High-throughput Research
High-throughput Research - Related Videos
09:44
High-throughput Screening for Chemical Modulators of Post-transcriptionally Regulated Genes
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2015
Here we describe a cell-based reporter gene assay as a valuable tool to screen chemical libraries for compounds modulating post-transcriptional control mechanisms exerted through 3’ UTR.
06:19
High-Throughput Screening to Obtain Crystal Hits for Protein Crystallography
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Cited by 16 •
2023
This protocol details high-throughput crystallization screening, ranging from the 1,536 microassay plate preparation to the end of a 6 week experimental time window. Details are included about the sample setup, the imaging obtained, and how users can perform analyses using an artificial intelligence-enabled graphical user interface to quickly and efficiently identify macromolecular crystallization conditions.
08:09
High-throughput Titration of Luciferase-expressing Recombinant Viruses
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Cited by 14 •
2014
This article presents a high-throughput luciferase expression-based method of titrating various RNA and DNA viruses using automated and manual liquid handlers.
08:10
Multi-enzyme Screening Using a High-throughput Genetic Enzyme Screening System
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Cited by 7 •
2016
This work presents a method of high-throughput screening using a universal genetic enzyme screening system that can be theoretically applied to over 200 enzymes. Here, the single screening system identifies three different enzymes (lipase, cellulase, and alkaline phosphatase) by simply changing the substrate used (p-nitrophenyl acetate, p-nitrophenyl-β-D-cellobioside, and phenyl phosphate).
06:50
A High-Throughput Electrochemiluminescence 7-Plex Assay Simultaneously Screening for Type 1 Diabetes and Multiple Autoimmune Diseases
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Cited by 3 •
2020
We model a simple multiplexed ECL assay that combines 7 autoantibody assays together. The assay is capable of screening for T1D and multiple other autoimmune diseases, simultaneously, including celiac disease, autoimmune thyroid disease, and autoimmune polyglandular syndrome 1.
FAQs
Several elements work together: miniaturized test systems reduce the scale of individual experiments, automated handling manages many measurements, and standardized workflows make results more consistent across the set. Computational analysis then organizes and compares the resulting data. This coordination matters because throughput alone is not sufficient; reliable comparison depends on controlling how experiments are performed and evaluated.
Parallel testing is valuable when researchers need to compare many variables, materials, or designs under a common experimental strategy. Running these evaluations together can reveal performance trends that may be difficult to see from isolated experiments. In engineering, those trends provide evidence for selecting promising candidates and directing later modeling, optimization, or more focused testing.
Compared with a lower-scale experimental approach, high-throughput research emphasizes breadth, repeatable execution, and rapid comparison across a large set of conditions or candidates. Its advantage is not simply producing more measurements, but creating a structured dataset that supports systematic analysis. The approach is therefore especially useful when the research question requires screening rather than examining only one design.
A typical workflow begins by defining the variables, materials, manufacturing conditions, or device architectures to compare. Researchers then arrange miniaturized tests, apply automated handling, and follow a standardized procedure for collecting measurements. Computational analysis brings the results together for comparison. The final dataset can support candidate selection, identify trends, and determine which options merit further investigation.
Engineering studies may apply the approach to materials, catalysts, manufacturing conditions, and device architectures, depending on the target technology. Miniaturized test systems and automated handling are central enabling components, while standardized workflows provide consistency between measurements. Together, these elements help researchers evaluate many candidate configurations while reducing the resources and development time required for sequential assessment.
In engineering, the method supports early-stage screening before substantial development effort is committed. Researchers can compare material candidates, catalyst options, processing conditions, or device designs and use the results to prioritize promising directions. Because the measurements are assembled into a larger dataset, the same work can also inform modeling and optimization, linking experimental evidence with next-generation technology design.