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.

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.

Unbiased High-Throughput Drug Combination Pilot Screening Identifies Synergistic Drug Combinations Effective against Patient-Derived and Drug-Resistant Melanoma Cell Lines

We describe the development, optimization, and validation of 384-well growth inhibition assays for six patient-derived melanoma cell lines (PDMCLs), three wild type (WT) for BRAF and three with V600E-BRAF mutations. We conducted a pilot drug combination (DC) high-throughput screening (HTS) of 45 pairwise 4×4 DC matrices prepared from 10 drugs in the PDMCL assays: two B-Raf inhibitors (BRAFi), a MEK inhibitor (MEKi), and a methylation agent approved for melanoma; cytotoxic topoisomerase II and DNA methyltransferase chemotherapies; and drugs targeting the base excision DNA repair enzyme APE1 (apurinic/apyrimidinic endonuclease-1/redox effector factor-1), SRC family tyrosine kinases, the heat shock protein 90 (HSP90) molecular chaperone, and histone deacetylases.Pairwise DCs between dasatinib and three drugs approved for melanoma therapy-dabrafenib, vemurafenib, or trametinib-were flagged as synergistic in PDMCLs. Exposure to fixed DC ratios of the SRC inhibitor dasatinib with the BRAFis or MEKis interacted synergistically to increase PDMCL sensitivity to growth inhibition and enhance cytotoxicity independently of PDMCL BRAF status. These DCs synergistically inhibited the growth of mouse melanoma cell lines that either were dabrafenib-sensitive or had acquired resistance to dabrafenib with cross resistance to vemurafenib, trametinib, and dasatinib. Dasatinib DCs with dabrafenib, vemurafenib, or trametinib activated apoptosis and increased cell death in melanoma cells independently of their BRAF status or their drug resistance phenotypes. These preclinical in vitro studies provide a data-driven rationale for the further investigation of DCs between dasatinib and BRAFis or MEKis as candidates for melanoma combination therapies with the potential to improve outcomes and/or prevent or delay the emergence of disease resistance.

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.

A statistical framework for assessing pharmacological responses and biomarkers using uncertainty estimates

High-throughput testing of drugs across molecular-characterised cell lines can identify candidate treatments and discover biomarkers. However, the cells' response to a drug is typically quantified by a summary statistic from a best-fit dose-response curve, whilst neglecting the uncertainty of the curve fit and the potential variability in the raw readouts. Here, we model the experimental variance using Gaussian Processes, and subsequently, leverage uncertainty estimates to identify associated biomarkers with a new Bayesian framework. Applied to in vitro screening data on 265 compounds across 1074 cancer cell lines, our models identified 24 clinically established drug-response biomarkers, and provided evidence for six novel biomarkers by accounting for association with low uncertainty. We validated our uncertainty estimates with an additional drug screen of 26 drugs, 10 cell lines with 8 to 9 replicates. Our method is applicable to any dose-response data without replicates, and improves biomarker discovery for precision medicine.

High-Throughput Cryo-EM Enabled by User-Free Preprocessing Routines

Single-particle cryoelectron microscopy (cryo-EM) continues to grow into a mainstream structural biology technique. Recent developments in data collection strategies alongside new sample preparation devices herald a future where users will collect multiple datasets per microscope session. To make cryo-EM data processing more automatic and user-friendly, we have developed an automatic pipeline for cryo-EM data preprocessing and assessment using a combination of deep-learning and image-analysis tools. We have verified the performance of this pipeline on a number of datasets and extended its scope to include sample screening by the user-free assessment of the qualities of a series of datasets under different conditions. We propose that our workflow provides a decision-free solution for cryo-EM, making data preprocessing more generalized and robust in the high-throughput era as well as more convenient for users from a range of backgrounds.

Oxygen consumption rate of Caenorhabditis elegans as a high-throughput endpoint of toxicity testing using the Seahorse XFe96 Extracellular Flux Analyzer

Caenorhabditis elegans presents functioning, biologically relevant phenotypes and is frequently used as a bioindicator of toxicity. However, most C. elegans in vivo effect-assessment methods are laborious and time consuming. Therefore, we developed a novel method to measure the oxygen consumption rate of C. elegans as a sublethal endpoint of toxicity. This protocol was tested by exposing 50 larval stage one C. elegans individuals for 48 h (at 20 °C) to different concentrations of two toxicants i.e. benzylcetyldimethylammonium chloride (BAC-C16) and cadmium (Cd). Following exposures, the oxygen consumption rate of the C. elegans individuals were measured using the high-throughput functionality of the Seahorse XFe96 Extracellular Flux Analyzer. Dose-response curves for BAC-C16 (R2 = 0.93; P = 0.001) and Cd (R2 = 0.98; P = 0.001) were created. Furthermore, a strong, positive correlation was evidenced between C. elegans oxygen consumption rate and a commonly used, ecologically relevant endpoint of toxicity (growth inhibition) for BAC-C16 (R2 = 0.93; P = 0.0001) and Cd (R2 = 0.91; P = 0.0001). The data presented in this study show that C. elegans oxygen consumption rate can be used as a promising functional measurement of toxicity.

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.

Sequencing-based microsatellite instability testing using as few as six markers for high-throughput clinical diagnostics

Microsatellite instability (MSI) testing of colorectal cancers (CRCs) is used to screen for Lynch syndrome (LS), a hereditary cancer-predisposition, and can be used to predict response to immunotherapy. Here, we present a single-molecule molecular inversion probe and sequencing-based MSI assay and demonstrate its clinical validity according to existing guidelines. We amplified 24 microsatellites in multiplex and trained a classifier using 98 CRCs, which accommodates marker specific sensitivities to MSI. Sample classification achieved 100% concordance with the MSI Analysis System v1.2 (Promega) in three independent cohorts, totaling 220 CRCs. Backward-forward stepwise selection was used to identify a 6-marker subset of equal accuracy to the 24-marker panel. Assessment of assay detection limits showed that the 24-marker panel is marginally more robust to sample variables than the 6-marker subset, detecting as little as 3% high levels of MSI DNA in sample mixtures, and requiring a minimum of 10 template molecules to be sequenced per marker for >95% accuracy. BRAF c.1799 mutation analysis was also included to streamline LS testing, with all c.1799T>A variants being correctly identified. The assay, therefore, provides a cheap, robust, automatable, and scalable MSI test with internal quality controls, suitable for clinical cancer diagnostics.

Quality control of cervical cytology using a 3-type HPV mRNA test increases screening program sensitivity of cervical intraepithelial neoplasia grade 2+ in young Norwegian women-A cohort study

Within 2021, Norway intends to complete implementation of HPV DNA-based primary screening for cervical cancer for women 34–69 years, while continue cytology-based screening for women 25–33 years. Over the recent years, the incidence of cervical cancer has increased by 30% among women younger than 40 years. In this subset of women, nearly 30% were diagnosed with a normal smear, as most recent smear, prior the cancer diagnosis. This observation demands quality control of normal smears. The aim of this study was to assess increase in program sensitivity of CIN2+ after follow-up of women with false negative Pap-smears testing positive for a 3-type (-16, -18, -45) HPV mRNA test in a cohort design over one screening interval. 521 women, aged 23–39 years, and no prior history of CIN1+ or HSIL, with an ASC-US or worse smear (ASC-US+) and 1444 women with normal screening cytology comprised the study cohorts. The positivity rate for the 3-type HPV mRNA was 1.9% (28/1444). Rescreening revealed 23 women with ASC-US, two women with LSIL, two women with ASC-H, and one woman with AGUS. If the HPV mRNA-positivity rate and histology findings from samples rescreened were applied to all women with normal cytology, an estimated increase in screening sensitivity of 16.4% (95% CI:15.3–17.5) for CIN2+ and 17.3% (95% CI:16.2–18.4) for CIN3+ were achieved. By rescreening less than 2% of women with normal cytology positive for a 3-type HPV mRNA test, we achieved a significant increase in screening program sensitivity.

