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This study presents a straightforward and reliable method for determining the viability of Acanthamoeba cysts. A standard method for determining Acanthamoeba cyst viability in an in vitro cytotoxicity analysis is required to ensure that the double-walled and sturdy cysts are affected by the substance tested. In this study, a new approach was used to determine the cysticidal potential of redox Cleland’s reagent, dithiothreitol (DTT), against Acanthamoeba cysts. This approach constitutes a significant breakthrough, as the cyst form of Acanthamoeba is known for its high resistance to various chemicals and drugs used to treat infections of the central nervous system and eyes caused by Acanthamoeba. Cyst viability was evaluated based on the intensity of the cyst population under fluorescence produced by propidium iodide (PI) dye and measured using an enzyme-linked immunosorbent assay (ELISA) reader at an absorbance of 636 nm. The results were validated using high-content screening (HCS). For analysis, an individual cell was imaged and examined for phenotypic changes in the Acanthamoeba cyst at the cyst population level. Fluorescence intensity of the cysts in each well in a 96-well plate was measured using Image J software. HCS is an automated technique that uses fluorescence microscopy to produce quantitative data.

Despite intensive research efforts in recent decades, cancer remains a leading cause of death worldwide. The chalcone family is a promising group of phytochemicals for therapeutic use against cancer development. Naturally-occurring chalcones, as well as synthetic chalcone analogues, have shown many beneficial biological properties, including anti-inflammatory, antioxidant, and anti-cancer activities. In this report, trans-chalcone (TChal) was found to increase cell death in breast cancer cells, assessed using high content screening. Subsequently, using antibody array analysis, TChal was found to increase heme oxygenase-1 (HO-1) expression in TChal-treated breast cancer cells. Blocking of HO-1 by siRNA in breast cancer cells diminished the effect of TChal on cell growth inhibition. TChal-fed mice also showed less tumor growth compared to vehicle-fed mice. Overall, we found that TChal increases HO-1 expression in breast cancer cells, thereby enhancing anti-tumorigenesis. Our results suggest that HO-1 expression could be a potential new target of TChal for anti-tumorigenesis in breast cancer.

Diagnosis and cure for rare diseases represent a great challenge for the scientific community who often comes up against the complexity and heterogeneity of clinical picture associated to a high cost and time-consuming drug development processes. Here we show a drug repurposing strategy applied to nephropathic cystinosis, a rare inherited disorder belonging to the lysosomal storage diseases. This approach consists in combining mechanism-based and cell-based screenings, coupled with an affordable computational analysis, which could result very useful to predict therapeutic responses at both molecular and system levels. Then, we identified potential drugs and metabolic pathways relevant for the pathophysiology of nephropathic cystinosis by comparing gene-expression signature of drugs that share common mechanisms of action or that involve similar pathways with the disease gene-expression signature achieved with RNA-seq.

Graphical abstract, Highlights • Cigarettes were compared to a tobacco heating product (THP1.0 T) in a comparative study. • Traditional and contemporary IVMN approaches were investigated. • Multiple cell types and scoring methods were employed. • Negative responses were observed for THP1.0 T irrespective of approach. • Contemporary approaches proved viable tools for screening purposes., In vitro studies have supported the toxicological evaluation of chemicals and complex mixtures including cigarette smoke and novel tobacco and nicotine products which include tobacco heating products (THP). This new environment requires faster testing, higher throughput and appropriate in vitro studies, to support product innovation and development. In this study, total particulate matter (TPM) from a commercially available THP and a reference cigarette (3R4F) were assessed up to 500 μg/mL using two in vitro micronucleus techniques. V79 and TK6 cells were assessed using conventional OECD 487 manual scoring techniques, whereas, CHO cells were assessed using contemporary, automated high content screening approaches (Cellomics ArrayScan® VTI). V79 cells gave the most consistent response with all three treatment conditions producing a clear positive genotoxic response. Human TK6 cells only produced dose-dependent response, indicative of a weak-positive response. CHO cells demonstrated a positive response with TPM using long (24 h) -S9 conditions. All three cell lines equally demonstrated a negative response with THP TPM up to 500 μg/mL. In conclusion, THP TPM did not increase micronuclei formation above control levels even at doses far exceeding that tested with reference cigarette smoke, in most cases up to 10x the dose delivered compared to that of cigarette smoke. This study supports the growing belief that THPs are less risky than conventional cigarettes and that 21st century screening techniques can be employed to support product design and decision making, as a potential 1st screen prior to more traditional assessments.

