Measuring stomatal and guard cell metrics for plant physiology and growth using StoManager1.

Automated guard cell detection and measurement are vital for understanding plant physiological performance and ecological functioning in global water and carbon cycles. Most current methods for measuring guard cells and stomata are laborious, time-consuming, prone to bias, and limited in scale. We developed StoManager1, a high-throughput tool utilizing geometrical, mathematical algorithms, and convolutional neural networks to automatically detect, count, and measure over 30 guard cell and stomatal metrics, including guard cell and stomatal area, length, width, stomatal aperture area/guard cell area, orientation, stomatal evenness, divergence, and aggregation index. Combined with leaf functional traits, some of these StoManager1-measured guard cell and stomatal metrics explained 90% and 82% of tree biomass and intrinsic water use efficiency (iWUE) variances in hardwoods, making them substantial factors in leaf physiology and tree growth. StoManager1 demonstrated exceptional precision and recall (mAP@0.5 over 0.96), effectively capturing diverse stomatal properties across over 100 species. StoManager1 facilitates the automation of measuring leaf stomatal and guard cells, enabling broader exploration of stomatal control in plant growth and adaptation to environmental stress and climate change. This has implications for global gross primary productivity (GPP) modeling and estimation, as integrating stomatal metrics can enhance predictions of plant growth and resource usage worldwide. Easily accessible open-source code and standalone Windows executable applications are available on a GitHub repository (https://github.com/JiaxinWang123/StoManager1) and Zenodo (https://doi.org/10.5281/zenodo.7686022).

Nucleic acid hybridization-based detection of pathogenic RNA using microscale thermophoresis.

Infectious diseases are one of the world's leading causes of morbidity. Their rapid spread emphasizes the need for accurate and fast diagnostic methods for large-scale screening. Here, we describe a robust method for the detection of pathogens based on microscale thermophoresis (MST). The method involves the hybridization of a fluorescently labeled DNA probe to a target RNA and the assessment of thermophoretic migration of the resulting complex in solution within a 2 to 30-time window. We found that the thermophoretic migration of the nucleic acid-based probes is primarily determined by the fluorescent molecule used, rather than the nucleic acid sequence of the probe. Furthermore, a panel of uniformly labeled probes that bind to the same target RNA yields a more responsive detection pattern than a single probe, and moreover, can be used for the detection of specific pathogen variants. In addition, intercalating agents (ICA) can be used to alter migration directionality to improve detection sensitivity and resolving power by several orders of magnitude. We show that this approach can rapidly diagnose viral SARS-CoV2, influenza H1N1, artificial pathogen targets, and bacterial infections. Furthermore, it can be used for anti-microbial resistance testing within 2 h, demonstrating its diagnostic potential for early pathogen detection.

High-Throughput-Methyl-Reading (HTMR) assay: a solution based on nucleotide methyl-binding proteins enables large-scale screening for DNA/RNA methyltransferases and demethylases.

Epigenetic therapy has significant potential for cancer treatment. However, few small potent molecules have been identified against DNA or RNA modification regulatory proteins. Current approaches for activity detection of DNA/RNA methyltransferases and demethylases are time-consuming and labor-intensive, making it difficult to subject them to high-throughput screening. Here, we developed a fluorescence polarization-based 'High-Throughput Methyl Reading' (HTMR) assay to implement large-scale compound screening for DNA/RNA methyltransferases and demethylases-DNMTs, TETs, ALKBH5 and METTL3/METTL14. This assay is simple to perform in a mix-and-read manner by adding the methyl-binding proteins MBD1 or YTHDF1. The proteins can be used to distinguish FAM-labelled substrates or product oligonucleotides with different methylation statuses catalyzed by enzymes. Therefore, the extent of the enzymatic reactions can be coupled with the variation of FP binding signals. Furthermore, this assay can be effectively used to conduct a cofactor competition study. Based on the assay, we identified two natural products as candidate compounds for DNMT1 and ALKBH5. In summary, this study outlines a powerful homogeneous approach for high-throughput screening and evaluating enzymatic activity for DNA/RNA methyltransferases and demethylases that is cheap, easy, quick, and highly sensitive.