'Fit-for-purpose?' - challenges and opportunities for applications of blockchain technology in the future of healthcare

Blockchain is a shared distributed digital ledger technology that can better facilitate data management, provenance and security, and has the potential to transform healthcare. Importantly, blockchain represents a data architecture, whose application goes far beyond Bitcoin - the cryptocurrency that relies on blockchain and has popularized the technology. In the health sector, blockchain is being aggressively explored by various stakeholders to optimize business processes, lower costs, improve patient outcomes, enhance compliance, and enable better use of healthcare-related data. However, critical in assessing whether blockchain can fulfill the hype of a technology characterized as 'revolutionary' and 'disruptive', is the need to ensure that blockchain design elements consider actual healthcare needs from the diverse perspectives of consumers, patients, providers, and regulators. In addition, answering the real needs of healthcare stakeholders, blockchain approaches must also be responsive to the unique challenges faced in healthcare compared to other sectors of the economy. In this sense, ensuring that a health blockchain is 'fit-for-purpose' is pivotal. This concept forms the basis for this article, where we share views from a multidisciplinary group of practitioners at the forefront of blockchain conceptualization, development, and deployment.

Optimizing 1D 1H-NMR profiling of plant samples for high throughput analysis: extract preparation, standardization, automation and spectra processing

INTRODUCTION

Proton nuclear magnetic resonance spectroscopy (1H-NMR)-based metabolomic profiling has a range of applications in plant sciences.

OBJECTIVES

The aim of the present work is to provide advice for minimizing uncontrolled variability in plant sample preparation before and during NMR metabolomic profiling, taking into account sample composition, including its specificity in terms of pH and paramagnetic ion concentrations, and NMR spectrometer performances.

METHODS

An automation of spectrometer preparation routine standardization before NMR acquisition campaign was implemented and tested on three plant sample sets (extracts of durum wheat spikelet, Arabidopsis leaf and root, and flax leaf, root and stem). We performed 1H-NMR spectroscopy in three different sites on the wheat sample set utilizing instruments from two manufacturers with different probes and magnetic field strengths. The three collections of spectra were processed separately with the NMRProcFlow web tool using intelligent bucketing, and the resulting buckets were subjected to multivariate analysis.

RESULTS

Comparability of large- (Arabidopsis) and medium-size (flax) datasets measured at 600 MHz and from the wheat sample set recorded at the three sites (400, 500 and 600 MHz) was exceptionally good in terms of spectral quality. The coefficient of variation of the full width at half maximum (FWHM) and the signal-to-noise ratio (S/N) of two selected peaks was comprised between 5 and 10% depending on the size of sample set and the spectrometer field. EDTA addition improved citrate and malate resonance patterns for wheat sample sets. A collection of 22 samples of wheat spikelet extracts was used as a proof of concept and showed that the data collected at the three sites on instruments of different field strengths and manufacturers yielded the same discrimination pattern of the biological groups.

CONCLUSION

Standardization or automation of several steps from extract preparation to data reduction improves data quality for small to large collections of plant samples of different origins.

Iterative screen optimization maximizes the efficiency of macromolecular crystallization

Advances in X-ray crystallography have streamlined the process of determining high-resolution three-dimensional macromolecular structures. However, a rate-limiting step in this process continues to be the generation of crystals that are of sufficient size and quality for subsequent diffraction experiments. Here, iterative screen optimization (ISO), a highly automated process in which the precipitant concentrations of each condition in a crystallization screen are modified based on the results of a prior crystallization experiment, is described. After designing a novel high-throughput crystallization screen to take full advantage of this method, the value of ISO is demonstrated by using it to successfully crystallize a panel of six diverse proteins. The results suggest that ISO is an effective method to obtain macromolecular crystals, particularly for proteins that crystallize under a narrow range of precipitant concentrations.

NiftyPET: a High-throughput Software Platform for High Quantitative Accuracy and Precision PET Imaging and Analysis

We present a standalone, scalable and high-throughput software platform for PET image reconstruction and analysis. We focus on high fidelity modelling of the acquisition processes to provide high accuracy and precision quantitative imaging, especially for large axial field of view scanners. All the core routines are implemented using parallel computing available from within the Python package NiftyPET, enabling easy access, manipulation and visualisation of data at any processing stage. The pipeline of the platform starts from MR and raw PET input data and is divided into the following processing stages: (1) list-mode data processing; (2) accurate attenuation coefficient map generation; (3) detector normalisation; (4) exact forward and back projection between sinogram and image space; (5) estimation of reduced-variance random events; (6) high accuracy fully 3D estimation of scatter events; (7) voxel-based partial volume correction; (8) region- and voxel-level image analysis. We demonstrate the advantages of this platform using an amyloid brain scan where all the processing is executed from a single and uniform computational environment in Python. The high accuracy acquisition modelling is achieved through span-1 (no axial compression) ray tracing for true, random and scatter events. Furthermore, the platform offers uncertainty estimation of any image derived statistic to facilitate robust tracking of subtle physiological changes in longitudinal studies. The platform also supports the development of new reconstruction and analysis algorithms through restricting the axial field of view to any set of rings covering a region of interest and thus performing fully 3D reconstruction and corrections using real data significantly faster. All the software is available as open source with the accompanying wiki-page and test data.

Evaluation and calibration of high-throughput predictions of chemical distribution to tissues

Toxicokinetics (TK) provides critical information for integrating chemical toxicity and exposure assessments in order to determine potential chemical risk (i.e., the margin between toxic doses and plausible exposures). For thousands of chemicals that are present in our environment, in vivo TK data are lacking. The publicly available R package "httk" (version 1.8, named for "high throughput TK") draws from a database of in vitro data and physico-chemical properties in order to run physiologically-based TK (PBTK) models for 553 compounds. The PBTK model parameters include tissue:plasma partition coefficients (K_p) which the httk software predicts using the model of Schmitt (Toxicol In Vitro 22 (2):457-467, 2008). In this paper we evaluated and modified httk predictions, and quantified confidence using in vivo literature data. We used 964 rat K_p measured by in vivo experiments for 143 compounds. Initially, predicted K_p were significantly larger than measured K_p for many lipophilic compounds (log₁₀ octanol:water partition coefficient > 3). Hence the approach for predicting K_p was revised to account for possible deficiencies in the in vitro protein binding assay, and the method for predicting membrane affinity was revised. These changes yielded improvements ranging from a factor of 10 to nearly a factor of 10,000 for 83 K_p across 23 compounds with only 3 K_p worsening by more than a factor of 10. The vast majority (92%) of K_p were predicted within a factor of 10 of the measured value (overall root mean squared error of 0.59 on log₁₀-transformed scale). After applying the adjustments, regressions were performed to calibrate and evaluate the predictions for 12 tissues. Predictions for some tissues (e.g., spleen, bone, gut, lung) were observed to be better than predictions for other tissues (e.g., skin, brain, fat), indicating that confidence in the application of in silico tools to predict chemical partitioning varies depending upon the tissues involved. Our calibrated model was then evaluated using a second data set of human in vivo measurements of volume of distribution (V_ss) for 498 compounds reviewed by Obach et al. (Drug Metab Dispos 36(7):1385-1405, 2008). We found that calibration of the model improved performance: a regression of the measured values as a function of the predictions has a slope of 1.03, intercept of - 0.04, and R² of 0.43. Through careful evaluation of predictive methods for chemical partitioning into tissues, we have improved and calibrated these methods and quantified confidence for TK predictions in humans and rats.

