HCS-Neurons: identifying phenotypic changes in multi-neuron images upon drug treatments of high-content screening

Human coculture model of astrocytes and SH-SY5Y cells to test the neurotoxicity of chemicals

In this study, we established a coculture model comprising human neuroblastoma SH-SY5Y cells and induced pluripotent stem cell-derived astrocytes, faithfully replicating the human brain environment for in vitro neurotoxicity assessment. We optimized the cell differentiation duration and cell ratios to obtain images conducive to neurite outgrowth evaluation. Subsequently, the neurotoxic effects in the coculture and monoculture of SH-SY5Y cells were confirmed using neurotoxic agents such as acrylamide (ACR) and hydrogen peroxide (H2O2). Disparities in the neurotoxic impacts of ACR and H2O2 within the coculture were mirrored in the expression of genes associated with early neuronal injury. Notably, the reduction in neurite outgrowth induced by neurotoxic agents revealed the coculture's lower sensitivity compared to monocultures. Furthermore, the coculture system exhibited distinct effects of test agents on nerve damage and manifested protective influences on nerve cells. The proposed methodology holds promise for large-scale chemical neurotoxicity screening through neurite change measurements. This in vitro coculture model, accounting for cell interactions, emerges as a valuable tool in toxicity testing, offering insights into the potential effects of chemicals within the human body.

Contribution of Human Pluripotent Stem Cell-Based Models to Drug Discovery for Neurological Disorders

One of the major obstacles to the identification of therapeutic interventions for central nervous system disorders has been the difficulty in studying the step-by-step progression of diseases in neuronal networks that are amenable to drug screening. Recent advances in the field of human pluripotent stem cell (PSC) biology offers the capability to create patient-specific human neurons with defined clinical profiles using reprogramming technology, which provides unprecedented opportunities for both the investigation of pathogenic mechanisms of brain disorders and the discovery of novel therapeutic strategies via drug screening. Many examples not only of the creation of human pluripotent stem cells as models of monogenic neurological disorders, but also of more challenging cases of complex multifactorial disorders now exist. Here, we review the state-of-the art brain cell types obtainable from PSCs and amenable to compound-screening formats. We then provide examples illustrating how these models contribute to the definition of new molecular or functional targets for drug discovery and to the design of novel pharmacological approaches for rare genetic disorders, as well as frequent neurodegenerative diseases and psychiatric disorders.

StackHCV: a web-based integrative machine-learning framework for large-scale identification of hepatitis C virus NS5B inhibitors

Fast and accurate identification of inhibitors with potency against HCV NS5B polymerase is currently a challenging task. As conventional experimental methods is the gold standard method for the design and development of new HCV inhibitors, they often require costly investment of time and resources. In this study, we develop a novel machine learning-based meta-predictor (termed StackHCV) for accurate and large-scale identification of HCV inhibitors. Unlike the existing method, which is based on single-feature-based approach, we first constructed a pool of various baseline models by employing a wide range of heterogeneous molecular fingerprints with five popular machine learning algorithms (k-nearest neighbor, multi-layer perceptron, partial least squares, random forest and support vectors machine). Secondly, we integrated these baseline models in order to develop the final meta-based model by means of the stacking strategy. Extensive benchmarking experiments showed that StackHCV achieved a more accurate and stable performance as compared to its constituent baseline models on the training dataset and also outperformed the existing predictor on the independent test dataset. To facilitate the high-throughput identification of HCV inhibitors, we built a web server that can be freely accessed at http://camt.pythonanywhere.com/StackHCV . It is expected that StackHCV could be a useful tool for fast and precise identification of potential drugs against HCV NS5B particularly for liver cancer therapy and other clinical applications.

iBitter-Fuse: A Novel Sequence-Based Bitter Peptide Predictor by Fusing Multi-View Features

Accurate identification of bitter peptides is of great importance for better understanding their biochemical and biophysical properties. To date, machine learning-based methods have become effective approaches for providing a good avenue for identifying potential bitter peptides from large-scale protein datasets. Although few machine learning-based predictors have been developed for identifying the bitterness of peptides, their prediction performances could be improved. In this study, we developed a new predictor (named iBitter-Fuse) for achieving more accurate identification of bitter peptides. In the proposed iBitter-Fuse, we have integrated a variety of feature encoding schemes for providing sufficient information from different aspects, namely consisting of compositional information and physicochemical properties. To enhance the predictive performance, the customized genetic algorithm utilizing self-assessment-report (GA-SAR) was employed for identifying informative features followed by inputting optimal ones into a support vector machine (SVM)-based classifier for developing the final model (iBitter-Fuse). Benchmarking experiments based on both 10-fold cross-validation and independent tests indicated that the iBitter-Fuse was able to achieve more accurate performance as compared to state-of-the-art methods. To facilitate the high-throughput identification of bitter peptides, the iBitter-Fuse web server was established and made freely available online. It is anticipated that the iBitter-Fuse will be a useful tool for aiding the discovery and de novo design of bitter peptides.