Lung imaging and autopsy reports among COVID-19 patients show elevated lung scarring (fibrosis). Early data from COVID-19 patients as well as previous studies from severe acute respiratory syndrome, Middle East respiratory syndrome, and other respiratory disorders show that the extent of lung fibrosis is associated with a higher mortality, prolonged ventilator dependence, and poorer long-term health prognosis. Current treatments to halt or reverse lung fibrosis are limited; thus, the rapid development of effective antifibrotic therapies is a major global medical need that will continue far beyond the current COVID-19 pandemic. Reproducible fibrosis screening assays with high signal-to-noise ratios and disease-relevant readouts such as extracellular matrix (ECM) deposition (the hallmark of fibrosis) are integral to any antifibrotic therapeutic development. Therefore, we have established an automated high-throughput and high-content primary screening assay measuring transforming growth factor-β (TGFβ)-induced ECM deposition from primary human lung fibroblasts in a 384-well format. This assay combines longitudinal live cell imaging with multiparametric high-content analysis of ECM deposition. Using this assay, we have screened a library of 2743 small molecules representing approved drugs and late-stage clinical candidates. Confirmed hits were subsequently profiled through a suite of secondary lung fibroblast phenotypic screening assays quantifying cell differentiation, proliferation, migration, and apoptosis. In silico target prediction and pathway network analysis were applied to the confirmed hits. We anticipate this suite of assays and data analysis tools will aid the identification of new treatments to mitigate against lung fibrosis associated with COVID-19 and other fibrotic diseases.

In microscopy-based drug screens, fluorescent markers carry critical information on how compounds affect different biological processes. However, practical considerations, such as the labor and preparation formats needed to produce different image channels, hinders the use of certain fluorescent markers. Consequently, completed screens may lack biologically informative but experimentally impractical markers. Here, we present a deep learning method for overcoming these limitations. We accurately generated predicted fluorescent signals from other related markers and validated this new machine learning (ML) method on two biologically distinct datasets. We used the ML method to improve the selection of biologically active compounds for Alzheimer's disease (AD) from a completed high-content high-throughput screen (HCS) that had only contained the original markers. The ML method identified novel compounds that effectively blocked tau aggregation, which had been missed by traditional screening approaches unguided by ML. The method improved triaging efficiency of compound rankings over conventional rankings by raw image channels. We reproduced this ML pipeline on a biologically independent cancer-based dataset, demonstrating its generalizability. The approach is disease-agnostic and applicable across diverse fluorescence microscopy datasets.

An immunostimulatory glycolipid molecule from the intestinal protozoan parasite Entamoeba histolytica (Eh) and its synthetic analogs derived from its phosphatidylinositol-b-anchor (EhPIb) previously showed considerable immunotherapeutic effects against Leishmania major infection in vitro and in vivo. Here, we describe a high content screening assay, based on primary murine macrophages. Parasites detection is based on a 90 kDA heat shock protein-specific staining, enabling the detection of several Leishmania species. We validated the assay using L. major, L. braziliensis, L. donovani, and L. infantum as well as investigated the anti-leishmanial activity of six immunostimulatory EhPIb-compounds (Eh-1 to Eh-6). Macrophages infected with dermotropic species were more sensitive towards treatment with the compounds as their viability showed a stronger reduction compared to macrophages infected with viscerotropic species. Most compounds caused a significant reduction of the infection rates and the parasite burdens depending on the infecting species. Only compound Eh-6 was found to have activity against all Leishmania species. Considering the challenges in anti-leishmanial drug discovery, we developed a multi-species screening assay capable of utilizing non-recombinant parasite strains, and demonstrated its usefulness by screening macrophage-targeting EhPIb-compounds showing their potential for the treatment of cutaneous and visceral leishmaniasis.

This paper proposes a new non-invasive, low-cost, and fully automated platform to quantitatively analyze dynamics of human-induced pluripotent stem cell-derived cardiomyocytes (hiPS-CMs) at the single-cell level by holographic image-based tracking for cardiotoxicity screening. A dense Farneback optical flow method and holographic imaging informatics were combined to characterize the contractile motion of a single CM, which obviates the need for costly equipment to monitor a CM's mechanical beat activity. The reliability of the proposed platform was tested by single-cell motion characterization, synchronization analysis, motion speed measurement of fixed CMs versus live CMs, and noise sensitivity. The applicability of the motion characterization method was tested to determine the pharmacological effects of two cardiovascular drugs, isoprenaline (166 nM) and E−4031 (500 μM). The experiments were done using single CMs and multiple cells, and the results were compared to control conditions. Cardiomyocytes responded to isoprenaline by increasing the action potential (AP) speed and shortening the resting period, thus increasing the beat frequency. In the presence of E−4031, the AP speed was decreased, and the resting period was prolonged, thus decreasing the beat frequency. The findings offer insights into single hiPS-CMs’ contractile motion and a deep understanding of their kinetics at the single-cell level for cardiotoxicity screening.

Characterising context‐dependent gene functions is crucial for understanding the genetic bases of health and disease. To date, inference of gene functions from large‐scale genetic perturbation screens is based on ad hoc analysis pipelines involving unsupervised clustering and functional enrichment. We present Knowledge‐ and Context‐driven Machine Learning (KCML), a framework that systematically predicts multiple context‐specific functions for a given gene based on the similarity of its perturbation phenotype to those with known function. As a proof of concept, we test KCML on three datasets describing phenotypes at the molecular, cellular and population levels and show that it outperforms traditional analysis pipelines. In particular, KCML identified an abnormal multicellular organisation phenotype associated with the depletion of olfactory receptors, and TGFβ and WNT signalling genes in colorectal cancer cells. We validate these predictions in colorectal cancer patients and show that olfactory receptors expression is predictive of worse patient outcomes. These results highlight KCML as a systematic framework for discovering novel scale‐crossing and context‐dependent gene functions. KCML is highly generalisable and applicable to various large‐scale genetic perturbation screens., KCML is a Knowledge‐ and Context‐driven Machine Learning framework that systematically analyses large‐scale genetic screens. KCML utilises gene ontology to identify phenotypes associated with a particular gene function in a given cellular context.