Evaluation and calibration of high-throughput predictions of chemical distribution to tissues

Toxicokinetics (TK) provides critical information for integrating chemical toxicity and exposure assessments in order to determine potential chemical risk (i.e., the margin between toxic doses and plausible exposures). For thousands of chemicals that are present in our environment, in vivo TK data are lacking. The publicly available R package "httk" (version 1.8, named for "high throughput TK") draws from a database of in vitro data and physico-chemical properties in order to run physiologically-based TK (PBTK) models for 553 compounds. The PBTK model parameters include tissue:plasma partition coefficients (Kp) which the httk software predicts using the model of Schmitt (Toxicol In Vitro 22 (2):457-467, 2008). In this paper we evaluated and modified httk predictions, and quantified confidence using in vivo literature data. We used 964 rat Kp measured by in vivo experiments for 143 compounds. Initially, predicted Kp were significantly larger than measured Kp for many lipophilic compounds (log10 octanol:water partition coefficient > 3). Hence the approach for predicting Kp was revised to account for possible deficiencies in the in vitro protein binding assay, and the method for predicting membrane affinity was revised. These changes yielded improvements ranging from a factor of 10 to nearly a factor of 10,000 for 83 Kp across 23 compounds with only 3 Kp worsening by more than a factor of 10. The vast majority (92%) of Kp were predicted within a factor of 10 of the measured value (overall root mean squared error of 0.59 on log10-transformed scale). After applying the adjustments, regressions were performed to calibrate and evaluate the predictions for 12 tissues. Predictions for some tissues (e.g., spleen, bone, gut, lung) were observed to be better than predictions for other tissues (e.g., skin, brain, fat), indicating that confidence in the application of in silico tools to predict chemical partitioning varies depending upon the tissues involved. Our calibrated model was then evaluated using a second data set of human in vivo measurements of volume of distribution (Vss) for 498 compounds reviewed by Obach et al. (Drug Metab Dispos 36(7):1385-1405, 2008). We found that calibration of the model improved performance: a regression of the measured values as a function of the predictions has a slope of 1.03, intercept of - 0.04, and R2 of 0.43. Through careful evaluation of predictive methods for chemical partitioning into tissues, we have improved and calibrated these methods and quantified confidence for TK predictions in humans and rats.

InSourcerer: a high-throughput method to search for unknown metabolite modifications by mass spectrometry

RATIONALE

Using mass spectrometry, the analysis of known metabolite structures has become feasible in a systematic high-throughput fashion. Nevertheless, the identification of previously unknown structures remains challenging, partially because many unidentified variants originate from known molecules that underwent unexpected modifications. Here, we present a method for the discovery of unknown metabolite modifications and conjugate metabolite isoforms in a high-throughput fashion.

METHODS

The method is based on user-controlled in-source fragmentation which is used to induce loss of weakly bound modifications. This is followed by the comparison of product ions from in-source fragmentation and collision-induced dissociation (CID). Diagonal MS² -MS³ matching allows the detection of unknown metabolite modifications, as well as substructure similarities. As the method relies heavily on the advantages of in-source fragmentation and its ability to 'magically' elucidate unknown modification, we have named it inSourcerer as a portmanteau of in-source and sorcerer.

RESULTS

The method was evaluated using a set of 15 different cytokinin standards. Product ions from in-source fragmentation and CID were compared. Hierarchical clustering revealed that good matches are due to the presence of common substructures. Plant leaf extract, spiked with a mix of all 15 standards, was used to demonstrate the method's ability to detect these standards in a complex mixture, as well as confidently identify compounds already present in the plant material.

CONCLUSIONS

Here we present a method that incorporates a classic liquid chromatography/mass spectrometry (LC/MS) workflow with fragmentation models and computational algorithms. The assumptions upon which the concept of the method was built were shown to be valid and the method showed that in-source fragmentation can be used to pinpoint structural similarities and indicate the occurrence of a modification.

Rapid screening and identification of chemical hazards in surface and drinking water using high resolution mass spectrometry and a case-control filter

Access to clean, safe drinking water poses a serious challenge to regulators, and requires analytical strategies capable of rapid screening and identification of potentially hazardous chemicals, specifically in situations when threats to water quality or security require rapid investigations and potential response. This study describes a fast and efficient chemical hazard screening strategy for characterising trace levels of polar organic contaminants in water matrices, based on liquid chromatography high resolution mass spectrometry with post-acquisition 'case-control' data processing. This method allowed for a rapid response time of less than 24 h for the screening of target, suspect and non-target unknown chemicals via direct injection analysis, and a second, more sensitive analysis option requiring sample pre-concentration. The method was validated by fortifying samples with a range of pesticides, pharmaceuticals and personal care products (n = 46); with >90% of target compounds positively screened in samples at 1 ng mL^-1, and 46% at 0.1 ng mL^-1 when analysed via direct injection. To simulate a contamination event samples were fortified with compounds not present in the commercial library (designated 'non-target compounds'; fipronil and fenitrothion), tentatively identified at 0.2 and 1 ng mL^-1, respectively; and a compound not included in any known commercial library or public database (designated 'unknown' compounds; 8Cl^- perfluorooctanesulfonic acid), at 0.8 ng mL^-1. The method was applied to two 'real-case' scenarios: (1) the assessment of drinking water safety during a high-profile event in Brisbane, Australia; and (2) to screen treated, re-circulated drinking water and pre-treated (raw) water. The validated workflow was effective for rapid prioritisation and screening of suspect and non-target potential hazards at trace levels, and could be applied to a wide range of matrices and investigations where comparison of organic contaminants between an affected and control site and or timeframe is warranted.

A note on statistical repeatability and study design for high-throughput assays

Characterizing the technical precision of measurements is a necessary stage in the planning of experiments and in the formal sample size calculation for optimal design. Instruments that measure multiple analytes simultaneously, such as in high-throughput assays arising in biomedical research, pose particular challenges from a statistical perspective. The current most popular method for assessing precision of high-throughput assays is by scatterplotting data from technical replicates. Here, we question the statistical rationale of this approach from both an empirical and theoretical perspective, illustrating our discussion using four example data sets from different genomic platforms. We demonstrate that such scatterplots convey little statistical information of relevance and are potentially highly misleading. We present an alternative framework for assessing the precision of high-throughput assays and planning biomedical experiments. Our methods are based on repeatability-a long-established statistical quantity also known as the intraclass correlation coefficient. We provide guidance and software for estimation and visualization of repeatability of high-throughput assays, and for its incorporation into study design. © 2016 The Authors. Statistics in Medicine Published by John Wiley & Sons Ltd.

