Decoding phenotypic screening: A comparative analysis of image representations

Biomedical imaging techniques such as high content screening (HCS) are valuable for drug discovery, but high costs limit their use to pharmaceutical companies. To address this issue, The JUMP-CP consortium released a massive open image dataset of chemical and genetic perturbations, providing a valuable resource for deep learning research. In this work, we aim to utilize the JUMP-CP dataset to develop a universal representation model for HCS data, mainly data generated using U2OS cells and CellPainting protocol, using supervised and self-supervised learning approaches. We propose an evaluation protocol that assesses their performance on mode of action and property prediction tasks using a popular phenotypic screening dataset. Results show that the self-supervised approach that uses data from multiple consortium partners provides representation that is more robust to batch effects whilst simultaneously achieving performance on par with standard approaches. Together with other conclusions, it provides recommendations on the training strategy of a representation model for HCS images.

ZeBraInspector, a platform for the automated segmentation and analysis of body and brain volumes in whole 5 days post-fertilization zebrafish following simultaneous visualization with identical orientations

In recent years, the zebrafish has become a well-established laboratory model. We describe here the ZeBraInspector (ZBI) platform for high-content 3D imaging (HCI) of 5 days post-fertilization zebrafish eleuthero-embryos (EEs). This platform includes a mounting method based on 3D-printed stamps to create a grid of wells in an agarose cast, facilitating batch acquisitions with a fast-confocal laser scanning microscope. We describe reference labeling in cleared fish with a fluorescent lipophilic dye. Based on this labeling, the ZBI software registers. EE 3D images, making it possible to visualize numerous identically oriented EEs on a single screen, and to compare their morphologies and any fluorescent patterns at a glance. High-resolution 2D snapshots can be extracted. ZBI software is therefore useful for diverse high-content analyses (HCAs). Following automated segmentation of the lipophilic dye signal, the ZBI software performs volumetric analyses on whole EEs and their nervous system white matter. Through two examples, we illustrate the power of these analyses for obtaining statistically significant results from a small number of samples: the characterization of a phenotype associated with a neurodevelopmental mutation, and of the defects caused by treatments with a toxic anti-cancer compound.

Past and future of trypanosomatids high-throughput phenotypic screening

Diseases caused by trypanosomatid parasites affect millions of people mainly living in developing countries. Novel drugs are highly needed since there are no vaccines and available treatment has several limitations, such as resistance, low efficacy, and high toxicity. The drug discovery process is often analogous to finding a needle in the haystack. In the last decades a so-called rational drug design paradigm, heavily dependent on computational approaches, has promised to deliver new drugs in a more cost-effective way. Paradoxically however, the mainstay of these computational methods is data-driven, meaning they need activity data for new compounds to be generated and available in databases. Therefore, high-throughput screening (HTS) of compounds still is a much-needed exercise in drug discovery to fuel other rational approaches. In trypanosomatids, due to the scarcity of validated molecular targets and biological complexity of these parasites, phenotypic screening has become an essential tool for the discovery of new bioactive compounds. In this article we discuss the perspectives of phenotypic HTS for trypanosomatid drug discovery with emphasis on the role of image-based, high-content methods. We also propose an ideal cascade of assays for the identification of new drug candidates for clinical development using leishmaniasis as an example.

Deep Learning Approach for Discovery of In Silico Drugs for Combating COVID-19

Early diagnosis of pandemic diseases such as COVID-19 can prove beneficial in dealing with difficult situations and helping radiologists and other experts manage staffing more effectively. The application of deep learning techniques for genetics, microscopy, and drug discovery has created a global impact. It can enhance and speed up the process of medical research and development of vaccines, which is required for pandemics such as COVID-19. However, current drugs such as remdesivir and clinical trials of other chemical compounds have not shown many impressive results. Therefore, it can take more time to provide effective treatment or drugs. In this paper, a deep learning approach based on logistic regression, SVM, Random Forest, and QSAR modeling is suggested. QSAR modeling is done to find the drug targets with protein interaction along with the calculation of binding affinities. Then deep learning models were used for training the molecular descriptor dataset for the robust discovery of drugs and feature extraction for combating COVID-19. Results have shown more significant binding affinities (greater than −18) for many molecules that can be used to block the multiplication of SARS-CoV-2, responsible for COVID-19.

Technologies for High-Throughput Identification of Antibiotic Mechanism of Action

There are two main strategies for antibiotic discovery: target-based and phenotypic screening. The latter has been much more successful in delivering first-in-class antibiotics, despite the major bottleneck of delayed Mechanism-of-Action (MOA) identification. Although finding new antimicrobial compounds is a very challenging task, identifying their MOA has proven equally challenging. MOA identification is important because it is a great facilitator of lead optimization and improves the chances of commercialization. Moreover, the ability to rapidly detect MOA could enable a shift from an activity-based discovery paradigm towards a mechanism-based approach. This would allow to probe the grey chemical matter, an underexplored source of structural novelty. In this study we review techniques with throughput suitable to screen large libraries and sufficient sensitivity to distinguish MOA. In particular, the techniques used in chemical genetics (e.g., based on overexpression and knockout/knockdown collections), promoter-reporter libraries, transcriptomics (e.g., using microarrays and RNA sequencing), proteomics (e.g., either gel-based or gel-free techniques), metabolomics (e.g., resourcing to nuclear magnetic resonance or mass spectrometry techniques), bacterial cytological profiling, and vibrational spectroscopy (e.g., Fourier-transform infrared or Raman scattering spectroscopy) were discussed. Ultimately, new and reinvigorated phenotypic assays bring renewed hope in the discovery of a new generation of antibiotics.

