RU2828670C1 - Method for studying distribution of neurons in retinotopic zones on histological section of corpus geniculatum laterale of animals - Google Patents

Abstract

FIELD: medicine.

SUBSTANCE: invention can be used to study the distribution of neurons in retinotopic zones on a histological section of the corpus geniculatum laterale of animals. Sections are made and stained, the sections are photographed, the outer boundaries of the layers of the dorsal nucleus of the corpus geniculatum laterale and the boundaries between these layers are detected, and the location of neurons on the section is marked. Marker marks the outer boundaries of layers A and A1, the inner boundary between these layers, as well as the colored neurons. Selected boundaries are approximated by sets of rectilinear segments, outer boundaries of layers are divided into specified number of segments of equal length, numbered for each of layers from left to right, ends of segments approximating external boundaries with identical numbers are connected in pairs to obtain segments. Thereafter, the area of each segment is measured, distribution of neurons between segments is determined as density of neurons and distribution of neurons within a segment based on distance from neurons to outer boundary of layer and boundary between layers.

EFFECT: invention enables to quantitatively analyze morphometric and spatial characteristics of neurons of eye-specific layers of the dorsal nucleus of the corpus geniculatum laterale and based on this to judge the degree of change in the functions of these neurons depending on the position of their receptive fields in the field of view.

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Description

The invention relates to the study of the physiology of vision and can be used to study the pathology of the visual system in animal models.

Histological images (images of histological preparations) are widely used to study the condition of tissues and organs of humans and animals; in particular, decisions are made on the severity of diseases and the assessment of the results of possible treatment based on their analysis [1]. One of the fundamental stages of histological image analysis is segmentation: local, in which some individual known structures (e.g., cell nuclei) are highlighted in the image, or global, in which the image is divided into informative zones [2]. Global segmentation can be performed either manually, by a specialist, or automatically. In the latter case, a "grid" structure is selected based on a priori information about the organ or tissue being studied, dividing it into certain segments [e.g., 3]. The nodes of these grids can then be modified interactively [e.g., 4].

Histological image analysis is also used in research to study the effects of drugs or environmental factors on the microstructure of organs and tissues, their ontogenetic and age-related changes, in particular in studies of the structures of the visual analyzer in humans and animals. Among the neural structures of the brain, the main projection thalamic nucleus, through which 90% of information enters the visual cortex, is the lateral geniculate nucleus (LGN). Neurons located in different layers of the dorsal nucleus of the LGN (LGNd) receive inputs either from the retina of the left or from the retina of the right eye, i.e. the receptive fields of neurons in one layer are located on one of the retinas. Such a layered structure makes the LGNd a convenient object for identifying differences in the effects of different forms of binocular experience impairment on monocular visual pathways. In a section of the LGNd of a cat, layers A and A1 can be distinguished, bordering each other (hereinafter, the internal border); layer A is located above layer A1. The outer boundaries of the layers (the upper boundary of layer A and the lower boundary of layer A1) are easily distinguishable. At the same time, various functional and morphological populations of neurons are distributed unevenly relative to the boundaries of the layers [5, 6].

The electrophysiological method demonstrated the retinotopic organization of the NKTd: areas close to the central vertical meridian of the visual field project to the medial area of the nucleus, while areas distant from it project to the lateral area; the representation of the central horizontal meridian is located in the middle part of the NKTd, while the top of the visual field projects to the caudal part of the NKTd, and the bottom of the visual field projects to the rostral part [7]. Thus, neurons with receptive fields in different zones of the visual field are located in different areas of the layer. Since the NKTd has a complex three-dimensionally curved shape, the shape of layers A and A1 in the frontal section differs depending on the rostrocaudal position of such a section within the NKTd. Layers in the rostral part of the NKTd, which represent the bottom of the visual field, have a curved shape, while layers in the middle and caudal parts of the NKTd have straight boundaries [8].

Visual impairments have different effects on visual functions in different parts of the visual field [9, 10], on such morphological characteristics of different areas of the NKT layers as the size of neurons and their distribution density [11, 12, 13], as well as on the brightness of staining of neuronal bodies and neuropil [11, 14].

For quantitative analysis of disturbances of morpho-functional organization of NKTd, the following procedure of grid construction was proposed in [11]. NKTd sections were selected, on which layers A and A1 had rectilinear external boundaries; the binocular part of layers A and A1 was divided into 4 sections of approximately equal area by lowering perpendiculars from the external boundary of layer A to the external boundary of layer A1; small rectangular areas were selected in the centers of the obtained sections, in which the density of neurons was calculated. The disadvantage of this method is, firstly, the need to work only with sections having sufficiently rectilinear external boundaries of layers. And, secondly, the impossibility of assessing the internal organization of layers A and A1.

