A Transcriptomic Map of Brain Aging in Mice
The amount of data on aging in animal models is only going to grow. Many large initiatives are focused on generating as much of a map of the biochemistry of aging as presently possible, and their output already far outpaces the ability of the broader scientific community to synthesize and understand this data. The new transcriptomic map noted here is an example of the type, and enormous research that will keep researchers busy in the decades ahead. A comprehensive understanding of the detailed progression of aging is perhaps less important to the near future of therapies to treat aging, however, as these will emerge from what is presently known of the contributing causes of aging, not from an understanding of the damage done by those causative mechanisms.
Biological ageing can be defined as a gradual loss of homeostasis across various aspects of molecular and cellular function. Mammalian brains consist of thousands of cell types, which may be differentially susceptible or resilient to ageing. Here we present a comprehensive single-cell RNA sequencing dataset containing roughly 1.2 million high-quality single-cell transcriptomes of brain cells from young adult and aged mice of both sexes, from regions spanning the forebrain, midbrain, and hindbrain. High-resolution clustering of all cells results in 847 cell clusters and reveals at least 14 age-biased clusters that are mostly glial types. At the broader cell subclass and supertype levels, we find age-associated gene expression signatures and provide a list of 2,449 unique differentially expressed genes (age-DE genes) for many neuronal and non-neuronal cell types.
Whereas most age-DE genes are unique to specific cell types, we observe common signatures with ageing across cell types, including a decrease in expression of genes related to neuronal structure and function in many neuron types, major astrocyte types and mature oligodendrocytes, and an increase in expression of genes related to immune function, antigen presentation, inflammation, and cell motility in immune cell types and some vascular cell types. Finally, we observe that some of the cell types that demonstrate the greatest sensitivity to ageing are concentrated around the third ventricle in the hypothalamus, including tanycytes, ependymal cells, and certain neuron types in the arcuate nucleus, dorsomedial nucleus, and paraventricular nucleus that express genes canonically related to energy homeostasis. Many of these types demonstrate both a decrease in neuronal function and an increase in immune response. These findings suggest that the third ventricle in the hypothalamus may be a hub for ageing in the mouse brain.
Overall, this study systematically delineates a dynamic landscape of cell-type-specific transcriptomic changes in the brain associated with normal ageing that will serve as a foundation for the investigation of functional changes in ageing and the interaction of ageing and disease.