Class A capsid assembly modulator apoptotic elimination of hepatocytes with high HBV core antigen level in vivo is dependent on de novo core protein translation

Capsid assembly is critical in the hepatitis B virus (HBV) life cycle, mediated by the viral core protein. Capsid assembly is the target for new anti-viral therapeutics known as capsid assembly modulators (CAMs) of which the CAM-aberrant (CAM-A) class induces aberrant shaped core protein structures and leads to hepatocyte cell death. This study aimed to identify the mechanism of action of CAM-A modulators leading to HBV-infected hepatocyte elimination where CAM-A-mediated hepatitis B surface antigen (HBsAg) reduction was evaluated in a stable HBV replicating cell line and in AAV-HBV-transduced C57BL/6, C57BL/6 SCID, and HBV-infected chimeric mice with humanized livers. Results showed that in vivo treatment with CAM-A modulators induced pronounced reductions in hepatitis B e antigen (HBeAg) and HBsAg, associated with a transient alanine amino transferase (ALT) increase. Both HBsAg and HBeAg reductions and ALT increase were delayed in C57BL/6 SCID and chimeric mice, suggesting that adaptive immune responses may indirectly contribute. However, CD8+ T cell depletion in transduced wild-type mice did not impact antigen reduction, indicating that CD8+ T cell responses are not essential. Transient ALT elevation in AAV-HBV-transduced mice coincided with a transient increase in endoplasmic reticulum stress and apoptosis markers, followed by detection of a proliferation marker. Microarray data revealed antigen presentation pathway (major histocompatibility complex class I molecules) upregulation, overlapping with the apoptosis. Combination treatment with HBV-specific siRNA demonstrated that CAM-A-mediated HBsAg reduction is dependent on de novo core protein translation. To conclude, CAM-A treatment eradicates HBV-infected hepatocytes with high core protein levels through the induction of apoptosis, which can be a promising approach as part of a regimen to achieve functional cure.

IMPORTANCE

Treatment with hepatitis B virus (HBV) capsid assembly modulators that induce the formation of aberrant HBV core protein structures (CAM-A) leads to programmed cell death, apoptosis, of HBV-infected hepatocytes and subsequent reduction of HBV antigens, which differentiates CAM-A from other CAMs. The effect is dependent on the de novo synthesis and high levels of core protein.

A flexible, image-based, high-throughput platform encompassing in-depth cell profiling to identify broad-spectrum coronavirus antivirals with limited off-target effects

The recent pandemic caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) posed a major threat to global health. Although the World Health Organization ended the public health emergency status, antiviral drugs are needed to address new variants of SARS-CoV-2 and future pandemics. To identify novel broad-spectrum coronavirus drugs, we developed a high-content imaging platform compatible with high-throughput screening. The platform is broadly applicable as it can be adapted to include various cell types, viruses, antibodies, and dyes. We demonstrated that the antiviral activity of compounds against SARS-CoV-2 variants (Omicron BA.5 and Omicron XBB.1.5), SARS-CoV, and human coronavirus 229E could easily be assessed. The inclusion of cellular dyes and immunostaining in combination with in-depth image analysis enabled us to identify compounds that induced undesirable phenotypes in host cells, such as changes in cell morphology or in lysosomal activity. With the platform, we screened ∼900K compounds and triaged hits, thereby identifying potential candidate compounds carrying broad-spectrum activity with limited off-target effects. The flexibility and early-stage identification of compounds with limited host cell effects provided by this high-content imaging platform can facilitate coronavirus drug discovery. We anticipate that its rapid deployability and fast turnaround can also be applied to combat future pandemics.

Remodeling oncogenic transcriptomes by small molecules targeting NONO

Much of the human proteome is involved in mRNA homeostasis, but most RNA-binding proteins lack chemical probes. Here we identify electrophilic small molecules that rapidly and stereoselectively decrease the expression of transcripts encoding the androgen receptor and its splice variants in prostate cancer cells. We show by chemical proteomics that the compounds engage C145 of the RNA-binding protein NONO. Broader profiling revealed that covalent NONO ligands suppress an array of cancer-relevant genes and impair cancer cell proliferation. Surprisingly, these effects were not observed in cells genetically disrupted for NONO, which were instead resistant to NONO ligands. Reintroduction of wild-type NONO, but not a C145S mutant, restored ligand sensitivity in NONO-disrupted cells. The ligands promoted NONO accumulation in nuclear foci and stabilized NONO-RNA interactions, supporting a trapping mechanism that may prevent compensatory action of paralog proteins PSPC1 and SFPQ. These findings show that NONO can be co-opted by covalent small molecules to suppress protumorigenic transcriptional networks.

