High-Throughput Metabolic Pattern Screening Strategy for Early Colorectal and Gastric Cancers Based on Covalent Organic Frameworks-Assisted Laser Desorption/Ionization Mass Spectrometry.

Precise early diagnosis and staging are conducive to improving the prognosis of colorectal cancer (CRC) and gastric cancer (GC) patients. However, due to intrusive inspections and limited sensitivity, the prevailing diagnostic methods impede precisely large-scale screening. In this work, we reported a high-throughput serum metabolic patterns (SMP) screening strategy based on covalent organic frameworks-assisted laser desorption/ionization mass spectrometry (hf-COFsLDI-MS) for early diagnosis and staging of CRC and GC. Notably, 473 high-quality SMP were extracted without any tedious sample pretreatment and coupled with multiple machine learning algorithms; the area under the curve (AUC) value is 0.938 with 96.9% sensitivity for early CRC diagnosis, and the AUC value is 0.974 with 100% sensitivity for early GC diagnosis. Besides, the discrimination of CRC and GC is accomplished with an AUC value of 0.966 for the validation set. Also, the screened-out features were identified by MS/MS experiments, and 8 metabolites were identified as the biomarkers for CRC and GC. Finally, the corresponding disordered metabolic pathways were revealed, and the staging of CRC and GC was completed. This work provides an alternative high-throughput screening strategy for CRC and GC and highlights the potential of metabolic molecular diagnosis in clinical applications.

A High-Throughput Approach for Photosynthesis Studies in a Brassicaceae Panel.

The study of natural variations in photosynthesis in the Brassicaceae family offers the possibility of identifying mechanisms to enhance photosynthetic efficiency in crop plants. Indeed, this family, and particularly its tribe Brassiceae, has been shown to harbor species that have a higher-than-expected photosynthetic efficiency, possibly as a result of a complex evolutionary history. Over the past two decades, methods have been developed to measure photosynthetic efficiency based on chlorophyll fluorescence. Chlorophyll fluorescence measurements are performed with special cameras, such as the FluorCams, which can be included in robotic systems to create high-throughput phenotyping platforms. While these platforms have so far demonstrated high efficiency in measuring small model species like Arabidopsis thaliana, they have the drawback of limited adaptability to accommodate different plant sizes. As a result, the range of species that can be analyzed is restricted. This chapter presents our approach to analyze the photosynthetic parameters: ϕPSII and Fv/Fm for a panel of Brassicaceae species, including a high-photosynthesis species, Hirschfeldia incana, and the adaptations to the phenotyping platform that are required to accommodate this varied group of plants.

An Overview of High-Throughput Crop Phenotyping: Platform, Image Analysis, Data Mining, and Data Management.

In this chapter, we explore the application of high-throughput crop phenotyping facilities for phenotype data acquisition and the extraction of significant information from the collected data through image processing and data mining methods. Additionally, the construction and outlook of crop phenotype databases are introduced and the need for global cooperation and data sharing is emphasized. High-throughput crop phenotyping significantly improves accuracy and efficiency compared to traditional measurements, making significant contributions to overcoming bottlenecks in the phenotyping field and advancing crop genetics.

High-throughput visualization mutation screening technology to enhance the specificity of CadR based whole-cell cadmium biosensor.

As a heavy metal pollutant, Cd2+ often enters the human body through the food chain causing great harm to human health. Whole-cell biosensor is an emerging technology for rapid on-site detection of heavy metals with the advantages of inexpensive, fast to mass-produce, and strong in anti-interference resistance, but suffering from insatisfactory specificity. In this study, a strategy of Adjacent Site Saturation Mutation (ASSM) was designed to improve the specificity of transcription factor CadR, which acted as the recognition element and determined the specificity of whole cell Cd2+ biosensors. A specific saturated library was constructed using the strategy of adjacent mutation. After two rounds of high-throughput visual screening, a whole-cell biosensor with good response to Cd2+, and with significant weakened Hg2+ interference was obtained. The optimized whole-cell biosensor showed a linear dynamic concentration range from 500 nM to 100 µM, a detection limit of 0.079 µM, and has satisfactory specificity and anti-interference. The ASSM strategy proposed in this study can provide a new method for the application of synthetic biology in food safety detection, indicating the importance of whole-cell biosensors for the detection of heavy metals.

