High-throughput Applications Research Articles

Advances in the application of colorectal cancer organoids in precision medicine

Colorectal cancer (CRC) ranks among the most prevalent gastrointestinal tumors globally and poses a significant threat to human health. In recent years, tumor organoids have emerged as ideal models for clinical disease research owing to their ability to closely mimic the original tumor tissue and maintain a stable phenotypic structure. Organoid technology has found widespread application in basic tumor research, precision therapy, and new drug development, establishing itself as a reliable preclinical model in CRC research. This has significantly advanced individualized and precise tumor therapies. Additionally, the integration of single-cell technology has enhanced the precision of organoid studies, offering deeper insights into tumor heterogeneity and treatment response, thereby contributing to the development of personalized treatment approaches. This review outlines the evolution of colorectal cancer organoid technology and highlights its strengths in modeling colorectal malignancies. This review also summarizes the progress made in precision tumor medicine and addresses the challenges in organoid research, particularly when organoid research is combined with single-cell technology. Furthermore, this review explores the future potential of organoid technology in the standardization of culture techniques, high-throughput screening applications, and single-cell multi-omics integration, offering novel directions for future colorectal cancer research.

Expert elicitation to assess real-world productivity gains in laser powder bed fusion

Purpose Laser powder bed fusion (LPBF) additive manufacturing (AM) enables the design of complex parts using materials that are otherwise difficult to fabricate. Due to the high cost of machines, the parts produced by LBPF are expensive. Both researchers and industry are therefore focused on lowering costs by improving productivity while ensuring part quality. The purpose of this study is to quantify the productivity gains from using laser beam shaping, multi-laser printing and the use of large build chambers to print larger size parts. Design/methodology/approach This paper performs an expert elicitation with 18 experts. Findings This paper finds that experts believe that larger parts are less likely to print successfully. Increasing the part footprint is more detrimental to print success than increasing part height. Experts also believe that beam shaping is expected to provide limited print time improvement (median 4% reduction, 90% CI: 2%–25%) while improving part quality, whereas going from one to two lasers is expected to provide a median of 25% (90% CI: 10%–45%) print time improvement but degrade part quality. Through cost analysis of a representative part, this paper shows that the uncertainty in build success rates for large parts dominates expected cost reductions from laser beam shaping or multi-laser printing. Research limitations/implications The study has three key limitations. First, it is possible that the sample of experts who agreed to take the survey biases the results. By definition, these are individuals who are willing to share what they know. There may be other experts who have a different view of the efficacy of the technologies evaluated here, but that view might be based on proprietary knowledge, which those experts are unable to share. Second, an elicitation captures what is known at a moment in time. As technology improves and as widespread deployment results in learning, the most consequential finding − that experts believed that success rates for large builds are likely to be low − may become less valid. Third, the overarching goal of this study is to assess technologies to improve AM productivity for high performance metal parts. A single study can only partially achieve this goal. The selection of technologies is constrained by both the desire to keep the study tractable and the suitability of expert elicitation as a method. For example, expert elicitation is not appropriate to assess the efficacy of technologies where sufficient empirical data or analytical techniques exist. Practical implications The results show that AM research and policy initiatives, including standards and regulatory schemes, must support efforts to improve the repeatability and reliability of the technological innovations that are needed to deploy AM in cost-critical or high throughput applications. These results also reinforce the criticality of workforce development components of existing (and future) AM policy initiatives. The elicitation revealed a significant number of factors that must be considered and potentially managed to ensure successful builds. Notably, no experts interviewed discussed all factors. While this may be a consequence of availability bias, it suggests that inexperienced AM users and nonexpert decision-makers, including managers, who would like to adopt new AM technologies, may be unaware of the myriad mechanisms by which build failure can occur and may fail to take mitigating action. This result contradicts a common belief that complicated parts can be fabricated with little to no expertise (assuming access to a design file for the part). Workforce development programs will be essential to help AM users develop the knowledge required to successfully implement metal AM. Originality/value Several strategies, including increasing the build volume to print larger parts or more parts at a time, using multiple lasers and beam shaping are proposed to improve the productivity of AM. However, the real-world efficacy of these strategies is not known. This work pools the judgment of experts to give decision-makers some insight into the current, real-world efficacy of these approaches.

