Phenotypic screening of small molecule libraries by high throughput cell imaging

Repurposing High-Throughput Image Assays Enables Biological Activity Prediction for Drug Discovery

In both academia and the pharmaceutical industry, large-scale assays for drug discovery are expensive and often impractical, particularly for the increasingly important physiologically relevant model systems that require primary cells, organoids, whole organisms, or expensive or rare reagents. We hypothesized that data from a single high-throughput imaging assay can be repurposed to predict the biological activity of compounds in other assays, even those targeting alternate pathways or biological processes. Indeed, quantitative information extracted from a three-channel microscopy-based screen for glucocorticoid receptor translocation was able to predict assay-specific biological activity in two ongoing drug discovery projects. In these projects, repurposing increased hit rates by 50- to 250-fold over that of the initial project assays while increasing the chemical structure diversity of the hits. Our results suggest that data from high-content screens are a rich source of information that can be used to predict and replace customized biological assays.

Accelerating glioblastoma drug discovery: Convergence of patient-derived models, genome editing and phenotypic screening

Patients diagnosed with glioblastoma (GBM) continue to face a bleak prognosis. It is critical that new effective therapeutic strategies are developed. GBM stem cells have molecular hallmarks of neural stem and progenitor cells and it is possible to propagate both non-transformed normal neural stem cells and GBM stem cells, in defined, feeder-free, adherent culture. These primary stem cell lines provide an experimental model that is ideally suited to cell-based drug discovery or genetic screens in order to identify tumour-specific vulnerabilities. For many solid tumours, including GBM, the genetic disruptions that drive tumour initiation and growth have now been catalogued. CRISPR/Cas-based genome editing technologies have recently emerged, transforming our ability to functionally annotate the human genome. Genome editing opens prospects for engineering precise genetic changes in normal and GBM-derived neural stem cells, which will provide more defined and reliable genetic models, with critical matched pairs of isogenic cell lines. Generation of more complex alleles such as knock in tags or fluorescent reporters is also now possible. These new cellular models can be deployed in cell-based phenotypic drug discovery (PDD). Here we discuss the convergence of these advanced technologies (iPS cells, neural stem cell culture, genome editing and high content phenotypic screening) and how they herald a new era in human cellular genetics that should have a major impact in accelerating glioblastoma drug discovery.

Leishmania hijacking of the macrophage intracellular compartments

Leishmania spp., transmitted to humans by the bite of the sandfly vector, are responsible for the three major forms of leishmaniasis, cutaneous, diffuse mucocutaneous and visceral. Leishmania spp. interact with membrane receptors of neutrophils and macrophages. In macrophages, the parasite is internalized within a parasitophorous vacuole and engages in a particular intracellular lifestyle in which the flagellated, motile Leishmania promastigote metacyclic form differentiates into non-motile, metacyclic amastigote form. This phenomenon is induced by Leishmania-triggered events leading to the fusion of the parasitophorous vacuole with vesicular members of the host cell endocytic pathway including recycling endosomes, late endosomes and the endoplasmic reticulum. Maturation of the parasitophorous vacuole leads to the intracellular proliferation of the Leishmania amastigote forms by acquisition of host cell nutrients while escaping host defense responses.

Enhancing the discovery and development of immunotherapies for cancer using quantitative and systems pharmacology: Interleukin-12 as a case study

Recent clinical successes of immune checkpoint modulators have unleashed a wave of enthusiasm associated with cancer immunotherapy. However, this enthusiasm is dampened by persistent translational hurdles associated with cancer immunotherapy that mirror the broader pharmaceutical industry. Specifically, the challenges associated with drug discovery and development stem from an incomplete understanding of the biological mechanisms in humans that are targeted by a potential drug and the financial implications of clinical failures. Sustaining progress in expanding the clinical benefit provided by cancer immunotherapy requires reliably identifying new mechanisms of action. Along these lines, quantitative and systems pharmacology (QSP) has been proposed as a means to invigorate the drug discovery and development process. In this review, I discuss two central themes of QSP as applied in the context of cancer immunotherapy. The first theme focuses on a network-centric view of biology as a contrast to a "one-gene, one-receptor, one-mechanism" paradigm prevalent in contemporary drug discovery and development. This theme has been enabled by the advances in wet-lab capabilities to assay biological systems at increasing breadth and resolution. The second theme focuses on integrating mechanistic modeling and simulation with quantitative wet-lab studies. Drawing from recent QSP examples, large-scale mechanistic models that integrate phenotypic signaling-, cellular-, and tissue-level behaviors have the potential to lower many of the translational hurdles associated with cancer immunotherapy. These include prioritizing immunotherapies, developing mechanistic biomarkers that stratify patient populations and that reflect the underlying strength and dynamics of a protective host immune response, and facilitate explicit sharing of our understanding of the underlying biology using mechanistic models as vehicles for dialogue. However, creating such models require a modular approach that assumes that the biological networks remain similar in health and disease. As oncogenesis is associated with re-wiring of these biological networks, I also describe an approach that combines mechanistic modeling with quantitative wet-lab experiments to identify ways in which malignant cells alter these networks, using Interleukin-12 as an example. Collectively, QSP represents a new holistic approach that may have profound implications for how translational science is performed.