AptaShield: A Universal Signal-Transduction System for Fast and High-Throughput Optical Molecular Biosensing.

Biosensing technologies are often described to provide facile, sensitive, and minimally to noninvasive detection of molecular analytes across diverse scientific, environmental, and clinical diagnostic disciplines. However, commercialization has been very limited mostly due to the difficulty of biosensor reconfiguration for different analyte(s) and limited high-throughput capabilities. The immobilization of different biomolecular probes (e.g., antibodies, peptides, and aptamers) requires the sensor surface chemistry to be tailored to provide optimal probe coupling, orientation, and passivation and prevent nonspecific interactions. To overcome these challenges, here we report the development of a solution-phase biosensor consisting of an engineered aptamer, the AptaShield, capable of universally binding to any antigen recognition site (Fab') of fluorescently labeled immunoglobulins (IgG) produced in rabbits. The resulting AptaShield biosensor relies on a low affinity dynamic equilibrium between the fluorescently tagged aptamer and IgG to generate a specific Förster resonance energy transfer (FRET) signal. As the analyte binds to the IgG, the AptaShield DNA aptamer-IgG complex dissociates, leading to an analyte concentration-dependent decrease of the FRET signal. The biosensor demonstrates high selectivity, specificity, and reproducibility for analyte quantification in different biological fluids (e.g., urine and blood serum) in a one-step and low sample volume (0.5-6.25 µL) format. The AptaShield provides a universal signal transduction mechanism as it can be coupled to different rabbit antibodies without the need for aptamer modification, therefore representing a robust high-throughput solution-phase technology suitable for point-of-care applications, overcoming the current limitations of gold standard enzyme-linked immunosorbent assays (ELISA) for molecular profiling.

Cell Painting-based bioactivity prediction boosts high-throughput screening hit-rates and compound diversity.

Identifying active compounds for a target is a time- and resource-intensive task in early drug discovery. Accurate bioactivity prediction using morphological profiles could streamline the process, enabling smaller, more focused compound screens. We investigate the potential of deep learning on unrefined single-concentration activity readouts and Cell Painting data, to predict compound activity across 140 diverse assays. We observe an average ROC-AUC of 0.744 ± 0.108 with 62% of assays achieving ≥0.7, 30% ≥0.8, and 7% ≥0.9. In many cases, the high prediction performance can be achieved using only brightfield images instead of multichannel fluorescence images. A comprehensive analysis shows that Cell Painting-based bioactivity prediction is robust across assay types, technologies, and target classes, with cell-based assays and kinase targets being particularly well-suited for prediction. Experimental validation confirms the enrichment of active compounds. Our findings indicate that models trained on Cell Painting data, combined with a small set of single-concentration data points, can reliably predict the activity of a compound library across diverse targets and assays while maintaining high hit rates and scaffold diversity. This approach has the potential to reduce the size of screening campaigns, saving time and resources, and enabling primary screening with more complex assays.

High throughput screening aids clinical decision-making in refractory acute myeloid leukaemia.

BACKGROUND: Despite advances in therapeutics for adverse-risk acute myeloid leukaemia (AML), overall survival remains poor, especially in refractory disease. Comprehensive tumour profiling and pre-clinical drug testing can identify effective personalised therapies. CASE: We describe a case of ETV6-MECOM fusion-positive refractory AML, where molecular analysis and in vitro high throughput drug screening identified a tolerable, novel targeted therapy and provided rationale for avoiding what could have been a toxic treatment regimen. Ruxolitinib combined with hydroxyurea led to disease control and enhanced quality-of-life in a patient unsuitable for intensified chemotherapy or allogeneic stem cell transplantation. CONCLUSION: This case report demonstrates the feasibility and role of combination pre-clinical high throughput screening to aid decision making in high-risk leukaemia. It also demonstrates the role a JAK1/2 inhibitor can have in the palliative setting in select patients with AML.

