High Resolution 4d Data
High Resolution 4d Data - Related Videos
12:00
4D Microscopy of Yeast
0 Views •
Cited by 12 •
2019
This protocol describes the analysis of fluorescently labeled intracellular compartments in budding yeast using multi-color 4D (time-lapse 3D) confocal microscopy. The imaging parameters are chosen to capture adequate signals while limiting photodamage. Custom ImageJ plugins allow labeled structures to be tracked and quantitatively analyzed.
08:59
4D Imaging of Protein Aggregation in Live Cells
0 Views •
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
0 Views •
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.
10:04
Sample Drift Correction Following 4D Confocal Time-lapse Imaging
0 Views •
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.
06:52
4D Printed Bifurcated Stents with Kirigami-Inspired Structures
0 Views •
Cited by 16 •
2019
Using a 3D printer, a shape memory polymer filament is extruded to form a branched tubular structure. The structure is patterned and shaped such that it can contract into a compact form once folded and then return to its formed shape when heated.
FAQs
Alignment places successive optical sections and time points into a consistent spatial framework. This allows researchers to compare the same cells, tissues, or structures as development proceeds rather than treating each image separately. Reliable alignment supports later segmentation and tracking, making observed movements and shape changes more interpretable across the complete volumetric time series.
Segmentation identifies cells or tissue regions within each volumetric dataset, while tracking connects those identified structures across successive time points. Together, these computational steps convert image sequences into measurements of division, migration, and shape change. Tracking can also reveal lineage relationships, which helps relate individual cellular behaviors to larger developmental events.
A three-dimensional snapshot shows structure at one moment, but adding time reveals how that structure changes. This temporal information can distinguish stable arrangements from active cell behaviors such as migration, division, or reshaping. In developmental biology, those dynamics help researchers connect cellular activity with tissue morphogenesis and construct quantitative models of embryonic development.
The workflow begins with time-lapse imaging that collects successive optical sections during development. These sections are combined into volumetric datasets and aligned across time. Researchers then apply computational segmentation and cell tracking to identify structures and follow them through the sequence. The resulting measurements describe changes in position, division, shape, and lineage over the observation period.
Analysis can reveal when and where cells divide, migrate, or change shape, as well as how those behaviors relate to lineage relationships. At the tissue level, the measurements show how cellular actions contribute to morphogenesis. These outcomes provide a quantitative basis for examining developmental organization rather than relying only on qualitative inspection of images.
Researchers can compare measured cellular behaviors and tissue changes with the expected developmental progression to identify abnormalities. Disruptions in division, migration, shape change, or lineage relationships may help explain altered tissue morphogenesis. Because the data preserve both spatial structure and temporal progression, they also support quantitative models that examine how cellular changes produce developmental defects.