High-Content Screening with siRNA Optimizes a Cell Biological Approach to Drug Discovery: Defining the Role of P53 Activation in the Cellular Response to Anticancer Drugs

Utilization of Multidimensional Data in the Analysis of Ultra-High-Throughput High Content Phenotypic Screens

High Content Screening (HCS) platforms can generate large amounts of multidimensional data. To take full advantage of all the rich contextual information provided by these screens, a combination of traditional as well as nontraditional hit identification and prioritization strategies is required. Here, we describe the workflow and analytics of multidimensional high content data to differentiate, group, and prioritize hits.

Biologically Relevant Heterogeneity: Metrics and Practical Insights

Heterogeneity is a fundamental property of biological systems at all scales that must be addressed in a wide range of biomedical applications, including basic biomedical research, drug discovery, diagnostics, and the implementation of precision medicine. There are a number of published approaches to characterizing heterogeneity in cells in vitro and in tissue sections. However, there are no generally accepted approaches for the detection and quantitation of heterogeneity that can be applied in a relatively high-throughput workflow. This review and perspective emphasizes the experimental methods that capture multiplexed cell-level data, as well as the need for standard metrics of the spatial, temporal, and population components of heterogeneity. A recommendation is made for the adoption of a set of three heterogeneity indices that can be implemented in any high-throughput workflow to optimize the decision-making process. In addition, a pairwise mutual information method is suggested as an approach to characterizing the spatial features of heterogeneity, especially in tissue-based imaging. Furthermore, metrics for temporal heterogeneity are in the early stages of development. Example studies indicate that the analysis of functional phenotypic heterogeneity can be exploited to guide decisions in the interpretation of biomedical experiments, drug discovery, diagnostics, and the design of optimal therapeutic strategies for individual patients.

Robust Hit Identification by Quality Assurance and Multivariate Data Analysis of a High-Content, Cell-Based Assay

Recent technological advances in high-content screening instrumentation have increased its ease of use and throughput, expanding the application of high-content screening to the early stages of drug discovery. However, high-content screens produce complex data sets, presenting a challenge for both extraction and interpretation of meaningful information. This shifts the high-content screening process bottleneck from the experimental to the analytical stage. In this article, the authors discuss different approaches of data analysis, using a phenotypic neurite outgrowth screen as an example. Distance measurements and hierarchical clustering methods lead to a profound understanding of different high-content screening readouts. In addition, the authors introduce a hit selection procedure based on machine learning methods and demonstrate that this method increases the hit verification rate significantly (up to a factor of 5), compared to conventional hit selection based on single readouts only. ( Journal of Biomolecular Screening 2007:1042-1049)

A metric and workflow for quality control in the analysis of heterogeneity in phenotypic profiles and screens

Heterogeneity is well recognized as a common property of cellular systems that impacts biomedical research and the development of therapeutics and diagnostics. Several studies have shown that analysis of heterogeneity: gives insight into mechanisms of action of perturbagens; can be used to predict optimal combination therapies; and can be applied to tumors where heterogeneity is believed to be associated with adaptation and resistance. Cytometry methods including high content screening (HCS), high throughput microscopy, flow cytometry, mass spec imaging and digital pathology capture cell level data for populations of cells. However it is often assumed that the population response is normally distributed and therefore that the average adequately describes the results. A deeper understanding of the results of the measurements and more effective comparison of perturbagen effects requires analysis that takes into account the distribution of the measurements, i.e. the heterogeneity. However, the reproducibility of heterogeneous data collected on different days, and in different plates/slides has not previously been evaluated. Here we show that conventional assay quality metrics alone are not adequate for quality control of the heterogeneity in the data. To address this need, we demonstrate the use of the Kolmogorov-Smirnov statistic as a metric for monitoring the reproducibility of heterogeneity in an SAR screen, describe a workflow for quality control in heterogeneity analysis. One major challenge in high throughput biology is the evaluation and interpretation of heterogeneity in thousands of samples, such as compounds in a cell-based screen. In this study we also demonstrate that three heterogeneity indices previously reported, capture the shapes of the distributions and provide a means to filter and browse big data sets of cellular distributions in order to compare and identify distributions of interest. These metrics and methods are presented as a workflow for analysis of heterogeneity in large scale biology projects.

Identifying and quantifying heterogeneity in high content analysis: application of heterogeneity indices to drug discovery

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.

