High-throughput Workflow Research Articles

AI-Assisted High-Throughput Tissue Microarray Workflow

Immunohistochemical (IHC) studies of formalin-fixed paraffin-embedded (FFPE) samples are a gold standard in oncology for tumor characterization, and the identification of prognostic and predictive markers. However, despite the abundance of archived FFPE samples, their research use is limited due to the labor-intensive nature of IHC on large cohorts. This study aimed to create a high-throughput workflow using modern technologies to facilitate IHC biomarker studies on large patient groups. Semiautomatic constructed tissue microarrays (TMAs) were created for two tumor patient cohorts and IHC stained for seven antibodies (ABs). AB expression in the tumor and surrounding stroma was quantified using the AI-supported image analysis software QuPath. The data were correlated with clinicopathological information using an R-script, all results were automatically compiled into formatted reports. By minimizing labor time to 7.7%—compared to whole-slide studies—the established workflow significantly reduced human and material resource consumption. It successfully correlated AB expression with overall patient survival and additional clinicopathological data, providing publication-ready figures and tables. The AI-assisted high-throughput TMA workflow, validated on two patient cohorts, streamlines modern histopathological research by offering cost and time efficiency compared to traditional whole-slide studies. It maintains research quality and preserves patient tissue while significantly reducing material and human resources, making it ideal for high-throughput research centers and collaborations.

Abstract B001: CircRNA-derived neoantigen identification in cancer vaccine development

Abstract Immunotherapy has been a popular topic in combating cancer for its great performance in specificity, versatility and durability recently. Cancer vaccines targeting neoantigens have shown clinical efficacy against cancer, eliciting long-lasting immune response and offering a more selective therapeutic approach with less side effects. However, rare mutation rates and low expression levels of neoantigen make it challenging to identify suitable neoantigens. Traditionally, researchers focus on the canonical mRNA transcriptome and proteome to find potential targets. Recent studies suggest that exploring the non-canonical transcriptome and proteome, particularly circular RNAs (circRNAs), can expand the search. CircRNAs, endogenously formed by the unique back-splicing events during pre-RNA processing, can serve as templates for protein synthesis. Some of these non-canonical peptides are specifically expressed in cancer cells, making circRNAs a valid source for neoantigen discovery. We have developed high-throughput circRNA reporter screening methods and mass spectrometry-based peptidomic workflow to pinpoint translating circRNAs and their derived peptides. Using these techniques, we have identified circRNA-derived neoantigens in breast cancer (BC) cell lines that can be used as possible targets for cancer vaccines. These circRNA-derived neoantigens are immunogenic and can trigger dendritic cell (DC) maturation and T cell activation in vitro. Next, we will test these findings by transferring to in vivo mouse system. We will assess cancer vaccine efficacy by targeting the neoantigens using syngeneic mouse tumor models. Our long- term goal is to apply the circRNA neoantigen identification technology to BC patient samples to facilitate cancer vaccine development and improve cancer immunotherapy. Our work will demonstrate the potential of circRNA-derived non canonical neoantigens in cancer vaccines, serving as a crucial first step to prove their effectiveness in therapy. By expanding our search beyond the traditional transcriptome and proteome, we will explore new targets and transform the neoantigen-based treatment. This innovative effort shifts the current understanding of neoantigen sources and opens up new possibilities for targeted cancer therapy strategies. Citation Format: Yi-Cian Zheng, Chun-Kan Chen. CircRNA-derived neoantigen identification in cancer vaccine development [abstract]. In: Proceedings of the AACR Special Conference in Cancer Research: RNAs as Drivers, Targets, and Therapeutics in Cancer; 2024 Nov 14-17; Bellevue, Washington. Philadelphia (PA): AACR; Mol Cancer Ther 2024;23(11_Suppl):Abstract nr B001.

A high-throughput framework for lattice dynamics

We develop an automated high-throughput workflow for calculating lattice dynamical properties from first principles including those dictated by anharmonicity. The pipeline automatically computes interatomic force constants (IFCs) up to 4th order from perturbed training supercells, and uses the IFCs to calculate lattice thermal conductivity, coefficient of thermal expansion, and vibrational free energy and entropy. It performs phonon renormalization for dynamically unstable compounds to obtain real effective phonon spectra at finite temperatures and calculates the associated free energy corrections. The methods and parameters are chosen to balance computational efficiency and result accuracy, assessed through convergence testing and comparisons with experimental measurements. Deployment of this workflow at a large scale would facilitate materials discovery efforts toward functionalities including thermoelectrics, contact materials, ferroelectrics, aerospace components, as well as general phase diagram construction.

