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<p>Imaging-based spatial transcriptomics (ST) is evolving as a pivotal technology in studying tumor biology and associated microenvironments. However, the strengths of the commercially available ST platforms in studying spatial biology have not been systematically evaluated using rigorously controlled experiments. We use serial 5 μm sections of formalin-fixed, paraffin-embedded surgically resected lung adenocarcinoma and pleural mesothelioma samples in tissue microarrays to compare the performance of the ST platforms (CosMx, MERFISH, and Xenium (uni/multi-modal)) in reference to bulk RNA sequencing, multiplex immunofluorescence, GeoMx, and hematoxylin and eosin staining data. In addition to an objective assessment of automatic cell segmentation and phenotyping, we perform a manual phenotyping evaluation to assess pathologically meaningful comparisons between ST platforms. Here, we show the intricate differences between the ST platforms, reveal the importance of parameters such as probe design in determining the data quality, and suggest reliable workflows for accurate spatial profiling and molecular discovery.</p>

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Comparison of imaging based single-cell resolution spatial transcriptomics profiling platforms using formalin-fixed paraffin-embedded tumor samples

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年9月26日

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  • Nejla Ozirmak Lermi,
  • Max Molina Ayala,
  • Sharia Hernandez,
  • Wei Lu,
  • Khaja Khan,
  • Alejandra Serrano,
  • Idania Lubo,
  • Leticia Hamana,
  • Katarzyna Tomczak,
  • Sean Barnes,
  • Jinzhuang Dou,
  • Qingnan Liang,
  • Ahmed N. Alrawi,
  • Claudio A. Arrechedera,
  • Kimberly S. Ayers,
  • Caludia A. Bedoya,
  • Elizabeth Burton,
  • Connie A. Chon,
  • Randy A. Chu,
  • Shadarra D. Crosby,
  • Jonathan Do,
  • Cibelle FP Lima,
  • Fu Szu-Chin,
  • Andy Futreal,
  • Ana L. Garcia,
  • Celia Garica-Prieto,
  • Swati Gite,
  • Curtis Gumbs,
  • Kristin J. Hargraves,
  • Meng He,
  • Chacha Horombe,
  • Heladio P. Ibarguen,
  • Stacy Jackson,
  • Jeena J. Jacob,
  • Isha Khanduri,
  • Walter K. Kinyua,
  • Mark Knafl,
  • Wenhua Lang,
  • Latasha D. Little,
  • Saradhi Mallampati,
  • Mary GT Mendoza,
  • Funda Meric-Bernstam,
  • Mohammad M. Mohammad,
  • Mario LM Piubelli,
  • Sabitha Prabhakaran,
  • Kenna R. Shaw,
  • Xiaofei Song,
  • Sandesh Subramanya,
  • Baohua Sun,
  • Shumaila Virani,
  • Wanlin Wang,
  • Scott E. Woodman,
  • Mingchu Xu,
  • Jianhua Zhang,
  • Qingxiu C. Zhang,
  • Shanyu Zhang,
  • Maria Gabriela Raso,
  • Ximing Tang,
  • Mei Jiang,
  • Beatriz Sanchez-Espiridion,
  • Annikka Weissferdt,
  • John Heymach,
  • Jianjun Zhang,
  • Boris Sepesi,
  • Tina Cascone,
  • Anne Tsao,
  • Mehmet Altan,
  • Reza Mehran,
  • Don Gibbons,
  • Ignacio Wistuba,
  • Cara Haymaker,
  • Ken Chen,
  • Luisa M. Solis Soto

摘要

Imaging-based spatial transcriptomics (ST) is evolving as a pivotal technology in studying tumor biology and associated microenvironments. However, the strengths of the commercially available ST platforms in studying spatial biology have not been systematically evaluated using rigorously controlled experiments. We use serial 5 μm sections of formalin-fixed, paraffin-embedded surgically resected lung adenocarcinoma and pleural mesothelioma samples in tissue microarrays to compare the performance of the ST platforms (CosMx, MERFISH, and Xenium (uni/multi-modal)) in reference to bulk RNA sequencing, multiplex immunofluorescence, GeoMx, and hematoxylin and eosin staining data. In addition to an objective assessment of automatic cell segmentation and phenotyping, we perform a manual phenotyping evaluation to assess pathologically meaningful comparisons between ST platforms. Here, we show the intricate differences between the ST platforms, reveal the importance of parameters such as probe design in determining the data quality, and suggest reliable workflows for accurate spatial profiling and molecular discovery.