Facile preparation of a photoactivatable surface on a 96-well plate: a versatile and multiplex cell migration assay platform

A novel photoactivatable 96-well plate based on photocleavable PEG and poly-d-lysine serves as a useful high-throughput cell migration assay platform.

An inverse small molecule screen to design a chemically defined medium supporting long-term growth of Drosophila cell lines

Drosophila cell culture is used as a model system with multiple applications including the identification of new therapeutic targets in screens, the study of conserved signal transduction pathway mechanisms, and as an expression system for recombinant proteins. However, in vitro methods for Drosophila cell and organ cultures are relatively undeveloped. To characterize the minimal requirements for long-term maintenance of Drosophila cell lines, we developed an inverse screening strategy to identify small molecules and synergies stimulating proliferation in a chemically defined medium. In this chemical-genetics approach, a compound-protein interaction database is used to systematically score genetic targets on a screen-wide scale to extract further information about cell growth. In the pilot screen, we focused on two well-characterized cell lines, Clone 8 (Cl.8) and Schneider 2 (S2). Validated factors were investigated for their ability to maintain cell growth over multiple passages in the chemically defined medium (CDM). The polyamine spermidine proved to be the critical component that enables the CDM to support long-term maintenance of Cl.8 cells. Spermidine supplementation upregulates DNA synthesis for Cl.8 and S2 cells and increases MAPK signaling for Cl.8 cells. The CDM also supports the long-term growth of Kc167 cells. Our target scoring approach validated the importance of polyamines, with enrichment for multiple polyamine ontologies found for both cell lines. Future iterations of the screen will enable the identification of compound combinations optimized for specific applications-maintenance and generation of new cell lines or the production and purification of recombinant proteins-thus increasing the versatility of Drosophila cell culture as both a genetic and biochemical model system. Our cumulative target scoring approach improves on traditional chemical-genetics methods and is extensible to biological processes in other species.

Finding novel pharmaceuticals in the systems biology era using multiple effective drug targets, phenotypic screening and knowledge of transporters: where drug discovery went wrong and how to fix it

Despite the sequencing of the human genome, the rate of innovative and successful drug discovery in the pharmaceutical industry has continued to decrease. Leaving aside regulatory matters, the fundamental and interlinked intellectual issues proposed to be largely responsible for this are: (a) the move from 'function-first' to 'target-first' methods of screening and drug discovery; (b) the belief that successful drugs should and do interact solely with single, individual targets, despite natural evolution's selection for biochemical networks that are robust to individual parameter changes; (c) an over-reliance on the rule-of-5 to constrain biophysical and chemical properties of drug libraries; (d) the general abandoning of natural products that do not obey the rule-of-5; (e) an incorrect belief that drugs diffuse passively into (and presumably out of) cells across the bilayers portions of membranes, according to their lipophilicity; (f) a widespread failure to recognize the overwhelmingly important role of proteinaceous transporters, as well as their expression profiles, in determining drug distribution in and between different tissues and individual patients; and (g) the general failure to use engineering principles to model biology in parallel with performing 'wet' experiments, such that 'what if?' experiments can be performed in silico to assess the likely success of any strategy. These facts/ideas are illustrated with a reasonably extensive literature review. Success in turning round drug discovery consequently requires: (a) decent systems biology models of human biochemical networks; (b) the use of these (iteratively with experiments) to model how drugs need to interact with multiple targets to have substantive effects on the phenotype; (c) the adoption of polypharmacology and/or cocktails of drugs as a desirable goal in itself; (d) the incorporation of drug transporters into systems biology models, en route to full and multiscale systems biology models that incorporate drug absorption, distribution, metabolism and excretion; (e) a return to 'function-first' or phenotypic screening; and (f) novel methods for inferring modes of action by measuring the properties on system variables at all levels of the 'omes. Such a strategy offers the opportunity of achieving a state where we can hope to predict biological processes and the effect of pharmaceutical agents upon them. Consequently, this should both lower attrition rates and raise the rates of discovery of effective drugs substantially.

Chapter 17: bioimage informatics for systems pharmacology

Recent advances in automated high-resolution fluorescence microscopy and robotic handling have made the systematic and cost effective study of diverse morphological changes within a large population of cells possible under a variety of perturbations, e.g., drugs, compounds, metal catalysts, RNA interference (RNAi). Cell population-based studies deviate from conventional microscopy studies on a few cells, and could provide stronger statistical power for drawing experimental observations and conclusions. However, it is challenging to manually extract and quantify phenotypic changes from the large amounts of complex image data generated. Thus, bioimage informatics approaches are needed to rapidly and objectively quantify and analyze the image data. This paper provides an overview of the bioimage informatics challenges and approaches in image-based studies for drug and target discovery. The concepts and capabilities of image-based screening are first illustrated by a few practical examples investigating different kinds of phenotypic changes caEditorsused by drugs, compounds, or RNAi. The bioimage analysis approaches, including object detection, segmentation, and tracking, are then described. Subsequently, the quantitative features, phenotype identification, and multidimensional profile analysis for profiling the effects of drugs and targets are summarized. Moreover, a number of publicly available software packages for bioimage informatics are listed for further reference. It is expected that this review will help readers, including those without bioimage informatics expertise, understand the capabilities, approaches, and tools of bioimage informatics and apply them to advance their own studies.

