High-throughput Investigation Research Articles

High Throughput Screening and Molecular Docking Analysis on DNA Polymerase (Pol) Β Cancer Variant K289M

DNA polymerase (pol) β plays a critical role in DNA repair mechanisms, and mutations in this enzyme have been implicated in various cancers. Because it may play a role in oncogenesis by altering the fidelity of DNA synthesis, the K289M variation of pol β is particularly interesting. This study uses Python molecular graphic, Cavity based docking, SwissDock, Structure validation server among other computational tools to undertake high-throughput screening and molecular docking investigations of DNA pol β cancer variant K289M (PDB ID: 6NKR). Ligands such as Boceprevir, Nirmatrelvir, Ritonavir, Carmofur, and Lopinavir were docked to identify potential inhibitors targeting this variant. Structural validation using the SAVES server and topology analysis using PDBSum helped assess the quality of the protein structure. Our results highlight several binding pockets with strong ligand affinities, suggesting potential therapeutic interventions for targeting pol β K289M in cancer.

Heron: Visualizing and Controlling Chemical Reaction Explorations and Networks.

Automated and high-throughput quantum chemical investigations into chemical processes have become feasible in great detail and broad scope. This results in an increase in complexity of the tasks and in the amount of generated data. An efficient and intuitive way for an operator to interact with these data and to steer virtual experiments is required. Here, we introduce Heron, a graphical user interface that allows for advanced human-machine interactions with quantum chemical exploration campaigns into molecular structure and reactivity. Heron offers access to interactive and automated explorations of chemical reactions with standard electronic structure modules, haptic force feedback, microkinetic modeling, and refinement of data by automated correlated calculations including black-box complete active space calculations. It is tailored to the exploration and analysis of vast chemical reaction networks. We show how interoperable modules enable advanced workflows and pave the way for routine low-entrance-barrier access to advanced modeling techniques.

Clinical cell-surface targets in metastatic and primary solid cancers.

Therapies against cell-surface targets (CSTs) represent an emerging treatment class in solid malignancies. However, high-throughput investigations of CST expression across cancer types have been reliant on data sets of mostly primary tumors, despite therapeutic use most commonly in metastatic disease. We identified a total of 818 clinical trials of CST therapies with 78 CSTs. We assembled a data set spanning RNA-seq and microarrays in 7,927 benign samples, 16,866 primary tumor samples, and 6,124 metastatic tumor samples. We also utilized single-cell RNA-seq data from 36 benign tissues and 558 primary and metastatic tumor samples, and matched RNA versus protein expression in 29 benign tissue samples, 1,075 tumor samples, and 942 cell lines. High RNA expression accurately predicted high protein expression across CST therapies in benign tissues, tumor samples, and cell lines. We compared metastatic versus primary tumor expression, identified potential opportunities for repositioning, and matched cell lines to tumor types based on CST and global RNA expression. We evaluated single-cell heterogeneity across tumors, and identified rare normal cell subpopulations that may contribute to toxicity. Finally, we identified combinations of CST therapies for which bispecific approaches could improve tumor specificity. This study helps better define the landscape of CST expression in metastatic and primary cancers.

DORQ-seq: high-throughput quantification of femtomol tRNA pools by combination of cDNA hybridization and Deep sequencing.

Due to its high modification content tRNAs are notoriously hard to quantify by reverse transcription and RNAseq. Bypassing numerous biases resulting from concatenation of enzymatic treatments, we here report a hybrid approach that harnesses the advantages of hybridization-based and deep sequencing-based approaches. The method renders obsolete any RNAseq related workarounds and correction factors that affect accuracy, sensitivity, and turnaround time. Rather than by reverse transcription, quantitative information on the isoacceptor composition of a tRNA pool is transferred to a cDNA mixture in a single step procedure, thereby omitting all enzymatic conversations except for the subsequent barcoding PCR. As a result, a detailed tRNA composition matrix can be obtained from femtomolar amounts of total tRNA. The method is fast, low in cost, and its bioinformatic data workup surprisingly simple. These properties make the approach amenable to high-throughput investigations including clinical samples, as we have demonstrated by application to a collection of variegated biological questions, each answered with novel findings. These include tRNA pool quantification of polysome-bound tRNA, of tRNA modification knockout strains under stress conditions, and of Alzheimer patients' brain tissues.

