Advances in quantitative analysis of astrocytes using machine learning

Abstract

Astrocytes, a subtype of glial cells, are starshaped cells that are involved in the homeostasis and blood flow control of the central nervous system (CNS). They are known to provide structural and functional support to neurons, including the regulation of neuronal activation through extracellular ion concentrations, the regulation of energy dynamics in the brain through the transfer of lactate to neurons, and the modulation of synaptic transmission via the release of neurotransmitters such as glutamate and adenosine triphosphate. In addition, astrocytes play a critical role in neuronal reconstruction after brain injury, including neurogenesis, synaptogenesis, angiogenesis, repair of the blood-brain barrier, and glial scar formation after traumatic brain injury (Zhou et al., 2020). The multifunctional role of astrocytes in the CNS with tasks requiring close contact with their targets is reflected by their morphological complexity, with processes and ramifications occurring over multiple scales where interactions are plastic and can change depending on the physiological conditions. Another major feature of astrocytes is reactive astrogliosis, a process occurring in response to traumatic brain injury, neurological diseases, or infection which involves substantial morphological alterations and is often accompanied by molecular, cytoskeletal, and functional changes that ultimately play a key role in the disease outcome (Schiweck et al., 2018). Because morphological changes in astrocytes correlate so significantly with brain injury and the development of pathologies of the CNS, there is a major interest in methods to reliably detect and accurately quantify such morphological alterations. We review below the recent progress in the quantitative analysis of images of astrocytes. We remark that, while our discussion is focused on astrocytes, the same methods discussed below can be applied to other types of complex glial cells.