Label-free cell assays to determine compound uptake or drug action using MALDI-TOF mass spectrometry.

Cell-based assays for compound screening and profiling are fundamentally important in life sciences, chemical biology and pharmaceutical research. Most cell assays measure the amount of a single reporter molecule or cellular endpoint, and require the use of fluorescence or other labeled materials. Consequently, there is high demand for label-free technologies that enable multiple biomolecules or endpoints to be measured simultaneously. Here, we describe how to develop, optimize and validate MALDI-TOF mass spectrometry (MS) cell assays that can be used to measure cellular uptake of transporter substrates, to monitor cellular drug target engagement or to discover cellular drug-response markers. In uptake assays, intracellular accumulation of a transporter substrate and its inhibition by test compounds is measured. In drug response assays, changes to multiple cellular metabolites or to abundant posttranslational protein modifications are monitored as reporters of drug activity. We detail a ten-part optimization protocol with every part taking 1-2 d that leads to a final 2 d optimized procedure, which includes cell treatment, transfer, MALDI MS-specific sample preparation, quantification using stable-isotope-labeled standards, MALDI-TOF MS data acquisition, data processing and analysis. Key considerations for validation and automation of MALDI-TOF MS cell assays are outlined. Overall, label-free MS cell-based assays offer speed, sensitivity, accuracy and versatility in drug research.

BonMOLière: Small-Sized Libraries of Readily Purchasable Compounds, Optimized to Produce Genuine Hits in Biological Screens across the Protein Space.

Experimental screening of large sets of compounds against macromolecular targets is a key strategy to identify novel bioactivities. However, large-scale screening requires substantial experimental resources and is time-consuming and challenging. Therefore, small to medium-sized compound libraries with a high chance of producing genuine hits on an arbitrary protein of interest would be of great value to fields related to early drug discovery, in particular biochemical and cell research. Here, we present a computational approach that incorporates drug-likeness, predicted bioactivities, biological space coverage, and target novelty, to generate optimized compound libraries with maximized chances of producing genuine hits for a wide range of proteins. The computational approach evaluates drug-likeness with a set of established rules, predicts bioactivities with a validated, similarity-based approach, and optimizes the composition of small sets of compounds towards maximum target coverage and novelty. We found that, in comparison to the random selection of compounds for a library, our approach generates substantially improved compound sets. Quantified as the "fitness" of compound libraries, the calculated improvements ranged from +60% (for a library of 15,000 compounds) to +184% (for a library of 1000 compounds). The best of the optimized compound libraries prepared in this work are available for download as a dataset bundle ("BonMOLière").

Evaluation of ELISA-Based Multiplex Peptides for the Detection of Human Serum Antibodies Induced by Zika Virus Infection across Various Countries.

Zika virus (ZIKV) is a mosquito-borne Flavivirus with a positive-sense RNA genome, which are generally transmitted through the bite of an infected Aedes mosquito. ZIKV infections could be associated with neurological sequelae that, and otherwise produces similar clinical symptoms as other co-circulating pathogens. Past infection with one member of the Flavivirus genus often induces cross-reactive antibodies against other flaviruses. These attributes complicate the ability to differentially diagnose ZIKV infection from other endemic mosquito-borne viruses, making it both a public health issue as well as a diagnostic challenge. We report the results from serological analyses using arbovirus-specific peptides on 339 samples that were previously collected from 6 countries. Overall, we found that our multiplexed peptide-based ELISA was highly efficient for identifying ZIKV antibodies as early as 2 weeks post infection, and that it correlates with microneutralization, plaque reduction neutralization tests (PRNTs) and commercial tests for ZIKV in previously characterized samples. We observed that seropositivity varied by patient cohort, reflecting the sampling period in relation to the 2015-2016 ZIKV outbreak. This work evaluates the accuracy, specificity, and sensitivity of our peptide-based ELISA method for detecting ZIKV antibodies from geographically diverse regions. These findings can contribute to ongoing serological methods development and can be adapted for use in future studies.

