Tomogram Analysis
Tomogram Analysis - Related Videos
10:25
Deep Learning-Based Segmentation of Cryo-Electron Tomograms
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Cited by 53 •
2022
This is a method for training a multi-slice U-Net for multi-class segmentation of cryo-electron tomograms using a portion of one tomogram as a training input. We describe how to infer this network to other tomograms and how to extract segmentations for further analyses, such as subtomogram averaging and filament tracing.
07:29
Determination of Molecular Structures of HIV Envelope Glycoproteins using Cryo-Electron Tomography and Automated Sub-tomogram Averaging
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Cited by 16 •
2011
The protocol describes a high-throughput approach to determining structures of membrane proteins using cryo-electron tomography and 3D image processing. It covers the details of specimen preparation, data collection, data processing and interpretation, and concludes with the production of a representative target for the approach, the HIV-1 Envelope glycoprotein. These computational procedures are designed in a way that enables researchers and students to work remotely and contribute to data...
15:04
A Neuronal and Astrocyte Co-Culture Assay for High Content Analysis of Neurotoxicity
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Cited by 46 •
2009
This article describes a novel protocol and reagent set designed for sensitive measurement of neurotoxic effects of compounds and treatments on co-cultures of neurons and astrocytes using high content analysis. Results demonstrate that high content analysis represents an exciting novel technology for neurotoxicity assessment.
10:39
Automated Analysis of Intracellular Phenotypes of Salmonella Using ImageJ
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Cited by 3 •
2022
Salmonella invades and replicates inside intestinal epithelial cells both in Salmonella-specific vacuoles and free in the cytosol (hyper-replication). A high-throughput fluorescence microscopy-based protocol is described here to quantify the intracellular phenotypes of Salmonella by two complementary image analyses through ImageJ, reaching single-cell resolution and scoring.
11:24
High-throughput Imaging and Analysis Workflow for Evaluating Skin Cell Phenotypes and Proliferation States in Tissue Samples
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Cited by 1 •
2025
The combination of iterative-bleaching-extends-multiplexity (IBEX) and a commercial nucleotide labeling assay (Click-iT EdU) enables the detection and categorization of dividing cell types in highly dynamic processes in fixed frozen murine tissue sections. Furthermore, a novel open-source image processing pipeline provides high-throughput image acquisition and analysis.
FAQs
Different views provide complementary information about structures that a single two-dimensional image may overlap or obscure. Combining these observations supports a more complete reconstruction of cellular or tissue organization, making it possible to examine the same feature across spatial dimensions. This added context helps distinguish genuine structural relationships from appearances caused by a particular viewing plane.
Segmentation separates recognizable cellular components within the reconstructed image, while contrast analysis helps distinguish regions that differ in visual signal. Together, these steps allow researchers to measure features such as shape and volume rather than relying only on visual inspection. The resulting measurements can support comparisons among organelles, cells, tissues, or molecular assemblies under different conditions.
Three-dimensional interpretation can expose how biological components are positioned relative to one another, including their shape, volume, and spatial relationships. Features that overlap in a two-dimensional view may become distinguishable when examined through reconstructed depth. This perspective is especially useful when morphology must be related to cellular organization or to functional differences between biological states.
A typical workflow begins with image data collected across different angles or depths, followed by computational reconstruction of the three-dimensional organization. Researchers then visualize the result, distinguish components through segmentation or contrast analysis, and quantify features such as shape, volume, and position. The measurements can subsequently be compared across samples, developmental stages, experimental conditions, or disease states.
Researchers would favor this approach when the biological question depends on depth, three-dimensional organization, or relationships among nearby components. It is useful for examining cells, organelles, tissues, and molecular assemblies whose arrangement may be obscured in conventional images. The method also supports structural comparisons when samples differ by developmental stage, experimental treatment, or disease state.
By quantifying morphology in three dimensions, the approach supplies structural measurements that can be compared with biological conditions or stages. Changes in shape, volume, or spatial organization may therefore be evaluated across samples rather than described only qualitatively. In biology, this supports investigations linking cellular and tissue architecture with functional differences, developmental progression, or disease-associated structural change.