Multiplex, high-throughput method to study cancer and immune cell mechanotransduction.

Studying cellular mechanoresponses during cancer metastasis is limited by sample variation or complex protocols that current techniques require. Metastasis is governed by mechanotransduction, whereby cells translate external stimuli, such as circulatory fluid shear stress (FSS), into biochemical cues. We present high-throughput, semi-automated methods to expose cells to FSS using the VIAFLO96 multichannel pipetting device custom-fitted with 22 G needles, increasing the maximum FSS 94-fold from the unmodified tips. Specifically, we develop protocols to semi-automatically stain live samples and to fix, permeabilize, and intracellularly process cells for flow cytometry analysis. Our first model system confirmed that the pro-apoptotic effects of TRAIL therapeutics in prostate cancer cells can be enhanced via FSS-induced Piezo1 activation. Our second system implements this multiplex methodology to show that FSS exposure (290 dyn cm-2) increases activation of murine bone marrow-derived dendritic cells. These methodologies greatly improve the mechanobiology workflow, offering a high-throughput, multiplex approach.

Behavioral Assays Dissecting NMDA Receptor Function in Zebrafish.

Zebrafish are a powerful system to study brain development and to dissect the activity of complex circuits. One advantage is that they display complex behaviors, including prey capture, learning, responses to photic and acoustic stimuli, and social interaction (Dreosti et al., Front Neural Circuits 9:39, 2015; Bruckner et al., PLoS Biol 20:e3001838, 2022; Zoodsma et al., Mol Autism 13:38, 2022) that can be probed to assess brain function. Many of these behaviors are easily assayed at early larval stages, offering a noninvasive and high-throughput readout of nervous system function. Additionally, larval zebrafish readily uptake small molecules dissolved in water making them ideal for behavioral-based drug screens. Together, larval zebrafish and their behavioral repertoire offer a means to rapidly dissect brain circuitry and can serve as a template for high-throughput small molecule screens.NMDA receptor subunits are highly conserved in zebrafish compared to mammals (Zoodsma et al., Mol Autism 13:38, 2022; Cox et al., Dev Dyn 234:756-766, 2005; Zoodsma et al., J Neurosci 40:3631-3645, 2020). High amino acid and domain structure homology between humans and zebrafish underlie conserved functional similarities. Here we describe a set of behavioral assays that are useful to study the NMDA receptor activity in brain function.

High-throughput proteomic analysis of extracellular vesicles from saliva by chemical probe-based array.

Extracellular vesicles (EVs) are cell-released, nucleus-free particles with a double-membrane structure that effectively prevents degradation of internal components by a variety of salivary enzymes. Saliva is an easily accessible biofluid that contains a wealth of valuable information for disease diagnosis and monitoring and especially reflect respiratory and digestive tract diseases. However, the lack of efficient and high-throughput methods for proteomic analysis of salivary biomarkers poses a significant challenge. Herein, we designed a salivary EV amphiphile-dendrimer supramolecular probe (SEASP) array which enables efficient enrichment and in situ detection of EVs protein biomarkers. Detergent Tween-20 washing of SEASP arrays removes high abundance of heteroproteins from saliva well. This array shows good analytical performance in the linear range of 10 µL-150 µL (LOD = 0.4 µg protein, or 10 µL saliva), exhibiting a good recovery (80.0 %). Compared to ultracentrifugation (UC), this procedure provides simple and convenient access to high-purity EVs (1.3 × 109 particles per mg protein) with good physiological status and structure. Coupling with mass spectrometry based proteomic analysis, differentially expressed proteins as selected asthma biomarkers have been screened. Then, we validated the proteomics primary screening results through clinical samples (100 µL each) using the SEASP array. Utilizing the dual antibody fluorescence technology, SEASP enables the simultaneous high-throughput detection of two proteins. Therefore, the EVs marker protein CD81 could be used as an internal standard to normalize the number of EVs, which was stably expressed in EVs. Proteomics and array results suggested that HNRNPU (P = 4.9 * 10-6) and MUC5B (P = 4.7 * 10-11) are promising protein biomarkers for infantile asthma. HNRNPU and MUC5B may be associated with disease onset and subtypes. The SEASP arrays provide a significant advancement in the field of salivary biomarker. The array enables high-throughput in situ protein detection for highly viscous and complex biological samples. It provides a rapid, low-cost, highly specific screening procedure and experimental basis for early disease screening and diagnosis in the field of liquid biopsy.

Recent advances in computational and experimental protein-ligand affinity determination techniques.

