计算机科学
多路复用
转录组
仿形(计算机编程)
数字化病理学
DNA微阵列
图像分辨率
工作流程
分割
计算生物学
病理
空间分析
基因表达谱
腺癌
快照(计算机存储)
基本事实
H&E染色
模式识别(心理学)
生物
像素
热点(地质)
高分辨率
生物信息学
人工智能
微阵列
作者
Nejla Ozirmak Lermi,Max Molina Ayala,Sharia Hernandez,Wei Lu,Khaja Khan,Alejandra G. Serrano,Idania Carolina Lubo Julio,Leticia Hamana,Katarzyna Tomczak,Sean Barnes,Jinzhuang Dou,Qingnan Liang,RTI Team,Ahmed Al-Rawi,Claudio A. Arrechedera,Kimberly S. Ayers,Claudia Bedoya,Elizabeth M. Burton,Connie A. Chon,Randy Chu
标识
DOI:10.1038/s41467-025-63414-1
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. Spatial cell distribution within a tissue microenvironment is a rapidly advancing field. Here, authors assess three commercially available single-cell resolution spatial transcriptomics approaches (CosMx, MERFISH, and Xenium) to inform which technology outperforms for immune profiling of solid tumors using patient samples.
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