Zebrafish is a highly advantageous model animal for drug screening and toxicity evaluation thanks to its amenability to optical imaging (i.e., transparency), possession of organ structures similar to humans, and the ease with which disease models can be established. However, current zebrafish drug screening technologies and devices suffer from limitations such as low level of automation and throughput, and low accuracy caused by the heterogeneity among individual zebrafish specimens. To address these issues, we herein develop a high-throughput zebrafish drug screening system. This system is capable of maintaining optimal culturing conditions and simultaneously monitoring and analyzing the movement of 288 zebrafish larvae under various external conditions, such as drug combinations. Moreover, to eliminate the effect of heterogeneity, locomotion of participating zebrafish is assessed and grouped before experiments. It is demonstrated that in contrast to the experimental results without pre-selection, which shows ∼20 % damaged motor function (i.e., degree of attenuation), the drug-induced variations among zebrafish with equivalent mobility reaches ∼80 %. Overall, our high-throughput zebrafish drug screening system overcomes current limitations by improving automation, throughput, and accuracy, resulting in enhanced detection of drug-induced variations in zebrafish motor function.

Quantifying viral growth rates is key to understanding evolutionary dynamics and the potential for mutants to escape antiviral drugs. Defining evolutionary escape paths and their impact on viral fitness allows for the development of drugs that are resistant to escape. In the case of HIV, combination antiretroviral therapy can successfully prevent or treat infection, but it relies on strict adherence to prevent escape. Here, we present a method termed QuickFit that enables the quantification of viral fitness by employing large numbers of parallel viral cultures to measure growth rates accurately. QuickFit consistently recapitulated HIV growth measurements obtained by traditional approaches, but with significantly higher throughput and lower rates of error. This method represents a promising tool for rapid and consistent evaluation of viral fitness.

Yeast plays a significant role in a variety of fields. In particular, it is extensively used as a model organism in genetics and cellular biology studies, and is employed in the production of vaccines, pharmaceuticals, and biofuels. Traditional \"bulk\"-based studies on yeast growth often overlook cellular variability, emphasizing the need for single-cell analysis. Micro-droplets, tiny liquid droplets with high surface-area-to-volume ratios, offer a promising platform for investigating single or a small number of cells, allowing precise control and monitoring of individual cell behaviors. Microfluidic devices, which facilitate the generation of micro-droplets, are advantageous due to their reduced volume requirements and ability to mimic in vivo micro-environments. This study introduces a custom-designed microfluidic device to encapsulate yeasts in micro-droplets under various conditions in a parallel manner. The results reveal that optimal glucose concentrations promoted yeast growth while cycloheximide and Cu2+ ions inhibited it. This platform enhances yeast cultivation strategies and holds potential for high-throughput single-cell investigations in more complex organisms.

Rapid detection of plant phenotypic traits is crucial for plant breeding and cultivation. Traditional measurement methods are carried out by rich-experienced agronomists, which are time-consuming and labor-intensive. However, with the increasing demand for rapid and high-throughput testing in tea plants traits, digital breeding and smart cultivation of tea plants rely heavily on precise plant phenotypic trait measurement techniques, among which hyperspectral imaging (HSI) technology stands out for its ability to provide real-time and rich-information. In this paper, we provide a comprehensive overview of the principles of hyperspectral imaging technology, the processing methods of cubic data, and relevant algorithms in tea plant phenomics, reviewing the progress of applying hyperspectral imaging technology to obtain information on tea plant phenotypes, growth conditions, and quality indicators under environmental stress. Lastly, we discuss the challenges faced by HSI technology in the detection of tea plant phenotypic traits from different perspectives, propose possible solutions, and envision the potential development prospects of HSI technology in the digital breeding and smart cultivation of tea plants. This review aims to provide theoretical and technical support for the application of HSI technology in detecting tea plant phenotypic information, further promoting the trend of developing high quality and high yield tea leaves.

BACKGROUND: Microbial robustness is crucial for developing cell factories that maintain consistent performance in a challenging environment such as large-scale bioreactors. Although tools exist to assess and understand robustness at a phenotypic level, the underlying metabolic and genetic mechanisms are not well defined, which limits our ability to engineer more strains with robust functions.
RESULTS: This study encompassed four steps. (I) Fitness and robustness were analyzed from a published dataset of yeast mutants grown in multiple environments. (II) Genes and metabolic processes affecting robustness or fitness were identified, and 14 of these genes were deleted in Saccharomyces cerevisiae CEN.PK113-7D. (III) The mutants bearing gene deletions were cultivated in three perturbation spaces mimicking typical industrial processes. (IV) Fitness and robustness were determined for each mutant in each perturbation space. We report that robustness varied according to the perturbation space. We identified genes associated with increased robustness such as MET28, linked to sulfur metabolism; as well as genes associated with decreased robustness, including TIR3 and WWM1, both involved in stress response and apoptosis.
CONCLUSIONS: The present study demonstrates how phenomics datasets can be analyzed to reveal the relationship between phenotypic response and associated genes. Specifically, robustness analysis makes it possible to study the influence of single genes and metabolic processes on stable microbial performance in different perturbation spaces. Ultimately, this information can be used to enhance robustness in targeted strains.

