High-throughput Detection
High-throughput Detection - 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.
10:05
High-throughput Detection Method for Influenza Virus
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Cited by 8 •
2012
This method describes the use of Infrared dye based imaging system for detection of H1N1 in bronchioalveolar lavage (BAL) fluid of infected mice at a high sensitivity. This methodology can be performed in a 96- or 384-well plate, requires <10 μl volume of test material and has the potential for concurrent screening of multiple pathogens.
11:00
High-throughput Detection of Respiratory Pathogens in Animal Specimens by Nanoscale PCR
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Cited by 11 •
2016
High-throughput testing of DNA and RNA based pathogens by nanoscale PCR is described using a syndromic canine and equine respiratory PCR panel.
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.
12:49
Detection of miRNA Targets in High-throughput Using the 3'LIFE Assay
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Cited by 8 •
2015
Luminescent identification of functional elements in 3’ untranslated regions (3’UTRs) (3’LIFE) is a technique to identify functional regulation in 3’UTRs by miRNAs or other regulatory factors. This protocol utilizes high-throughput methodology such as 96-well transfection and luciferase assays to screen hundreds of putative interactions for functional repression.
FAQs
Automated liquid handling moves samples and reagents through many measurements with less hands-on intervention, while microplates and microfluidic formats organize those measurements in compact, parallel arrangements. Together, these components support rapid processing of large experimental libraries and can reduce the sample volume required for each analysis, making broad screening more practical in bioengineering studies.
Optical and electrical sensors convert biological, chemical, or physical events into measurable signals. Their outputs provide the basis for identifying targets, quantifying responses, or comparing samples across an experiment. Selecting an appropriate signal type allows the same general workflow to examine different materials, biomolecules, cells, or engineered systems.
Software processes the signals generated across many samples and organizes them for identification, quantification, or comparison. This computational step helps researchers evaluate large libraries systematically rather than interpreting measurements one at a time. The resulting comparisons can reveal promising candidates or engineered designs that merit more detailed follow-up analysis.
Effective large-scale screening depends on coordinating the sample format, liquid-handling workflow, sensor type, and software analysis. These elements must support measurements across many samples while limiting sample use and manual effort. Their integration determines whether researchers can rapidly compare the members of an experimental library and extract results suitable for selecting candidates.
A typical workflow begins by arranging samples in a microplate or microfluidic format, followed by automated liquid handling and signal measurement with optical or electrical sensors. Software then processes the resulting data to identify, quantify, or compare targets. Candidates or engineered systems showing useful results can subsequently undergo more detailed study.
Researchers choose this approach when they need to evaluate many biomolecules, cells, materials, or engineered systems efficiently. It is especially relevant to drug discovery, biomarker analysis, diagnostic development, and optimization of biological designs. By supporting rapid comparison of large libraries, the method helps narrow broad experimental programs to candidates for deeper investigation.
The measurements can identify targets, quantify signals, and compare responses across numerous samples. In drug discovery, these results help locate promising candidates within a large library. For biomarker and diagnostic work, the same capabilities support analysis of biological signals and comparison among samples, contributing to development and evaluation of diagnostic approaches.