Multiplexed high content screening assays create a systems cell biology approach to drug discovery

High content screening (HCS) has emerged as an important platform technology for early drug discovery from target identification through in vitro ADME/Tox. The focus is now on implementing multiplexed assays, developing and using advanced reagents and developing and harnessing more sophisticated informatics tools. Multiplexed HCS assays have the potential to dramatically improve the early drug discovery process by creating systems cell biology profiles on the activities of compounds. It is predicted that multiplexed HCS assays will accelerate the overall workflow and produce deeper functional knowledge, thereby permitting better decisions on what compounds to pursue.:

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.

A systems approach for analysis of high content screening assay data with topic modeling

Background

High Content Screening (HCS) has become an important tool for toxicity assessment, partly due to its advantage of handling multiple measurements simultaneously. This approach has provided insight and contributed to the understanding of systems biology at cellular level. To fully realize this potential, the simultaneously measured multiple endpoints from a live cell should be considered in a probabilistic relationship to assess the cell's condition to response stress from a treatment, which poses a great challenge to extract hidden knowledge and relationships from these measurements.

Method

In this work, we applied a text mining method of Latent Dirichlet Allocation (LDA) to analyze cellular endpoints from in vitro HCS assays and related to the findings to in vivo histopathological observations. We measured multiple HCS assay endpoints for 122 drugs. Since LDA requires the data to be represented in document-term format, we first converted the continuous value of the measurements to the word frequency that can processed by the text mining tool. For each of the drugs, we generated a document for each of the 4 time points. Thus, we ended with 488 documents (drug-hour) each having different values for the 10 endpoints which are treated as words. We extracted three topics using LDA and examined these to identify diagnostic topics for 45 common drugs located in vivo experiments from the Japanese Toxicogenomics Project (TGP) observing their necrosis findings at 6 and 24 hours after treatment.

Results

We found that assay endpoints assigned to particular topics were in concordance with the histopathology observed. Drugs showing necrosis at 6 hour were linked to severe damage events such as Steatosis, DNA Fragmentation, Mitochondrial Potential, and Lysosome Mass. DNA Damage and Apoptosis were associated with drugs causing necrosis at 24 hours, suggesting an interplay of the two pathways in these drugs. Drugs with no sign of necrosis we related to the Cell Loss and Nuclear Size assays, which is suggestive of hepatocyte regeneration.

Conclusions

The evidence from this study suggests that topic modeling with LDA can enable us to interpret relationships of endpoints of in vitro assays along with an in vivo histological finding, necrosis. Effectiveness of this approach may add substantially to our understanding of systems biology.

Cell-based fuzzy metrics enhance high-content screening (HCS) assay robustness

High-content screening (HCS) allows the exploration of complex cellular phenotypes by automated microscopy and is increasingly being adopted for small interfering RNA genomic screening and phenotypic drug discovery. We introduce a series of cell-based evaluation metrics that have been implemented and validated in a mono-parametric HCS for regulators of the membrane trafficking protein caveolin 1 (CAV1) and have also proved useful for the development of a multiparametric phenotypic HCS for regulators of cytoskeletal reorganization. Imaging metrics evaluate imaging quality such as staining and focus, whereas cell biology metrics are fuzzy logic-based evaluators describing complex biological parameters such as sparseness, confluency, and spreading. The evaluation metrics were implemented in a data-mining pipeline, which first filters out cells that do not pass a quality criterion based on imaging metrics and then uses cell biology metrics to stratify cell samples to allow further analysis of homogeneous cell populations. Use of these metrics significantly improved the robustness of the monoparametric assay tested, as revealed by an increase in Z' factor, Kolmogorov-Smirnov distance, and strict standard mean difference. Cell biology evaluation metrics were also implemented in a novel supervised learning classification method that combines them with phenotypic features in a statistical model that exceeded conventional classification methods, thus improving multiparametric phenotypic assay sensitivity.

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.

