Profiling alveolar macrophage responses to inhaled compounds using in vitro high content image analysis

One of the main hurdles in the development of new inhaled medicines is the frequent observation of foamy macrophage (FM) responses in non-clinical studies in experimental animals, which raises safety concerns and hinders progress into clinical trials. We have investigated the potential of a novel multi-parameter high content image analysis (HCIA) assay as an in vitro safety screening tool to predict drug induced FM. Rat (NR8383) and human U937-derived alveolar macrophages were exposed in vitro to a panel of model compounds with different biological activity, including inhaled bronchodilators, inhaled corticosteroids (ICS), phospholipidosis inducers and proapoptotic agents. An HCIA was utilized to produce drug-induced cell response profiles based on individual cell health, morphology and lipid content parameters. The profiles of both rat and human macrophage cell lines differentiated between cell responses to marketed inhaled drugs and compounds known to induce phospholipidosis and apoptosis. Hierarchical clustering of the aggregated data allowed identification of distinct cell profiles in response to exposure to phospholipidosis and apoptosis inducers. Additionally, in NR8383 cell responses formed two distinct clusters, associated with increased vacuolation with or without lipid accumulation. U937 cells presented a similar trend but appeared less sensitive to drug exposure and presented a narrower range of responses. These results indicate that our multi-parameter HCIA assay is suitable to generate characteristic drug-induced macrophage response profiles, thus enabling differentiation of foamy macrophage phenotypes associated with phospholipidosis and apoptosis. This approach shows great potential as pre-clinical in vitro screening tool for safety assessment of candidate inhaled medicines.

Challenges and opportunities for modeling monogenic and complex disorders of the human retina via induced pluripotent stem cell technology

The human retina is a highly structured and complex neurosensory tissue central to perceiving and processing visual signals. In a healthy individual, the close interplay between the neuronal retina, the adjacent retinal pigment epithelium and the underlying blood supply, the choriocapillaris, is critical for maintaining eyesight over a lifetime. An impairment of this delicate and metabolically highly active system, caused by genetic alteration, environmental impact or both, results in a multitude of pathological phenotypes of the retina. Understanding and treating these disease processes are motivated by a marked medical need in young as well as in older patients. While naturally occurring or gene-manipulated animal models have been used successfully in ophthalmological research for many years, recent advances in induced pluripotent stem cell technology have opened up new avenues to generate patient-derived retinal model systems. Here, we explore to what extent these cellular models can be useful to mirror human pathologies in vitro ultimately allowing to analyze disease mechanisms and testing treatment options in the cell type of interest on an individual patient-specific genetic background.

Object detection networks and augmented reality for cellular detection in fluorescence microscopy

Object detection networks are high-performance algorithms famously applied to the task of identifying and localizing objects in photography images. We demonstrate their application for the classification and localization of cells in fluorescence microscopy by benchmarking four leading object detection algorithms across multiple challenging 2D microscopy datasets. Furthermore we develop and demonstrate an algorithm that can localize and image cells in 3D, in close to real time, at the microscope using widely available and inexpensive hardware. Furthermore, we exploit the fast processing of these networks and develop a simple and effective augmented reality (AR) system for fluorescence microscopy systems using a display screen and back-projection onto the eyepiece. We show that it is possible to achieve very high classification accuracy using datasets with as few as 26 images present. Using our approach, it is possible for relatively nonskilled users to automate detection of cell classes with a variety of appearances and enable new avenues for automation of fluorescence microscopy acquisition pipelines.