Use of artificial intelligence to enhance phenotypic drug discovery

Research and development (R&D) productivity across the pharmaceutical industry has received close scrutiny over the past two decades, especially taking into consideration reports of attrition rates and the colossal cost for drug development. The respective merits of the two main drug discovery approaches, phenotypic and target based, have divided opinion across the research community, because each hold different advantages for identifying novel molecular entities with a successful path to the market. Nevertheless, both have low translatability in the clinic. Artificial intelligence (AI) and adoption of machine learning (ML) tools offer the promise of revolutionising drug development, and overcoming obstacles in the drug discovery pipeline. Here, we assess the potential of target-driven and phenotypic-based approaches and offer a holistic description of the current state of the field, from both a scientific and industry perspective. With the emerging partnerships between AI/ML and pharma still in their relative infancy, we investigate the potential and current limitations with a particular focus on phenotypic drug discovery. Finally, we emphasise the value of public-private partnerships (PPPs) and cross-disciplinary collaborations to foster innovation and facilitate efficient drug discovery programmes.

Targeting phosphatidylinositol 3-kinase gamma (PI3Kγ): Discovery and development of its selective inhibitors

Phosphatidylinositol 3-kinase gamma (PI3Kγ) has been regarded as a promising drug target for the treatment of advanced solid tumors, leukemia, lymphoma, and inflammatory and autoimmune diseases. However, the high level of structural conservation among the members of the PI3K family and the diverse physiological roles of Class I PI3K isoforms (α, β, δ, and γ) highlight the importance of isoform selectivity in the development of PI3Kγ inhibitors. In this review, we provide an overview of the structural features of PI3Kγ that influence γ-isoform selectivity and discuss the structure-selectivity-activity relationship of existing clinical PI3Kγ inhibitors. Additionally, we summarize the experimental and computational techniques utilized to identify PI3Kγ inhibitors. The insights gained so far could be used to overcome the main challenges in development and accelerate the discovery of PI3Kγ-selective inhibitors.

Single-Cell Resolution and Quantitation of Targeted Glucocorticoid Delivery in the Thymus

Glucocorticoids are lipid-soluble hormones that signal via the glucocorticoid receptor (GR), a ligand-dependent transcription factor. Circulating glucocorticoids derive from the adrenals, but it is now apparent that paracrine glucocorticoid signaling occurs in multiple tissues. Effective local glucocorticoid concentrations and whether glucocorticoid delivery can be targeted to specific cell subsets are unknown. We use fluorescence detection of chromatin-associated GRs as biosensors of ligand binding and observe signals corresponding to steroid concentrations over physiological ranges in vitro and in vivo. In the thymus, where thymic epithelial cell (TEC)-synthesized glucocorticoids antagonize negative selection, we find that CD4⁺CD8⁺TCR^hi cells, a small subset responding to self-antigens and undergoing selection, are specific targets of TEC-derived glucocorticoids and are exposed to 3-fold higher levels than other cells. These results demonstrate and quantitate targeted delivery of paracrine glucocorticoids. This approach may be used to assess in situ nuclear receptor signaling in a variety of physiological and pathological contexts.

Development of a 3-D Organoid System Using Human Induced Pluripotent Stem Cells to Model Idiopathic Autism

Autism spectrum condition (ASC) is a complex set of behavioral and neurological responses reflecting a likely interaction between autism susceptibility genes and the environment. Autism represents a spectrum in which heterogeneous genetic backgrounds are expressed with similar heterogeneity in the affected domains of communication, social interaction, and behavior. The impact of gene-environment interactions may also account for differences in underlying neurology and wide variation in observed behaviors. For these reasons, it has been difficult for geneticists and neuroscientists to build adequate systems to model the complex neurobiology causes of autism. In addition, the development of therapeutics for individuals with autism has been painstakingly slow, with most treatment options reduced to repurposed medications developed for other neurological diseases. Adequately developing therapeutics that are sensitive to the genetic and neurobiological diversity of individuals with autism necessitates personalized models of ASC that can capture some common pathways that reflect the neurophysiological and genetic backgrounds of varying individuals. Testing cohorts of individuals with and without autism for these potentially convergent pathways on a scalable platform for therapeutic development requires large numbers of samples from a diverse population. To date, human induced pluripotent stem cells (iPSCs) represent one of the best systems for conducting these types of assays in a clinically relevant and scalable way. The discovery of the four Yamanaka transcription factors (OCT3/4, SOX2, c-Myc, and KLF4) [1] allows for the induction of iPSCs from fibroblasts [2], peripheral blood mononuclear cells (PBMCs, i.e. lymphocytes and monocytes) [3, 4], or dental pulp cells [5] that retain the original genetics of the individual from which they were derived [6], making iPSCs a powerful tool to model neurophysiological conditions. iPSCs are a readily renewable cell type that can be developed on a small scale for boutique-style proof-of-principle phenotypic studies and scaled to an industrial level for drug screening and other high-content assays. This flexibility, along with the ability to represent the true genetic diversity of autism, underscores the importance of using iPSCs to model neurophysiological aspects of ASC.