INTRODUCTION: Modern drug discovery revolves around designing ligands that target the chosen biomolecule, typically proteins. For this, the evaluation of affinities of putative ligands is crucial. This has given rise to a multitude of dedicated computational and experimental methods that are constantly being developed and improved. AREAS COVERED: In this review, the authors reassess both the industry mainstays and the newest trends among the methods for protein - small-molecule affinity determination. They discuss both computational affinity predictions and experimental techniques, describing their basic principles, main limitations, and advantages. Together, this serves as initial guide to the currently most popular and cutting-edge ligand-binding assays employed in rational drug design. EXPERT OPINION: The affinity determination methods continue to develop toward miniaturization, high-throughput, and in-cell application. Moreover, the availability of data analysis tools has been constantly increasing. Nevertheless, cross-verification of data using at least two different techniques and careful result interpretation remain of utmost importance.

Using DNA-encoded libraries of fragments for hit discovery of challenging therapeutic targets.

INTRODUCTION: The effectiveness of Fragment-based drug design (FBDD) for targeting challenging therapeutic targets has been hindered by two factors: the small library size and the complexity of the fragment-to-hit optimization process. The DNA-encoded library (DEL) technology offers a compelling and robust high-throughput selection approach to potentially address these limitations. AREA COVERED: In this review, the authors propose the viewpoint that the DEL technology matches perfectly with the concept of FBDD to facilitate hit discovery. They begin by analyzing the technical limitations of FBDD from a medicinal chemistry perspective and explain why DEL may offer potential solutions to these limitations. Subsequently, they elaborate in detail on how the integration of DEL with FBDD works. In addition, they present case studies involving both de novo hit discovery and full ligand discovery, especially for challenging therapeutic targets harboring broad drug-target interfaces. EXPERT OPINION: The future of DEL-based fragment discovery may be promoted by both technical advances and application scopes. From the technical aspect, expanding the chemical diversity of DEL will be essential to achieve success in fragment-based drug discovery. From the application scope side, DEL-based fragment discovery holds promise for tackling a series of challenging targets.

Magnetic Microtweezers for High-Throughput Bioseparation in Sub-Nanoliter Droplets.

Multiomics studies at single-cell level require small volume manipulation, high throughput analysis, and multiplexed detection, characteristics that droplet microfluidics can tackle. However, the initial step of molecule bioseparation remains challenging. Here, we describe a unique magnetic device to trap and extract magnetic particles in sub-nanoliter droplets, for compartmentalisation of detection steps. Relying on electrodeposition of NiFe structures and microfluidic manipulation, the extraction of 1 µm diameter magnetic particles was achieved at high throughput (20 droplets per second) with an efficiency close to 100% in 450 pL droplets. The first demonstration of its adaptability to single-cell analysis is demonstrated with the extraction of mRNA. Using a purified nucleic acid solution, this unique magnetic configuration was able to reach a RNA extraction rate of 72%. This is the first demonstration of a physical separation in droplets at high throughput at single-cell scale.

Using Dimensionality Reduction to Visualize Phenotypic Changes in High-Throughput Microscopy.

High-throughput microscopy has enabled screening of cell phenotypes at unprecedented scale. Systematic identification of cell phenotype changes (such as cell morphology and protein localization changes) is a major analysis goal. Because cell phenotypes are high-dimensional, unbiased approaches to detect and visualize the changes in phenotypes are still needed. Here, we suggest that changes in cellular phenotype can be visualized in reduced dimensionality representations of the image feature space. We describe a freely available analysis pipeline to visualize changes in protein localization in feature spaces obtained from deep learning. As an example, we use the pipeline to identify changes in subcellular localization after the yeast GFP collection was treated with hydroxyurea.

Microfluidic device for the high-throughput and selective encapsulation of single target cells.

Droplet-based microfluidic technologies for encapsulating single cells have rapidly evolved into powerful tools for single-cell analysis. In conventional passive single-cell encapsulation techniques, because cells arrive randomly at the droplet generation section, to encapsulate only a single cell with high precision, the average number of cells per droplet has to be decreased by reducing the average frequency at which cells arrive relative to the droplet generation rate. Therefore, the encapsulation efficiency for a given droplet generation rate is very low. Additionally, cell sorting operations are required prior to the encapsulation of target cells for specific cell type analysis. To address these challenges, we developed a cell encapsulation technology with a cell sorting function using a microfluidic chip. The microfluidic chip is equipped with an optical detection section to detect the optical information of cells and a sorting section to encapsulate cells into droplets by controlling a piezo element, enabling active encapsulation of only the single target cells. For a particle population including both targeted and non-targeted particles arriving at an average frequency of up to 6000 particles per s, with an average number of particles per droplet of 0.45, our device maintained a high purity above 97.9% for the single-target-particle droplets and achieved an outstanding throughput, encapsulating up to 2900 single target particles per s. The proposed encapsulation technology surpasses the encapsulation efficiency of conventional techniques, provides high efficiency and flexibility for single-cell research, and shows excellent potential for various applications in single-cell analysis.