Integrated compound profiling screens identify the mitochondrial electron transport chain as the molecular target of the natural products manassantin, sesquicillin, and arctigenin

Phenotypic compound screens can be used to identify novel targets in signaling pathways and disease processes, but the usefulness of these screens depends on the ability to quickly determine the target and mechanism of action of the molecules identified as hits. One fast route to discovering the mechanism of action of a compound is to profile its properties and to match this profile with those of compounds of known mechanism of action. In this work, the Novartis collection of over 12,000 pure natural products was screened for effects on early zebrafish development. The largest phenotypic class of hits, which caused developmental arrest without necrosis, contained known electron transport chain inhibitors and many compounds of unknown mechanism of action. High-throughput transcriptional profiling revealed that these compounds are mechanistically related to one another. Metabolic and biochemical assays confirmed that all of the molecules that induced developmental arrest without necrosis inhibited the electron transport chain. These experiments demonstrate that the electron transport chain is the target of the natural products manassantin, sesquicillin, and arctigenin. The overlap between the zebrafish and transcriptional profiling screens was not perfect, indicating that multiple profiling screens are necessary to fully characterize molecules of unknown function. Together, zebrafish screening and transcriptional profiling represent sensitive and scalable approaches for identifying bioactive compounds and elucidating their mechanism of action.

Small inhibitors of ADP-ribosylation factor activation and function in mammalian cells

Small GTP-binding proteins of the ADP-ribosylation factor (Arf) family control various cell functional responses including protein transport and recycling between different cellular compartments, phagocytosis, proliferation, cytoskeletal remodelling, and migration. The activity of Arfs is tightly regulated. GTPase-activating proteins (GAPs) inactivate Arfs by stimulating GTP hydrolysis, and guanine nucleotide exchange factors (GEFs) stimulate the conversion of inactive GDP-bound Arf to the active GTP-bound conformation. There is increasing evidence that Arf small GTPases contribute to cancer growth and invasion. Increased expression of Arf6 and of Arf-GEPs, or deregulation Arf-GAP functions have been correlated with enhanced invasive capacity of tumor cells and metastasis. The spatiotemporal specificity of Arf activation is dictated by their GEFs that integrate various signals in stimulated cells. Brefeldin A (BFA), which inactivates a subset of Arf-GEFs, has been very useful for assessing the function of Golgi-localized Arfs. However, specific inhibitors to investigate the individual function of BFA-sensitive and insensitive Arf-GEFs are lacking. In recent years, specific screens have been developed, and new inhibitors with improved selectivity and potency to study cell functional responses regulated by BFA-sensitive and BFA-insensitive Arf pathways have been identified. These inhibitors have been instrumental for our understanding of the spatiotemporal activation of Arf proteins in cells and demonstrate the feasibility of developing small molecules interfering with Arf activation to prevent tumor invasion and metastasis.

Ranking of multidimensional drug profiling data by fractional-adjusted bi-partitional scores

Motivation: The recent development of high-throughput drug profiling (high content screening or HCS) provides a large amount of quantitative multidimensional data. Despite its potentials, it poses several challenges for academia and industry analysts alike. This is especially true for ranking the effectiveness of several drugs from many thousands of images directly. This paper introduces, for the first time, a new framework for automatically ordering the performance of drugs, called fractional adjusted bi-partitional score (FABS). This general strategy takes advantage of graph-based formulations and solutions and avoids many shortfalls of traditionally used methods in practice. We experimented with FABS framework by implementing it with a specific algorithm, a variant of normalized cut—normalized cut prime (FABS-NC^′), producing a ranking of drugs. This algorithm is known to run in polynomial time and therefore can scale well in high-throughput applications.

Results: We compare the performance of FABS-NC^′ to other methods that could be used for drugs ranking. We devise two variants of the FABS algorithm: FABS-SVM that utilizes support vector machine (SVM) as black box, and FABS-Spectral that utilizes the eigenvector technique (spectral) as black box. We compare the performance of FABS-NC^′ also to three other methods that have been previously considered: center ranking (Center), PCA ranking (PCA), and graph transition energy method (GTEM). The conclusion is encouraging: FABS-NC^′ consistently outperforms all these five alternatives. FABS-SVM has the second best performance among these six methods, but is far behind FABS-NC^′: In some cases FABS-NC^′ produces over half correctly predicted ranking experiment trials than FABS-SVM.

Availability: The system and data for the evaluation reported here will be made available upon request to the authors after this manuscript is accepted for publication.

Contact:yxy128@berkeley.edu

Inhibitors of the cellular trafficking of ricin

Throughout the last decade, efforts to identify and develop effective inhibitors of the ricin toxin have focused on targeting its N-glycosidase activity. Alternatively, molecules disrupting intracellular trafficking have been shown to block ricin toxicity. Several research teams have recently developed high-throughput phenotypic screens for small molecules acting on the intracellular targets required for entry of ricin into cells. These screens have identified inhibitory compounds that can protect cells, and sometimes even animals against ricin. We review these newly discovered cellular inhibitors of ricin intoxication, discuss the advantages and drawbacks of chemical-genetics approaches, and address the issues to be resolved so that the therapeutic development of these small-molecule compounds can progress.

