The brain vasculature comprises diverse specialized cells that are essential for brain function, yet their spatial organization remains poorly understood. Here, we construct a comprehensive cerebrovascular cell atlas encompassing 314,535 transcriptomes that captures the arteriovenous axis and defines consensus cell states. We then perform spatial transcriptomics to map 1,529,740 cells across the human temporal cortex and hippocampus, uncovering stereotyped micro-communities termed vascular cell ensembles. These ensembles comprise specialized subsets of endothelial cells, mural cells, fibroblasts, and perivascular macrophages that align with the arteriovenous architecture to coordinate segment-specific functions, such as neurovascular coupling, blood-brain barrier transport, and immune surveillance. By overlaying genetic risk and pharmacologic reactivity, we identify ensemble-specific susceptibilities and candidate therapeutic targets across neurological diseases, including small vessel disease and stroke. This study provides a resource to dissect the spatial and functional logic underlying human cerebrovascular biology and establishes a blueprint for decoding neurological disease susceptibility and therapeutic response.
Jerry C. Wang, Damian A Sanchez, Santhosh Arul et al.· Cell· 0 citations
Biomedical research is increasingly constrained by repetitive, fragmented workflows that slow discovery. We introduce Biomni, a general-purpose biomedical artificial intelligence agent that autonomously executes diverse research tasks. To map the biomedical action space, Biomni's action-discovery agent mines tools, databases, and protocols from thousands of publications across 25 domains, building a unified agentic environment. Its general-purpose architecture integrates large language model reasoning with retrieval-augmented planning and code-based execution, dynamically composing workflows without predefined templates. Systematic benchmarking shows strong generalization across heterogeneous tasks-causal gene prioritization, drug repurposing, rare-disease diagnosis, microbiome analysis, and molecular cloning-without task-specific tuning. Real-world case studies demonstrate Biomni interpreting multi-modal datasets, optimizing protein stability, orchestrating wet-lab instruments, and generating experimentally testable protocols. Biomni envisions artificial intelligence augmenting human scientists and accelerating discovery.
Kexin Huang, Serena Zhang, Hanchen Wang et al.· Science· 19 citations· ⚡2