Acoustic Ejection Mass Spectrometry: A Fully Automatable Technology for High-Throughput Screening in Drug Discovery.

Acoustic droplet ejection (ADE)-open port interface (OPI)-mass spectrometry (MS) has recently been introduced as a versatile analytical method that combines fast and contactless acoustic sampling with sensitive and accurate electrospray ionization (ESI)-MS-based analyte detection. The potential of the technology to provide label-free measurements in subsecond analytical cycle times makes it an attractive option for high-throughput screening (HTS). Here, we report the first implementation of ADE-OPI-MS in a fully automated HTS environment, based on the example of a biochemical assay aiming at the identification of small-molecule inhibitors of the cyclic guanosine monophosphate-adenosine monophosphate (GMP-AMP) synthase (cGAS). First, we describe the optimization of the method to enable sensitive and accurate determination of enzyme activity and inhibition in miniaturized 1536-well microtiter plate format. Then we show both results from a validation single-concentration screen using a test set of 5500 compounds, and the subsequent concentration-response testing of selected hits in direct comparison with a previously established matrix-assisted laser desorption/ionization time-of-flight (MALDI-TOF) readout. Finally, we present the development of an in-line OPI cleaning procedure aiming to match the instrument robustness required for large-scale HTS campaigns. Overall, this work points to critical method development parameters and provides guidance for the establishment of integrated ADE-OPI-MS as HTS-compatible technology for early drug discovery.

Luminescence Energy Transfer-Based Screening and Target Engagement Approaches for Chemical Biology and Drug Discovery.

Luminescence is characterized by the spontaneous emission of light resulting from either chemical or biological reactions. Because of their high sensitivity, reduced background interference, and applicability to numerous situations, luminescence-based assay strategies play an essential role in early-stage drug discovery. Newer developments in luminescence-based technologies have dramatically affected the ability of researchers to investigate molecular binding events. At the forefront of these developments are the nano bioluminescence resonance energy transfer (NanoBRET) and amplified luminescent proximity homogeneous assay (Alpha) technologies. These technologies have opened up numerous possibilities for analyzing the molecular biophysical properties of complexes in environments such as cell lysates. Moreover, NanoBRET enables the validation and quantitation of the interactions between therapeutic targets and small molecules in live cells, representing an essential benchmark for preclinical drug discovery. Both techniques involve proximity-based luminescence energy transfer, in which excited-state energy is transferred from a donor to an acceptor, where the efficiency of transfer depends on proximity. Both approaches can be applied to high-throughput compound screening in biological samples, with the NanoBRET assay providing opportunities for live-cell screening. Representative applications of both technologies for assessing physical interactions and associated challenges are discussed.

Cell-based assay for ciliopathy patients to improve accurate diagnosis using ALPACA.

Skeletal ciliopathies are a group of disorders caused by dysfunction of the cilium, a small signaling organelle present on nearly every vertebrate cell. This group of disorders is marked by genetic and clinical heterogeneity, which complicates accurate diagnosis. In this study, we developed a robust, standardized immunofluorescence approach to accurately diagnose a subset of these disorders. Hereto we determined and compared the cilium phenotype of healthy individuals to patients from three different ciliopathy subgroups, using skin-derived fibroblasts. The cilium phenotype assay consists of three parameters; (1) ciliogenesis, based on the presence or absence of cilium markers, (2) cilium length, measured by the combined signal of an axonemal and a cilium membrane marker, and (3) retrograde intraflagellar transport (IFT), quantified by the area of the ciliary tip. Analysis of the cilium phenotypic data yielded comparable and reproducible results and in addition, displayed identifiable clusters for healthy individuals and two ciliopathy subgroups, i.e. ATD and CED. Our results illustrate that standardized analysis of the cilium phenotype can be used to discriminate between ciliopathy subgroups. Therefore, we believe that standardization of functional assays analyzing cilium phenotypic data can provide additional proof for conclusive diagnosis of ciliopathies, which is essential for routine diagnostic care.