Expression of G protein-coupled receptor 19 in human lung cancer cells is triggered by entry into S-phase and supports G(2)-M cell-cycle progression

It has long been known that G protein-coupled receptors (GPCR) are subject to illegitimate expression in tumor cells. Presumably, hijacking the normal physiologic functions of GPCRs contributes to all biologic capabilities acquired during tumorigenesis. Here, we searched for GPCRs that were expressed in lung cancer: the mRNA encoding orphan G protein-coupled receptor 19 (GPR19) was found frequently overexpressed in tissue samples obtained from patients with small cell lung cancer. Several observations indicate that overexpression of Gpr19 confers a specific advantage to lung cancer cells by accelerating transition through the cell-cycle. (i) Knockdown of Gpr19 mRNA by RNA interference reduced cell growth of human lung cancer cell lines. (ii) Cell-cycle progression through G(2)-M-phase was impaired in cells transfected with siRNAs directed against Gpr19 and this was associated with increased protein levels of cyclin B1 and phosphorylated histone H3. (iii) The expression levels of Gpr19 mRNA varied along the cell-cycle with a peak observed in S-phase. (iv) The putative control of Gpr19 expression by E2F transcription factors was verified by chromatin immunoprecipitation: antibodies directed against E2F-1 to -4 allowed for the recovery of the Gpr19 promoter. (v) Removal of E2F binding sites in the Gpr19 promoter diminished the expression of a luciferase reporter. (vi) E2f and Gpr19 expression correlated in lung cancer patient samples. To the best of knowledge, this is the first example of a GPCR showing cell-cycle-specific mRNA expression. Our data also validate GPR19 as a candidate target when overexpressed in lung cancer.

Early safety assessment using cellular systems biology yields insights into mechanisms of action

The integration of high-content screening (HCS) readers with organ-specific cell models, panels of functional biomarkers, and advanced informatics is a powerful approach to identifying the toxic liabilities of compounds early in the development process and forms the basis of "early safety assessment." This cellular systems biology (CSB) approach (CellCiphr profile) has been used to integrate rodent and human cellular hepatic models with panels of functional biomarkers measured at multiple time points to profile both the potency and specificity of the cellular toxicological response. These profiles also provide initial insights on the mechanism of the toxic response. The authors describe here mechanistic assay profiles designed to further dissect the toxic mechanisms of action and elucidate subtle effects apparent in subpopulations of cells. They measured 8 key mechanisms of toxicity with multiple biomarker feature measurements made simultaneously in populations of living primary hepatocytes and HepG2 cells. Mining the cell population response from these mechanistic profiles revealed the concentration dependence and nature of the heterogeneity of the response, as well as relationships between the functional responses. These more detailed mechanistic profiles define differences in compound activities that are not apparent in the average population response. Because cells and tissues encounter wide ranges of drug doses in space and time, these mechanistic profiles build on the CellCiphr profile and better reflect the complexity of the response in vivo.

A Personal Perspective on High-Content Screening (HCS)

High-content screening (HCS) was introduced in 1997 based on light microscope imaging technologies to address the need for an automated platform that could analyze large numbers of individual cells with subcellular resolution using standard microplates. Molecular specificity based on fluorescence was a central element of the platform taking advantage of the growing list of reagent classes and the ability to multiplex. In addition, image analysis coupled to data management, data mining, and data visualization created a tool that focused on biological information and knowledge to begin exploring the functions of genes identified in the genomics revolution. This overview looks at the development of HCS, the evolution of the technologies, and the market up to the present day. In addition, the options for adopting uniform definitions is suggested along with a perspective on what advances are needed to continue building the value of HCS in biomedical research, drug discovery, and development and diagnostics.

HCS Road

The effective analysis and interpretation of high-content screening (HCS) data requires joining results to information on experimental treatments and controls, normalizing data, and selecting hits or fitting concentration-response curves. HCS data have unique requirements that are not supported by traditional high-throughput screening databases, including the ability to designate separate positive and negative controls for different measurements in multiplexed assays; the ability to capture information on the cell lines, fluorescent reagents, and treatments in each assay; the ability to store and use individual-cell and image data; and the ability to support HCS readers and software from multiple vendors along with third-party image analysis tools. To address these requirements, the authors developed an enterprise system for the storage and processing of HCS images and results. This system, HCS Road, supports target identification, lead discovery, lead evaluation, and lead profiling activities. A dedicated client supports experimental design, data review, and core analyses and displays images together with results for assay development, hit assessment, and troubleshooting. Data can be exported to third-party applications for further analysis and exploration. HCS Road provides a single source for high-content results across the organization, regardless of the group or instrument that produced them.

Review Article: High-Content Screening: A Decade of Evolution

In the past decade, high-content screening has become a highly developed approach to obtaining richly descriptive quantitative phenotypic data using automated microscopy. From early use in drug screening, the technique has evolved to embrace a diverse range of applications in both academic and industrial sectors and is now widely recognized as providing an efficient and effective approach to large-scale programs investigating cell biology in situ and in context.