Identification and Clinical Translation of Biomarker Signatures: Statistical Considerations

Powerful machine learning tools exist to extract biological patterns for diagnosis or prediction from high-dimensional datasets. Simultaneous advances in high-throughput profiling technologies have led to a rapid acceleration of biomarker discovery investigations across all areas of medicine. However, the translation of biomarker signatures into clinically useful tools has thus far been difficult. In this chapter, several important considerations are discussed that influence such translation in the context of classifier design. These include aspects of variable selection that go beyond classification accuracy, as well as effects of variability on assay stability and sample size. The consideration of such factors may lead to an adaptation of biomarker discovery approaches, aimed at an optimal balance of performance and clinical translatability.

Automated Gravimetric Calibration to Optimize the Accuracy and Precision of TECAN Freedom EVO Liquid Handler

High-throughput screening technologies are increasingly integrated into the formulation development process of biopharmaceuticals. The performance of liquid handling systems is dependent on the ability to deliver accurate and precise volumes of specific reagents to ensure process quality. We have developed an automated gravimetric calibration procedure to adjust the accuracy and evaluate the precision of the TECAN Freedom EVO liquid handling system. Volumes from 3 to 900 µL using calibrated syringes and fixed tips were evaluated with various solutions, including aluminum hydroxide and phosphate adjuvants, β-casein, sucrose, sodium chloride, and phosphate-buffered saline. The methodology to set up liquid class pipetting parameters for each solution was to split the process in three steps: (1) screening of predefined liquid class, including different pipetting parameters; (2) adjustment of accuracy parameters based on a calibration curve; and (3) confirmation of the adjustment. The run of appropriate pipetting scripts, data acquisition, and reports until the creation of a new liquid class in EVOware was fully automated. The calibration and confirmation of the robotic system was simple, efficient, and precise and could accelerate data acquisition for a wide range of biopharmaceutical applications.

Technical Advances of the Recombinant Antibody Microarray Technology Platform for Clinical Immunoproteomics

In the quest for deciphering disease-associated biomarkers, high-performing tools for multiplexed protein expression profiling of crude clinical samples will be crucial. Affinity proteomics, mainly represented by antibody-based microarrays, have during recent years been established as a proteomic tool providing unique opportunities for parallelized protein expression profiling. But despite the progress, several main technical features and assay procedures remains to be (fully) resolved. Among these issues, the handling of protein microarray data, i.e. the biostatistics parts, is one of the key features to solve. In this study, we have therefore further optimized, validated, and standardized our in-house designed recombinant antibody microarray technology platform. To this end, we addressed the main remaining technical issues (e.g. antibody quality, array production, sample labelling, and selected assay conditions) and most importantly key biostatistics subjects (e.g. array data pre-processing and biomarker panel condensation). This represents one of the first antibody array studies in which these key biostatistics subjects have been studied in detail. Here, we thus present the next generation of the recombinant antibody microarray technology platform designed for clinical immunoproteomics.

Development and Validation of a Universal High-Throughput UDP-Glycosyltransferase Assay with a Time-Resolved FRET Signal

Glycosyltransferase enzymes play diverse metabolic and regulatory roles by catalyzing the transfer of sugar molecules to protein, lipid, and carbohydrate acceptors, and they are increasingly of interest as therapeutic targets in a number of diseases, including metabolic disorders, cancer, and infectious diseases. The glycosyltransferases are a challenging target class from an assay development perspective because of the diversity of both donor and acceptor substrates and the lack of suitable glycan detection methods. However, many glycosyltransferases use uridine 5'-diphosphate (UDP) sugars as donor substrates, and detection of the free UDP reaction product provides a generic approach for measuring the activity of those enzymes. To exploit this approach for a broadly applicable high-throughput screening (HTS) assay for discovery of glycosyltransferase inhibitors, we developed a Transcreener(®) assay for immunodetection of UDP with a time-resolved Förster resonance energy transfer (TR-FRET) signal. We optimized the assay for detection of glycosyltransferase activity with nucleotide diphosphate (NDP) sugars at concentrations from 10 μM to 1 mM, achieving Z' values of 0.6 or higher. The assay was validated by orthogonal pooled screening with 8,000 compounds using polypeptide N-acetylgalactosaminyltransferase T3 as the target, and the hits were confirmed using an orthogonal readout. The reagents and signal were both stable for more than 8 h at room temperature, insuring robust performance in automated HTS environments. The TR-FRET-based UDP detection assay provides a broadly applicable approach for screening glycosyltransferases that use a UDP-sugar donor.

Accessing the High-Throughput Screening Data Landscape

The progress of high-throughput screening (HTS) techniques is changing the chemical data landscape by producing massive biological data from tested compounds. Public data repositories (e.g., PubChem) receive HTS data provided by various institutes and this data pool is being updated on a daily basis. The goal of these data sharing efforts is to let users quickly obtain the biological data of target compounds. Without a universal chemical identifier, the repositories (e.g., PubChem) provide users various methods to query and retrieve chemical properties and biological data by several different chemical identifiers (e.g., SMILES, InChIKey, and IUPAC name). The major challenge for most users, especially computational modelers, is obtaining the biological data for a large dataset of compounds (e.g., thousands of drug molecules) instead of a single compound. This chapter aims to introduce the steps to access the public data repositories for target compounds with specific emphasis on the automatic data downloading for large datasets.

Correction of Microplate Data from High-Throughput Screening

High-throughput screening (HTS) makes it possible to collect cellular response data from a large number of cell lines and small molecules in a timely and cost-effective manner. The errors and noises in the microplate-formatted data from HTS have unique characteristics, and they can be generally grouped into three categories: run-wise (temporal, multiple plates), plate-wise (background pattern, single plate), and well-wise (single well). In this chapter, we describe a systematic solution for identifying and correcting such errors and noises, mainly basing on pattern recognition and digital signal processing technologies.

CurveP Method for Rendering High-Throughput Screening Dose-Response Data into Digital Fingerprints

The nature of high-throughput screening (HTS) puts certain limits on optimal test conditions for each particular sample, therefore, on top of usual data normalization, additional parsing is often needed to account for incomplete read outs or various artifacts that arise from signal interferences.CurveP is a heuristic, user-tunable, curve-cleaning algorithm that attempts to find a minimum set of corrections, which would give a monotonic dose-response curve. After applying the corrections, the algorithm proceeds to calculate a set of numeric features, which can be used as a fingerprint characterizing the sample, or as a vector of independent variables (e.g., molecular descriptors in case of chemical substances testing). The resulting output can be a part of HTS data analysis or can be used as input for a broad spectrum of computational applications, such as Quantitative Structure-Activity Relationship (QSAR) modeling, computational toxicology, bio- and cheminformatics.

High-throughput screening against thioredoxin glutathione reductase identifies novel inhibitors with potential therapeutic value for schistosomiasis

BACKGROUND

Schistosomiasis, a parasitic disease also known as bilharzia and snail fever, is caused by different species of flatworms, such as Schistosoma mansoni (S. mansoni). Thioredoxin glutathione reductase (TGR) from S. mansoni (SmTGR) is a well-characterized drug target for schistosomiasis, yet no anti-SmTGR compounds have reached clinical trials, suggesting that therapeutic development against schistosomiasis might benefit from additional scaffolds targeting this enzyme.

