Modern High-throughput Screening Research Articles

Mid-infrared chemical imaging of living cells enabled by plasmonic metasurfaces.

Mid-Infrared (MIR) chemical imaging provides rich chemical information of biological samples in a label-free and non-destructive manner. Yet, its adoption to live-cell analysis is limited by the strong attenuation of MIR light in water, often necessitating cell culture geometries that are incompatible with the prolonged viability of cells and with standard high-throughput workflow. Here, we introduce a new approach to MIR microscopy, where cells are imaged through their localized near-field interaction with a plasmonic metasurface. Chemical contrast of distinct molecular groups provided sub-cellular resolution images of the proteins, lipids, and nucleic acids in the cells that were collected using an inverted MIR microscope. Time-lapse imaging of living cells demonstrated that their behaviors, including motility, viability, and substrate adhesion, can be monitored over extended periods of time using low-power MIR light. The presented approach provides a method for the non-perturbative MIR imaging of living cells, which is well-suited for integration with modern high-throughput screening technologies for the label-free, high-content chemical imaging of living cells.

High-Throughput GPCRome Screen of Pollutants Reveals the Activity of Polychlorinated Biphenyls at Melatonin and Sphingosine-1-phosphate Receptors.

Exposure to environmental pollutants is linked to numerous toxic outcomes, warranting concern about the effect of pollutants on human health. To assess the threat of pollutant exposure, it is essential to understand their biological activity. Unfortunately, gaps remain for many pollutants' specific biological activity and molecular targets. A superfamily of signaling proteins, G-protein-coupled receptors (GPCRs), has been shown as potential targets for pollutant activity. However, research investigating the pollutant activity at the GPCRome is scarce. This work explores pollutant activity across a library of human GPCRs by leveraging modern high-throughput screening techniques devised for drug discovery and pharmacology. We designed and implemented a pilot screen of eight pollutants at 314 human GPCRs and discovered specific polychlorinated biphenyl (PCB) activity at sphingosine-1-phosphate and melatonin receptors. The method utilizes open-source resources available to academic and governmental institutions to enable future campaigns that screen large numbers of pollutants. Thus, we present a novel high-throughput approach to assess the biological activity and specific targets of pollutants.

Multifocal hyperspectral Raman imaging setup for multi-well plates

High-throughput screening has become widespread in drug discovery. This increases the need for efficient, parallelized spectral imaging techniques. However, current designs focus on microplate readers without the capability of measuring hyperspectral images or microscopy setups. In this work, a novel setup for parallelized hyperspectral Raman spectroscopy is presented. Utilizing a unique combination of a diffractive element and a micro-lens array in a tailored optical setup, it was possible to acquire 64 high-resolution Raman spectra from a multi-well plate simultaneously. This allows an enormous reduction in measurement time in comparison to sequential readers. Thus, the presented parallelized hyperspectral Raman spectroscopic setup provides a time saving way for modern high-throughput screening applications and the advantage of chemically selective real-time comparison between the different micro-well spots.

3D Bioprinted Hydrogel Microfluidic Devices for Parallel Drug Screening.

Conventional high-throughput screening (HTS) platforms suffer from the need for large cell volumes, high reagent consumption, significant assembly cost, and handling efforts. The assembly of three-dimensional (3D) bioprinted hydrogel-based microfluidic chips within platforms can address these problems. We present a continuous and seamless manufacturing approach to create a bioprinted microfluidic chips with a circular pattern scalable toward HTS platforms. Digital light processing 3D bioprinting is used to tune the local permeability of our chip, made of polyethylene glycol diacrylate and cell-laden gelatin methacryloyl, for creating predefined gradients of biochemical properties. We measured the flow-induced physical characteristics, the mass transport of drug agents, and the biological features of the proposed chip. We measured reactive oxygen species from the encapsulated cells through an integrated process and showed the capacity of the hydrogel-based chip for creating drug/agent gradients. This work introduces a chip design based on a hydrogel that can be changed and could be used for modern HTS platforms such as in vitro organoids.