Multi-omics analysis identifies FoxO1 as a regulator of macrophage function through metabolic reprogramming

Macrophages are plastic cells that can switch among different states according to bioenergetic or biosynthetic requirements. Our previous work demonstrated that the transcription factor Forkhead Box Protein 1 (FoxO1) plays a pivotal role in regulating the function of macrophages, but the underlying mechanisms are still unclear. Here we identify FoxO1 as a regulator of macrophage function through metabolic reprogramming. Transcriptomic and proteomic analyses showed that the deficiency of FoxO1 results in an alternatively activated (M2) phenotype of macrophages, with lower expression of inflammatory response- and migration-associated genes. Using the high content screening and analysis technology, we found that deletion of FoxO1 in macrophages slows their migration rate and impairs their function to limit tumor cell growth in vitro. Next, we demonstrated that glycolysis is inhibited in FoxO1-deficient macrophages, which leads to the observed functional changes and the reduced tumor suppression capability. This prospective study shows that FoxO1 serves as a bridge between metabolism and macrophage function.

Automated screening of AURKA activity based on a genetically encoded FRET biosensor using fluorescence lifetime imaging microscopy

Fluorescence Lifetime Imaging Microscopy (FLIM) is a robust tool to measure Förster Resonance Energy Transfer (FRET) between two fluorescent proteins, mainly when using genetically-encoded FRET biosensors. It is then possible to monitor biological processes such as kinase activity with a good spatiotemporal resolution and accuracy. Therefore, it is of interest to improve this methodology for future high content screening purposes. We here implement a time-gated FLIM microscope that can image and quantify fluorescence lifetime with a higher speed than conventional techniques such as Time-Correlated Single Photon Counting (TCSPC). We then improve our system to perform automatic screen analysis in a 96-well plate format. Moreover, we use a FRET biosensor of AURKA activity, a mitotic kinase involved in several epithelial cancers. Our results show that our system is suitable to measure FRET within our biosensor paving the way to the screening of novel compounds, potentially allowing to find new inhibitors of AURKA activity.

A dataset of images and morphological profiles of 30 000 small-molecule treatments using the Cell Painting assay

Background

Large-scale image sets acquired by automated microscopy of perturbed samples enable a detailed comparison of cell states induced by each perturbation, such as a small molecule from a diverse library. Highly multiplexed measurements of cellular morphology can be extracted from each image and subsequently mined for a number of applications.

Findings

This microscopy dataset includes 919 265 five-channel fields of view, representing 30 616 tested compounds, available at “The Cell Image Library” (CIL) repository. It also includes data files containing morphological features derived from each cell in each image, both at the single-cell level and population-averaged (i.e., per-well) level; the image analysis workflows that generated the morphological features are also provided. Quality-control metrics are provided as metadata, indicating fields of view that are out-of-focus or containing highly fluorescent material or debris. Lastly, chemical annotations are supplied for the compound treatments applied.

Conclusions

Because computational algorithms and methods for handling single-cell morphological measurements are not yet routine, the dataset serves as a useful resource for the wider scientific community applying morphological (image-based) profiling. The dataset can be mined for many purposes, including small-molecule library enrichment and chemical mechanism-of-action studies, such as target identification. Integration with genetically perturbed datasets could enable identification of small-molecule mimetics of particular disease- or gene-related phenotypes that could be useful as probes or potential starting points for development of future therapeutics.

Determination of Hepatotoxicity in iPSC-Derived Hepatocytes by Multiplexed High Content Assays

We present here methods for assessing hepatotoxicity by high content imaging and image analysis. The assays focus on the characterization of toxic effects using a variety of phenotypic endpoint readouts. Multi-parametric automated image analysis is used in the protocols to increase assay sensitivity and provide important information about possible in vitro toxicity mechanisms. iPSC-derived hepatocytes were used as a model for the hepatotoxicity assays, but the methods would also be suitable for other liver toxicity cell models. The methods contain detailed step-by-step descriptions of the cell treatment, staining, image acquisition, and image analysis.

Quality Control for High-Throughput Imaging Experiments Using Machine Learning in Cellprofiler

Robust high-content screening of visual cellular phenotypes has been enabled by automated microscopy and quantitative image analysis. The identification and removal of common image-based aberrations is critical to the screening workflow. Out-of-focus images, debris, and auto-fluorescing samples can cause artifacts such as focus blur and image saturation, contaminating downstream analysis and impairing identification of subtle phenotypes. Here, we describe an automated quality control protocol implemented in validated open-source software, leveraging the suite of image-based measurements generated by CellProfiler and the machine-learning functionality of CellProfiler Analyst.