METHODS

A high-throughput screening (HTS) assay in vitro against SmTGR was developed and applied to a diverse compound library. SmTGR activity was quantified with ThioGlo®, a reagent that fluoresces upon binding to the free sulfhydryl groups of the reaction product GSH (reduced glutathione).

RESULTS

We implemented an HTS effort against 59,360 synthetic compounds. In the primary screening, initial hits (928 or 1.56 %) showing greater than 90 % inhibition on SmTGR activity at a final concentration of 10 μM for each compound were identified. Further tests were carried out to confirm the effects of these hits and to explore the concentration-dependent response characteristics. As a result, 74 of them (0.12 %) representing 17 chemical scaffolds were confirmed and showed a great concentration-dependent inhibitory trend against SmTGR, including structures previously shown to be lethal to schistosomal growth. Of these, two scaffolds displayed a limited structure-activity relationship. When tested in cultured larvae, 39 compounds had cidal activity in 48 h, and five of them killed larvae completely at 3.125 μM. Of these, three compounds also killed adult worms ex vivo at concentrations between 5 μM and 10 μM.

CONCLUSION

These confirmed hits may serve as starting points for the development of novel therapeutics to combat schistosomiasis.

Pre-analytical sample quality: metabolite ratios as an intrinsic marker for prolonged room temperature exposure of serum samples

Advances in the "omics" field bring about the need for a high number of good quality samples. Many omics studies take advantage of biobanked samples to meet this need. Most of the laboratory errors occur in the pre-analytical phase. Therefore evidence-based standard operating procedures for the pre-analytical phase as well as markers to distinguish between 'good' and 'bad' quality samples taking into account the desired downstream analysis are urgently needed. We studied concentration changes of metabolites in serum samples due to pre-storage handling conditions as well as due to repeated freeze-thaw cycles. We collected fasting serum samples and subjected aliquots to up to four freeze-thaw cycles and to pre-storage handling delays of 12, 24 and 36 hours at room temperature (RT) and on wet and dry ice. For each treated aliquot, we quantified 127 metabolites through a targeted metabolomics approach. We found a clear signature of degradation in samples kept at RT. Storage on wet ice led to less pronounced concentration changes. 24 metabolites showed significant concentration changes at RT. In 22 of these, changes were already visible after only 12 hours of storage delay. Especially pronounced were increases in lysophosphatidylcholines and decreases in phosphatidylcholines. We showed that the ratio between the concentrations of these molecule classes could serve as a measure to distinguish between 'good' and 'bad' quality samples in our study. In contrast, we found quite stable metabolite concentrations during up to four freeze-thaw cycles. We concluded that pre-analytical RT handling of serum samples should be strictly avoided and serum samples should always be handled on wet ice or in cooling devices after centrifugation. Moreover, serum samples should be frozen at or below -80°C as soon as possible after centrifugation.

A microfluidic platform for high-throughput multiplexed protein quantitation

We present a high-throughput microfluidic platform capable of quantitating up to 384 biomarkers in 4 distinct samples by immunoassay. The microfluidic device contains 384 unit cells, which can be individually programmed with pairs of capture and detection antibody. Samples are quantitated in each unit cell by four independent MITOMI detection areas, allowing four samples to be analyzed in parallel for a total of 1,536 assays per device. We show that the device can be pre-assembled and stored for weeks at elevated temperature and we performed proof-of-concept experiments simultaneously quantitating IL-6, IL-1β, TNF-α, PSA, and GFP. Finally, we show that the platform can be used to identify functional antibody combinations by screening 64 antibody combinations requiring up to 384 unique assays per device.

Evaluation of the sensitivity, responsiveness and reproducibility of primary rainbow trout hepatocyte vitellogenin expression as a screening assay for estrogen mimics

Vitellogenin (Vtg) induction in primary fish hepatocytes has been proposed as an in vitro screening assay for ER agonists and antagonists, but has not yet been used extensively as a high-throughput screening tool due to poor reproducibility, sensitivity and overall feasibility. The present work has evaluated the role of seasonal variation, normalization, optimal culture and assay conditions on the sensitivity, responsiveness and reproducibility of in vitro vtg gene mRNA and protein expression in rainbow trout (Oncorhynchus mykiss) primary hepatocytes using the xenoestrogen 17α-ethynylestradiol (EE2) as a test chemical. The results show that primary hepatocytes display a relatively high individual and seasonal variation in both Vtg mRNA and protein induction potential, although less variance was observed in assay sensitivity. Data normalization of assay response to maximum (3 nM EE2) and minimum (DMSO) Vtg production dramatically reduced this variance and led to improved assay reproducibility. A time-dependent response was observed both for mRNA and protein expression, reaching maximum Vtg induction after 96 h of exposure, although reproducible concentration response curves for both Vtg mRNA and protein could be obtained already after 48 h. A need for chemical re-exposure of the hepatocytes was identified to be important for sustaining exposure concentrations in extended studies (>48 h), whereas different plate formats (96, 24 or 6 wells) did not affect the bioassay performance. In conclusion, standardization of hepatocyte bioassay and test conditions as well as data-normalization procedures are proposed to be instrumental for more consistent and comparable results in future use of this in vitro assay.

Rediscovery rate estimation for assessing the validation of significant findings in high-throughput studies

It is common and advised practice in biomedical research to validate experimental or observational findings in a population different from the one where the findings were initially assessed. This practice increases the generalizability of the results and decreases the likelihood of reporting false-positive findings. Validation becomes critical when dealing with high-throughput experiments, where the large number of tests increases the chance to observe false-positive results. In this article, we review common approaches to determine statistical thresholds for validation and describe the factors influencing the proportion of significant findings from a 'training' sample that are replicated in a 'validation' sample. We refer to this proportion as rediscovery rate (RDR). In high-throughput studies, the RDR is a function of false-positive rate and power in both the training and validation samples. We illustrate the application of the RDR using simulated data and real data examples from metabolomics experiments. We further describe an online tool to calculate the RDR using t-statistics. We foresee two main applications. First, if the validation study has not yet been collected, the RDR can be used to decide the optimal combination between the proportion of findings taken to validation and the size of the validation study. Secondly, if a validation study has already been done, the RDR estimated using the training data can be compared with the observed RDR from the validation data; hence, the success of the validation study can be assessed.

High-throughput micro-plate HCI-vanillin assay for screening tannin content in sorghum grain

BACKGROUND

The HCI-vanillin assay is a well-accepted method for determining tannin content in sorghum but is limited to small sample sets due to the time-consuming nature of the method. The objective was to develop an accurate and repeatable high-throughput 96-well plate assay for breeders to screen large sample sets of sorghum for tannin content. Validation of the high-throughput assay was tested on 25 sorghums suspected to contain tannin.

RESULTS

Approximately 30 measurements per day were completed using the conventional assay compared to 224 measurements using the 96-well platform. The correlation between the two tannin assays was 0.98. The coefficient of variation (CV) was 3.54% and 3.21% for the 96-well and conventional method, respectively. The 96-well assay exhibited good repeatability, with the inter-plate CV between 2.77% and 4.85%.

CONCLUSION

The high-throughput 96-well HCI-vanillin assay exhibited an eightfold increase in the number of measurements completed and was as accurate as the conventional HCI-vanillin assay.