Enhancing the Small-Scale Screenable Biological Space beyond Known Chemogenomics Libraries with Gray Chemical Matter─Compounds with Novel Mechanisms from High-Throughput Screening Profiles.

Phenotypic assays have become an established approach to drug discovery. Greater disease relevance is often achieved through cellular models with increased complexity and more detailed readouts, such as gene expression or advanced imaging. However, the intricate nature and cost of these assays impose limitations on their screening capacity, often restricting screens to well-characterized small compound sets such as chemogenomics libraries. Here, we outline a cheminformatics approach to identify a small set of compounds with likely novel mechanisms of action (MoAs), expanding the MoA search space for throughput limited phenotypic assays. Our approach is based on mining existing large-scale, phenotypic high-throughput screening (HTS) data. It enables the identification of chemotypes that exhibit selectivity across multiple cell-based assays, which are characterized by persistent and broad structure activity relationships (SAR). We validate the effectiveness of our approach in broad cellular profiling assays (Cell Painting, DRUG-seq, and Promotor Signature Profiling) and chemical proteomics experiments. These experiments revealed that the compounds behave similarly to known chemogenetic libraries, but with a notable bias toward novel protein targets. To foster collaboration and advance research in this area, we have curated a public set of such compounds based on the PubChem BioAssay dataset and made it available for use by the scientific community.

AI is a viable alternative to high throughput screening: a 318-target study.

High throughput screening (HTS) is routinely used to identify bioactive small molecules. This requires physical compounds, which limits coverage of accessible chemical space. Computational approaches combined with vast on-demand chemical libraries can access far greater chemical space, provided that the predictive accuracy is sufficient to identify useful molecules. Through the largest and most diverse virtual HTS campaign reported to date, comprising 318 individual projects, we demonstrate that our AtomNet® convolutional neural network successfully finds novel hits across every major therapeutic area and protein class. We address historical limitations of computational screening by demonstrating success for target proteins without known binders, high-quality X-ray crystal structures, or manual cherry-picking of compounds. We show that the molecules selected by the AtomNet® model are novel drug-like scaffolds rather than minor modifications to known bioactive compounds. Our empirical results suggest that computational methods can substantially replace HTS as the first step of small-molecule drug discovery.

Virtual Screening of a Chemically Diverse "Superscaffold" Library Enables Ligand Discovery for a Key GPCR Target.

The advent of ultra-large libraries of drug-like compounds has significantly broadened the possibilities in structure-based virtual screening, accelerating the discovery and optimization of high-quality lead chemotypes for diverse clinical targets. Compared to traditional high-throughput screening, which is constrained to libraries of approximately one million compounds, the ultra-large virtual screening approach offers substantial advantages in both cost and time efficiency. By expanding the chemical space with compounds synthesized from easily accessible and reproducible reactions and utilizing a large, diverse set of building blocks, we can enhance both the diversity and quality of the discovered lead chemotypes. In this study, we explore new chemical spaces using reactions of sulfur(VI) fluorides to create a combinatorial library consisting of several hundred million compounds. We screened this virtual library for cannabinoid type II receptor (CB2) antagonists using the high-resolution structure in conjunction with a rationally designed antagonist, AM10257. The top-predicted compounds were then synthesized and tested in vitro for CB2 binding and functional antagonism, achieving an experimentally validated hit rate of 55%. Our findings demonstrate the effectiveness of reliable reactions, such as sulfur fluoride exchange, in diversifying ultra-large chemical spaces and facilitate the discovery of new lead compounds for important biological targets.