Leveraging Cell Painting Images to Expand the Applicability Domain and Actively Improve Deep Learning Quantitative Structure-Activity Relationship Models

The search for chemical hit material is a lengthy and increasingly expensive drug discovery process. To improve it, ligand-based quantitative structure-activity relationship models have been broadly applied to optimize primary and secondary compound properties. Although these models can be deployed as early as the stage of molecule design, they have a limited applicability domain─if the structures of interest differ substantially from the chemical space on which the model was trained, a reliable prediction will not be possible. Image-informed ligand-based models partly solve this shortcoming by focusing on the phenotype of a cell caused by small molecules, rather than on their structure. While this enables chemical diversity expansion, it limits the application to compounds physically available and imaged. Here, we employ an active learning approach to capitalize on both of these methods' strengths and boost the model performance of a mitochondrial toxicity assay (Glu/Gal). Specifically, we used a phenotypic Cell Painting screen to build a chemistry-independent model and adopted the results as the main factor in selecting compounds for experimental testing. With the additional Glu/Gal annotation for selected compounds we were able to dramatically improve the chemistry-informed ligand-based model with respect to the increased recognition of compounds from a 10% broader chemical space.

Identification of Z-Tyr-Ala-CHN2, a Cathepsin L Inhibitor with Broad-Spectrum Cell-Specific Activity against Coronaviruses, including SARS-CoV-2

The ongoing COVID-19 pandemic, caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), is partly under control by vaccination. However, highly potent and safe antiviral drugs for SARS-CoV-2 are still needed to avoid development of severe COVID-19. We report the discovery of a small molecule, Z-Tyr-Ala-CHN2, which was identified in a cell-based antiviral screen. The molecule exerts sub-micromolar antiviral activity against SARS-CoV-2, SARS-CoV-1, and human coronavirus 229E. Time-of-addition studies reveal that Z-Tyr-Ala-CHN2 acts at the early phase of the infection cycle, which is in line with the observation that the molecule inhibits cathepsin L. This results in antiviral activity against SARS-CoV-2 in VeroE6, A549-hACE2, and HeLa-hACE2 cells, but not in Caco-2 cells or primary human nasal epithelial cells since the latter two cell types also permit entry via transmembrane protease serine subtype 2 (TMPRSS2). Given their cell-specific activity, cathepsin L inhibitors still need to prove their value in the clinic; nevertheless, the activity profile of Z-Tyr-Ala-CHN2 makes it an interesting tool compound for studying the biology of coronavirus entry and replication.

The Image Data Explorer: Interactive exploration of image-derived data

Many bioimage analysis projects produce quantitative descriptors of regions of interest in images. Associating these descriptors with visual characteristics of the objects they describe is a key step in understanding the data at hand. However, as many bioimage data and their analysis workflows are moving to the cloud, addressing interactive data exploration in remote environments has become a pressing issue. To address it, we developed the Image Data Explorer (IDE) as a web application that integrates interactive linked visualization of images and derived data points with exploratory data analysis methods, annotation, classification and feature selection functionalities. The IDE is written in R using the shiny framework. It can be easily deployed on a remote server or on a local computer. The IDE is available at https://git.embl.de/heriche/image-data-explorer and a cloud deployment is accessible at https://shiny-portal.embl.de/shinyapps/app/01_image-data-explorer.