A High-Throughput (HTS) Assay for Enzyme Reaction Phenotyping in Human Recombinant P450 Enzymes Using LC-MS/MS

Enzyme reaction phenotyping is employed extensively during the early stages of drug discovery to identify the enzymes responsible for the metabolism of new chemical entities (NCEs). Early identification of metabolic pathways facilitates prediction of potential drug-drug interactions associated with enzyme polymorphism, induction, or inhibition, and aids in the design of clinical trials. Incubation of NCEs with human recombinant enzymes is a popular method for such work because of the specificity, simplicity, and high-throughput nature of this approach for phenotyping studies. The availability of a relative abundance factor and calculated intersystem extrapolation factor for the expressed recombinant enzymes facilitates easy scaling of in vitro data, enabling in vitro-in vivo extrapolation. Described in this unit is a high-throughput screen for identifying enzymes involved in the metabolism of NCEs. Emphasis is placed on the analysis of the human recombinant enzymes CYP1A2, CYP2C8, CYP2C9, CYP2C19, CYP2D6, CYP2B6, and CYP3A4, including the calculation of the intrinsic clearance for each.

Baculovirus superinfection: a probable restriction factor on the surface display of proteins for library screening

In addition to the expression of recombinant proteins, baculoviruses have been developed as a platform for the display of complex eukaryotic proteins on the surface of virus particles or infected insect cells. Surface display has been used extensively for antigen presentation and targeted gene delivery but is also a candidate for the display of protein libraries for molecular screening. However, although baculovirus gene libraries can be efficiently expressed and displayed on the surface of insect cells, target gene selection is inefficient probably due to super-infection which gives rise to cells expressing more than one protein. In this report baculovirus superinfection of Sf9 cells has been investigated by the use of two recombinant multiple nucleopolyhedrovirus carrying green or red fluorescent proteins under the control of both early and late promoters (vAcBacGFP and vAcBacDsRed). The reporter gene expression was detected 8 hours after the infection of vAcBacGFP and cells in early and late phases of infection could be distinguished by the fluorescence intensity of the expressed protein. Simultaneous infection with vAcBacGFP and vAcBacDsRed viruses each at 0.5 MOI resulted in 80% of infected cells co-expressing the two fluorescent proteins at 48 hours post infection (hpi), and subsequent infection with the two viruses resulted in similar co-infection rate. Most Sf9 cells were re-infectable within the first several hours post infection, but the re-infection rate then decreased to a very low level by 16 hpi. Our data demonstrate that Sf9 cells were easily super-infectable during baculovirus infection, and super-infection could occur simultaneously at the time of the primary infection or subsequently during secondary infection by progeny viruses. The efficiency of super-infection may explain the difficulties of baculovirus display library screening but would benefit the production of complex proteins requiring co-expression of multiple polypeptides.

Perspectives on validation of high-throughput assays supporting 21st century toxicity testing

In vitro high-throughput screening (HTS) assays are seeing increasing use in toxicity testing. HTS assays can simultaneously test many chemicals but have seen limited use in the regulatory arena, in part because of the need to undergo rigorous, time-consuming formal validation. Here we discuss streamlining the validation process, specifically for prioritization applications. By prioritization, we mean a process in which less complex, less expensive, and faster assays are used to prioritize which chemicals are subjected first to more complex, expensive, and slower guideline assays. Data from the HTS prioritization assays is intended to provide a priori evidence that certain chemicals have the potential to lead to the types of adverse effects that the guideline tests are assessing. The need for such prioritization approaches is driven by the fact that there are tens of thousands of chemicals to which people are exposed, but the yearly throughput of most guideline assays is small in comparison. The streamlined validation process would continue to ensure the reliability and relevance of assays for this application. We discuss the following practical guidelines: (1) follow current validation practice to the extent possible and practical; (2) make increased use of reference compounds to better demonstrate assay reliability and relevance; (3) de-emphasize the need for cross-laboratory testing; and (4) implement a web-based, transparent, and expedited peer review process.

High throughput en face mapping of arterial permeability using tile scanning confocal microscopy

Elevated uptake of plasma macromolecules by the arterial wall has been implicated in the initiation of atherosclerosis. Here we describe a new method for mapping such uptake in laboratory animals. Albumin was labelled with a fluorescent dye and administered intravenously. After 10 min, the aorta was fixed in situ, excised and opened. En face confocal microscopy employing a computer-controlled stage was used to obtain contiguous tiles, each consisting of a stack of images of fluorescence emission at different depths in the wall. To obtain two-dimensional maps, intensities were summed in each column of voxels starting at the endothelial surface and extending 10 μm into the wall. Variation in the sensitivity of the system with time and in all three spatial directions was assessed and corrected using calibration standards and model specimens. In immature rabbits, uptake around aorto-intercostal branches was greatest in an arrowhead-shaped region around the downstream half of each ostium, and at its lateral margins. Uptake around branches in mature rabbits was more uniform; it was highest upstream of the ostium. Patches and streaks of high uptake were also seen at non-branch locations in the descending thoracic aorta. Transport was more uniform around branches in mice, except for small regions of high uptake at the ostial rim and at the leading edge of an intimal cushion upstream of the ostium, where lesions develop. The technique provides accurate quantification in three dimensions over large areas; it has high throughput, sensitivity and resolution and is suitable for widespread use.

Fast-mode duplex qPCR for BCR-ABL1 molecular monitoring: innovation, automation, and harmonization

Reverse transcription quantitative polymerase chain reaction (RTqPCR)is currently the most sensitive tool available for the routine monitoring of disease level in patients undergoing treatment for BCRABL1 associated malignancies. Considerable effort has been invested at both the local and international levels to standardise the methodology and reporting criteria used to assess this critical metric. In an effort to accommodate the demands of increasing sample throughput and greater standardization, we adapted the current best-practice guidelines to encompass automation platforms and improved multiplex RT-qPCR technology.

Toward the establishment of standardized in vitro tests for lipid-based formulations, part 1: method parameterization and comparison of in vitro digestion profiles across a range of representative formulations

The Lipid Formulation Classification System Consortium is an industry-academia collaboration, established to develop standardized in vitro methods for the assessment of lipid-based formulations (LBFs). In this first publication, baseline conditions for the conduct of digestion tests are suggested and a series of eight model LBFs are described to probe test performance across different formulation types. Digestion experiments were performed in vitro using a pH-stat apparatus and danazol employed as a model poorly water-soluble drug. LBF digestion (rate and extent) and drug solubilization patterns on digestion were examined. To evaluate cross-site reproducibility, experiments were conducted at two sites and highly consistent results were obtained. In a further refinement, bench-top centrifugation was explored as a higher throughput approach to separation of the products of digestion (and compared with ultracentrifugation), and conditions under which this method was acceptable were defined. Drug solubilization was highly dependent on LBF composition, but poorly correlated with simple performance indicators such as dispersion efficiency, confirming the utility of the digestion model as a means of formulation differentiation.

Salivary cortisol as a diagnostic tool for Cushing's syndrome and adrenal insufficiency: improved screening by an automatic immunoassay

BACKGROUND

Salivary cortisol is increasingly used to assess patients with suspected hypo- and hypercortisolism. This study established disease-specific reference ranges for an automated electrochemiluminescence immunoassay (ECLIA).