PEPITA: Parallelized High-Throughput Quantification of Ototoxicity and Otoprotection in Zebrafish Larvae.

Drug-induced hearing injury (ototoxicity) is a common, debilitating side effect of many antibiotic regimens that can be worsened by adverse drug interactions. Such adverse drug interactions are often not detected until after drugs are already on the market because of the difficulty of measuring all possible drug combinations. While in vivo mammalian assays to screen for ototoxic damage exist, they are currently time-consuming, costly, and limited in throughput, which limits their utility in assessing drug interaction outcomes. To facilitate more rapid quantification of ototoxicity and assessment of adverse drug interactions that impact ototoxicity, we have developed a high-throughput workflow we call parallelized evaluation of protection and injury for toxicity assessment (PEPITA). PEPITA uses zebrafish larvae to quantify ototoxic damage and protection. Previous work has shown that hair cells (HCs) in the zebrafish lateral line are very similar to human inner ear HCs, meaning zebrafish are a viable model to test drug-induced ototoxicity. In PEPITA, we expose zebrafish larvae to different combinations of drugs, fluorescently label the HCs, and subsequently use microscopy to quantify the brightness of the fluorescently labeled HCs as an assay for ototoxic damage and hair-cell viability. PEPITA is a reproducible, low-cost, technically accessible, and high-throughput assay. These advantages allow many experiments to be conducted in parallel, paving the way for systematic evaluation of drug-induced hearing injury and other multidrug interactions. Key features • Analysis of drug-induced hair cell damage associated with ototoxicity using Danio rerio • Ototoxicity assessment performed in vivo • Uses microscopy to generate images to assay ototoxicity quantitatively. • Enables testing of various combinations of drugs at various doses to determine toxicity-associated drug-drug interaction outcomes (synergy, antagonism).

End-To-End Automated Intact Protein Mass Spectrometry for High-Throughput Screening and Characterization of Bispecific and Multispecific Antibodies.

Bispecific antibodies (bsAbs) and multispecific antibodies (msAbs) represent a promising frontier in therapeutic antibody development, offering unique capabilities not achievable with traditional monoclonal antibodies. Despite their potential, significant challenges remain due to their increased molecular complexity. One prominent challenge is the correct assembly of light and heavy chains, as improper pairing leads to mispaired or incompletely assembled species that lack therapeutic efficacy and possess undesired properties, impairing the developability, manufacturability, and safety. There is a critical need for rapid, sensitive analytical tools to monitor and control these undesired species and ensure the quality assessment of bsAbs and msAbs. To address this need, we present a novel high-throughput, format-agnostic intact mass workflow that significantly enhances the efficiency of detecting and quantifying biotherapeutic products and related impurities. This workflow integrates automated sample preparation, novel high-resolution rapid mass detection powered by SampleStream-MS, and an advanced data analysis pipeline. It offers increased throughput and data quality while substantially reducing analysis turnover time and labor. This was demonstrated in a pilot program where ∼800 multispecific antibodies were processed in 10 working days. The article details the evaluation and validation of our method, demonstrating its repeatability and intermediate precision in terms of measurement accuracy and relative quantification of various product-related species. We underscore the transformative potential of this end-to-end high-throughput workflow in expediting bispecific and multispecific antibody discovery, optimizing production processes, and ensuring high-quality development and manufacturing for therapeutic antibodies.

Rapid QC-MS: Interactive Dashboard for Synchronous Mass Spectrometry Data Acquisition Quality Control.

