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Communication with the hematopoietic system is a vital component of regulating brain function in health and disease. Traditionally, the major routes considered for this neuroimmune communication are by individual molecules such as cytokines carried by blood, by neural transmission, or, in more severe pathologies, by the entry of peripheral immune cells into the brain. In addition, functional mRNA from peripheral blood can be directly transferred to neurons via extracellular vesicles (EVs), but the parameters that determine their uptake are unknown. Using varied animal models that stimulate neuronal activity by peripheral inflammation, optogenetics, and selective proteasome inhibition of dopaminergic (DA) neurons, we show that the transfer of EVs from blood is triggered by neuronal activity in vivo. Importantly, this transfer occurs not only in pathological stimulation but also by neuronal activation caused by the physiological stimulus of novel object placement. This discovery suggests a continuous role of EVs under pathological conditions as well as during routine cognitive tasks in the healthy brain.
Neurons collect their inputs from other neurons by sending out arborized dendritic structures. However, the relationship between the shape of dendrites and the precise organization of synaptic inputs in the neural tissue remains unclear. Inputs could be distributed in tight clusters, entirely randomly or else in a regular grid-like manner. Here, we analyze dendritic branching structures using a regularity index R, based on average nearest neighbor distances between branch and termination points, characterizing their spatial distribution. We find that the distributions of these points depend strongly on cell types, indicating possible fundamental differences in synaptic input organization. Moreover, R is independent of cell size and we find that it is only weakly correlated with other branching statistics, suggesting that it might reflect features of dendritic morphology that are not captured by commonly studied branching statistics. We then use morphological models based on optimal wiring principles to study the relation between input distributions and dendritic branching structures. Using our models, we find that branch point distributions correlate more closely with the input distributions while termination points in dendrites are generally spread out more randomly with a close to uniform distribution. We validate these model predictions with connectome data. Finally, we find that in spatial input distributions with increasing regularity, characteristic scaling relationships between branching features are altered significantly. In summary, we conclude that local statistics of input distributions and dendrite morphology depend on each other leading to potentially cell type specific branching features.