Direct single-molecule imaging for diagnostic and blood screening assays.

Every year, over 100 million units of donated blood undergo mandatory screening for HIV, hepatitis B, hepatitis C, and syphilis worldwide. Often, donated blood is also screened for human T cell leukemia-lymphoma virus, Chagas, dengue, Babesia, cytomegalovirus, malaria, and other infections. Several billion diagnostic tests are performed annually around the world to measure more than 400 biomarkers for cardiac, cancer, infectious, and other diseases. Considering such volumes, every improvement in assay performance and/or throughput has a major impact. Here, we show that medically relevant assay sensitivities and specificities can be fundamentally improved by direct single-molecule imaging using regular epifluorescence microscopes. In current microparticle-based assays, an ensemble of bound signal-generating molecules is measured as a whole. By contrast, we acquire intensity profiles to identify and then count individual fluorescent complexes bound to targets on antibody-coated microparticles. This increases the signal-to-noise ratio and provides better discrimination over nonspecific effects. It brings the detection sensitivity down to the attomolar (10-18 M) for model assay systems and to the low femtomolar (10-16 M) for measuring analyte in human plasma. Transitioning from counting single-molecule peaks to averaging pixel intensities at higher analyte concentrations enables a continuous linear response from 10-18 to 10-5 M. Additionally, our assays are insensitive to microparticle number and volume variations during the binding reaction, eliminating the main source of uncertainties in standard assays. Altogether, these features allow for increased assay sensitivity, wide linear detection ranges, shorter incubation times, simpler assay protocols, and minimal reagent consumption.

Brief Guide: Experimental Strategies for High-Quality Hit Selection from Small-Molecule Screening Campaigns.

Small-molecule screening is a powerful approach to identify modulators of either specific biological targets or cellular pathways with phenotypic endpoints. A myriad of assay technologies are available to assess the activity of enzymes, monitor protein-protein interactions, measure transcription factor activity in reporter assays, or detect cellular features and activities using high-content imaging. A common challenge during small-molecule screening is, however, the presence of hit compounds generating assay interference, thereby producing false-positive hits. Thus, efforts are needed to uncover such interferences to prioritize high-quality hits for further analysis. This process encompasses (1) computational approaches to flag undesirable compounds, and (2) the use of experimental approaches like counter, orthogonal, and cellular fitness screens to identify and eliminate artifacts. In this brief guide, we provide an overview for first-time users, highlighting experimental screening strategies to prioritize high-quality bioactive hits from high-throughput screening/high-content screening (HTS/HCS) campaigns.

A Novel High-Throughput FLIPR Tetra-Based Method for Capturing Highly Confluent Kinetic Data for Structure-Kinetic Relationship Guided Early Drug Discovery.

Target engagement by small molecules is necessary for producing a physiological outcome. In the past, a lot of emphasis was placed on understanding the thermodynamics of such interactions to guide structure-activity relationships. It is becoming clearer, however, that understanding the kinetics of the interaction between a small-molecule inhibitor and the biological target [structure-kinetic relationship (SKR)] is critical for selection of the optimum candidate drug molecule for clinical trial. However, the acquisition of kinetic data in a high-throughput manner using traditional methods can be labor intensive, limiting the number of molecules that can be tested. As a result, in-depth kinetic studies are often carried out on only a small number of compounds, and usually at a later stage in the drug discovery process. Fundamentally, kinetic data should be used to drive key decisions much earlier in the drug discovery process, but the throughput limitations of traditional methods preclude this. A major limitation that hampers acquisition of high-throughput kinetic data is the technical challenge in collecting substantially confluent data points for accurate parameter estimation from time course analysis. Here, we describe the use of the fluorescent imaging plate reader (FLIPR), a charge-coupled device (CCD) camera technology, as a potential high-throughput tool for generating biochemical kinetic data with smaller time intervals. Subsequent to the design and optimization of the assay, we demonstrate the collection of highly confluent time-course data for various kinase protein targets with reasonable throughput to enable SKR-guided medicinal chemistry. We select kinase target 1 as a special case study with covalent inhibition, and demonstrate methods for rapid and detailed analysis of the resultant kinetic data for parameter estimation. In conclusion, this approach has the potential to enable rapid kinetic studies to be carried out on hundreds of compounds per week and drive project decisions with kinetic data at an early stage in drug discovery.

