Jasmine Plummer, Felipe Segato Dezem, David P. Cook, Jiwoon Park, Luke Zhang, Yutian Liu, Maycon Marção, Hannah DuBose, Wani Arjumand, Kellie Wise, Michael J. Roach, Kate Harvey, Taopeng Wang, Kirk B. Jensen, Natalia S. Morosini, Roberto De Gregorio, Alicia Alonso, Shauna L Houlihan, Robert E. Schwartz, Erika Hissong, Catherine Snopkowski, Jeffrey L. Wrana, Natalie K. Ryan, Lisa M. Butler, George Church, Alexander Swarbrick, Christopher E. Mason, Luciano G. Martelotto
Spatial transcriptomics lacks standardized metrics for evaluating imaging-based in situ hybridization technologies across sites. In this study, we generated the Spatial Touchstone (ST) dataset from six tissue types across several global sites with centralized sectioning, analyzed on both Xenium and CosMx platforms. These platforms were selected for their widespread use and distinct chemistries. We assessed reproducibility, sensitivity, dynamic ranges, signal-to-noise ratio, false discovery rates, cell type annotation and congruence with single-cell profiling. This study offers ST standardized operating procedures (STSOPs) and an open-source software, SpatialQM, enabling evaluation of samples across all technical metrics and direct imputation of cell annotations. The generated imaging-based spatial transcriptomics data repository comprises 254 spatial profiles, incorporating both public and newly generated ST datasets in a web-based application, which enables analysis and comparison of user data against an extensive collection of imaging-based datasets. Finally, we establish best practices and metrics to evaluate and integrate imaging-based multi-omics data from single cells into spatial transcriptomics to spatial proteomics.