Assessment of DNA Double Strand Break Repair Activity Using High-throughput and Quantitative Luminescence-based Reporter Assays.

The repair of DNA double strand breaks (DSBs) is crucial for the maintenance of genome stability and cell viability. DSB repair (DSBR) in cells is mediated through several mechanisms: homologous recombination (HR), non-homologous end joining (NHEJ), microhomology-mediated end joining (MMEJ), and single strand annealing (SSA). Cellular assays are essential to measure the proficiency and modulation of these pathways in response to various stimuli. Here, we present a suite of extrachromosomal reporter assays that each measure the reconstitution of a nanoluciferase reporter gene by one of the four major DSBR pathways in cells. Upon transient transfection into cells of interest, repair of pathway-specific reporter substrates can be measured in under 24 h by the detection of Nanoluciferase (NanoLuc) luminescence. These robust assays are quantitative, sensitive, titratable, and amenable to a high-throughput screening format. These properties provide broad applications in DNA repair research and drug discovery, complementing the currently available toolkit of cellular DSBR assays.

Rapid Gene Silencing Followed by Antimicrobial Susceptibility Testing for Target Validation in Antibiotic Discovery.

Mycobacterium tuberculosis is the main causative agent of tuberculosis (TB)-an ancient yet widespread global infectious disease to which 1.6 million people lost their lives in 2021. Antimicrobial resistance (AMR) has been an ongoing crisis for decades; 4.95 million deaths were associated with antibiotic resistance in 2019. While AMR is a multi-faceted problem, drug discovery is an urgent part of the solution and is at the forefront of modern research.The landscape of drug discovery for TB has undoubtedly been transformed by the development of high-throughput gene-silencing techniques that enable interrogation of every gene in the genome, and their relative contribution to fitness, virulence, and AMR. A recent advance in this area is CRISPR interference (CRISPRi). The application of this technique to antimicrobial susceptibility testing (AST) is the subject of ongoing research in basic science.CRISPRi technology can be used in conjunction with the high-throughput SPOT-culture growth inhibition assay (HT-SPOTi) to rapidly evaluate and assess gene essentiality including non-essential, conditionally essential (by using appropriate culture conditions), and essential genes. In addition, the HT-SPOTi method can develop drug susceptibility and drug resistance profiles.This technology is further useful for drug discovery groups who have designed target-based inhibitors rationally and wish to validate the primary mechanisms of their novel compounds' antibiotic action against the proposed target.

Modified HT-SPOTi: An Antimicrobial Susceptibility Testing to Evaluate Formulated Therapeutic Combinations Against Bacterial Growth and Viability.

Antimicrobial resistance (AMR) poses a serious threat to global health, potentially causing 10 million deaths per year globally by 2050. To tackle AMR, researchers from all around the world have generated a selection of various formulated (viz. nanoparticulate, liposomal) therapeutic combinations to be evaluated for new antimicrobial drug discovery. To meet the urgent need for accelerating new antibacterial drug development, we need rapid but reliable whole-cell assay methods and models to test formulated therapeutic combinations against several pathogens in different in vitro conditions as models of actual infections.Over the past two decades, high-throughput spot-culture growth inhibition assay (HT-SPOTi) has been demonstrated to be a gold-standard drug susceptibility method for evaluating novel chemotherapeutic entities and existing drugs against various microbes of global concern. Our modified HT-SPOTi method serves the purpose of evaluating drug combinations against Gram-positive/negative microorganisms as well as acid-fast bacilli. The newly developed and modified HT-SPOTi assay builds upon the limitations of our previously published method to incorporate antimicrobial susceptibility testing with formulated therapeutic combinations. The modified HT-SPOTi is compared with a range of other antimicrobial susceptibility testing methods and validated using a library of existing antibiotics as well as formulated therapeutic combinations. The modified HT-SPOTi assay can serve as an efficient and reliable high-throughput drug screening platform to discover new potential antimicrobial molecules, including as part of therapeutic formulations.This chapter describes the generation of drug susceptibility profile for formulated therapeutic combinations using modified HT-SPOTi in a semi-automated system.

