Dual adeno-associated virus system for selective and sparse labeling of astrocytes

Astrocytes are the most abundant glial cells in the central nervous system. They perform a diverse array of functions, with a critical role in structural integrity, synapse formation, and neurotransmission. These cells exhibit substantial regional heterogeneity and display variable responses to different neurological diseases. Such diversity in astrocyte morphology and function is essential for understanding both normal brain function and the underlying mechanisms of neurological disorders. To investigate this heterogeneity, we developed a novel method for the selective and sparse labeling of astrocytes in various brain regions. This technique utilizes a dual adeno-associated virus system that allows for the expression of Cre recombinase and enhanced green fluorescent protein under the control of the glial fibrillary acidic protein (GfaABC1D) promoter. The system was tested in C57BL/6J mice and successfully labeled astrocytes across multiple brain regions. The method enabled the detailed visualization of individual astrocytes-including their intricate peripheral processes-through three-dimensional reconstructions from confocal microscopy images. Furthermore, the labeling efficiency of this dual adeno-associated virus technology was validated by examining astrocyte function in a spared nerve injury model and through chemogenetic modulation. This innovative approach holds great promise for future research because it enables a more comprehensive understanding of astrocyte variation not only in spared nerve injury but also in a broad spectrum of neurological diseases. The ability to selectively label and study astrocytes in different brain regions provides a powerful tool for exploring the complexities of these essential cells and their roles in physiological and pathological conditions.

PHP.eB; Sholl analysis; astrocytes; chemogenetic modulation; dual-adeno-associated virus system; glial fibrillary acidic protein (GfaABC1D) promoter; hierarchical clustering approach; morphological parameter analysis; spared nerve injury; sparse labeling.