SLAS Technology最新文献

Automation of diagnostic assays generally aims to increase reproducibility and throughput while decreasing human errors and hands-on time. Here, we introduce a protocol for the automated chemical conjugation of glycans to color-coded magnetic beads using the KingFisher Flex magnetic particle processor. The resulting glycan-coupled magnetic beads allow the detection of anti-glycan antibodies of different isotypes from various species. By generating anti-glycan antibody profiles, monoclonal antibodies can be screened for their specificity and cross-reactivity, while anti-glycan antibody profiles from different human body fluids can aid in predicting response to treatment or outcome of disease. This efficient, scalable protocol can also be adapted to attach proteins and other biomolecules to beads, making it useful for a wider range of applications that require bead-based laboratory methods.

This study aimed to develop a streamlined method for evaluating the dilution ratio of drug dose–response plates created by automated liquid handlers in the early stages of drug discovery. The quantitative techniques commonly used for this purpose have restrictions due to their limited linear dynamic range and inaccuracies in assessing serial dilution performance. To address this challenge, we describe a method based on acoustic ejection mass spectrometry (AEMS). The method involves using standard compounds and an internal standard to evaluate each dilution point in quality control (QC) plates. The samples are transferred to a chromatography-free tandem mass spectrometry system through an acoustic source, enabling the analysis of one sample per three seconds from a microtiter plate. This approach provides precise, accurate, label-free, and rapid data acquisition to support high-throughput screening efforts.

Transepithelial electrical resistance (TEER) is a widely used technique for quantifying the permeability of epithelial and endothelial cell layers. However, traditional methods of measuring TEER are limited to single timepoint measurements and can subject cells to an altered environment during the measurement. Here, we assessed the validity of TEER measurements by the ECIS TEER96 device, which is designed to take continuous TEER measurements of a cell culture system in a standard laboratory incubator. We found that the instrument accurately measures TEER across TEER values ranging from 10 to 2050 Ω*cm² and is more accurate than the manual epithelial voltohmmeter electrode at high TEER values. Furthermore, the high-resolution measurements provided by the device allowed for a unique insight into the mechanisms and kinetics of cells in vitro. To demonstrate the continuous measurement capability of the device, we tracked the formation of an MDCKI cell monolayer until TEER plateaued. Furthermore, we treated Caco-2 monolayers with different concentrations of DMSO and the antimicrobial and surfactant compound benzethonium chloride to measure disruptions to barrier integrity. Treatment of both compounds resulted in concentration-dependent loss of barrier integrity. Our results suggest that the ECIS TEER96 device is a reliable and convenient option for measuring TEER in cell cultures and can provide valuable insights into the behavior of cells in vitro. This technology will be especially useful for increasing throughput of drug permeability assays, inflammation studies, and gaining better understanding of disease states in a cell culture system.

Background

Skin cutaneous melanoma (SKCM) is one of the fastest developing malignancies with strong aggressive ability and no proper curative treatments. Numerous studies illustrated the importance of N6-methyladenosine (m6A) RNA modification to tumorigenesis. The aim of this study was to identify novel prognostic signature by using m6A-related lncRNAs, thus to improve the survival for SKCM patients and guide SKCM therapy.

Methods

We downloaded the Presentational Matrix data from The Cancer Genome Atlas (TCGA) and analyzed all the expressed lncRNAs among 468 SKCM samples. Pearson correlation analysis was performed to assess the correlations between lncRNAs and 29 m6A-related genes. Least absolute shrinkage and selection operator (LASSO), univariate and multivariate Cox regression analysis were performed to construct m6A-related lncRNAs prognostic signature (m6A-LPS). The accuracy and prognostic value of this signature were validated by using receiver operating characteristic (ROC) curves, Kaplan-Meier (K-M) survival analysis, univariate COX or multivariate COX analyses. After calculating risk scores, patients were divided into low- and high-risk subgroups by the median value of risk scores.

Results

A total of 2973 lncRNAs were found expressed among SKCM tissues. Prognostic analysis showed that 98 lncRNAs had a significant effect on the survival of SKCM patients. The m6A-LPS was validated using K-M and ROC analysis and the predictive accuracy of the risk score was also high according to the AUC of the ROC curve in training and testing sets. A nomogram based on tumor stage, gender and risk score that had a strong ability to forecast the 1-, 2-, 3-, 5-year OS of SKCM patients confirmed by calibrations. Enrichment analysis indicated that malignancy-associated biological processes and pathways were more common in the high-risk subgroup.

