Musu Yuan, Kaidi Jin, Hanying Yan, Amelia Schroeder, Chunyu Luo, Sicong Yao, Bernhard Dumoulin, Jonathan Levinsohn, Tianhao Luo, Jean R. Clemenceau, Inyeop Jang, Minji Kim, Yunhe Liu, Minghua Deng, Emma E. Furth, Parker C. Wilson, Anupma Nayak, Idania Carolina Lubo Julio, Luisa M. Solis Soto, Linghua Wang, Jeong Hwan Park, Katalin Suszták, Tae Hyun Hwang, Mingyao Li
Spatial omics technologies have transformed biomedical research by enabling high-resolution molecular profiling while preserving the native tissue architecture. These advances provide unprecedented insights into tissue structure and function. However, the high cost and time-intensive nature of spatial omics experiments necessitate careful experimental design, particularly in selecting regions of interest (ROIs) from large tissue sections. Currently, ROI selection is performed manually, which introduces subjectivity, inconsistency and a lack of reproducibility. Previous studies have shown strong correlations between spatial molecular patterns and histological features, suggesting that readily available and cost-effective histology images can be leveraged to guide spatial omics experiments. Here we present Smart Spatial omics (S2-omics), an end-to-end workflow that automatically selects ROIs from histology images with the goal of maximizing molecular information content in the ROIs. Through comprehensive evaluations across multiple spatial omics platforms and tissue types, we demonstrate that S2-omics enables systematic and reproducible ROI selection and enhances the robustness and impact of downstream biological discovery.