To eliminate the last problem, the following procedure was proposed in [15]. The sections of the NKTd were selected that had rectilinear outer boundaries of the layers. The medial pole of the A1 layer was taken as 0% of the length, and the lateral pole as 100% of the length. For the normalized correlation of the spatial coordinates of neurons with the retinotopic map of the NKTd, the ratio of the distance from the neuron to the beginning of the A1 layer to the full length of the A1 layer was determined. This method is the closest to the proposed one and is accepted as a prototype.

The technical problem is that the procedures for isolating retinotopic zones in both of the above-mentioned methods are suitable only if the outer boundaries of the layers have a close to rectilinear shape. Meanwhile, in many NKTd sections, the outer boundaries of the fields have a curved shape, which is poorly approximated by a single straight line segment. As a result, such sections are excluded from further quantitative analysis, which does not allow us to estimate such parameters as the number of neurons, the density of neurons, their position relative to the layer boundary, the distribution of neurons within the layer, and the brightness of a part of the layer over the entire projection of the visual field. Segmentation of such sections using the known technical means presented in [16] is also difficult due to the lack of objective criteria for segmentation in a semi-automatic mode. There are no suitable models for generating grids for NKTd segmentation. Meanwhile, it is known that the position of the bodies of certain neuron populations relative to the layer boundaries changes during postnatal development [6]; a change in cell density is used as a morphological correlate of visual impairments or their restoration [17]; changes in the brightness of the color of neurons and neuropil also indicate a change in their function [14].

To resolve this problem, it is proposed to develop a method for studying the distribution of neurons in retinotopic zones on a histological section of animal NKT, which includes preparing and staining sections, photographing sections, identifying the outer boundaries of NKTd layers and the boundaries between these layers, marking the location of neurons on the section, marking the outer boundaries of layers A and A1, the inner boundary between the layers, and also stained neurons, approximating the identified boundaries with sets of straight line segments, dividing the outer boundaries of the layers into a given number of segments of equal length, numbered for each of the layers from left to right, connecting the ends of the segments approximating the outer boundaries with the same numbers in pairs, obtaining segments corresponding to the functional retinotopic zones, and then measuring the area of each segment, determining the distribution of neurons between segments (neuron density) and within the segment (based on the distance from the neurons to the outer boundary of the layer and the boundary between the layers).

The technical result will be the ability to determine the area and average brightness of segments, the distribution of neurons within a segment and between segments on NKTd sections of any shape due to a new method for identifying topical zones. As a result, the information content of the data obtained during section analysis increases, and it becomes possible to analyze a larger number of sections from one animal. This, firstly, will allow obtaining more detailed information on the localization of disorders in the visual field and, secondly, will lead to a decrease in the required number of animals in the sample, which is consistent with the international principle of conducting research on animals 3R.

Thus, the proposed method allows quantitative analysis of the morphometric and spatial characteristics of neurons in the eye-specific layers of the NKTd and, based on this, to judge the degree of change in the functions of these neurons depending on the position of their receptive fields in the visual field. Such data may be important in studying the formation of visual neurons in ontogenesis under normal conditions and in modifying visual experience.

Fig. 1A shows an image of a section of the right hemisphere NKT, with lines drawn on it indicating the outer boundaries of layers A and A1 and the boundary between these layers.

Fig. 1B shows the division of the layers of the NKT into segments corresponding to retinotopic zones, and the isolation of neurons in each segment of each layer.

Fig. 2 shows the result of counting the number of neurons in the selected ten segments of layers A and A1 of the NKTd in absolute units (Fig. 2A) and the density of neurons (Fig. 2B).

Fig. 3 shows the average brightness on the additive RGB color scale, calculated as the average value of the intensities of the R-, G- and B-components of the color of each pixel of the image of a given segment of layer A and A1.

Fig. 4 shows the percentage of neurons located in a given bin within a selected segment of layer A and A1, calculated based on the obtained distances from the neuron position to the layer boundaries. Each layer of the NKTd was divided into three bins of equal width, corresponding to the upper (B), middle (C), and lower (H) parts of the layer. The number of neurons in a given segment of a given layer is taken as 100%.

To implement the method, the following operations must be performed.

1. Make cuts.

2. Stain the sections.

3. Photograph (digitize) the sections.

4. On the image of the section, the boundaries of layers A and A1 of the NKTd and the boundary between the layers are marked with lines and the location of the neurons is marked with dots.

5. Retinotopic zones and their parameters are identified, for this:

5.1 Find the coordinates of the points that form the lines defining the boundaries of the layers and the boundary between the layers.

5.2 Divide the lines defining the outer boundaries of the layers into a given number (for example, 10) of segments of equal length.

