Perineuronal nets (PNNs) represent a highly condensed specialized form of brain extracellular matrix (ECM) enwrapping mostly parvalbumin‐positive interneurons in the brain in a mesh‐like fashion. PNNs not only regulate the onset and completion of the critical period during postnatal brain development, control cell excitability, and synaptic transmission but are also implicated in several brain disorders including schizophrenia. Holes in the perineuronal nets, harboring the synaptic contacts, along with hole‐surrounding ECM barrier can be viewed as PNN compartmentalization units that might determine the properties of synapses and heterosynaptic communication. In this study, we developed a novel open‐source script for Fiji (ImageJ) to semi‐automatically quantify structural alterations of PNNs such as the number of PNN units, area, mean intensity of PNN marker expression in 2D and 3D, shape parameters of PNN units in the ketamine‐treated Sprague–Dawley rat model of schizophrenia using high‐resolution confocal microscopic images. We discovered that the mean intensity of ECM within PNN units is inversely correlated with the area and the perimeter of the PNN holes. The intensity, size, and shape of PNN units proved to be three major principal factors to describe their variability. Ketamine‐treated rats had more numerous but smaller and less circular PNN units than control rats. These parameters allowed to correctly classify individual PNNs as derived from control or ketamine‐treated groups with ≈85% reliability. Thus, the proposed multidimensional analysis of PNN units provided a robust and comprehensive morphometric fingerprinting of fine ECM structure abnormalities in the experimental model of schizophrenia.

Perineuronal net (PNN) is a highly structured portion of the CNS extracellular matrix (ECM) regulating synaptic plasticity and a range of pathologic conditions including posttraumatic regeneration and epilepsy. Here we studied Wisteria floribunda agglutinin-stained histological sections to quantify the PNN size and enrichment of chondroitin sulfates in mouse brain and spinal cord. Somatosensory cortex sections were examined during the period of PNN establishment at postnatal days 14, 21 and 28. The single cell PNN size and the chondroitin sulfate intensity were quantified for all cortex layers and specifically for the cortical layer IV which has the highest density of PNN-positive neurons. We demonstrate that the chondroitin sulfate proteoglycan staining intensity is increased between P14 and P28 while the PNN size remains unchanged. We then addressed posttraumatic changes of the PNN expression in laminae 6 and 7 of cervical spinal cord following hemisection injury. We demonstrate increase of the chondroitin sulfate content at 1.6–1.8 mm rostrally from the injury site and increase of the density of PNN-bearing cells at 0.4–1.2 mm caudally from the injury site. We further demonstrate decrease of the single cell PNN area at 0.2 mm caudally from the injury site suggesting that the PNN ECM takes part in the posttraumatic tissue rearrangement in the spinal cord. Our results demonstrate new insights on the PNN structure dynamics in the developing and posttraumatic CNS.

Perineuronal nets (PNN) are a special highly structured type of extracellular matrix encapsulating synapses on large populations of CNS neurons. PNN undergo structural changes in schizophrenia, epilepsy, Alzheimer’s disease, stroke, post-traumatic conditions, and some other brain disorders. The functional role of the PNN microstructure in brain pathologies has remained largely unstudied until recently. Here, we review recent research implicating PNN microstructural changes in schizophrenia and other disorders. We further concentrate on high-resolution studies of the PNN mesh units surrounding synaptic boutons to elucidate fine structural details behind the mutual functional regulation between the ECM and the synaptic terminal. We also review some updates regarding PNN as a potential pharmacological target. Artificial intelligence (AI)-based methods are now arriving as a new tool that may have the potential to grasp the brain’s complexity through a wide range of organization levels—from synaptic molecular events to large scale tissue rearrangements and the whole-brain connectome function. This scope matches exactly the complex role of PNN in brain physiology and pathology processes, and the first AI-assisted PNN microscopy studies have been reported. To that end, we report here on a machine learning-assisted tool for PNN mesh contour tracing.