High-throughput Method Research Articles

Whole Cell Luminescence-Based Screen for Inhibitors of the Bacterial Sec Machinery.

There is a pressing need for new antibiotics to combat rising resistance to those already in use. The bacterial general secretion (Sec) system has long been considered a good target for novel antimicrobials thanks to its irreplacable role in maintaining cell envelope integrity, yet the lack of a robust, high-throughput method to screen for Sec inhibition has so far hampered efforts to realize this potential. Here, we have adapted our recently developed in vitro assay for Sec activity─based on the split NanoLuc luciferase─to work at scale and in living cells. A simple counterscreen allows compounds that specifically target Sec to be distinguished from those with other effects on cellular function. As proof of principle, we have applied this assay to a library of 5000 compounds and identified a handful of moderately effective in vivo inhibitors of Sec. Although these hits are unlikely to be potent enough to use as a basis for drug development, they demonstrate the efficacy of the screen. We therefore anticipate that the methods presented here will be scalable to larger compound libraries, in the ultimate quest for Sec inhibitors with clinically relevant properties.

A high-throughput screening method for GM soybean events based on single universal primer multiplex PCR and capillary electrophoresis

A high-throughput screening method for GM soybean events based on single universal primer multiplex PCR and capillary electrophoresis

Estimating Maize Crop Height and Aboveground Biomass Using Multi-Source Unmanned Aerial Vehicle Remote Sensing and Optuna-Optimized Ensemble Learning Algorithms

Accurately assessing maize crop height (CH) and aboveground biomass (AGB) is crucial for understanding crop growth and light-use efficiency. Unmanned aerial vehicle (UAV) remote sensing, with its flexibility and high spatiotemporal resolution, has been widely applied in crop phenotyping studies. Traditional canopy height models (CHMs) are significantly influenced by image resolution and meteorological factors. In contrast, the accumulated incremental height (AIH) extracted from point cloud data offers a more accurate estimation of CH. In this study, vegetation indices and structural features were extracted from optical imagery, nadir and oblique photography, and LiDAR point cloud data. Optuna-optimized models, including random forest regression (RFR), light gradient boosting machine (LightGBM), gradient boosting decision tree (GBDT), and support vector regression (SVR), were employed to estimate maize AGB. Results show that AIH99 has higher accuracy in estimating CH. LiDAR demonstrated the highest accuracy, while oblique photography and nadir photography point clouds were slightly less accurate. Fusion of multi-source data achieved higher estimation accuracy than single-sensor data. Embedding structural features can mitigate spectral saturation, with R2 ranging from 0.704 to 0.939 and RMSE ranging from 0.338 to 1.899 t/hm2. During the entire growth cycle, the R2 for LightGBM and RFR were 0.887 and 0.878, with an RMSE of 1.75 and 1.76 t/hm2. LightGBM and RFR also performed well across different growth stages, while SVR showed the poorest performance. As the amount of nitrogen application gradually decreases, the accumulation and accumulation rate of AGB also gradually decrease. This high-throughput crop-phenotyping analysis method offers advantages, such as speed and high accuracy, providing valuable references for precision agriculture management in maize fields.

Design of Oxidation-Resistant Alloys Using Combinatorial Approaches with Chemically Graded Materials

This work introduces a new high-throughput method to characterize the oxidation behavior of chemically graded Ni-based alloys in order to feed databases destined to numerical metallurgy approaches. A Ni–wCr–3Al (w ∈ [0, 30]) chemically graded material was obtained from two homogeneous samples by a diffusion couple method at 1300 °C for 100 h. The composition range was selected in order to observe the three types of oxidation behavior identified in the reference work of Giggins and Pettit (Giggins and Pettit in Journal of The Electrochemical Society 118:1782, 1971). The excellent agreement between simulated and experimental diffusion profiles validated the experimental method used to manufacture the chemically graded material (CGM). The CGM was then oxidized at 1200 °C in air. Surface and cross-section characterization was conducted by SEM/EDS and Raman spectroscopy to identify the oxides formed on the CGM. To accelerate the Raman characterization treatment, a method linking principal component analysis and K-means unsupervised clustering algorithm was developed. It allowed for the identification of the oxide type without peak indexation issues and is well suited for CGM. These results show that results similar to well-recognized reference experiments (Giggins and Pettit in Journal of The Electrochemical Society 118:1782, 1971) can be achieved using only one CGM.

