High-throughput Screening Technologies Research Articles

Chemistry‐driven Hit‐to‐lead Optimization Guided by Structure‐based Approaches

For several decades, hit identification for drug discovery has been facilitated by developments in both fragment-based and high-throughput screening technologies. However, a major bottleneck in drug discovery projects continues to be the optimization of primary hits from screening campaigns in order to derive lead compounds. Computational chemistry or molecular modeling can play an important role during this hit-to-lead (H2L) stage by both suggesting putative optimizations and decreasing the number of compounds to be experimentally synthesized and evaluated. However, it is also crucial to consider the feasibility of organically synthesizing these virtually designed compounds. Furthermore, the generated molecules should have reasonable physicochemical properties and be medicinally relevant. This review focuses on chemistry-driven and structure-based computational methods that can be used to tackle the difficult problem of H2L optimization, with emphasis being placed on the strategy developed in our laboratory.

CRISPR/Cas-based customization of pooled CRISPR libraries.

Pooled CRISPR libraries are widely used in high-throughput screening to study various biological processes. Various pooled CRISPR libraries have been shared for CRISPR screens and useful tools have been developed to construct researcher’s own libraries, however, many researchers are struggling to create their own pooled CRISPR libraries: it is a time-consuming, labor-intensive, and expensive process. In this study, we develop a simple method to customize conventional pooled CRISPR libraries using the CRISPR/Cas9 system. We show that conventional pooled CRISPR libraries can be modified by eliminating gRNAs that target positive genes, enabling the identification of unknown target genes in CRISPR screening. CRISPR/Cas9 system can be applied as a precise tool for customizing conventional pooled CRISPR libraries and will broaden the scope of high-throughput screening technology.

Nanoliter Centrifugal Liquid Dispenser Coupled with Superhydrophobic Microwell Array Chips for High-Throughput Cell Assays.

Microfluidic systems have been regarded as a potential platform for high-throughput screening technology in drug discovery due to their low sample consumption, high integration, and easy operation. The handling of small-volume liquid is an essential operation in microfluidic systems, especially in investigating large-scale combination conditions. Here, we develop a nanoliter centrifugal liquid dispenser (NanoCLD) coupled with superhydrophobic microwell array chips for high-throughput cell-based assays in the nanoliter scale. The NanoCLD consists of a plastic stock block with an array of drilled through holes, a reagent microwell array chip (reagent chip), and an alignment bottom assembled together in a fixture. A simple centrifugation at 800 rpm can dispense ~160 nL reagents into microwells in 5 min. The dispensed reagents are then delivered to cells by sandwiching the reagent chip upside down with another microwell array chip (cell chip) on which cells are cultured. A gradient of doxorubicin is then dispensed to the cell chip using the NanoCLD for validating the feasibility of performing drug tests on our microchip platform. This novel nanoliter-volume liquid dispensing method is simple, easy to operate, and especially suitable for repeatedly dispensing many different reagents simultaneously to microwells.

Identification and Correction of Additive and Multiplicative Spatial Biases in Experimental High-Throughput Screening

Data generated by high-throughput screening (HTS) technologies are prone to spatial bias. Traditionally, bias correction methods used in HTS assume either a simple additive or, more recently, a simple multiplicative spatial bias model. These models do not, however, always provide an accurate correction of measurements in wells located at the intersection of rows and columns affected by spatial bias. The measurements in these wells depend on the nature of interaction between the involved biases. Here, we propose two novel additive and two novel multiplicative spatial bias models accounting for different types of bias interactions. We describe a statistical procedure that allows for detecting and removing different types of additive and multiplicative spatial biases from multiwell plates. We show how this procedure can be applied by analyzing data generated by the four HTS technologies (homogeneous, microorganism, cell-based, and gene expression HTS), the three high-content screening (HCS) technologies (area, intensity, and cell-count HCS), and the only small-molecule microarray technology available in the ChemBank small-molecule screening database. The proposed methods are included in the AssayCorrector program, implemented in R, and available on CRAN.

Small-Molecule Screening for Genetic Diseases.