Consistently collecting high-quality liquid chromatography-coupled tandem mass spectrometry (LC-MS/MS) data is a time-consuming hurdle for untargeted workflows. Analytical controls such as internal and biological standards are commonly included in high-throughput workflows, helping researchers recognize low-integrity specimens regardless of their biological source. However, evaluating these standards as data are collected has remained a considerable bottleneck─in both person-hours and accuracy. Here we present Rapid QC-MS, an automated, interactive dashboard for assessing LC-MS/MS data quality. Minutes after a new data file is written, a browser-viewable dashboard is updated with quality control results spanning multiple performance dimensions such as instrument sensitivity, in-run retention time shifts, and mass accuracy drift. Rapid QC-MS provides interactive visualizations that help users recognize acute deviations in these performance metrics, as well as gradual drifts over periods of hours, days, months, or years. Rapid QC-MS is open-source, simple to install, and highly configurable. By integrating open-source Python libraries and widely used MS analysis software, it can adapt to any LC-MS/MS workflow. Rapid QC-MS runs locally and offers optional remote quality control by syncing with Google Drive. Furthermore, Rapid QC-MS can operate in a semiautonomous fashion, alerting users to specimens with potentially poor analytical integrity via frequently used messaging applications. Initially developed for integration with Thermo Orbitrap workflows, Rapid QC-MS offers a fast, straightforward approach to help users collect high-quality untargeted LC-MS/MS data by eliminating many of the most time-consuming steps in manual data curation. Download for free: https://github.com/czbiohub-sf/Rapid-QC-MS.

ASMI: An automated, low-cost indenter for soft matter

The automated soft matter indenter (ASMI) is a platform for rapidly performing mechanical characterization of samples with elastic moduli in the range 7 kPa to 67 MPa with a sample acquisition time between 1 and 10 min. It is a low-cost system based upon open-source software, a modified mill, and an educational force sensor with a total bill of materials <$500. This system tests batches of up to 96 samples based on a standard well-plate sample holder without requiring any human intervention. Using the ASMI, users can obtain mechanical data in a programmable manner that enables high-throughput workflows, precisely testing time-dependent phenomena, and integration with other processing steps for closed-loop optimization.

High-resolution acoustic ejection mass spectrometry for high-throughput library screening

An approach is described for high-throughput quality assessment of drug candidate libraries using high-resolution acoustic ejection mass spectrometry (AEMS). Sample introduction from 1536-well plates is demonstrated for this application using 2.5 nL acoustically dispensed sample droplets into an Open Port Interface (OPI) with pneumatically assisted electrospray ionization at a rate of one second per sample. Both positive and negative ionization are shown to be essential to extend the compound coverage of this protease inhibitor-focused library. Specialized software for efficiently interpreting this data in 1536-well format is presented. A new high-throughput method for quantifying the concentration of the components (HTQuant) is proposed that neither requires adding an internal standard to each well nor further encumbers the high-throughput workflow. This approach for quantitation requires highly reproducible peak areas, which is shown to be consistent within 4.4 % CV for a 1536-well plate analysis. An approach for troubleshooting the workflow based on the background ion current signal is also presented. The AEMS data is compared to the industry standard LC/PDA/ELSD/MS approach and shows similar coverage but at 180-fold greater throughput. Despite the same ionization process, both methods confirmed the presence of a small percentage of compounds in wells that the other did not. The data for this relatively small, focused library is compared to a larger, more chemically diverse library to indicate that this approach can be more generally applied beyond this single case study. This capability is particularly timely considering the growing implementation of artificial intelligence strategies that require the input of large amounts of high-quality data to formulate predictions relevant to the drug discovery process. The molecular structures of the 872-compound library analyzed here are included to begin the process of correlating molecular structures with ionization efficiency and other parameters as an initial step in this direction.

Simple and robust high-throughput serum proteomics workflow with low-microflow LC–MS/MS

Clinical proteomics has substantially advanced in identifying and quantifying proteins from biofluids, such as blood, contributing to the discovery of biomarkers. The throughput and reproducibility of serum proteomics for large-scale clinical sample analyses require improvements. High-throughput analysis typically relies on automated equipment, which can be costly and has limited accessibility. In this study, we present a rapid, high-throughput workflow low-microflow LC–MS/MS method without automation. This workflow was optimized to minimize the preparation time and costs by omitting the depletion and desalting steps. The developed method was applied to data-independent acquisition (DIA) analysis of 235 samples, and it consistently yielded approximately 6000 peptides and 600 protein groups, including 33 FDA-approved biomarkers. Our results demonstrate that an 18-min DIA high-throughput workflow, assessed through intermittently collected quality control samples, ensures reproducibility and stability even with 2 µL of serum. It was successfully used to analyze serum samples from patients with diabetes having chronic kidney disease (CKD), and could identify five dysregulated proteins across various CKD stages.

Key Aspects in Designing High-Throughput Workflows in Electrocatalysis Research: A Case Study on IrCo Mixed-Metal Oxides.