Examining the evidence of non-monotonic dose-response in Androgen Receptor agonism high-throughput screening assay

Modern High-Throughput Screening (HTS) techniques allow to determine in vitro bioactivity of tens of thousands of chemicals within a relatively short period of time and tested compounds are usually interpreted as either active or inactive. The interpretation is mostly based on the assumption of monotonic dose-response. This approach ignores potential abnormal dose-response relationships, such as non-monotonic dose-response (NMDR). NMDR presents a serious challenge to toxicologists and pharmacologists, since they undermine the usefulness of such concepts as lowest-observed-adverse-effect level (LOAEL) and no-observed-adverse-effect level (NOAEL). The possible presence of the NMDR in Androgen receptor (AR) agonism was examined for a structurally diverse set of chemicals (~8 300 unique compounds) from Tox21 project library. The source of activity data is Tox21 AR agonism luciferase-based HTS on the MDA-MB-453 cell line. The examination of curve fitting for 35,328 dose-response data entries was based on modified version of existing criteria for determination of NMDR. The bias that arises from compounds' cytotoxicity and interference with firefly luciferase protein was also studied. The examination has shown evidence of NMDR for several compounds, including known AR antagonists (e. g. Cyproterone acetate) and other known endocrine disruptors (e. g. Tranilast). Compounds were divided into 3 groups based on chemical class, known biological activity profile and the shape of dose-response curve. The challenges of using HTS data to determine NMDR and benefits of this analysis are discussed

Developing a cell-bound detection system for the screening of oxidase activity using the fluorescent peroxide sensor roGFP2-Orp1.

Accurate yet efficient high-throughput screenings have emerged as essential technology for enzyme engineering via directed evolution. Modern high-throughput screening platforms for oxidoreductases are commonly assisted by technologies such as surface display and rely on emulsification techniques to facilitate single-cell analysis via fluorescence-activated cell sorting. Empowered by the dramatically increased throughput, the screening of significantly larger sequence spaces in acceptable time frames is achieved but usually comes at the cost of restricted applicability. In this work, we tackle this problem by utilizing roGFP2-Orp1 as a fluorescent one-component detection system for enzymatic H2O2 formation. We determined the kinetic parameters of the roGFP2-Orp1 reaction with H2O2 and established an efficient immobilization technique for the sensor on Saccharomyces cerevisiae cells employing the lectin Concanavalin A. This allowed to realize a peroxide-sensing shell on enzyme-displaying cells, a system that was successfully employed to screen for H2O2 formation of enzyme variants in a whole-cell setting.

Student-Designed High-Throughput Assays to Assess Effects of Growth Insults in Budding Yeast.

Modern high-throughput screening is a versatile method used widely in academic and industrial labs for genetic and pharmacological testing, but is underutilized in undergraduate laboratories. We designed an open inquiry-based laboratory activity exploiting a small-scale high-throughput screening assay of the budding yeast S. cerevisiae to promote active learning and introduce students to modern data collection and analysis techniques. In this activity, students at both the high school and university levels generate growth curves of S. cerevisiae using a microplate reader after treatment with their insult of choice. This lab presents an ideal opportunity for both high school and university students to actively learn about the process of science as well as high-throughput data collection and analysis in a cost-effective manner.

CrossCheck: an open-source web tool for high-throughput screen data analysis

Modern high-throughput screening methods allow researchers to generate large datasets that potentially contain important biological information. However, oftentimes, picking relevant hits from such screens and generating testable hypotheses requires training in bioinformatics and the skills to efficiently perform database mining. There are currently no tools available to general public that allow users to cross-reference their screen datasets with published screen datasets. To this end, we developed CrossCheck, an online platform for high-throughput screen data analysis. CrossCheck is a centralized database that allows effortless comparison of the user-entered list of gene symbols with 16,231 published datasets. These datasets include published data from genome-wide RNAi and CRISPR screens, interactome proteomics and phosphoproteomics screens, cancer mutation databases, low-throughput studies of major cell signaling mediators, such as kinases, E3 ubiquitin ligases and phosphatases, and gene ontological information. Moreover, CrossCheck includes a novel database of predicted protein kinase substrates, which was developed using proteome-wide consensus motif searches. CrossCheck dramatically simplifies high-throughput screen data analysis and enables researchers to dig deep into the published literature and streamline data-driven hypothesis generation. CrossCheck is freely accessible as a web-based application at http://proteinguru.com/crosscheck.

