High Throughput Phenotyping
High Throughput Phenotyping - Related Videos
07:23
High-throughput Method for Observing Motility Phenotypes in Pseudomonas aeruginosa
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2025
This protocol presents a rapid and efficient method for identifying genetic factors involved in various types of motilities in Pseudomonas aeruginosa.
15:28
A Microscopic Phenotypic Assay for the Quantification of Intracellular Mycobacteria Adapted for High-throughput/High-content Screening
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Cited by 30 •
2014
Here, we describe a phenotypic assay applicable to the High-throughput/High-content screens of small-interfering synthetic RNA (siRNA), chemical compound, and Mycobacterium tuberculosis mutant libraries. This method relies on the detection of fluorescently labeled Mycobacterium tuberculosis within fluorescently labeled host cell using automated confocal microscopy.
05:30
Mouse Eye Enucleation for Remote High-throughput Phenotyping
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Cited by 19 •
2011
The dissection technique illustrates enucleation of the mouse eye for tissue fixation to perform phenotyping in high-throughput screens.
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.
07:55
High Throughput Yeast Strain Phenotyping with Droplet-Based RNA Sequencing
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Cited by 3 •
2020
A bottleneck in the ‘design-build-test’ cycle of microbial engineering is the speed at which we can perform functional screens of strains. We describe a high-throughput method for strain screening applied to hundreds to thousands of yeast cells per experiment that utilizes droplet-based RNA sequencing.
FAQs
Parallelization allows many samples to be examined within a shared experimental framework, while consistent measurement reduces variation introduced by handling, imaging, recording, or behavioral testing. Together, these features make quantitative comparisons more reliable across samples and conditions. In neuroscience, that consistency is essential for detecting subtle changes in neuronal or disease-related phenotypes rather than confusing technical variation with biology.
Computational feature extraction converts observations from images, activity recordings, or behavioral assays into measurable characteristics that can be compared statistically. This approach supports analysis of complex traits that may be difficult to evaluate consistently by visual inspection alone. Researchers can then identify patterns across large datasets and distinguish conditions associated with changes in morphology, network activity, cellular responses, or behavior.
The approach can characterize several levels of neuronal biology, including neuronal morphology, network activity, cellular responses, and disease-related phenotypes. The relevant measurement depends on the experimental question and assay used. Examining these traits at scale helps researchers compare how genes, drugs, or environmental conditions influence distinct aspects of nervous-system biology, rather than relying on a single readout.
A high-throughput strategy emphasizes breadth, rapid measurement, and consistent comparison across many samples, whereas a smaller study may focus on intensive examination of fewer specimens or conditions. The broader sampling can reveal subtle or variable effects that are difficult to recognize in limited datasets. Findings from this stage can guide selection of mechanisms or phenotypes for more detailed follow-up.
A typical workflow begins by conducting parallelized experiments across many samples or conditions, followed by automated imaging, activity recording, or behavioral measurement. The resulting observations undergo computational feature extraction and statistical analysis. This sequence produces quantitative comparisons among phenotypes and conditions, allowing researchers to identify meaningful effects before investing in deeper mechanistic investigation.
It is particularly useful when researchers need to compare how many genes, drug conditions, or environmental exposures affect neuronal systems. Measuring multiple samples rapidly can expose subtle effects and disease-related patterns that may otherwise remain unnoticed. The resulting comparisons help prioritize promising phenotypes or candidate mechanisms for focused experiments, linking large-scale screening with subsequent neuroscience research.