With the growing interest of the electrochemical community in high-throughput (HT) experimentation as a powerful tool in accelerating materials discovery, the implementation of HT methodologies and the design of HT workflows has gained traction. We identify 6 aspects essential to HT workflow design in electrochemistry and beyond to ease the incorporation of HT methods in the community's research and to assist in their improvement. We study IrCo mixed-metal oxides (MMOs) for the oxygen evolution reaction (OER) in acidic media using the mentioned aspects to provide a practical example of possible workflow design pitfalls and strategies to counteract them.

An Optimized Liquid Chromatography-Mass Spectrometry Method for Ganglioside Analysis in Cell Lines.

Gangliosides are glycosphingolipids composed of a sialylated glycan head group and a ceramide backbone. These anionic lipids form lipid rafts and play crucial roles in regulating various proteins involved in signal transduction, adhesion, and cell-cell recognition. Neuroblastoma, a pediatric cancer of the sympathetic nervous system, is treated with intensive chemotherapy, radiation, and an antibody targeting the GD2 ganglioside. Gangliosides are critical in neuroblastoma development and serve as therapeutic targets, making it essential to establish a reliable, rapid, and cost-effective method for profiling gangliosides, particularly one capable of isomeric separation of intact species. In this study, liquid chromatography-mass spectrometry (LC-MS) was optimized using standard gangliosides, followed by the optimization of sphingolipid extraction methods from cell lines by comparing Folch and absolute methanol extraction techniques. Percent recovery and the number of identified sphingolipids were used to evaluate the analytical merits of these methods. A standard gangliosides calibration curve demonstrated excellent linearity (R2 = 0.9961-0.9975). The ZIC-HILIC column provided the best separation of ganglioside GD1 isomers with a 25 min runtime. GD1a elutes before GD1b on the ZIC-HILIC column. Absolute methanol yielded better percent recovery (96 ± 7) and identified 121 different sphingolipids, the highest number between the two extraction methods. The optimized method was applied to profile gangliosides in neuroblastoma (COG-N-683), pancreatic cancer (PSN1), breast cancer (MDA-MB-231BR), and brain tumor (CRL-1620) cell lines. The ganglioside profile of the neuroblastoma cell line COG-N-683 showed an inverse relationship between GD1 and GD2. Ceramide, Hex1Cer, GM1, and GM3 were highly abundant in CRL-1620, PSN1, and MDA-MB-231BR, respectively. These results suggest that our method provides a sensitive, reliable, and high-throughput workflow for ganglioside profiling across different cell types.

B-159 Fast Cleanup for 19 Steroid Analytes in Serum Using Supported Liquid Extraction (SLE)

Abstract Background Liquid chromatography-tandem mass spectrometry (LC-MS/MS) is a powerful tool that holds promise in designing the future of steroid analysis in the clinical laboratory. It is already widely used in steroid analysis in endocrinology and is being utilized increasingly in newer applications for individual steroids and panels relevant in clinical research. LC-MS/MS is gaining user base owing to greater sensitivity and specificity and gradually even replacing some of the legacy test methods. Improved sample preparation, modern chromatography methods, and sensitive, faster scanning mass spectrometers have all played a role in improving LC-MS/MS. Steroid analysis for clinical research can require very low limits of detection which demand high recoveries and very clean extracted samples. Supported Liquid Extraction (SLE) can provide very clean extracted samples with less method development and a streamlined workflow compared to solid phase extraction (SPE). Laboratories desire high-throughput methods which consolidate analytes into one panel with fast chromatographic run times. Chromatographic separation of steroid isomers with the same m/z is necessary for accurate quantitation by LCMS/MS. Meeting these criteria can be challenging in a single LC-MS/MS method. Methods Each 200 µL serum sample was pretreated with 200 µL of pH 6 Ammonium Acetate buffer (aq) before being loaded onto a Novum™ PRO SLE, MAX 96-well plate, following the standard SLE protocol. Samples were eluted using 2 × 900 µL of Dichloromethane or Ethyl Acetate. After evaporation of the elution solvent, samples were reconstituted in 100 µL of 0.5 mM Ammonium Fluoride / Methanol (60:40, v/v). A Kinetex™ 2.6 μm C18, 50 x 3.0 mm column using a gradient of 0.5 mM Ammonium Fluoride (aq) and Methanol on an Agilent® 1290 Infinity UHPLC and SCIEX® 7500 Triple Quad™ were used for quantitation. Results Here, we present an effective sample extraction method and LC-MS/MS analysis method for the quantitation of a 19-analyte steroid panel from serum. The Kinetex 2.6 μm C18 column provided a fast 8-minute chromatographic separation and good selectivity for steroid analytes in both positive ion mode and negative ion mode. Calibration curves with a linear fit and 1/x weighting, showed good linearity with R2 of &amp;gt;0.99 for all analytes except 17-OH-Pregnenolone and DHEAS when using Dichloromethane as the elution solvent, and R2 of &amp;gt;0.980 for all analytes when using Ethyl Acetate as the elution the solvent. Accuracy of samples using Dichloromethane as the elution solvent were 80-112 %, except for 17-OH-Pregnenolone and DHEAS. Accuracy of all samples using the Ethyl Acetate elution solvent were 91-118 %. Conclusions The-96 well plate extraction method was robust, reproducible, and can be automated. SLE using Novum Pro MAX combined with a fast LC method provides a simple, rapid, high-throughput workflow for identification and quantitation of 19 steroids from serum.