Construction and application of 3-fucosyllactose whole-cell biosensor for high-throughput screening of overproducers.

Biosensor-based high-throughput screening is efficient for improving industrial microorganisms. There is a severe shortage of human milk oligosaccharides (HMOs) biosensors. This study established a 3-fucosyllactose (3-FL, a kind of HMOs) whole-cell biosensor by coupling cell growth with production. To construct and optimize the biosensor, an Escherichia coli 3-FL producer was engineered by deleting the manA, yihS and manX genes, directing the mannose flux solely to 3-FL synthesis. Then, an α-L-fucosidase was introduced to hydrolyze 3-FL to fucose which was used as the only carbon source for cell growth. Using the biosensor, the 3-FL production of a screened mutant was improved by 25 % to 42.05 ± 1.28 g/L. The productivity reached 1.17 g/L/h, the highest level reported by now. The csrB mutant obtained should be a new clue for the 3-FL overproduction mechanism. In summary, this study provided a novel approach to construct HMOs biosensors for strain improvement.

A Multi-Drug Concentration Gradient Mixing Chip: A Novel Platform for High-Throughput Drug Combination Screening.

Combinatorial drug therapy has emerged as a critically important strategy in medical research and patient treatment and involves the use of multiple drugs in concert to achieve a synergistic effect. This approach can enhance therapeutic efficacy while simultaneously mitigating adverse side effects. However, the process of identifying optimal drug combinations, including their compositions and dosages, is often a complex, costly, and time-intensive endeavor. To surmount these hurdles, we propose a novel microfluidic device capable of simultaneously generating multiple drug concentration gradients across an interlinked array of culture chambers. This innovative setup allows for the real-time monitoring of live cell responses. With minimal effort, researchers can now explore the concentration-dependent effects of single-agent and combination drug therapies. Taking neural stem cells (NSCs) as a case study, we examined the impacts of various growth factors-epithelial growth factor (EGF), platelet-derived growth factor (PDGF), and fibroblast growth factor (FGF)-on the differentiation of NSCs. Our findings indicate that an overdose of any single growth factor leads to an upsurge in the proportion of differentiated NSCs. Interestingly, the regulatory effects of these growth factors can be modulated by the introduction of additional growth factors, whether singly or in combination. Notably, a reduced concentration of these additional factors resulted in a decreased number of differentiated NSCs. Our results affirm that the successful application of this microfluidic device for the generation of multi-drug concentration gradients has substantial potential to revolutionize drug combination screening. This advancement promises to streamline the process and accelerate the discovery of effective therapeutic drug combinations.

The Construction and Application of a New Screening Method for Phosphodiesterase Inhibitors.

Phosphodiesterases (PDEs), a superfamily of enzymes that hydrolyze cyclic adenosine monophosphate (cAMP) and cyclic guanosine monophosphate (cGMP), are recognized as a therapeutic target for various diseases. However, the current screening methods for PDE inhibitors usually experience problems due to complex operations and/or high costs, which are not conducive to drug development in respect of this target. In this study, a new method for screening PDE inhibitors based on GloSensor technology was successfully established and applied, resulting in the discovery of several novel compounds of different structural types with PDE inhibitory activity. Compared with traditional screening methods, this method is low-cost, capable of dynamically detecting changes in substrate concentration in live cells, and can be used to preliminarily determine the type of PDEs affected by the detected active compounds, making it more suitable for high-throughput screening for PDE inhibitors.

Current progress in high-throughput screening for drug repurposing.

High-throughput screening (HTS) is a simple, rapid and cost-effective solution to determine active candidates from large library of compounds. HTS is gaining attention from Pharmaceuticals and Biotechnology companies for accelerating their drug discovery programs. Conventional drug discovery program is time consuming and expensive. In contrast drug repurposing approach is cost-effective and increases speed of drug discovery as toxicity profile is already known. The present chapter highlight HTS technology including microplate, microfluidics, lab-on-chip, organ-on-chip for drug repurposing. The current chapter also highlights the application of HTS for bacterial infections and cancer.

Metabolism and cytotoxicity studies of the two hallucinogens 1cP-LSD and 4-AcO-DET in human liver and zebrafish larvae models using LC-HRMS/MS and a high-content screening assay.

