Comparative analysis of the methods for quantitative study of the perineuronal net microstructure

N.S. Lipachev a*, A.S. Dvoeglazova a**, A.A. Sadreeva b***, A.V. Aganov a****, M.N. Paveliev c*****

aKazan Federal University, Kazan, 420008 Russia

bI.M. Sechenov First Moscow State Medical University, Moscow, 119991 Russia

cNeuroscience Center, University of Helsinki, Helsinki, 00290 Finland

E-mail: *nikita.lipachev@gmail.com, **dvoeglazovaanastasia@list.ru,

***aminasa.android@gmail.com, ****Albert.Aganov@kpfu.ru, *****paveliev@outlook.com

Received September 19, 2022


ORIGINAL ARTICLE

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DOI: 10.26907/2542-064X.2022.4.519-534

For citation: Lipachev N.S., Dvoeglazova A.S., Sadreeva A.A., Aganov A.V., Paveliev M.N. Comparative analysis of the methods for quantitative study of the perineuronal net microstructure. Uchenye Zapiski Kazanskogo Universiteta. Seriya Estestvennye Nauki, 2022, vol. 164, no. 4, pp. 519–534. doi: 10.26907/2542-064X.2022.4.519-534. (In Russian)

Abstract

Perineuronal nets (PNN) are a special and highly structured type of the CNS extracellular matrix. In past few years, the important role of PNN in the normal physiology of the CNS and the changes in their expression associated with some pathologies have been shown, thus suggesting that PNN are involved in the pathogenesis of a number of brain and spinal cord diseases. Until recently, no quantitative studies have focused on the spatial geometry of the PNN meshes. In 2021 and 2022, we published two quantitative studies of the PNN microstructure in the cerebral cortex based on two new, different methods developed by us to analyze high-resolution confocal microscopy data. This article summarizes the results of a comparative analysis of these two methods for quantitative study of the PNN microstructure using microscopy data on the medial prefrontal cortex in an experimental model of schizophrenia. A high correlation was found between the two methods for the mesh area and the linear dimensions of the three-dimensional mesh structure. No correlation was observed for the two-dimensional shape parameters of the mesh. The obtained results demonstrate that the two methods are complementary and have additive value for future quantitative studies of the PNN microstructure.

Keywords: perineuronal net, extracellular matrix, neuron, central nervous system

Acknowledgments. We thank Professor A.E. Dityatev (German Center for Neurodegenerative Diseases, Magdeburg, Germany) for providing us with the confocal images of perineuronal nets and Associate Professor A.V. Zakharov (Kazan Federal University) for his assistance with manus­cript preparation.

This study was supported by the Russian Foundation for Basic Research (project no. 20-31-70001).

Figure Captions

Fig. 1. Comparison of mesh contours on the confocal plane obtained by the M1 and M2 methods. In each pair of images, the unprocessed fragment of the confocal image is shown on the left, and the contours applied to this fragment obtained by the M1 method (magenta) and the M2 method (cyan) are shown on the right: a–h – the area value by M2 is larger than the area value by M1; e–h – the area of M2 is larger than the area of M1 and the mesh is not clearly defined; i–p – the area of M1 is   approximately equal to the area of M2; i–k – the contours of M1 and M2 are significantly different; j–p – the contours of M1 and M2 almost completely coincide; q–t – the area of M1 is larger than the area of M2.

Fig. 2. Mean area (a) and perimeter (b) of meshes of the perineuronal net, both obtained by the M1 and M2 methods. Asterisks indicate significant differences based on the results of the rank sum test (*p < 0.05; **p < 0.01; ***p < 0.001). Group size: 8 animals, 1250 meshes. The bottom and top borders of the rectangles correspond to the 25th and 75th percentiles, respectively. The line inside the rectangle is the median.

Fig. 3. Differences in the mesh area values obtained by the M1 and M2 methods. Frequency distribution histograms for the values of the parameters M2–M1 (a) and M2/M1 (b).

Fig. 4. Correlation of the area (a) and perimeter (b) values of meshes obtained by the M1 and M2 methods. Linear regression is shown by a straight line. R is the correlation coefficient. The dotted line at 45? is shown for the analysis of the bias towards larger values for M2.

Fig. 5. Comparison of the mesh contours on the confocal plane obtained by the M1 and M2 methods: the shape of the mesh differs significantly between M1 and M2.

Fig. 6. Lack of correlation between the values of the parameters characterizing the shape of the mesh, obtained by the M1 and M2 methods: a – circularity; b – aspect ratio; c – solidity.

Fig. 7. Correlation of the intensity values of pixels in the contour (a) and all pixels of the mesh (b), obtained by the M1 and M2 methods. The dotted line at 45? is shown for the analysis of the bias towards larger values for M2.

Fig. 8. Analysis of the mesh thickness on the neuron surface along the Z axis of the confocal stack: a, b – the analyzed mesh; c–d – fluorescence intensity maps along the mesh contour in the confocal stack, plotted for the M1 (c) and M2 (d) contours. The yellow lines mark the confocal plane with the highest mean intensity. The green lines mark the upper and the lower mesh boundaries based on the intensity threshold.

Fig. 9. Comparative analysis of the mesh thickness measurements obtained by the M1 and M2 methods: a – mean values of the mesh thickness. The bottom and top borders of the rectangles correspond to the 25th and 75th percentiles. The line inside the rectangle is the median; b – correlation of the thickness values; c – differences in the mesh thickness values. Frequency distribution histograms for the M2 – M1 parameter values.

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