B-231 Evaluation of the KingFisher Flex for DNA Isolation From FFPE Tissue Utilizing Autolys M Tubes and the MagMAX FFPE DNA/RNA Ultra Kit

Abstract Background Our institution is facing increasing demand for oncology testing that requires high quality DNA extracted from formalin-fixed paraffin-embedded (FFPE) tissue. The current DNA extraction workflow involves a time and labor-intensive manual process that limits our ability to accommodate increasing downstream clinical testing volumes. We evaluated the KingFisher (KF) Flex utilizing Autolys M Tubes and the MagMAX FFPE DNA/RNA Ultra Kit as a possible high-throughput, automated DNA isolation workflow option that would decrease hands-on time and be conducive to volume growth without the need for additional staff. Methods DNA from 52 FFPE tissue samples (n=24 unique cases previously extracted by the current “manual method”) were extracted using Autolys M tubes, the MagMAX FFPE DNA/RNA Ultra Kit, and the KF Flex (Thermo Fisher Scientific Waltham, MA) over 4 “automated method” runs. Method modifications to the published procedure were introduced over the runs to determine any effects on DNA quality/quantity. Timings were taken during each run to compare hands-on and total run time against that of the manual method. Precision experiments were performed by extracting 4 replicates of sample on a single run (intra-assay precision) and 2 samples from 2 cases over 2 runs (inter-assay precision). Three cases were processed using varying tissue input levels to assess input-yield correlation. DNA yield was measured using HS dsDNA Qubit (Thermo Fisher Scientific, Waltham, MA). A subset of samples was tested by digital droplet PCR (ddPCR) (n=10), chromosomal microsomal microarray (CMA) (n=1), and next-generation sequencing (NGS) (n=3). Variants and QC metrics of the KF DNA were compared to those of the manual method DNA. Results The average total DNA yield was 3% lower from samples extracted following the manufacturer recommended KF procedure compared to matched samples extracted using the manual method (n=28 samples from 23 unique cases). A CV of 2.8% was observed in an intra-assay experiment (n=4 replicates from 1 case) and 3.0% in an average inter-assay experiment (n=2 replicates from 2 cases). There was a direct correlation between the tissue input amount (4 amounts of varied tissue input) and the total DNA yield (n=3 samples). We observed concordant fractional abundance values for ddPCR samples and concordant variant detection for NGS and CMA samples. We did not see a significant difference in the quality metrics tracked for any of the downstream testing assays. Conclusions This evaluation demonstrated that, as compared to our manual method, the automated method resulted in comparable DNA yields and downstream assay results. The incorporation of this automation would significantly increase our FFPE DNA extraction capacity without requiring additional staff while reducing hands-on time per run by about six hours. We would also be able to reduce ergonomic hazards related to tube cap manipulation, eliminate use of hazardous reagents, and leverage the plate format to better integrate extracted DNA into our clinical sample preparation workflows. These operational benefits highlight the value of automating high-throughput workflows.

Size photometry and fluorescence imaging of immobilized immersed extracellular vesicles.