Automatic cleaning in acoustic ejection mass spectrometry: Enhancing the system robustness for large-scale high-throughput analysis of complex samples

The rapid evolution of high-throughput mass spectrometry (HT-MS) technologies has positioned MS as a pivotal analytical tool across diverse disciplines. Its significance is particularly pronounced in high-throughput drug discovery and development, where MS plays a critical role throughout various phases. Acoustic ejection mass spectrometry (AEMS) is a recent addition to the HT-MS landscape, showcasing a balanced performance high analytical throughput and high data quality. Particularly, AEMS's in-line dilution feature allows the direct analysis of large-scale, complex reaction solutions without the need for sample cleanup, making it a popular choice for large-scale high-throughput screenings. However, the substantial volume of complex matrix introduces concerns about system robustness, specifically regarding the potential clogging of the sample transfer line. This study addresses this challenge by introducing an integrated automatic washing feature to the AEMS system. This enhancement significantly improves system robustness without imposing any additional demands on assay execution time. Demonstrating an extended electrode lifetime, the cleaning approach proves effective in maintaining system performance over prolonged periods, showcasing its potential for continuous large-sample-scale high-throughput analysis applications.

M2ara: unraveling metabolomic drug responses in whole-cell MALDI mass spectrometry bioassays.

Fast computational evaluation and classification of concentration responses for hundreds of metabolites represented by their mass-to-charge (m/z) ratios is indispensable for unraveling complex metabolomic drug actions in label-free, whole-cell Matrix-Assisted Laser Desorption/Ionization Mass Spectrometry (MALDI MS) bioassays. In particular, the identification of novel pharmacodynamic biomarkers to determine target engagement, potency and potential polypharmacology of drug-like compounds in high-throughput applications requires robust data interpretation pipelines. Given the large number of mass features in cell-based MALDI MS bioassays, reliable identification of true biological response patterns and their differentiation from any measurement artefacts that may be present is critical. To facilitate the exploration of metabolomic responses in complex MALDI MS datasets, we present a novel software tool, M2ara. Implemented as a user-friendly R-based shiny application, it enables rapid evaluation of Molecular High Content Screening (MHCS) assay data. Furthermore, we introduce the concept of Curve Response Score (CRS) and CRS fingerprints to enable rapid visual inspection and ranking of mass features. In addition, these CRS fingerprints allow direct comparison of cellular effects among different compounds. Beyond cellular assays, our computational framework can also be applied to MALDI MS-based (cell-free) biochemical assays in general. The software tool, code and examples are available at https://github.com/CeMOS-Mannheim/M2ara and https://dx.doi.org/10.6084/m9.figshare.25736541. Supplementary material is available at Bioinformatics online.

High throughput application of the NanoBiT Biochemical Assay for the discovery of selective inhibitors of the interaction of PI3K-p110α with KRAS

The NanoBiT Biochemical Assay (NBBA) was designed as a biochemical format of the NanoBiT cellular assay, aiming to screen weak protein-protein interactions (PPIs) in mammalian cell lysates. Here we present a High Throughput Screening (HTS) application of the NBBA to screen small molecule and fragment libraries to identify compounds that block the interaction of KRAS-G12D with phosphatidylinositol 3-kinase (PI3K) p110α. This interaction promotes PI3K activity, resulting in the promotion of cell growth, proliferation and survival, and is required for tumour initiation and growth in mouse lung cancer models, whilst having little effect on the health of normal adult mice, establishing the significance of the p110α/KRAS interaction as an oncology drug target. Despite the weak binding affinity of the p110α/KRAS interaction (KD = 3 μM), the NBBA proved to be robust and displayed excellent Z’-factor statistics during the HTS primary screening of 726,000 compounds, which led to the identification of 8,000 active compounds. A concentration response screen comparing KRAS/p110α with two closely related PI3K isoforms, p110δ and p110γ, identified selective p110α-specific compounds and enabled derivation of an IC50 for these hits. We identified around 30 compounds showing greater than 20-fold selectivity towards p110α versus p110δ and p110γ with IC50 < 2 μM. By using Differential Scanning Fluorimetry (DSF) we confirmed several compounds that bind directly to purified p110α. The most potent hits will be followed up by co-crystallization with p110α to aid further elucidation of the nature of the interaction and extended optimisation of these compounds.