Background Fanconi anemia is a rare disease clinically characterized by malformations, bone marrow failure and an increased risk of solid tumors and hematologic malignancies. The only therapies available are hematopoietic stem cell transplantation for bone marrow failure or leukemia, and surgical resection for solid tumors. Therefore, there is still an urgent need for new therapeutic options. With this aim, we developed a novel high-content cell-based screening assay to identify drugs with therapeutic potential in FA. Results A TALEN-mediated FANCA-deficient U2OS cell line was stably transfected with YFP-FANCD2 fusion protein. These cells were unable to form fluorescent foci or to monoubiquitinate endogenous or exogenous FANCD2 upon DNA damage and were more sensitive to mitomycin C when compared to the parental wild type counterpart. FANCA correction by retroviral infection restored the cell line’s ability to form FANCD2 foci and ubiquitinate FANCD2. The feasibility of this cell-based system was interrogated in a high content screening of 3802 compounds, including a Prestwick library of 1200 FDA-approved drugs. The potential hits identified were then individually tested for their ability to rescue FANCD2 foci and monoubiquitination, and chromosomal stability in the absence of FANCA. Conclusions While, unfortunately, none of the compounds tested were able to restore cellular FANCA-deficiency, our study shows the potential capacity to screen large compound libraries in the context of Fanconi anemia therapeutics in an optimized and cost-effective platform.

High-content phenotypic screening has become the approach of choice for drug discovery due to its ability to extract drug-specific multi-layered data. In the field of epigenetics, such screening methods have suffered from a lack of tools sensitive to selective epigenetic perturbations. Here we describe a novel approach, Microscopic Imaging of Epigenetic Landscapes (MIEL), which captures the nuclear staining patterns of epigenetic marks and employs machine learning to accurately distinguish between such patterns. We validated the MIEL platform across multiple cells lines and using dose-response curves, to insure the fidelity and robustness of this approach for high content high throughput drug discovery. Focusing on noncytotoxic glioblastoma treatments, we demonstrated that MIEL can identify and classify epigenetically active drugs. Furthermore, we show MIEL was able to accurately rank candidate drugs by their ability to produce desired epigenetic alterations consistent with increased sensitivity to chemotherapeutic agents or with induction of glioblastoma differentiation., eLife digest Each cell contains a complete copy of a person’s genes coded in their DNA. However, for a cell to perform its specific role, it only needs a small fraction of this genetic information. The mechanisms that control which genes a cell is using fall under the umbrella of ‘epigenetics’ (meaning beyond genetics). These mechanisms involve changes in the way that DNA is organized inside the cell nucleus and changes in how accessible different parts of the genome are to various cellular components. DNA is long and fragile so, to maintain its integrity, it is wrapped around protein complexes called histones. Adding chemical modifications to histones is one of the main epigenetic mechanisms that cells use to regulate which genes are turned on and off. Several methods allow researchers to read patterns of histone modification and use this information to derive what state a cell is in and how it might behave. Improving these methods is of particular interest in drug development, where this information could reveal the effects, and side-effects, of new treatments. Unfortunately, existing techniques are costly in both time and money, and they are not well suited to analyzing epigenetic changes caused by the large numbers of compounds tested during drug development. To overcome this barrier, Farhy et al. have developed a new system called ‘Microscopic Imaging of the Epigenetic Landscape’ (MIEL for short). The system allows them to quickly analyze the epigenetic changes caused by each of a large number of different chemical compounds when they are used on cells. MIEL tags different histone modifications by staining each with a different color, and then uses automated microscopy to produce images. It then extracts information from these images using advanced image analysis tools. The changes induced by different drugs can then be compared and categorized using machine learning algorithms. To test the MIEL system, Farhy et al. grew brain cancer cells (derived from human tumors) in the lab, and treated them with compounds that target proteins involved in histone modifications. Using their newly created pipeline, Farhy et al. were able to identify the unique epigenetic changes caused by these compounds, and train the system to correctly predict which type of drug the cells had been treated with. In a different set of experiments Farhy et al. demonstrate the utility of their new pipeline in identifying drugs that induce a set of epigenetic changes associated with a reduced ability to regrow tumors. This new system could help screen thousands of compounds for their epigenetic effects, which could aid the design of new treatments for many diseases.

Three-dimensional (3D) cell culture is often mentioned in the context of regenerative medicine, for example, for the replacement of ischemic myocardium with tissue-engineered muscle constructs. Additionally, 3D cell culture is used, although less commonly, in basic research, toxicology, and drug development. These applications have recently benefited from innovations in stem cell technologies allowing the mass-production of hiPSC-derived cardiomyocytes or other cardiovascular cells, and from new culturing methods including organ-on-chip and bioprinting technologies. On the analysis side, improved sensors, computer-assisted image analysis, and data collection techniques have lowered the bar for switching to 3D cell culture models. Nevertheless, 3D cell culture is not as widespread or standardized as traditional cell culture methods using monolayers of cells on flat surfaces. The many possibilities of 3D cell culture, but also its limitations, drawbacks and methodological pitfalls, are less well-known. This article reviews currently used cardiovascular 3D cell culture production methods and analysis techniques for the investigation of cardiotoxicity, in drug development and for disease modeling.