Phenotypic high-throughput screening identifies aryl hydrocarbon receptor agonism as common inhibitor of toxin-induced retinal pigment epithelium cell death.

The retinal pigment epithelium (RPE) is essential to maintain retinal function, and RPE cell death represents a key pathogenic stage in the progression of several blinding ocular diseases, including age-related macular degeneration (AMD). To identify pathways and compounds able to prevent RPE cell death, we developed a phenotypic screening pipeline utilizing a compound library and high-throughput screening compatible assays on the human RPE cell line, ARPE-19, in response to different disease relevant cytotoxic stimuli. We show that the metabolic by-product of the visual cycle all-trans-retinal (atRAL) induces RPE apoptosis, while the lipid peroxidation by-product 4-hydroxynonenal (4-HNE) promotes necrotic cell death. Using these distinct stimuli for screening, we identified agonists of the aryl hydrocarbon receptor (AhR) as a consensus target able to prevent both atRAL mediated apoptosis and 4-HNE-induced necrotic cell death. This works serves as a framework for future studies dedicated to screening for inhibitors of cell death, as well as support for the discussion of AhR agonism in RPE pathology.

Graph Convolutional Network-Enhanced Model for Screening Persistent, Mobile, and Toxic and Very Persistent and Very Mobile Substances.

The global management for persistent, mobile, and toxic (PMT) and very persistent and very mobile (vPvM) substances has been further strengthened with the rapid increase of emerging contaminants. The development of a ready-to-use and publicly available tool for the high-throughput screening of PMT/vPvM substances is thus urgently needed. However, the current model building with the coupling of conventional algorithms, small-scale data set, and simplistic features hinders the development of a robust model for screening PMT/vPvM with wide application domains. Here, we construct a graph convolutional network (GCN)-enhanced model with feature fusion of a molecular graph and molecular descriptors to effectively utilize the significant correlation between critical descriptors and PMT/vPvM substances. The model is built with 213,084 substances following the latest PMT classification criteria. The application domains of the GCN-enhanced model assessed by kernel density estimation demonstrate the high suitability for high-throughput screening PMT/vPvM substances with both a high accuracy rate (86.6%) and a low false-negative rate (6.8%). An online server named PMT/vPvM profiler is further developed with a user-friendly web interface (http://www.pmt.zj.cn/). Our study facilitates a more efficient evaluation of PMT/vPvM substances with a globally accessible screening platform.

MALDI-TOF Mass Spectrometry-Based Assay for Measuring Covalent Target Engagement of KRAS G12C Inhibitors.

MALDI-TOF mass spectrometry enables high-throughput screening of covalent fragment libraries and SAR compound progressions of selective KRAS G12C inhibitors. Using the MALDI-TOF platform instead of the more traditional ESI-MS TOF/orbitrap instrumentation can radically shorten sample acquisition time, allowing up to 384 samples to be screened in 30 min. The typical throughput for a covalent library screen is 1152 samples per 8 h, including processing, calculation, and reporting steps. The throughput can be doubled without any significant assay modification.

High-Throughput Cell-Based Screening of Small Molecule KRAS Signaling Inhibitors Using a Homogeneous Time-Resolved Fluorescence (HTRF) Assay.

With recent advances proving that effective inhibition of KRAS is possible, there have been significant efforts made to develop inhibitors of specific mutant alleles. Here we describe a detailed protocol that employs homogeneous time-resolved fluorescence (HTRF) to identify compounds acting on KRAS signaling in malignant cell lines. This method allows for high-throughput, cell-based screens of large compound libraries for the development of RAS-targeted therapeutics.

Studying RAS Interactions in Live Cells with BRET.