Drug repositioning in sarcomas and other rare tumors

Drug repositioning in sarcomas and other rare tumors

A parallel generalized relaxation method for high-performance image segmentation on GPUs

Fast and scalable software modules for image segmentation are needed for modern high-throughput screening platforms in Computational Biology. Indeed, accurate segmentation is one of the main steps to be applied in a basic software pipeline aimed to extract accurate measurements from a large amount of images. Image segmentation is often formulated through a variational principle, where the solution is the minimum of a suitable functional, as in the case of the Ambrosio–Tortorelli model. Euler–Lagrange equations associated with the above model are a system of two coupled elliptic partial differential equations whose finite-difference discretization can be efficiently solved by a generalized relaxation method, such as Jacobi or Gauss–Seidel, corresponding to a first-order alternating minimization scheme. In this work we present a parallel software module for image segmentation based on the Parallel Sparse Basic Linear Algebra Subprograms (PSBLAS), a general-purpose library for parallel sparse matrix computations, using its Graphics Processing Unit (GPU) extensions that allow us to exploit in a simple and transparent way the performance capabilities of both multi-core CPUs and of many-core GPUs. We discuss performance results in terms of execution times and speed-up of the segmentation module running on GPU as well as on multi-core CPUs, in the analysis of 2D gray-scale images of mouse embryonic stem cells colonies coming from biological experiments.

Direct synthesis of imino-C-nucleoside analogues and other biologically active iminosugars.

Iminosugars have attracted increasing attention as chemical probes, chaperones and leads for drug discovery. Despite several clinical successes, their de novo synthesis remains a significant challenge that also limits their integration with modern high-throughput screening technologies. Herein, we describe a unique synthetic strategy that converts a wide range of acetaldehyde derivatives into iminosugars and imino-C-nucleoside analogues in two or three straightforward transformations. We also show that this strategy can be readily applied to the rapid production of indolizidine and pyrrolizidine iminosugars. The high levels of enantio- and diastereoselectivity, excellent overall yields, convenience and broad substrate scope make this an appealing process for diversity-oriented synthesis, and should enable drug discovery efforts.

The future for early-stage tuberculosis drug discovery.

There is an urgent need for new and better drugs to treat tuberculosis due to lengthy and complex treatment regimens and a rising problem of drug resistance. Drug discovery efforts have increased over the past few years, with a larger focus on modern high-throughput screening technologies. A combination of target-based approaches, with the traditional empirical means of drug identification, has been complemented by the use of target-based phenotypic screens only recently made possibly with newer genetic tools. Using these approaches, a number of promising compound series have been discovered. However, significant problems remain in developing these into drugs. This review highlights recent advances in TB drug discovery, including an overview of screening campaigns, lessons learned and future directions.

UGPA: unified Gene Pathway Analyzer package for high-throughput genome-wide screening data provides mechanistic overview on human diseases

BackgroundGene set or pathway analysis (GPA) can provide a comprehensive mechanistic overview in delineating molecular pathoetiology of human diseases. Existing tools that can handle for GPA on both mutational and gene expressional aspects are limited. This leads to the development of uGPA (unified Gene Pathway Analyzer). MethodsuGPA package provides a unified solution to knowledge base-driven competitive GPA that can analyze genome-wide screening data derived from multiple platforms (next generation sequencing or microarray) and sample sources (genomic or transcriptomic) on both mutational and gene expressional perspectives. It allows for a quick assessment of perspective outline of gene sets on the studied disease through enrichment test modeled by cumulative hypergeometric distribution and reports the gene components detected with events (mutation or differential gene expression) in the gene sets. ResultsuGPA package has been successfully verified to be applicable on input data generated from NGS-based platforms (transcriptome sequencing and whole-exome sequencing). Genome-wide screening data from other platforms should also apply. ConclusionsuGPA package delivers a simple, flexible and platform-independent functionality to multiple aspects of GPA. It equips clinicians and scientists with a useful tool in studying molecular mechanism of human diseases using modern high-throughput genome-wide screening strategy, which assists personalized therapy.

Using information from historical high-throughput screens to predict active compounds.