High-throughput combination assay for studying biofilm formation of uropathogenic Escherichia coli.

Uropathogenic Escherichia coli, the most common cause for urinary tract infections, forms biofilm enhancing its antibiotic resistance. To assess the effects of compounds on biofilm formation of uropathogenic Escherichia coli UMN026 strain, a high-throughput combination assay using resazurin followed by crystal violet staining was optimized for 384-well microplate. Optimized assay parameters included, for example, resazurin and crystal violet concentrations, and incubation time for readouts. For the assay validation, quality parameters Z' factor, coefficient of variation, signal-to-noise, and signal-to-background were calculated. Microplate uniformity, signal variability, edge well effects, and fold shift were also assessed. Finally, a screening with known antibacterial compounds was conducted to evaluate the assay performance. The best conditions found were achieved by using 12 µg/mL resazurin for 150 min and 0.023% crystal violet. This assay was able to detect compounds displaying antibiofilm activity against UMN026 strain at sub-inhibitory concentrations, in terms of metabolic activity and/or biomass.

Scalable lipid droplet microarray fabrication, validation, and screening.

High throughput screening of small molecules and natural products is costly, requiring significant amounts of time, reagents, and operating space. Although microarrays have proven effective in the miniaturization of screening for certain biochemical assays, such as nucleic acid hybridization or antibody binding, they are not widely used for drug discovery in cell culture due to the need for cells to internalize lipophilic drug candidates. Lipid droplet microarrays are a promising solution to this problem as they are capable of delivering lipophilic drugs to cells at dosages comparable to solution delivery. However, the scalablility of the array fabrication, assay validation, and screening steps has limited the utility of this approach. Here we take several new steps to scale up the process for lipid droplet array fabrication, assay validation in cell culture, and drug screening. A nanointaglio printing process has been adapted for use with a printing press. The arrays are stabilized for immersion into aqueous solution using a vapor coating process. In addition to delivery of lipophilic compounds, we found that we are also able to encapsulate and deliver a water-soluble compound in this way. The arrays can be functionalized by extracellular matrix proteins such as collagen prior to cell culture as the mechanism for uptake is based on direct contact with the lipid delivery vehicles rather than diffusion of the drug out of the microarray spots. We demonstrate this method for delivery to 3 different cell types and the screening of 92 natural product extracts on a microarray covering an area of less than 0.1 cm2. The arrays are suitable for miniaturized screening, for instance in high biosafety level facilities where space is limited and for applications where cell numbers are limited, such as in functional precision medicine.

CODEX: COunterfactual Deep learning for the in silico EXploration of cancer cell line perturbations.

MOTIVATION: High-throughput screens (HTS) provide a powerful tool to decipher the causal effects of chemical and genetic perturbations on cancer cell lines. Their ability to evaluate a wide spectrum of interventions, from single drugs to intricate drug combinations and CRISPR-interference, has established them as an invaluable resource for the development of novel therapeutic approaches. Nevertheless, the combinatorial complexity of potential interventions makes a comprehensive exploration intractable. Hence, prioritizing interventions for further experimental investigation becomes of utmost importance. RESULTS: We propose CODEX (COunterfactual Deep learning for the in silico EXploration of cancer cell line perturbations) as a general framework for the causal modeling of HTS data, linking perturbations to their downstream consequences. CODEX relies on a stringent causal modeling strategy based on counterfactual reasoning. As such, CODEX predicts drug-specific cellular responses, comprising cell survival and molecular alterations, and facilitates the in silico exploration of drug combinations. This is achieved for both bulk and single-cell HTS. We further show that CODEX provides a rationale to explore complex genetic modifications from CRISPR-interference in silico in single cells. AVAILABILITY AND IMPLEMENTATION: Our implementation of CODEX is publicly available at https://github.com/sschrod/CODEX. All data used in this article are publicly available.