Cell-based screening has become the major compound interrogation strategy in Chagas disease drug discovery. Several different cell lines have been deployed as host cells in screening assays. However, host cell characteristics and host-parasite interactions may play an important role when assessing anti-T. cruzi compound activity, ultimately impacting on hit discovery. To verify this hypothesis, four distinct mammalian cell lines (U2OS, THP-1, Vero and L6) were used as T. cruzi host cells in High Content Screening assays. Rates of infection varied greatly between different host cells. Susceptibility to benznidazole also varied, depending on the host cell and parasite strain. A library of 1,280 compounds was screened against the four different cell lines infected with T. cruzi, resulting in the selection of a total of 82 distinct compounds as hits. From these, only two hits were common to all four cell lines assays (2.4%) and 51 were exclusively selected from a single assay (62.2%). Infected U2OS cells were the most sensitive assay, as 55 compounds in total were identified as hits; infected THP-1 yielded the lowest hit rates, with only 16 hit compounds. Of the selected hits, compound FPL64176 presented selective anti-T. cruzi activity and could serve as a starting point for the discovery of new anti-chagasic drugs.

Objective Lipid droplet (LD) deposition in adipose tissue is a critical factor leading to metabolic dysfunction. Various herbal medicines in traditional Chinese medicine (TCM) are used to treat hyperlipidemia, type 2 diabetes, obesity, and other diseases. The objective of this study was to identify potential anti-adipogenic agents from TCM herbal compounds. Methods One hundred and twenty compounds were evaluated in terms of their effect on adipocyte differentiation through image-based high content screening. Anti-adipogenic effects of identified hits were further confirmed at various concentrations. In addition, drug-induced liver injury assay was performed with HepG2 cells to test the hepatotoxicity of hit compounds. Results Berbamine (BBM), a chemical isolated from barberry, and a derivative of BBM, berbamine dihydrochloride (BBMD), reduced LDs formation by more than 50%. Dose-dependent effects were observed and the IC 50 values of the two hits, BBM and BBMD, were determined as 1.88 μM and 0.95 μM, respectively. Moreover, BBM induced mild HepG2 cell injury, while its dihydrochloride—BBMD did not exhibit hepatotoxicity within 40 μM. Conclusion This study demonstrates that BBMD may be a potential therapeutic candidate for disorders associated with elevated LDs accumulation.

SARS-CoV-2 infection has led to a global health crisis, and yet our understanding of the disease and potential treatment options remains limited. The infection occurs through binding of the virus with angiotensin converting enzyme 2 (ACE2) on the cell membrane. Here, we established a screening strategy to identify drugs that reduce ACE2 levels in human embryonic stem cell (hESC)-derived cardiac cells and lung organoids. Target analysis of hit compounds revealed androgen signaling as a key modulator of ACE2 levels. Treatment with antiandrogenic drugs reduced ACE2 expression and protected hESC-derived lung organoids against SARS-CoV-2 infection. Finally, clinical data on COVID-19 patients demonstrated that prostate diseases, which are linked to elevated androgen, are significant risk factors and that genetic variants that increase androgen levels are associated with higher disease severity. These findings offer insights on the mechanism of disproportionate disease susceptibility in men and identify antiandrogenic drugs as candidate therapeutics for COVID-19., Graphical Abstract, Highlights • Drug screens on hESC cardiac cells identify modulators of SARS-CoV-2 receptor ACE2 • Targets of drugs that reduce ACE2 converge on androgen signaling pathway • Androgen signaling inhibition reduces SARS-CoV-2 infection in hESC lung organoids • Elevated androgen increases COVID-19 susceptibility and severity in men, Men are more susceptibility to severe COVID-19 complications. Fattahi and colleagues leverage stem cell models, high-throughput drug screens, and patient records to demonstrate that male sex hormones regulate SARS-CoV-2 receptors and increase disease severity in men. They identify drug candidates that reduce viral infection by inhibiting these hormones.

Thoroughly understanding the molecular mechanisms responsible for the biological properties of pluripotent stem cells, as well as for the processes involved in reprograming, differentiation, and transition between Naïve and Primed pluripotent states, is of great interest in basic and applied research. Although pluripotent cells have been extensively characterized in terms of their transcriptome and miRNome, a comprehensive understanding of how these gene products specifically impact their biology, depends on gain- or loss-of-function experimental approaches capable to systematically interrogate their function. We review all studies carried up to date that used arrayed screening approaches to explore the function of these genetic elements on those biological contexts, using focused or genome-wide genetic libraries. We further discuss the limitations and advantages of approaches based on assays with population-level primary readouts, derived from single-parameter plate readers, or cell-level primary readouts, obtained using multiparametric flow cytometry or quantitative fluorescence microscopy (i.e., high-content screening). Finally, we discuss technical limitation and future perspectives, highlighting how the integration of screening data may lead to major advances in the field of stem cell research and therapy. Electronic supplementary material The online version of this article (10.1186/s13287-018-1124-6) contains supplementary material, which is available to authorized users.