Modern high-throughput screening (HTS) is a well-established approach for hit finding in drug discovery that is routinely employed in the pharmaceutical industry to screen more than a million compounds within a few weeks. However, as the industry shifts to more disease-relevant but more complex phenotypic screens, the focus has moved to piloting smaller but smarter chemically/biologically diverse subsets followed by an expansion around hit compounds. One standard method for doing this is to train a machine-learning (ML) model with the chemical fingerprints of the tested subset of molecules and then select the next compounds based on the predictions of this model. An alternative approach would be to take advantage of the wealth of bioactivity information contained in older (full-deck) screens using so-called HTS fingerprints, where each element of the fingerprint corresponds to the outcome of a particular assay, as input to machine-learning algorithms. We constructed HTS fingerprints using two collections of data: 93 in-house assays and 95 publicly available assays from PubChem. For each source, an additional set of 51 and 46 assays, respectively, was collected for testing. Three different ML methods, random forest (RF), logistic regression (LR), and naïve Bayes (NB), were investigated for both the HTS fingerprint and a chemical fingerprint, Morgan2. RF was found to be best suited for learning from HTS fingerprints yielding area under the receiver operating characteristic curve (AUC) values >0.8 for 78% of the internal assays and enrichment factors at 5% (EF(5%)) >10 for 55% of the assays. The RF(HTS-fp) generally outperformed the LR trained with Morgan2, which was the best ML method for the chemical fingerprint, for the majority of assays. In addition, HTS fingerprints were found to retrieve more diverse chemotypes. Combining the two models through heterogeneous classifier fusion led to a similar or better performance than the best individual model for all assays. Further validation using a pair of in-house assays and data from a confirmatory screen--including a prospective set of around 2000 compounds selected based on our approach--confirmed the good performance. Thus, the combination of machine-learning with HTS fingerprints and chemical fingerprints utilizes information from both domains and presents a very promising approach for hit expansion, leading to more hits. The source code used with the public data is provided.

Network Medicine and High Throughput Screening

A new paradigm is emerging in modern drug discovery. It is a fusion of traditional and modern medicine, phenotypic and targeted drug discovery, or systems and reductionist thinking. This is exemplified by using a combination of network medicine and high throughput screening. It blends the use of physiologically relevant biological systems with the high throughput and statistical robustness of modern assay technologies. The basic principles of network theory and tools of network medicine are described. Scale-free networks and their organizing principles are discussed. They are emergent properties of living, autopoietic systems. This includes networks of people who do high throughput screening (HTS), and microscopic networks of ions, metabolites, DNA, RNA, proteins, lipids, carbohydrates, viruses, bacteria, fungi, human cells and tissues. Databases have been constructed based on the metabolome, genome, transcriptome, proteome, lipidome, glycocode, virome, bacteriome and many others. Modern HTS can be used to examine the interactions of many parts of the complex human network. High content screening (HCS) can look at perturbations that occur when test compounds are added to single cells. Allo-network drugs can have effects far beyond a single protein and can be transmitted to other cells. Interactions and hidden connections can be revealed, with the goal of developing new drugs that have few, if any harmful side effects and are effective against multi-drug resistant cancer cells or bacteria.

A Review of Traditional Japanese Medicines and their Potential Mechanism of Action

Traditional Japanese herbal, or Kampo medicine was developed and modified from Chinese herbal medicine. After the Japanese government approved Kampo for clinical use, much attention has been paid to establishing scientific evidence for the effectiveness of these medicines. Recent progress has been made in elucidating the mechanisms of action of some types of Kampo medicine, including rikkunshito (RKT), daikenchuto, and yokukansan. In this review, we focused on identifying the target molecules and the active ingredients of RKT. Thus far, many target molecules have been implicated in the mechanism of action of Kampo medicines, such as ion channels, enzymes, and receptors. In particular, G protein-coupled receptors are attractive candidates for explaining herbal medicine activity. This is particularly true of RKT, which is composed of 8 independent, crude drug extracts. Recent reports have shown that RKT elicits its effects through dual action to the G protein-coupled receptors: inhibition of serotonergic 5-HT2C and 5-HT2B receptors and activation of ghrelin receptors via specific ingredients of RKT. In addition, we suggest that the identification of the effective ingredients from Kampo medicines could contribute to the discovery and development of new drugs by means of modern high-throughput drug screening technology.