METHODS

Unstimulated saliva from 62 patients with hypothalamic-pituitary disease was collected at 0800 h. A peak serum cortisol level below 500 nmol/l during the insulin tolerance test (ITT) was used to identify hypocortisolism. Receiver-operating characteristic (ROC) analysis allowed establishment of lower and upper cutoffs with at least 95% specificity for adrenal insufficiency and adrenal sufficiency. Saliva from 40 patients with confirmed hypercortisolism, 45 patients with various adrenal masses, and 115 healthy subjects was sampled at 2300 h and after low-dose dexamethasone suppression at 0800 h . ROC analysis was used to calculate thresholds with at least 95% sensitivity for hypercortisolism. Salivary cortisol was measured with an automated ECLIA.

RESULTS

When screening for secondary adrenal insufficiency, a lower cutoff of 3.2 nmol/l and an upper cutoff of 13.2 nmol/l for unstimulated salivary cortisol allowed a highly specific diagnosis (i.e. similar to the ITT result) in 26% of patients. For identification of hypercortisolism, cutoffs of 6.1 nmol/l (sensitivity 95%, specificity 91%, area under the curve (AUC) 0.97) and 2.0 nmol/l (sensitivity 97%, specificity 86%, AUC 0.97) were established for salivary cortisol at 2300 h and for dexamethasone-suppressed salivary cortisol at 0800 h.

CONCLUSIONS

The newly established thresholds facilitated initial screening for secondary adrenal insufficiency and allowed excellent identification of hypercortisolism. Measurement by an automated immunoassay will allow broader use of salivary cortisol as a diagnostic tool.

Using iRT, a normalized retention time for more targeted measurement of peptides

Multiple reaction monitoring (MRM) has recently become the method of choice for targeted quantitative measurement of proteins using mass spectrometry. The method, however, is limited in the number of peptides that can be measured in one run. This number can be markedly increased by scheduling the acquisition if the accurate retention time (RT) of each peptide is known. Here we present iRT, an empirically derived dimensionless peptide-specific value that allows for highly accurate RT prediction. The iRT of a peptide is a fixed number relative to a standard set of reference iRT-peptides that can be transferred across laboratories and chromatographic systems. We show that iRT facilitates the setup of multiplexed experiments with acquisition windows more than four times smaller compared to in silico RT predictions resulting in improved quantification accuracy. iRTs can be determined by any laboratory and shared transparently. The iRT concept has been implemented in Skyline, the most widely used software for MRM experiments.

A high-throughput screen identifies a new natural product with broad-spectrum antibacterial activity

Due to the inexorable invasion of our hospitals and communities by drug-resistant bacteria, there is a pressing need for novel antibacterial agents. Here we report the development of a sensitive and robust but low-tech and inexpensive high-throughput metabolic screen for novel antibiotics. This screen is based on a colorimetric assay of pH that identifies inhibitors of bacterial sugar fermentation. After validation of the method, we screened over 39,000 crude extracts derived from organisms that grow in the diverse ecosystems of Costa Rica and identified 49 with reproducible antibacterial effects. An extract from an endophytic fungus was further characterized, and this led to the discovery of three novel natural products. One of these, which we named mirandamycin, has broad-spectrum antibacterial activity against Escherichia coli, Pseudomonas aeruginosa, Vibrio cholerae, methicillin-resistant Staphylococcus aureus, and Mycobacterium tuberculosis. This demonstrates the power of simple high throughput screens for rapid identification of new antibacterial agents from environmental samples.

Use of parallel validation high-throughput screens to reduce false positives and identify novel dengue NS2B-NS3 protease inhibitors

Dengue virus (DENV), a mosquito-borne member of the family Flaviviridae, is a significant global pathogen affecting primarily tropical and subtropical regions of the world and placing tremendous burden on the limited medical infrastructure that exists in many of the developing countries located within these regions. Recent outbreaks in developed countries, including Australia (Hanna et al., 2009), France (La Ruche et al., 2010), Taiwan (Kuan et al., 2010), and the USA (CDC, 2010), lead many researchers to believe that continued emergence into more temperate latitudes is likely. A primary concern is that there are no approved vaccines or antiviral therapies to treat DENV infections. Since the viral NS2B-NS3 protease (DENV NS2B-NS3pro) is required for virus replication, it provides a strategic target for the development of antiviral drugs. In this study, proof-of-concept high-throughput screenings (HTSs) were performed to unambiguously identify dengue 2 virus (DEN2V) NS2B-NS3pro inhibitors from a library of 2000 compounds. Validation screens were performed in parallel to concurrently eliminate insoluble, auto-fluorescing, and/or nonspecific inhibitors. Kinetic analyses of the hits revealed that parallel substrate fluorophore (AMC) interference controls and trypsin inhibition controls were able to reduce false positive rates due to solubility and fluorophore interference while the trypsin inhibition control additionally eliminated non-specific inhibitors. We identified five DEN2V NS2B-NS3pro inhibitors that also inhibited the related West Nile virus (WNV) protease (NS2B-NS3pro), but did not inhibit the trypsin protease. Biochemical analyses revealed various mechanisms of inhibition including competitive and mixed noncompetitive inhibition, with the lowest K(i) values being 12±1.5 μM for DEN2V NS2B-NS3pro and 2±0.2 μM for WNV NS2B-NS3pro.

Chemiluminescence enzyme immunoassay for the determination of sulfamethoxydiazine

Sulfamethoxydiazine (SMD), which is often used for animal disease treatment, is harmful to human health. No SMD residue should be detected in food in some countries, such as USA and Japan. Therefore, it is significant to develop a high-throughput, high-sensitivity and accurate method for the determination of the content of SMD in food. In this paper, chemiluminescence enzyme immunoassay (CLEIA) was developed for quantification of SMD. For this method, the limit of detection was 3.2 pg/ml, the linear range was from 10 to 2000 pg/ml, the within-day and inter-day precision were below 13% and below 18%, respectively, and the recovery was from 85% to 105%. Milk and egg were selected as samples to be examined with this method, and the result indicated that this CLEIA method was suitable for screening and quality control of food.

High-throughput analysis of synthetic peptides for the immunodiagnosis of canine visceral leishmaniasis

Background

Visceral leishmaniasis is the most severe form of leishmaniasis. Approximately 20% of zoonotic human visceral leishmaniasis worldwide is caused by Leishmania infantum, which is also known as Leishmania chagasi in Latin America, and disease incidence is increasing in urban and peri-urban areas of the tropics. In this form of disease, dogs are the main reservoirs. Diagnostic methods used to identify Leishmania infected animals are not able to detect all of the infected ones, which can compromise the effectiveness of disease control. Therefore, to contribute to the improvement of diagnostic methods for canine visceral leishmaniasis (CVL), we aimed to identify and test novel antigens using high-throughput analysis.

Methodology/Principal Findings

Immunodominant proteins from L. infantum were mapped in silico to predict B cell epitopes, and the 360 predicted peptides were synthesized on cellulose membranes. Immunoassays were used to select the most reactive peptides, which were then investigated with canine sera. Next, the 10 most reactive peptides were synthesized using solid phase peptide synthesis protocol and tested using ELISA. The sensitivity and specificity of these peptides were also compared to the EIE-LVC Bio-Manguinhos kit, which is recommended by the Brazilian Ministry of Health for use in leishmaniasis control programs. The sensitivity and specificity of the selected synthesized peptides was as high as 88.70% and 95.00%, respectively, whereas the EIE-LVC kit had a sensitivity of 13.08% and 100.00% of specificity. Although the tests based on synthetic peptides were able to diagnose up to 94.80% of asymptomatic dogs with leishmaniasis, the EIE-LVC kit failed to detect the disease in any of the infected asymptomatic dogs.