Characterizing medaka visual features using a high-throughput optomotor response assay.

Medaka fish (Oryzias latipes) is a powerful model to study genetics underlying the developmental and functional traits of the vertebrate visual system. We established a simple and high-throughput optomotor response (OMR) assay utilizing medaka larvae to study visual functions including visual acuity and contrast sensitivity. Our assay presents multiple adjustable stripes in motion to individual fish in a linear arena. For that the OMR assay employs a tablet display and the Fish Stripes software to adjust speed, width, color, and contrast of the stripes. Our results demonstrated that optomotor responses were robustly induced by black and white stripes presented from below in the linear-pool-arena. We detected robust strain specific differences in the OMR when comparing long established medaka inbred strains. We observed an interesting training effect upon the initial exposure of larvae to thick stripes, which allowed them to better respond to narrower stripes. The OMR setup and protocol presented here provide an efficient tool for quantitative phenotype mapping, addressing visual acuity, trainability of cortical neurons, color sensitivity, locomotor response, retinal regeneration and others. Our open-source setup presented here provides a crucial prerequisite for ultimately addressing the genetic basis of those processes.

Quality considerations and major pitfalls for high throughput DNA-based newborn screening for severe combined immunodeficiency and spinal muscular atrophy.

BACKGROUND: Many newborn screening programs worldwide have introduced screening for diseases using DNA extracted from dried blood spots (DBS). In Germany, DNA-based assays are currently used to screen for severe combined immunodeficiency (SCID), spinal muscular atrophy (SMA), and sickle cell disease (SCD). METHODS: This study analysed the impact of pre-analytic DNA carry-over in sample preparation on the outcome of DNA-based newborn screening for SCID and SMA and compared the efficacy of rapid extraction versus automated protocols. Additionally, the distribution of T cell receptor excision circles (TREC) on DBS cards, commonly used for routine newborn screening, was determined. RESULTS: Contaminations from the punching procedure were detected in the SCID and SMA assays in all experimental setups tested. However, a careful evaluation of a cut-off allowed for a clear separation of true positive polymerase chain reaction (PCR) amplifications. Our rapid in-house extraction protocol produced similar amounts compared to automated commercial systems. Therefore, it can be used for reliable DNA-based screening. Additionally, the amount of extracted DNA significantly differs depending on the location of punching within a DBS. CONCLUSIONS: Newborn screening for SMA and SCID can be performed reliably. It is crucial to ensure that affected newborns are not overlooked. Therefore a carefully consideration of potential contaminating factors and the definition of appropriate cut-offs to minimise the risk of false results are of special concern. It is also important to note that the location of punching plays a pivotal role, and therefore an exact quantification of TREC numbers per µl may not be reliable and should therefore be avoided.

Enabling high-throughput enzyme discovery and engineering with a low-cost, robot-assisted pipeline.

As genomic databases expand and artificial intelligence tools advance, there is a growing demand for efficient characterization of large numbers of proteins. To this end, here we describe a generalizable pipeline for high-throughput protein purification using small-scale expression in E. coli and an affordable liquid-handling robot. This low-cost platform enables the purification of 96 proteins in parallel with minimal waste and is scalable for processing hundreds of proteins weekly per user. We demonstrate the performance of this method with the expression and purification of the leading poly(ethylene terephthalate) hydrolases reported in the literature. Replicate experiments demonstrated reproducibility and enzyme purity and yields (up to 400 µg) sufficient for comprehensive analyses of both thermostability and activity, generating a standardized benchmark dataset for comparing these plastic-degrading enzymes. The cost-effectiveness and ease of implementation of this platform render it broadly applicable to diverse protein characterization challenges in the biological sciences.

A Novel Fluorescence-Based Microplate Assay for High-Throughput Screening of hSULT1As Inhibitors.