Two effective methods for correcting experimental high-throughput screening data

Rapid advances in biomedical sciences and genetics have increased the pressure on drug development companies to promptly translate new knowledge into treatments for disease. Impelled by the demand and facilitated by technological progress, the number of compounds evaluated during the initial high-throughput screening (HTS) step of drug discovery process has steadily increased. As a highly automated large-scale process, HTS is prone to systematic error caused by various technological and environmental factors. A number of error correction methods have been designed to reduce the effect of systematic error in experimental HTS (Brideau et al., 2003; Carralot et al., 2012; Kevorkov and Makarenkov, 2005; Makarenkov et al., 2007; Malo et al., 2010). Despite their power to correct systematic error when it is present, the applicability of those methods in practice is limited by the fact that they can potentially introduce a bias when applied to unbiased data. We describe two new methods for eliminating systematic error from HTS data based on a prior knowledge of the error location. This information can be obtained using a specific version of the t-test or of the χ(2) goodness-of-fit test as discussed in Dragiev et al. (2011). We will show that both new methods constitute an important improvement over the standard practice of not correcting for systematic error at all as well as over the B-score correction procedure (Brideau et al., 2003) which is widely used in the modern HTS. We will also suggest a more general data preprocessing framework where the new methods can be applied in combination with the Well Correction procedure (Makarenkov et al., 2007). Such a framework will allow for removing systematic biases affecting all plates of a given screen as well as those relative to some of its individual plates.

A biomimetic polyketide-inspired approach to small-molecule ligand discovery

The discovery of new compounds for the pharmacological manipulation of protein function often embraces the screening of compound collections, and it is widely recognized that natural products offer beneficial characteristics as protein ligands. Much effort has therefore been focused on “natural product-like” libraries, yet the synthesis and screening of such libraries is often limited by one or more of the following: modest library sizes and structural diversity, conformational heterogeneity, and the costs associated with the substantial infrastructure of modern high-throughput screening centers. Here, we describe the design and execution of an approach to this broad problem by merging principles associated biologically-inspired oligomerization and the structure of polyketide-derived natural products. A novel class of chiral and conformationally-constrained oligomers is described (termed “chiral oligomers of pentenoic amides” – COPAs) that offers compatibility with split-and-pool methods and can be screened en masse in a batch mode. We demonstrate that a COPA library containing 160,000 compounds is a useful source of novel protein ligands by identifying a non-covalent synthetic ligand to the DNA-binding domain of the p53 transcription factor.

Toxicogenomics revolution in the optimisation of pharmaceutical drug development and drug safety evaluations.

Modern high throughput screening methods in toxicology need to be developed. These new approaches are necessary to provide more insight into potential human toxicity than the old traditional methods. Toxicogenomics investigate the changes in gene expression profile following exposure to a toxicant. It offers the potential to identify a human toxicant earlier in drug development and to detect human-specific toxicants that can cause no adverse reaction in rats. New transcript profiling technologies enable simultaneous measurement of the changes in expression of many hundreds or many thousands of genes. The availability of these methods has brought about significant revolutionary changes in many areas of investigative biology, where analyses of patterns of gene expression, rather than of individual genes, are being used. Toxicogenomics offers new opportunities for both mechanistic toxicity research and predictive toxicology. This paper gives an overview of the impact of toxicogenomics on the revolution of pharmaceutical drug development and drug safety evaluation. The basic approach within the frame work of high throughput screening is also a debate for discussion. The challenging issues with this novel technology in the selection screening of biomarkers for different therapeutic end points are also examined in this review. Key words: Toxicogenomics, genomics, transcript profiling, biomarkers, microarray, drug development, drug safety.

Detecting host factors involved in virus infection by observing the clustering of infected cells in siRNA screening images

Motivation: Detecting human proteins that are involved in virus entry and replication is facilitated by modern high-throughput RNAi screening technology. However, hit lists from different laboratories have shown only little consistency. This may be caused by not only experimental discrepancies, but also not fully explored possibilities of the data analysis. We wanted to improve reliability of such screens by combining a population analysis of infected cells with an established dye intensity readout.Results: Viral infection is mainly spread by cell–cell contacts and clustering of infected cells can be observed during spreading of the infection in situ and in vivo. We employed this clustering feature to define knockdowns which harm viral infection efficiency of human Hepatitis C Virus. Images of knocked down cells for 719 human kinase genes were analyzed with an established point pattern analysis method (Ripley's K-function) to detect knockdowns in which virally infected cells did not show any clustering and therefore were hindered to spread their infection to their neighboring cells. The results were compared with a statistical analysis using a common intensity readout of the GFP-expressing viruses and a luciferase-based secondary screen yielding five promising host factors which may suit as potential targets for drug therapy.Conclusion: We report of an alternative method for high-throughput imaging methods to detect host factors being relevant for the infection efficiency of viruses. The method is generic and has the potential to be used for a large variety of different viruses and treatments being screened by imaging techniques.Contact: r.eils@dkfz.de; r.koenig@dkfz.deSupplementary information: Supplementary data are available at Bioinformatics online.