Identification and validation of bioactive small molecule target through phenotypic screening

For effective bioactive small molecule discovery and development into new therapeutic drug, a systematic screening and target protein identification is required. Different from the conventional screening system, herein phenotypic screening in combination with multi-omics-based target identification and validation (MOTIV) is introduced. First, phenotypic screening provides visual effect of bioactive small molecules in the cell or organism level. It is important to know the effect on the cell or organism level since small molecules affect not only a single target but the entire cellular mechanism within a cell or organism. Secondly, MOTIV provides systemic approach to discover the target protein of bioactive small molecule. With the chemical genomics and proteomics approach of target identification methods, various target protein candidates are identified. Then network analysis and validations of these candidates result in identifying the biologically relevant target protein and cellular mechanism. Overall, the combination of phenotypic screening and MOTIV will provide an effective approach to discover new bioactive small molecules and their target protein and mechanism identification.

INTEGRATED ALGORITHMS FOR IMAGE ANALYSIS AND CLASSIFICATION OF NUCLEAR DIVISION FOR HIGH-CONTENT CELL-CYCLE SCREENING

Advances in fluorescent probing and microscopic imaging technology provide important tools for biomedical research in studying the structures and functions of cells and molecules. Such studies require the processing and analysis of huge amounts of image data, and manual image analysis is very time consuming, thus costly, and also potentially inaccurate and poor reproducibility. In this paper, we present and combine several advanced computational, probabilistic, and fuzzy-set methods for the computerized classification of cell nuclei in different mitotic phases. We tested our proposed methods with real image sequences recorded over a period of twenty-four hours at every fifteen minutes with a time-lapse fluorescence microscopy. The experimental results have shown that the proposed methods are effective for the task of classification.

Comparison of segmentation algorithms for fluorescence microscopy images of cells

The analysis of fluorescence microscopy of cells often requires the determination of cell edges. This is typically done using segmentation techniques that separate the cell objects in an image from the surrounding background. This study compares segmentation results from nine different segmentation techniques applied to two different cell lines and five different sets of imaging conditions. Significant variability in the results of segmentation was observed that was due solely to differences in imaging conditions or applications of different algorithms. We quantified and compared the results with a novel bivariate similarity index metric that evaluates the degree of underestimating or overestimating a cell object. The results show that commonly used threshold-based segmentation techniques are less accurate than k-means clustering with multiple clusters. Segmentation accuracy varies with imaging conditions that determine the sharpness of cell edges and with geometric features of a cell. Based on this observation, we propose a method that quantifies cell edge character to provide an estimate of how accurately an algorithm will perform. The results of this study will assist the development of criteria for evaluating interlaboratory comparability.

A quantitative, facile, and high-throughput image-based cell migration method is a robust alternative to the scratch assay

Cell migration is a key phenotype for a number of therapeutically important biological responses, including angiogenesis. A commonly used method to assess cell migration is the scratch assay, which measures the movement of cells into a wound made by physically scoring a confluent cell monolayer to create an area devoid of cells. Although this method has been adequate for qualitative characterization of migration inhibitors, it does not provide the highly reproducible results required for quantitative compound structure-activity relationship evaluation because of the inconsistent size and placement of the wound area within the microplate well. The Oris™ Cell Migration Assay presents a superior alternative to the scratch assay, permitting formation of precisely placed and homogeneously sized cell-free areas into which migration can occur without releasing factors from wounded or dead cells or damaging the underlying extracellular matrix. Herein the authors compare results from the scratch and Oris™ cell migration assays using an endothelial progenitor cell line and the Src kinase inhibitor dasatinib. They find that using the Acumen™ Explorer laser microplate cytometer in combination with the Oris™ Cell Migration Assay plate provides a robust, efficient, and cost-effective cell migration assay exhibiting excellent signal to noise, plate uniformity, and statistical validation metrics.

Phenotype recognition with combined features and random subspace classifier ensemble

Background

Automated, image based high-content screening is a fundamental tool for discovery in biological science. Modern robotic fluorescence microscopes are able to capture thousands of images from massively parallel experiments such as RNA interference (RNAi) or small-molecule screens. As such, efficient computational methods are required for automatic cellular phenotype identification capable of dealing with large image data sets. In this paper we investigated an efficient method for the extraction of quantitative features from images by combining second order statistics, or Haralick features, with curvelet transform. A random subspace based classifier ensemble with multiple layer perceptron (MLP) as the base classifier was then exploited for classification. Haralick features estimate image properties related to second-order statistics based on the grey level co-occurrence matrix (GLCM), which has been extensively used for various image processing applications. The curvelet transform has a more sparse representation of the image than wavelet, thus offering a description with higher time frequency resolution and high degree of directionality and anisotropy, which is particularly appropriate for many images rich with edges and curves. A combined feature description from Haralick feature and curvelet transform can further increase the accuracy of classification by taking their complementary information. We then investigate the applicability of the random subspace (RS) ensemble method for phenotype classification based on microscopy images. A base classifier is trained with a RS sampled subset of the original feature set and the ensemble assigns a class label by majority voting.

Results

Experimental results on the phenotype recognition from three benchmarking image sets including HeLa, CHO and RNAi show the effectiveness of the proposed approach. The combined feature is better than any individual one in the classification accuracy. The ensemble model produces better classification performance compared to the component neural networks trained. For the three images sets HeLa, CHO and RNAi, the Random Subspace Ensembles offers the classification rates 91.20%, 98.86% and 91.03% respectively, which compares sharply with the published result 84%, 93% and 82% from a multi-purpose image classifier WND-CHARM which applied wavelet transforms and other feature extraction methods. We investigated the problem of estimation of ensemble parameters and found that satisfactory performance improvement could be brought by a relative medium dimensionality of feature subsets and small ensemble size.

