High Throughput Measurement
High Throughput Measurement - Related Videos
09:44
High-throughput Screening for Chemical Modulators of Post-transcriptionally Regulated Genes
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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.
08:58
High-Throughput Measurement and Classification of Organic P in Environmental Samples
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06:19
High-Throughput Screening to Obtain Crystal Hits for Protein Crystallography
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Cited by 16 •
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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.
07:07
A High-throughput Method for Measurement of Glomerular Filtration Rate in Conscious Mice
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Cited by 65 •
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11:03
High Throughput Measurement of Extracellular DNA Release and Quantitative NET Formation in Human Neutrophils In Vitro
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FAQs
High Throughput Measurement gains efficiency by coordinating parallelized workflows, miniaturized assays, multiplexed detection, automation, and computational processing. Parallelization lets many samples or experimental conditions be handled together, while multiplexing captures several biological measurements within the same analytical framework. Computation then organizes the resulting large dataset so researchers can compare responses systematically rather than examining each condition in isolation.
In immunology and infection studies, multiplexed detection can capture several immune or infection-related features across the same set of samples or conditions. This supports parallel comparison of immune-cell responses, antibody production, cytokine production, pathogen growth, and treatment effects. The resulting measurements help distinguish patterns among experimental groups and reveal how multiple biological responses vary together.
The main influences are the samples selected, the experimental conditions compared, and the biological features measured. High Throughput Measurement can expose response variability across diverse samples, but meaningful interpretation depends on comparing those samples and conditions systematically. Including treatment effects, immune responses, pathogen growth, or production of antibodies and cytokines changes which patterns the dataset can reveal.
Computational processing is needed because parallelized and multiplexed experiments generate large datasets rather than a single result. It organizes measurements for comparison across samples and conditions, allowing researchers to examine response variability and relate immune or infection-associated features to broader disease mechanisms and clinical outcomes. Without this comparative step, the scale of collected information would be difficult to use systematically.
A typical workflow combines parallelized sample or condition handling with miniaturized assays, automated operations, multiplexed detection, and computational analysis. Researchers structure measurements across selected samples or experimental conditions, collect multiple biological signals, and then process the dataset to compare responses. The workflow can therefore be adapted to immune-cell activity, antibody or cytokine production, pathogen growth, or treatment effects.
Researchers apply these measurements when they need to examine many biological samples or experimental conditions in immunology and infection research. Supported uses include biomarker identification, host-pathogen studies, drug screening, and analysis of response variability. The approach is especially useful when investigators want to compare treatment effects or connect immune measurements with disease mechanisms and clinical outcomes.
These datasets can reveal patterns in immune-cell responses, antibody or cytokine production, pathogen growth, and treatment effects across diverse samples. Such patterns may support biomarker identification and help characterize host-pathogen relationships. They also provide a basis for studying why responses vary and for linking complex immune measurements with disease mechanisms or clinical outcomes.