Characterization and bioinformatic filtering of ambient gRNAs in single-cell CRISPR screens using CLEANSER.

Recent technological developments in single-cell RNA-seq CRISPR screens enable high-throughput investigation of the genome. Through transduction of a gRNA library to a cell population followed by transcriptomic profiling by scRNA-seq, it is possible to characterize the effects of thousands of genomic perturbations on global gene expression. A major source of noise in scRNA-seq CRISPR screens are ambient gRNAs, which are contaminating gRNAs that likely originate from other cells. If not properly filtered, ambient gRNAs can result in an excess of false positive gRNA assignments. Here, we utilize CRISPR barnyard assays to characterize ambient gRNA noise in single-cell CRISPR screens. We use these datasets to develop and train CLEANSER, a mixture model that identifies and filters ambient gRNA noise. This model takes advantage of the bimodal distribution between native and ambient gRNAs and includes both gRNA and cell-specific normalization parameters, correcting for confounding technical factors that affect individual gRNAs and cells. The output of CLEANSER is the probability that a gRNA-cell assignment is in the native distribution over the ambient distribution. We find that ambient gRNA filtering methods impact differential gene expression analysis outcomes and that CLEANSER outperforms alternate approaches by increasing gRNA-cell assignment accuracy.

STRAIGHT-IN: a platform for rapidly generating panels of genetically modified human pluripotent stem cell lines.

Targeted integration of large DNA cargoes (>10 kb) or genomic replacements in mammalian cells, such as human pluripotent stem cells (hPS cells), remains challenging. Here we describe a platform termed serine and tyrosine recombinase-assisted integration of genes for high-throughput investigation (STRAIGHT-IN) to circumvent this. First, a landing pad cassette is precisely inserted or used to replace specific genomic regions. The site-specific integrase Bxb1 then enables DNA constructs, including those >50 kb, to be integrated into the genome, while Cre recombinase excises auxiliary DNA sequences to prevent postintegrative silencing. Using a strategy whereby the positive selection marker is only expressed if the donor plasmid carrying the payload is correctly targeted, we can obtain 100% enrichment for cells containing the DNA payload. Procedures for expressing Cre efficiently also mean that a clonal isolation step is no longer essential to derive the required genetically modified hPS cells containing the integrated DNA, potentially reducing clonal variability. Furthermore, STRAIGHT-IN facilitates rapid and multiplexed generation of genetically matched hPS cells when multiple donor plasmids are delivered simultaneously. STRAIGHT-IN has various applications, which include integrating complex genetic circuits for synthetic biology, as well as creating panels of hPS cells lines containing, as necessary, hundreds of disease-linked variants for disease modeling and drug discovery. After establishing the hPS cell line containing the landing pad, the entire procedure, including donor plasmid synthesis, takes 1.5-3 months, depending on whether single or multiple DNA payloads are integrated. This protocol only requires the researcher to be skilled in molecular biology and standard cell culture techniques.

A Droplet-Based Microfluidic Platform for High-Throughput Culturing of Yeast Cells in Various Conditions.

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.