The genetic determinants of many diseases, including monogenic diseases and cancers, have been identified; nevertheless, targeted therapy remains elusive for most. High-throughput screening (HTS) of small molecules, including high-content analysis (HCA), has been an important technology for the discovery of molecular tools and new therapeutics. HTS can be based on modulation of a known disease target (called reverse chemical genetics) or modulation of a disease-associated mechanism or phenotype (forward chemical genetics). Prominent target-based successes include modulators of transthyretin, used to treat transthyretin amyloidoses, and the BCR-ABL kinase inhibitor Gleevec, used to treat chronic myelogenous leukemia. Phenotypic screening successes include modulators of cystic fibrosis transmembrane conductance regulator, splicing correctors for spinal muscular atrophy, and histone deacetylase inhibitors for cancer. Synthetic lethal screening, in which chemotherapeutics are screened for efficacy against specific genetic backgrounds, is a promising approach that merges phenotype and target. In this article, we introduce HTS technology and highlight its contributions to the discovery of drugs and probes for monogenic diseases and cancer.

Expansion of cat-ELCCA for the Discovery of Small Molecule Inhibitors of the Pre-let-7-Lin28 RNA-Protein Interaction.

Dysregulation of microRNA (miRNA) expression has been linked to many human diseases; however, because of the challenges associated with RNA-targeted drug discovery, additional approaches are needed for probing miRNA biology. The emerging regulatory role of miRNA-binding proteins in miRNA maturation presents such an alternative strategy. Exploiting our laboratory’s click chemistry-based high-throughput screening (HTS) technology, catalytic enzyme-linked click chemistry assay or cat-ELCCA, we have designed a modular method by which to discover new chemical tools for manipulating pre-miRNA–miRNA–binding protein interactions. Using the pre-let-7d–Lin28 interaction as proof-of-concept, the results presented demonstrate how HTS using cat-ELCCA can enable the discovery of small molecules targeting RNA–protein interactions.

Skyline Computation for Low-Latency Image-Activated Cell Identification

High-throughput label-free single cell screening technology has been studied for noninvasive analysis of various kinds of cells. We tackle the cell identification task in the cell sorting system as a continuous skyline computation. Skyline Computation is a method for extracting interesting entries from a large population with multiple attributes. Jointed rooted-tree (JR-tree) is continuous skyline computation algorithm that manages entries using a rooted-tree structure. JR-tree delays extend the tree to deeper levels to accelerate tree construction and traversal. In this study, we proposed the JR-tree-based parallel skyline computation accelerator. We implemented it on a field-programmable gate array (FPGA). We evaluated our proposed software and hardware algorithms against an existing software algorithm using synthetic and real-world datasets.

Glycointeractions in bacterial pathogenesis.

Many important interactions between bacterial pathogens and their hosts are highly specific binding events that involve host or pathogen carbohydrate structures (glycans). Glycan interactions can mediate adhesion, invasion and immune evasion and can act as receptors for toxins. Several bacterial pathogens can also enzymatically alter host glycans to reveal binding targets, degrade the host cell glycans or alter the function of host glycoproteins. In recent years, high-throughput screening technologies, such as lectin, glycan and mucin microarrays, have transformed the field by identifying new bacterial-host glycointeractions, which are crucial for colonization, persistence and disease. In this Review, we discuss interactions involving both host and bacterial glycans that have a role in bacterial pathogenesis. We also highlight recent technological advances that have illuminated the glycoscience of microbial pathogenesis.

Development of a cost effective and robust AlphaScreen® platform for application.

The use of AlphaScreen® detection has allowed researchers to examine a wide variety of molecular interactions for use in high-throughput screening. However, the cost of Alpha reagents can often be prohibitory for extended screening campaigns or for young investigators with limited funding. To reduce assay costs, many labs have focused on miniaturization, while there have been limited efforts to scale down Alpha reagents. Thus, we describe the optimization of an AlphaScreen detection platform by systematically reducing the Alpha reagents down to 2.5μg/ml beads, without compromising assay integrity. We demonstrate that reducing bead concentration reduces detection costs substantially while yielding robust statistics. We believe this simple new protocol will enhance the future utilization of AlphaScreen technology in high-throughput screening.

Cell-Free Optogenetic Gene Expression System.

Optogenetic tools provide a new and efficient way to dynamically program gene expression with unmatched spatiotemporal precision. To date, their vast potential remains untapped in the field of cell-free synthetic biology, largely due to the lack of simple and efficient light-switchable systems. Here, to bridge the gap between cell-free systems and optogenetics, we studied our previously engineered one component-based blue light-inducible Escherichia coli promoter in a cell-free environment through experimental characterization and mathematical modeling. We achieved >10-fold dynamic expression and demonstrated rapid and reversible activation of the target gene to generate oscillatory response. The deterministic model developed was able to recapitulate the system behavior and helped to provide quantitative insights to optimize dynamic response. This in vitro optogenetic approach could be a powerful new high-throughput screening technology for rapid prototyping of complex biological networks in both space and time without the need for chemical induction.