Conclusions/Significance

Our study shows that ELISA using synthetic peptides is a technique with great potential for diagnosing CVL; furthermore, the use of these peptides in other diagnostic methodologies, such as immunochromatographic tests, could be beneficial to CVL control programs.

Globally, the number of new human cases of visceral leishmaniasis (VL) is estimated to be approximately 500,000 per year. This is the most severe of all forms of leishmaniasis, and the zoonotic form of VL, caused by Leishmania infantum (also known as Leishmania chagasi), represents 20% of human visceral leishmaniasis worldwide; additionally, its prevalence is increasing in urban and peri-urban areas of the tropics. In Brazil, the identification and elimination of infected dogs, which act as a reservoir for Leishmania parasites, is a control measure employed in addition to the use of insecticides against the vectors and the identification and treatment of infected humans. Currently, the diagnostic methods employed to identify infected animals are not able to detect all of these dogs, which compromises the effectiveness of control measures. Moreover, one of the most important issues in controlling VL is the difficulty of diagnosing asymptomatic dogs, which act as parasite reservoirs. Therefore, to contribute to the improvement of the diagnostic methods for CVL, we aimed to identify and characterize new antigens that were more sensitive and specific and could be applied in epidemiologic surveys.

Inhibitors of SARS-CoV entry--identification using an internally-controlled dual envelope pseudovirion assay

Severe acute respiratory syndrome-associated coronavirus (SARS-CoV) emerged as the causal agent of an endemic atypical pneumonia, infecting thousands of people worldwide. Although a number of promising potential vaccines and therapeutic agents for SARS-CoV have been described, no effective antiviral drug against SARS-CoV is currently available. The intricate, sequential nature of the viral entry process provides multiple valid targets for drug development. Here, we describe a rapid and safe cell-based high-throughput screening system, dual envelope pseudovirion (DEP) assay, for specifically screening inhibitors of viral entry. The assay system employs a novel dual envelope strategy, using lentiviral pseudovirions as targets whose entry is driven by the SARS-CoV Spike glycoprotein. A second, unrelated viral envelope is used as an internal control to reduce the number of false positives. As an example of the power of this assay a class of inhibitors is reported with the potential to inhibit SARS-CoV at two steps of the replication cycle, viral entry and particle assembly. This assay system can be easily adapted to screen entry inhibitors against other viruses with the careful selection of matching partner virus envelopes.

Uniformity trials in plant freezing assays involving microtitre plates

The primary aim of this study was to measure the inherent experimental variability in plant freezing assays involving microtitre plates. Laurus nobilis leaf strips were used as experimental material. Data analysis involved variability measurements among and within microtitre plates. Statistically significant variability (p < 0.05) was observed in both cases. The second aim was to test the effectiveness of five experimental designs, in controlling the experimental error. According to our results the variability in microtitre plate freezing assays can be controlled by the use of blocked experimental designs and single well plots.

Rapid end-point quantitation of prion seeding activity with sensitivity comparable to bioassays

A major problem for the effective diagnosis and management of prion diseases is the lack of rapid high-throughput assays to measure low levels of prions. Such measurements have typically required prolonged bioassays in animals. Highly sensitive, but generally non-quantitative, prion detection methods have been developed based on prions' ability to seed the conversion of normally soluble protease-sensitive forms of prion protein to protease-resistant and/or amyloid fibrillar forms. Here we describe an approach for estimating the relative amount of prions using a new prion seeding assay called real-time quaking induced conversion assay (RT-QuIC). The underlying reaction blends aspects of the previously described quaking-induced conversion (QuIC) and amyloid seeding assay (ASA) methods and involves prion-seeded conversion of the alpha helix-rich form of bacterially expressed recombinant PrP^C to a beta sheet-rich amyloid fibrillar form. The RT-QuIC is as sensitive as the animal bioassay, but can be accomplished in 2 days or less. Analogous to end-point dilution animal bioassays, this approach involves testing of serial dilutions of samples and statistically estimating the seeding dose (SD) giving positive responses in 50% of replicate reactions (SD₅₀). Brain tissue from 263K scrapie-affected hamsters gave SD₅₀ values of 10¹¹-10¹²/g, making the RT-QuIC similar in sensitivity to end-point dilution bioassays. Analysis of bioassay-positive nasal lavages from hamsters affected with transmissible mink encephalopathy gave SD₅₀ values of 10^3.5–10^5.7/ml, showing that nasal cavities release substantial prion infectivity that can be rapidly detected. Cerebral spinal fluid from 263K scrapie-affected hamsters contained prion SD₅₀ values of 10^2.0–10^2.9/ml. RT-QuIC assay also discriminated deer chronic wasting disease and sheep scrapie brain samples from normal control samples. In principle, end-point dilution quantitation can be applied to many types of prion and amyloid seeding assays. End point dilution RT-QuIC provides a sensitive, rapid, quantitative, and high throughput assay of prion seeding activity.

Prion diseases are deadly infectious neurodegenerative disorders of mammals which involve the misfolding of host prion protein. To better manage these diseases, we need to be able to detect and quantify the infectious particles, or prions, in biological samples. However, current tests lack the sensitivity, speed and/or quantitative capabilities required for many important applications in medicine, agriculture, wildlife biology and research. To address this problem, we have developed a new prion assay that is highly sensitive, rapid, and quantitative. This assay takes advantage of the ability of miniscule amounts of infectious prions to seed the misfolding of large excesses of normal prion protein in test tube reactions. Quantitation is achieved by testing a range of sample dilutions and determining loss of seeding activity, i.e. the end-point dilution. Similar analyses have long been used to quantify prions by inoculation into animals; however, such bioassays take months or years to perform and are both animal-intensive and expensive. Our new method provides a more practical means of detecting and quantifying prions. So far, we have applied this assay to prions from sheep, deer, and hamsters, and have found surprisingly high levels of prions in the nasal and cerebral spinal fluids of infected hamsters.

Development and validation of a cell-based assay for the nuclear receptor retinoid-related orphan receptor gamma

The nuclear receptor retinoid-related orphan receptor gamma (RORγ) has become an attractive target for drug discovery due to its important role in the development and differentiation of Th17 cells, a subset of T cells that produce interleukin-17 and are involved in the pathogenesis of human inflammatory and autoimmune diseases. To facilitate the drug discovery efforts in this area, we have developed a cellular assay for screening for RORγ inverse agonists. We stably engineered a tetracycline-inducible Gal4 DNA-binding domain/RORγ ligand-binding domain fusion protein into an upstream activation sequence driven-beta-lactamase reporter gene cell line. Due to its constitutive activity, the induced Gal4-RORγ expression leads to increased reporter activity, which can be knocked down using RORγ ligand-binding domain-specific RNA interference oligos. Using this assay, we tested several recently reported ligands for RORγ and observed varying levels of partial inverse agonist activity at μM concentrations. Additionally, we screened a small library of biologically active compounds with this assay and demonstrated its robustness and usefulness in high-throughput screening and follow-up studies for this emerging drug target.