Systematic benchmarking of imaging spatial transcriptomics platforms in FFPE tissues

Huan Wang, Ruixu Huang, Jack Nelson, Ce Gao, Miles Tran, Anna Yeaton, Sachi Krishna, Kristen Felt, Kathleen L. Pfaff, Teri Bowman, Scott J. Rodig, Kevin Wei (), Brittany A. Goods () and Samouil L. Farhi ()
Additional contact information
Huan Wang: Broad Institute of MIT and Harvard, Spatial Technology Platform
Ruixu Huang: Molecular and Systems Biology and Program in Quantitative Biomedical Sciences at Dartmouth College, Thayer School of Engineering
Jack Nelson: Broad Institute of MIT and Harvard, Spatial Technology Platform
Ce Gao: Brigham and Women’s Hospital at Harvard Medical School, Division of Rheumatology, Inflammation, and Immunity
Miles Tran: Brigham and Women’s Hospital at Harvard Medical School, Division of Rheumatology, Inflammation, and Immunity
Anna Yeaton: Broad Institute of MIT and Harvard, Spatial Technology Platform
Sachi Krishna: Broad Institute of MIT and Harvard, Spatial Technology Platform
Kristen Felt: Brigham & Women’s Hospital and Dana-Farber Cancer Institute, ImmunoProfile
Kathleen L. Pfaff: Dana-Farber Cancer Institute, Center for Immuno-Oncology, Tissue Biomarker Laboratory
Teri Bowman: Brigham and Women’s Hospital, Department of Pathology
Scott J. Rodig: Dana-Farber Cancer Institute, Center for Immuno-Oncology, Tissue Biomarker Laboratory
Kevin Wei: Brigham and Women’s Hospital at Harvard Medical School, Division of Rheumatology, Inflammation, and Immunity
Brittany A. Goods: Molecular and Systems Biology and Program in Quantitative Biomedical Sciences at Dartmouth College, Thayer School of Engineering
Samouil L. Farhi: Broad Institute of MIT and Harvard, Spatial Technology Platform

Nature Communications, 2025, vol. 16, issue 1, 1-17

Abstract: Abstract 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.

Date: 2025
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DOI: 10.1038/s41467-025-64990-y

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