High Content Screening
High Content Screening - Related Videos
13:32
High Content Screening in Neurodegenerative Diseases
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Cited by 8 •
2012
We describe a methodology combining automated cell culturing with high-content imaging to visualize and quantify multiple cellular processes and structures, in a high-throughput manner. Such methods can aid in the further functional annotation of genomes as well as identify disease gene networks and potential drug targets.
06:40
A Robust Method for the Large-Scale Production of Spheroids for High-Content Screening and Analysis Applications
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Cited by 4 •
2021
This protocol details a method for the production of three different types of spheroids in a manner that makes them suitable for large-scale high-content screening and analysis. In addition, examples are presented showing how they can be analyzed at spheroid and individual cell levels.
15:04
A Neuronal and Astrocyte Co-Culture Assay for High Content Analysis of Neurotoxicity
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Cited by 46 •
2009
This article describes a novel protocol and reagent set designed for sensitive measurement of neurotoxic effects of compounds and treatments on co-cultures of neurons and astrocytes using high content analysis. Results demonstrate that high content analysis represents an exciting novel technology for neurotoxicity assessment.
11:38
High Content Screening Analysis to Evaluate the Toxicological Effects of Harmful and Potentially Harmful Constituents (HPHC)
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Cited by 23 •
2016
The objective of the study was to assess the biological impact of 15 cigarette smoke constituents using a combination of an impedance-based real time cell analyzer and a high-content screening (HCS)-based platform for toxicological assessment in vitro. This study provides information on effective doses, toxicity and modes of action of the tested compounds.
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.
FAQs
The workflow links three analytical stages: automated image acquisition, object identification, and quantitative feature extraction. Images can be collected across many microplate wells under different experimental conditions, then analyzed consistently for cell number, morphology, protein localization, or viability. This integration makes cellular responses comparable across conditions rather than relying on an isolated measurement.
Multiple features capture different aspects of the same cellular response, such as changes in morphology occurring alongside altered protein localization or viability. Examining these measurements together can reveal complex phenotypic responses and relationships that a single assay signal may miss. This broader characterization helps distinguish cellular outcomes and supports more systematic biological comparisons.
Image-analysis software converts microscopy data into measurable biological variables by identifying objects in each image and quantifying their characteristics. Depending on the experiment, those characteristics may include cell number, morphology, protein localization, or viability. The resulting measurements transform large collections of images into datasets that can be compared across wells and experimental conditions.
A typical workflow begins with robotic application of experimental conditions to microplate wells. The system then acquires fluorescence or bright-field images from those wells, after which software identifies objects and measures selected cellular features. Researchers can compare the resulting measurements across conditions to characterize phenotypic responses and determine which observations warrant follow-up experiments.
The workflow uses microplate wells to organize experimental conditions, robotic systems to apply those conditions, and automated microscopy to capture cellular images. Both fluorescence and bright-field imaging are supported in the described approach. Image-analysis software then processes the acquired data, allowing cellular characteristics to be quantified consistently across the plate.
Researchers apply this approach when they need to examine complex cellular responses across many experimental conditions. Its supported uses include drug discovery, toxicity testing, functional genomics, disease modeling, and investigations of cellular pathways. The ability to measure several features in parallel makes it useful for comparing phenotypes systematically rather than evaluating only one biological readout.
The method produces quantitative datasets describing features such as cell number, morphology, protein localization, and viability under defined conditions. These data support systematic comparison of cellular phenotypes and can reveal relationships among measured features. Results can then guide follow-up experiments focused on particular responses, pathways, disease models, or candidate treatments.