The continuous emergence of new psychoactive substances (NPS) attracted a great deal of attention within recent years. Lately, the two hallucinogenic NPS 1cP-LSD and 4-AcO-DET have appeared on the global market. Knowledge about their metabolism to identify potential metabolic targets for analysis and their cytotoxic properties is lacking. The aim of this work was thus to study their in vitro and in vivo metabolism in pooled human liver S9 fraction (pHLS9) and in zebrafish larvae (ZL) by means of liquid chromatography-high-resolution tandem mass spectrometry. Monooxygenases involved in the initial metabolic steps were elucidated using recombinant human isozymes. Investigations on their cytotoxicity were performed on the human hepatoma cell line HepG2 using a multiparametric, fluorescence-based high-content screening assay. This included measurement of CYP-enzyme mediated effects by means of the unspecific CYP inhibitor 1-aminbenzotriazole (ABT). Several phase I metabolites of both compounds and two phase II metabolites of 4-AcO-DET were produced in vitro and in vivo. After microinjection of 1cP-LSD into the caudal vein of ZL, three out of seven metabolites formed in pHLS9 were also detected in ZL. Twelve 4-AcO-DET metabolites were identified in ZL after exposure via immersion bath and five of them were found in pHLS9 incubations. Notably, unique metabolites of 4-AcO-DET were only produced by ZL, whereas 1cP-LSD specific metabolites were found both in ZL and in pHLS9. No toxic effects were observed for 1cP-LSD and 4-AcO-DET in HepG2 cells, however, two parameters were altered in incubations containing 4-AcO-DET together with ABT compared with incubations without ABT but in concentrations far above expected in vivo concentration. Further investigations should be done with other hepatic cell lines expressing higher levels of CYP enzymes.

Ultrapotent influenza hemagglutinin fusion inhibitors developed through SuFEx-enabled high-throughput medicinal chemistry.

Seasonal and pandemic-associated influenza strains cause highly contagious viral respiratory infections that can lead to severe illness and excess mortality. Here, we report on the optimization of our small-molecule inhibitor F0045(S) targeting the influenza hemagglutinin (HA) stem with our Sulfur-Fluoride Exchange (SuFEx) click chemistry-based high-throughput medicinal chemistry (HTMC) strategy. A combination of SuFEx- and amide-based lead molecule diversification and structure-guided design led to identification and validation of ultrapotent influenza fusion inhibitors with subnanomolar EC50 cellular antiviral activity against several influenza A group 1 strains. X-ray structures of six of these compounds with HA indicate that the appended moieties occupy additional pockets on the HA surface and increase the binding interaction, where the accumulation of several polar interactions also contributes to the improved affinity. The compounds here represent the most potent HA small-molecule inhibitors to date. Our divergent HTMC platform is therefore a powerful, rapid, and cost-effective approach to develop bioactive chemical probes and drug-like candidates against viral targets.

Development, High-Throughput Profiling, and Biopanning of a Large Phage Display Single-Domain Antibody Library.

Immunoglobulin G-based monoclonal antibodies (mAbs) have been effective in treating various diseases, but their large molecular size can limit their penetration of tissue and efficacy in multifactorial diseases, necessitating the exploration of alternative forms. In this study, we constructed a phage display library comprising single-domain antibodies (sdAbs; or "VHHs"), known for their small size and remarkable stability, using a total of 1.6 × 109 lymphocytes collected from 20 different alpacas, resulting in approximately 7.16 × 1010 colonies. To assess the quality of the constructed library, next-generation sequencing-based high-throughput profiling was performed, analyzing approximately 5.65 × 106 full-length VHH sequences, revealing 92% uniqueness and confirming the library's diverse composition. Systematic characterization of the library revealed multiple sdAbs with high affinity for three therapeutically relevant antigens. In conclusion, our alpaca sdAb phage display library provides a versatile resource for diagnostics and therapeutics. Furthermore, the library's vast natural VHH antibody repertoire offers insights for generating humanized synthetic sdAb libraries, further advancing sdAb-based therapeutics.

Machine Learning-Accelerated High-Throughput Computational Screening: Unveiling Bimetallic Nanoparticles with Peroxidase-Like Activity.

Bimetallic nanoparticles (NPs) with peroxidase-like (POD-like) activity play a crucial role in biosensing, disease treatment, environmental management, and other fields. However, their development is impeded by a vast range of tunable properties in components and structures, making the establishment of structure-effect relationships and the discovery of active materials challenging. Addressing this, we established robust scaling relationships by meticulously analyzing the catalytic reaction networks of pure metal NPs, which laid the volcano-shaped correlation between the activity and O* adsorption energy. Utilizing these relationships, we introduced an innovative and versatile descriptor of the NPs, which was then integrated into a machine learning-accelerated high-throughput computational workflow, significantly boosting the predictive accuracy for the POD-like activity of bimetallic NPs. Our methodological approach enabled the successful prediction of activities for 1260 bimetallic NPs, leading to the identification of several highly effective catalysts. Furthermore, we distilled several strategies for designing efficient bimetallic NPs based on our screening results.