A unified approach to investigating 4 dpf zebrafish larval behaviour through a standardised light/dark assay

Zebrafish are a dynamic research model in the domains of neuropsychopharmacology, biological psychiatry and behaviour. Working with larvae ≤4 days post-fertilisation (dpf) offers an avenue for high-throughput investigation whilst aligning with the 3Rs principles of animal research. The light/dark assay, which is the most widely used behavioural assay for larval neuropharmacology research, lacks experimental reliability and standardisation. This study aimed to formulate a robust, reproducible and standardised light/dark behavioural assay using 4 dpf zebrafish larvae. Considerable between-batch and inter-individual variability was found, which we rectified with a normalisation approach to ensure a reliable foundation for analysis. We then identified that 5-min light/dark transition periods are optimal for locomotor activity. We also found that a 30-min acclimation in the light was found to produce significantly increased dark phase larval locomotion. Next, we confirmed the pharmacological predictivity of the standardised assay using ethanol which, as predicted, caused hyperlocomotion at low concentrations and hypolocomotion at high concentrations. Finally, the assay was validated by assessing the behavioural phenotype of hyperactive transgenic (adgrl3.1−/−) larvae, which was rescued with psychostimulant medications. Our standardised assay not only provides a clear experimental and analytical framework to work with 4 dpf larvae, but also facilitates between-laboratory collaboration using our normalisation approach.

The sweet symphony of N-glycans in myeloid malignancies

Although the involvement of glycan structures in diseases has long been recognized, their detailed and high-throughput investigation has only recently been made possible due to technological advancements. For this reason, glycosylation is a generally understudied phenomenon, however it could provide critical information on the pathobiology of many disorders by virtue of its widespread abundance and critical role in protein function. Here, we focus on myeloid malignancies, conditions for which the survival rates are often poor and curative therapeutic options are generally limited. We review the current literature on (1) N-glycosylation of major hematopoietic growth receptors found mutated in myeloid malignancies, (2) chemoresistance through intracellular glycan-related processes, and (3) mechanisms by which altered N-glycosylation contributes to interactions between myeloid blasts and bone marrow stromal cells leading to niche hijacking. For each topic, we describe the related pathobiology and its (potential) clinical implications. The combination of glycoproteomic and genomic information is expected to result in a deeper molecular understanding of the pathobiology of these diseases, which could subsequently be used for improving prognostication and therapeutic strategies.

Brain Chimeroids reveal individual susceptibility to neurotoxic triggers.

Interindividual genetic variation affects the susceptibility to and progression of many diseases1,2. However, efforts to study how individual human brains differ in normal development and disease phenotypes are limited by the paucity of faithful cellular human models, and the difficulty of scaling current systems to represent multiple people. Here we present human brain Chimeroids, a highly reproducible, multidonor human brain cortical organoid model generated by the co-development of cells from a panel of individual donors in a single organoid. By reaggregating cells from multiple single-donor organoids at the neural stem cell or neural progenitor cell stage, we generate Chimeroids in which each donor produces all cell lineages of the cerebral cortex, even when using pluripotent stem cell lines with notable growth biases. We used Chimeroids to investigate interindividual variation in the susceptibility to neurotoxic triggers that exhibit high clinical phenotypic variability: ethanol and the antiepileptic drug valproic acid. Individual donors varied in both the penetrance of the effect on target cell types, and the molecular phenotype within each affected cell type. Our results suggest that human genetic background may be an important mediator of neurotoxin susceptibility and introduce Chimeroids as a scalable system for high-throughput investigation of interindividual variation in processes of brain development and disease.

High Throughput LC-MS/MS Method for the Quantitation of Mefenamic Acid in Rat Plasma by Protein Precipitation Technique Using 96 Well Plate Format

