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

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.

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Publication Details

Journal
Nature Communications
Published
2025-09-26
DOI
https://doi.org/10.1038/s41467-025-63414-1
Citations
8
Primary Topic
Single-cell and spatial transcriptomics
Type
article
Field-Weighted Citation Impact
6.45

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article

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

Leticia Hamana, Shanyu Zhang, Scott E. Woodman, Nejla Ozirmak Lermi et al.
8 citations
Nature Communications
Single-cell and spatial transcriptomics
6.45
article

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

Leticia Hamana, Shanyu Zhang, Scott E. Woodman, Nejla Ozirmak Lermi, Don L. Gibbons, Khaja Khan, Shumaila Virani, Claudia Bedoya, Mohammad M. Mohammad, Shadarra Crosby, Ximing Tang, Idania Carolina Lubo Julio, Cibelle Lima, Chacha Horombe, Elizabeth M. Burton, Meng He, Kristin J. Hargraves, Beatriz Sánchez‐Espiridión, Mehmet Altan, Alejandra G. Serrano, Jinzhuang Dou, Sharia Hernandez, John Victor Heymach, Cara Haymaker, Randy Chu, Baohua Sun, Xiaofei Song, Chae Yoon Chon, Reza John Mehran, Anne S. Tsao, Mei Jiang, Boris Sepesi, Tina Cascone, Mario Luiz Marques Piubelli, Qingnan Liang, Kenna Shaw, P. Andrew Futreal, Qingxiu Zhang, Wanlin Wang, Maria Gabriela Raso, M Mendoza, Annikka Weissferdt, Isha Khanduri, Stacy Jackson, Katarzyna Tomczak, Funda Meric‐Bernstam, Luisa M. Solis Soto, Swati Gite, Mark Knafl, Sean Barnes, Saradhi Mallampati, Sandesh Subramanya, Walter Kinyua, Heladio P. Ibarguen, Ana Lineth García, Curtis Gumbs, Latasha Little, Claudio A. Arrechedera, Jianjun Zhang, Ken Chen, Mingchu Xu, Wei Lu, Marta Ayala, Ignacio Wistuba, Kelsey Ayers, Jonathan Do, Sabitha Prabhakaran, Jeena Jacob, Jianhua Zhang, Wenhua Lang, Ahmed Al-Rawi, Fu Szu-Chin, Celia Garica-Prieto
article en
8 citations

Abstract

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.

Nature CommunicationsVol. 16(1)
The University of Texas MD Anderson Cancer Center (US)
National Institutes of Health, University of Texas MD Anderson Cancer Center, National Cancer Institute
Openalex Percentile: Top 3%
Single-cell and spatial transcriptomics
6.45
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