Review of high-content screening applications in toxicology

High-content screening (HCS) technology combining automated microscopy and quantitative image analysis can address biological questions in academia and the pharmaceutical industry. Various HCS experimental applications have been utilized in the research field of in vitro toxicology. In this review, we describe several HCS application approaches used for studying the mechanism of compound toxicity, highlight some challenges faced in the toxicological community, and discuss the future directions of HCS in regards to new models, new reagents, data management, and informatics. Many specialized areas of toxicology including developmental toxicity, genotoxicity, developmental neurotoxicity/neurotoxicity, hepatotoxicity, cardiotoxicity, and nephrotoxicity will be examined. In addition, several newly developed cellular assay models including induced pluripotent stem cells (iPSCs), three-dimensional (3D) cell models, and tissues-on-a-chip will be discussed. New genome-editing technologies (e.g., CRISPR/Cas9), data analyzing tools for imaging, and coupling with high-content assays will be reviewed. Finally, the applications of machine learning to image processing will be explored. These new HCS approaches offer a huge step forward in dissecting biological processes, developing drugs, and making toxicology studies easier.

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.

High content screening for drug discovery from traditional Chinese medicine

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

CellProfiler and KNIME: Open-Source Tools for High-Content Screening

High-content screening (HCS) has established itself in the world of the pharmaceutical industry as an essential tool for drug discovery and drug development. HCS is currently starting to enter the academic world and might become a widely used technology. Given the diversity of problems tackled in academic research, HCS could experience some profound changes in the future, mainly with more imaging modalities and smart microscopes being developed. One of the limitations in the establishment of HCS in academia is flexibility and cost. Flexibility is important to be able to adapt the HCS setup to accommodate the multiple different assays typical of academia. Many cost factors cannot be avoided, but the costs of the software packages necessary to analyze large datasets can be reduced by using open-source software. We present and discuss the open-source software CellProfiler for image analysis and KNIME for data analysis and data mining that provide software solutions, which increase flexibility and keep costs low.

A novel inhibitor of inducible NOS dimerization protects against cytokine-induced rat beta cell dysfunction

BACKGROUND AND PURPOSE

Beta cell apoptosis is a major feature of type 1 diabetes, and pro-inflammatory cytokines are key drivers of the deterioration of beta cell mass through induction of apoptosis. Mitochondrial stress plays a critical role in mediating apoptosis by releasing cytochrome C into the cytoplasm, directly activating caspase-9 and its downstream signalling cascade. We aimed to identify new compounds that protect beta cells from cytokine-induced activation of the intrinsic (mitochondrial) pathway of apoptosis.

EXPERIMENTAL APPROACH

Diabetogenic media, composed of IL-1β, IFN-γ and high glucose, were used to induce mitochondrial stress in rat insulin-producing INS1E cells, and a high-content image-based screen of small molecule modulators of Casp9 pathway was performed.

KEY RESULTS

A novel small molecule, ATV399, was identified from a high-content image-based screen for compounds that inhibit cleaved caspase-9 activation and subsequent beta cell apoptosis induced by a combination of IL-1β, IFN-γ and high glucose, which together mimic the pathogenic diabetic milieu. Through medicinal chemistry optimization, potency was markedly improved (6-30 fold), with reduced inhibitory effects on CYP3A4. Improved analogues, such as CAT639, improved beta cell viability and insulin secretion in cytokine-treated rat insulin-producing INS1E cells and primary dispersed islet cells. Mechanistically, CAT639 reduced the production of NO by allosterically inhibiting dimerization of inducible NOS (iNOS) without affecting its mRNA levels.

CONCLUSION AND IMPLICATIONS

Taken together, these studies demonstrate a successful phenotypic screening campaign resulting in identification of an inhibitor of iNOS dimerization that protects beta cell viability and function through modulation of mitochondrial stress induced by cytokines.

Biochemistry and biomedicine of quantum dots: from biodetection to bioimaging, drug discovery, diagnostics, and therapy

According to recent research, nanotechnology based on quantum dots (QDs) has been widely applied in the field of bioimaging, drug delivery, and drug analysis. Therefore, it has become one of the major forces driving basic and applied research. The application of nanotechnology in bioimaging has been of concern. Through in vitro labeling, it was found that luminescent QDs possess many properties such as narrow emission, broad UV excitation, bright fluorescence, and high photostability. The QDs also show great potential in whole-body imaging. The QDs can be combined with biomolecules, and hence, they can be used for targeted drug delivery and diagnosis. The characteristics of QDs make them useful for application in pharmacy and pharmacology. This review focuses on various applications of QDs, especially in imaging, drug delivery, pharmaceutical analysis, photothermal therapy, biochips, and targeted surgery. Finally, conclusions are made by providing some critical challenges and a perspective of how this field can be expected to develop in the future.