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.

Generating 'omic knowledge': the role of informatics in high content screening

High Content Screening (HCS) and High Content Analysis (HCA) have emerged over the past 10 years as a powerful technology for both drug discovery and systems biology. Founded on the automated, quantitative image analysis of fluorescently labeled cells or engineered cell lines, HCS provides unparalleled levels of multi-parameter data on cellular events and is being widely adopted, with great benefits, in many aspects of life science from gaining a better understanding of disease processes, through better models of toxicity, to generating systems views of cellular processes. This paper looks at the role of informatics and bioinformatics in both enabling and driving HCS to further our understanding of both the genome and the cellome and looks into the future to see where such deep knowledge could take us.

Machine vision-assisted analysis of structure-localization relationships in a combinatorial library of prospective bioimaging probes

With a combinatorial library of bioimaging probes, it is now possible to use machine vision to analyze the contribution of different building blocks of the molecules to their cell-associated visual signals. For this purpose, cell-permeant, fluorescent styryl molecules were synthesized by condensation of 168 aldehyde with 8 pyridinium/quinolinium building blocks. Images of cells incubated with fluorescent molecules were acquired with a high content screening instrument. Chemical and image feature analysis revealed how variation in one or the other building block of the styryl molecules led to variations in the molecules' visual signals. Across each pair of probes in the library, chemical similarity was significantly associated with spectral and total signal intensity similarity. However, chemical similarity was much less associated with similarity in subcellular probe fluorescence patterns. Quantitative analysis and visual inspection of pairs of images acquired from pairs of styryl isomers confirm that many closely-related probes exhibit different subcellular localization patterns. Therefore, idiosyncratic interactions between styryl molecules and specific cellular components greatly contribute to the subcellular distribution of the styryl probes' fluorescence signal. These results demonstrate how machine vision and cheminformatics can be combined to analyze the targeting properties of bioimaging probes, using large image data sets acquired with automated screening systems.

Translocation Biosensors - Cellular System Integrators to Dissect CRM1-Dependent Nuclear Export by Chemicogenomics

Fluorescent protein biosensors are powerful cellular systems biology tools for dissecting the complexity of cellular processes with high spatial and temporal resolution. As regulated nucleo-cytoplasmic transport is crucial for the modulation of numerous (patho)physiological cellular responses, a detailed understanding of its molecular mechanism would open up novel options for a rational manipulation of the cell. In contrast to genetic approaches, we here established and employed high-content cellular translocation biosensors applicable for dissecting nuclear export by chemicogenomics. A431 cell lines, stably expressing a translocation biosensor composed of glutathione S-transferase, GFP and a rational combination of nuclear import and export signals, were engineered by antibiotic selection and flow cytometry sorting. Using an optimized nuclear translocation algorithm, the translocation response could be robustly quantified on the Cellomics Arrayscan^® VTI platform. Subsequent to assay optimization, the assay was developed into a higher density 384-well format high-content assay and employed for the screening of the 17K ChemBioNet compound collection. This library was selected on the basis of a genetic algorithm used to identify maximum common chemical substructures in a database of annotated bioactive molecules and hence, is well-placed in the chemical space covered by bioactive compounds. Automated multiparameter data analysis combined with visual inspection allowed us to identify and to rationally discriminate true export inhibitors from false positives, which included fluorescent compounds or cytotoxic substances that dramatically affected the cellular morphology. A total of 120 potential hit compounds were selected for Cellomics Arrayscan^® VTI based rescreening. The export inhibitory activity of 20 compounds effective at concentrations < 25 μM were confirmed by fluorescence microscopy in several cell lines. Interestingly, kinetic analysis allowed the identification of inhibitors capable to interfere with the export receptor CRM1-mediated nuclear export not only in an irreversible, but also in a reversible fashion. In sum, exploitation of biosensor based screening allows the identification of chemicogenomic tools applicable for dissecting nucleo-cytoplasmic transport in living cells.

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.

Single cell cytometry of protein function in RNAi treated cells and in native populations

Background

High Content Screening has been shown to improve results of RNAi and other perturbations, however significant intra-sample heterogeneity is common and can complicate some analyses. Single cell cytometry can extract important information from subpopulations within these samples. Such approaches are important for immune cells analyzed by flow cytometry, but have not been broadly available for adherent cells that are critical to the study of solid-tumor cancers and other disease models.

