Fragmented metadata in spatial omics archives has rendered large volumes of multimodal molecular-histological data inaccessible as 'dark data'. Here, we introduce SpatialDataAgent, an agentic workflow for autonomous spatial omics data curation, combining schema-constrained evidence evaluation with a self-refining standardization agent. Applied to a decade of GEO records, SpatialDataAgent identified 769 paired H&E-spatial transcriptomics (ST) datasets, representing a 6.4-fold scale expansion over existing manually curated baselines. Within the benchmarking window, the framework achieved a 141% increase in high-confidence (Class A) paired datasets, which were automatically filtered and assembled to establish HESRT (a datalake containing 29.2 million spots/cells), establishing a blueprint for evidence-grounded autonomous curation of multimodal biomedical archives.
Ji, J.-H., Zou, Q., Cheng, J., She, Z., Hao, Y., Liu, W., Zhang, D., Wang, Z., Yu, J.-T., Yuan, Z.
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