STATEMENT OF SIGNIFICANCE

Quantum dots (QDs) is an emerging field of interdisciplinary subject that involves physics, chemistry, materialogy, biology, medicine, and so on. In addition, nanotechnology based on QDs has been applied in depth in biochemistry and biomedicine. Some forward-looking fields emphatically reflected in some extremely vital areas that possess inspiring potential applicable prospects, such as immunoassay, DNA analysis, biological monitoring, drug discovery, in vitro labelling, in vivo imaging, and tumor target are closely connected to human life and health and has been the top and forefront in science and technology to date. Furthermore, this review has not only involved the traditional biochemical detection but also particularly emphasized its potential applications in life science and biomedicine.

Live-Cell High Content Screening in Drug Development

In the past decade, automated microscopy has become an important tool for the drug discovery and development process. The establishment of imaging modalities as screening tools depended on technological breakthroughs in the domain of automated microscopy and automated image analysis. These types of assays are often referred to as high content screening or high content analysis (HCS/HCA). The driving force to adopt imaging for drug development is the quantity and quality of cellular information that can be collected and the enhanced physiological relevance of cellular screening compared to biochemical screening. Most imaging in drug development is performed on fixed cells as this allows uncoupling the preparation of the cells from the acquisition of the images. Live-cell imaging is technically challenging, but is very useful for many aspects of the drug development pipeline such as kinetic studies of compound mode of action or to analyze the motion of cellular components. Most vendors of HCS microscopy systems offer the option of environmental chambers and onboard pipetting on their platforms. This reflects the wish and desire of many customers to have the ability to perform live-cell assays on their HCS automated microscopes. This book chapter summarizes the challenges and advantages of live-cell imaging in drug discovery. Examples of applications are presented and the motivation to perform these assays in kinetic mode is discussed.

Target identification by image analysis

Covering: 1997 to the end of 2015Each biologically active compound induces phenotypic changes in target cells that are characteristic for its mode of action. These phenotypic alterations can be directly observed under the microscope or made visible by labelling structural elements or selected proteins of the cells with dyes. A comparison of the cellular phenotype induced by a compound of interest with the phenotypes of reference compounds with known cellular targets allows predicting its mode of action. While this approach has been successfully applied to the characterization of natural products based on a visual inspection of images, recent studies used automated microscopy and analysis software to increase speed and to reduce subjective interpretation. In this review, we give a general outline of the workflow for manual and automated image analysis, and we highlight natural products whose bacterial and eucaryotic targets could be identified through such approaches.

Anti-cancer agents in Saudi Arabian herbals revealed by automated high-content imaging

Natural products have been used for medical applications since ancient times. Commonly, natural products are structurally complex chemical compounds that efficiently interact with their biological targets, making them useful drug candidates in cancer therapy. Here, we used cell-based phenotypic profiling and image-based high-content screening to study the mode of action and potential cellular targets of plants historically used in Saudi Arabia’s traditional medicine. We compared the cytological profiles of fractions taken from Juniperus phoenicea (Arar), Anastatica hierochuntica (Kaff Maryam), and Citrullus colocynthis (Hanzal) with a set of reference compounds with established modes of action. Cluster analyses of the cytological profiles of the tested compounds suggested that these plants contain possible topoisomerase inhibitors that could be effective in cancer treatment. Using histone H2AX phosphorylation as a marker for DNA damage, we discovered that some of the compounds induced double-strand DNA breaks. Furthermore, chemical analysis of the active fraction isolated from Juniperus phoenicea revealed possible anti-cancer compounds. Our results demonstrate the usefulness of cell-based phenotypic screening of natural products to reveal their biological activities.

A method for characterizing phenotypic changes in highly variable cell populations and its application to high content screening of Arabidopsis thaliana protoplasts

Quantitative image analysis procedures are necessary for the automated discovery of effects of drug treatment in large collections of fluorescent micrographs. When compared to their mammalian counterparts, the effects of drug conditions on protein localization in plant species are poorly understood and underexplored. To investigate this relationship, we generated a large collection of images of single plant cells after various drug treatments. For this, protoplasts were isolated from six transgenic lines of A. thaliana expressing fluorescently tagged proteins. Eight drugs at three concentrations were applied to protoplast cultures followed by automated image acquisition. For image analysis, we developed a cell segmentation protocol for detecting drug effects using a Hough transform-based region of interest detector and a novel cross-channel texture feature descriptor. In order to determine treatment effects, we summarized differences between treated and untreated experiments with an L₁ Cramér-von Mises statistic. The distribution of these statistics across all pairs of treated and untreated replicates was compared to the variation within control replicates to determine the statistical significance of observed effects. Using this pipeline, we report the dose dependent drug effects in the first high-content Arabidopsis thaliana drug screen of its kind. These results can function as a baseline for comparison to other protein organization modeling approaches in plant cells. © 2017 International Society for Advancement of Cytometry.

