Highly parallel production of designer organoids by mosaic patterning of progenitors.

Organoids derived from human stem cells are a promising approach for disease modeling, regenerative medicine, and fundamental research. However, organoid variability and limited control over morphological outcomes remain as challenges. One open question is the extent to which engineering control over culture conditions can guide organoids to specific compositions. Here, we extend a DNA "velcro" cell patterning approach, precisely controlling the number and ratio of human induced pluripotent stem cell-derived progenitors contributing to nephron progenitor (NP) organoids and mosaic NP/ureteric bud (UB) tip cell organoids within arrays of microwells. We demonstrate long-term control over organoid size and morphology, decoupled from geometric constraints. We then show emergent trends in organoid tissue proportions that depend on initial progenitor cell composition. These include higher nephron and stromal cell representation in mosaic NP/UB organoids vs. NP-only organoids and a "goldilocks" initial cell ratio in mosaic organoids that optimizes the formation of proximal tubule structures.

Sample-Wise and Gene-Wise Comparisons Confirm a Greater Similarity of RNA and Protein Expression Data at the Level of Molecular Pathways and Suggest an Approach for the Data Quality Check in High-Throughput Expression Databases.

Identification of genes and molecular pathways with congruent profiles in the proteomic and transcriptomic datasets may result in the discovery of promising transcriptomic biomarkers that would be more relevant to phenotypic changes. In this study, we conducted comparative analysis of 943 paired RNA and proteomic profiles obtained for the same samples of seven human cancer types from The Cancer Genome Atlas (TCGA) and NCI Clinical Proteomic Tumor Analysis Consortium (CPTAC) [two major open human cancer proteomic and transcriptomic databases] that included 15,112 protein-coding genes and 1611 molecular pathways. Overall, our findings demonstrated statistically significant improvement of the congruence between RNA and proteomic profiles when performing analysis at the level of molecular pathways rather than at the level of individual gene products. Transition to the molecular pathway level of data analysis increased the correlation to 0.19-0.57 (Pearson) and 0.14-057 (Spearman), or 2-3-fold for some cancer types. Evaluating the gain of the correlation upon transition to the data analysis the pathway level can be used to refine the omics data by identifying outliers that can be excluded from the comparison of RNA and proteomic profiles. We suggest using sample- and gene-wise correlations for individual genes and molecular pathways as a measure of quality of RNA/protein paired molecular data. We also provide a database of human genes, molecular pathways, and samples related to the correlation between RNA and protein products to facilitate an exploration of new cancer transcriptomic biomarkers and molecular mechanisms at different levels of human gene expression.

Green and Versatile High Throughput Microwell Oxidation-Based Spectrophotometric Methods for Determination of Galidesivir in Capsules.

BACKGROUND: Galidesivir (GDV) is a promising new antiviral drug for the potent and safe treatment of a broad spectrum of viral diseases, including COVID-19. In the literature, no analytical method exists for the determination of GDV in bulk and dosage form. OBJECTIVE: The aim of this study was the development of versatile green and simple microwell spectrophotometric methods (MW-SPMs) for the determination of GDV in its bulk form and capsules. METHODS: Three MW-SPMs were developed involving the oxidation of GDV by ammonium metavanadate (AMV), chromium trioxide (CTO), and potassium iodate (PIO) in an acid medium. The reactions were carried out in 96-well plates at room temperature and the absorbances of chromogenic reaction products were measured by an absorbance microplate reader at 780, 595, and 475 nm for AMV, CTO, and PIO, respectively. Variables influencing the reactions were carefully investigated and optimized. RESULTS: Linear relations with excellent correlation coefficients (0.9991-0.9997) were found between the absorbances and GDV concentrations in a range of 25-500 µg/mL. The limits of detection were ≥8.3 µg/mL. The accuracy and precision of the three MW-SPMs were confirmed by recovery and replicate analysis, respectively. The recovery values were 98.6-101.2% and the relative standard deviations were ≤1.02%. The proposed MW-SPMs were successfully applied to the analysis of GDV in bulk drug and capsules with high accuracy and precisions. The greenness of MW-SPMs was confirmed by three comprehensive metric tools. CONCLUSIONS: The proposed MW-SPMs combined the inherent advantages of microwell-based analysis and the use of common laboratory reagents for the involved reaction. These advantages include high-throughput, readily automation, reduced samples/reagents volume, precise measurements, and versatility. The advantages of the use of common laboratory reagents include availability, consistency, compatibility, safety, and cost-effectiveness. HIGHLIGHTS: Overall, the proposed MW-SPMs are versatile valuable tools for the quantitation of GDV during its pharmaceutical manufacturing.