A quantitative intracellular peptide binding assay reveals recognition determinants and context dependence of short linear motifs.

Transient protein-protein interactions play key roles in controlling dynamic cellular responses. Many examples involve globular protein domains that bind to peptide sequences known as Short Linear Motifs (SLiMs), which are enriched in intrinsically disordered regions of proteins. Here we describe a novel functional assay for measuring SLiM binding, called Systematic Intracellular Motif Binding Analysis (SIMBA). In this method, binding of a foreign globular domain to its cognate SLiM peptide allows yeast cells to proliferate by blocking a growth arrest signal. A high-throughput application of the SIMBA method involving competitive growth and deep sequencing provides rapid quantification of the relative binding strength for thousands of SLiM sequence variants, and a comprehensive interrogation of SLiM sequence features that control their recognition and potency. We show that multiple distinct classes of SLiM-binding domains can be analyzed by this method, and that the relative binding strength of peptides in vivo correlates with their biochemical affinities measured in vitro. Deep mutational scanning provides high-resolution definitions of motif recognition determinants and reveals how sequence variations at non-core positions can modulate binding strength. Furthermore, mutational scanning of multiple parent peptides that bind human tankyrase ARC or YAP WW domains identifies distinct binding modes and uncovers context effects in which the preferred residues at one position depend on residues elsewhere. The findings establish SIMBA as a fast and incisive approach for interrogating SLiM recognition via massively parallel quantification of protein-peptide binding strength in vivo.

A microvascularized in vitro liver model for disease modeling and drug discovery

Drug discovery for complex liver diseases faces alarming attrition rates. The lack of non-clinical models that recapitulate key aspects of liver (patho)-physiology is likely contributing to the inefficiency of developing effective treatments. Of particular notice is the common omission of an organized microvascular component despite its importance in maintaining liver function and its involvement in the development of several pathologies. Increasing the complexity ofin vitromodels is usually associated with a lack of scalability and robustness which hinders their implementation in drug development pipelines. Here, we describe a comprehensive liver microphysiological system comprising stellates, liver-derived endothelial cells and hepatocytes conceived within a scalable and automated platform. We show that endothelial cells self-organize in a microvascular network when co-cultured with stellates in a hydrogel. In a tri-culture, hepatocytes polarize accordingly, with a basolateral side facing blood vessels and an apical side facing bile-canaliculi-like structures. Stellates interact and surround the hollow microvessels. Steatosis was induced by exogenous administration of fatty acids which could be prevented by co-administration of firsocostat. Administration of TGF-βresulted in an activated stellate cells phenotype which could be prevented by the co-administration of SB-431542. The model was implemented on a microtiter plate format comprising 64 chips which enabled the development of a fully automated, multiplexed fibrosis assay with a robust Z' factor suitable for high-throughput applications.

A Universal Plug-and-Play Approach to In Situ Multinuclear Magnetic Resonance Analysis of Electrochemical Phenomena in Commercial Battery Cells.

Advancing electrochemical energy storage devices relies on versatile analytical tools capable of revealing the molecular mechanisms behind the function and degradation of battery materials in situ. The nuclear magnetic resonance phenomenon plays a pivotal role in fundamental studies of energy materials and devices because of its exceptional sensitivity to local environments and the dynamics of many electrochemically relevant elements. The jelly roll architecture is one of the most energy-dense and, therefore, most popular concepts implemented in pouch, prismatic, and cylindrical Li- and Na-ion cells. Such widely commercialized designs, however, represent a significant obstacle for a range of powerful in situ magnetic resonance-based methodologies due to negligible radio frequency electromagnetic field penetration through conductive metal casings and current collectors. In this work, we introduce an experimental setup that enables direct RF wave transmission through the cell terminals and current collectors, and provides efficient excitation and detection of NMR signals. An RF adapter designed as a plug-and-play device effectively turns the battery cell into an NMR probe that can be tuned to a broad range of Larmor frequencies. Due to its exceptional sensitivity, versatility, and multinuclear capability, the proposed methodology is suitable for fundamental research and industrial high-throughput screening applications. In situ NMR lineshapes provide a direct quantitative description of the chemical environments of electrochemically active elements and enable new metrics for the accurate assessment of state-of-charge (SoC) and state-of-health (SoH). Specifically, in commercial pouch cells based on lithium cobalt oxide, lithium nickel manganese cobalt oxide, and sodium nickel iron manganese oxide chemistries, 7Li and 23Na NMR data unambiguously show electrochemical transformations between intercalated and metallic forms of charge carrier ions, and reveal anisotropic magnetic properties of the electrode coating.