Biologically Relevant Heterogeneity: Metrics and Practical Insights

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

A 1536 Colorimetric SPAP Reporter Assay: Comparison with 96- and 384-Well Formats

We report the successful miniaturization of a functional cell-based reporter gene assay. Utilizing interleukin-1beta (IL-1/β)-induced secreted placental alkaline phosphatase (SPAP)-catalyzed colorimetric readout, we reduced the assay volume to 10 μl using a Greiner 1536-well microplate. Our experiences of assay development, liquid handling (using a Hydra® 96; Robbins Scientific, Sunnyvale, CA), and detection (using the SpectraImage and SpectraFluor-Plus plate readers, Tecan Austria GmbH, Grodig, Austria) in 1536 wells are discussed. The effect of a set of 1,280 compounds in this SPAP reporter assay were compared between 96-, 384-, and 1536-well formats and were shown to be very similar. We conclude that cell-based reporter gene assays using SPAP-catalyzed color readouts are sensitive and highly reproducible in 1536-well plates and should be considered as a cost-effective alternative to luciferase reporters for miniaturized assay formats. Finally, we review the prospects for the implementation of routine HTS in 1536-well plates based around the instrumentation investigated.

Large-scale image-based screening and profiling of cellular phenotypes

Cellular phenotypes are observable characteristics of cells resulting from the interactions of intrinsic and extrinsic chemical or biochemical factors. Image-based phenotypic screens under large numbers of basal or perturbed conditions can be used to study the influences of these factors on cellular phenotypes. Hundreds to thousands of phenotypic descriptors can also be quantified from the images of cells under each of these experimental conditions. Therefore, huge amounts of data can be generated, and the analysis of these data has become a major bottleneck in large-scale phenotypic screens. Here, we review current experimental and computational methods for large-scale image-based phenotypic screens. Our focus is on phenotypic profiling, a computational procedure for constructing quantitative and compact representations of cellular phenotypes based on the images collected in these screens. © 2016 International Society for Advancement of Cytometry.

Getting the whole picture: High content screening using three-dimensional cellular model systems and whole animal assays

Phenotypic or High Content Screening (HCS) is becoming more widely used for primary screening campaigns in drug discovery. Currently the vast majority of HCS campaigns are using cell lines grown in well-established monolayer cultures (2D tissue culture). There is widespread recognition that the more biologically relevant 3D tissue culture technologies such as spheroids and organoids and even whole animal assays will eventually be run as primary HCS. Upgrading the IT infrastructure to cope with the increase in data volumes requires investments in hardware (and software) and this will be manageable. However, the main bottleneck for the effective adoption and use of 3D tissue culture and whole animal assays in HCS is anticipated to be the development of software for the analysis of 3D images. In this review we summarize the current state of the available software and how they may be applied to analyzing 3D images obtained from a HCS campaign. © 2016 International Society for Advancement of Cytometry.

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

Deciphering the effects of compounds on molecular events within living cells is becoming an increasingly important component of drug discovery. In a model application of the industrial drug discovery process, the authors profiled a panel of 22 compounds using hierarchical cluster analysis of multiparameter high-content screening measurements from nearly 500,000 cells per microplate. RNAi protein knockdown methodology was used with high-content screening to dissect the effects of 2 anticancer drugs on multiple target activities. Camptothecin activated p53 in A549 lung carcinoma cells pretreated with scrambled siRNA, exhibited concentration-dependent cell cycle blocks, and induced moderate microtubule stabilization. Knockdown of camptothecin-induced p53 protein expression with p53 siRNA inhibited the G1/S blocking activity of the drug and diminished its microtubule-stabilizing activity. Paclitaxel activated p53 protein at low concentrations but exhibited G2/M cell cycle blocking activity at higher concentrations where microtubules were stabilized. In cells treated with p53 siRNA, paclitaxel failed to activate p53 protein, but the knockdown did not have a significant effect on the ability of paclitaxel to stabilize microtubules or induce a G2/M cell cycle block. Thus, this model application of the use of RNAi technology within the context of high-content screening shows the potential to provide massive amounts of combinatorial cell biological information on the temporal and spatial responses that cells mount to treatment by promising therapeutic candidates.

Identification of Gap Junction Blockers Using Automated Fluorescence Microscopy Imaging

Gap junctions coordinate electrical signals and facilitate metabolic synchronization between cells. In this study, the authors have developed a novel assay for the identification of gap junction blockers using fluorescence microscopy imaging-based high-content screening technology. In the assay, the communication between neighboring cells through gap junctions was measured by following the redistribution of a fluorescent marker. The movement of calcein dye from dye-loaded donor cells to dye-free acceptor cells through gap junctions overexpressed on cell surface membranes was monitored using automated fluorescence microscopy imaging in a high-throughput compatible format. The fluorescence imaging technology consisted of automated focusing, image acquisition, image processing, and data mining. The authors have successfully performed a high-throughput screening of a 486,000- compound program with this assay, and they were able to identify false positives without additional experiments. Selective and pharmacologically interesting compounds were identified for further optimization. ( Journal of Biomolecular Screening 2003:489-499)

Quantification of the Proliferation Index of Human Dermal Fibroblast Cultures with the ArrayScan™ High-Content Screening Reader

