Microscopic Quantification
Microscopic Quantification - Related Videos
15:28
A Microscopic Phenotypic Assay for the Quantification of Intracellular Mycobacteria Adapted for High-throughput/High-content Screening
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Cited by 30 •
2014
Here, we describe a phenotypic assay applicable to the High-throughput/High-content screens of small-interfering synthetic RNA (siRNA), chemical compound, and Mycobacterium tuberculosis mutant libraries. This method relies on the detection of fluorescently labeled Mycobacterium tuberculosis within fluorescently labeled host cell using automated confocal microscopy.
12:29
Two Algorithms for High-throughput and Multi-parametric Quantification of Drosophila Neuromuscular Junction Morphology
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Cited by 11 •
2017
Two image analysis algorithms, "Drosophila NMJ Morphometrics" and "Drosophila NMJ Bouton Morphometrics" were created, to automatically quantify nine morphological features of the Drosophila neuromuscular junction (NMJ).
09:57
Workflow for High-content, Individual Cell Quantification of Fluorescent Markers from Universal Microscope Data, Supported by Open Source Software
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Cited by 5 •
2014
Presented is a flexible informatics workflow enabling multiplexed image-based analysis of fluorescently labeled cells. The workflow quantifies nuclear and cytoplasmic markers and computes marker translocation between these compartments. Procedures are provided for perturbation of cells using siRNA and reliable methodology for marker detection by indirect immunofluorescence in 96-well formats.
11:32
Real-Time, High-Throughput Microscopic Quantification of Human Neutrophil Extracellular Trap Release and Assessing the Pharmacology of Antagonists
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Cited by 3 •
2024
This defined protocol describes a real-time high-throughput microscopy approach to visualize and quantify human neutrophil extracellular trap (NET) release in vitro. The reproducible method allows investigation of the characteristics and kinetics of NET release upon stimulation with distinct NETosis inducers and enables assessment of the pharmacology of NETosis antagonists.
04:57
Proper Care and Cleaning of the Microscope
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Cited by 2 •
2008
Keeping the microscope optics clean is important for high-quality imaging. Dust, fingerprints, excess immersion oil, or mounting medium on or in a microscope causes reduction in contrast and resolution. DIC is especially sensitive to contamination and scratches on the lens surfaces. This protocol details the procedure for keeping the microscope clean.
FAQs
Calibration helps ensure that measurements reflect biological differences rather than inconsistent image-acquisition conditions. Researchers can then compare signals, structures, or spatial patterns across experimental conditions more reliably. This step is especially important when evaluating changes in neurons, synapses, cellular compartments, or molecular markers, because unstandardized imaging can reduce the reproducibility and interpretability of numerical results.
Regions of interest define which portions of an image enter the analysis, while segmentation separates structures or signals for measurement. Their boundaries directly influence particle counts, area, length, and fluorescence intensity values. Consistent selection and processing therefore help ensure that comparisons represent meaningful differences between samples rather than changes caused by analyzing different spatial regions or structures.
The measurement should match the biological feature being evaluated. Particle counting can characterize discrete objects such as synapses or labeled structures, whereas area and length measurements describe cellular compartments or neural organization. Fluorescence intensity analysis is suited to molecular markers or signals. Selecting the appropriate output makes the numerical analysis more directly relevant to the hypothesis being tested.
Representative sampling and appropriate statistical analysis determine whether image-based measurements support a dependable comparison. Sampling should capture the structures or regions relevant to the biological question, while statistical methods help evaluate differences between experimental conditions. Together, these practices reduce the risk that conclusions about development, injury, disease, or treatment reflect limited or uneven observations.
A typical workflow begins by calibrating image acquisition, then defining the regions of interest and selecting a suitable measurement approach. Researchers may segment structures, count particles, measure area or length, or analyze fluorescence intensity. They then organize the numerical results for comparison between conditions and apply appropriate statistical analysis to evaluate the biological question.
In neuroscience, the approach supports quantitative evaluation of neurons, synapses, cellular compartments, and molecular markers. Researchers can use these measurements to characterize neural organization or compare changes associated with development, injury, disease, and treatment. Converting microscopy observations into numerical data makes it easier to test hypotheses about structural or signal-related differences across experimental conditions.