Garlic-derived Exosomes Alleviate Osteoarthritis Through Inhibiting the MAPK Signaling Pathway.

Osteoarthritis (OA) is the most common degenerative joint disease affecting millions of people worldwide. Garlic-derived exosomes (GDEs) are nanoparticles extracted from garlic that exhibit anti-inflammatory effects on other diseases, but the effect of GDEs on OA has not been elucidated. In this study, GDEs were extracted and characterized. Chondrocytes were treated with IL-1β and incubated with GDEs in vitro, and the expression of cartilage matrix components (collagen II and aggrecan) and matrix degrading enzymes (MMP3 and MMP9) was evaluated via Western blotting. Changes in the MAPK pathway was also examined using Western blotting. The transcriptomic changes associated with GDE intervention were evaluated using high-throughput RNA-seq method. In vivo, we used anterior cruciate ligament transection (ACLT) combined with destabilization of the medial meniscus (DMM) surgery to establish a mouse OA model, and GDEs was intraarticularly injected into the joint cavity. The therapeutic effect of GDE was evaluated by behavioral and histopathological analysis. The results showed that IL-1β treatment inhibited the expression of collagen II and aggrecan, and upregulated the expression of MMP3 and MMP9, while GDE intervention alleviated these effects. GDEs also inhibited the phosphorylation of ERK, JNK, and P38. In vivo, GDE alleviated the sensitivity to heat stimulation and altered walking gait in a mouse OA model. Histopathological analysis indicated that GDE intervention ameliorated joint destruction in the knee joint without obvious toxicity. The results proved that GDEs alleviated the progression of OA in vitro and in vivo, and may be a potential disease-modifying drug for OA.

Integrating Computational Design and Experimental Approaches for Next-Generation Biologics.

Therapeutic protein engineering has revolutionized medicine by enabling the development of highly specific and potent treatments for a wide range of diseases. This review examines recent advances in computational and experimental approaches for engineering improved protein therapeutics. Key areas of focus include antibody engineering, enzyme replacement therapies, and cytokine-based drugs. Computational methods like structure-based design, machine learning integration, and protein language models have dramatically enhanced our ability to predict protein properties and guide engineering efforts. Experimental techniques such as directed evolution and rational design approaches continue to evolve, with high-throughput methods accelerating the discovery process. Applications of these methods have led to breakthroughs in affinity maturation, bispecific antibodies, enzyme stability enhancement, and the development of conditionally active cytokines. Emerging approaches like intracellular protein delivery, stimulus-responsive proteins, and de novo designed therapeutic proteins offer exciting new possibilities. However, challenges remain in predicting in vivo behavior, scalable manufacturing, immunogenicity mitigation, and targeted delivery. Addressing these challenges will require continued integration of computational and experimental methods, as well as a deeper understanding of protein behavior in complex physiological environments. As the field advances, we can anticipate increasingly sophisticated and effective protein therapeutics for treating human diseases.