Human sulfotransferase 1As (hSULT1As) play a crucial role in the metabolic clearance and detoxification of a diverse range of endogenous and exogenous substances, as well as in the bioactivation of some procarcinogens and promutagens. Pharmacological inhibiting hSULT1As activities may enhance the in vivo effects of most hSULT1As drug substrates and offer protective strategies against the hSULT1As-mediated bioactivation of procarcinogens. To date, a fluorescence-based high-throughput assay for the efficient screening of hSULT1As inhibitors has not yet been reported. In this work, a fluorogenic substrate (HN-241) for hSULT1As was developed through scaffold-seeking and structure-guided molecular optimization. Under physiological conditions, HN-241 could be readily sulfated by hSULT1As to form HN-241 sulfate, which emitted brightly fluorescent signals around 450 nm. HN-241 was then used for establishing a novel fluorescence-based microplate assay, which strongly facilitated the high-throughput screening of hSULT1As inhibitors. Following the screening of an in-house natural product library, several polyphenolic compounds were identified with anti-hSULT1As activity, while pectolinarigenin and hinokiflavone were identified as potent inhibitors against three hSULT1A isozymes. Collectively, a novel fluorescence-based microplate assay was developed for the high-throughput screening and characterization of hSULT1As inhibitors, which offered an efficient and facile approach for identifying potent hSULT1As inhibitors from compound libraries.

'Small volume-big problem': culturing Yarrowia lipolytica in high-throughput micro-formats.

With the current progress in the 'design' and 'build' stages of the 'design-build-test-learn' cycle, many synthetic biology projects become 'test-limited'. Advances in the parallelization of microbes cultivations are of great aid, however, for many species down-scaling leaves a metabolic footprint. Yarrowia lipolytica is one such demanding yeast species, for which scaling-down inevitably leads to perturbations in phenotype development. Strictly aerobic metabolism, propensity for filamentation and adhesion to hydrophobic surfaces, spontaneous flocculation, and high acidification of media are just several characteristics that make the transfer of the micro-scale protocols developed for the other microbial species very challenging in this case. It is well recognized that without additional 'personalized' optimization, either MTP-based or single-cell-based protocols are useless for accurate studies of Y. lipolytica phenotypes. This review summarizes the progress in the scaling-down and parallelization of Y. lipolytica cultures, highlighting the challenges that occur most frequently and strategies for their overcoming. The problem of Y. lipolytica cultures down-scaling is illustrated by calculating the costs of micro-cultivations, and determining the unintentionally introduced, thus uncontrolled, variables. The key research into culturing Y. lipolytica in various MTP formats and micro- and pico-bioreactors is discussed. Own recently developed and carefully pre-optimized high-throughput cultivation protocol is presented, alongside the details from the optimization stage. We hope that this work will serve as a practical guide for those working with Y. lipolytica high-throughput screens.

A Guide to Biodetection in Droplets.

Droplet-based methods for optical biodetection enable unprecedented high-throughput experimental parameters. The methods, however, remain underused due to the accompanying multidisciplinary and complicated experimental workflows. Here, we provide a tutorial for droplet-based optical biodetection workflows with a focus on the key aspect of label selection. By discussing and guiding readers through recent state-of-the-art studies, we aim to make droplet-based approaches more accessible to the general scientific public.

Biomarkers for aging in Caenorhabditis elegans high throughput screening.

Aging is characterized by a functional decline in organism fitness over time due to a complex combination of genetic and environmental factors [ 1-4]. With an increasing elderly population at risk of age-associated diseases, there is a pressing need for research dedicated to promoting health and longevity through anti-aging interventions. The roundworm Caenorhabditis elegans is an established model organism for aging studies due to its short life cycle, ease of culture, and conserved aging pathways. These benefits also make the worm well-suited for high-throughput screening (HTS) methods to study biomarkers of the molecular changes, cellular dysfunction, and physiological decline associated with aging. Within this review, we offer a summary of recent advances in HTS techniques to study biomarkers of aging in C. elegans.

Synergizing Chemical Structures and Bioassay Descriptions for Enhanced Molecular Property Prediction in Drug Discovery.

