BRAINCELL-AID: An Agentic AI Created Brain Cell Type Resource for Community Annotation

Authors:Rongbin Li, Wenbo Chen, Zhao Li, Rodrigo Munoz-Castaneda, Jinbo Li, Neha S. Maurya, Arnav Solanki, Huan He, Hanwen Xing, Meaghan Ramlakhan, Zachary Wise, Nelson Johansen, Zhuhao Wu, Hua Xu, Michael Hawrylycz, W. Jim Zheng

Abstract:Single-cell RNA sequencing has transformed our ability to identify diverse cell types and their transcriptomic signatures. However, annotating these signatures-especially those involving poorly characterized genes-remains a major challenge. Traditional methods, such as Gene Set Enrichment Analysis (GSEA), depend on well-curated annotations and often perform poorly in these contexts. Large Language Models (LLMs) offer a promising alternative but struggle to represent complex biological knowledge within structured ontologies. To address this, we present BRAINCELL-AID (BRAINCELL-AID: this https URL), a novel multi-agent AI system that integrates free-text descriptions with ontology labels to enable more accurate and robust gene set annotation. By incorporating retrieval-augmented generation (RAG), we developed a robust agentic workflow that refines predictions using relevant PubMed literature, reducing hallucinations and enhancing interpretability. Using this workflow, we achieved correct annotations for 77% of mouse gene sets among their top predictions. Applying this approach, we annotated 5,322 brain cell clusters from the comprehensive mouse brain cell atlas generated by the BRAIN Initiative Cell Census Network, enabling novel insights into brain cell function by identifying region-specific gene co-expression patterns and inferring functional roles of gene ensembles. BRAINCELL-AID also identifies Basal Ganglia-related cell types with neurologically meaningful descriptions. Hence, we create a valuable resource to support community-driven cell type annotation.

Artificial Intelligence (cs.AI)

arXiv:2510.17064 [cs.AI]

(or arXiv:2510.17064v4 [cs.AI] for this version)