High-content drug screening for rare diseases

Per definition, rare diseases affect only a small number of subjects within a given population. Taken together however, they represent a considerable medical burden, which remains poorly addressed in terms of treatment. Compared to other diseases, obstacles to the development of therapies for rare diseases include less extensive physiopathology knowledge, limited number of patients to test treatments, and poor commercial interest from the industry. Recently, advances in high-throughput and high-content screening (HTS and HCS) have been fostered by the development of specific routines that use robot- and computer-assisted technologies to automatize tasks, allowing screening of a large number of compounds in a short period of time, using experimental model of diseases. These approaches are particularly relevant for drug repositioning in rare disease, which restricts the search to compounds that have already been tested in humans, thereby reducing the need for extensive preclinical tests. In the future, these same tools, combined with computational modeling and artificial neural network analyses, may also be used to predict individual clinical responses to drugs in a personalized medicine approach.

High throughput toxicity screening and intracellular detection of nanomaterials

With the growing numbers of nanomaterials (NMs), there is a great demand for rapid and reliable ways of testing NM safety—preferably using in vitro approaches, to avoid the ethical dilemmas associated with animal research. Data are needed for developing intelligent testing strategies for risk assessment of NMs, based on grouping and read‐across approaches. The adoption of high throughput screening (HTS) and high content analysis (HCA) for NM toxicity testing allows the testing of numerous materials at different concentrations and on different types of cells, reduces the effect of inter‐experimental variation, and makes substantial savings in time and cost. HTS/HCA approaches facilitate the classification of key biological indicators of NM‐cell interactions. Validation of in vitroHTS tests is required, taking account of relevance to in vivo results. HTS/HCA approaches are needed to assess dose‐ and time‐dependent toxicity, allowing prediction of in vivo adverse effects. Several HTS/HCA methods are being validated and applied for NM testing in the FP7 project NANoREG, including Label‐free cellular screening of NM uptake, HCA, High throughput flow cytometry, Impedance‐based monitoring, Multiplex analysis of secreted products, and genotoxicity methods—namely High throughput comet assay, High throughput in vitro micronucleus assay, and γH2AX assay. There are several technical challenges with HTS/HCA for NM testing, as toxicity screening needs to be coupled with characterization of NMs in exposure medium prior to the test; possible interference of NMs with HTS/HCA techniques is another concern. Advantages and challenges of HTS/HCA approaches in NM safety are discussed. WIREs Nanomed Nanobiotechnol 2017, 9:e1413. doi: 10.1002/wnan.1413

For further resources related to this article, please visit the WIREs website.

Quantitative and in toto imaging in ascidians: working toward an image-centric systems biology of chordate morphogenesis

Developmental biology relies heavily on microscopy to image the finely controlled cell behaviors that drive embryonic development. Most embryos are large enough that a field of view with the resolution and magnification needed to resolve single cells will not span more than a small region of the embryo. Ascidian embryos, however, are sufficiently small that they can be imaged in toto with fine subcellular detail using conventional microscopes and objectives. Unlike other model organisms with particularly small embryos, ascidians have a chordate embryonic body plan that includes a notochord, hollow dorsal neural tube, heart primordium and numerous other anatomical details conserved with the vertebrates. Here we compare the size and anatomy of ascidian embryos with those of more traditional model organisms, and relate these features to the capabilities of both conventional and exotic imaging methods. We review the emergence of Ciona and related ascidian species as model organisms for a new era of image-based developmental systems biology. We conclude by discussing some important challenges in ascidian imaging and image analysis that remain to be solved.

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.

