4d Confocal Imaging
4d Confocal Imaging - Related Videos
10:04
Sample Drift Correction Following 4D Confocal Time-lapse Imaging
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Cited by 222 •
2014
Time-lapse microscopy allows the visualization of developmental processes. Growth or drift of samples during image acquisition reduces the ability to accurately follow and measure cell movements during development. We describe the use of open source image processing software to correct for three dimensional sample drift over time.
09:16
Analyzing Craniofacial Morphogenesis in Zebrafish Using 4D Confocal Microscopy
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Cited by 18 •
2014
Time-lapse confocal imaging is a powerful technique useful for characterizing embryonic development. Here, we describe the methodology and characterize craniofacial morphogenesis in wild-type, as well as pdgfra, smad5, and smo mutant embryos.
08:59
4D Imaging of Protein Aggregation in Live Cells
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Cited by 4 •
2013
Cellular viability depends on timely and efficient management of protein misfolding. Here we describe a method for visualizing the different potential fates of a misfolded protein: refolding, degradation, or sequestration in inclusions. We demonstrate the use of a folding sensor, Ubc9ts, for monitoring proteostasis and aggregation quality control in live cells using 4D microscopy.
12:38
4D Multimodality Imaging of Citrobacter rodentium Infections in Mice
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Cited by 19 •
2013
Multi-modality imaging is a valuable approach for studying bacterial colonization in small animal models. This protocol outlines infection of mice with bioluminescent Citrobacter rodentium and the longitudinal monitoring of bacterial colonization using composite 3D diffuse light imaging tomography with μCT imaging to create a 4D movie of C. rodentium infection.
07:07
4D Light-sheet Imaging of Zebrafish Cardiac Contraction
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Cited by 8 •
2024
This protocol utilizes light-sheet imaging to investigate cardiac contractile function in zebrafish larvae and gain insights into cardiac mechanics through cell tracking and interactive analysis.
FAQs
The method first collects fluorescence from optical sections at successive depths, preserving information from different positions within a specimen. These slices are then reconstructed into a three-dimensional volume. Repeating the depth-resolved acquisition at later time points produces a sequence of volumes, allowing structural changes to be examined as a temporal progression rather than as isolated images.
Repeated acquisitions provide the temporal dimension needed to distinguish ongoing biological changes from a single structural snapshot. By comparing reconstructed volumes collected at successive times, researchers can examine movement, alterations in morphology, and changing organization within a biological system. This time-resolved perspective is especially relevant when cells, tissues, or engineered structures evolve within complex environments.
Fluorescence supplies the signal used to distinguish structures within each optical section. Collecting that signal at successive depths allows the system to represent spatial organization throughout a three-dimensional volume. When the same depth-resolved process is repeated over time, fluorescence-based structural information supports analysis of how labeled biological features move, reorganize, or change in morphology.
A single three-dimensional volume describes spatial organization at one point in time, whereas 4D Confocal Imaging adds repeated observations of that volume. The additional temporal information makes it possible to study dynamic behavior, including movement and evolving morphology. This distinction matters in systems where cell migration, tissue organization, or biological responses cannot be interpreted from one static reconstruction.
A typical workflow begins by collecting fluorescence from optical sections through successive depths. The sections are reconstructed into a three-dimensional volume, and the acquisition is repeated at later time points. The resulting sequence of volumes forms the dataset for examining structural changes over time. Analysis can then focus on morphology, movement, organization, or biological responses.
In bioengineering, this approach is useful when researchers need to observe how cells and structures behave within complex environments. Supported applications include characterizing cell migration, tissue organization, interactions between cells and biomaterials, and the development of engineered tissues. Its spatial and temporal information helps connect observed structural changes with evolving biological behavior.
The datasets support quantitative analysis of morphology, movement, and biological responses. Researchers can follow how structures change across reconstructed volumes and time points, assess the organization of tissues, and examine cellular behavior in relation to biomaterials or engineered tissue development. These measurements provide a basis for describing dynamic changes rather than relying only on endpoint observations.