Systematic benchmarking of high-throughput subcellular spatial transcriptomics platforms across human tumors

Pengfei Ren, Rui Zhang, Yunfeng Wang, Peng Zhang, Ce Luo, Suyan Wang, Xiaohong Li, Zongxu Zhang, Yanping Zhao, Yufeng He, Haorui Zhang, Yufeng Li, Zhidong Gao, Xiuping Zhang, Yahui Zhao, Zhihua Liu, Yuanguang Meng (), Zhe Zhang () and Zexian Zeng ()
Additional contact information
Pengfei Ren: Peking University
Rui Zhang: Peking University
Yunfeng Wang: Peking University
Peng Zhang: Peking University
Ce Luo: Peking University
Suyan Wang: Peking University Chengdu Academy for Advanced Interdisciplinary Biotechnologies
Xiaohong Li: Peking University
Zongxu Zhang: Peking University
Yanping Zhao: Tsinghua University
Yufeng He: Peking University
Haorui Zhang: Peking University
Yufeng Li: Chinese PLA Medical School
Zhidong Gao: Peking University People’s Hospital
Xiuping Zhang: Chinese PLA Medical School
Yahui Zhao: Chinese Academy of Medical Sciences and Peking Union Medical College
Zhihua Liu: Chinese Academy of Medical Sciences and Peking Union Medical College
Yuanguang Meng: Seventh Medical Center of Chinese PLA General Hospital
Zhe Zhang: Seventh Medical Center of Chinese PLA General Hospital
Zexian Zeng: Peking University

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

Abstract: Abstract Recent advancements in spatial transcriptomics technologies have significantly enhanced resolution and throughput, underscoring an urgent need for systematic benchmarking. Here, we generate serial tissue sections from colon adenocarcinoma, hepatocellular carcinoma, and ovarian cancer samples for systematic evaluation. Using these uniformly processed samples, we generate spatial transcriptomics data across four high-throughput platforms with subcellular resolution: Stereo-seq v1.3, Visium HD FFPE, CosMx 6K, and Xenium 5K. To establish ground truth datasets, we profile proteins on tissue sections adjacent to all platforms using CODEX and perform single-cell RNA sequencing on the same samples. Leveraging manual nuclear segmentation and detailed annotations, we systematically assess each platform’s performance across capture sensitivity, specificity, diffusion control, cell segmentation, cell annotation, spatial clustering, and concordance with adjacent CODEX. The uniformly generated and processed multi-omics dataset could advance computational method development and biological discoveries. The dataset is accessible via SPATCH, a user-friendly web server for visualization and download.

Date: 2025
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DOI: 10.1038/s41467-025-64292-3

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