iPMI: Machine Learning-Aided Identification of Parametrial Invasion in Women with Early-Stage Cervical Cancer

Radical hysterectomy is a recommended treatment for early-stage cervical cancer. However, the procedure is associated with significant morbidities resulting from the removal of the parametrium. Parametrial cancer invasion (PMI) is found in a minority of patients but the efficient system used to predict it is lacking. In this study, we develop a novel machine learning (ML)-based predictive model based on a random forest model (called iPMI) for the practical identification of PMI in women. Data of 1112 stage IA-IIA cervical cancer patients who underwent primary surgery were collected and considered as the training dataset, while data from an independent cohort of 116 consecutive patients were used as the independent test dataset. Based on these datasets, iPMI-Econ was then developed by using basic clinicopathological data available prior to surgery, while iPMI-Power was also introduced by adding pelvic node metastasis and uterine corpus invasion to the iPMI-Econ. Both 10-fold cross-validations and independent test results showed that iPMI-Power outperformed other well-known ML classifiers (e.g., logistic regression, decision tree, k-nearest neighbor, multi-layer perceptron, naive Bayes, support vector machine, and extreme gradient boosting). Upon comparison, it was found that iPMI-Power was effective and had a superior performance to other well-known ML classifiers in predicting PMI. It is anticipated that the proposed iPMI may serve as a cost-effective and rapid approach to guide important clinical decision-making.

High-Content Screening for Protein-Protein Interaction Modulators Using Proximity Ligation Assay in Primary Neurons

The proximity ligation assay (PLA) allows the detection and subcellular localization of protein-protein interactions with high specificity. We recently developed a high-content screening model based on primary hippocampal neurons cultured in 384-well plates and screened a library of ∼1100 compounds using a PLA between tau and bridging integrator 1, a genetic risk factor for Alzheimer's disease. We developed image-segmentation and spot-detection algorithms to delineate PLA signals in the axonal network, but not in cell bodies, from confocal images acquired via a high-throughput microscope. To compare data generated from different plates and through different experiments, we developed a computational routine to optimize the image analysis parameters for each plate and devised a range of quality-control measures to ultimately identify compounds that consistently increase or decrease our read-out. We provide the following protocols. © 2019 by John Wiley & Sons, Inc. Basic Protocol 1: Routine culture of rat postnatal hippocampal neurons in 384-well plates Basic Protocol 2: Compound incubation using the high-content screening platform Support Protocol 1: Preparation of intermediate plates for compound screening Support Protocol 2: Preparation of intermediate plates for hit validation (dose-response curves) Basic Protocol 3: Proximity ligation assay in 384-well plates Basic Protocol 4: Image acquisition and analysis Support Protocol 3: Optimization of analysis parameters Basic Protocol 5: Identification of hits Basic Protocol 6: Validation of hits based on dose-response curves.

StackIL6: a stacking ensemble model for improving the prediction of IL-6 inducing peptides

The release of interleukin (IL)-6 is stimulated by antigenic peptides from pathogens as well as by immune cells for activating aggressive inflammation. IL-6 inducing peptides are derived from pathogens and can be used as diagnostic biomarkers for predicting various stages of disease severity as well as being used as IL-6 inhibitors for the suppression of aggressive multi-signaling immune responses. Thus, the accurate identification of IL-6 inducing peptides is of great importance for investigating their mechanism of action as well as for developing diagnostic and immunotherapeutic applications. This study proposes a novel stacking ensemble model (termed StackIL6) for accurately identifying IL-6 inducing peptides. More specifically, StackIL6 was constructed from twelve different feature descriptors derived from three major groups of features (composition-based features, composition-transition-distribution-based features and physicochemical properties-based features) and five popular machine learning algorithms (extremely randomized trees, logistic regression, multi-layer perceptron, support vector machine and random forest). To enhance the utility of baseline models, they were effectively and systematically integrated through a stacking strategy to build the final meta-based model. Extensive benchmarking experiments demonstrated that StackIL6 could achieve significantly better performance than the existing method (IL6PRED) and outperformed its constituent baseline models on both training and independent test datasets, which thereby support its excellent discrimination and generalization abilities. To facilitate easy access to the StackIL6 model, it was established as a freely available web server accessible at http://camt.pythonanywhere.com/StackIL6. It is anticipated that StackIL6 can help to facilitate rapid screening of promising IL-6 inducing peptides for the development of diagnostic and immunotherapeutic applications in the future.

