A brain-wide analysis of perineuronal nets in the mouse brain reveals complex interactions with parvalbumin neurons and correlations with gene expression

A growing body of literature shows that the extracellular matrix plays an important role in the proper functioning of the brain. In particular, extracellular matrix networks, called perineuronal nets (PNNs), are known to aggregate around the cell bodies and proximal dendrites of nerve cells during postnatal development. Our knowledge of PNNs, however, is limited to a few brain areas, and so far a complete characterization of their distribution in the entire brain is missing. PNNs are recognized to surround different cell types in distinct brain regions, such as parvalbumin-positive (PV) interneurons in cortical areas, and evidence shows that they are responsible for restricting plasticity, thereby affecting for example memory processes or the ability to recover from CNS damage. Understanding how the extracellular matrix is differentially aggregated throughout all structures of the brain may suggest unique functions of PNNs and highlight their role in brain physiology. To address this issue, here we generated a brain-wide atlas of PNNs and PV interneurons in the adult mouse. We used immuno-/lectin-histochemistry and computational methods for automated PNNs and cell detection in serial coronal sections of the whole brain. Importantly, the atlas is aligned to a common 3D reference brain volume, the Allen Brain Common Coordinate Framework. This allowed us to exploit the open whole-brain connectivity atlas and gene expression dataset to study correlations with PNN presence. We analyzed different PNNs metrics to give a quantitative description of their distribution and this dataset was correlated to the PV cells dataset to evaluate their relationship. Using this information, we unveiled differences in PNNs distribution and their relationship with PV cells among distinct brain areas. Furthermore, we demonstrated relevant correlations between gene expression and connectivity patterns with PNN abundance suggesting PNNs as a marker of low plasticity structures and high fidelity, highly reliable connections. These results represent the first quantitative analysis of PNNs and PV cells in the entire mouse brain that could provide an important basis for studying the effects of the extracellular matrix on brain physiology