SpatialDataAgent: Autonomous Curation of a Decade of Public Spatial Omics at Scale

Spatial transcriptomics maps gene activity within tissue sections. When paired with tissue images, these measurements connect molecular activity to visible tissue structures, providing a basis for studying cellular organization and developing image-based molecular analyses. Yet public spatial omics data often remain difficult to reuse—stored across non-standard archives where coordinates, histology images, and molecular counts frequently diverge, use proprietary containers, or are mixed with dissociated single-cell data. Here, we present SpatialDataAgent, an autonomous system that converts public deposits into standardized multimodal datasets at scale. SpatialDataAgent is designed to preserve source-deposited measurements through source-grounded coordinate transformations and explicit data-preservation constraints: it grounds coordinate mappings in deposited metadata, uses a catalog of 26 specialized domain tools for spatial formats, and checks candidate alignment through automated visual diagnostics. Evaluated on 400 reference studies, our screening module identifies eligible datasets with 91.75% classification accuracy and a 0.913 macro-F1 score. In a controlled evaluation across 40 diverse GEO studies (331 samples per condition), domain workflow support increases human-reviewed visual alignment from 54.7% to 77.6% (+23.0 percentage points; 95% bootstrap CI: 8.6–45.3), reaching 81.0% when coupled with visual quality feedback. Using SpatialDataAgent, we construct and release HESRT (Histology-paired Expression atlas of Spatially Resolved Transcriptomics), an open atlas of 4,468 human-reviewed multimodal samples comprising 37.05 million spatial observations across multiple species and technologies. HESRT makes matched images and gene measurements available for studies of tissue organization and image-based molecular modeling.