Background A variety of neurological disorders including neurodegenerative diseases and infection by neurotropic viruses can cause structural and functional changes in the central nervous system (CNS), resulting in long-term neurological sequelae. An improved understanding of the pathogenesis of these disorders is important for developing efficacious interventions. Human induced pluripotent stem cells (hiPSCs) offer an extraordinary window for modeling pathogen-CNS interactions, and other cellular interactions, in three-dimensional (3D) neuronal cultures that can recapitulate several aspects of in vivo brain tissue. Methods Herein, we describe a prototype of scaffold-free hiPSC-based adherent 3D (A-3D) human neuronal cultures in 96-well plates. To test their suitability for drug screening, A-3D neuronal cultures were infected with herpes simplex virus type 1 (HSV-1) with or without acyclovir. Results The half maximal inhibitory concentration (IC50) of acyclovir was 3.14 μM and 3.12 μM determined using flow cytometry and the CX7 High Content Screening platform, respectively. Conclusions Our A-3D neuronal cultures provide an unprecedented opportunity for high-content drug screening programs to treat human CNS infections. Electronic supplementary material The online version of this article (10.1186/s13287-018-0881-6) contains supplementary material, which is available to authorized users.

Several dyes are currently available for use in detecting differentiation of mesenchymal cells into adipocytes. Dyes, such as Oil Red O, are cheap, easy to use and widely utilized by laboratories analyzing the adipogenic potential of mesenchymal cells. However, they are not specific to changes in gene transcription. We have developed a gene-specific differentiation assay to analyze when a mesenchymal cell has switched its fate to an adipogenic lineage. Immuno-labelling against fatty acid binding protein-4 (FABP4), a lineage-specific marker of adipogenic differentiation, enabled visualization and quantification of differentiated cells. The ability to quantify adipogenic differentiation potential of mesenchymal cells in a 96 well microplate format has promising implications for a number of applications. Hundreds of clinical trials involve the use of adult mesenchymal stromal cells and it is currently difficult to correlate therapeutic outcomes within and especially between such clinical trials. This simple high-throughput FABP4 assay provides a quantitative assay for assessing the differentiation potential of patient-derived cells and is a robust tool for comparing different isolation and expansion methods. This is particularly important given the increasing recognition of the heterogeneity of the cells being administered to patients in mesenchymal cell products. The assay also has potential utility in high throughput drug screening, particularly in obesity and pre-diabetes research.

In this study, we describe the application of a transformed Chlamydia trachomatis strain constitutively expressing the red fluorescent protein mCherry, to allow real-time monitoring of the infection cycle and screening for agents that block replication of C. trachomatis. The red fluorescent C. trachomatis strain was detected autonomously without antibody staining and was equally susceptible to doxycycline as the wild type strain. A high-throughput screening assay was developed using the transformed strain and automated fluorescence microscopy. The assay was used in a pilot screen of a 349 compound library containing natural products from Australian flora and fauna. Compounds with anti-chlamydial activity were tested for dose response and toxicity to host cells and two non-toxic compounds had 50% effective concentration (EC50) values in the low micromolar range. Natural products are valuable sources for drug discovery and the identified Chlamydia growth inhibition may be starting points for future drug development. Live cell imaging was used to visualize growth of the red fluorescent C. trachomatis strain over time. The screening assay reduced workload and reagents compared to an assay requiring immunostaining and could further be used to monitor the development of Chlamydia inclusions and anti-chlamydial effect in real time.

High throughput screening determines the effects of many conditions on a given biological target. Currently, to estimate the effects of those conditions on other targets requires either strong modeling assumptions (e.g. similarities among targets) or separate screens. Ideally, data-driven experimentation could be used to learn accurate models for many conditions and targets without doing all possible experiments. We have previously described an active machine learning algorithm that can iteratively choose small sets of experiments to learn models of multiple effects. We now show that, with no prior knowledge and with liquid handling robotics and automated microscopy under its control, this learner accurately learned the effects of 48 chemical compounds on the subcellular localization of 48 proteins while performing only 29% of all possible experiments. The results represent the first practical demonstration of the utility of active learning-driven biological experimentation in which the set of possible phenotypes is unknown in advance. DOI: http://dx.doi.org/10.7554/eLife.10047.001, eLife digest Biomedical scientists have invested significant effort into making it easy to perform lots of experiments quickly and cheaply. These “high throughput” methods are the workhorses of modern “systems biology” efforts. However, we simply cannot perform an experiment for every possible combination of different cell type, genetic mutation and other conditions. In practice this has led researchers to either exhaustively test a few conditions or targets, or to try to pick the experiments that best allow a particular problem to be explored. But which experiments should we pick? The ones we think we can predict the outcome of accurately, the ones for which we are uncertain what the results will be, or a combination of the two? Humans are not particularly well suited for this task because it requires reasoning about many possible outcomes at the same time. However, computers are much better at handling statistics for many experiments, and machine learning algorithms allow computers to “learn” how to make predictions and decisions based on the data they’ve previously processed. Previous computer simulations showed that a machine learning approach termed “active learning” could do a good job of picking a series of experiments to perform in order to efficiently learn a model that predicts the results of experiments that were not done. Now, Naik et al. have performed cell biology experiments in which experiments were chosen by an active learning algorithm and then performed using liquid handling robots and an automated microscope. The key idea behind the approach is that you learn more from an experiment you can’t predict (or that you predicted incorrectly) than from just confirming your confident predictions. The results of the robot-driven experiments showed that the active learning approach outperforms strategies a human might use, even when the potential outcomes of individual experiments are not known beforehand. The next challenge is to apply these methods to reduce the cost of achieving the goals of large projects, such as The Cancer Genome Atlas. DOI: http://dx.doi.org/10.7554/eLife.10047.002

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.

