Systematic benchmarking of imaging spatial transcriptomics platforms in FFPE tissues - CosMx Data

Emerging imaging spatial transcriptomics (iST) platforms and coupled analytical methods can recover cell-to-cell interactions, groups of spatially covarying genes, and gene signatures associated with pathological features, and are thus particularly well-suited for applications in formalin fixed paraffin embedded (FFPE) tissues. Here, we benchmark the performance of three commercial iST platforms—10X Xenium, Vizgen MERSCOPE, and Nanostring CosMx—on serial sections from tissue microarrays (TMAs) containing 17 tumor and 16 normal tissue types for both relative technical and biological performance. On matched genes, we find that Xenium consistently generates higher transcript counts per gene without sacrificing specificity. Xenium and CosMx measure RNA transcripts in concordance with orthogonal single-cell transcriptomics. All three platforms can perform spatially resolved cell typing with varying degrees of sub-clustering capabilities, with Xenium and CosMx finding slightly more clusters than MERSCOPE, albeit with different false discovery rates and cell segmentation error frequencies. Taken together, our analyses provide a comprehensive benchmark to guide the choice of iST method as researchers design studies with precious samples in this rapidly evolving field. In this study, we compared currently available FFPE-compatible iST platforms on matched tissue samples. We prepared a set of samples representative of typical archival FFPE tissues, comprised of 33 different tumor and normal tissue types, and acquired matched data from sequential sections according to the manufacturer’s best practices at the time of writing, generating a dataset of > 5.0 M cells. We analyzed the relative sensitivity and specificity of each method on shared transcripts and further quantified the concordance of the iST data across each platform with paired scRNA-seq data collected by 10x Chromium Single Cell Gene Expression FLEX. Then we focused on cell-level comparisons, evaluating the out-of-the-box segmentation for each platform based on detected genes and transcripts and coexpression patterns of known disjoint markers. Finally, we cross-compared the ability of each platform to identify cell type clusters with breast and breast cancer tissues as an example use case. Taken together, our work provides the first head-to-head comparison of these platforms across multiple archival healthy and cancerous FFPE tissue types.