The precise prediction of molecular properties can greatly accelerate the development of new drugs. However, in silico molecular property prediction approaches have been limited so far to assays for which large amounts of data are available. In this study, we develop a new computational approach leveraging both the textual description of the assay of interest and the chemical structure of target compounds. By combining these two sources of information via self-supervised learning, our tool can provide accurate predictions for assays where no measurements are available. Remarkably, our approach achieves state-of-the-art performance on the FS-Mol benchmark for zero-shot prediction, outperforming a wide variety of deep learning approaches. Additionally, we demonstrate how our tool can be used for tailoring screening libraries for the assay of interest, showing promising performance in a retrospective case study on a high-throughput screening campaign. By accelerating the early identification of active molecules in drug discovery and development, this method has the potential to streamline the identification of novel therapeutics.

Construction and application of H1R ligand screening materials based on SMA stabilization and his-tag covalent immobilization of membrane proteins.

The histamine H1 receptor (H1R) plays a pivotal role in allergy initiation and undergoes the necessity of devising a high-throughput screening approach centered on H1R to screen novel ligands effectively. This study suggests a method employing styrene maleic acid (SMA) extraction and His-tag covalent bonding to immobilize H1R membrane proteins, minimizing the interference of nonspecific proteins interference while preserving native protein structure and maximizing target exposure. This approach was utilized to develop a novel material for high-throughput ligand screening and implemented in cell membrane chromatography (CMC). An H1R-His-SMALPs/CMC model was established and its chromatographic performance (selectivity, specificity and lifespan) validated, demonstrating a significant enhancement in lifespan compared to previous CMC models. Subsequently, this model facilitated high-throughput screening of H1R ligands in the compound library and preliminary activity verification of potential H1R antagonists. Identification of a novel H1R antagonist laid the foundation for further development in this area.

Method development with high-throughput enhanced matrix removal followed by UHPLC-QqQ-MS/MS for analysis of grape polyphenol metabolites in human urine.

Grape and grape derived products contain many bioactive phenolics which have a variety of impacts on health. Following oral ingestion, the phenolic compounds and their metabolites may be detectable in human urine. However, developing a reliable method for the analysis of phenolic compounds in urine is challenging. In this work, we developed and validated a new high-throughput, sensitive and reproducible analytical method for the simultaneous analysis of 31 grape phenolic compounds and metabolites using Oasis PRiME HLB cleanup for sample preparation combined with ultra-performance liquid chromatography with triple quadrupole tandem mass spectrometry (UHPLC-QqQ-MS/MS). Using this new method, the accuracy achieved was 69.3 % ∼ 134.9 % (except for six compounds), and the recovery achieved was 52.4 % ∼ 134.7 % (except for two very polar compounds). For each of the 31 target analytes, the value of intra-day precision was less than 14.3 %. The value of inter-day precision was slightly higher than intra-day precision, with a range of 0.7 % ∼ 19.1 %. We report for the first time on the effect of gender and BMI on the accuracy and recovery of human urine samples, and results from analysis of variance (ANOVA), and principal component analysis (PCA) indicated there was no difference in the value of accuracy and recovery between different gender or BMI (>30) using our purposed cleanup and UHPLC-QqQ-MS/MS method. Overall, this newly developed method could serve as a powerful tool for analyzing grape phenolic compounds and metabolites in human urine samples.

Boosting the Accuracy and Chemical Space Coverage of the Detection of Small Colloidal Aggregating Molecules Using the BAD Molecule Filter.