Superluminescent variants of Gaussia luciferase in living animal deep-tissue imaging

Gaussia luciferase (Gluc) is widely recognized as a powerful bioluminescent tool for biosensing and molecular imaging. However, its utility in mammalian deep tissue imaging and high-throughput applications is hampered by two primary limitations: the emission of blue light and the transient nature of its bioluminescence reaction. In this study, we have engineered two novel luciferases, BeM and SuM, by incorporating known amino acid mutations that have demonstrated the ability to stabilize, enhance, or shift the wavelength of Gluc-catalyzed luminescence towards the red spectrum. The BeM and SuM-coelenterazine systems exhibited a more than 100-fold increase in bioluminescence intensity in vitro, and over a 10-fold enhancement in vivo, compared to the conventional firefly luciferase-D-luciferin system. This significant improvement enables the noninvasive deep tissue imaging of a small number of cells within living mice. BeM and SuM emerge as the most efficient Gaussia luciferase variants to date, showcasing their immense potential as bioengineered light sources. Their applications could span various fields, ranging from fundamental scientific research to clinical diagnostics and therapeutic interventions, marking a significant leap forward in bioluminescence technology.

Transfer learning for molecular property predictions from small datasets

Machine learning has emerged as a new tool in chemistry to bypass expensive experiments or quantum-chemical calculations, for example, in high-throughput screening applications. However, many machine learning studies rely on small datasets, making it difficult to efficiently implement powerful deep learning architectures such as message passing neural networks. In this study, we benchmark common machine learning models for the prediction of molecular properties on two small datasets, for which the best results are obtained with the message passing neural network PaiNN as well as SOAP molecular descriptors concatenated to a set of simple molecular descriptors tailored to gradient boosting with regression trees. To further improve the predictive capabilities of PaiNN, we present a transfer learning strategy that uses large datasets to pre-train the respective models and allows us to obtain more accurate models after fine-tuning on the original datasets. The pre-training labels are obtained from computationally cheap ab initio or semi-empirical models, and both datasets are normalized to mean zero and standard deviation one to align the labels’ distributions. This study covers two small chemistry datasets, the Harvard Organic Photovoltaics dataset (HOPV, HOMO–LUMO-gaps), for which excellent results are obtained, and the FreeSolv dataset (solvation energies), where this method is less successful, probably due to a complex underlying learning task and the dissimilar methods used to obtain pre-training and fine-tuning labels. Finally, we find that for the HOPV dataset, the final training results do not improve monotonically with the size of the pre-training dataset, but pre-training with fewer data points can lead to more biased pre-trained models and higher accuracy after fine-tuning.

High accuracy meets high throughput for near full-length 16S ribosomal RNA amplicon sequencing on the Nanopore platform.