Review of high-content screening applications in toxicology

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

Automated Neuron Detection in High-Content Fluorescence Microscopy Images Using Machine Learning

The study of neuronal morphology in relation to function, and the development of effective medicines to positively impact this relationship in patients suffering from neurodegenerative diseases, increasingly involves image-based high-content screening and analysis. The first critical step toward fully automated high-content image analyses in such studies is to detect all neuronal cells and distinguish them from possible non-neuronal cells or artifacts in the images. Here we investigate the performance of well-established machine learning techniques for this purpose. These include support vector machines, random forests, k-nearest neighbors, and generalized linear model classifiers, operating on an extensive set of image features extracted using the compound hierarchy of algorithms representing morphology, and the scale-invariant feature transform. We present experiments on a dataset of rat hippocampal neurons from our own studies to find the most suitable classifier(s) and subset(s) of features in the common practical setting where there is very limited annotated data for training. The results indicate that a random forests classifier using the right feature subset ranks best for the considered task, although its performance is not statistically significantly better than some support vector machine based classification models.

In vitro acute and developmental neurotoxicity screening: an overview of cellular platforms and high-throughput technical possibilities

Neurotoxicity and developmental neurotoxicity are important issues of chemical hazard assessment. Since the interpretation of animal data and their extrapolation to man is challenging, and the amount of substances with information gaps exceeds present animal testing capacities, there is a big demand for in vitro tests to provide initial information and to prioritize for further evaluation. During the last decade, many in vitro tests emerged. These are based on animal cells, human tumour cell lines, primary cells, immortalized cell lines, embryonic stem cells, or induced pluripotent stem cells. They differ in their read-outs and range from simple viability assays to complex functional endpoints such as neural crest cell migration. Monitoring of toxicological effects on differentiation often requires multiomics approaches, while the acute disturbance of neuronal functions may be analysed by assessing electrophysiological features. Extrapolation from in vitro data to humans requires a deep understanding of the test system biology, of the endpoints used, and of the applicability domains of the tests. Moreover, it is important that these be combined in the right way to assess toxicity. Therefore, knowledge on the advantages and disadvantages of all cellular platforms, endpoints, and analytical methods is essential when establishing in vitro test systems for different aspects of neurotoxicity. The elements of a test, and their evaluation, are discussed here in the context of comprehensive prediction of potential hazardous effects of a compound. We summarize the main cellular characteristics underlying neurotoxicity, present an overview of cellular platforms and read-out combinations assessing distinct parts of acute and developmental neurotoxicology, and highlight especially the use of stem cell-based test systems to close gaps in the available battery of tests.

High Content Imaging (HCI) on Miniaturized Three-Dimensional (3D) Cell Cultures

High content imaging (HCI) is a multiplexed cell staining assay developed for better understanding of complex biological functions and mechanisms of drug action, and it has become an important tool for toxicity and efficacy screening of drug candidates. Conventional HCI assays have been carried out on two-dimensional (2D) cell monolayer cultures, which in turn limit predictability of drug toxicity/efficacy in vivo; thus, there has been an urgent need to perform HCI assays on three-dimensional (3D) cell cultures. Although 3D cell cultures better mimic in vivo microenvironments of human tissues and provide an in-depth understanding of the morphological and functional features of tissues, they are also limited by having relatively low throughput and thus are not amenable to high-throughput screening (HTS). One attempt of making 3D cell culture amenable for HTS is to utilize miniaturized cell culture platforms. This review aims to highlight miniaturized 3D cell culture platforms compatible with current HCI technology.