High-throughput tagging of endogenous loci for rapid characterization of protein function.

To facilitate the interrogation of protein function at scale, we have developed high-throughput insertion of tags across the genome (HITAG). HITAG enables users to rapidly produce libraries of cells, each with a different protein of interest C-terminally tagged. HITAG is based on a modified strategy for performing Cas9-based targeted insertions, coupled with an improved approach for selecting properly tagged lines. Analysis of the resulting clones generated by HITAG reveals high tagging specificity, with most successful tagging events being indel free. Using HITAG, we fuse mCherry to a set of 167 stress granule-associated proteins and elucidate the features that drive a subset of proteins to strongly accumulate within these transient RNA-protein granules.

New methods for drug synergy prediction: A mini-review.

In this mini-review, we explore the new prediction methods for drug combination synergy relying on high-throughput combinatorial screens. The fast progress of the field is witnessed in the more than thirty original machine learning methods published since 2021, a clear majority of them based on deep learning techniques. We aim to put these articles under a unifying lens by highlighting the core technologies, the data sources, the input data types and synergy scores used in the methods, as well as the prediction scenarios and evaluation protocols that the articles deal with. Our finding is that the best methods accurately solve the synergy prediction scenarios involving known drugs or cell lines while the scenarios involving new drugs or cell lines still fall short of an accurate prediction level.

Screening and characterization of 133 physiologically-relevant environmental chemicals for reproductive toxicity.

Reproduction is a functional outcome that relies on complex cellular, tissue, and organ interactions that span the developmental period to adulthood. Thus, the assessment of its disruption by environmental chemicals would benefit significantly from scalable and innovative approaches to testing using functionally comparable reproductive models such as the nematode C. elegans. We adapted a previously described low-throughput in vivo chromosome segregation assay using C. elegans predictive of reproductive toxicity and leveraged available public data sources (ToxCast, ICE) to screen and characterize 133 physiologically-relevant chemicals in a high-throughput manner. The screening outcome was further validated in a second, independent in vivo assay assessing embryonic viability. In total, 13 chemicals were classified as reproductive toxicants with the two most active chemicals belonging to the large family of Quaternary Ammonium Compounds (QACs) commonly used as disinfectants but with limited available reproductive toxicity data. We compared the results from the C. elegans assay with ToxCast in vitro data compiled from 700+ cell response assays and 300+ signaling pathways-based assays. We did not observe a difference in the bioactivity or in the average potency (AC50) between the top and bottom chemicals. However, the intended target categories were significantly different between the classified chemicals with, in particular, an over-representation of steroid hormone targets for the high Z-score chemicals. Taken together, these results point to the value of in vivo models that scale to high-throughput level for reproductive toxicity assessment and to the need to prioritize the assessment of QACs impacts on reproduction.

Machine Learning Models Identify Inhibitors of New Delhi Metallo-ß-lactamase.

The worldwide spread of the metallo-ß-lactamases (MBL), especially New Delhi metallo-ß-lactamase-1 (NDM-1), is threatening the efficacy of ß-lactams, which are the most potent and prescribed class of antibiotics in the clinic. Currently, FDA-approved MBL inhibitors are lacking in the clinic even though many strategies have been used in inhibitor development, including quantitative high-throughput screening (qHTS), fragment-based drug discovery (FBDD), and molecular docking. Herein, a machine learning-based prediction tool is described, which was generated using results from HTS of a large chemical library and previously published inhibition data. The prediction tool was then used for virtual screening of the NIH Genesis library, which was subsequently screened using qHTS. A novel MBL inhibitor was identified and shown to lower minimum inhibitory concentrations (MICs) of Meropenem for a panel of E. coli and K. pneumoniae clinical isolates expressing NDM-1. The mechanism of inhibition of this novel scaffold was probed utilizing equilibrium dialyses with metal analyses, native state electrospray ionization mass spectrometry, UV-vis spectrophotometry, and molecular docking. The uncovered inhibitor, compound 72922413, was shown to be 9-hydroxy-3-[(5-hydroxy-1-oxa-9-azaspiro[5.5]undec-9-yl)carbonyl]-4H-pyrido[1,2-a]pyrimidin-4-one.