High-throughput cell-based assays are becoming a powerful approach in the drug discovery process. The ArrayScan™ high-content screening (HCS) reader is a cytometer based on a fully automated fluorescence microscope that is able to obtain quantitative information on the intensity and localization of fluorescence signals within single cells over a wide cell population. The aim of this work was to set up an automated HCS multiparameter analysis for the quantification of the in vitro proliferation index of normal human dermal fibroblast (NHDF) cultures. The authors stimulated starved NHDF with insulin-like growth factor-1, platelet-derived growth factor, epidermal growth factor, fibroblast growth factor, or serum, and they quantified the proliferation index by measuring the expression of Ki-67 antigen, the incorporation of bromodeoxyuridine (BrdU), and the phosphorylation of the retinoblastoma protein (pRb). This approach also allowed quantification of the mitotic index by phospho-histone H3 staining and the percentage of cells in the S-phase by BrdU incorporation. The proliferation data from the ArrayScan™ assays were validated by comparison with a reference enzyme-linked immunosorbent assay (ELISA) and by flow cytometry. The measured proliferation indices were highly reproducible in repeated measures and independent experiments. The authors therefore propose that the ArrayScan™ HCS system could be used for high-throughput multiparameter analysis and quantification of the proliferation of cellular cultures.

Morphology and Phenotype of Peripheral Erythrocytes of Fish: A Rapid Screening of Images by Using Software

Many information of biological study as stained cells analysis under microscope cannot be obtained rich information like detail morphology, shape, size, proper intensity etc. but image analysis software can easily be detected all these parameters within short duration. The cells types can be yeast cells to mammalian cells. An attempt has been made to detect cellular abnormalities from an image of metronidazole (MTZ) treated compared to control images of peripheral erythrocytes of fish by using non-commercial, open-source, CellProfiler (CP) image analysis software (Ver. 2.1.0). The comparative results were obtained after analysis the software. In conclusion, this image based screening of Giemsa stained fish erythrocytes can be a suitable tool in biological research for primary toxicity prediction at DNA level alongwith cellular phenotypes. Moreover, still suggestions are needed in relation to accuracy of present analysis for Giemsa stained fish erythrocytes because previous works have been carried out images of cells with fluorescence dye.

The development of high-content screening (HCS) technology and its importance to drug discovery

INTRODUCTION

High-content screening (HCS) was introduced about twenty years ago as a promising analytical approach to facilitate some critical aspects of drug discovery. Its application has spread progressively within the pharmaceutical industry and academia to the point that it today represents a fundamental tool in supporting drug discovery and development.

AREAS COVERED

Here, the authors review some of significant progress in the HCS field in terms of biological models and assay readouts. They highlight the importance of high-content screening in drug discovery, as testified by its numerous applications in a variety of therapeutic areas: oncology, infective diseases, cardiovascular and neurodegenerative diseases. They also dissect the role of HCS technology in different phases of the drug discovery pipeline: target identification, primary compound screening, secondary assays, mechanism of action studies and in vitro toxicology.

EXPERT OPINION

Recent advances in cellular assay technologies, such as the introduction of three-dimensional (3D) cultures, induced pluripotent stem cells (iPSCs) and genome editing technologies (e.g., CRISPR/Cas9), have tremendously expanded the potential of high-content assays to contribute to the drug discovery process. Increasingly predictive cellular models and readouts, together with the development of more sophisticated and affordable HCS readers, will further consolidate the role of HCS technology in drug discovery.

Building cell models and simulations from microscope images

The use of fluorescence microscopy has undergone a major revolution over the past twenty years, both with the development of dramatic new technologies and with the widespread adoption of image analysis and machine learning methods. Many open source software tools provide the ability to use these methods in a wide range of studies, and many molecular and cellular phenotypes can now be automatically distinguished. This article presents the next major challenge in microscopy automation, the creation of accurate models of cell organization directly from images, and reviews the progress that has been made towards this challenge.

Dynamic cytotoxic profiles of sulfur mustard in human dermal cells determined by multiparametric high-content analysis

Sulfur mustard (SM) is a well known chemical warfare agent that poses a major threat to military personnel and also populace. It targets multiple macromolecules, and its toxic effects are mediated by complex mechanisms. However, the sequence and manner of SM-induced cellular and molecular events underpinning the pathological processes are not fully elucidated. Effective therapeutic agents against SM poisoning are also lacking. The present study aimed to determine the dynamic cytotoxic profiles of SM in primary cultured human epidermal keratinocytes-fetal (HEK-f) and human dermal fibroblasts-adult (HDF-a) by establishing a high content analysis (HCA)-based multiparametric toxicity assay panel. SM was found to produce multiple, concentration-dependent cellular responses, including abnormal cellular morphology, cycle arrest, apoptosis, necrosis, mitochondrial membrane potential imbalance, increased membrane permeability, oxidative stress, DNA damage, and lysosome impairment. Time-course analysis indicated that the cellular and molecular responses related to the highly reactive targets of SM, such as glutathione depletion, reactive oxygen species release, DNA and lysosomal damage, and actin microfilament architecture modification, were congenerous initial events for SM injury. Moreover, this study demonstrated a novel finding that SM induced autophagy, and it was closely related to lysosome alterations in both cell types. Higher susceptibility of HEK-f cells to SM was associated with early lysosomal damage and decreased autophagy activity. Multiparametric HCA also revealed the concentration-dependent cytoprotective effect of hydroxychloroquine in HDF-a cells. The above results provided overall and objective evidence for elucidating the cytotoxic mechanism of SM, and also a good scientific base for further research on countermeasures against SM injury.