Conclusions

The characteristics of curvelet transform of being multiscale and multidirectional suit the description of microscopy images very well. It is empirically demonstrated that the curvelet-based feature is clearly preferred to wavelet-based feature for bioimage descriptions. The random subspace ensemble of MLPs is much better than a number of commonly applied multi-class classifiers in the investigated application of phenotype recognition.

Cell migration and invasion assays as tools for drug discovery

Cell migration and invasion are processes that offer rich targets for intervention in key physiologic and pathologic phenomena such as wound healing and cancer metastasis. With the advent of high-throughput and high content imaging systems, there has been a movement towards the use of physiologically relevant cell-based assays earlier in the testing paradigm. This allows more effective identification of lead compounds and recognition of undesirable effects sooner in the drug discovery screening process. This article will review the effective use of several principle formats for studying cell motility: scratch assays, transmembrane assays, microfluidic devices and cell exclusion zone assays.

Initial impact of the sequencing of the human genome

The sequence of the human genome has dramatically accelerated biomedical research. Here I explore its impact, in the decade since its publication, on our understanding of the biological functions encoded in the genome, on the biological basis of inherited diseases and cancer, and on the evolution and history of the human species. I also discuss the road ahead in fulfilling the promise of genomics for medicine.

Primary cells and stem cells in drug discovery: emerging tools for high-throughput screening

Many drug discovery screening programs employ immortalized cells, recombinantly engineered to express a defined molecular target. Several technologies are now emerging that render it feasible to employ more physiologically, and clinically relevant, cell phenotypes. Consequently, numerous approaches use primary cells, which retain many functions seen in vivo, as well as endogenously expressing the target of interest. Furthermore, stem cells, of either embryonic or adult origin, as well as those derived from differentiated cells, are now finding a place in drug discovery. Collectively, these cells are expanding the utility of authentic human cells, either as screening tools or as therapeutics, as well as providing cells derived directly from patients. Nonetheless, the growing use of phenotypically relevant cells (including primary cells or stem cells) is not without technical difficulties, particularly when their envisioned use lies in high-throughput screening (HTS) protocols. In particular, the limited availability of homogeneous primary or stem cell populations for HTS mandates that novel technologies be developed to accelerate their adoption. These technologies include detection of responses with very few cells as well as protocols to generate cell lines in abundant, homogeneous populations. In parallel, the growing use of changes in cell phenotype as the assay readout is driving greater use of high-throughput imaging techniques in screening. Taken together, the greater availability of novel primary and stem cell phenotypes as well as new detection technologies is heralding a new era of cellular screening. This convergence offers unique opportunities to identify drug candidates for disorders at which few therapeutics are presently available.

In vivo imaging in cancer

Imaging has become an indispensable tool in the study of cancer biology and in clinical prognosis and treatment. The rapid advances in high resolution fluorescent imaging at single cell level and MR/PET/CT image registration, combined with new molecular probes of cell types and metabolic states, will allow the physical scales imaged by each to be bridged. This holds the promise of translation of basic science insights at the single cell level to clinical application. In this article, we describe the recent advances in imaging at the macro- and micro-scale and how these advances are synergistic with new imaging agents, reporters, and labeling schemes. Examples of new insights derived from the different scales of imaging and relevant probes are discussed in the context of cancer progression and metastasis.

Image analysis benchmarking methods for high-content screen design

The recent development of complex chemical and small interfering RNA (siRNA) collections has enabled large-scale cell-based phenotypic screening. High-content and high-throughput imaging are widely used methods to record phenotypic data after chemical and small interfering RNA treatment, and numerous image processing and analysis methods have been used to quantify these phenotypes. Currently, there are no standardized methods for evaluating the effectiveness of new and existing image processing and analysis tools for an arbitrary screening problem. We generated a series of benchmarking images that represent commonly encountered variation in high-throughput screening data and used these image standards to evaluate the robustness of five different image analysis methods to changes in signal-to-noise ratio, focal plane, cell density and phenotype strength. The analysis methods that were most reliable, in the presence of experimental variation, required few cells to accurately distinguish phenotypic changes between control and experimental data sets. We conclude that by applying these simple benchmarking principles an a priori estimate of the image acquisition requirements for phenotypic analysis can be made before initiating an image-based screen. Application of this benchmarking methodology provides a mechanism to significantly reduce data acquisition and analysis burdens and to improve data quality and information content.

Targeting lymphatic vessel functions through tyrosine kinases

The lymphatic vascular system is actively involved in tissue fluid homeostasis, immune surveillance and fatty acid transport. Pathological conditions can arise from injury to the lymphatics, or they can be recruited in the context of cancer to facilitate metastasis. Protein tyrosine kinases are central players in signal transduction networks and regulation of cell behavior. In the lymphatic endothelium, tyrosine kinases are involved in processes such as the maintenance of existing lymphatic vessels, growth and maturation of new vessels and modulation of their identity and function. As such, they are attractive targets for both existing inhibitors and the development of new inhibitors which affect lymphangiogenesis in pathological states such as cancer. RNAi screening provides an opportunity to identify the functional role of tyrosine kinases in the lymphatics. This review will discuss the role of tyrosine kinases in lymphatic biology and the potential use of inhibitors for anti-lymphangiogenic therapy.