Immunofluorescence analysis of individual extracellular vesicles (EVs) in common fluorescence microscopes is gaining popularity due to its accessibility and high fluorescence sensitivity; however, EV number and size are only measurable using fluorescent stains requiring extensive sample manipulations. Here we introduce highly sensitive label-free EV size photometry (SP) based on interferometric scattering (iSCAT) imaging of immersed EVs immobilized on a glass coverslip. We implement SP on a common inverted epifluorescence microscope with LED illumination and a simple 50:50 beamsplitter, permitting seamless integration of SP with fluorescence imaging (SPFI). We present a high-throughput SPFI workflow recording >10,000 EVs in 7min over ten 88×88µm2 fields of view, pre- and post-incubation imaging to suppress background, along with automated image alignment, aberration correction, spot detection and EV sizing. We achieve an EV sizing range from 37 to ∼220nm in diameter with a dual 440 and 740nm SP illumination scheme, and suggest that this range can be extended by more advanced image analysis or additional hardware customization. We benchmark SP to flow cytometry using calibrated silica nanoparticles and demonstrate superior, label-free sensitivity. We showcase SPFI's potential for EV analysis by experimentally distinguishing surface and volumetric EV dyes, observing the deformation of EVs adsorbed to a surface, and by uncovering distinct subpopulations in <100nm-in-diameter EVs with fluorescently tagged membrane proteins.

High-throughput dataset of impurity adsorption on common catalysts in biomass upgrading applications

An extensive dataset consisting of adsorption energies of pernicious impurities present in biomass upgrading processes on common catalysts and support materials has been generated. This work aims to inform catalyst and process development for the conversion of biomass-derived feedstocks to fuels and chemicals. A high-throughput workflow was developed to execute density functional theory calculations for a diverse set of atomic (Al, B, Ca, Cl, Fe, K, Mg, Mn, N, Na, P, S, Si, Zn) and molecular (COS, H2S, HCl, HCN, K2O, KCl, NH3) species on 35 unique surfaces for transition-metal (Ag, Au, Co, Cu, Fe, Ir, Ni, Pd, Pt, Re, Rh, Ru) and metal-oxide (Al2O3, MgO, anatase-TiO2, rutile-TiO2, ZnO, ZrO2) catalysts and supports. Approximately 3,000 unique adsorption geometries and corresponding adsorption energies were obtained.

Mid-infrared chemical imaging of living cells enabled by plasmonic metasurfaces.

Mid-Infrared (MIR) chemical imaging provides rich chemical information of biological samples in a label-free and non-destructive manner. Yet, its adoption to live-cell analysis is limited by the strong attenuation of MIR light in water, often necessitating cell culture geometries that are incompatible with the prolonged viability of cells and with standard high-throughput workflow. Here, we introduce a new approach to MIR microscopy, where cells are imaged through their localized near-field interaction with a plasmonic metasurface. Chemical contrast of distinct molecular groups provided sub-cellular resolution images of the proteins, lipids, and nucleic acids in the cells that were collected using an inverted MIR microscope. Time-lapse imaging of living cells demonstrated that their behaviors, including motility, viability, and substrate adhesion, can be monitored over extended periods of time using low-power MIR light. The presented approach provides a method for the non-perturbative MIR imaging of living cells, which is well-suited for integration with modern high-throughput screening technologies for the label-free, high-content chemical imaging of living cells.

An Improved Method and Device for Nucleic Acid Isolation Using a High-Salt Gel Electroelution Trap.

The success of DNA analytical methods, including long-read sequencing, depends on the availability of high-quality, purified DNA. Previously, we developed a method and device for isolating high-molecular-weight (HMW) DNA for long-read sequencing using a high-salt gel electroelution trap. Here, we present an improved version of this method for purifying nucleic acids with high yield and purity from even the most challenging biological samples. The proposed method is a significant improvement over the previously published procedure, offering a simple, fast, and efficient solution for isolating HMW DNA and smaller DNA and RNA molecules. The method utilizes vertical gel electrophoresis in two nested, partially overlapping electrophoretic columns. The upper, smaller-diameter column has a thin layer of agarose gel at the bottom, which separates nucleic acids from impurities, and an electrophoresis buffer on top. After the target nucleic acid has been gel-purified on the upper column, a larger-diameter column with a layer of high-salt gel overlaid with electrophoresis buffer is inserted from below. The purified nucleic acid is then electroeluted into the buffer-filled gap between the separating gel and the high-salt gel, where excess counterions from the high-salt gel slow its migration and cause it to accumulate. The proposed vertical purification system outperforms the previously described horizontal system in terms of ease of use, speed, scalability, and compatibility with high-throughput workflows. Furthermore, the vertical system allows for the sequential purification of several nucleic acid species from the same sample using interchangeable salt-gel columns.