Passive immunotherapy with a novel antibody against 3pE-modified Aβ demonstrates potential for enhanced efficacy and favorable safety in combination with BACE inhibitor treatment in plaque-depositing mice

The imbalance between production and clearance of amyloid β (Aβ) peptides and their resulting accumulation in the brain is an early and crucial step in the pathogenesis of Alzheimer's disease (AD). Therefore, Aβ is strongly positioned as a promising and extensively validated therapeutic target for AD. Investigational disease-modifying approaches aiming at reducing cerebral Aβ concentrations include prevention of de novo production of Aβ through inhibition of β-site amyloid precursor protein cleaving enzyme 1 (BACE1), and clearance of Aβ deposits via passive Aβ immunotherapy. We have developed a novel, high affinity antibody against Aβ peptides bearing a pyroglutamate residue at amino acid position 3 (3pE), an Aβ species abundantly present in plaque deposits in AD brains. Here, we describe the preclinical characterization of this antibody, and demonstrate a significant reduction in amyloid burden in the absence of microhemorrhages in different mouse models with established plaque deposition. Moreover, we combined antibody treatment with chronic BACE1 inhibitor treatment and demonstrate significant clearance of pre-existing amyloid deposits in transgenic mouse brain, without induction of microhemorrhages and other histopathological findings. Together, these data confirm significant potential for the 3pE-specific antibody to be developed as a passive immunotherapy approach that balances efficacy and safety. Moreover, our studies suggest further enhanced treatment efficacy and favorable safety after combination of the 3pE-specific antibody with BACE1 inhibitor treatment.

Development of a cellular high-content, immunofluorescent HBV core assay to identify novel capsid assembly modulators that induce the formation of aberrant HBV core structures

Hepatitis B Virus (HBV) core protein has multiple functions in the viral life cycle and is an attractive target for new anti-viral therapies. Capsid assembly modulators (CAMs) target the core protein and induce the formation of either morphologically normal (CAM-N) or aberrant structures (CAM-A), both devoid of genomic material. To date a diverse family of CAM-N chemotypes has been identified, but in contrast, described CAM-As are based on the heteroaryldihydropyrimidine (HAP) scaffold. We used the HBV-inducible HepG2.117 cell line with immunofluorescent labeling of HBV core to develop and validate a cellular high-content image-based assay where aggregated core structures are identified using image analysis spot texture features. Treatment with HAPs led to a dose- and time-dependent formation of aggregated core appearing as dot-like structures in the cytoplasm and nucleus. By combining a biochemical and cellular screening approach, a compound was identified as a novel non-HAP scaffold able to induce dose-dependent formation of aberrant core structures, which was confirmed by electron microscopy and native gel electrophoresis. This compound displayed anti-HBV activity in HepG2.117 cells, providing proof-of-concept for our screening approach. We believe our combined biochemical and cellular high-content screening method will aid in expanding the range of CAM-A chemotypes.

SpheroidJ: An Open-Source Set of Tools for Spheroid Segmentation

BACKGROUND AND OBJECTIVES

Spheroids are the most widely used 3D models for studying the effects of different micro-environmental characteristics on tumour behaviour, and for testing different preclinical and clinical treatments. In order to speed up the study of spheroids, imaging methods that automatically segment and measure spheroids are instrumental; and, several approaches for automatic segmentation of spheroid images exist in the literature. However, those methods fail to generalise to a diversity of experimental conditions. The aim of this work is the development of a set of tools for spheroid segmentation that works in a diversity of settings.

METHODS

In this work, we have tackled the spheroid segmentation task by first developing a generic segmentation algorithm that can be easily adapted to different scenarios. This generic algorithm has been employed to reduce the burden of annotating a dataset of images that, in turn, has been employed to train several deep learning architectures for semantic segmentation. Both our generic algorithm and the constructed deep learning models have been tested with several datasets of spheroid images where the spheroids were grown under several experimental conditions, and the images acquired using different equipment.

RESULTS

The developed generic algorithm can be particularised to different scenarios; however, those particular algorithms fail to generalise to different conditions. By contrast, the best deep learning model, constructed using the HRNet-Seg architecture, generalises properly to a diversity of scenarios. In order to facilitate the dissemination and use of our algorithms and models, we present SpheroidJ, a set of open-source tools for spheroid segmentation.

CONCLUSIONS

In this work, we have developed an algorithm and trained several models for spheroid segmentation that can be employed with images acquired under different conditions. Thanks to this work, the analysis of spheroids acquired under different conditions will be more reliable and comparable; and, the developed tools will help to advance our understanding of tumour behaviour.

