Dynamic Domain Imaging
Dynamic Domain Imaging - Related Videos
02:26
Conservation of Protein Domains Over Different Proteins
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2020
Protein domains are small structurally independent units that are part of a single amino acid chain. Although these domains are often structurally independent, they may rely on synergistic effects to perform their functions as part of a larger protein. Protein domains may be conserved within the same organism, as well as across different organisms. A limited set of protein domains often duplicate and recombine during evolution. These domains can be organized in different combinations to form...
14:21
Optical Frequency Domain Imaging of Ex vivo Pulmonary Resection Specimens: Obtaining One to One Image to Histopathology Correlation
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Cited by 21 •
2013
A method to image ex vivo pulmonary resection specimens with optical frequency domain imaging (OFDI) and obtain precise correlation to histology is described, which is essential to developing specific OFDI interpretation criteria for pulmonary pathology. This method is applicable to other tissue types and imaging techniques to obtain precise imaging to histology correlation for accurate image interpretation and assessment. Imaging criteria established with this technique would then be...
11:24
Targeted Labeling of Neurons in a Specific Functional Micro-domain of the Neocortex by Combining Intrinsic Signal and Two-photon Imaging
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Cited by 19 •
2012
A method is described for labeling neurons with fluorescent dyes in predetermined functional micro-domains of the neocortex. First, intrinsic signal optical imaging is used to obtain a functional map. Then two-photon microscopy is used to label and image neurons within a micro-domain of the map.
02:33
Spontaneous Calcium Imaging for Compartment-Specific Dynamics in Astrocytes
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2025
Source: Bannai, H., et. al., Dissection of Local Ca2+ Signals in Cultured Cells by Membrane-targeted Ca2+ Indicators. J. Vis. Exp. (2019)The video demonstrates fluorescence imaging to observe compartment-specific spontaneous calcium dynamics in astrocytes by sequentially monitoring calcium-sensitive proteins localized on the plasma membrane and endoplasmic reticulum.
02:42
In Vivo Two-Photon Imaging of Microglial Dynamics in the Mouse Hippocampus
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2025
Source: Kamei, R., et. al., In Vivo Chronic Two-Photon Imaging of Microglia in the Mouse Hippocampus. J. Vis. Exp. (2022)The video demonstrates the setup and imaging process for in vivo two-photon microscopy of microglial dynamics in the hippocampal CA1 (Cornu ammonis 1) and dentate gyrus, ensuring optimal optical alignment and structural integrity assessment.
FAQs
The approach links time-lapse image acquisition with computational segmentation, which identifies spatial domains and estimates their boundaries. Quantitative tracking then follows changes in position, size, and intensity across successive images. This converts visual observations into spatial and temporal measurements, allowing researchers to compare local structural changes with biological activity over the course of an experiment.
Computational segmentation separates domains from surrounding regions so that each domain can be analyzed consistently throughout an image sequence. Accurate boundary identification supports measurements of domain size, intensity, and spatial arrangement. It also makes it possible to recognize events such as formation, movement, fusion, or dissolution rather than relying only on qualitative visual inspection.
A single image can show domain organization at one moment but cannot establish how that organization changes. Temporal measurements reveal whether domains form, move, merge, or disappear, while spatial measurements show where those changes occur. Together, they help connect local structural dynamics with biological function and expose heterogeneity that a static view may conceal.
A typical workflow begins by acquiring a time-lapse sequence that captures the system as it changes. Researchers then apply computational segmentation to identify domains and their boundaries, followed by quantitative tracking across frames. The resulting data can describe domain size, intensity, location, and dynamic events, providing a structured basis for interpreting evolving biological organization.
In engineered tissues and biomaterials, the method can quantify how spatial domains develop or reorganize during use or development. Measurements of boundaries, size, intensity, movement, fusion, or dissolution provide evidence of changing organization within the engineered system. These observations can support evaluation of how local structure evolves and how that evolution relates to biological function.
The measurements provide quantitative evidence about heterogeneity and changing organization in systems whose properties evolve over time. By comparing domain behavior across engineered tissues, biomaterials, or cellular arrangements, researchers can evaluate whether a design produces the intended spatial and temporal patterns. This supports more informed assessment and refinement of dynamic biological systems.