High-throughput multi-parameter profiling of electrophysiological drug effects in human embryonic stem cell derived cardiomyocytes using multi-electrode arrays

Human stem cell derived cardiomyocytes (hESC-CM) provide a potential model for development of improved assays for pre-clinical predictive drug safety screening. We have used multi-electrode array (MEA) analysis of hESC-CM to generate multi-parameter data to profile drug impact on cardiomyocyte electrophysiology using a panel of 21 compounds active against key cardiac ion channels. Our study is the first to apply multi-parameter phenotypic profiling and clustering techniques commonly used for high-content imaging and microarray data to the analysis of electrophysiology data obtained by MEA analysis. Our data show good correlations with previous studies in stem cell derived cardiomyocytes and demonstrate improved specificity in compound risk assignment over convention single-parametric approaches. These analyses indicate great potential for multi-parameter MEA data acquired from hESC-CM to enable drug electrophysiological liabilities to be assessed in pre-clinical cardiotoxicity assays, facilitating informed decision making and liability management at the optimum point in drug development.

The Use of Antibodies in Small-Molecule Drug Discovery

Antibodies are powerful research tools that can be used in many areas of biology to probe, measure, and perturb various biological structures. Successful drug discovery is dependent on the correct identification of a target implicated in disease, coupled with the successful selection, optimization, and development of a candidate drug. Because of their specific binding characteristics, with regard to specificity, affinity, and avidity, coupled with their amenability to protein engineering, antibodies have become a key tool in drug discovery, enabling the quantification, localization, and modulation of proteins of interest. This review summarizes the application of antibodies and other protein affinity reagents as specific research tools within the drug discovery process.

Fast and accurate automated cell boundary determination for fluorescence microscopy

Detailed measurement of cell phenotype information from digital fluorescence images has the potential to greatly advance biomedicine in various disciplines such as patient diagnostics or drug screening. Yet, the complexity of cell conformations presents a major barrier preventing effective determination of cell boundaries, and introduces measurement error that propagates throughout subsequent assessment of cellular parameters and statistical analysis. State-of-the-art image segmentation techniques that require user-interaction, prolonged computation time and specialized training cannot adequately provide the support for high content platforms, which often sacrifice resolution to foster the speedy collection of massive amounts of cellular data. This work introduces a strategy that allows us to rapidly obtain accurate cell boundaries from digital fluorescent images in an automated format. Hence, this new method has broad applicability to promote biotechnology.

Quantitative high content imaging of cellular adaptive stress response pathways in toxicity for chemical safety assessment

Over the past decade, major leaps forward have been made on the mechanistic understanding and identification of adaptive stress response landscapes underlying toxic insult using transcriptomics approaches. However, for predictive purposes of adverse outcome several major limitations in these approaches exist. First, the limited number of samples that can be analyzed reduces the in depth analysis of concentration-time course relationships for toxic stress responses. Second these transcriptomics analysis have been based on the whole cell population, thereby inevitably preventing single cell analysis. Third, transcriptomics is based on the transcript level, totally ignoring (post)translational regulation. We believe these limitations are circumvented with the application of high content analysis of relevant toxicant-induced adaptive stress signaling pathways using bacterial artificial chromosome (BAC) green fluorescent protein (GFP) reporter cell-based assays. The goal is to establish a platform that incorporates all adaptive stress pathways that are relevant for toxicity, with a focus on drug-induced liver injury. In addition, cellular stress responses typically follow cell perturbations at the subcellular organelle level. Therefore, we complement our reporter line panel with reporters for specific organelle morphometry and function. Here, we review the approaches of high content imaging of cellular adaptive stress responses to chemicals and the application in the mechanistic understanding and prediction of chemical toxicity at a systems toxicology level.