High throughput screening for expanded CTG repeats in myotonic dystrophy type 1 using melt curve analysis.

BACKGROUND: Myotonic dystrophy type 1 (DM1) is caused by CTG repeat expansions in the DMPK gene and is the most common form of muscular dystrophy. Patients can have long delays from onset to diagnosis, since clinical signs and symptoms are often nonspecific and overlapping with other disorders. Clinical genetic testing by Southern blot or triplet-primed PCR (TP-PCR) is technically challenging and cost prohibitive for population surveys. METHODS: Here, we present a high throughput, low-cost screening tool for CTG repeat expansions using TP-PCR followed by high resolution melt curve analysis with saturating concentrations of SYBR GreenER dye. RESULTS: We determined that multimodal melt profiles from the TP-PCR assay are a proxy for amplicon length stoichiometry. In a screen of 10,097 newborn blood spots, melt profile analysis accurately reflected the tri-modal distribution of common alleles from 5 to 35 CTG repeats, and identified the premutation and full expansion alleles. CONCLUSION: We demonstrate that robust detection of expanded CTG repeats in a single tube can be achieved from samples derived from specimens with minimal template DNA such as dried blood spots (DBS). This technique is readily adaptable to large-scale testing programs such as population studies and newborn screening programs.

Transforming early pharmaceutical assessment of genotoxicity: applying statistical learning to a high throughput, multi end point in vitro micronucleus assay.

To provide a comprehensive analysis of small molecule genotoxic potential we have developed and validated an automated, high-content, high throughput, image-based in vitro Micronucleus (IVM) assay. This assay simultaneously assesses micronuclei and multiple additional cellular markers associated with genotoxicity. Acoustic dosing (≤ 2 mg) of compound is followed by a 24-h treatment and a 24-h recovery period. Confocal images are captured [Cell Voyager CV7000 (Yokogawa, Japan)] and analysed using Columbus software (PerkinElmer). As standard the assay detects micronuclei (MN), cytotoxicity and cell-cycle profiles from Hoechst phenotypes. Mode of action information is primarily determined by kinetochore labelling in MN (aneugencity) and γH2AX foci analysis (a marker of DNA damage). Applying computational approaches and implementing machine learning models alongside Bayesian classifiers allows the identification of, with 95% accuracy, the aneugenic, clastogenic and negative compounds within the data set (Matthews correlation coefficient: 0.9), reducing analysis time by 80% whilst concurrently minimising human bias. Combining high throughput screening, multiparametric image analysis and machine learning approaches has provided the opportunity to revolutionise early Genetic Toxicology assessment within AstraZeneca. By multiplexing assay endpoints and minimising data generation and analysis time this assay enables complex genotoxicity safety assessments to be made sooner aiding the development of safer drug candidates.

Bisindolylmaleimide IX: A novel anti-SARS-CoV2 agent targeting viral main protease 3CLpro demonstrated by virtual screening pipeline and in-vitro validation assays.