Small subunit (SSU) ribosomal RNA (rRNA) gene amplicon sequencing is a foundational method in microbial ecology. Currently, short-read platforms are commonly employed for high-throughput applications of SSU rRNA amplicon sequencing, but at the cost of poor taxonomic classification due to limited fragment lengths. The Oxford Nanopore Technologies (ONT) platform can sequence full-length SSU rRNA genes, but its lower raw-read accuracy has so-far limited accurate taxonomic classification and de novo feature generation. Here, we present a sequencing workflow, termed ssUMI, that combines unique molecular identifier (UMI)-based error correction with newer (R10.4+) ONT chemistry and sample barcoding to enable high throughput near full-length SSU rRNA (e.g. 16S rRNA) amplicon sequencing. The ssUMI workflow generated near full-length 16S rRNA consensus sequences with 99.99% mean accuracy using a minimum subread coverage of 3×, surpassing the accuracy of Illumina short reads. The consensus sequences generated with ssUMI were used to produce error-free de novo sequence features with no false positives with two microbial community standards. In contrast, Nanopore raw reads produced erroneous de novo sequence features, indicating that UMI-based error correction is currently necessary for high-accuracy microbial profiling with R10.4+ ONT sequencing chemistries. We showcase the cost-competitive scalability of the ssUMI workflow by sequencing 87 time-series wastewater samples and 27 human gut samples, obtaining quantitative ecological insights that were missed by short-read amplicon sequencing. ssUMI, therefore, enables accurate and low-cost full-length 16S rRNA amplicon sequencing on Nanopore, improving accessibility to high-resolution microbiome science.

Digital PCR threshold robustness analysis and optimization using dipcensR.

Digital polymerase chain reaction (dPCR) is a best-in-class molecular biology technique for the accurate and precise quantification of nucleic acids. The recent maturation of dPCR technology allows the quantification of up to thousands of targeted nucleic acids per instrument per day. A key step in the dPCR data analysis workflow is the classification of partitions into two classes based on their partition intensities: partitions either containing or lacking target nucleic acids of interest. Much effort has been invested in the design and tailoring of automated dPCR partition classification procedures, and such procedures will be increasingly important as the technology ventures into high-throughput applications. However, automated partition classification is not fail-safe, and evaluation of its accuracy ishighly advised. This accuracy evaluation is a manual endeavor and is becoming a bottleneck for high-throughput dPCR applications. Here, we introduce dipcensR, the first data-analysis procedure that automates the assessment of any linear partition classifier's partition classification accuracy, offering potentially substantial efficiency gains. dipcensR is based on a robustness evaluation of said partition classification and flags classifications with low robustness as needing review. Additionally, dipcensR's robustness analysis underpins (optional) automatic optimization of partition classification to achieve maximal robustness. A freely available R implementation supports dipcensR's use.

Validation of Saliva as the Clinical Specimen Type for a University-Wide COVID-19 Surveillance Program.

At the beginning of the COVID-19 pandemic, the Georgia Institute of Technology made the decision to keep the university doors open for on-campus attendance. To manage COVID-19 infection rates, internal resources were applied to develop and implement a mass asymptomatic surveillance program. The objective was to identify infections early for proper follow-on verification testing, contact tracing, and quarantine/isolation as needed. Program success depended on frequent and voluntary sample collection from over 40,000 students, faculty, and staff personnel. At that time, the nasopharyngeal (NP) swab, not saliva, was the main accepted sample type for COVID-19 testing. However, due to collection discomfort and the inability to be self-collected, the NP swab was not feasible for voluntary and frequent self-collection. Therefore, saliva was selected as the clinical sample type and validated. A saliva collection kit and a sample processing and analysis workflow were developed. The results of a clinical sample-type comparison study between co-collected and matched NP swabs and saliva samples showed 96.7% positive agreement and 100% negative agreement. During the Fall 2020 and Spring 2021 semesters, 319,988 samples were collected and tested. The program resulted in maintaining a low overall mean positivity rate of 0.78% and 0.54% for the Fall 2020 and Spring 2021 semesters, respectively. For this high-throughput asymptomatic COVID-19 screening application, saliva was an exceptionally good sample type.

Lens-free on-chip 3D microscopy based on wavelength-scanning Fourier ptychographic diffraction tomography