An automated new technique for scoring the in vivo micronucleus assay with image analysis

The mammalian erythrocyte micronucleus assay is frequently used to assess chemical-induced damage to the chromosomes or the mitotic apparatus of erythroblasts. Because quantitative analysis of micronuclei by microscopy is time consuming and laborious, several automatic scoring methodologies with image analysis have been reported. However, there have been cases in which it was difficult to examine the proportion of polychromatic erythrocytes (PCEs) among total erythrocytes as an index for bone marrow (BM) toxicity, and sample slide preparation has proven to be laborious with existing automatic methods. We developed an automatic scoring system with image analysis for the rodent erythrocyte micronucleus assay using 12-well plates employing high-content screening analyser. In our method, micronucleated PCEs (MNPCEs), PCEs and erythrocytes were identified from three kinds of images: bright field image, fluorescence image with Hoechst 33342, and fluorescence image with propidium iodide. The frequencies of MNPCEs and PCEs were subsequently calculated. A comparison of automatic and manual scoring was carried out using BM and peripheral blood (PB) obtained from mice treated with stepwise doses of mitomycin C. The scores obtained by automatic analysis corresponded to those obtained by manual scoring; the frequencies of MNPCEs in BM and PB obtained by automatic scoring were 132 and 113%, respectively, of those obtained by manual scoring, and the corresponding frequencies of PCEs were 95 and 120%, respectively. Furthermore, we performed five repeats of the examinations of mouse BM and PB treated with mitomycin C or vinblastine sulphate and showed that automatic scoring was equivalent to manual scoring in reproducibility. Meanwhile, the scoring data obtained by manual scoring tended to vary among observers. These results suggest that our automatic scoring system with image analysis is superior to manual microscopy scoring in terms of speed and objectivity, comparable in reproducibility and useful for the in vivo micronucleus assay.

High-content assays for hepatotoxicity using induced pluripotent stem cell-derived cells

Development of predictive in vitro assays for early toxicity evaluation is extremely important for improving the drug development process and reducing drug attrition rates during clinical development. High-content imaging-based in vitro toxicity assays are emerging as efficient tools for safety and efficacy testing to improve drug development efficiency. In this report we have used an induced pluripotent stem cell (iPSC)-derived hepatocyte cell model having a primary tissue-like phenotype, unlimited availability, and the potential to compare cells from different individuals. We examined a number of assays and phenotypic markers and developed automated screening methods for assessing multiparameter readouts of general and mechanism-specific hepatotoxicity. Endpoints assessed were cell viability, nuclear shape, average and integrated cell area, mitochondrial membrane potential, phospholipid accumulation, cytoskeleton integrity, and apoptosis. We assayed compounds with known mechanisms of toxicity and also evaluated a diverse hepatotoxicity library of 240 compounds. We conclude that high-content automated screening assays using iPSC-derived hepatocytes are feasible, provide information about mechanisms of toxicity, and can facilitate the safety assessment of drugs and chemicals.

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

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

Whole-organ cell shape analysis reveals the developmental basis of ascidian notochord taper

Here we use in toto imaging together with computational segmentation and analysis methods to quantify the shape of every cell at multiple stages in the development of a simple organ: the notochord of the ascidian Ciona savignyi. We find that cell shape in the intercalated notochord depends strongly on anterior-posterior (AP) position, with cells in the middle of the notochord consistently wider than cells at the anterior or posterior. This morphological feature of having a tapered notochord is present in many chordates. We find that ascidian notochord taper involves three main mechanisms: Planar Cell Polarity (PCP) pathway-independent sibling cell volume asymmetries that precede notochord cell intercalation; the developmental timing of intercalation, which proceeds from the anterior and posterior towards the middle; and the differential rates of notochord cell narrowing after intercalation. A quantitative model shows how the morphology of an entire developing organ can be controlled by this small set of cellular mechanisms.