An advanced and validated high-throughput liquid chromatography-tandem mass spectrometry (LC-MS/MS) approach has been established to accurately determine the concentration of mefenamic acid in rat plasma. This method utilizes mefenamic acid D4 as the internal standard (ISTD). The analyte and ISTD underwent protein precipitation in a 96-well plate format and were then separated on a BDS Hypersil C8 column (3 μm, 100 x 4.6 mm) using a precise isocratic mobile phase of 2 mM ammonium formate by 0.1% formic acid and acetonitrile (30:70 v/v). The detection was performed on a triple quadrupole mass spectrometer utilizing positive ionization consideration. The approach demonstrated a concentration range of 20.659 to 20067.772 ng/mL, thru relative regaining reaching from 69.1 to 74.3%. The inter-batch precision was excellent, with a coefficient of variation (%CV) of ≤ 7.8%. Additionally, the inter-batch accuracy at four different quality control-points ranged from 97.0 to 100.4%, demonstrating high levels of accuracy. This method has been thoroughly validated and is extremely specific, accurate, and precise. It is perfectly suited for high-throughput investigation of mefenamic acid in rat plasma, making it ideal for routine analysis.

Generation of isogenic allelic series of LMNA-mutated hiPSC lines using the novel and highly-efficient targeting platform, STRAIGHT-IN

Abstract Funding Acknowledgements Type of funding sources: Public grant(s) – National budget only. Main funding source(s): The Netherlands Organisation for Health Research and Development ZonMW Background Human induced pluripotent stem cells (hiPSCs) have demonstrated enormous potential for advancing in vitro disease modelling and genetically modified hiPSCs carrying disease-associated variants are particularly useful tools for this purpose. Until now, strategies for efficient and rapid integration of large DNA payloads (>10 kb) into specific genomic regions are still limited, thus precluding efficient and rapid targeting of hiPSC lines. Purpose and Methods To overcome this, the STRAIGHT-IN platform (Serine and Tyrosine Recombinase Assisted Integration of Genes for High-Throughput INvestigation) was developed that combined different classes of site-specific recombinases with CRISPR-Cas9 mediated homologous recombination and allowed stringent site-specific replacement of large genomic fragments in hiPSCs. Here, we use STRAIGHT-IN to simultaneously generate a library of genetically matched hiPSC lines carrying multiple heterozygous mutations in LMNA gene. Mutations in LMNA, encoding Lamin A/C protein, have been associated with 5-10% cases of dilated cardiomyopathy (DCM) with conduction defects. Results Through detailed online searches of the LMNA database and literature, we identified and selected 11 LMNA mutations to insert into a wild-type hiPSC line based on the following criteria: (i) mutations associated with cardiac abnormalities, in terms of structural (DCM/heart failure) and arrhythmic phenotypes; (ii) mutations with known familiar/pedigree-relationship; (iii) different types of mutations (missense and nonsense). We then applied the workflow of STRAIGHT-IN consisting of: (1) replacing the entire LMNA genomic locus (33 kb) on one allele with a Landing Pad (LP) cassette containing Serine Recombinase (Bxb1) recognition sites; (2) reintroducing LMNA gene into the LP cassette through integration of Bxb1-Donor plasmids carrying the LMNA variants of interest via Bxb1 expression; (3) expressing a tyrosine recombinase (Cre) to excise the majority of the auxiliary exogenous DNA sequences. Conclusions STRAIGHT-IN allows simultaneous generation of a panel of 11 isogenic hiPSC lines carrying selected LMNA mutations rapidly (5-6 weeks), efficiently and cost-effectively, thus facilitating the production of specific mutated hiPSC lines for disease modelling and personalized medicine applications.

High-throughput investigation of stability and Li diffusion of doped solid electrolytes via neural network potential without configurational knowledge

