Retinal Layer Tracing
Retinal Layer Tracing - Related Videos
03:20
Identification of Mouse Retinal Ganglion Cell Subtypes Through Fluorescent Tracing
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2025
Source: Laboissonniere, et. al. Single-cell RNA-Seq of Defined Subsets of Retinal Ganglion Cells. J. Vis. Exp. (2017). This video demonstrates a fluorescent tracing technique to identify retinal ganglion cell (RGC) subtypes in a transgenic mouse retina. The process involves securing the isolated retina in a recording chamber, maintaining cell viability with oxygenated perfusion, and targeting fluorescently labeled RGCs using a pipette with a tracer. By analyzing dendritic morphology and...
14:33
Morphometric Analyses of Retinal Sections
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Cited by 6 •
2012
This video demonstrates three types of morphometric analyses of the retina, which include measuring the inner nuclear layer thickness, quantifying the number of retinal ganglion cells (RGCs) and measuring the sizes of RGCs. The technique can offer a simple but scientific platform for morphometric analyses.
13:06
Viral Tracing of Genetically Defined Neural Circuitry
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Cited by 10 •
2012
A method of tracing synaptically connected neurons is described. We use TVA specificity of an upstream cell to probe whether a cell population of interest receives synaptic input from genetically defined cell types.
09:17
Using Retinal Imaging to Study Dementia
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Cited by 19 •
2017
The retina shares prominent similarities with the brain and thus represents a unique window to study vasculature and neuronal structure in the brain non-invasively. This protocol describes a method to study dementia using retinal imaging techniques. This method can potentially aid in diagnosis and risk assessment of dementia.
07:02
Trace Fear Conditioning in Mice
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Cited by 32 •
2014
In the following experiment we describe a protocol for trace fear conditioning in mice. This type of associative memory includes a trace period that separates the neutral stimulus and the unconditioned stimulus.
FAQs
Boundary detection relies on transitions in image intensity, texture, or visible anatomical organization. These signals indicate where one retinal layer ends and another begins, allowing a boundary to be followed across an image rather than judged from a single location. The reliability of the trace depends on whether these interfaces remain recognizable throughout the imaged region.
Thickness measurements convert traced anatomical boundaries into quantitative descriptions of retinal structure. Researchers can use them to examine neuronal organization and compare structural differences associated with development, injury, neurodegeneration, or treatment effects. Because the measurements are standardized, they also provide a common basis for relating anatomical change to visual function across imaging-based studies.
Manual annotation places boundaries through direct visual judgment, whereas segmentation algorithms identify and follow boundaries computationally. Both approaches can be used with microscopy or optical coherence tomography images, but their results still require careful inspection when interfaces are difficult to distinguish. This combination supports quantitative analysis while acknowledging that image interpretation can remain challenging.
Weak contrast can make the transition between neighboring layers difficult to recognize, while pathology can distort the usual anatomical arrangement. In either situation, a boundary may not follow the expected appearance across the image. Careful review is therefore important before using the trace for thickness comparisons, because structural measurements may otherwise reflect image ambiguity or distortion rather than genuine biological change.
A typical workflow begins by examining a microscopy or optical coherence tomography image, locating visible interfaces between retinal layers, and following each boundary across the image. The boundaries may be marked manually or generated with segmentation algorithms. The completed traces then require review, especially in regions with weak contrast or altered anatomy, before thickness or organizational measurements are compared.
Researchers apply the method when they need quantitative evidence of retinal structural organization or change. Relevant study contexts include retinal development, injury, neurodegeneration, and evaluation of treatment effects. By converting image features into layer-specific measurements, tracing helps investigators compare anatomy between conditions and assess whether observed structural differences accompany changes in visual function.
The main outcomes are standardized measurements of retinal thickness and descriptions of neuronal organization. These data can reveal structural change across experimental or clinical conditions and help connect retinal anatomy with visual function. In treatment studies, comparable measurements can also support assessment of structural effects, provided that boundaries are reviewed consistently where contrast is weak or anatomy is distorted.