Designs and concept reliance of a fully automated high-content screening platform

High-content screening (HCS) is becoming an accepted platform in academic and industry screening labs and does require slightly different logistics for execution. To automate our stand-alone HCS microscopes, namely, an alpha IN Cell Analyzer 3000 (INCA3000), originally a Praelux unit hooked to a Hudson Plate Crane with a maximum capacity of 50 plates per run, and the IN Cell Analyzer 2000 (INCA2000), in which up to 320 plates could be fed per run using the Thermo Fisher Scientific Orbitor, we opted for a 4 m linear track system harboring both microscopes, plate washer, bulk dispensers, and a high-capacity incubator allowing us to perform both live and fixed cell-based assays while accessing both microscopes on deck. Considerations in design were given to the integration of the alpha INCA3000, a new gripper concept to access the onboard nest, and peripheral locations on deck to ensure a self-reliant system capable of achieving higher throughput. The resulting system, referred to as Hestia, has been fully operational since the new year, has an onboard capacity of 504 plates, and harbors the only fully automated alpha INCA3000 unit in the world.

The use of integrated and intelligent testing strategies in the prediction of toxic hazard and in risk assessment

There is increasing concern that insurmountable differences between humans and laboratory animals limit the relevance and reliability for hazard identification and risk assessment purposes of animal data produced by traditional toxicity test procedures. A way forward is offered by the emerging new technologies, which can be directly applied to human material or even to human beings themselves. This promises to revolutionise the evaluation of the safety of chemicals and chemical products of various kinds and, in particular, pharmaceuticals. The available and developing technologies are summarised and it is emphasised that they will need to be used selectively, in integrated and intelligent testing strategies, which, in addition to being scientifically sound, must be manageable and affordable. Examples are given of proposed testing strategies for general chemicals, cosmetic ingredients, candidate pharmaceuticals, inhaled substances, nanoparticles and neurotoxicity.

Technologies supporting analytical cytology: clinical, research and drug discovery applications

The tools and techniques developed for analytical cytology have become invaluable in expanding the development of cancer screening programs and biomarker discovery for personalized medicine. Detecting cellular, molecular, and functional changes of diseased tissue as defined by quantitative analytical methodologies has enhanced the field of medical diagnostics and prognostics. The focus of this review is to outline applications and recent technical advances in flow cytometry, laser scanning cytometry, image cytometry, and quantitative image analysis, as they pertain to clinical, research, and drug discovery applications.

Phaedra, a Protocol-Driven System for Analysis and Validation of High-Content Imaging and Flow Cytometry

High-content screening has brought new dimensions to cellular assays by generating rich data sets that characterize cell populations in great detail and detect subtle phenotypes. To derive relevant, reliable conclusions from these complex data, it is crucial to have informatics tools supporting quality control, data reduction, and data mining. These tools must reconcile the complexity of advanced analysis methods with the user-friendliness demanded by the user community. After review of existing applications, we realized the possibility of adding innovative new analysis options. Phaedra was developed to support workflows for drug screening and target discovery, interact with several laboratory information management systems, and process data generated by a range of techniques including high-content imaging, multicolor flow cytometry, and traditional high-throughput screening assays. The application is modular and flexible, with an interface that can be tuned to specific user roles. It offers user-friendly data visualization and reduction tools for HCS but also integrates Matlab for custom image analysis and the Konstanz Information Miner (KNIME) framework for data mining. Phaedra features efficient JPEG2000 compression and full drill-down functionality from dose-response curves down to individual cells, with exclusion and annotation options, cell classification, statistical quality controls, and reporting.

High content screening in neurodegenerative diseases

The functional annotation of genomes, construction of molecular networks and novel drug target identification, are important challenges that need to be addressed as a matter of great urgency^1-4. Multiple complementary 'omics' approaches have provided clues as to the genetic risk factors and pathogenic mechanisms underlying numerous neurodegenerative diseases, but most findings still require functional validation⁵. For example, a recent genome wide association study for Parkinson's Disease (PD), identified many new loci as risk factors for the disease, but the underlying causative variant(s) or pathogenic mechanism is not known^{6, 7}. As each associated region can contain several genes, the functional evaluation of each of the genes on phenotypes associated with the disease, using traditional cell biology techniques would take too long.