Conclusion

Collectively, m6A-related lncRNAs exert as potential biomarkers for prognostic stratification of SKCM patients and may assist clinicians achieving individualized treatment for SKCM.

The Droplet Microarray (DMA) has emerged as a tool for high-throughput biological and chemical applications by enabling miniaturization and parallelization of experimental processes. Due to its ability to hold hundreds of nanoliter droplets, the DMA enables simple screening and analysis of samples such as cells and biomolecules. However, handling of nanoliter volumes poses a challenge, as manual recovery of nanoliter volumes is not feasible, and traditional laboratory equipment is not suited to work with such low volumes, and small array formats. To tackle this challenge, we developed the Automated Nanoliter Droplet Selection device (ANDeS), a robotic system for automated collection and transfer of nanoliter samples from DMA.

ANDeS can automatically collect volumes from 50 to 350 nL from the flat surface of DMA with a movement accuracy of ±30 µm using fused silica capillaries. The system can automatically collect and transfer the droplets from DMA chip into other platforms, such as microtiter plates, conical tubes or another DMA. In addition, to ensure high throughput and multiple droplet collection, the uptake of multiple droplets within a single capillary, separated by air gaps to avoid mixing of the samples within the capillary, was optimized and demonstrated.

This study shows the potential of ANDeS in laboratory applications by using it for the collection and transfer of biological samples, contained in nanoliter droplets, for subsequent analysis. The experimental results demonstrate the ability of ANDeS to increase the versatility of the DMA platform by allowing for automated retrieval of nanoliter samples from DMA, which was not possible manually on the level of individual droplets. Therefore, it widens the variety of analytical techniques that can be used for the analysis of content of individual droplets and experiments performed using DMA. Thus, ANDeS opens up opportunities to expand the development of miniaturized assays in such fields as cell screening, omics analysis and combinatorial chemistry.

3D cell culture models are important tools in translational research but have been out of reach for high-throughput screening due to complexity, requirement of large cell numbers and inadequate standardization. Microfluidics and culture model miniaturization technologies could overcome these challenges. Here, we present a high-throughput workflow to produce and characterize the formation of miniaturized spheroids using deep learning. We train a convolutional neural network (CNN) for cell ensemble morphology classification for droplet microfluidic minispheroid production, benchmark it against more conventional image analysis, and characterize minispheroid assembly determining optimal surfactant concentrations and incubation times for minispheroid production for three cell lines with different spheroid formation properties. Notably, this format is compatible with large-scale spheroid production and screening. The presented workflow and CNN offer a template for large scale minispheroid production and analysis and can be extended and re-trained to characterize morphological responses in spheroids to additives, culture conditions and large drug libraries.

Human induced pluripotent stem cells (hiPSCs) have demonstrated great promise for a variety of applications that include cell therapy and regenerative medicine. Production of clinical grade hiPSCs requires reproducible manufacturing methods with stringent quality-controls such as those provided by image-controlled robotic processing systems. In this paper we present an automated image analysis method for identifying and picking hiPSC colonies for clonal expansion using the CellX^TM robotic cell processing system. This method couples a light weight deep learning segmentation approach based on the U-Net architecture to automatically segment the hiPSC colonies in full field of view (FOV) high resolution phase contrast images with a standardized approach for suggesting pick locations. The utility of this method is demonstrated using images and data obtained from the CellX^TM system where clinical grade hiPSCs were reprogrammed, clonally expanded, and differentiated into retinal organoids for use in treatment of patients with inherited retinal degenerative blindness.

Rapid and accurate measurements of immune protein markers are essential for diagnosis and treatment in all clinical settings. The recent pandemic has revealed a stark need for developing new tools and assays that could be rapidly used in diverse settings and provide useful information to clinicians. Here, we describe the development and test application of a novel one-step CRP/IP-10 duplex assay for the LightDeck platform capable of delivering reproducible and accurate measurements in under eight minutes. We used the optimized assay to measure CRP and IP-10 levels in human blood and serum samples from healthy, SARS-CoV-2 (COVID-19) positive, and influenza-like illness (ILI) presenting patients. Our results agreed with previously published analyte levels and enabled us to make statistically significant comparisons relevant to multiple clinical parameters. Our duplex assay is a simple and powerful tool for aiding prognostic decision-making in diverse settings.