Results

We have directly quantitated the effect of resolving RNAi treatments at the single cell level in experimental systems for both exogenous and endogenous targets. Analyzing the effect of an siRNA that targets GFP at the single cell level permits a stronger measure of the absolute function of the siRNA by gating to eliminate background levels of GFP intensities. Extending these methods to endogenous proteins, we have shown that well-level results of the knockdown of PTEN results in an increase in phospho-S6 levels, but at the single cell level, the correlation reveals the role of other inputs into the pathway. In a third example, reduction of STAT3 levels by siRNA causes an accumulation of cells in the G1 phase of the cell cycle, but does not induce apoptosis or necrosis when compared to control cells that express the same levels of STAT3. In a final example, the effect of reduced p53 levels on increased adriamycin sensitivity for colon carcinoma cells was demonstrated at the whole-well level using siRNA knockdown and in control and untreated cells at the single cell level.

Conclusion

We find that single cell analysis methods are generally applicable to a wide range of experiments in adherent cells using technology that is becoming increasingly available to most laboratories. It is well-suited to emerging models of signaling dysfunction, such as oncogene addition and oncogenic shock. Single cell cytometry can demonstrate effects on cell function for protein levels that differ by as little as 20%. Biological differences that result from changes in protein level or pathway activation state can be modulated directly by RNAi treatment or extracted from the natural variability intrinsic to cells grown under normal culture conditions.

Cell cycle dependent and schedule-dependent antitumor effects of sorafenib combined with radiation

The antineoplastic drug sorafenib (BAY 43-9006) is a multikinase inhibitor that targets the serine-threonine kinase B-Raf as well as several tyrosine kinases. Given the numerous molecular targets of sorafenib, there are several potential anticancer mechanisms of action, including induction of apoptosis, cytostasis, and antiangiogenesis. We observed that sorafenib has broad activity in viability assays in several human tumor cell lines but selectively induces apoptosis in only some lines. Sorafenib was found to decrease Mcl-1 levels in most cell lines tested, but this decrease did not correlate with apoptotic sensitivity. Sorafenib slows cell cycle progression and prevents irradiated cells from reaching and accumulating at G2-M. In synchronized cells, sorafenib causes a reversible G1 delay, which is associated with decreased levels of cyclin D1, Rb, and phosphorylation of Rb. Although sorafenib does not affect intrinsic radiosensitivity using in vitro colony formation assays, it significantly reduces colony size. In HCT116 xenograft tumor growth delay experiments in mice, sorafenib alters radiation response in a schedule-dependent manner. Radiation treatment followed sequentially by sorafenib was found to be associated with the greatest tumor growth delay. This study establishes a foundation for clinical testing of sequential fractionated radiation followed by sorafenib in gastrointestinal and other malignancies.

Increasing the robustness and validity of RNAi screens

RNAi screening in mammalian cells has become a valuable method to identify and describe genetic relationships in both basic biology and disease mechanisms. Multiple efforts are underway to standardize how RNAi screening data are reported, including establishing experimental criteria for defining a validated hit from a screen, and the extent to which the primary screening data themselves are reported. These discussions have identified several key areas that require consistency, or at least understanding, before RNAi screening data can be used generally. Successfully addressing these targeted areas would broaden the use of RNAi screening data beyond advancing one or a few hits into validation experiments, to enable verification of primary screening data, and to facilitate comparisons between sample groups based on screening profiles. Areas for improving RNAi screening include general guidelines for validating hits from screens, the creation of standardized reporting structures for RNAi screening data, such as Minimum Information About an RNAi Experiment (MIARE), statistical methods for analyzing screening data that explicitly account for differences between screening RNAi reagents versus small molecules, and technical improvements to RNAi screening that improve the analysis of gene knockdowns, including multiparametric approaches, such as high-content screening. This review will discuss how these approaches can improve RNAi screening data at the community level and for an individual researcher trying to manage an RNAi screen.

Implementation of high-content assay for inhibitors of mitogen-activated protein kinase phosphatases

Small molecule inhibitors of protein tyrosine kinases have become both powerful chemical probes of biological processes and clinically effective therapeutics. In contrast, few small molecule inhibitors of protein tyrosine phosphatases have been identified and none are currently approved for clinical use. New cell-based high-content methods have been developed that should enable investigators to probe for selective inhibitors of diseases-relevant protein phosphatases. Details of these methods are described herein.

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.