Discovery of JNJ-63576253, a Next-Generation Androgen Receptor Antagonist Active Against Wild-Type and Clinically Relevant Ligand Binding Domain Mutations in Metastatic Castration-Resistant Prostate Cancer

Numerous mechanisms of resistance arise in response to treatment with second-generation androgen receptor (AR) pathway inhibitors in metastatic castration-resistant prostate cancer (mCRPC). Among these, point mutations in the ligand binding domain can transform antagonists into agonists, driving the disease through activation of AR signaling. To address this unmet need, we report the discovery of JNJ-63576253, a next-generation AR pathway inhibitor that potently abrogates AR signaling in models of human prostate adenocarcinoma. JNJ-63576253 is advancing as a clinical candidate with potential effectiveness in the subset of patients who do not respond to or are progressing while on second-generation AR-targeted therapeutics.

Tales of 1,008 small molecules: phenomic profiling through live-cell imaging in a panel of reporter cell lines

Phenomic profiles are high-dimensional sets of readouts that can comprehensively capture the biological impact of chemical and genetic perturbations in cellular assay systems. Phenomic profiling of compound libraries can be used for compound target identification or mechanism of action (MoA) prediction and other applications in drug discovery. To devise an economical set of phenomic profiling assays, we assembled a library of 1,008 approved drugs and well-characterized tool compounds manually annotated to 218 unique MoAs, and we profiled each compound at four concentrations in live-cell, high-content imaging screens against a panel of 15 reporter cell lines, which expressed a diverse set of fluorescent organelle and pathway markers in three distinct cell lineages. For 41 of 83 testable MoAs, phenomic profiles accurately ranked the reference compounds (AUC-ROC ≥ 0.9). MoAs could be better resolved by screening compounds at multiple concentrations than by including replicates at a single concentration. Screening additional cell lineages and fluorescent markers increased the number of distinguishable MoAs but this effect quickly plateaued. There remains a substantial number of MoAs that were hard to distinguish from others under the current study's conditions. We discuss ways to close this gap, which will inform the design of future phenomic profiling efforts.

Image-based high-content screening in drug discovery

While target-based drug discovery strategies rely on the precise knowledge of the identity and function of the drug targets, phenotypic drug discovery (PDD) approaches allow the identification of novel drugs based on knowledge of a distinct phenotype. Image-based high-content screening (HCS) is a potent PDD strategy that characterizes small-molecule effects through the quantification of features that depict cellular changes among or within cell populations, thereby generating valuable data sets for subsequent data analysis. However, these data can be complex, making image analysis from large HCS campaigns challenging. Technological advances in image acquisition, processing, and analysis as well as machine-learning (ML) approaches for the analysis of multidimensional data sets have rendered HCS as a viable technology for small-molecule drug discovery. Here, we discuss HCS concepts, current workflows as well as opportunities and challenges of image-based phenotypic screening and data analysis.

Phenotypic Image Analysis Software Tools for Exploring and Understanding Big Image Data from Cell-Based Assays

Phenotypic image analysis is the task of recognizing variations in cell properties using microscopic image data. These variations, produced through a complex web of interactions between genes and the environment, may hold the key to uncover important biological phenomena or to understand the response to a drug candidate. Today, phenotypic analysis is rarely performed completely by hand. The abundance of high-dimensional image data produced by modern high-throughput microscopes necessitates computational solutions. Over the past decade, a number of software tools have been developed to address this need. They use statistical learning methods to infer relationships between a cell's phenotype and data from the image. In this review, we examine the strengths and weaknesses of non-commercial phenotypic image analysis software, cover recent developments in the field, identify challenges, and give a perspective on future possibilities.

Genetically Engineered iPSC-Derived FTDP-17 MAPT Neurons Display Mutation-Specific Neurodegenerative and Neurodevelopmental Phenotypes

Tauopathies such as frontotemporal dementia (FTD) remain incurable to date, partially due to the lack of translational in vitro disease models. The MAPT gene, encoding the microtubule-associated protein tau, has been shown to play an important role in FTD pathogenesis. Therefore, we used zinc finger nucleases to introduce two MAPT mutations into healthy donor induced pluripotent stem cells (iPSCs). The IVS10+16 mutation increases the expression of 4R tau, while the P301S mutation is pro-aggregant. Whole-transcriptome analysis of MAPT IVS10+16 neurons reveals neuronal subtype differences, reduced neural progenitor proliferation potential, and aberrant WNT/SHH signaling. Notably, these neurodevelopmental phenotypes could be recapitulated in neurons from patients carrying the MAPT IVS10+16 mutation. Moreover, the additional pro-aggregant P301S mutation revealed additional phenotypes, such as an increased calcium burst frequency, reduced lysosomal acidity, tau oligomerization, and neurodegeneration. This series of iPSCs could serve as a platform to unravel a potential link between pathogenic 4R tau and FTD.