In vitro and in vivo high-throughput assays for the testing of anti-Trypanosoma cruzi compounds

BACKGROUND

The two available drugs for treatment of T. cruzi infection, nifurtimox and benznidazole (BZ), have potential toxic side effects and variable efficacy, contributing to their low rate of use. With scant economic resources available for antiparasitic drug discovery and development, inexpensive, high-throughput and in vivo assays to screen potential new drugs and existing compound libraries are essential.

METHODS

In this work, we describe the development and validation of improved methods to test anti-T. cruzi compounds in vitro and in vivo using parasite lines expressing the firefly luciferase (luc) or the tandem tomato fluorescent protein (tdTomato). For in vitro assays, the change in fluorescence intensity of tdTomato-expressing lines was measured as an indicator of parasite replication daily for 4 days and this method was used to identify compounds with IC(50) lower than that of BZ.

FINDINGS

This method was highly reproducible and had the added advantage of requiring relatively low numbers of parasites and no additional indicator reagents, enzymatic post-processes or laborious visual counting. In vivo, mice were infected in the footpads with fluorescent or bioluminescent parasites and the signal intensity was measured as a surrogate of parasite load at the site of infection before and after initiation of drug treatment. Importantly, the efficacy of various drugs as determined in this short-term (<2 weeks) assay mirrored that of a 40 day treatment course.

CONCLUSION

These methods should make feasible broader and higher-throughput screening programs needed to identify potential new drugs for the treatment of T. cruzi infection and for their rapid validation in vivo.

Trypanoside, anti-tuberculosis, leishmanicidal, and cytotoxic activities of tetrahydrobenzothienopyrimidines

The synthesis of 2-(5,6,7,8-tetrahydro[1]benzothieno[2,3-d]pyrimidin-4-yl)hydrazone-derivatives (BTPs) and their in vitro evaluation against Trypanosoma cruzi trypomastigotes, Mycobacterium tuberculosis, Leishmania amazonensis axenic amastigotes, and six human cancer cell lines is described. The in vivo activity of the most active and least toxic compounds against T. cruzi and L. amazonensis was also studied. BTPs constitute a new family of drug leads with potential activity against infectious diseases. Due to their drug-like properties, this series of compounds can potentially serve as templates for future drug-optimization and drug-development efforts for use as therapeutic agents in developing countries.

A novel wounding device suitable for quantitative biochemical analysis of wound healing and regeneration of cultured epithelium

We describe the fabrication and use of an in vitro wounding device that denudes cultured epithelium in patterns designed to leave behind strips or islands of cells sufficiently narrow or small to ensure that all the remaining cells become rapidly activated and then migrate, dedifferentiate, and proliferate in near synchrony. The design ensures that signals specific to regenerating cells do not become diluted by quiescent differentiated cells that are not affected by wound-induced activation. The device consists of a flat circular disk of rubber, engraved to produce alternating ridges and grooves in patterns of concentric circles or parallel lines. The disk is mounted at the end of a pneumatically controlled piston assembly. Application of controlled pressure and circular or linear movement of the disk on cultures produced highly reproducible wounding patterns. The near-synchronous regenerative activity of cell bands or islands allowed the collection of samples large enough for biochemical studies to sensitively detect alterations involving mRNA for several early response genes and protein phosphorylation in major signaling pathways. The method is versatile, easy to use and reproducible, and should facilitate biochemical, proteomic, and genomic studies of wound-induced regeneration of cultured epithelium.

Chemical substructures that enrich for biological activity

MOTIVATION

Certain chemical substructures are present in many drugs. This has led to the claim of 'privileged' substructures which are predisposed to bioactivity. Because bias in screening library construction could explain this phenomenon, the existence of privilege has been controversial.

RESULTS

Using diverse phenotypic assays, we defined bioactivity for multiple compound libraries. Many substructures were associated with bioactivity even after accounting for substructure prevalence in the library, thus validating the privileged substructure concept. Determinations of privilege were confirmed in independent assays and libraries. Our analysis also revealed 'underprivileged' substructures and 'conditional privilege'-rules relating combinations of substructure to bioactivity. Most previously reported substructures have been flat aromatic ring systems. Although we validated such substructures, we also identified three-dimensional privileged substructures. Most privileged substructures display a wide variety of substituents suggesting an entropic mechanism of privilege. Compounds containing privileged substructures had a doubled rate of bioactivity, suggesting practical consequences for pharmaceutical discovery.

Identification of an anti-MRSA dihydrofolate reductase inhibitor from a diversity-oriented synthesis

The screening of a diversity-oriented synthesis library followed by structure-activity relationship investigations have led to the discovery of an anti-MRSA agent which operates as an inhibitor of Staphylococcus aureus dihydrofolate reductase.