Abstract P5-01-01: Personalized neoadjuvant treatment planning using optical metabolic imaging

Abstract Currently, there are no reliable methods to optimize treatment regimens for individual breast cancer patients. Oncologists choose drug treatments based on expression levels of tumor cell signaling receptors (i.e. HER2, ER, PR) and other factors, and assess whether the treatment is effective after significant time has passed. Unfortunately, over one third of patients exhibit resistance to their initial treatment, increasing their risk of future metastasis and death. Morbidities from sub-optimal drug regimens could be reduced with a personalized drug screen for breast cancer at the time of diagnosis. With the vast number of therapeutic options available to patients (>50 drugs approved with more on the way), a high-throughput screening technology is needed to accurately evaluate how a patient will respond to these options. Here we present Optical Metabolic Imaging (OMI) of tumor-derived organoids as a predictive drug screening platform for individual breast cancer patients. Changes in cell metabolism precede changes in tumor volume and thus present an earlier marker of treatment response. OMI is sensitive to these early changes by exploiting the intrinsic fluorescent properties of NAD(P)H and FAD, coenzymes of metabolic reactions. OMI endpoints include the optical redox ratio (the fluorescence intensity of NAD(P)H divided by the fluorescence intensity of FAD), as well as the fluorescence lifetimes of NAD(P)H and FAD. The redox ratio reflects the cellular redox balance, and the fluorescence lifetimes report on the binding activity of these coenzymes. OMI has the unique ability to non-invasively monitor metabolism in living, intact samples on the single-cell level, and can thus quantify heterogeneity in drug response. Changes were quantified at the single-cell level using the OMI Index, a linear combination of the optical redox ratio and the mean NAD(P)H and FAD fluorescence lifetimes. This index was derived using a multivariate analysis of variance and has been shown previously to correlate with treatment response in human cancer cells. OMI also allows for high-throughput screening of potential cancer drugs and drug combinations on patient biopsy samples cultured ex vivo. These samples are grown as organoids in a 3D matrix that mimics the natural tumor environment. Organoids were successfully generated from core needle biopsies of untreated breast tumors. These organoids were treated with the patient's prescribed neoadjuvant therapy, and early metabolic changes were quantified using OMI. Organoids grew from a variety of untreated breast tumor subtypes, and early metabolic changes could be resolved at the single-cell level after only 24 hours of treatment in vitro. In parallel, each patient's Residual Cancer Burden (RCB) score was quantified by a surgical pathologist after neoadjuvant treatment and served as gold standard validation of tumor drug response. Results from an early cohort of patients suggest that OMI could be used to predict patient clinical response to therapy. A linear combination of OMI variables measured in vitro in only 48 hours correlated strongly with patient RCB score (Pearson correlation coefficient=0.97, n=5). This methodology could allow oncologists to determine the ideal treatment regimen for their patients at the time of diagnosis. Citation Format: Sharick JT, Walsh AJ, Sanders ME, Kelley MC, Meszoely IM, Hooks MA, Burkard ME, Esbona K, Choudhary A, Skala MC. Personalized neoadjuvant treatment planning using optical metabolic imaging [abstract]. In: Proceedings of the 2017 San Antonio Breast Cancer Symposium; 2017 Dec 5-9; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2018;78(4 Suppl):Abstract nr P5-01-01.

Biomonitoring application in the exposome assessment

Biomonitoring can be applied to assess internal exposure and environmental exposure by exposure markers with providing internal exposure to biological characterization and individual exposure information, which is a key tool to evaluate the risk exposure to disease by biological alternation information. With the development of high throughput, broad spectrum and high efficiency screening and detection technology, biomonitoring is defined as traditional biological monitoring (targeted monitoring) and non targeted monitoring analysis (exposomic approaches). An exposomic approach differs from traditional biomonitoring in that it can theoretically include all exposures of potential health significance, whether they are derived from exogenous sources. Both traditional and nontraditional biomonitoring methods should be used to understand the complexity of exposures faced throughout the lifespan. Through hybrid approaches, emerging techniques and the integration of bioinformatics, and developing the detection methods for low abundance chemicals, improving the differentiation ability between endogenous and exogenous chemical, the health outcomes and exposures can be widely recognized and characterized, which can finally contribute to improving the precise prevention and intervention for diseases under the new exposomic model.