There is also a need to understand the molecular networks that link genetic mutations to the phenotypes they cause. It is expected that disease phenotypes are the result of multiple interactions that have been disrupted. Reconstruction of these networks using traditional molecular methods would be time consuming. Moreover, network predictions from independent studies of individual components, the reductionism approach, will probably underestimate the network complexity⁸. This underestimation could, in part, explain the low success rate of drug approval due to undesirable or toxic side effects. Gaining a network perspective of disease related pathways using HT/HC cellular screening approaches, and identifying key nodes within these pathways, could lead to the identification of targets that are more suited for therapeutic intervention.

High-throughput screening (HTS) is an ideal methodology to address these issues^9-12. but traditional methods were one dimensional whole-well cell assays, that used simplistic readouts for complex biological processes. They were unable to simultaneously quantify the many phenotypes observed in neurodegenerative diseases such as axonal transport deficits or alterations in morphology properties^{13, 14}. This approach could not be used to investigate the dynamic nature of cellular processes or pathogenic events that occur in a subset of cells. To quantify such features one has to move to multi-dimensional phenotypes termed high-content screening (HCS)^{4, 15-17}. HCS is the cell-based quantification of several processes simultaneously, which provides a more detailed representation of the cellular response to various perturbations compared to HTS.

HCS has many advantages over HTS^{18, 19}, but conducting a high-throughput (HT)-high-content (HC) screen in neuronal models is problematic due to high cost, environmental variation and human error. In order to detect cellular responses on a 'phenomics' scale using HC imaging one has to reduce variation and error, while increasing sensitivity and reproducibility.

Herein we describe a method to accurately and reliably conduct shRNA screens using automated cell culturing²⁰ and HC imaging in neuronal cellular models. We describe how we have used this methodology to identify modulators for one particular protein, DJ1, which when mutated causes autosomal recessive parkinsonism²¹.

Combining the versatility of HC imaging with HT methods, it is possible to accurately quantify a plethora of phenotypes. This could subsequently be utilized to advance our understanding of the genome, the pathways involved in disease pathogenesis as well as identify potential therapeutic targets.

Extracting fluorescent reporter time courses of cell lineages from high-throughput microscopy at low temporal resolution

The extraction of fluorescence time course data is a major bottleneck in high-throughput live-cell microscopy. Here we present an extendible framework based on the open-source image analysis software ImageJ, which aims in particular at analyzing the expression of fluorescent reporters through cell divisions. The ability to track individual cell lineages is essential for the analysis of gene regulatory factors involved in the control of cell fate and identity decisions. In our approach, cell nuclei are identified using Hoechst, and a characteristic drop in Hoechst fluorescence helps to detect dividing cells. We first compare the efficiency and accuracy of different segmentation methods and then present a statistical scoring algorithm for cell tracking, which draws on the combination of various features, such as nuclear intensity, area or shape, and importantly, dynamic changes thereof. Principal component analysis is used to determine the most significant features, and a global parameter search is performed to determine the weighting of individual features. Our algorithm has been optimized to cope with large cell movements, and we were able to semi-automatically extract cell trajectories across three cell generations. Based on the MTrackJ plugin for ImageJ, we have developed tools to efficiently validate tracks and manually correct them by connecting broken trajectories and reassigning falsely connected cell positions. A gold standard consisting of two time-series with 15,000 validated positions will be released as a valuable resource for benchmarking. We demonstrate how our method can be applied to analyze fluorescence distributions generated from mouse stem cells transfected with reporter constructs containing transcriptional control elements of the Msx1 gene, a regulator of pluripotency, in mother and daughter cells. Furthermore, we show by tracking zebrafish PAC2 cells expressing FUCCI cell cycle markers, our framework can be easily adapted to different cell types and fluorescent markers.