Solid electrolytes hold substantial promise as vital components of all-solid-state batteries. Enhancing their performance necessitates simultaneous improvements in their stability and lithium conductivity. These properties can be calculated using first-principles simulations, provided that the crystal structure of the material and the diffusion pathway through the material are known. However, solid electrolytes typically incorporate dopants to enhance their properties, necessitating the optimization of the dopant configuration for the simulations. Yet, performing such calculations via the first-principles approach is so costly that existing approaches usually rely on predetermined dopant configurations informed by existing knowledge or are limited to systems doped with only a few atoms. The proposed method enables the optimization of the dopant configuration with the support of neural network potential (NNP). Our approach entails the use of molecular dynamics to analyze the diffusion after the optimization of the dopant configuration. The application of our approach to Li10\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$${_{10}}$$\\end{document}MP2\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$${_{2}}$$\\end{document}S12-x\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$${_{12-x}}$$\\end{document}Ox\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$${_{x}}$$\\end{document} (M = Ge, Si, or Sn) reproduce the experimental results well. Furthermore, analysis of the lithium diffusion pathways suggests that the activation energy of diffusion undergoes a percolation transition. This study demonstrates the effectiveness of NNPs in the systematic exploration of solid electrolytes.

Comprehensive exploration of halide double perovskites Cs2BʹGeCl6 (Bʹ: Zn, Cd) for affordable energy technologies: a high-throughput investigation

Comprehensive exploration of halide double perovskites Cs2BʹGeCl6 (Bʹ: Zn, Cd) for affordable energy technologies: a high-throughput investigation

Molecular detection of exosomal miRNAs of blood serum for prognosis of colorectal cancer

Colorectal cancer (CRC) is the third most common cancer affecting people. The discovery of new, non-invasive, specific, and sensitive molecular biomarkers for CRC may assist in the diagnosis and support therapeutic decision making. Exosomal miRNAs have been demonstrated in carcinogenesis and CRC development, which makes these miRNAs strong biomarkers for CRC. Deep sequencing allows a robust high-throughput informatics investigation of the types and abundance of exosomal miRNAs. Thus, exosomal miRNAs can be efficiently examined as diagnostic biomarkers for disease screening. In the present study, a number of 660 mature miRNAs were detected in patients diagnosed with CRC at different stages. Of which, 29 miRNAs were differentially expressed in CRC patients compared with healthy controls. Twenty-nine miRNAs with high abundance levels were further selected for subsequent analysis. These miRNAs were either highly up-regulated (e.g., let-7a-5p, let-7c-5p, let-7f-5p, let-7d-3p, miR-423-5p, miR-3184-5p, and miR-584) or down-regulated (e.g., miR-30a-5p, miR-99-5p, miR-150-5p, miR-26-5p and miR-204-5p). These miRNAs influence critical genes in CRC, leading to either tumor growth or suppression. Most of the reported diagnostic exosomal miRNAs were shown to be circulating in blood serum. The latter is a novel miRNA that was found in exosomal profile of blood serum. Some of the predicted target genes of highly expressed miRNAs participate in several cancer pathways, including CRC pathway. These target genes include tumor suppressor genes, oncogenes and DNA repair genes. Main focus was given to multiple critical signaling cross-talking pathways including transforming growth factor β (TGFβ) signaling pathways that are directly linked to CRC. In conclusion, we recommend further analysis in order to experimentally confirm exact relationships between selected differentially expressed miRNAs and their predicted target genes and downstream functional consequences.

Atomic displacement threshold energies and defect generation in GaN, AlN, and AlGaN: A high-throughput molecular dynamics investigation

Gallium nitride, aluminum nitride, and their ternary alloys form an important class of wide-bandgap semiconductors employed in a variety of applications, including radiation-hard electronics. To better understand the effects of irradiation in these materials, molecular dynamics simulations were employed to determine the threshold recoil energies to permanently displace atoms from crystalline sites. Threshold displacement energies were calculated with the lattices at 0 K. Thermal effects are found to lower the threshold energies by ∼1 eV. The threshold energy knockout events observed result in Frenkel pair defects. The electronic structure and dynamics of these Frenkel pair defects are analyzed and the consequences for device operation are discussed.

