Rigor and Reproducibility of Spatial Transcriptomics Performed on Clinically Sourced Human Tissues

Spatial transcriptomic profiling enables precise quantification of gene expression with simultaneous localization of expression profiles onto tissue structures. Several implementations of these approaches have been released as commercialized platforms that will allow multiple laboratories to improve our understanding of human disease mechanisms. There is also intense interest in applying these methods in clinical trials or as laboratory-developed tests to aid in the diagnosis of disease. However, before these technologies can be broadly deployed in clinical research and diagnostics, it is necessary to thoroughly understand their performance in real-world conditions. In this study, we vet the technical reproducibility, data normalization methods, and assay sensitivity focusing predominantly on one widely used spatial transcriptomics methodology, digital spatial profiling. We also compare its performance with a single molecular imager, a newer platform with single-cell resolution. Using clinically sourced human kidney tissues and biopsies as exemplars, we find that digital spatial profiling exhibits high rigor and reproducibility. We show that normalization approaches can impact the biological interpretation of spatial transcriptomics data. Although there is good concordance between multicellular and single-cell resolution methods, there are tradeoffs in cost, execution time, and sensitivity of detection, which may affect which approach is chosen. Our study lays a practical foundation for the incorporation of spatial transcriptomics methods into clinical workflows.