Quantitative characterization of mitosis-blocked tetraploid cells using high content analysis

A range of cellular evidence supporting a G1 tetraploidy checkpoint was obtained from different assay methods including flow cytometry, immunoblotting, and microscopy. Cancer research would benefit if these cellular properties could instead be measured by a single, quantitative, automated assay method, such as high content analysis (HCA). Thus, nocodazole-treated cells were fluorescently labeled for different cell cycle-associated properties, including DNA content, retinoblastoma (Rb) and histone H3 phosphorylation, p53 and p21(WAF1) expression, nuclear and cell sizes, and cell morphology, and automatically imaged, analyzed, and correlated using HCA. HCA verified that nocodazole-induced mitosis block resulted in tetraploid cells. Rb and histone H3 were maximally hyperphosphorylated by 24 h of nocodazole treatment, accompanied by cell and nuclear size decreases and cellular rounding. Cells remained tetraploid and mononucleated with longer treatments, but other targets reverted to G1 levels, including Rb and histone H3 dephosphorylation accompanied by cellular respreading. This was accompanied by increased p53 and p21(WAF1) expression levels. The range of effects accompanying nocodazole-induced block of mitosis and the resulting tetraploid cells' reversal to a pseudo-G1 state can be quantitatively measured by HCA in an automated manner, recommending this assay method for the large-scale biology challenges of modern cancer drug discovery.

Systems cell biology based on high-content screening

A new discipline of biology has emerged since 2004, which we call "systems cell biology" (SCB). Systems cell biology is the study of the living cell, the basic unit of life, an integrated and interacting network of genes, proteins, and myriad metabolic reactions that give rise to function. SCB takes advantage of high-content screening platforms, but delivers more detailed profiles of cellular systemic function, including the application of advanced reagents and informatics tools to sophisticated cellular models. Therefore, an SCB profile is a cellular systemic response as measured by a panel of reagents that quantify a specific set of biomarkers.

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.

Forward in reverse: how reverse genetics complements chemical genetics

Pharmaceutical discovery has a renewed interest in physiological screening, while chemical genomics initiatives will soon provide a large amount of cellular assay data. However, there is no single robust approach to connect active compounds with their targets, limiting their experimental and therapeutic use. Systematic matching of chemical genetic phenotypes with reverse genetic phenotypes would provide a valuable starting point for many investigations.

Tubulin assembly, taxoid site binding, and cellular effects of the microtubule-stabilizing agent dictyostatin

(-)-Dictyostatin is a sponge-derived, 22-member macrolactone natural product shown to cause cells to accumulate in the G2/M phase of the cell cycle, with changes in intracellular microtubules analogous to those observed with paclitaxel treatment. Dictyostatin also induces assembly of purified tubulin more rapidly than does paclitaxel, and nearly as vigorously as does dictyostatin's close structural congener, (+)-discodermolide (Isbrucker et al. (2003), Biochem. Pharmacol. 65, 75-82). We used synthetic (-)-dictyostatin to study its biochemical and cytological activities in greater detail. The antiproliferative activity of dictyostatin did not differ greatly from that of paclitaxel or discodermolide. Like discodermolide, dictyostatin retained antiproliferative activity against human ovarian carcinoma cells resistant to paclitaxel due to beta-tubulin mutations and caused conversion of cellular soluble tubulin pools to microtubules. Detailed comparison of the abilities of dictyostatin and discodermolide to induce tubulin assembly demonstrated that the compounds had similar potencies. Dictyostatin inhibited the binding of radiolabeled discodermolide to microtubules more potently than any other compound examined, and dictyostatin and discodermolide had equivalent activity as inhibitors of the binding of both radiolabeled epothilone B and paclitaxel to microtubules. These results are consistent with the idea that the macrocyclic structure of dictyostatin represents the template for the bioactive conformation of discodermolide.

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

High-content screening has emerged as a newand powerful technique for identifying small-molecule modulators ofmammalian cell biology. The authors describe the development and execution of a high-content screen to identify smallmolecules that induce mitotic arrest in mammalian cancer cells. Many widely used chemotherapeutics, such as Taxol ®and vinblastine, induce mitotic arrest, and the creation of new drugs that also induce mitotic arrest may have tremendous therapeutic value. In their screen, the authors employed a simple DNA stain (DAPI) and a sensitive nonparametric statistical test to identify compounds from an internal collection of 13,000 high-quality lead-like small molecules. Subsequent analysis of 1 active compound indicated that it induces mitotic arrest, assessed using a high-content phosphohistone H3 detection assay, and caused cell proliferation defects inmultiple cancer cell lines. The active compound, a quinazolinone originating from a natural product-like subset of the screened compounds, is active in cells at 500nMand appears to act by inhibiting the polymerization of tubulin.

