Stochastic Subcellular Organization of Dense-Core Vesicles Revealed by Point Pattern Analysis

Deletion of β-Neurexins in Mice Alters the Distribution of Dense-Core Vesicles in Presynapses of Hippocampal and Cerebellar Neurons

Communication between neurons through synapses includes the release of neurotransmitter-containing synaptic vesicles (SVs) and of neuromodulator-containing dense-core vesicles (DCVs). Neurexins (Nrxns), a polymorphic family of cell surface molecules encoded by three genes in vertebrates (Nrxn1–3), have been proposed as essential presynaptic organizers and as candidates for cell type-specific or even synapse-specific regulation of synaptic vesicle exocytosis. However, it remains unknown whether Nrxns also regulate DCVs. Here, we report that at least β-neurexins (β-Nrxns), an extracellularly smaller Nrxn variant, are involved in the distribution of presynaptic DCVs. We found that conditional deletion of all three β-Nrxn isoforms in mice by lentivirus-mediated Cre recombinase expression in primary hippocampal neurons reduces the number of ultrastructurally identified DCVs in presynaptic boutons. Consistently, colabeling against marker proteins revealed a diminished population of chromogranin A- (ChrgA-) positive DCVs in synapses and axons of β-Nrxn-deficient neurons. Moreover, we validated the impaired DCV distribution in cerebellar brain tissue from constitutive β-Nrxn knockout (β-TKO) mice, where DCVs are normally abundant and β-Nrxn isoforms are prominently expressed. Finally, we observed that the ultrastructure and marker proteins of the Golgi apparatus, responsible for packaging neuropeptides into DCVs, seem unchanged. In conclusion, based on the validation from the two deletion strategies in conditional and constitutive KO mice, two neuronal populations from the hippocampus and cerebellum, and two experimental protocols in cultured neurons and in the brain tissue, this study presented morphological evidence that the number of DCVs at synapses is altered in the absence of β-Nrxns. Our results therefore point to an unexpected contribution of β-Nrxns to the organization of neuropeptide and neuromodulator function, in addition to their more established role in synaptic vesicle release.

Order Through Disorder: The Characteristic Variability of Systems

Randomness characterizes many processes in nature, and therefore its importance cannot be overstated. In the present study, we investigate examples of randomness found in various fields, to underlie its fundamental processes. The fields we address include physics, chemistry, biology (biological systems from genes to whole organs), medicine, and environmental science. Through the chosen examples, we explore the seemingly paradoxical nature of life and demonstrate that randomness is preferred under specific conditions. Furthermore, under certain conditions, promoting or making use of variability-associated parameters may be necessary for improving the function of processes and systems.

Kinesin-3 Responds to Local Microtubule Dynamics to Target Synaptic Cargo Delivery to the Presynapse

Neurons in the CNS establish thousands of en passant synapses along their axons. Robust neurotransmission depends on the replenishment of synaptic components in a spatially precise manner. Using live-cell microscopy and single-molecule reconstitution assays, we find that the delivery of synaptic vesicle precursors (SVPs) to en passant synapses in hippocampal neurons is specified by an interplay between the kinesin-3 KIF1A motor and presynaptic microtubules. Presynaptic sites are hotspots of dynamic microtubules rich in GTP-tubulin. KIF1A binds more weakly to GTP-tubulin than GDP-tubulin and competes with end-binding (EB) proteins for binding to the microtubule plus end. A disease-causing mutation within KIF1A that reduces preferential binding to GDP- versus GTP-rich microtubules disrupts SVP delivery and reduces presynaptic release upon neuronal stimulation. Thus, the localized enrichment of dynamic microtubules along the axon specifies a localized unloading zone that ensures the accurate delivery of SVPs, controlling presynaptic strength in hippocampal neurons.

Moran's I quantifies spatio-temporal pattern formation in neural imaging data

Motivation

Neural activities of the brain occur through the formation of spatio-temporal patterns. In recent years, macroscopic neural imaging techniques have produced a large body of data on these patterned activities, yet a numerical measure of spatio-temporal coherence has often been reduced to the global order parameter, which does not uncover the degree of spatial correlation. Here, we propose to use the spatial autocorrelation measure Moran’s I, which can be applied to capture dynamic signatures of spatial organization. We demonstrate the application of this technique to collective cellular circadian clock activities measured in the small network of the suprachiasmatic nucleus (SCN) in the hypothalamus.

Results

We found that Moran’s I is a practical quantitative measure of the degree of spatial coherence in neural imaging data. Initially developed with a geographical context in mind, Moran’s I accounts for the spatial organization of any interacting units. Moran’s I can be modified in accordance with the characteristic length scale of a neural activity pattern. It allows a quantification of statistical significance levels for the observed patterns. We describe the technique applied to synthetic datasets and various experimental imaging time-series from cultured SCN explants. It is demonstrated that major characteristics of the collective state can be described by Moran’s I and the traditional Kuramoto order parameter R in a complementary fashion.

Availability and implementation

Python 2.7 code of illustrative examples can be found in the Supplementary Material.

Supplementary information

Supplementary data are available at Bioinformatics online.