High-throughput preparation and quick characterization of oxidation behaviors of complex Al–Cr compositional gradient coatings on a novel Co–Al–W–based superalloy prepared using multi-arc ion plating technology

The early oxidation behaviors of complex Al–Cr compositional gradient coatings on a novel Co–Al–W substrate prepared by multi-arc ion plating technology (MIPT) at 1000 °C for 20–60 min were rapidly characterized. The as-deposited coatings with compositions ranging from (55%–90%Al, 45%–10%Cr) exhibited the laminated structure α-Al + Al80Cr20+Al8Cr5+α-Cr. After annealing at 640 °C for 40 h, a two-layered coating composed of (Co(Al, Cr) + Al8Co18Cr4) and IRL formed, and the thickness of the coating increased from the 14–18 μm of the as-deposited state to 25–35 μm. During oxidation at 1000 °C for 20–60 min, a three-layer structure consisting of an outer α-Al2O3+α-Cr2O3+CoCr2O4 layer, a central unoxidized Al–Cr layer, and an inner μ-Co7(W0.55Cr0.45) 6+B2–CoAl layer formed on the substrate. The Al–Cr coatings with higher Cr content had a looser oxide layer due to the CoCr2O4 phase, which might degrade the intended protective effect of the Al–Cr coatings. Thus, the Al–Cr coatings with higher Al content were suitable for preventing oxidation behavior. This work proposes a high-throughput screening method for rapid characterization of the early oxidation mechanism of the complex Al–Cr compositional gradient coatings.

Cell-free synthesis of infective phages from in vitro assembled phage genomes for efficient phage engineering and production of large phage libraries.

Bacteriophages are promising alternatives to traditional antimicrobial treatment of bacterial infections. To further increase the potential of phages, efficient engineering methods are needed. This study investigates an approach to phage engineering based on phage rebooting and compares selected methods of assembly and rebooting of phage genomes. GG assembly of phage genomes and subsequent rebooting by cell-free transcription-translation reactions yielded the most efficient phage engineering and allowed production of a proof-of-concept T7 phage library of 1.8 × 107 phages. We obtained 7.5 × 106 different phages, demonstrating generation of large and diverse libraries suitable for high-throughput screening of mutant phenotypes. Implementing versatile and high-throughput phage engineering methods allows vastly accelerated and improved phage engineering, bringing us closer to applying effective phages to treat infections in the clinic.

Retraction Note to: An alternative high-throughput staining method for detection of neutral lipids in green microalgae for biodiesel applications

Retraction Note to: An alternative high-throughput staining method for detection of neutral lipids in green microalgae for biodiesel applications

Leaf rolling detection in maize under complex environments using an improved deep learning method

Leaf rolling is a common adaptive response that plants have evolved to counteract the detrimental effects of various environmental stresses. Gaining insight into the mechanisms underlying leaf rolling alterations presents researchers with a unique opportunity to enhance stress tolerance in crops exhibiting leaf rolling, such as maize. In order to achieve a more profound understanding of leaf rolling, it is imperative to ascertain the occurrence and extent of this phenotype. While traditional manual leaf rolling detection is slow and laborious, research into high-throughput methods for detecting leaf rolling within our investigation scope remains limited. In this study, we present an approach for detecting leaf rolling in maize using the YOLOv8 model. Our method, LRD-YOLO, integrates two significant improvements: a Convolutional Block Attention Module to augment feature extraction capabilities, and a Deformable ConvNets v2 to enhance adaptability to changes in target shape and scale. Through experiments on a dataset encompassing severe occlusion, variations in leaf scale and shape, and complex background scenarios, our approach achieves an impressive mean average precision of 81.6%, surpassing current state-of-the-art methods. Furthermore, the LRD-YOLO model demands only 8.0 G floating point operations and the parameters of 3.48 M. We have proposed an innovative method for leaf rolling detection in maize, and experimental outcomes showcase the efficacy of LRD-YOLO in precisely detecting leaf rolling in complex scenarios while maintaining real-time inference speed.

RNA-binding motif protein 28 enhances angiogenesis by improving STAT3 translation in hepatocellular carcinoma

Hepatocellular carcinoma (HCC) is a prevalent malignant tumor characterized by extensive angiogenesis. However, the underlying mechanisms of HCC pathogenesis remain unclear. Previous studies have shown that RNA-binding proteins (RBPs) are implicated in HCC pathogenesis. In this study, we observed that increased RBM28 expression in HCC tissues was positively correlated with tumor microvascular density and negatively correlated with patient prognosis. Overexpression of RBM28 in HCC cells promoted tubule formation in human umbilical vein endothelial cells, whereas inhibition of RBM28 had the opposite effect, furthermore, the role of RBM28 in the progression of HCC was assessed using transgenic mouse models and chemically induced HCC models. We used various molecular assays and high-throughput detection methods to evaluate the role of RBM28 in promoting angiogenesis in HCC. Increased RBM28 expression in HCC directly binds to STAT3 mRNA, recruiting EIF4E to increase STAT3 expression and enhancing the secretion and expression of vascular endothelial growth factor A; consequently, promoting neovascularization in HCC. The potential of RBM28 as a viable diagnostic and therapeutic target for HCC was assessed using multi-cohort clinical samples and animal models. In summary, our results provide insights into the pathogenesis, clinical diagnosis, and treatment of HCC.

Novel selection of recycled PET surrogates based on non-targeted screening of non-intentionally added substances and chemometrics

Waste PET is utilized to produce recycled PET (rPET) through bottle-to-bottle recycling in order to reduce environmental pollution. However, non-intentionally added substances (NIAS) may be generated during rPET production, potentially posing a threat to food safety. In this study, a non-target screening of 13 batches with a total of 39 rPET samples was performed using GC-QTOF-MS and 240 compounds were tentatively identified. It was worth noting that the detection of phthalate esters in rPET from China, such as bis(2-ethylhexyl) phthalate, exhibits a higher diversity (17 different types) and frequency (7.8 %–100 %) in comparison with related studies. Subsequently, based on the principles for selection of surrogates used in challenge test, seven molecular descriptors were selected as variables for compound characterization, followed by principal component analysis (PCA) for representation. Based on this analysis and referencing the EFSA and FDA surrogate suggestion lists, eight surrogates were proposed for China, namely chlorobenzene, toluene, benzophenone, bis(2-ethylhexyl) terephthalate, methyl salicylate, methyl stearate, 1-phenyldecane and phenylcyclohexane. High-throughput detection methods were developed for each surrogate with the regression correlation coefficients over 0.999, spiked recoveries ranging from 99.3-107.5 %, LODs of 0.003–2.192 ng/mL, LOQs of 0.009–7.306 ng/mL, RSD of 0.71–4.23 %, respectively. This research provides foundational data and methodological support for the evaluation of rPET challenge testing in China, offering technical support for subsequent safety assessments of rPET.

Maximizing efficiency in sedimentary ancient DNA analysis: a novel extract pooling approach

In the last few decades, the field of ancient DNA has taken a new direction towards using sedimentary ancient DNA (sedaDNA) for studying human and mammalian population dynamics as well as past ecosystems. However, the screening of numerous sediment samples from archaeological sites remains a time-consuming and costly endeavor, particularly when targeting hominin DNA. Here, we present a novel high-throughput method that facilitates the fast and efficient analysis of sediment samples by applying a pooled testing approach. This method combines multiple extracts, enabling early parallelization of laboratory procedures and effective aDNA screening. Pooled samples with detectable aDNA signals undergo detailed analysis, while empty pools are discarded. We have successfully applied our method to multiple sediment samples from Middle and Upper Paleolithic sites in Europe, Asia, and Africa. Notably, our results reveal that an aDNA signal remains discernible even when pooled with four negative samples. We also demonstrate that the DNA yield of double-stranded libraries increases significantly when reducing the extract input, potentially mitigating the effects of inhibition. By embracing this innovative approach, researchers can analyze large numbers of sediment samples for aDNA preservation, achieving significant cost reductions of up to 70% and reducing hands-on laboratory time to one-fifth.

A statistical simulation model to guide the choices of analytical methods in arrayed CRISPR screen experiments.

An arrayed CRISPR screen is a high-throughput functional genomic screening method, which typically uses 384 well plates and has different gene knockouts in different wells. Despite various computational workflows, there is currently no systematic way to find what is a good workflow for arrayed CRISPR screening data analysis. To guide this choice, we developed a statistical simulation model that mimics the data generating process of arrayed CRISPR screening experiments. Our model is flexible and can simulate effects on phenotypic readouts of various experimental factors, such as the effect size of gene editing, as well as biological and technical variations. With two examples, we showed that the simulation model can assist making principled choice of normalization and hit calling method for the arrayed CRISPR data analysis. This simulation model is implemented in an R package and can be downloaded from Github.

From Cell to Gene: Deciphering the Mechanism of Heart Failure With Single-Cell Sequencing.

Heart failure (HF) is a prevalent cardiovascular disease with significant morbidity and mortality rates worldwide. Due to the intricate structure of the heart, diverse cell types, and the complex pathogenesis of HF, further in-depth investigation into the underlying mechanismsis required. The elucidation of the heterogeneity of cardiomyocytes and the intercellular communication network is particularly important. Traditional high-throughput sequencing methods provide an average measure of gene expression, failing to capture the "heterogeneity" between cells and impacting the accuracy of gene function knowledge. In contrast, single-cell sequencing techniques allow for the amplification of the entire genome or transcriptome at the individual cell level, facilitating the examination of gene structure and expression with unparalleled precision. This approach offers valuable insights into disease mechanisms, enabling the identification of changes in cellular components and gene expressions during hypertrophy associated with HF. Moreover, it reveals distinct cell populations and their unique roles in the HF microenvironment, providing a comprehensive understanding of the cellular landscape that underpins HF pathogenesis. This review focuses on the insights provided by single-cell sequencing techniques into the mechanisms underlying HF and discusses the challenges encountered in current cardiovascular research.

Phage Immunoprecipitation and Sequencing—a Versatile Technique for Mapping the Antibody Reactome

Characterizing the antibody reactome for circulating antibodies provide insight into pathogen exposure, allergies, and autoimmune diseases. This is important for biomarker discovery, clinical diagnosis, and prognosis of disease progression, as well as population-level insights into the immune system. The emerging technology phage display immunoprecipitation and sequencing (PhIP-seq) is a high-throughput method for identifying antigens/epitopes of the antibody reactome. In PhIP-seq, libraries with sequences of defined lengths and overlapping segments are bioinformatically designed using naturally occurring proteins and cloned into phage genomes to be displayed on the surface. These libraries are used in immunoprecipitation experiments of circulating antibodies. This can be done with parallel samples from multiple sources, and the DNA inserts from the bound phages are barcoded and subjected to next-generation sequencing for hit determination. PhIP-seq is a powerful technique for characterizing the antibody reactome that has undergone rapid advances in recent years. In this review, we comprehensively describe the history of PhIP-seq and discuss recent advances in library design and applications.

Computational Exploration of Pd-Based Heusler alloys for permanent Magnets: Density Functional Theory and High-Throughput methods

In this work, we have explored the potential of a relatively new class of Pd-based Heusler alloys as permanent magnets. Our approach began with a high-throughput study using the Automatic Flow (AFLOW) electronic structure database to identify Pd₂YZ-type Heusler systems with tetragonal symmetry and high magnetization. For the alloys that passed our initial selection filters, we performed a detailed investigation of their magnetic properties using density functional theory (DFT) calculations. These calculations included assessments of magnetocrystalline anisotropy energy (EMCA) and exchange interactions. The selected candidates, which met all our criteria, were then subjected to Monte Carlo simulations to analyze the temperature dependence of their magnetization and determine their Curie temperatures (TC). Our results indicate that Pd₂FeNi and Pd₂FePt are promising candidates for permanent magnets. Pd₂FeNi exhibited an EMCA of 2.4 MJ/m3 and a TC of 504 K, while Pd₂FePt showed an EMCA of 4.11 MJ/m3 and a TC of 463 K. Both Curie temperatures are significantly above room temperature by more than 160 K, demonstrating the alloys’ potential for high-temperature applications and their overall suitability as efficient, rare earth-free permanent magnets.