Systems Cell Biology Knowledge Created from High Content Screening

High content screening (HCS), the large-scale automated analysis of the temporal and spatial changes in cells and cell constituents in arrays of cells, has the potential to create enormous systems cell biology knowledge bases. HCS is being employed along with the continuum of the early drug discovery process, including lead optimization where new knowledge is being used to facilitate the decision-making process. We demonstrate methodology to build new systems cell biology knowledge using a multiplexed HCS assay, designed with the aid of knowledge-mining tools, to measure the phenotypic response of a panel of human tumor cell types to a panel of natural product-derived microtubule-targeted anticancer agents and their synthetic analogs. We show how this new systems cell biology knowledge can be used to design a lead compound optimization strategy for at least two members of the panel, (-)-laulimalide and (+)-discodermolide, that exploits cell killing activity while minimally perturbing the regulation of the cell cycle and the stability of microtubules. Furthermore, this methodology can also be applied to basic biomedical research on cells.

Screening for apoptosis???classical and emerging techniques

There has been a rapid development of cell-based assays and screening methods to identify promising apoptosis-inducing drug candidates for the treatment of cancer. Distinguishing between the complex processes involved in apoptosis and other forms of cell death requires information on both biochemical and morphological processes in the cell. Traditionally, many assays have been limited to measuring, for example, caspase activity using fluorogenic substrates. However, these screening assays provide only limited information on the complex processes involved in apoptosis. In this review we describe some of the available apoptosis assays amenable to high-throughput screening. In particular, image-based high-content screening assays to evaluate multiple biochemical and morphological parameters in apoptotic cells are described. Through combining the imaging of cells in microtiter plates with powerful image analysis algorithms, one can acquire deeper knowledge on multiple biochemical or morphological pathways at the single-cell level at an early stage in the development of novel anti-cancer drugs.

The Benzo[c]phenanthridine Alkaloid, Sanguinarine, Is a Selective, Cell-active Inhibitor of Mitogen-activated Protein Kinase Phosphatase-1

Mitogen-activated protein kinase phosphatase-1 (MKP-1) is a dual specificity phosphatase that is overexpressed in many human tumors and can protect cells from apoptosis caused by DNA-damaging agents or cellular stress. Small molecule inhibitors of MKP-1 have not been reported, in part because of the lack of structural guidance for inhibitor design and definitive assays for MKP-1 inhibition in intact cells. Herein we have exploited a high content chemical complementation assay to analyze a diverse collection of pure natural products for cellular MKP-1 inhibition. Using two-dimensional Kolmogorov-Smirnov statistics, we identified sanguinarine, a plant alkaloid with known antibiotic and antitumor activity but no primary cellular target, as a potent and selective inhibitor of MKP-1. Sanguinarine inhibited cellular MKP-1 with an IC50 of 10 microM and showed selectivity for MKP-1 over MKP-3. Sanguinarine also inhibited MKP-1 and the MKP-1 like phosphatase, MKP-L, in vitro with IC50 values of 17.3 and 12.5 microM, respectively, and showed 5-10-fold selectivity for MKP-3 and MKP-1 over VH-1-related phosphatase, Cdc25B2, or protein-tyrosine phosphatase 1B. In a human tumor cell line with high MKP-1 levels, sanguinarine caused enhanced ERK and JNK/SAPK phosphorylation. A close congener of sanguinarine, chelerythrine, also inhibited MKP-1 in vitro and in whole cells, and activated ERK and JNK/SAPK. In contrast, sanguinarine analogs lacking the benzophenanthridine scaffold did not inhibit MKP-1 in vitro or in cells nor did they cause ERK or JNK/SAPK phosphorylation. These data illustrate the utility of a chemical complementation assay linked with multiparameter high content cellular screening.

Genome scale cytometry: High content analysis for high throughput RNAi phenotype profiling

The combination of RNAi-mediated knockdown of gene expression and high content screening (HCS) allows the determination of the contribution of single genes to a variety of cellular effects varying between growth and survival to subtle alterations in cellular morphology and phenotype. This review examines the current status of research in combining these tools.: