Automated High-content Screening
Automated 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.
07:50
Facilitating Drug Discovery: An Automated High-content Inflammation Assay in Zebrafish
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Cited by 39 •
2012
Here we describe a novel high-content chemically induced inflammation assay aiming at the identification of immune-modulatory bioactives. We have successfully combined automated microscopy with custom developed software scripts enabling automated quantification of the inflammatory response as well as further data processing, analysis, mining, and storage.
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.
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.
FAQs
Image-analysis algorithms turn microscopy data into comparable cellular measurements by identifying individual cells, quantifying fluorescent markers, and evaluating morphology, subcellular localization, viability, or pathogen burden. Measuring these features together is important because a treatment or genetic perturbation may alter several aspects of a cell simultaneously. The resulting multiparametric profile helps connect visible phenotypes with possible biological mechanisms.
Standardized imaging conditions reduce variation introduced during acquisition, allowing differences among samples to be interpreted as experimental effects rather than inconsistent image capture. Automated sample handling applies the same operations across many samples. In Automated High-content Screening, this consistency supports systematic comparisons of immune-cell states, infection-related changes, and responses to experimental treatments across large sample sets.
Fluorescent markers provide molecularly linked signals, whereas morphology, localization, and viability describe broader cellular states. Combining these readouts lets a screen relate marker changes to structural or survival-related consequences instead of relying on a single measurement. For infection studies, pathogen burden can be evaluated alongside host-cell features, helping characterize how infection and cellular responses change together.
Linking image-derived phenotypes to genetic perturbations or experimental treatments is central to mechanism discovery. If a perturbation produces a distinctive combination of marker, morphology, localization, viability, or pathogen-burden changes, investigators can use that pattern to identify biologically informative responses. This design also supports biomarker identification by highlighting measurements that distinguish cellular states or treatment outcomes.
A typical workflow begins with robotic sample handling, followed by fluorescent imaging under standardized conditions and computational processing of captured images. The analysis identifies cells and extracts multiple features, after which measurements are compared across treatments or genetic perturbations. This sequence converts large image collections into structured phenotypic data for prioritizing informative experimental conditions.
In immunology, the approach can assess immune-cell activation by measuring coordinated changes in cellular markers and appearance. In infection research, it can track host-pathogen interactions, infection dynamics, and pathogen burden while also recording effects on host-cell viability or morphology. These parallel measurements provide a broader view of how immune cells and infectious processes respond to perturbation.
For therapeutic-response studies, researchers can compare cellular phenotypes across candidate treatments and identify conditions associated with altered pathogen burden or host responses, when those measurements are included in the assay. The same strategy can help prioritize candidate drugs or vaccine strategies by linking treatment exposure to measurable cellular outcomes across many samples.