A high-content subtractive screen for selecting small molecules affecting internalization of GPCRs

G-protein-coupled receptors (GPCRs) are pivotal in cellular responses to the environment and are common drug targets. Identification of selective small molecules acting on single GPCRs is complicated by the shared machinery coupling signal transduction to physiology. Here, we demonstrate a high-content screen using a panel of GPCR assays to identify receptor selective molecules acting within the kinase/phosphatase inhibitor family. A collection of 88 kinase and phosphatase inhibitors was screened against seven agonist-induced GPCR internalization cell models as well as transferrin uptake in human embryonic kidney cells. Molecules acting on a single receptor were identified through excluding pan-specific compounds affecting housekeeping endocytosis or disrupting internalization of multiple receptors. We identified compounds acting on a sole GPCR from activities in a broad range of chemical structures that could not be easily sorted by conventional means. Selective analysis can therefore rapidly select compounds selectively affecting GPCR activity with specificity to one receptor class through high-content screening.

Intact mammalian cell function on semiconductor nanowire arrays: new perspectives for cell-based biosensing

Nanowires (NWs) are attracting more and more interest due to their potential cellular applications, such as delivery of compounds or sensing platforms. Arrays of vertical indium-arsenide (InAs) NWs are interfaced with human embryonic kidney cells and rat embryonic dorsal root ganglion neurons. A selection of critical cell functions and pathways are shown not to be impaired, including cell adhesion, membrane integrity, intracellular enzyme activity, DNA uptake, cytosolic and membrane protein expression, and the neuronal maturation pathway. The results demonstrate the low invasiveness of InAs NW arrays, which, combined with the unique physical properties of InAs, open up their potential for cellular investigations.

A rapid, inexpensive high throughput screen method for neurite outgrowth

Neurite outgrowth assays are the most common phenotypic screen to assess chemical effects on neuronal cells. Current automated assays involve expensive equipment, lengthy sample preparation and handling, costly reagents and slow rates of data acquisition and analysis. We have developed a high throughput screen (HTS) for neurite outgrowth using a robust neuronal cell model coupled to fast and inexpensive visualization methods, reduced data volume and rapid data analysis. Neuroscreen-1 (NS-1) cell, a subclone of PC12, possessing rapid growth and enhanced sensitivity to NGF was used as a model neuron. This method reduces preparation time by using cells expressing GFP or native cells stained with HCS CellMask(™) Red in a multiplexed 30 min fixation and staining step. A 2x2 camera binning process reduced both image data files and analysis times by 75% and 60% respectively, compared to current protocols. In addition, eliminating autofocus steps during montage generation reduced data collection time. Pharmacological profiles for stimulation and inhibition of neurite outgrowth by NGF and SU6656 were comparable to current standard method utilizing immunofluorescence detection of tubulin. Potentiation of NGF-induced neurite outgrowth by members of a 1,120-member Prestwick compound library as assayed using this method identified six molecules, including etoposide, isoflupredone acetate, fludrocortisone acetate, thioguanosine, oxyphenbutazone and gibberellic acid, that more than doubled the neurite mass primed by 2 ng/ml NGF. This simple procedure represents an important routine approach in high throughput screening of large chemical libraries using the neurite outgrowth phenotype as a measure of the effects of chemical molecules on neuronal cells.

From single genes to gene networks: high-throughput-high-content screening for neurological disease

Neuronal development, function, and the subsequent degeneration of the brain are still an enigma in both the normal and pathologic states, and there is an urgent need to find better targets for developing therapeutic intervention. Current techniques to deconstruct the architecture of brain and disease-related pathways are best suited for following up on single genes but would take an impractical amount of time for the leads from the current wave of genetic and genomic data. New technical developments have made combined high-throughput-high-content (HT-HC) cellular screens possible, which have the potential to contextualize the information, gathered from a combination of genetic and genomic approaches, into networks and functional biology and can be utilized for the identification of therapeutic targets. Herein we discuss the potential impact of HT-HC cellular screens on medical neuroscience.

A Personal Perspective on High-Content Screening (HCS)

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