Comprehensive Analysis of Allulose Production: A Review and Update.

Advancements in D-allulose production have seen significant strides in recent years, focusing on enzymatic conversion methods. Key developments include traditional immobilization techniques, the discovery of novel enzymes, directed evolution studies, and biosynthesis through metabolic pathway modification. Enzymatic conversion, particularly utilizing D-allulose 3-epimerase, remains fundamental for industrial-scale production. Innovative immobilization strategies, such as functionalized nano-beads and magnetic MOF nanoparticles, have significantly enhanced enzyme stability and reusability. Directed evolution has led to improved enzyme thermostability and catalytic efficiency, while synthetic biology methods, including phosphorylation-driven and thermodynamics-driven pathways, have optimized production processes. High-throughput screening methods have been crucial in identifying and refining enzyme variants for industrial applications. Collectively, these advancements not only enhance production efficiency and cost-effectiveness but also adhere to sustainable and economically viable manufacturing practices. The past five years have witnessed critical developments with significant potential impact on the commercial viability and global demand for allulose.

High-Throughput Bioprinting Method for Modeling Vascular Permeability in Standard Six-well Plates with Size and Pattern Flexibility.

Vascular permeability is a key factor in developing therapies for disorders associated with compromised endothelium, such as endothelial dysfunction in coronary arteries and impaired function of the blood-brain barrier. Existing fabrication techniques do not adequately replicate the geometrical variation in vascular networks in the human body, which substantially influences disease progression; moreover, these techniques often involve multi-step fabrication procedures that hinder the high-throughput production necessary for pharmacological testing. This paper presents a bioprinting protocol for creating multiple vascular tissues with desired patterns and sizes directly on standard six-well plates, overcoming existing resolution and productivity challenges in bioprinting technology. A simplified fabrication approach was established to construct six hollow, perfusable channels within a hydrogel, which were subsequently lined with human umbilical vein endothelial cells to form a functional and mature endothelium. The computer-controlled nature of 3D bioprinting ensures high reproducibility and requires fewer manual fabrication steps than traditional methods. This highlights VOP's potential as an efficient high-throughput platform for modeling vascular permeability and advancing drug discovery.

Integrating ATAC-Seq and RNA-Seq Reveals the Signal Regulation Involved in the Artemia Embryonic Reactivation Process.

Embryonic diapause is a common evolutionary adaptation observed across a wide range of organisms. Artemia is one of the classic animal models for diapause research. The current studies of Artemia diapause mainly focus on the induction and maintenance of the embryonic diapause, with little research on the molecular regulatory mechanism of Artemia embryonic reactivation. The first 5 h after embryonic diapause breaking has been proved to be most important for embryonic reactivation in Artemia. In this work, two high-throughput sequencing methods, ATAC-seq and RNA-seq, were integrated to study the signal regulation process in embryonic reactivation of Artemia at 5 h after diapause breaking. Through the GO and KEGG enrichment analysis of the high-throughput datasets, it was showed that after 5 h of diapause breaking, the metabolism and regulation of Artemia cyst were quite active. Several signal transduction pathways were identified in the embryonic reactivation process, such as G-protein-coupled receptor (GPCR) signaling pathway, cell surface receptor signaling pathway, hormone-mediated signaling pathway, Wnt, Notch, mTOR signaling pathways, etc. It indicates that embryonic reactivation is a complex process regulated by multiple signaling pathways. With the further protein structure analysis and RT-qPCR verification, 11 GPCR genes were identified, in which 5 genes function in the embryonic reactivation stage and the other 6 genes contribute to the diapause stage. The results of this work reveal the signal transduction pathways and GPCRs involved in the embryonic reactivation process of Artemia cysts. These findings offer significant clues for in-depth research on the signal regulatory mechanisms of the embryonic reactivation process and valuable insights into the mechanism of animal embryonic diapause.