoters controlled Enhanced-Green Fluorescent Protein expression upon exposure to Cadmium Chloride (CdCl2), Sodium Arsenate (NaAsO2) and Paraquat. In agreement with previous studies based on a micro-well format, we could observe a chemical-specific response, identified through differences in dynamics and amplitude. We especially determined IC50 values for CdCl2 and NaAsO2, in agreement with published data. Individual cell identification via image-based screening allowed us to perform multiparametric analyses.

CONCLUSIONS

Using pre/sub lethal cell stress instead of cell mortality, we highlighted the high significance and the superior sensitivity of both stress promoter activation reporting and cell morphology parameters in measuring the cell response to a toxicant. These results demonstrate the first generation of high-throughput and high-content assays, capable of assessing chemical hazards in vitro within the REACH policy framework.

Image-Based Screening for the Identification of Novel Proteasome Inhibitors

The proteasome is a new, interesting target in cancer drug therapy, and the proteasome inhibitor bortezomib has shown an effect in myeloma patients. It is of interest to efficiently discover and evaluate new proteasome inhibitors. The authors describe the development of an image-based screening assay for the identification of compounds with proteasome-inhibiting activity. The stably transfected human embryo kidney cell line HEK 293 ZsGreen Proteasome Sensor Cell Line expressing the ZsProSensor-1 fusion protein was used for screening and evaluation of proteasome inhibitors. Inhibition of the proteasome leads to accumulation of the green fluorescent protein ZsGreen, which is measured in the ArrayScan® High Content Screening system, in which cell morphology is studied simultaneously. When screening the LOPAC1280 substance library, several compounds with effect on the proteasome were found; among the hits were disulfiram and ammonium pyrrolidinedithiocarbamate (PDTC). Cytotoxic analysis of disulfiram and PDTC showed that the compounds induced cytotoxicity in the myeloma cell line RPMI 8226. The average Z' value for the assay was 0.66. The results indicate that the assay rapidly identifies new proteasome-inhibiting substances, and it will be further used as a tool for image-based screening of other chemically diverse compound libraries.

Chemical genetics: an evolving toolbox for target identification and lead optimization

Chemical genetics combines chemistry with biology as a means of exploring the function of unknown proteins or identifying the proteins responsible for a particular phenotype. Chemical genetics is thus a valuable tool in the identification of novel drug targets. This chapter describes the application of chemical genetics in traditional and systems-based approaches to drug target discovery and the tools/approaches that appear most promising for guiding future pharmaceutical development.

High-content screening moves to the front of the line

High-content screening (HCS) has been used in late-stage drug discovery for a decade. In the past few years, technological advances have expanded the role of HCS into the early stages of drug discovery, including high-throughput screening and hit-to-lead studies. More recently, computational advances in image analysis and technological advancements in general cell biology have extended the utility of HCS into target validation and basic biological studies, including RNAi screening. The use of HCS in target validation is expanding the work that can be done at this stage, especially the range of targets that can be characterized, and putting it into a more biological context.