Heterogeneity within the human population and within diseased tissues necessitates a personalized medicine approach to diagnostics and the treatment of diseases. Functional assays at the single-cell level can contribute to uncovering heterogeneity and ultimately assist in improved treatment decisions based on the presence of outlier cells. We aim to develop a platform for high-throughput, single-cell-based assays using well-characterized hydrodynamic cell isolation arrays which allow for precise cell and fluid handling. Here, we demonstrate the ability to extract spatial and temporal information about several intracellular components using a single fluorescent channel, eliminating the problem of overlapping fluorescence emission spectra. Integrated with imaging technologies such as wide field-of-view lens-free fluorescent imaging, fiber-optic array scanning technology, and microlens arrays, use of a single fluorescent channel will reduce the cost of reagents and optical components. Specifically, we sequentially stain hydrodynamically trapped cells with three biochemical labels all sharing the same fluorescence excitation and emission spectrum. These markers allow us to analyze the amount of DNA, and compare nucleus-to-cytoplasm ratio, as well as glycosylation of surface proteins. By imaging cells in real-time we enable measurements of temporal localization of cellular components and intracellular reaction kinetics, the latter is used as a measurement of multi-drug resistance. Demonstrating the efficacy of this single-cell analysis platform is the first step in designing and implementing more complete assays, aimed toward improving diagnosis and personalized treatments to complex diseases.

Over the last decade, imaging as a detection mode for cell based assays has opened a new world of opportunities to measure "phenotypic endpoints" in both current and developing biological models. These "high content" methods combine multiple measurements of cell physiology, whether it comes from sub-cellular compartments, multicellular structures, or model organisms. The resulting multifaceted data can be used to derive new insights into complex phenomena from cell differentiation to compound pharmacology and toxicity. Exploring the major application areas through review of the growing compendium of literature provides evidence that this technology is having a tangible impact on drug discovery and the life sciences.

The coupling of Ca(2+) influx to synaptic vesicle (SV) recycling in nerve terminals is essential for neurotransmitter release and thus neuronal communication. Both of these parameters have been monitored using fluorescent reporter dyes such as fura-2 and FM1-43 in single central nerve terminals. However, their simultaneous monitoring has been hampered by the proximity of their fluorescence spectra, resulting in significant contamination of their signals by bleedthrough. We have developed an assay that simultaneously monitors both SV recycling and changes in intracellular free Ca(2+) ([Ca(2+)](i)) in cultured neurons using the reporter dyes FM4-64 and fura-2AM. By monitoring both fura-2 and FM4-64 emission in the far red range, we were able to visualize functionally independent readouts of both SV recycling and [Ca(2+)](i) independent of fluorescence bleedthrough. We were also able to incorporate an assay of cell viability without any fluorescence bleedthrough from either fura-2 or FM4-64 signals, using the dye SYTOX Green. We propose that this assay of three key neuronal functions could be simply translated into a high content screening format for studies investigating small molecule inhibitors of these processes.

In this paper, we describe a new bioimage informatics system developed for high content screening (HCS) applications with the goal to extract and analyze phenotypic features of hundreds of thousands of mitotic cells simultaneously. The system introduces the algorithm of multi-phenotypic mitotic analysis (MMA) and integrates that with algorithms of correlation analysis and compound clustering used in gene microarray studies. The HCS–MMA system combines different phenotypic information of cellular images obtained from three-channel acquisitions to distinguish and label individual cells at various phases of mitosis. The proposed system can also be used to extract and count the number of cells in each phase in cell-based assay experiments and archive the extracted data into a structured database for more sophisticated statistical and data analysis. To recognize different mitotic phases, binary patterns are set up based on a known biological mitotic spindle model to characterize cellular morphology of actin, microtubules, and DNA. To illustrate its utility, the HCS–MMA system has been applied to screen the quantitative response of 320 different drug compounds in suppressing Monastrol. The results are validated and evaluated by comparing the performance of HCS–MMA with visual analysis, as well as clustering of the drug compounds under evaluation.

International audience; Pesticides as well as many other environmental pollutants are considered as risk factors for the initiation and the progression of cancer. In order to evaluate the in vitro effects of chemicals present in the diet, we began by combining viability, real-time cellular impedance and high throughput screening data to identify a concentration "zone of interest" for the six xenobiotics selected: endosulfan, dioxin, carbaryl, carbendazim, p ' p ' DDE and hydroquinone. We identified a single concentration of each pollutant allowing a modulation of the impedance in the absence of vital changes (nuclear integrity, mitochondrial membrane potential, cell death). Based on the number of observed modulations known to be involved in hepatic homeostasis dysfunction that may lead to cancer progression such as cell cycle and apoptosis regulators, EMT biomarkers and signal transduction pathways, we then ranked the pollutants in terms of their toxicity. Endosulfan, was able to strongly modulate all the studied cellular processes in HepG2 cells, followed by dioxin, then carbendazim. While p,p,p ' DDE, carbaryl and hydroquinone seemed to affect fewer functions, their effects nevertheless warrant close scrutiny. Our in vitro data indicate that these xenobiotics may contribute to the evolution and worsening of hepatocarcinoma, whether via the induction of the EMT process and/or via the deregulation of liver key processes such as cell cycle and resistance to apoptosis.

