Stereological Quantification
Stereological Quantification - Related Videos
11:25
Knowing What Counts: Unbiased Stereology in the Non-human Primate Brain
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Cited by 22 •
2009
The anatomical organization of the primate brain can provide important insights into normal and pathological conditions in humans. Unbiased stereology is a method for accurately and efficiently estimating the total neuron number (or other cell type) in a given reference space1.
08:19
Stereological Estimation of Dopaminergic Neuron Number in the Mouse Substantia Nigra Using the Optical Fractionator and Standard Microscopy Equipment
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Cited by 45 •
2017
This work presents a step-by-step protocol for the unbiased stereological estimation of dopaminergic neuronal cell numbers in the mouse substantia nigra using standard microscopy equipment (i.e., a light microscope, a motorized object table (x, y, z plane), and public domain software for digital image analysis.
09:45
Histological Quantification of Chronic Myocardial Infarct in Rats
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Cited by 14 •
2016
The post-mortem assessment of myocardial infarction (MI) in rodents is based on quantification of the infarct on stained heart sections. We describe an accurate method to quantify the infarct size using systematic sampling of harvested rat hearts from base to apex and image analyses of trichrome-stained histological sections.
09:15
Stereological Estimation of Cholinergic Fiber Length in the Nucleus Basalis of Meynert of the Mouse Brain
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2020
Neuronal fiber length within a three-dimensional structure of a brain region is a reliable parameter to quantify specific neuronal structural integrity or degeneration. This article details a stereological quantification method to measure cholinergic fiber length within the nucleus basalis of Meynert in mice as an example.
08:44
Invertebrate Lifespan Quantification
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2023
Many animals naturally stop growing upon reaching adulthood, after which they undergo aging or "senescence" until dying. The amount of time between an organism\'s birth and death is called its lifespan, which can be influenced by various biological and environmental factors. By exposing organisms to different growth conditions, scientists can better understand the factors affecting lifespan. Flies and worms are ideal organisms to perform such experiments, given their short generation time and...
FAQs
Systematic random sampling selects tissue sections or sampling locations according to a defined pattern that begins from a random starting point. This approach helps ensure that measurements represent the broader specimen rather than an unusually dense, sparse, or otherwise atypical region. In medical research, representative sampling strengthens comparisons between healthy and diseased tissues.
Point counting uses geometric sampling points to estimate the volume occupied by a structure within tissue sections. The number of points intersecting a defined tissue component provides a basis for calculating its proportional representation. This makes point counting useful for assessing tissue composition, lesion volume, or other volume-related changes across experimental groups.
Unbiased counting frames provide a standardized geometric field for identifying and counting structures in sampled sections. Their use supports consistent decisions about which cells or features belong in the count, reducing the influence of arbitrary field selection. Consequently, researchers can generate more reproducible estimates of cell populations and compare those estimates across conditions.
Depending on the sampling tool and tissue feature examined, stereological quantification can estimate number, volume, length, or surface area. This range allows investigators to study cell populations, lesion volume, vascular networks, and tissue composition rather than relying on a single numerical outcome. The selected measurement should match the biological structure and the research question.
A typical workflow begins by selecting representative tissue sections through systematic random sampling, then applying an appropriate geometric tool, such as point counting or an unbiased counting frame. The investigator records observations from the sampled fields and uses them to generate a population-level estimate. The resulting measurements can then support comparisons among tissues or experimental groups.
The method is useful when medical investigators need reproducible quantitative outcomes from histological or pathological tissue. Applications include comparing healthy and diseased tissues, measuring lesion volume, evaluating vascular networks, characterizing tissue composition, and assessing cell populations in experimental disease models. It also helps strengthen analyses of treatment effects by converting structural observations into comparable measurements.