Predicting Neuroinflammation in Morphine Tolerance for Tolerance Therapy from Immunostaining Images of Rat Spinal Cord

Long-term morphine treatment leads to tolerance which attenuates analgesic effect and hampers clinical utilization. Recent studies have sought to reveal the mechanism of opioid receptors and neuroinflammation by observing morphological changes of cells in the rat spinal cord. This work proposes a high-content screening (HCS) based computational method, HCS-Morph, for predicting neuroinflammation in morphine tolerance to facilitate the development of tolerance therapy using immunostaining images for astrocytes, microglia, and neurons in the spinal cord. HCS-Morph first extracts numerous HCS-based features of cellular phenotypes. Next, an inheritable bi-objective genetic algorithm is used to identify a minimal set of features by maximizing the prediction accuracy of neuroinflammation. Finally, a mathematic model using a support vector machine with the identified features is established to predict drug-treated images to assess the effects of tolerance therapy. The dataset consists of 15 saline controls (1 μl/h), 15 morphine-tolerant rats (15 μg/h), and 10 rats receiving a co-infusion of morphine (15 μg/h) and gabapentin (15 μg/h, Sigma). The three individual models of astrocytes, microglia, and neurons for predicting neuroinflammation yielded respective Jackknife test accuracies of 96.67%, 90.00%, and 86.67% on the 30 rats, and respective independent test accuracies of 100%, 90%, and 60% on the 10 co-infused rats. The experimental results suggest that neuroinflammation activity expresses more predominantly in astrocytes and microglia than in neuron cells. The set of features for predicting neuroinflammation from images of astrocytes comprises mean cell intensity, total cell area, and second-order geometric moment (relating to cell distribution), relevant to cell communication, cell extension, and cell migration, respectively. The present investigation provides the first evidence for the role of gabapentin in the attenuation of morphine tolerance from phenotypic changes of astrocytes and microglia. Based on neuroinflammation prediction, the proposed computer-aided image diagnosis system can greatly facilitate the development of tolerance therapy with anti-inflammatory drugs.

Histone acetylation inhibitors promote axon growth in adult dorsal root ganglia neurons

Intrinsic mechanisms that guide damaged axons to regenerate following spinal cord injury remain poorly understood. Manipulation of posttranslational modifications of key proteins in mature neurons could reinvigorate growth machinery after injury. One such modification is acetylation, a reversible process controlled by two enzyme families, the histone deacetylases (HDACs) and the histone acetyl transferases (HATs), acting in opposition. Whereas acetylated histones in the nucleus are associated with upregulation of growth-promoting genes, deacetylated tubulin in the axoplasm is associated with more labile microtubules, conducive to axon growth. This study investigates the effects of HAT and HDAC inhibitors on cultured adult dorsal root ganglia (DRG) neurons and shows that inhibition of HATs by anacardic acid or CPTH2 improves axon outgrowth, whereas inhibition of HDACs by TSA or tubacin inhibits axon growth. Anacardic acid increased the number of axons able to cross an inhibitory chondroitin sulfate proteoglycan border. Histone acetylation but not tubulin acetylation level was affected by HAT inhibitors, whereas tubulin acetylation levels were increased in the presence of the HDAC inhibitor tubacin. Although the microtubule-stabilizing drug taxol did not have an effect on the lengths of DRG axons, nocodazole decreased axon lengths. Determining the mechanistic basis will require future studies, but this study shows that inhibitors of HAT can augment axon growth in adult DRG neurons, with the potential of aiding axon growth over inhibitory substrates produced by the glial scar.

Rat embryonic hippocampus and induced pluripotent stem cell derived cultured neurons recover from laser-induced subaxotomy

Axonal injury and stress have long been thought to play a pathogenic role in a variety of neurodegenerative diseases. However, a model for studying single-cell axonal injury in mammalian cells and the processes of repair has not been established. The purpose of this study was to examine the response of neuronal growth cones to laser-induced axonal damage in cultures of embryonic rat hippocampal neurons and induced pluripotent stem cell (iPSC) derived human neurons. A 532-nm pulsed [Formula: see text] picosecond laser was focused to a diffraction limited spot at a precise location on an axon using a laser energy/power that did not rupture the cell membrane (subaxotomy). Subsequent time series images were taken to follow axonal recovery and growth cone dynamics. After laser subaxotomy, axons thinned at the damage site and initiated a dynamic cytoskeletal remodeling process to restore axonal thickness. The growth cone was observed to play a role in the repair process in both hippocampal and iPSC-derived neurons. Immunofluorescence staining confirmed structural tubulin damage and revealed initial phases of actin-based cytoskeletal remodeling at the damage site. The results of this study indicate that there is a repeatable and cross-species repair response of axons and growth cones after laser-induced damage.