Visualizing quantitative microscopy data: History and challenges

Data visualization is a fundamental aspect of science. In the context of microscopy-based studies, visualization typically involves presentation of the images themselves. However, data visualization is challenging when microscopy experiments entail imaging of millions of cells, and complex cellular phenotypes are quantified in a high-content manner. Most well-established visualization tools are inappropriate for displaying high-content data, which has driven the development of new visualization methodology. In this review, we discuss how data has been visualized in both classical and high-content microscopy studies; as well as the advantages, and disadvantages, of different visualization methods.

Phenotypic Screening of Small-Molecule Inhibitors: Implications for Therapeutic Discovery and Drug Target Development in Traumatic Brain Injury

The inability of central nervous system (CNS) neurons to regenerate damaged axons and dendrites following traumatic brain injury (TBI) creates a substantial obstacle for functional recovery. Apoptotic cell death, deposition of scar tissue, and growth-repressive molecules produced by glia further complicate the problem and make it challenging for re-growing axons to extend across injury sites. To date, there are no approved drugs for the treatment of TBI, accentuating the need for relevant leads. Cell-based and organotypic bioassays can better mimic outcomes within the native CNS microenvironment than target-based screening methods and thus should speed the discovery of therapeutic agents that induce axon or dendrite regeneration. Additionally, when used to screen focused chemical libraries such as small-molecule protein kinase inhibitors, these assays can help elucidate molecular mechanisms involved in neurite outgrowth and regeneration as well as identify novel drug targets. Here, we describe a phenotypic cellular (high content) screening assay that utilizes brain-derived primary neurons for screening small-molecule chemical libraries.

The evolution of drug discovery: from phenotypes to targets, and back

Cumulative scientific and technological advances over the past two centuries have transformed drug discovery from a largely serendipitous process into the high tech pipelines of today.

Microfluidic Image Cytometry for Single-Cell Phenotyping of Human Pluripotent Stem Cells

A microfluidic human pluripotent stem cell (hPSC) array has been developed for robust and reproducible hPSC culture methods to assess chemically defined serum- and feeder-free culture conditions. This microfluidic platform, combined with image cytometry, enables the systematic analysis of multiple simultaneously detected marker expression in individual cells, for screening of various chemically defined media across hPSC lines, and the study of phenotypic responses.

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

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

Fluorescent protein biosensors applied to microphysiological systems

This mini-review discusses the evolution of fluorescence as a tool to study living cells and tissues in vitro and the present role of fluorescent protein biosensors (FPBs) in microphysiological systems (MPSs). FPBs allow the measurement of temporal and spatial dynamics of targeted cellular events involved in normal and perturbed cellular assay systems and MPSs in real time. FPBs evolved from fluorescent analog cytochemistry (FAC) that permitted the measurement of the dynamics of purified proteins covalently labeled with environmentally insensitive fluorescent dyes and then incorporated into living cells, as well as a large list of diffusible fluorescent probes engineered to measure environmental changes in living cells. In parallel, a wide range of fluorescence microscopy methods were developed to measure the chemical and molecular activities of the labeled cells, including ratio imaging, fluorescence lifetime, total internal reflection, 3D imaging, including super-resolution, as well as high-content screening. FPBs evolved from FAC by combining environmentally sensitive fluorescent dyes with proteins in order to monitor specific physiological events such as post-translational modifications, production of metabolites, changes in various ion concentrations, and the dynamic interaction of proteins with defined macromolecules in time and space within cells. Original FPBs involved the engineering of fluorescent dyes to sense specific activities when covalently attached to particular domains of the targeted protein. The subsequent development of fluorescent proteins (FPs), such as the green fluorescent protein, dramatically accelerated the adoption of studying living cells, since the genetic "labeling" of proteins became a relatively simple method that permitted the analysis of temporal-spatial dynamics of a wide range of proteins. Investigators subsequently engineered the fluorescence properties of the FPs for environmental sensitivity that, when combined with targeted proteins/peptides, created a new generation of FPBs. Examples of FPBs that are useful in MPS are presented, including the design, testing, and application in a liver MPS.

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

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

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

One of the greatest challenges in biomedical research, drug discovery and diagnostics is understanding how seemingly identical cells can respond differently to perturbagens including drugs for disease treatment. Although heterogeneity has become an accepted characteristic of a population of cells, in drug discovery it is not routinely evaluated or reported. The standard practice for cell-based, high content assays has been to assume a normal distribution and to report a well-to-well average value with a standard deviation. To address this important issue we sought to define a method that could be readily implemented to identify, quantify and characterize heterogeneity in cellular and small organism assays to guide decisions during drug discovery and experimental cell/tissue profiling. Our study revealed that heterogeneity can be effectively identified and quantified with three indices that indicate diversity, non-normality and percent outliers. The indices were evaluated using the induction and inhibition of STAT3 activation in five cell lines where the systems response including sample preparation and instrument performance were well characterized and controlled. These heterogeneity indices provide a standardized method that can easily be integrated into small and large scale screening or profiling projects to guide interpretation of the biology, as well as the development of therapeutics and diagnostics. Understanding the heterogeneity in the response to perturbagens will become a critical factor in designing strategies for the development of therapeutics including targeted polypharmacology.