5.3 Connect the vertices of the obtained segments with the same numbers in pairs.

5.4 Determine the area of the resulting sectors.

5.5 Divide each sector into three bins of equal height, corresponding to the upper, middle and lower parts of the layer.

5.6 Determine the average brightness in the sector.

6. Populations of neurons are identified in terms of retinotopic zones.

6.1 Find the coordinates of the points that marked the location of the neurons.

6.2 Each of the neurons is assigned to one of the sectors of one of the layers of the NKTd.

6.3 Determine the distance from each neuron to the outer boundary of the layer and to the boundary between layers.

7. Determine the parameters of the selected neuron populations.

7.1 Determine the number of neurons in each population.

7.2 Find the distribution of neurons by sectors (dependence of the neuron density on the sector number).

7.3 Based on the position of the neuron relative to the layer boundaries, the neuron is assigned to one of the bins, i.e. the distribution of neurons within the sector is found. The number of neurons in the sector is taken as 100%.

The essence of the method is explained by an example:

After perfusion fixation of the brain in animals with experimentally induced strabismus, frontal sections of the thalamus containing NKTd were prepared on a freezing microtome. To identify neuronal cell bodies, an immunohistochemical reaction was performed with monoclonal antibodies SMI-32, which bind to non-phosphorylated domains of the heavy protein of neurofilaments, one of the main elements of the neuronal cytoskeleton, and label mainly Y-type neurons [18]. On digitized images of NKTd sections, using various graphic markers, the expert marked the outer boundaries of layers A and A1 (e.g., in blue), the inner boundary between these layers (e.g., in red), and also stained neurons (e.g., green dots), the staining intensity of which, due to the immunohistochemical reaction, was higher than that of the background (Fig. 1A). The selected boundaries were approximated by sets of straight line segments. The resulting sets of segments approximating the outer boundaries of the layers were divided into a given number of fragments of equal length (for example, ten). The vertices of the segments were numbered from left to right for each of the layers (in this example, from 1 to 11, see Fig. 1B). Then the vertices that had the same ordinal numbers in layers A and A1 were connected to each other. As a result, each of the layers of the NKTd was divided into a given (in this example, ten) number of segments (Fig. 1B). The shape of the segments was similar to the shape of the retinotopic zones shown on the maps [8]: the population of neurons in each sector formed the desired functional retinotopic zone. In contrast to the known prototype method of analyzing sections, in which the boundaries of layers A and A1 of the NKTd are oriented rectilinearly, and the location of neurons is projected onto a straight line parallel to the boundary of the layers, the proposed method allows one to isolate populations of neurons within the functional retinotopic zones on sections of the NKTd with boundaries of any curvature.

Similarly [15], the coordinates of neurons are found as the coordinates of the points by which they were marked. Based on these coordinates, the segment to which a given neuron belongs is determined, and then the number of neurons in these segments (Fig. 2A). Then, taking into account the measurement of the area of each segment, the density of neurons is calculated - the number of neurons per unit area (Fig. 2B). An analysis of the distribution of neurons by segments shows that in segments 8-10, in which neurons of the retinotopic zone of the periphery of the visual field are located, the number and density of neurons in layers A and A1 differ significantly. Next, the distance between neurons and the boundaries of the layer is calculated and on its basis it is determined, for example, whether a neuron lies in the upper, middle or lower part of the layer (Fig. 3). Analysis of the distribution of neurons within a segment (the number of neurons in each segment of each layer is taken as 100%) shows that, as in the norm, in most segments, the minimum number of Y-neurons is located in the center of the layer (in bin C, the percentage of neurons is lower than in bin B or in bin H), which is in good agreement with literary data [5, 15].

The average brightness in segments 6-10 (Fig. 4) is higher for layer A1, since it has fewer darkly colored neuronal bodies, and the brightness of the background (neuropil) is also reduced.

Differences in these indicators between layers A and A1 indicate disturbances caused by the animal's strabismus.

Thus, the proposed method allows dividing layers A and A1 into functional zones (segments) on histological sections of the NKTd according to retinotopic projections of the visual field, studying the distribution of neurons within a segment and between segments, and the average brightness of a segment. The proposed method opens up opportunities for studying the distribution of neurons on sections of the NKTd of any shape and identifying structural features that arise during ontogenesis or are caused by visual experience impairment. Purpose - in histological studies for biology and medicine when studying layered structures of the brain, as well as when modeling visual experience impairments on laboratory animals.

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Claims (1)

A method for studying the distribution of neurons in retinotopic zones on a histological section of the lateral geniculate body of animals, including the preparation and staining of sections, photographing sections, identifying the outer boundaries of the layers of the dorsal geniculate nucleus of the NGT and the boundary between these layers, marking the location of neurons on the section, characterized in that the outer boundaries of layers A and A1, the inner boundary between these layers, as well as the stained neurons, are marked with a marker, the selected boundaries are approximated by sets of rectilinear segments, the outer boundaries of the layers are divided into a given number of segments of equal length, numbered for each of the layers from left to right, the ends of the segments approximating the outer boundaries, with the same numbers are connected in pairs, obtaining segments, after which the area of each segment is measured, the distribution of neurons between segments is determined as the density of neurons and the distribution of neurons within a segment based on the distance from the neurons to the outer boundary of the layer and the boundary between the layers.