The concept of drug discovery through stem cell biology is based on technological developments whose genesis is now coincident. The first is automated cell microscopy with concurrent advances in image acquisition and analysis, known as high content screening (HCS). The second is patient-derived stem cells for modelling the cell biology of brain diseases. HCS has developed from the requirements of the pharmaceutical industry for high throughput assays to screen thousands of chemical compounds in the search for new drugs. HCS combines new fluorescent probes with automated microscopy and computational power to quantify the effects of compounds on cell functions. Stem cell biology has advanced greatly since the discovery of genetic reprogramming of somatic cells into induced pluripotent stem cells (iPSCs). There is now a rush of papers describing their generation from patients with various diseases of the nervous system. Although the majority of these have been genetic diseases, iPSCs have been generated from patients with complex diseases (schizophrenia and sporadic Parkinson’s disease). Some genetic diseases are also modelled in embryonic stem cells generated from blastocysts rejected during in vitro fertilisation. Neural stem cells have been isolated from post-mortem brain of Alzheimer’s patients and neural stem cells generated from biopsies of the olfactory organ of patients is another approach. These “olfactory neurosphere-derived” cells demonstrate robust disease-specific phenotypes in patients with schizophrenia and Parkinson’s disease. High content screening is already in use to find small molecules for the generation and differentiation of embryonic stem cells and induced pluripotent stem cells. The challenges for using stem cells for drug discovery are to develop robust stem cell culture methods that meet the rigorous requirements for repeatable, consistent quantities of defined cell types at the industrial scale necessary for high content screening.

Several chalcones were synthesized and their in vitro cytotoxicity against various human cell lines, including human breast adenocarcinoma cell line MCF-7, human lung adenocarcinoma cell line A549, human prostate cancer cell line PC3, human adenocarcinoma cell line HT-29 (colorectal cancer) and human normal liver cell line WRL-68 was evaluated. Most of the compounds being active cytotoxic agents, four of them with minimal IC50 values were chosen and studied in detail with MCF-7 cells. The compounds 1, 5, 23, and 25 were capable in eliciting apoptosis in MCF-7 cells as shown by multiparameter cytotoxicity assay and caspase-3/7, -8, and -9 activities (p < 0.05). The ROS level showed 1.3-fold increase (p < 0.05) at the low concentrations used and thus it was concluded that the compounds increased the ROS level eventually leading to apoptosis in MCF-7 cells through intrinsic as well as extrinsic pathways.

8 páginas, figuras., The discovery of novel targets that can be pharmacologically exploited to lead to a better disease outcome has long been an aim of biomedical research. At present, the technology and robotisation available have pushed the search for novel molecules to a high-throughput screening (HTS) context, making it possible to screen several hundreds of compounds or genes in a single day. High-content screenings (HCS) have added a refined complexity to the screening processes, as the information drawn from an image-based assay is more complete than the monoparametric readouts obtained in classical HTS assays. Here, we review the development of HCS platforms to identify molecules influencing FOXO nuclear relocation and activation as pharmacological targets, their applicability and the future directions of the screening field.

Cytomics aims at understanding the functional relationships between cellular phenotypes (cytome) and metabolic pathways (proteome) that result from a combination of genetically defined mechanisms (genome) and environmental conditions [1,2]. Although flow-cytometry is able to measure the optical properties of single cells at a rate of >1000 cells per minute it has a limited capability of mapping individual events. To accurately quantify (sub-) cellular characteristics within a natural context there is a fast-growing need for image-based cytometry. Images, obtained with fluorescence microscopy, provide the exact information on signal intensity, location and distribution of specific molecules within intact cell systems (tissue or monolayers) and allow for investigating cellular properties in relation to the cell-ecological context [3]. Previously, we have developed a cytometric approach for scoring DNA lesion endpoints in confocal images of murine fibroblasts [3]. We now present a generalized approach for multivariate phenotypic profiling of individual nuclei using automated fluorescence mosaic microscopy and optimized digital image processing tools. An indefinite number of fields, z-slices and channels can be analyzed; the only prerequisite is the presence of a nuclear counterstain, which is used for the generation of masks. To anticipate for erroneous segmentation of clustered nuclei in dense cell cultures we implemented an iterative conditional segmentation (ics) algorithm that uses both morphological and intensity information from the image (Figure 1). The method makes use of a priori knowledge about the size and shape of nuclei in stringent feedback selection of correctly segmented nuclei. Depending on the degree of clustering, segmentation performance varies between 95% and 100%. Complete analysis of nuclei and subnuclear features for a region of 25 images of 1000x1000 pixels, 3 z-slices and 3 channels only takes ~ 3 minutes or ~ 0.7sec/nucleus. Our method is insensitive to scaling, illumination heterogeneity and variability or non-uniformity of staining. We have successfully applied our system in cell cycle analysis, scoring of transfection efficiency and assessment of (localized) DNA damage in response to genotoxic stress and ionizing radiation.

