High-throughput single-cell omics of non-human primate brain tissue provides a powerful platform to investigate the molecular basis of brain aging. Here, we present a comprehensive transcriptomic and chromatin accessibility atlas of 2,955,873 nuclei from eight brain regions of 23 female cynomolgus macaques spanning the adult lifespan, including exceptionally old individuals. Our analyses reveal dynamic, cell-subtype- and region-specific age-related changes in core brain functions, including synaptic communication and axon myelination. We identify multicellular networks in the pons and medulla as a previously unrecognized hotspot of primate brain aging, highlighting white matter vulnerability as a central feature of aging. Integration with human brain aging and neurodegeneration datasets reveals both shared and divergent molecular mechanisms. We further define transcription factors and age-related chromatin remodeling programs linked to longevity and neurodegeneration. This spatiotemporal atlas establishes a foundational framework for understanding the cellular and regulatory architecture of primate brain aging and its links to disease.
Precise delineation of cortical layers is fundamental for understanding human brain organization, cell-type architecture, and disease-related tissue alterations. However, traditional anatomy-based methods often lack molecular resolution and suffer from inter-observer subjectivity. Here, we present gene expression-defined cortical layers (GD-Ls) using the BayesSpace algorithm, a high-resolution framework for cortical parcellation based on spatial transcriptomics. Compared with traditional anatomy-based approaches, GD-Ls more accurately resolve laminar boundaries and capture fine-scale laminar heterogeneity, including sublayer-like domains within L1, L3, and L6, as well as a molecularly distinct transition zone at the gray-white matter interface. Validation across diverse cortical lobes, multiple spatial platforms, and independent healthy postmortem datasets demonstrates that GD-Ls capture the intrinsic molecular architecture of the cortex irrespective of tissue source. Furthermore, cross-species analyses show that this framework is extensible to macaque and mouse cortices. Crucially, GD-Ls successfully identify subtle laminar disorganization and aberrant cellular and molecular signatures in pathologically altered tissues, which are often missed by conventional histology. Together, GD-Ls provide an objective and reproducible tool for standardized cortical mapping and for identifying early pathological signatures in the human brain. The source code is available on GitHub (https://github.com/YanrongWei/GD-Ls).
Yanrong Wei, Youzhe He, Yuyang Liu et al.· Genome Medicine· 0 citations