Imaging Workflow
Imaging Workflow - Related Videos
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.
11:39
A Quantitative Fitness Analysis Workflow
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Cited by 25 •
2012
Quantitative Fitness Analysis (QFA) is a complementary series of experimental and computational methods for estimating microbial culture fitnesses. QFA estimates the effect of genetic mutations, drugs or other applied treatments on microbe growth. Experiments scaling from focussed analysis of single cultures to thousands of parallel cultures can be designed.
09:23
Integrating Automated Simulation Workflows with 3D Visualization for Virtual Experiments in the Metaverse
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2026
A generalized, FAIR-compliant method is presented for domain-expert researchers seeking to integrate simulation and data-processing tools into automated workflows for 3D virtual experiments. A neutronics example demonstrates setting up a local Galaxy instance, wrapping OpenMC and file-conversion tools, launching workflows from Omniverse, and visualizing the converted 3D outputs.
10:52
A High-content Imaging Workflow to Study Grb2 Signaling Complexes by Expression Cloning
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Cited by 6 •
2012
A high-content screening method for the identification of novel signaling competent transmembrane receptors is described. This method is amenable to large-scale automation and allows predictions about in vivo protein binding and the sub-cellular localization of protein complexes in mammalian cells.
09:17
A Simple Migration/Invasion Workflow Using an Automated Live-cell Imager
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Cited by 1 •
2019
The current protocol describes an integrated method investigating cancer cell migration and invasion on a single platform in real-time, providing an easily reproducible and time-efficient option to study cell mobility and morphology.
FAQs
Calibration establishes a reference for image measurements, while noise reduction removes unwanted variation and uneven-illumination correction helps prevent brightness differences caused by the imaging field from being mistaken for biology. Together, these preprocessing steps make downstream measurements more consistent and interpretable. They are especially important when comparing cell intensity, morphology, or spatial patterns across images.
Controlled acquisition conditions reduce avoidable differences between images, giving later analysis a more stable basis for comparison. In biology, this matters because apparent changes in cell number, shape, intensity, or spatial relationships should reflect the samples or biological process being studied rather than inconsistent imaging conditions. This supports more interpretable observations.
Quantitative analysis can convert visual observations into measurable descriptors, including cell number, shape, intensity, and spatial relationships. Selecting features should follow the biological question: counts can describe abundance, shape can characterize morphology, intensity can capture image signal, and spatial relationships can examine organization. The resulting measurements connect images with biological interpretation.
A useful plan should specify how samples are prepared, how images are acquired under controlled conditions, which preprocessing operations are applied, and which features will be measured. It should also include how findings will be communicated, so the final results remain linked to the original biological question. This organization helps make observations consistent and interpretable.
Researchers can apply this approach to microscopy-based cell studies, tissue analysis, phenotyping, and time-resolved investigations of biological processes. The emphasis can change with the application: cell studies may focus on number or shape, tissue analysis may use spatial relationships, and time-resolved work can organize observations across biological change. These uses make image data more useful for comparison and interpretation.
An Imaging Workflow is valuable when a study must compare observations across samples, images, or stages of a biological process. Standardized preparation, acquisition, preprocessing, and analysis help reduce measurement bias and improve reproducibility. The workflow also provides a structured path from visual evidence to reported findings, which is important when image-based results need to support a specific biological question.