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

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

Rapid Assessment and Visualization of Normality in High-Content and Other Cell-Level Data and Its Impact on the Interpretation of Experimental Results

When investigators monitor effects on a population of cells following a perturbation, these events rarely occur in a classical normal (or Gaussian) distribution. A normal distribution is, however, explicitly assumed for events within a single well, in which mean values per well are used as an assay metric and, in general, measures of assay robustness, such as the Z' score and the V factor. Such analysis is not possible for many technologies; however, high-content screening (HCS) measures events of individual cells, which are averaged over the well. These individual cell-level measurements may be analyzed separately. This study quantifies the extent of nonnormality in experimental samples and their effects on determining the EC50 of a test compound and the assay robustness statistics. The results, based on five sets of publicly available data, indicate that the Z' or V-factor score can be improved by as much as 0.44 more than standard calculations, and the EC50 of a dose-response curve can be lowered by as much as fivefold when nonparametric methods are used, but not all data sets show a significant improvement. The effect on analysis depends in part on whether the greatest shift from normality occurs in the upper or lower range of the dose-response curve.

Translational paradigms in pharmacology and drug discovery

The translational sciences represent the core element in enabling and utilizing the output from the biomedical sciences and to improving drug discovery metrics by reducing the attrition rate as compounds move from preclinical research to clinical proof of concept. Key to understanding the basis of disease causality and to developing therapeutics is an ability to accurately diagnose the disease and to identify and develop safe and effective therapeutics for its treatment. The former requires validated biomarkers and the latter, qualified targets. Progress has been hampered by semantic issues, specifically those that define the end product, and by scientific issues that include data reliability, an overt reductionistic cultural focus and a lack of hierarchically integrated data gathering and systematic analysis. A necessary framework for these activities is represented by the discipline of pharmacology, efforts and training in which require recognition and revitalization.

The fall and rise of pharmacology--(re-)defining the discipline?

Pharmacology is an integrative discipline that originated from activities, now nearly 7000 years old, to identify therapeutics from natural product sources. Research in the 19th Century that focused on the Law of Mass Action (LMA) demonstrated that compound effects were dose-/concentration-dependent eventually leading to the receptor concept, now a century old, that remains the key to understanding disease causality and drug action. As pharmacology evolved in the 20th Century through successive biochemical, molecular and genomic eras, the precision in understanding receptor function at the molecular level increased and while providing important insights, led to an overtly reductionistic emphasis. This resulted in the generation of data lacking physiological context that ignored the LMA and was not integrated at the tissue/whole organism level. As reductionism became a primary focus in biomedical research, it led to the fall of pharmacology. However, concerns regarding the disconnect between basic research efforts and the approval of new drugs to treat 21st Century disease tsunamis, e.g., neurodegeneration, metabolic syndrome, etc. has led to the reemergence of pharmacology, its rise, often in the semantic guise of systems biology. Against a background of limited training in pharmacology, this has resulted in issues in experimental replication with a bioinformatics emphasis that often has a limited relationship to reality. The integration of newer technologies within a pharmacological context where research is driven by testable hypotheses rather than technology, together with renewed efforts in teaching pharmacology, is anticipated to improve the focus and relevance of biomedical research and lead to novel therapeutics that will contain health care costs.

A kinome-wide siRNA screen identifies multiple roles for protein kinases in hypoxic stress adaptation, including roles for IRAK4 and GAK in protection against apoptosis in VHL-/- renal carcinoma cells, despite activation of the NF-κB pathway

Hypoxia induces changes to cancer cells that make them more resistant to treatment. We have looked at signaling pathways that facilitate these changes by screening the human kinome for effects on hypoxic responses in SW480 colon cancer cells. Hits identified in the screen were examined for effects on multiple molecular responses to hypoxia, including the endoplasmic reticulum stress and DNA damage responses in colon, melanoma, and renal cancer lines. To validate the hits from the small interfering RNA studies, we developed cell lines expressing stable short hairpin RNAs (shRNAs) in the A498 renal carcinoma cell line. Several lines, including those expressing shRNAs against DYRK1B, GAK, IHPK2, IRAK4, and MATK, showed an inability to form spheroid cultures. In addition, shRNAs targeting IRAK4 and GAK were incapable of 2D growth under anoxia. In the GAK shRNA-expressing line, nuclear factor-κB (NF-κB) was localized to the nucleus, but in the IRAK4 shRNA line, NF-κB levels were increased but the extent of nuclear localization was unchanged. Dominant negative mutants of IRAK4 and GAK also showed strong apoptotic effects in A498 cells under anoxia, supporting a direct link between these kinases and survival of the VHL(-/-) RCC line, which is typically highly resistant to hypoxic stress as a result of high and constitutive levels of Hif-1α.

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.

Update on in vitro cytotoxicity assays for drug development

BACKGROUND

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

OBJECTIVE

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

METHODS

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

RESULTS/CONCLUSION

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