Deconstructing the perineuronal net: Cellular contributions and molecular composition of the neuronal extracellular matrix

Perineuronal nets (PNNs) are lattice-like substructures of the neural extracellular matrix that enwrap particular populations of neurons throughout the central nervous system. Previous work suggests that this structure plays a major role in modulating developmental neural plasticity and brain maturation. Understanding the precise role of these structures has been hampered by incomplete comprehension of their molecular composition and cellular contributions to their formation, which is studied herein using primary cortical cell cultures. By defining culture conditions to reduce (cytosine-β-d-arabinofuranoside/AraC addition) or virtually eliminate (elevated potassium chloride (KCl) and AraC application) glia, PNN components impacted by this cell type were identified. Effects of depolarizing KCl concentrations alone were also assessed. Our work identified aggrecan as the primary neuronal component of the PNN and its expression was dramatically up-regulated by both depolarization and glial cell inhibition and additionally, the development of aggrecan-positive PNNs was accelerated. Surprisingly, most of the other PNN components tested were made in a glial-dependent manner in our culture system. Interestingly, in the absence of these glial-derived components, an aggrecan- and hyaluronan-reactive PNN developed, demonstrating that these two components are sufficient for base PNN assembly. Other components were expressed in a glial-dependent manner. Overall, this work provides deeper insight into the complex interplay between neurons and glia in the formation of the PNN and improves our understanding of the molecular composition of these structures.

► Most assayed PNN components are produced in a glial-dependent manner. ► Aggrecan and subcomponents of hyaluronan are produced by neurons. ► Aggrecan is produced in an activity-dependent manner. ► Aggrecan and hyaluronan are necessary for the base formation of PNNs.