A novel high-throughput assay for the quantitative assessment of receptor trafficking

1. Receptor transport between intracellular compartments has important consequences for receptor function and is an exciting area of current study. Existing methods for studying receptor trafficking often require labour-intensive techniques or are difficult to quantify reliably. We report a novel high-throughput method that uses automated imaging and analysis tools to accurately quantify cannabinoid CB1 receptor trafficking. 2. Haemagglutinin (HA)-tagged CB1 was stably expressed in HEK-293 cells and cell surface or total receptors were detected immunocytochemically. Images of receptor and nuclear staining were acquired with an automated fluorescent microscope (Discovery-1; Molecular Devices, Sunnyvale, CA, USA) and quantified at high throughput with MetaMorph (Molecular Devices) software. The 'Granularity' assay measured internalization by counting receptor clusters that appear during receptor endocytosis, a well-established approach. Our assay, referred to as 'Total Grey Value per Cell' (TGVC), measures the total fluorescence above background, normalized to cell count. 3. Incubation with the cannabinoid agonist HU-210 (100 nmol/L) resulted in rapid CB1 internalization, reaching a maximum within 20 min. Whether quantified by Granularity or TGVC, the time-course of endocytosis could be modelled with exponentially derived curves and with similar half-lives. We demonstrate the sensitivity of our TGVC method by measuring the concentration dependence of CB1 internalization and its versatility by measuring downregulation following chronic agonist exposure, whereby total CB1 was reduced to approximately 55% of basal after 3 h. 4. The TGVC quantification method described is efficient, accurate and versatile and is likely to provide a valuable tool in receptor trafficking studies.

An image score inference system for RNAi genome-wide screening based on fuzzy mixture regression modeling

With recent advances in fluorescence microscopy imaging techniques and methods of gene knock down by RNA interference (RNAi), genome-scale high-content screening (HCS) has emerged as a powerful approach to systematically identify all parts of complex biological processes. However, a critical barrier preventing fulfillment of the success is the lack of efficient and robust methods for automating RNAi image analysis and quantitative evaluation of the gene knock down effects on huge volume of HCS data. Facing such opportunities and challenges, we have started investigation of automatic methods towards the development of a fully automatic RNAi-HCS system. Particularly important are reliable approaches to cellular phenotype classification and image-based gene function estimation. We have developed a HCS analysis platform that consists of two main components: fluorescence image analysis and image scoring. For image analysis, we used a two-step enhanced watershed method to extract cellular boundaries from HCS images. Segmented cells were classified into several predefined phenotypes based on morphological and appearance features. Using statistical characteristics of the identified phenotypes as a quantitative description of the image, a score is generated that reflects gene function. Our scoring model integrates fuzzy gene class estimation and single regression models. The final functional score of an image was derived using the weighted combination of the inference from several support vector-based regression models. We validated our phenotype classification method and scoring system on our cellular phenotype and gene database with expert ground truth labeling. We built a database of high-content, 3-channel, fluorescence microscopy images of Drosophila Kc(167) cultured cells that were treated with RNAi to perturb gene function. The proposed informatics system for microscopy image analysis is tested on this database. Both of the two main components, automated phenotype classification and image scoring system, were evaluated. The robustness and efficiency of our system were validated in quantitatively predicting the biological relevance of genes.

Cellular phenotype recognition for high-content RNA interference genome-wide screening

Genome-wide, cell-based screens using high-content screening (HCS) techniques and automated fluorescence microscopy generate thousands of high-content images that contain an enormous wealth of cell biological information. Such screens are key to the analysis of basic cell biological principles, such as control of cell cycle and cell morphology. However, these screens will ultimately only shed light on human disease mechanisms and potential cures if the analysis can keep up with the generation of data. A fundamental step toward automated analysis of high-content screening is to construct a robust platform for automatic cellular phenotype identification. The authors present a framework, consisting of microscopic image segmentation and analysis components, for automatic recognition of cellular phenotypes in the context of the Rho family of small GTPases. To implicate genes involved in Rac signaling, RNA interference (RNAi) was used to perturb gene functions, and the corresponding cellular phenotypes were analyzed for changes. The data used in the experiments are high-content, 3-channel, fluorescence microscopy images of Drosophila Kc167 cultured cells stained with markers that allow visualization of DNA, polymerized actin filaments, and the constitutively activated Rho protein Rac(V12). The performance of this approach was tested using a cellular database that contained more than 1000 samples of 3 predefined cellular phenotypes, and the generalization error was estimated using a cross-validation technique. Moreover, the authors applied this approach to analyze the whole high-content fluorescence images of Drosophila cells for further HCS-based gene function analysis.

Confocal light absorption and scattering spectroscopic microscopy monitors organelles in live cells with no exogenous labels

This article reports the development of an optical imaging technique, confocal light absorption and scattering spectroscopic (CLASS) microscopy, capable of noninvasively determining the dimensions and other physical properties of single subcellular organelles. CLASS microscopy combines the principles of light-scattering spectroscopy (LSS) with confocal microscopy. LSS is an optical technique that relates the spectroscopic properties of light elastically scattered by small particles to their size, refractive index, and shape. The multispectral nature of LSS enables it to measure internal cell structures much smaller than the diffraction limit without damaging the cell or requiring exogenous markers, which could affect cell function. Scanning the confocal volume across the sample creates an image. CLASS microscopy approaches the accuracy of electron microscopy but is nondestructive and does not require the contrast agents common to optical microscopy. It provides unique capabilities to study functions of viable cells, which are beyond the capabilities of other techniques.