SARS-CoV-2, the virus that causes COVID-19 consists of several enzymes with essential functions within its proteome. Here, we focused on repurposing approved and investigational drugs/compounds. We targeted seven proteins with enzymatic activities known to be essential at different stages of the viral cycle including PLpro, 3CLpro, RdRP, Helicase, ExoN, NendoU, and 2'-O-MT. For virtual screening, energy minimization of a crystal structure of the modeled protein was carried out using the Protein Preparation Wizard (Schrodinger LLC 2020-1). Following active site selection based on data mining and COACH predictions, we performed a high-throughput virtual screen of drugs and investigational molecules (n = 5903). The screening was performed against viral targets using three sequential docking modes (i.e., HTVS, SP, and XP). Virtual screening identified ∼290 potential inhibitors based on the criteria of energy, docking parameters, ligand, and binding site strain and score. Drugs specific to each target protein were further analyzed for binding free energy perturbation by molecular mechanics (prime MM-GBSA) and pruning the hits to the top 32 candidates. The top lead from each target pool was further subjected to molecular dynamics simulation using the Desmond module. The resulting top eight hits were tested for their SARS-CoV-2 anti-viral activity in-vitro. Among these, a known inhibitor of protein kinase C isoforms, Bisindolylmaleimide IX (BIM IX), was found to be a potent inhibitor of SARS-CoV-2. Further, target validation through enzymatic assays confirmed 3CLpro to be the target. This is the first study that has showcased BIM IX as a COVID-19 inhibitor thereby validating our pipeline.

High-dimensional variable selection for ordinal outcomes with error control.

Many high-throughput genomic applications involve a large set of potential covariates and a response which is frequently measured on an ordinal scale, and it is crucial to identify which variables are truly associated with the response. Effectively controlling the false discovery rate (FDR) without sacrificing power has been a major challenge in variable selection research. This study reviews two existing variable selection frameworks, model-X knockoffs and a modified version of reference distribution variable selection (RDVS), both of which utilize artificial variables as benchmarks for decision making. Model-X knockoffs constructs a 'knockoff' variable for each covariate to mimic the covariance structure, while RDVS generates only one null variable and forms a reference distribution by performing multiple runs of model fitting. Herein, we describe how different importance measures for ordinal responses can be constructed that fit into these two selection frameworks, using either penalized regression or machine learning techniques. We compared these measures in terms of the FDR and power using simulated data. Moreover, we applied these two frameworks to high-throughput methylation data for identifying features associated with the progression from normal liver tissue to hepatocellular carcinoma to further compare and contrast their performances.

Establishment of a rapid ELISPOT assay for influenza virus titration and neutralizing antibody detection.

Seasonal influenza is an acute respiratory infection causing around 500 000 global deaths annually. There is an unmet medical need to develop more effective antiviral drugs and vaccines against influenza infection. A rapid, accurate, high-throughput titration assay for influenza virus particles or neutralizing antibodies would be extremely useful in these research fields. However, commonly used methods such as tissue culture infective dose and plaque-forming units (PFU) for virus particle quantification, and the plaque reduction neutralization test (PRNT) for antibody determination are time-consuming, laborious, and have limited accuracy. In this study, we developed an efficient assay based on the enzyme-linked immunospot (ELISPOT) technique for the influenza virus and neutralizing antibody titration. Two broad-spectrum antibodies recognizing the nucleoproteins of influenza A and B viruses were used in the assay to broadly and highly sensitively detect influenza virus-infected cells at 16 hours postinfection. An optimized cell culture medium with no tosyl phenylalanyl chloromethyl ketone trypsin and high dose oseltamivir acid was used to improve quantitation accuracy. This ELISPOT assay displayed a good correlation (R2 = 0.9851) with the PFU assay when used to titrate 30 influenza virus isolates. The assay was also applied to measure influenza-neutralizing antibodies in 40 human sera samples, showing a good correlation (R2 = 0.9965) with the PRNT assay. This ELISPOT titration assay is a rapid, accurate, high-throughput assay for quantification of influenza virus and neutralizing antibodies, and provides a powerful tool for research into and development of drugs and vaccines against influenza.

Hit Discovery for Public Target Programs in the European Lead Factory: Experiences and Output from Assay Development and Ultra-High-Throughput Screening.