Bioluminescence resonance energy transfer (BRET) is a valuable technique for studying protein-protein interactions (PPIs) within live cells (Pfleger and Eidne, Nat Methods 3:165-174, 2006). Among the various BRET methodologies, a recent addition called NanoBRET has emerged, leveraging advancements in donor and acceptor technologies (Machleidt and Woodroofe, ACS Chem Biol 10:1797-1804, 2015). In this study, we present a developed methodology designed to measure PPIs involving the RAS protein family and their effectors and interactors at the plasma membrane. By utilizing the NanoLuc and HaloTag BRET pair, we provide evidence of a saturable interaction between KRAS4b-G12D and full-length RAF1. Conversely, the RAF1 R89L mutant, known to impede RAF1 binding to active RAS, exhibits nonspecific interactions. The assay exhibits remarkable signal-to-background ratios and is highly suitable for investigating the interactions of RAS with effectors, as well as for high-throughput screening assays.

The Discovery of New Inhibitors of Insulin-Regulated Aminopeptidase by a High-Throughput Screening of 400,000 Drug-like Compounds.

With the ambition to identify novel chemical starting points that can be further optimized into small drug-like inhibitors of insulin-regulated aminopeptidase (IRAP) and serve as potential future cognitive enhancers in the clinic, we conducted an ultra-high-throughput screening campaign of a chemically diverse compound library of approximately 400,000 drug-like small molecules. Three biochemical and one biophysical assays were developed to enable large-scale screening and hit triaging. The screening funnel, designed to be compatible with high-density microplates, was established with two enzyme inhibition assays employing either fluorescent or absorbance readouts. As IRAP is a zinc-dependent enzyme, the remaining active compounds were further evaluated in the primary assay, albeit with the addition of zinc ions. Rescreening with zinc confirmed the inhibitory activity for most compounds, emphasizing a zinc-independent mechanism of action. Additionally, target engagement was confirmed using a complementary biophysical thermal shift assay where compounds causing positive/negative thermal shifts were considered genuine binders. Triaging based on biochemical activity, target engagement, and drug-likeness resulted in the selection of 50 qualified hits, of which the IC50 of 32 compounds was below 3.5 µM. Despite hydroxamic acid dominance, diverse chemotypes with biochemical activity and target engagement were discovered, including non-hydroxamic acid compounds. The most potent compound (QHL1) was resynthesized with a confirmed inhibitory IC50 of 320 nM. Amongst these compounds, 20 new compound structure classes were identified, providing many new starting points for the development of unique IRAP inhibitors. Detailed characterization and optimization of lead compounds, considering both hydroxamic acids and other diverse structures, are in progress for further exploration.

High-Throughput µPAD with Cascade Signal Amplification through Dual Enzymes for arsM in Paddy Soil.

The arsM gene is a critical biomarker for the potential risk of arsenic exposure in paddy soil. However, on-site screening of arsM is limited by the lack of high-throughput point-of-use (POU) methods. Here, a multiplex CRISPR/Cas12a microfluidic paper-based analytical device (µPAD) was constructed for the high-throughput POU analysis of arsM, with cascade amplification driven by coupling crRNA-enhanced Cas12a and horseradish peroxidase (HRP)-modified probes. First, seven crRNAs were designed to recognize arsM, and their LODs and background signal intensities were evaluated. Next, a step-by-step iterative approach was utilized to develop and optimize coupling systems, which improved the sensitivity 32 times and eliminated background signal interference. Then, ssDNA reporters modified with HRP were introduced to further lower the LOD to 16 fM, and the assay results were visible to the naked eye. A multiplex channel microfluidic paper-based chip was developed for the reaction integration and simultaneous detection of 32 samples and generated a recovery rate between 87.70 and 114.05%, simplifying the pretreatment procedures and achieving high-throughput POU analysis. Finally, arsM in Wanshan paddy soil was screened on site, and the arsM abundance ranged from 1.05 × 106 to 6.49 × 107 copies/g; this result was not affected by the environmental indicators detected in the study. Thus, a coupling crRNA-based cascade amplification method for analyzing arsM was constructed, and a microfluidic device was developed that contains many more channels than previous paper chips, greatly improving the analytical performance in paddy soil samples and providing a promising tool for the on-site screening of arsM at large scales.