When cells are exposed to (ionising) radiation there is a rapid phosphorylation of a minor nucleosomal histone protein, H2AX, at the sites where double stranded breaks (DSB) occur. This phosphorylation is one of the earliest events in the repair cascade and extends over several mega base pairs surrounding the break. Nowadays it is generally accepted that the formation of γH2AX functions as a signal enhancer. Using immuno histochemistry we can visualise this phosphorylation as foci in the nucleus, where each foci represents a DSB [1]. For our research we use normal human primary fibroblasts (NHDF’s) to study the so called radiation-induced bystander effects which refer to the responses induced in non-irradiated cells, when neighbouring cells are irradiated. Although the exact pathways of transmission are yet to be determined, studies have shown that gap junction-mediated transport and secretion of soluble extracellular factors play an important role [2]. To exclude variation we first tried synchronisation of the fibroblasts using nocodazole or aphidicolin. Our attempts did not produce the desired synchronisation level. In addition, recent reports doubt the effectiveness of these products in cell synchronisation [3]. To resolve this problem, we used high content screening of cells together with specific cell cycle markers. One of these markers is 5-bromo-2-deoxyuridine (BrdU). BrdU, a synthetic nucleoside, is an analogue of thymidine that can be incorporated in replicating cells and specifically label S-phases [4]. Cells are cultured on membrane inserts, with a pore size of 0,4µm allowing soluble factors to pass but preventing the cells to interchange. These cells are irradiated with different doses and subsequently placed together with NHDF that are grown on cover glasses (see figure 1). Depending on the objectives BrdU is added 20-40 minutes before fixation. We found a differential pattern for γH2AX that we could specifically link to the cell cycle. During the S phase γH2AX is significantly more induced than during other phases of the cell cycle (see figure 2). This is probably due to the increased vulnerability caused by the unwinding of DNA during replication. 1. ¬¬S.H.Macphail, J.P.Banath, T.Y.Yu, E.H.Chu, H.Lambur, P.L.Olive, Int.J.Radiat.Biol. 79 (2003) P. 351-358. 2. H.Yang, N.Asaad, K.D.Held, Oncogene 24 (2005) p. 2096-2103. 3. S.Cooper, G.Iyer, M.Tarquini, P.Bissett, Cell Tissue Res. 324 (2006) p.237-242. 4. R.T.O'Keefe, S.C.Henderson, D.L.Spector, J.Cell Biol. 116 (1992) p.1095-1110.

Background The zebrafish has been established as the main vertebrate model system for whole organism screening applications. However, the lack of consistent positioning of zebrafish embryos within wells of microtiter plates remains an obstacle for the comparative analysis of images acquired in automated screening assays. While technical solutions to the orientation problem exist, dissemination is often hindered by the lack of simple and inexpensive ways of distributing and duplicating tools. Results Here, we provide a cost effective method for the production of 96-well plate compatible zebrafish orientation tools using a desktop 3D printer. The printed tools enable the positioning and orientation of zebrafish embryos within cavities formed in agarose. Their applicability is demonstrated by acquiring lateral and dorsal views of zebrafish embryos arrayed within microtiter plates using an automated screening microscope. This enables the consistent visualization of morphological phenotypes and reporter gene expression patterns. Conclusions The designs are refined versions of previously demonstrated devices with added functionality and strongly reduced production costs. All corresponding 3D models are freely available and digital design can be easily shared electronically. In combination with the increasingly widespread usage of 3D printers, this provides access to the developed tools to a wide range of zebrafish users. Finally, the design files can serve as templates for other additive and subtractive fabrication methods.

Background The genotoxic effect of cigarette smoke is routinely measured by treating cells with cigarette Particulate Matter (PM) at different dose levels in submerged cell cultures. However, PM exposure cannot be considered as a complete exposure as it does not contain the gas phase component of the cigarette smoke. The in vitro γH2AX assay by High Content Screening (HCS) has been suggested as a complementary tool to the standard battery of genotoxicity assays as it detects DNA double strand breaks in a high-throughput fashion. The aim of this study was to further optimise the in vitro γH2AX assay by HCS to enable aerosol exposure of human bronchial epithelial BEAS-2B cells at the air-liquid interface (ALI). Methods Whole mainstream cigarette smoke (WMCS) from two reference cigarettes (3R4F and M4A) were assessed for their genotoxic potential. During the study, a further characterisation of the Borgwaldt RM20S® aerosol exposure system to include single dilution assessment with a reference gas was also carried out. Results The results of the optimisation showed that both reference cigarettes produced a positive genotoxic response at all dilutions tested. However, the correlation between dose and response was low for both 3R4F and M4A (Pearson coefficient, r = -0.53 and -0.44 respectively). During the additional characterisation of the exposure system, it was observed that several pre-programmed dilutions did not perform as expected. Conclusions Overall, the in vitro γH2AX assay by HCS could be used to evaluate WMCS in cell cultures at the ALI. Additionally, the extended characterisation of the exposure system indicates that assessing the performance of the dilutions could improve the existing routine QC checks.