Data processing for high-throughput mass spectrometry in drug discovery.

INTRODUCTION: High-throughput mass spectrometry that could deliver > 10 times faster sample readout speed than traditional LC-based platforms has emerged as a powerful analytical technique, enabling the rapid analysis of complex biological samples. This increased speed of MS data acquisition has brought a critical demand for automatic data processing capabilities that should match or surpass the speed of data acquisition. Those data processing capabilities should serve the different requirements of drug discovery workflows. AREAS COVERED: This paper introduced the key steps of the automatic data processing workflows for high-throughput MS technologies. Specific examples and requirements are detailed for different drug discovery applications. EXPERT OPINION: The demand for automatic data processing in high-throughput mass spectrometry is driven by the need to keep pace with the accelerated speed of data acquisition. The seamless integration of processing capabilities with LIMS, efficient data review mechanisms, and the exploration of future features such as real-time feedback, automatic method optimization, and AI model training is crucial for advancing the drug discovery field. As technology continues to evolve, the synergy between high-throughput mass spectrometry and intelligent data processing will undoubtedly play a pivotal role in shaping the future of high-throughput drug discovery applications.

Development of a High-Throughput Analytical Method for Antimicrobials in Wastewater Using an Automated Pipetting and Solid-Phase Extraction System.

Antimicrobial resistance (AMR) has emerged and spread globally. Recent studies have also reported the presence of antimicrobials in a wide variety of aquatic environments. Conducting a nationwide monitoring survey of AMR in the environment to elucidate its status and to assess its impact on ecosystems and human health is of social importance. In this study, we developed a novel high-throughput analysis (HTA) system based on a 96-well plate solid-phase extraction (SPE), using automated pipetting and an SPE pre-treatment system. The effectiveness of the system as an HTA for antimicrobials in environmental water was verified by comparing it with a conventional manual analytical system in a domestic hospital over a period of two years and four months. The results of the manual analysis and HTA using a combination of automated pipetting and SPE systems were generally consistent, and no statistically significant difference was observed (p > 0.05) between the two systems. The agreement ratios between the measured concentrations based on the conventional and HTA methods were positively correlated with a correlation coefficient of r = 0.99. These results indicate that HTA, which combines automated pipetting and an SPE pre-treatment system for rapid, high-volume analysis, can be used as an effective approach for understanding the environmental contamination of antimicrobials at multiple sites. To the best of our knowledge, this is the first report to present the accuracy and agreement between concentrations based on a manual analysis and those measured using HTA in hospital wastewater. These findings contribute to a comprehensive understanding of antimicrobials in aquatic environments and assess the ecological and human health risks associated with antimicrobials and antimicrobial-resistant bacteria to maintain the safety of aquatic environments.

A GC×GC-MS method based on solid-state modulator for non-targeted metabolomics: Comparison with traditional GC-MS method.

The formidable challenge posed by the presence of extremely high amounts of compounds and large differences in concentrations in plasma significantly complicates non-targeted metabolomics analyses. In this study, a comprehensive two-dimensional gas chromatography-quadrupole mass spectrometry (GC×GC-qMS) method with a solid-state modulator (SSM) for non-targeted metabolomics in beagle plasma was first established based on a GC-MS method, and the qualitative and quantitative performance of the two platforms were compared. Identification of detected compounds was accomplished utilizing NIST database match scores, retention indices (RIs) and standards. Semi-quantification involved the calculation of peak area ratios to internal standards. Metabolite identification sheets were generated for plasma samples on both analytical platforms, featuring 22 representative metabolites chosen for validating qualitative accuracy, and for conducting comparisons of linearity, accuracy, precision, and sensitivity. The outcomes revealed a threefold increase in the number of identifiable metabolites on the GC×GC-MS platform, with lower limits of quantitation (LLOQs) reduced to 0.5-0.05 times those achieved on the GC-MS platform. Accuracy in quantification for both GC×GC-MS and GC-MS fell within the range of 85-115%, and the vast majority of intra- and inter-day precisions were within the range of 20%. These findings underscore that relative to the conventional GC-MS method, the GC×GC-MS method developed in this study, combined with SSM, exhibits enhanced qualitative capabilities, heightened sensitivity, and comparable accuracy and precision, rendering it more suitable for non-targeted metabolomics analyses.

A high-throughput DNA analysis method based on isothermal amplification on a suspension microarray for detecting mpox virus and viruses with comparable symptoms.