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Analysis of ZFN-engineered MAPT IVS10+16 with or without additional P301S mutation

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Neurodevelopmental phenotypes in ZFN and patient-derived MAPT IVS10+16 neurons

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Neurodegenerative phenotypes in MAPT IVS10+16/P301S double-mutant neurons

Protocols and characterization data for 2D, 3D, and slice-based tumor models from the PREDECT project

Two-dimensional (2D) culture of cancer cells in vitro does not recapitulate the three-dimensional (3D) architecture, heterogeneity and complexity of human tumors. More representative models are required that better reflect key aspects of tumor biology. These are essential studies of cancer biology and immunology as well as for target validation and drug discovery. The Innovative Medicines Initiative (IMI) consortium PREDECT (www.predect.eu) characterized in vitro models of three solid tumor types with the goal to capture elements of tumor complexity and heterogeneity. 2D culture and 3D mono- and stromal co-cultures of increasing complexity, and precision-cut tumor slice models were established. Robust protocols for the generation of these platforms are described. Tissue microarrays were prepared from all the models, permitting immunohistochemical analysis of individual cells, capturing heterogeneity. 3D cultures were also characterized using image analysis. Detailed step-by-step protocols, exemplary datasets from the 2D, 3D, and slice models, and refined analytical methods were established and are presented.

MightyScreen: An Open-Source Visualization Application for Screening Data Analysis

Screening is a methodology widely used in biological and biomedical research. There are numerous visualization methods to validate screening data quality but very few visualization applications capable of hit selection. Here, we present MightyScreen ( mightyscreen.net ), a novel web-based application designed for visual data evaluation as well as visual hit selection. We believe MightyScreen is an intuitive and interactive addition to conventional hit selection methods. We also provide study cases showing how MightyScreen is used to visually explore screening data and make hit selections.

Therapeutic efficacy of a respiratory syncytial virus fusion inhibitor

Respiratory syncytial virus is a major cause of acute lower respiratory tract infection in young children, immunocompromised adults, and the elderly. Intervention with small-molecule antivirals specific for respiratory syncytial virus presents an important therapeutic opportunity, but no such compounds are approved today. Here we report the structure of JNJ-53718678 bound to respiratory syncytial virus fusion (F) protein in its prefusion conformation, and we show that the potent nanomolar activity of JNJ-53718678, as well as the preliminary structure–activity relationship and the pharmaceutical optimization strategy of the series, are consistent with the binding mode of JNJ-53718678 and other respiratory syncytial virus fusion inhibitors. Oral treatment of neonatal lambs with JNJ-53718678, or with an equally active close analog, efficiently inhibits established acute lower respiratory tract infection in the animals, even when treatment is delayed until external signs of respiratory syncytial virus illness have become visible. Together, these data suggest that JNJ-53718678 is a promising candidate for further development as a potential therapeutic in patients at risk to develop respiratory syncytial virus acute lower respiratory tract infection.

Respiratory syncytial virus causes lung infections in children, immunocompromised adults, and in the elderly. Here the authors show that a chemical inhibitor to a viral fusion protein is effective in reducing viral titre and ameliorating infection in rodents and neonatal lambs.

High-throughput imaging: Focusing in on drug discovery in 3D

3D organotypic culture models such as organoids and multicellular tumor spheroids (MCTS) are becoming more widely used for drug discovery and toxicology screening. As a result, 3D culture technologies adapted for high-throughput screening formats are prevalent. While a multitude of assays have been reported and validated for high-throughput imaging (HTI) and high-content screening (HCS) for novel drug discovery and toxicology, limited HTI/HCS with large compound libraries have been reported. Nonetheless, 3D HTI instrumentation technology is advancing and this technology is now on the verge of allowing for 3D HCS of thousands of samples. This review focuses on the state-of-the-art high-throughput imaging systems, including hardware and software, and recent literature examples of 3D organotypic culture models employing this technology for drug discovery and toxicology screening.