The ability to conduct effective high throughput screening (HTS) campaigns in drug discovery is often hampered by the detection of false positives in these assays due to small colloidally aggregating molecules (SCAMs). SCAMs can produce artifactual hits in HTS by nonspecific inhibition of the protein target. In this work, we present a new computational prediction tool for detecting SCAMs based on their 2D chemical structure. The tool, called the boosted aggregation detection (BAD) molecule filter, employs decision tree ensemble methods, namely, the CatBoost classifier and the light gradient-boosting machine, to significantly improve the detection of SCAMs. In developing the filter, we explore models trained on individual data sets, a consensus approach using these models, and, third, a merged data set approach, each tailored for specific drug discovery needs. The individual data set method emerged as most effective, achieving 93% sensitivity and 90% specificity, outperforming existing state-of-the-art models by 20 and 5%, respectively. The consensus models offer broader chemical space coverage, exceeding 90% for all testing sets. This feature is an important aspect particularly for early stage medicinal chemistry projects, and provides information on applicability domain. Meanwhile, the merged data set models demonstrated robust performance, with a notable sensitivity of 79% in the comprehensive 10-fold cross-validation test set. A SHAP analysis of model features indicates the importance of hydrophobicity and molecular complexity as primary factors influencing the aggregation propensity. The BAD molecule filter is readily accessible for the public usage on https://molmodlab-aau.com/Tools.html. This filter provides a new, more robust tool for aggregate prediction in the early stages of drug discovery to optimize hit rates and reduce associated testing and validation overheads.

High-throughput classification of S. cerevisiae tetrads using deep learning.

Meiotic crossovers play a vital role in proper chromosome segregation and evolution of most sexually reproducing organisms. Meiotic recombination can be visually observed in Saccharomyces cerevisiae tetrads using linked spore-autonomous fluorescent markers placed at defined intervals within the genome, which allows for analysis of meiotic segregation without the need for tetrad dissection. To automate the analysis, we developed a deep learning-based image recognition and classification pipeline for high-throughput tetrad detection and meiotic crossover classification. As a proof of concept, we analyzed a large image data set from wild-type and selected gene knock-out mutants to quantify crossover frequency, interference, chromosome missegregation, and gene conversion events. The deep learning-based method has the potential to accelerate the discovery of new genes involved in meiotic recombination in S. cerevisiae such as the underlying factors controlling crossover frequency and interference.

High-throughput assays to assess variant effects on disease.

Interpreting the wealth of rare genetic variants discovered in population-scale sequencing efforts and deciphering their associations with human health and disease present a critical challenge due to the lack of sufficient clinical case reports. One promising avenue to overcome this problem is deep mutational scanning (DMS), a method of introducing and evaluating large-scale genetic variants in model cell lines. DMS allows unbiased investigation of variants, including those that are not found in clinical reports, thus improving rare disease diagnostics. Currently, the main obstacle limiting the full potential of DMS is the availability of functional assays that are specific to disease mechanisms. Thus, we explore high-throughput functional methodologies suitable to examine broad disease mechanisms. We specifically focus on methods that do not require robotics or automation but instead use well-designed molecular tools to transform biological mechanisms into easily detectable signals, such as cell survival rate, fluorescence or drug resistance. Here, we aim to bridge the gap between disease-relevant assays and their integration into the DMS framework.

High-Throughput Proteomics Enabled by a Fully Automated Dual-Trap and Dual-Column LC-MS.

This Technical Note describes a dual-column liquid chromatography system coupled to mass spectrometry (LC-MS) for high-throughput bottom-up proteomic analysis. This system made full use of two 2-position 10-port valves and a binary pump with an integrated loading pump of a commercial LC instrument to provide successive operation of two parallel subsystems. Each subsystem consisted of a set of trap columns and an analytical column. A T-junction union was used to split the mobile phase from the loading pump into two parts. This allowed one set of columns to be washed and equilibrated, followed by the injection of the next sample, while the previous sample was eluting and being analyzed on the other set of columns, thereby greatly increasing the analysis throughput. This approach showed high reproducibility for the analysis of HeLa tryptic digests with average relative standard deviation (RSD) values of 1.75%, 6.90%, and 5.19% for the identification number of proteins, peptides, and peptide-spectrum matches (PSMs), respectively, across 10 consecutive runs. The capacity for peptide and protein identification, as well as proteome depth, of the dual-column LC system was comparable to a conventional single-column system. Due to its simple equipment requirements and set up process, this method should be highly accessible for other laboratories.