BACKGROUND: Mpox is a zoonotic disease caused by mpox virus (MPXV) infection. Since May 2022, there has been a marked increase in human mpox cases in different regions. Rash, fever, and sore throat are typical signs of mpox. However, other viruses, such as the B virus (BV), herpes simplex virus types 1 (HSV-1), herpes simplex virus types 2 (HSV-2), and varicella zoster virus (VZV), can also infect people and cause comparable symptoms. Therefore, clinical symptoms and signs alone make distinguishing MPXV from these viruses difficult. RESULTS: In this study, we combined suspension microarray technology with recombinase-aided amplification technology (RAA) to establish a high-throughput, sensitive, and quantitative method for detecting MPXV and other viruses that can cause similar symptoms. The experimental results confirmed that the technique has outstanding sensitivity, with a minimum detection limit (LOD) of 0.1 fM and a linear range of 0.3 fM to 20 pM, spanning five orders of magnitude. The approach also exhibits exquisite selectivity, as the amplified signal can only be detected when the target virus nucleic acid is present. Additionally, serum recoveries ranging from 80.52% to 119.09% suggest that the detection outcomes are generally considered reliable. Moreover, the time required for detection using this high-throughput method is very short. After DNA extraction, the detection signal amplified by isothermal amplification on the bead array can be obtained in just 1 h. SIGNIFICANCE AND NOVELTY: Our research introduces a new technique that utilizes suspension microarray technology and isothermal amplification to create a high-throughput nucleic acid assay. This innovative method offers multiple benefits compared to current techniques, such as being cost-effective, time-efficient, highly sensitive, and having high throughput capabilities. Furthermore, the assay is applicable not only for detecting MPXV and viruses with similar symptoms, but also for clinical diagnostics, food safety, and environmental monitoring, rendering it an effective tool for screening harmful microorganisms.

A long-term storable gel-laden chip composite built in a multi-well plate enablingin situcell encapsulation for high-throughput liver model.

Hydrogels are widely used as scaffold materials for constructingin vitrothree-dimensional microphysiological systems. However, their high sensitivity to various external cues hinders the development of hydrogel-laden, microscale, and high-throughput chips. Here, we have developed a long-term storable gel-laden chip composite built in a multi-well plate, which enablesin situcell encapsulation and facilitates high-throughput analysis. Through optimized chemical crosslinking and freeze-drying method (C/FD), we have achieved a high-quality of gel-laden chip composite with excellent transparency, uniform porosity, and appropriate swelling and mechanical characteristics. Besides collagen, decellularized extracellular matrix with tissue-specific biochemical compound has been applied as chip composite. As a ready-to-use platform,in situcell encapsulation within the gel has been achieved through capillary force generated during gel reswelling. The liver-mimetic chip composite, comprising HepG2 cells or primary hepatocytes, has demonstrated favorable hepatic functionality and high sensitivity in drug testing. The developed fabrication process with improved stability of gels and storability allows chip composites to be stored at a wide range of temperatures for up to 28 d without any deformation, demonstrating off-the-shelf products. Consequently, this provides an exceptionally simple and long-term storable platform that can be utilized for an efficient tissue-specific modeling and various biomedical applications.

Cellular nucleus image-based smarter microscope system for single cell analysis.

Cell imaging technology is undoubtedly a powerful tool for studying single-cell heterogeneity due to its non-invasive and visual advantages. It covers microscope hardware, software, and image analysis techniques, which are hindered by low throughput owing to abundant hands-on time and expertise. Herein, a cellular nucleus image-based smarter microscope system for single-cell analysis is reported to achieve high-throughput analysis and high-content detection of cells. By combining the hardware of an automatic fluorescence microscope and multi-object recognition/acquisition software, we have achieved more advanced process automation with the assistance of Robotic Process Automation (RPA), which realizes a high-throughput collection of single-cell images. Automated acquisition of single-cell images has benefits beyond ease and throughout and can lead to uniform standard and higher quality images. We further constructed a single-cell image database-based convolutional neural network (Efficient Convolutional Neural Network, E-CNN) exceeding 20618 single-cell nucleus images. Computational analysis of large and complex data sets enhances the content and efficiency of single-cell analysis with the assistance of Artificial Intelligence (AI), which breaks through the super-resolution microscope's hardware limitation, such as specialized light sources with specific wavelengths, advanced optical components, and high-performance graphics cards. Our system can identify single-cell nucleus images that cannot be artificially distinguished with an accuracy of 95.3%. Overall, we build an ordinary microscope into a high-throughput analysis and high-content smarter microscope system, making it a candidate tool for Imaging cytology.