Molecular mechanism of respiratory syncytial virus fusion inhibitors

Respiratory syncytial virus (RSV) is a leading cause of pneumonia and bronchiolitis in young children and the elderly. Therapeutic small molecules have been developed that bind the RSV F glycoprotein and inhibit membrane fusion, yet their binding sites and molecular mechanisms of action remain largely unknown. Here we show that these inhibitors bind to a three-fold-symmetric pocket within the central cavity of the metastable prefusion conformation of RSV F. Inhibitor binding stabilizes this conformation by tethering two regions that must undergo a structural rearrangement to facilitate membrane fusion. Inhibitor-escape mutations occur in residues that directly contact the inhibitors or are involved in the conformational rearrangements required to accommodate inhibitor binding. Resistant viruses do not propagate as well as wild-type RSV in vitro, indicating a fitness cost for inhibitor escape. Collectively, these findings provide new insight into class I viral fusion proteins and should facilitate development of optimal RSV fusion inhibitors.

High-throughput fluorescence imaging approaches for drug discovery using in vitro and in vivo three-dimensional models

INTRODUCTION

High-resolution microscopy using fluorescent probes is a powerful tool to investigate individual cell structure and function, cell subpopulations and mechanisms underlying cellular responses to drugs. Additionally, responses to drugs more closely resemble those seen in vivo when cells are physically connected in three-dimensional (3D) systems (either 3D cell cultures or whole organisms), as opposed to traditional monolayer cultures. Combined, the use of imaging-based 3D models in the early stages of drug development has the potential to generate biologically relevant data that will increase the likelihood of success for drug candidates in human studies.

AREAS COVERED

The authors discuss current methods for the culturing of cells in 3D as well as approaches for the imaging of whole-animal models and 3D cultures that are amenable to high-throughput settings and could be implemented to support drug discovery campaigns. Furthermore, they provide critical considerations when discussing imaging these 3D systems for high-throughput chemical screenings.

EXPERT OPINION

Despite widespread understanding of the limitations imposed by the two-dimensional versus the 3D cellular paradigm, imaging-based drug screening of 3D cellular models is still limited, with only a few screens found in the literature. Image acquisition in high throughput, accurate interpretation of fluorescent signal, and uptake of staining reagents can be challenging, as the samples are in essence large aggregates of cells. The authors recognize these shortcomings that need to be overcome before the field can accelerate the utilization of these technologies in large-scale chemical screens.

Flow Cytometry: Impact on Early Drug Discovery

Modern flow cytometers can make optical measurements of 10 or more parameters per cell at tens of thousands of cells per second and more than five orders of magnitude dynamic range. Although flow cytometry is used in most drug discovery stages, "sip-and-spit" sampling technology has restricted it to low-sample-throughput applications. The advent of HyperCyt sampling technology has recently made possible primary screening applications in which tens of thousands of compounds are analyzed per day. Target-multiplexing methodologies in combination with extended multiparameter analyses enable profiling of lead candidates early in the discovery process, when the greatest numbers of candidates are available for evaluation. The ability to sample small volumes with negligible waste reduces reagent costs, compound usage, and consumption of cells. Improved compound library formatting strategies can further extend primary screening opportunities when samples are scarce. Dozens of targets have been screened in 384- and 1536-well assay formats, predominantly in academic screening lab settings. In concert with commercial platform evolution and trending drug discovery strategies, HyperCyt-based systems are now finding their way into mainstream screening labs. Recent advances in flow-based imaging, mass spectrometry, and parallel sample processing promise dramatically expanded single-cell profiling capabilities to bolster systems-level approaches to drug discovery.

Innovation in academic chemical screening: filling the gaps in chemical biology

Academic screening centers across the world have endeavored to discover small molecules that can modulate biological systems. To increase the reach of functional-genomic and chemical screening programs, universities, research institutes, and governments have followed their industrial counterparts in adopting high-throughput paradigms. As academic screening efforts have steadily grown in scope and complexity, so have the ideas of what is possible with the union of technology and biology. This review addresses the recent conceptual and technological innovation that has been propelling academic screening into its own unique niche. In particular, high-content and whole-organism screening are changing how academics search for novel bioactive compounds. Importantly, we recognize examples of successful chemical probe development that have punctuated the changing technology landscape.

