A Hierarchical Multimodal Knowledge Graph for Neural Cell-Type-Specific Regulation Integrating Single-Cell Transcriptomics and Literature Evidence
TL;DR
A neural-cell-centric multimodal knowledge graph that transforms fragmented regulatory evidence into a standardized, computable substrate and provides a structured basis for cross-study comparison, hypothesis generation and knowledge-guided reasoning in neural cell-type-specific regulation.
Abstract
The nervous system comprises highly diverse cell types governed by cell-type-specific molecular regulatory programs. However, regulatory evidence is scattered across unstructured literature and described using inconsistent cell-type nomenclature and granularity, hindering systematic integration and cross-study comparison. Here, we construct a neural-cell-centric multimodal knowledge graph that transforms fragmented regulatory evidence into a standardized, computable substrate. We establish a three-level hierarchical cell-type taxonomy anchored to the Cell Ontology (79 nodes), integrate two large-scale human brain single-cell transcriptomic datasets (over 4 million cells) to derive molecular fingerprints, and use a large language model to retain 25,812 curated regulatory evidence records from PubMed abstracts. The resulting Neo4j graph contains 41,532 directed relationships. For knowledge graph embedding, we export a deduplicated non-paper training subgraph containing 19,819 triples over 10,660 entities, supporting cell-type-specific link prediction that prioritizes candidate regulators and markers, illustrated here for microglia. This framework provides a structured basis for cross-study comparison, hypothesis generation and knowledge-guided reasoning in neural cell-type-specific regulation.