Everything, everywhere, almost at once.

A new platform that can follow the movement of individual proteins inside millions of cells in a single day will help contribute to existing knowledge of cell biology and identify new therapeutics.

Beagle: a near-edge X-ray absorption fine-structure spectroscopy data processing solution for beamline experiments at Pohang Accelerator Laboratory.

Near-edge X-ray absorption fine-structure (NEXAFS) spectroscopy is a powerful tool for identifying chemical bonding states at synchrotron radiation facilities. Advances in new materials require researchers in both academia and industry to measure tens to hundreds of samples during the available beam time on a synchrotron beamline, which is typically allocated to users. Automated measurement methods, along with analysis software, have been developed for beamlines. Automated measurements facilitate high-throughput experiments and accumulate vast amounts of measured spectral data. The analysis software supports various functions for analyzing the experimental data; however, these analysis methods are complicated, and learning them can be time-consuming. To process large amounts of spectral data, a new analysis software, dedicated to NEXAFS spectroscopy, that is easy to use and can provide results in a short time is desired. Herein, the development of Beagle is described, software calculating molecular orientation from NEXAFS spectroscopy data that can report results in a short time comparable with that required to measure one sample at the beamline. It was designed to progress in a single sequence from data loading to the printing of the results with a `click of a button'. The functions of the software include recognizing the dataset, correcting the background, normalizing the plot, calculating the electron yield and determining the molecular orientation. The analysis results can be saved as {\tt{.txt}} files (spectral data), {\tt{.pdf}} files (graphic images) and Origin files (spectral data and graphic images).

Recent advances in analytical techniques for high throughput experimentation.

High throughput experimentation is a growing and evolving field that allows to execute dozens to several thousands of experiments per day with relatively limited resources. Through miniaturization, typically a high degree of automation and the use of digital data tools, many parallel reactions or experiments at a time can be run in such workflows. High throughput experimentation also requires fast analytical techniques capable of generating critically important analytical data in line with the increased rate of experimentation. As traditional techniques usually do not deliver the speed required, some unique approaches are required to enable workflows to function as designed. This review covers the recent developments (2019-2020) in this field and was intended to give a comprehensive overview of the current "state-of-the-art."

Applications of ambient ionization mass spectrometry in 2020: An annual review.

Recent developments in mass spectrometry (MS) analyses have seen a concerted effort to reduce the complexity of analytical workflows through the simplification (or removal) of sample preparation and the shortening of run-to-run analysis times. Ambient ionization mass spectrometry (AIMS) is an exemplar MS-based technology that has swiftly developed into a popular and powerful tool in analytical science. This increase in interest and demonstrable applications is down to its capacity to enable the rapid analysis of a diverse range of samples, typically in their native state or following a minimalistic sample preparation approach. The field of AIMS is constantly improving and expanding, with developments of powerful and novel techniques, improvements to existing instrumentation, and exciting new applications added with each year that passes. This annual review provides an overview of applications of AIMS techniques over the past year (2020), with a particular focus on the application of AIMS in a number of key fields of research including biomedical sciences, forensics and security, food sciences, the environment, and chemical synthesis. Novel ambient ionization techniques are introduced, including picolitre pressure-probe electrospray ionization and fiber spray ionization, in addition to modifications and improvements to existing techniques such as hand-held devices for ease of use, and USB-powered ion sources for on-site analysis. In all, the information provided in this review supports the view that AIMS has become a leading approach in MS-based analyses and that improvements to existing methods, alongside the development of novel approaches, will continue across the foreseeable future.

Establishment of a high-throughput analysis method for the identification of key proteases of peptide drugs in vitro / 中国药科大学学报

@#In order to establish an effective analytical method to detect the key proteases which affect the metabolism and the plasma half-life of peptides in vivo, a method to analyze the key proteases of peptide drugs based on high performance liquid chromatography in vitro was established. The general enzymatic reaction conditions in vitro were as follows: pH was 7. 8 or 9. 0 and the buffer system was 0. 01 mol/L PBS or 50 mmol/L Tris buffer. The results of pramlintide detected by this method showed that kallikrein-related peptidase 5 and dipeptidyl peptidase 4 had the strongest hydrolysis on pramlintide. The result was consistent with that determined by microscale thermophoresis, which indicated that kallikrein-related peptidase 5 and dipeptidyl peptidase 4 were the key proteases of pramlintide. This analytical method provides the basis for high-throughput stability screening of peptides and can be used to analyze key proteases of peptide drugs. It can also provide guidance for optimizing the protease stability of peptide drugs.