Simulation of calcium signaling in fine astrocytic processes: Effect of spatial properties on spontaneous activity

Audrey Denizot, Misa Arizono, U Valentin Nägerl, Hédi Soula and Hugues Berry

PLOS Computational Biology, 2019, vol. 15, issue 8, 1-33

Abstract: Astrocytes, a glial cell type of the central nervous system, have emerged as detectors and regulators of neuronal information processing. Astrocyte excitability resides in transient variations of free cytosolic calcium concentration over a range of temporal and spatial scales, from sub-microdomains to waves propagating throughout the cell. Despite extensive experimental approaches, it is not clear how these signals are transmitted to and integrated within an astrocyte. The localization of the main molecular actors and the geometry of the system, including the spatial organization of calcium channels IP3R, are deemed essential. However, as most calcium signals occur in astrocytic ramifications that are too fine to be resolved by conventional light microscopy, most of those spatial data are unknown and computational modeling remains the only methodology to study this issue. Here, we propose an IP3R-mediated calcium signaling model for dynamics in such small sub-cellular volumes. To account for the expected stochasticity and low copy numbers, our model is both spatially explicit and particle-based. Extensive simulations show that spontaneous calcium signals arise in the model via the interplay between excitability and stochasticity. The model reproduces the main forms of calcium signals and indicates that their frequency crucially depends on the spatial organization of the IP3R channels. Importantly, we show that two processes expressing exactly the same calcium channels can display different types of calcium signals depending on the spatial organization of the channels. Our model with realistic process volume and calcium concentrations successfully reproduces spontaneous calcium signals that we measured in calcium micro-domains with confocal microscopy and predicts that local variations of calcium indicators might contribute to the diversity of calcium signals observed in astrocytes. To our knowledge, this model is the first model suited to investigate calcium dynamics in fine astrocytic processes and to propose plausible mechanisms responsible for their variability.Author summary: Astrocytes process information in the brain via calcium signals that can modulate neuronal communication. Astrocytic calcium signals are associated with brain functioning, including memory and learning, and are altered in the diseased brain. Astrocytic calcium signals display a huge spatio-temporal diversity, which mechanisms and functional roles are poorly understood. 80% of calcium signals occur in the gliapil, corresponding to astrocytic ramifications that are too thin to be detected by conventional light microscopy. Because of the small volumes at stake, we modeled astrocytic calcium signals in the gliapil with a stochastic spatially-explicit individual-based model. Our model successfully reproduces calcium signals that we measured in hippocampal astrocytic gliapil and sheds light to the importance of the localization of calcium sources. We predict that the diversity of calcium signals measured with fluorescent indicators might be partly due to local variations of the concentration of those indicators. We believe that this model will be useful to investigate the propagation of calcium signals within the sponge-like network of astrocytic processes, and eventually to better understand information processing in the brain.

Date: 2019
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
https://journals.plos.org/ploscompbiol/article?id=10.1371/journal.pcbi.1006795 (text/html)
https://journals.plos.org/ploscompbiol/article/fil ... 06795&type=printable (application/pdf)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:plo:pcbi00:1006795

DOI: 10.1371/journal.pcbi.1006795

Access Statistics for this article

More articles in PLOS Computational Biology from Public Library of Science
Bibliographic data for series maintained by ploscompbiol ().