Chemical genetics: elucidating biological systems with small-molecule compounds

Chemical genetics employs diverse small-molecule compounds to elucidate biological processes in a manner analogous to the mutagenesis strategies at the core of classical genetics. Screening small-molecule libraries for compounds that induce a phenotype of interest represents the forward chemical genetic approach, whereas the reverse approach involves small molecules targeting a single protein. Here, we review key differences between the goals for small-molecule screening in industry versus academia, recent developments in high-throughput screening, and publicly available resources of compound collections, screening facilities, and databases. A particularly exciting outcome of a chemical genetic screen is the discovery of a previously unknown role for a protein in a pathway together with compounds that affect the function of that protein. In illustrative cases, such discoveries have led to progress toward therapeutic development and more commonly have increased the size of the small molecule "toolbox" available to the research community for the study of biological processes.

High-throughput screening of cellular features using high-resolution light-microscopy; Application for profiling drug effects on cell adhesion

High-resolution light-microscopy and high-throughput screening are two essential methodologies for characterizing cellular phenotypes. Optimally combining these methodologies in cell-based screening to test detailed molecular and cellular responses to multiple perturbations constitutes a major challenge. Here we describe the development and application of a screening microscope platform that automatically acquires and interprets sub-micron resolution images at fast rates. The analysis pipeline is based on the quantification of multiple subcellular features and statistical comparisons of their distributions in treated vs. control cells. Using this platform, we screened 2200 natural extracts for their effects on the fine structure and organization of focal adhesions. This screen identified 15 effective extracts whose fractionation and characterization were further analyzed using the same microscope system. The significance of combining resolution, throughput and multi-parametric analyses for biomedical research and drug discovery is discussed.

A Cheminformatic Toolkit for Mining Biomedical Knowledge

PURPOSE

Cheminformatics can be broadly defined to encompass any activity related to the application of information technology to the study of properties, effects and uses of chemical agents. One of the most important current challenges in cheminformatics is to allow researchers to search databases of biomedical knowledge, using chemical structures as input.

MATERIALS AND METHODS

An important step towards this goal was the establishment of PubChem, an open, centralized database of small molecules accessible through the World Wide Web. While PubChem is primarily intended to serve as a repository for high throughput screening data from federally-funded screening centers and academic research laboratories, the major impact of PubChem could also reside in its ability to serve as a chemical gateway to biomedical databases such as PubMed.

CONCLUSION

This article will review cheminformatic tools that can be applied to facilitate annotation of PubChem through links to the scientific literature; to integrate PubChem with transcriptomic, proteomic, and metabolomic datasets; to incorporate results of numerical simulations of physiological systems into PubChem annotation; and ultimately, to translate data of chemical genomics screening efforts into information that will benefit biomedical researchers and physician scientists across all therapeutic areas.

Automated neurite extraction using dynamic programming for high-throughput screening of neuron-based assays

High-throughput screening (HTS) of cell-based assays has recently emerged as an important tool of drug discovery. The analysis and modeling of HTS microscopy neuron images, however, is particularly challenging. In this paper we present a novel algorithm for extraction and quantification of neurite segments from HTS neuron images. The algorithm is designed to be able to detect and link neurites even with complex neuronal structures and of poor imaging quality. Our proposed algorithm automatically detects initial seed points on a set of grid lines and estimates the ending points of the neurite by iteratively tracing the centerline points along the line path representing the neurite segment. The live-wire method is then applied to link the seed points and the corresponding ending points using dynamic programming techniques, thus enabling the extraction of the centerlines of the neurite segments accurately and robustly against noise, discontinuity, and other image artifacts. A fast implementation of our algorithm using dynamic programming is also provided in the paper. Any thin neurite and its segments with low intensity contrast can be well preserved by detecting the starting and ending points of the neurite. All these properties make the proposed algorithm attractive for high-throughput screening of neuron-based assays.

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

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

High-throughput fluorescence microscopy for systems biology

In this post-genomic era, we need to define gene function on a genome-wide scale for model organisms and humans. The fundamental unit of biological processes is the cell. Among the most powerful tools to assay such processes in the physiological context of intact living cells are fluorescence microscopy and related imaging techniques. To enable these techniques to be applied to functional genomics experiments, fluorescence microscopy is making the transition to a quantitative and high-throughput technology.

Using High-Throughput Screening Data To Discriminate Compounds with Single-Target Effects from Those with Side Effects

The most desirable compound leads from high-throughput assays are those with novel biological activities resulting from their action on a single biological target. Valuable resources can be wasted on compound leads with significant 'side effects' on additional biological targets; therefore, technical refinements to identify compounds that primarily have effects resulting from a single target are needed. This study explores the use of multiple assays of a chemical library and a statistic based on entropy to identify lead compound classes that have patterns of assay activity resulting primarily from small molecule action on a single target. This statistic, called the coincidence score, discriminates with 88% accuracy compound classes known to act primarily on a single target from compound classes with significant side effects on nonhomologous targets. Furthermore, a significant number of the compound classes predicted to have primarily single-target effects contain known bioactive compounds. We also show that a compound's known biological target or mechanism of action can often be suggested by its pattern of activities in multiple assays.

Cellular imaging in drug discovery

Traditional screening paradigms often focus on single targets. To facilitate drug discovery in the more complex physiological environment of a cell or organism, powerful cellular imaging systems have been developed. The emergence of these detection technologies allows the quantitative analysis of cellular events and visualization of relevant cellular phenotypes. Cellular imaging facilitates the integration of complex biology into the screening process, and addresses both high-content and high-throughput needs. This review describes how cellular imaging technologies contribute to the drug discovery process.