The European Lead Factory (ELF) consortium provides European academics and small and medium enterprises access to ~0.5 million unique compounds, a state-of-the-art ultra-high-throughput screening (u-HTS) platform, and industrial early drug discovery (DD) expertise with the aim of delivering innovative DD starting points. From 2013 to 2018, 154 proposals for eight target classes in seven therapeutic areas were submitted to the ELF consortium, 88 of which were accepted by the selection committee. During this period, 76 primary assays based on seven different readout technologies were optimized and mainly miniaturized to 1536-well plates. In total, 72 u-HTS campaigns were carried out, and follow-up work including hit triage through orthogonal, deselection, selectivity, and biophysical assays were finalized. This ambitious project showed that besides the quality of the compound library and the primary assay, the success of centralized u-HTS of large compound libraries across many target classes, various assay types, and different readout technologies is also largely dependent on the capacity and flexibility of the automation on one hand and the hit-triaging phase on the other, particularly because of undesired compound-assay interference. Thus far, the delivered hit lists from the ELF consortium have resulted in spinoffs, patents, in vivo proof of concepts, preclinical development programs, peer-reviewed publications, PhD theses, and much more, demonstrating early success indications.

Validation of a High-Throughput Calcium Mobilization Assay for the Human Trace Amine-Associated Receptor 1.

The human trace amine-associated receptor 1 (hTAAR1) is a G protein-coupled receptor (GPCR) that is widely expressed in monoaminergic nuclei in the central nervous system and has therapeutic potential for multiple diseases, including drug addiction and schizophrenia. Thus, identification of novel hTAAR1 ligands is critical to advancing our knowledge of hTAAR1 function and to the development of therapeutics for a wide range of diseases. Herein we describe the development of a robust, 3-addition high-throughput screening (HTS) calcium mobilization assay using stable CHO-Gαq16-hTAAR1 cells, which functionally couple hTAAR1 to the promiscuous Gαq16 protein and thus allow signal transduction to occur through mobilization of internal calcium. Our previously established 96-well hTAAR1 assay was first miniaturized to the 384-well format and optimized to provide an assay with a Z' factor of 0.84, which is indicative of a robust HTS assay. Using the 3-addition protocol, 22,000 compounds were screened and yielded a ~1% agonist hit rate and a ~0.2% antagonist hit rate. Of the antagonist hits, two confirmed hits are the most potent hTAAR1 antagonists identified to date (IC50 = 206 and 281 nM). While scientists have been studying hTAAR1 for years, the lack of suitable hTAAR1 antagonists has been a major roadblock for studying the basic pharmacology of hTAAR1. Thus, these new ligands will serve as valuable tools to study hTAAR1-mediated signaling mechanisms, therapeutic potential, and in vivo functions.

Comparison of Approaches for Determining Bioactivity Hits from High-Dimensional Profiling Data.

Phenotypic profiling assays are untargeted screening assays that measure a large number (hundreds to thousands) of cellular features in response to a stimulus and often yield diverse and unanticipated profiles of phenotypic effects, leading to challenges in distinguishing active from inactive treatments. Here, we compare a variety of different strategies for hit identification in imaging-based phenotypic profiling assays using a previously published Cell Painting data set. Hit identification strategies based on multiconcentration analysis involve curve fitting at several levels of data aggregation (e.g., individual feature level, aggregation of similarly derived features into categories, and global modeling of all features) and on computed metrics (e.g., Euclidean and Mahalanobis distance metrics and eigenfeatures). Hit identification strategies based on single-concentration analysis included measurement of signal strength (e.g., total effect magnitude) and correlation of profiles among biological replicates. Modeling parameters for each approach were optimized to retain the ability to detect a reference chemical with subtle phenotypic effects while limiting the false-positive rate to 10%. The percentage of test chemicals identified as hits was highest for feature-level and category-based approaches, followed by global fitting, whereas signal strength and profile correlation approaches detected the fewest number of active hits at the fixed false-positive rate. Approaches involving fitting of distance metrics had the lowest likelihood for identifying high-potency false-positive hits that may be associated with assay noise. Most of the methods achieved a 100% hit rate for the reference chemical and high concordance for 82% of test chemicals, indicating that hit calls are robust across different analysis approaches.