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STED Microscopy Uncovers Signals Between Molecules

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Long known for its ability to effectively examine molecular structure, STED has been adapted to acquire spatial and temporal data, helping to unveil the mystery of intercellular communication.

DOUGLAS FARMER, SENIOR EDITOR [email protected]

Stimulation emission depletion (STED) microscopy is a mainstay in superresolution imaging, since scientists established its capability to drastically improve the lateral resolution present in traditional fluorescence microscopy. In recent years, it has evolved into a method that can also be adjusted to capture the temporal and spatial data that is essential for studying rapid molecular dynamics in real time. Scientists are using STED in modern research to observe the way in which DNA binds and how these basic, but intricate, building blocks interact with each other in their biological...Read full article

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    Published: July 2024
    Glossary
    fluorescence
    Fluorescence is a type of luminescence, which is the emission of light by a substance that has absorbed light or other electromagnetic radiation. Specifically, fluorescence involves the absorption of light at one wavelength and the subsequent re-emission of light at a longer wavelength. The emitted light occurs almost instantaneously and ceases when the excitation light source is removed. Key characteristics of fluorescence include: Excitation and emission wavelengths: Fluorescent materials...
    superresolution
    Superresolution refers to the enhancement or improvement of the spatial resolution beyond the conventional limits imposed by the diffraction of light. In the context of imaging, it is a set of techniques and algorithms that aim to achieve higher resolution images than what is traditionally possible using standard imaging systems. In conventional optical microscopy, the resolution is limited by the diffraction of light, a phenomenon described by Ernst Abbe's diffraction limit. This limit sets a...
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    deep learning
    Deep learning is a subset of machine learning that involves the use of artificial neural networks to model and solve complex problems. The term "deep" in deep learning refers to the use of deep neural networks, which are neural networks with multiple layers (deep architectures). These networks, often called deep neural networks or deep neural architectures, have the ability to automatically learn hierarchical representations of data. Key concepts and components of deep learning include: ...
    Featuresstimulation emission depletionSTEDMicroscopyfluorescenceDNAsuperresolutiondoughnut holeStefan HellSTORMPALMlocalizationnodes of Ranviersingle-photon detectionPicoQuantLeicaAbberiorFLIMFCSMINSTEDMINFLUXlive-cell imagingnitrogen-vacancy centersphotobleachingdeep learningPIEZO1 channels

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