BJR-tree: fast skyline computation algorithm using dominance relation-based tree structure

High-throughput label-free single-cell screening technology has been studied for the noninvasive analysis of various kinds of cells. Selecting the prominent cells with extreme features from a large number of cells is an important and interesting problem, which we call the serendipitous searching problem (SSP). In the SSP, it is important to find entries located near the rind of the population in a multi-dimensional feature space. We tackle the SSP as a continuous skyline computation. Originally, the skyline computation was designed to extract interesting entries from a database with multi-attributes. The skyline points are continuously updated as the existing entries disappear and new entries arrive. In this paper, we propose a balanced jointed rooted tree (BJR-tree) algorithm and a non-dominated relation cache (ND-cache) for continuous skyline computation. The BJR-tree expresses the dominance relation as an arc and stores the “dominated” relations. The ND-cache complements the BJR-tree by reducing the recalculation of the dominance relations. The execution times of the BJR-tree and existing continuous skyline computation algorithms are compared on randomly constructed synthetic datasets with multiple temporal and spatial features. The BJR-tree is then evaluated on actually measured information of blood cells. On the two- and eight-dimensional synthetic datasets, the BJR-tree computed the continuous skylines approximately 3 and 70 times faster than LookOut, respectively. On real-world datasets, BJR-tree was approximately 2.4–3.2 times faster than LookOut.

Autoantibody Profiling Using Human Autoantigen Protein Array and AlphaScreen.

Autoantibodies that recognize self-antigens are believed to have close relationship diseases such as autoimmune diseases, cancer, and lifestyle diseases. Analysis of autoantibodies is essential for investigating pathology mechanisms, diagnosis, and therapeutics of these diseases. We developed autoantibody profiling assay using cell-free synthesized protein array and high-throughput screening technology. Our assay system can sensitively detect interaction between recombinant antigen protein and autoantibody and efficiently analyze autoantibody profiling in patients' sera.

Development and Implementation of an HTS-Compatible Assay for the Discovery of Selective Small-Molecule Ligands for Pre-microRNAs

microRNAs (miRNAs) are small gene regulatory RNAs, and their expression has been found to be dysregulated in a number of human diseases. To facilitate the discovery of small molecules capable of selectively modulating the activity of a specific miRNA, we have utilized new high-throughput screening technology targeting Dicer-mediated pre-miRNA maturation. Pilot screening of ~50,000 small molecules and ~33,000 natural product extract libraries against pre-miR-21 processing indicated the potential of our assay for this goal, yielding a campaign Z′ factor of 0.52 and an average plate signal-to-background (S/B) ratio of 13. Using two-dimensional screening against a second pre-miRNA, pre-let-7d, we evaluated the selectivity of confirmed hits. The results presented demonstrate how high-throughput screening can be used to identify selective small molecules for a target RNA.

FLECS Technology for High-Throughput Single-Cell Force Biology and Screening.

Dr. Ivan Pushkarsky from the Department of Bioengineering at UCLA and Forcyte Biotechnologies, Inc. was awarded The President's Innovation Award at the Annual Society of Biomolecular Imaging and Informatics meeting held in San Diego, September 2017. All cell types produce mechanical forces to serve important physiological roles. Since aberrant force-generating phenotypes directly lead to disease, cellular force-generation mechanisms are high-value targets for new therapies. Despite knowledge of their significance in disease, drug developers have had limited access to force-generating cellular phenotypes, especially in the context of high-throughput screening. To serve this valuable need, we have developed a platform microtechnology called "FLECS" that can acquire robust contractility data from 1000s of uniformly patterned single cells simultaneously and seamlessly integrates with the 96- and 384-well plate formats to facilitate large-scale drug screens. This perspective discusses the challenges facing existing laboratory methods for measuring cellular force in the context of drug discovery. It then provides an overview of the FLECS platform, describes how it was designed to overcome many of these challenges, and discusses some exciting work already accomplished with FLECS. It concludes by highlighting the platform nature of the technology and the potential value that it promises for a myriad of drug development efforts.

Omics-Based Platform for Studying Chemical Toxicity Using Stem Cells.