Using Infrared Thermography for High-Throughput Plant Phenotyping.

Infrared thermography offers a rapid, noninvasive method for measuring plant temperature, which provides a proxy for stomatal conductance and plant water status and can therefore be used as an index for plant stress. Thermal imaging can provide an efficient method for high-throughput screening of large numbers of plants. This chapter provides guidelines for using thermal imaging equipment and illustrative methodologies, coupled with essential considerations, to access plant physiological processes.

Droplet-based microfluidic platform for detecting agonistic peptides that are self-secreted by yeast expressing a G-protein-coupled receptor.

BACKGROUND: Single-cell droplet microfluidics is an important platform for high-throughput analyses and screening because it provides an independent and compartmentalized microenvironment for reaction or cultivation by coencapsulating individual cells with various molecules in monodisperse microdroplets. In combination with microbial biosensors, this technology becomes a potent tool for the screening of mutant strains. In this study, we demonstrated that a genetically engineered yeast strain that can fluorescently sense agonist ligands via the heterologous expression of a human G-protein-coupled receptor (GPCR) and concurrently secrete candidate peptides is highly compatible with single-cell droplet microfluidic technology for the high-throughput screening of new agonistically active peptides. RESULTS: The water-in-oil microdroplets were generated using a flow-focusing microfluidic chip to encapsulate engineered yeast cells coexpressing a human GPCR [i.e., angiotensin II receptor type 1 (AGTR1)] and a secretory agonistic peptide [i.e., angiotensin II (Ang II)]. The single yeast cells cultured in the droplets were then observed under a microscope and analyzed using image processing incorporating machine learning techniques. The AGTR1-mediated signal transduction elicited by the self-secreted Ang II peptide was successfully detected via the expression of a fluorescent reporter in single-cell yeast droplet cultures. The system could also distinguish Ang II analog peptides with different agonistic activities. Notably, we further demonstrated that the microenvironment of the single-cell droplet culture enabled the detection of rarely existing positive (Ang II-secreting) yeast cells in the model mixed cell library, whereas the conventional batch-culture environment using a shake flask failed to do so. Thus, our approach provided compartmentalized microculture environments, which can prevent the diffusion, dilution, and cross-contamination of peptides secreted from individual single yeast cells for the easy identification of GPCR agonists. CONCLUSIONS: We established a droplet-based microfluidic platform that integrated an engineered yeast biosensor strain that concurrently expressed GPCR and self-secreted the agonistic peptides. This offers individually isolated microenvironments that allow the culture of single yeast cells secreting these peptides and gaging their signaling activities, for the high-throughput screening of agonistic peptides. Our platform base on yeast GPCR biosensors and droplet microfluidics will be widely applicable to metabolic engineering, environmental engineering, and drug discovery.

Technological advancements in viral vector designing and optimization for therapeutic applications.

Viral vector engineering is critical to the advancement of several sectors of biotechnology, gene therapy, and vaccine development. These vectors were produced from viruses, were employed to deliver therapeutic genes or to alter biological processes. The potential for viral vectors to improve the precision, safety, and efficiency of therapeutic interventions has boosted their demand. The dynamic interplay between technological advancements and computational tools in establishing the landscape of viral vector engineering and vector optimization for therapeutic reasons is discussed in this chapter. It also emphasizes the importance of in silico techniques in maximizing vector potential for therapeutics and many phases of viral vector engineering, from genomic analysis to computer modelling and advancements to improve precise gene delivery. High-throughput screening propels the expedited process of vector selection, and computational techniques to analyze complex omics data to further enhance vector capabilities have been discussed. As in silico models reveal insights into off-target effects and integration sites, vector safety (biodistribution and toxicity) remains a crucial part and bridges the gap between preclinical and clinical investigations. Despite the limitations, this chapter depicts a future in which technology and computing merge to catapult viral vector therapy into an era of boundless possibilities.