High-throughput Microscopy
High-throughput Microscopy - Related Videos
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.
06:19
High-Throughput Screening to Obtain Crystal Hits for Protein Crystallography
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Cited by 16 •
2023
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.
09:37
Optimized Negative Staining: a High-throughput Protocol for Examining Small and Asymmetric Protein Structure by Electron Microscopy
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Cited by 71 •
2014
More than half of proteins are small proteins (molecular mass <200 kDa) that are challenging for both electron microscope imaging and three-dimensional reconstructions. Optimized negative staining is a robust and high-throughput protocol to obtain high contrast and relatively high resolution (~1 nm) images of small asymmetric proteins or complexes under different physiological conditions.
15:29
Microscopy-based Assays for High-throughput Screening of Host Factors Involved in Brucella Infection of Hela Cells
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Cited by 6 •
2016
Two assays for microscopy-based high-throughput screening of host factors involved in Brucella infection are described. The entry assay detects host factors required for Brucella entry and the endpoint assay those required for intracellular replication. While applicable for alternative approaches, siRNA screening in HeLa cells is used to illustrate the protocols.
07:20
Fabrication of Micro-Patterned Chip with Controlled Thickness for High-Throughput Cryogenic Electron Microscopy
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2022
A newly developed micro-patterned chip with graphene oxide windows is fabricated by applying microelectromechanical system techniques, enabling efficient and high-throughput cryogenic electron microscopy imaging of various biomolecules and nanomaterials.
FAQs
Standardization makes measurements comparable across the many samples or fields examined. Keeping image acquisition consistent allows differences in neuronal morphology, synaptic structures, or cell populations to be interpreted as biological variation rather than changes caused by imaging conditions. This consistency is especially important when comparing genetic or pharmacological perturbations across large experiments.
Automated sample handling and motorized stage movement enable the system to examine many samples or fields of view without relying on manual repositioning for each image. This supports efficient collection of repeated measurements under standardized conditions. In neuroscience experiments, that capacity makes it practical to compare cellular features across numerous conditions or perturbations.
Computational image analysis converts acquired images into quantitative measurements of biological features. Rather than relying only on visual inspection, researchers can evaluate phenotypes such as neuronal morphology, synaptic structures, or cell populations across many observations. The resulting measurements support systematic comparisons between experimental conditions and help reveal cellular patterns that manual imaging may overlook.
A typical workflow brings together sample handling or motorized stage movement, standardized image acquisition, and computational analysis. The system first presents multiple samples or fields for imaging, then collects comparable images under consistent conditions, and finally measures selected cellular or tissue features. This sequence links automated data collection with quantitative interpretation at scale.
In neuroscience, the measurements can focus on neuronal morphology, synaptic structures, cell populations, and responses to genetic or pharmacological perturbations. Examining these features across many samples allows researchers to compare how experimental conditions affect cellular organization or behavior. The approach therefore connects large-scale imaging with quantitative analysis of neural phenotypes.
The approach supports disease modeling and drug screening by producing comparable measurements across many experimental conditions. Researchers can examine cellular phenotypes associated with disease models or evaluate responses to pharmacological perturbations using the same quantitative framework. Its scale also helps identify cellular patterns that could be difficult to recognize through manual imaging alone.