Foundational model aided automatic high-throughput drug screening using self-controlled cohort study.

Developing medicine from scratch to governmental authorization and detecting adverse drug reactions (ADR) have barely been economical, expeditious, and risk-averse investments. The availability of large-scale observational healthcare databases and the popularity of large language models offer an unparalleled opportunity to enable automatic high-throughput drug screening for both repurposing and pharmacovigilance. To demonstrate a general workflow for automatic high-throughput drug screening with the following advantages: (i) the association of various exposure on diseases can be estimated; (ii) both repurposing and pharmacovigilance are integrated; (iii) accurate exposure length for each prescription is parsed from clinical texts; (iv) intrinsic relationship between drugs and diseases are removed jointly by bioinformatic mapping and large language model - ChatGPT; (v) causal-wise interpretations for incidence rate contrasts are provided. Using a self-controlled cohort study design where subjects serve as their own control group, we tested the intention-to-treat association between medications on the incidence of diseases. Exposure length for each prescription is determined by parsing common dosages in English free text into a structured format. Exposure period starts from initial prescription to treatment discontinuation. A same exposure length preceding initial treatment is the control period. Clinical outcomes and categories are identified using existing phenotyping algorithms. Incident rate ratios (IRR) are tested using uniformly most powerful (UMP) unbiased tests. We assessed 3,444 medications on 276 diseases on 6,613,198 patients from the Clinical Practice Research Datalink (CPRD), an UK primary care electronic health records (EHR) spanning from 1987 to 2018. Due to the built-in selection bias of self-controlled cohort studies, ingredients-disease pairs confounded by deterministic medical relationships are removed by existing map from RxNorm and nonexistent maps by calling ChatGPT. A total of 16,901 drug-disease pairs reveals significant risk reduction, which can be considered as candidates for repurposing, while a total of 11,089 pairs showed significant risk increase, where drug safety might be of a concern instead. This work developed a data-driven, nonparametric, hypothesis generating, and automatic high-throughput workflow, which reveals the potential of natural language processing in pharmacoepidemiology. We demonstrate the paradigm to a large observational health dataset to help discover potential novel therapies and adverse drug effects. The framework of this study can be extended to other observational medical databases.

Pharmaceutical metabolite identification in lettuce (Lactuca sativa) and earthworms (Eisenia fetida) using liquid chromatography coupled to high-resolution mass spectrometry and in silico spectral library.

Pharmaceuticals released into the aquatic and soil environments can be absorbed by plants and soil organisms, potentially leading to the formation of unknown metabolites that may negatively affect these organisms or contaminate the food chain. The aim of this study was to identify pharmaceutical metabolites through a triplet approach for metabolite structure prediction (software-based predictions, literature review, and known common metabolic pathways), followed by generating in silico mass spectral libraries and applying various mass spectrometry modes for untargeted LC-qTOF analysis. Therefore, Eisenia fetida and Lactuca sativa were exposed to a pharmaceutical mixture (atenolol, enrofloxacin, erythromycin, ketoprofen, sulfametoxazole, tetracycline) under hydroponic and soil conditions at environmentally relevant concentrations. Samples collected at different time points were extracted using QuEChERS and analyzed with LC-qTOF in data-dependent (DDA) and data-independent (DIA) acquisition modes, applying both positive and negative electrospray ionization. The triplet approach for metabolite structure prediction yielded a total of 3762 pharmaceutical metabolites, and anin silico mass spectral library was created based on these predicted metabolites. This approach resulted in the identification of 26 statistically significant metabolites (p < 0.05), with DDA + and DDA - outperforming DIA modes by successfully detecting 56/67 sample type:metabolite combinations. Lettuce roots had the highest metabolite count (26), followed by leaves (6) and earthworms (2). Despite the lower metabolite count, earthworms showed the highest peak intensities, closely followed by roots, with leaves displaying the lowest intensities. Common metabolic reactions observed included hydroxylation, decarboxylation, acetylation, and glucosidation, with ketoprofen-related metabolites being the most prevalent, totaling 12 distinct metabolites. In conclusion, we developed a high-throughput workflow combining open-source software with LC-HRMS for identifying unknown metabolites across various sample types.

A high-throughput workflow to analyze sequence-conformation relationships and explore hydrophobic patterning in disordered peptoids

A high-throughput workflow to analyze sequence-conformation relationships and explore hydrophobic patterning in disordered peptoids