The new strategy for chemical toxicity testing and modeling is to use in vitro human cell-based assays in conjunction with quantitative high-throughput screening (qHTS) technology, to identify molecular mechanisms and predict in vivo responses. Stem cells are more physiologically relevant than immortalized cell lines because of their unique proliferation and differentiation potentials. We established a robust two stem cells-two lineages assay system, encompassing human mesenchymal stem cells (hMSCs) along osteogenesis and human induced pluripotent stem cells (hiPSCs) along hepatogenesis. We performed qHTS phenotypic screening of LOPAC1280 and identified 38 preliminary hits for hMSCs. This was followed by validation of a selected number of hits and determination of their IC50 values and mechanistic studies of idarubicin and cantharidin treatments using proteomics and bioinformatics. In general, hiPSCs were more sensitive than hMSCs to chemicals, and differentiated progenies were less sensitive than their progenitors. We showed that chemical toxicity depends on both stem cell types and their differentiation stages. Proteomics identified and quantified over 3000 proteins for both stem cells. Bioinformatics identified apoptosis and G2/M as the top pathways conferring idarubicin toxicity. Our Omics-based assays of stem cells provide mechanistic insights into chemical toxicity and may help prioritize chemicals for in-depth toxicological evaluations.

Guidelines for Microplate Selection in High Content Imaging.

Since the inception of commercialized automated high content screening (HCS) imaging devices in the mid to late 1990s, the adoption of media vessels typically used to house and contain biological specimens for interrogation has transitioned from microscope slides and petri dishes into multi-well microtiter plates called microplates. The early 96- and 384-well microplates commonly used in other high-throughput screening (HTS) technology applications were often not designed for optical imaging. Since then, modifications and the use of next-generation materials with improved optical clarity have enhanced the quality of captured images, reduced autofocusing failures, and empowered the use of higher power magnification objectives to resolve fine detailed measurements at the subcellular pixel level. The plethora of microplates and their applications requires practitioners of high content imaging (HCI) to be especially diligent in the selection and adoption of the best plates for running longitudinal studies or larger screening campaigns. While the highest priority in experimental design is the selection of the biological model, the choice of microplate can alter the biological response and ultimately may change the experimental outcome. This chapter will provide readers with background, troubleshooting guidelines, and considerations for choosing an appropriate microplate.

High-Throughput Protein Engineering Improves the Antigenicity and Stability of Soluble HIV-1 Envelope Glycoprotein SOSIP Trimers.

Soluble envelope glycoprotein (Env) trimers (SOSIP.664 gp140) are attractive HIV-1 vaccine candidates, with structures that mimic the native membrane-bound Env spike (gp160). Since engineering trimers can be limited by the difficulty of rationally predicting beneficial mutations, here we used a more comprehensive mutagenesis approach with the goal of identifying trimer variants with improved antigenic and stability properties. We created 341 cysteine pairs at predicted points of stabilization throughout gp140, 149 proline residue substitutions at every residue of the gp41 ectodomain, and 362 space-filling residue substitutions at every hydrophobic and aromatic residue in gp140. The parental protein target, the clade B strain B41 SOSIP.664 gp140, does not bind the broadly neutralizing antibody PGT151 and so was used here to identify improved variants that also provide insight into the structural basis for Env antigenicity. Each of the 852 mutants was expressed in human cells and screened for antigenicity using four different monoclonal antibodies (MAbs), including PGT151. We identified 29 trimer variants with antigenic improvements derived from each of the three mutagenesis strategies. We selected four variants (Q203F, T538F, I548F, and M629P) for more comprehensive biochemical, structural, and antigenicity analyses. The T538F substitution had the most beneficial effect overall, including restoration of the PGT151 epitope. The improved B41 SOSIP.664 trimer variants identified here may be useful for vaccine and structural studies.IMPORTANCE Soluble Env trimers have become attractive HIV-1 vaccine candidates, but the prototype designs are capable of further improvement through protein engineering. Using a high-throughput screening technology (shotgun mutagenesis) to create and evaluate 852 variants, we were able to identify sequence changes that were beneficial to the antigenicity and stability of soluble trimers based on the clade B B41 env gene. The strategies described here may be useful for identifying a wider range of antigenically and structurally improved soluble trimers based on multiple genotypes for use in programs intended to create a broadly protective HIV-1 vaccine.

Identification and correction of spatial bias are essential for obtaining quality data in high-throughput screening technologies

Spatial bias continues to be a major challenge in high-throughput screening technologies. Its successful detection and elimination are critical for identifying the most promising drug candidates. Here, we examine experimental small molecule assays from the popular ChemBank database and show that screening data are widely affected by both assay-specific and plate-specific spatial biases. Importantly, the bias affecting screening data can fit an additive or multiplicative model. We show that the use of appropriate statistical methods is essential for improving the quality of experimental screening data. The presented methodology can be recommended for the analysis of current and next-generation screening data.