Quantitation of chronic and acute treatment effects on neuronal network activity using image and signal analysis: toward a high-content assay

Upon maturation, primary neuronal cultures form an interconnected network based on neurite outgrowth and synaptogenesis in which spontaneous electrical activity arises. Measurement of network activity allows quantification of neuronal health and maturation. A fluorescent indicator was used to monitor secondary calcium influxes after the occurrence of action potentials, allowing us to examine activity of hippocampal cultures via confocal live cell imaging. Subsequently, nuclear staining with DAPI allows accurate cell segmentation. To analyze the calcium recording in a robust, observer-independent manner, we implemented an automated image- and signal-processing algorithm and validated it against a visual, interactive procedure. Both methods yielded similar results on the emergence of synchronized activity and allowed robust quantitative measurement of acute and chronic modulation of drugs on network activity. Both the number of days in vitro (DIV) and neutralization of nerve growth factor (NGF) have a significant effect on synchronous burst frequency and correlation. Acute effects are demonstrated using 5-HT (serotonin) and ethylene glycol tetra-acetic acid. Automated analysis allowed measuring additional features, such as peak decay times and bursting frequency of individual neurons. Based on neuronal cell cultures in 96-well plates and accurate calcium recordings, the analysis method allows development of an integrated high-content screening assay. Because molecular biological techniques can be applied to assess the influence of genes on network activity, it is applicable for neurotoxicity or neurotrophics screening as well as development of in vitro disease models via, for example, pharmacologic manipulation or RNAi.

Light microscopy applications in systems biology: opportunities and challenges

Biological systems present multiple scales of complexity, ranging from molecules to entire populations. Light microscopy is one of the least invasive techniques used to access information from various biological scales in living cells. The combination of molecular biology and imaging provides a bottom-up tool for direct insight into how molecular processes work on a cellular scale. However, imaging can also be used as a top-down approach to study the behavior of a system without detailed prior knowledge about its underlying molecular mechanisms. In this review, we highlight the recent developments on microscopy-based systems analyses and discuss the complementary opportunities and different challenges with high-content screening and high-throughput imaging. Furthermore, we provide a comprehensive overview of the available platforms that can be used for image analysis, which enable community-driven efforts in the development of image-based systems biology.

Translation of a tumor microenvironment mimicking 3D tumor growth co-culture assay platform to high-content screening

For drug discovery, cell-based assays are becoming increasingly complex to mimic more realistically the nature of biological processes and their diversifications in diseases. Multicellular co-cultures embedded in a three-dimensional (3D) matrix have been explored in oncology to more closely approximate the physiology of the human tumor microenvironment. High-content analysis is the ideal technology to characterize these complex biological systems, although running such complex assays at higher throughput is a major endeavor. Here, we report on adapting a 3D tumor co-culture growth assay to automated microscopy, and we compare various imaging platforms (confocal vs. nonconfocal) with correlating automated image analysis solutions to identify optimal conditions and settings for future larger scaled screening campaigns. The optimized protocol has been validated in repeated runs where established anticancer drugs have been evaluated for performance in this innovative assay.

Profiling of toxicity and identification of distinct apoptosis profiles using a 384-well high-throughput flow cytometry screening platform

Methods and techniques used to detect apoptosis have benefited from advances in technologies such as flow cytometry. With a large arsenal of lasers, fluorescent labels, and readily accessible biological targets, it is possible to detect multiple targets with unique combinations of fluorescent spectral signatures from a single sample. Traditional flow cytometry has been limited as a screening tool as the sample throughput has been low, whereas the data analysis and generation of screening relevant results have been complex. The HTFC Screening System running ForeCyt software is an instrument platform designed to perform high-throughput, multiplexed screening with seamless transformation of flow cytometry data into screening hits. We report the results of a screen that simultaneously quantified caspase 3/7 activation, annexin V binding, cell viability, and mitochondrial integrity. Assay performance over 5 days demonstrated robustness, reliability, and performance of the assay. This system is high throughput in that a 384-well plate can be read and fully analyzed within 30 min and is sensitive with an assay window of at least 10-fold for all parameters and a Z' factor of ≥0.75 for all endpoints and time points. From a screen of 231 compounds, 11 representative toxicity profiles highlighting differential activation of apoptotic pathways were identified.