Lens-free on-chip microscopy is a powerful and promising high-throughput computational microscopy technique due to its unique advantage of creating high-resolution images across the full field-of-view (FOV) of the imaging sensor. Nevertheless, most current lens-free microscopy methods have been designed for imaging only two-dimensional thin samples. Lens-free on-chip tomography (LFOCT) with a uniform resolution across the entire FOV and at a subpixel level remains a critical challenge. In this paper, we demonstrated a new LFOCT technique and associated imaging platform based on wavelength scanning Fourier ptychographic diffraction tomography (wsFPDT). Instead of using angularly-variable illuminations, in wsFPDT, the sample is illuminated by on-axis wavelength-variable illuminations, ranging from 430 to 1200 nm. The corresponding under-sampled diffraction patterns are recorded, and then an iterative ptychographic reconstruction procedure is applied to fill the spectrum of the three-dimensional (3D) scattering potential to recover the sample’s 3D refractive index (RI) distribution. The wavelength-scanning scheme not only eliminates the need for mechanical motion during image acquisition and precise registration of the raw images but secures a quasi-uniform, pixel-super-resolved imaging resolution across the entire imaging FOV. With wsFPDT, we demonstrate the high-throughput, billion-voxel 3D tomographic imaging results with a half-pitch lateral resolution of 775 nm and an axial resolution of 5.43 μm across a large FOV of 29.85 mm2 and an imaging depth of >200 μm. The effectiveness of the proposed method was demonstrated by imaging various types of samples, including micro-polystyrene beads, diatoms, and mouse mononuclear macrophage cells. The unique capability to reveal quantitative morphological properties, such as area, volume, and sphericity index of single cell over large cell populations makes wsFPDT a powerful quantitative and label-free tool for high-throughput biological applications.

Sample-Specific MS/MS Methods in High-Throughput Mass Spectrometry.

The drug discovery process increasingly relies on high-throughput sample analysis to accelerate the identification of viable drug candidates. Recently, chromatographic-free high-throughput mass spectrometry (HT-MS) technologies have emerged, significantly increasing sample readout speed and enabling the analysis of large sample sets. These HT-MS platforms continuously acquire data from various samples into a single data file, presenting challenges in applying distinctive data acquisition methods to specific samples. This study introduces a novel approach that integrates real-time sample loading status to activate sample-specific MS/MS data acquisition methods on the high-throughput acoustic ejection mass spectrometry platform. Effective method switching and high signal reproducibility were demonstrated across different data acquisition window durations in multiple reaction monitoring (MRM), high-resolution MRM (MRMHR), and information-dependent acquisition modes. This advancement provides a user-friendly and robust solution to the method-setting challenges of HT-MS, expanding the implementation of HT-MS platforms in drug discovery and other high-throughput analytical applications.

Efficient and robust reverse genetics system for bovine rotavirus generation and its application for antiviral screening

Unveiling the molecular mechanisms underlying rotavirus replication and pathogenesis has been hampered by the lack of a reverse genetics (RG) system in the past. Since 2017, multiple plasmid-based RG systems for simian, human, and murine-like rotaviruses have been established. However, none of the described methods have supported the recovery of bovine rotaviruses (BRVs). Here, we established an optimized plasmid-based RG system for BRV culture-adapted strain (BRV G10P [15] BLR) and clinical isolates (BRV G6P [1] C73, G10P [11] HM26) based on a BHK-T7 cell clone stably expressing T7 polymerase. Furthermore, using this optimized RG system, we successfully rescued the reporter virus BRV rC73/Zs, rHM26/Zs and rBLR/Zs, harboring a genetically modified 1.8-kb segment 7 encoding full-length nonstructural protein 3 (NSP3) fused to ZsGreen, a 232-amino acid green fluorescent protein. Analysis of the stability of genomic insertions showed that the rC73/Zs and rBLR/Zs replicated efficiently and were genetically stable in seven rounds of serial passaging, while rHM26/Zs can be stabilized only up to the third generation, indicating that the BRV segment composition may influence the viral fitness. In addition, we adopted the recombinant reporter viruses for high-throughput screening application and discovered 12 candidates out of 1440 compounds with potential antiviral activities against rotavirus. In summary, this improved RG system of BRVs represents an important tool with great potential for understanding the molecular biology of BRV and facilitates the development of novel therapeutics and vaccines for BRV.

