High-content Imaging
High-content Imaging - Related Videos
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.
14:10
A High Content Imaging Assay for Identification of Botulinum Neurotoxin Inhibitors
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Cited by 3 •
2014
Botulinum neurotoxin is one of the most potent toxins among Category-A biothreat agents, yet a post-exposure therapeutic is not available. The high content imaging approach is a powerful methodology for identifying novel inhibitors as it enables multiparameter screening using biologically relevant motor neurons, the primary target of this toxin.
12:11
A Rhodopsin Transport Assay by High-Content Imaging Analysis
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Cited by 1 •
2019
Here, we described a high-content imaging method to quantify the transport of rhodopsin mutants associated with retinitis pigmentosa. A multiple-wavelength scoring analysis was used to quantify rhodopsin protein on the cell surface or in the whole cell.
11:37
Cellular Redox Profiling Using High-content Microscopy
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Cited by 7 •
2017
This paper presents a high-content microscopy workflow for simultaneous quantification of intracellular ROS levels, as well as mitochondrial membrane potential and morphology – jointly referred to as mitochondrial morphofunction – in living adherent cells using the cell-permeant fluorescent reporter molecules 5-(and-6)-chloromethyl-2',7'-dichlorodihydrofluorescein diacetate, acetyl ester (CM-H2DCFDA) and tetramethylrhodamine methylester (TMRM).
07:04
Determination of the Glycogen Content in Cyanobacteria
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Cited by 25 •
2017
Here, we present a reliable and easy assay to measure the glycogen content in cyanobacterial cells. The procedure entails precipitation, selectable depolymerization, and the detection of glucose residues. This method is suitable for both wildtype and genetically engineered strains and can facilitate the metabolic engineering of cyanobacteria.
FAQs
High-content imaging converts visual variation into comparable measurements through a linked imaging and analysis workflow. Robotic handling presents many experimental conditions to the microscope, while automated acquisition records images across selected channels or illumination modes. Computational analysis then extracts features such as cell number, morphology, protein localization, or organelle structure, allowing phenotypes to be compared quantitatively rather than judged only by eye.
Multichannel acquisition separates information from different fluorescent signals, while bright-field imaging provides a nonfluorescent imaging option. Combining image channels lets an experiment examine several cellular features in the same sample, such as morphology alongside protein localization or organelle structure. This multiplexed view helps connect changes in one feature with other measured characteristics under the same experimental condition.
Segmentation is the computational step that assigns image regions to individual cells, structures, or other analyzable objects. Its role is crucial because measurements such as cell number, morphology, and organelle structure depend on identifying relevant regions consistently. Once images are segmented, the system can summarize features across many samples and reveal subtle responses that manual visual inspection may miss.
Compared with conventional microscopy, the high-content approach emphasizes scale and standardized quantification. Instead of relying mainly on selected images or qualitative inspection, it evaluates large image sets across many experimental conditions and links visible phenotypes to numerical measurements. This design can improve reproducibility and make small biological responses easier to detect, particularly in screening or comparative experiments.
A typical workflow begins with robotic sample handling, followed by automated image capture using fluorescence, bright-field microscopy, or multiple channels. Computational segmentation then organizes the images into measurable regions, after which feature values are compared across experimental conditions. The resulting dataset can include cell counts, morphology, protein localization, and organelle structure, depending on the imaging design.
Researchers apply high-content imaging to phenotypic screening, drug discovery, and toxicity testing because it can compare many conditions while recording several cellular features. The method is also useful in disease modeling, where measurable changes in morphology, localization, or organelle structure can help characterize biological responses. Its value comes from connecting complex visual phenotypes with quantitative comparisons.
In biological techniques, this approach supports experiments that need both broad sampling and detailed cellular readouts. It can examine cells, tissues, or model organisms and relate changes in appearance to measurements collected across conditions. Such datasets help researchers evaluate cellular processes, identify responses that conventional microscopy may overlook, and interpret complex phenotypes using reproducible quantitative evidence.