Newborn disease screening increases survival, improves quality of life and reduces treatment costs for healthcare systems. Mass spectrometry (MS) is an effective method for metabolic screening. However, conventional analytical methods require biofluid handling and cooling conditions during transport, making the logistics difficult and expensive, especially for remote regions. \'Paper-spray\' (PS) ionization generates a charged solvent spray from samples deposited on paper strips. Therefore, samples can be applied on a suitable matrix and shipped as dried spots to diagnostic laboratories with standard postal or messenger services. We built a robotic platform, the \'Open SprayBot\', to automatically analyze paper-deposited samples via PS-MS and increase the sample throughput. The system is operated via RUMBA32 and Arduino Mega boards. A commercial syringe pump and power supply provide solvent application and electrical current required for PS-MS. The usability of the Open SprayBot was demonstrated by quantifying palmitoyl-l-carnitine, a common biomarker in newborn screening.

Extracellular vesicles (EVs), including exosomes, have significant potential for diagnostic and therapeutic applications. The lack of standardized methods for efficient and high-throughput isolation and analysis of EVs, however, has limited their widespread use in clinical practice. Surface epitope immunoaffinity (SEI) isolation utilizes affinity ligands, including antibodies, aptamers, or lectins, that target specific surface proteins present on EVs. Paramagnetic bead-SEI isolation represents a fit-for-purpose solution for the reproducible, high-throughput isolation of EVs from biofluids and downstream analysis of RNA, protein, and lipid biomarkers that is compatible with clinical laboratory workflows. This study evaluates a new SEI isolation method for enriching subpopulations of EVs. EVs were isolated from human plasma using a bead-based SEI method designed for on-bead and downstream analysis of EV-associated RNA and protein biomarkers. Western blot analysis confirmed the presence of EV markers in the captured nanoparticles. Mass spectrometry analysis of the SEI lysate identified over 1500 proteins, with the top 100 including known EV-associated proteins. microRNA (miRNA) sequencing followed by RT-qPCR analysis identified EV-associated miRNA transcripts. Using SEI, EVs were isolated using automated high-throughput particle moving instruments, demonstrating equal or higher protein and miRNA yield and recovery compared to manual processing. SEI is a rapid, efficient, and high-throughput method for isolating enriched populations of EVs; effectively reducing contamination and enabling the isolation of a specific subpopulation of EVs. In this study, high-throughput EV isolation and RNA extraction have been successfully implemented. This technology holds great promise for advancing the field of EV research and facilitating their application for biomarker discovery and clinical research.

Avian pathogenic Escherichia coli (APEC) cause avian colibacillosis and accurately distinguishing infectious isolates is critical for controlling its transmission. Multilocus sequence typing (MLST) is an accurate and efficient strain identification method for epidemiological surveillance. This research aimed to develop a fast and high-throughput workflow that simultaneously sequences the Achtman typing scheme\'s 7 housekeeping genes of multiple E. coli isolates using the Oxford Nanopore Technologies (ONT) platform for large-scale APEC study. E. coli strains were isolated from poultry farms, the housekeeping genes were amplified, and amplicons were sequenced on an R9.4 MinION flow cell using the Nanopore GridION sequencer (ONT, Oxford, UK) following the initial workflow (ONT-MLST). Moreover, the workflow was revised by introducing large-scale DNA extraction and multiplex PCR into the ONT-MLST workflow and applied to 242 new isolates, 18 isolates from the previous workflow, and 5 ATCC reference strains using Flongle flow cell on the Nanopore MinION Mk1C sequencer (ONT, Oxford, UK). Finally, the sequence type (ST) results of the 308 isolates collected from infected chickens and poultry farm environments were reported and analyzed. Data indicated that E. coli belonging to ST159, ST8578, and ST355 have the potential to infect multiple organs in broiler. In addition, zoonotic STs, ST69, ST10, ST38, and ST131, were detected from poultry farms. With the advantages of the high throughput of ONT, this study provides a rapid workflow for large-scale E. coli typing and identified frequently isolated sequence types related to APEC infection in poultry.

Genetic gains made by plant breeders are limited by generational cycling rates and flowering time. Several efforts have been made to reduce the time to switch from vegetative to reproductive stages in plants, but these solutions are usually species-specific and require flowering. The concept of in vitro nurseries is that somatic plant cells can be induced to form haploid cells that have undergone recombination (creating artificial gametes), which can then be used for cell fusion to enable breeding in a Petri dish. The induction of in vitro meiosis, however, is the largest current bottleneck to in vitro nurseries. To help overcome this, we previously described a high-throughput, bi-fluorescent, single cell system in Arabidopsis thaliana, which can be used to test the meiosis-like induction capabilities of candidate factors. In this present work, we validated the system using robust datasets (>4M datapoints) from extensive simulated meiosis induction tests. Additionally, we determined false-detection rates of the fluorescent cells used in this system as well as the ideal tissue source for factor testing.

High-entropy nanomaterials exhibit exceptional mechanical, physical, and chemical properties, finding applications in many industries. Peroxidases are metalloenzymes that accelerate the decomposition of hydrogen peroxide. This study uses the high-entropy approach to generate multimetal oxide-based nanozymes with peroxidase-like activity and explores their application as sensors in ex vivo bioassays. A library of 81 materials was produced using a coprecipitation method for rapid synthesis of up to 100 variants in a single plate. The A and B sites of the magnetite structure, (AA\')(BB\'B\'\')2O4, were substituted with up to six different cations (Cu/Fe/Zn/Mg/Mn/Cr). Increasing the compositional complexity improved the catalytic performance; however, substitutions of single elements also caused drastic reductions in the peroxidase-like activity. A generalized linear model was developed describing the relationship between material composition and catalytic activity. Binary interactions between elements that acted synergistically or antagonistically were identified, and a single parameter, the mean interaction effect, was observed to correlate highly with catalytic activity, providing a valuable tool for the design of high-entropy-inspired nanozymes.