Single-cell laser emitting cytometry for label-free nucleolus fingerprinting

The nucleolus, a recognized biomolecular condensate, serves as the hub for ribosome biogenesis within the cell nucleus. Its quantity and morphology are discernible indicators of cellular functional states. However, precise identification and quantification of nucleoli remain challenging without specific labeling, particularly for suspended cells, tissue-level analysis and high-throughput applications. Here we introduce a single-cell laser emitting cytometry (SLEC) for label-free nucleolus differentiation through light-matter interactions within a Fabry–Perot resonator. The separated gain medium enhances the threshold difference by 36-fold between nucleolus and its surroundings, enabling selective laser emissions at nucleolar area while maintaining lower-order mode. The laser emission image provides insights into structural inhomogeneity, temporal fluid-like dynamics, and pathological application. Lasing spectral fingerprint depicts the quantity and size of nucleoli within a single cell, showcasing the label-free flow cytometry for nucleolus. This approach holds promise for nucleolus-guided cell screening and drug evaluation, advancing the study of diseases such as cancer and neurodegenerative disorders.

A machine learning enhanced EMS mutagenesis probability map for efficient identification of causal mutations in Caenorhabditis elegans.

Chemical mutagenesis-driven forward genetic screens are pivotal in unveiling gene functions, yet identifying causal mutations behind phenotypes remains laborious, hindering their high-throughput application. Here, we reveal a non-uniform mutation rate caused by Ethyl Methane Sulfonate (EMS) mutagenesis in the C. elegans genome, indicating that mutation frequency is influenced by proximate sequence context and chromatin status. Leveraging these factors, we developed a machine learning enhanced pipeline to create a comprehensive EMS mutagenesis probability map for the C. elegans genome. This map operates on the principle that causative mutations are enriched in genetic screens targeting specific phenotypes among random mutations. Applying this map to Whole Genome Sequencing (WGS) data of genetic suppressors that rescue a C. elegans ciliary kinesin mutant, we successfully pinpointed causal mutations without generating recombinant inbred lines. This method can be adapted in other species, offering a scalable approach for identifying causal genes and revitalizing the effectiveness of forward genetic screens.

Artificial design of anisotropic magnetoelectric effect in Sr2IrO4/SrTiO3 superlattices

Magnetoelectric response in thin films is highly desirable for high-throughput and high-density microelectronic applications, like magnetic sensors. To sensitively detect the direction of magnetic field, the anisotropic magnetoelectric effect is required. Here, we artificially design an anisotropic magnetoelectric response in Sr2IrO4/SrTiO3 superlattices, where a strong (negligible) magnetoelectric response for magnetic fields along the z-direction (xy-plane) of Sr2IrO4/SrTiO3 interface was observed. A combination of perturbative calculations with numerical results and density functional theory calculations reveals that only the effective z-component Zeeman field within the Sr2IrO4 layers can change the electron occupation of the neighboring SrTiO3 layers, which is proportional to the polarization. Via controlling the periodicity of the superlattices, we can further control the anisotropy of magnetoelectric responses. This atomic-scale design of 3d/5d superlattices paves an alternative way toward controllable magnetoelectric effects with thin film integrations.

Advancing scanning probe microscopy simulations: A decade of development in probe-particle models

The Probe-Particle Model combine theories designed for the simulation of scanning probe microscopy experiments, employing non-reactive, flexible tip apices to achieve sub-molecular resolution. In the article we present the latest version of the Probe-Particle Model implemented in the open-source ppafm package, highlighting substantial advancements in accuracy, computational performance, and user-friendliness. To demonstrate this we provide a comprehensive review of approaches for simulating non-contact Atomic Force Microscopy. They vary in complexity from simple Lennard-Jones potential to the latest full density-based model. We compared those approaches with ab initio calculated references, showcasing their respective merits. All parts of the ppafm package have undergone acceleration by 1-2 orders of magnitude using OpenMP and OpenCL technologies. The updated package includes an interactive graphical user interface and seamless integration into the Python ecosystem via pip, facilitating advanced scripting and interoperability with other software. This adaptability positions ppafm as an ideal tool for high-throughput applications, including the training of machine learning models for the automatic recovery of atomic structures from nc-AFM measurements. We envision significant potential for this application in future single-molecule analysis, synthesis, and advancements in surface science in general. Additionally, we discuss simulations of other sub-molecular scanning-probe imaging techniques, such as bond-resolved scanning tunneling microscopy and kelvin probe force microscopy, all built on the robust foundation of the Probe-Particle Model. Altogether this demonstrates the broad impact of the model across diverse domains of on-surface science and molecular chemistry.