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Authors & Affiliations
Caterina Ciani, Giulio Pistorio, Marika Mearelli, Laura Pinfildi, Simone Cauzzo, Ester Bruno, Sun Zhenyang, Fabio Anzà, Julio Hechavarria, Jean-Marie Graic, Maurizio De Pittà, Chiara Magliaro, Carmen Falcone
Abstract
Over recent years, there has been an increasing need for a better understanding of the identity and the roles of astrocytes and their involvement in cognitive abilities. Indeed, they play a crucial role in brain functions, they show primate-specific features, and they are relevant in several diseases. Investigations on astrocytes across evolution have rarely been performed. However, a comprehensive comparison of astrocytes in a diverse range of mammals is pivotal for understanding the morphology across species and the related modifications in gene expression and functions. Our project aims to investigate astrocytes’ diversity across mammals, by characterizing the distribution and the single-cell morphology of different subpopulations of astrocytes within different cortical layers across mammals. In details, we analyzed samples from prefrontal cortex of: Primates (chimpanzee, rhesus macaque, human), Carnivora (tiger, lion, leopard), Artiodactila (cow, tursiops), Rodentia (mouse) and Chiroptera (Seba’s short-tailed bat). We immunostained these samples with various astrocyte markers (i.e., GFAP, ALDH1L1, S100β, GLAST) to compare: (1) the distribution of different astrocyte subpopulations, and (2) the single-cell astrocyte morphology reconstructed with an algorithm-driven segmentation. Our previous results showed an increase in ILA morphological complexity and density in primates. With this project, we will unlock unprecedented details of the distribution and the single-cell morphological complexity of different astrocyte subtypes across different layers and different mammals, with a special focus on primates and humans. Data obtained from this research have the potential to lead to new fascinating hypotheses on the role of astrocytes in primate neuroanatomical, behavioral and cognitive complexity.