A method for detecting the number of wheat ears based on the improved YOLOX model

ABSTRACT Ear count serves as a crucial parameter for estimating wheat yield, holding great significance in crop phenotypic parameter calculations, yield predictions and wheat ear density management. To accurately determine the number of wheat ears, this study utilized the Global Wheat Head Detection 2021 dataset, and introduced a wheat ear counting method based on the improved YOLOX model. An attention mechanism was integrated into the feature extraction network to bolster the semantic information of the feature map. This enhancement aimed to improve the capability of effectively detecting wheat ears. By using GIoU loss to optimize the loss function, the positioning accuracy of bounding box was improved. Experimental results show that, compared with other models, the improved YOLOX model has significantly improved the detection effect of different targets. Among them, the precision of the improved YOLOX model is 97.24%, the average precision is 94.15%, the accuracy is 99.76%, and the log average miss detection rate is 0.25. This study provides a reliable method for precision wheat ear identification, offering valuable applications in high-throughput investigation of wheat ear traits.

Design of a Multiparametric Perfusion Bioreactor System for Evaluating Sub-Normothermic Preservation of Rat Abdominal Wall Vascularized Composite Allografts.

Static cold storage (SCS), the current clinical gold standard for organ preservation, provides surgeons with a limited window of time between procurement and transplantation. In vascularized composite allotransplantation (VCA), this time limitation prevents many viable allografts from being designated to the best-matched recipients. Machine perfusion (MP) systems hold significant promise for extending and improving organ preservation. Most of the prior MP systems for VCA have been built and tested for large animal models. However, small animal models are beneficial for high-throughput biomolecular investigations. This study describes the design and development of a multiparametric bioreactor with a circuit customized to perfuse rat abdominal wall VCAs. To demonstrate its concept and functionality, this bioreactor system was employed in a small-scale demonstrative study in which biomolecular metrics pertaining to graft viability were evaluated non-invasively and in real time. We additionally report a low incidence of cell death from ischemic necrosis as well as minimal interstitial edema in machine perfused grafts. After up to 12 h of continuous perfusion, grafts were shown to survive transplantation and reperfusion, successfully integrating with recipient tissues and vasculature. Our multiparametric bioreactor system for rat abdominal wall VCA provides an advanced framework to test novel techniques to enhance normothermic and sub-normothermic VCA preservations in small animal models.

Identification of B cell subsets based on antigen receptor sequences using deep learning.

B cell receptors (BCRs) denote antigen specificity, while corresponding cell subsets indicate B cell functionality. Since each B cell uniquely encodes this combination, physical isolation and subsequent processing of individual B cells become indispensable to identify both attributes. However, this approach accompanies high costs and inevitable information loss, hindering high-throughput investigation of B cell populations. Here, we present BCR-SORT, a deep learning model that predicts cell subsets from their corresponding BCR sequences by leveraging B cell activation and maturation signatures encoded within BCR sequences. Subsequently, BCR-SORT is demonstrated to improve reconstruction of BCR phylogenetic trees, and reproduce results consistent with those verified using physical isolation-based methods or prior knowledge. Notably, when applied to BCR sequences from COVID-19 vaccine recipients, it revealed inter-individual heterogeneity of evolutionary trajectories towards Omicron-binding memory B cells. Overall, BCR-SORT offers great potential to improve our understanding of B cell responses.

Combinatorial and high-throughput investigation of growth nanotwin formation

This study examines the synthesis of growth nanotwins in CuNi alloys using combinatorial and high-throughput experimental techniques. 338 unique CuNi samples were synthesized via co-sputtering to create a material library encompassing composition, hardness, phase, and crystallographic data. The material library data in conjunction with scanning transmission electron microscopy was used to evaluate growth twinning over a wide compositional range (Cu – 6.8 to 58.8 at% Ni). A direct correlation between measured twin boundary spacings and the stacking fault energies underscored limitations of the current growth twin model caused by an underestimation of the free energy penalty for forming non-twinned grains. To address this, a refined model was developed to accurately capture the variation in twin boundary spacing and formation across all compositions. This model paves the way for high-throughput investigations into nanotwin synthesis in various alloy systems.