High-throughput Format
High-throughput Format - Related Videos
11:03
High Throughput Measurement of Extracellular DNA Release and Quantitative NET Formation in Human Neutrophils In Vitro
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Cited by 46 •
2016
High throughput assays are presented that in combination provide excellent tools to quantitate NET release from human neutrophils.
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.
05:51
Evaluating Leaf Responses to Microbial Secondary Metabolites Using A High-Throughput Format
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2025
This protocol describes a high-throughput approach for evaluating plant ion leakage, peroxidase activity, and callose production in the same sample.
09:59
A High-throughput Assay to Assess and Quantify Neutrophil Extracellular Trap Formation
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Cited by 8 •
2019
This protocol describes a highly sensitive and high throughput neutrophil extracellular trap (NET) assay for the semi-automated quantification of ex vivo NET formation by immunofluorescence three-dimensional confocal microscopy. This protocol can be used to evaluate NET formation and degradation after different stimuli and can be used to study potential NET-targeted therapies.
FAQs
Miniaturization allows each assay to use less reagent while preserving the ability to process many reactions in parallel. Automated liquid handling then transfers samples and reagents through a standardized workflow, reducing manual processing time and supporting consistent treatment across reactions. Together, these features increase sample capacity and make large experimental comparisons more practical.
Standardized workflows make conditions more uniform across the many samples or reactions processed in one experiment. This consistency supports reproducibility and allows researchers to compare results across biological samples, experimental conditions, or targets with greater confidence. In immunology and infection studies, standardization is especially useful when screening large sets of antibodies, immune responses, or potential interventions.
Quantitative readouts convert parallel assay reactions into measurements that can be compared systematically. Rather than relying only on individual observations, researchers can evaluate differences across many samples, conditions, or biological targets using the same measurement framework. This supports efficient identification of patterns, candidate responses, or promising targets within large experimental datasets.
A typical workflow combines miniaturized assays, automated liquid handling, standardized processing steps, and quantitative measurement. Samples or reactions are arranged so that many conditions can be handled in parallel, while automation manages reagent and sample transfers. The resulting measurements are then compared across the experiment, providing scalable data while limiting reagent consumption and processing time.
In immunology research, this approach can screen for pathogen-specific antibodies and measure responses from immune cells across many samples or conditions. Parallel processing makes it possible to examine numerous biological targets or response patterns efficiently, while standardized quantitative measurements support comparisons between samples. These capabilities help researchers evaluate immune activity at a broader scale.
In infection research, parallelized assays can examine host-pathogen interactions across many experimental conditions or biological targets. The same scalable strategy can support evaluation of candidate therapeutics and identification of diagnostic markers by generating comparable quantitative data from large sample sets. Its value lies in narrowing broad experimental possibilities into candidates suitable for further investigation.