Christian Thomsen, Birgit Truumees, Søren Nielsen, Boye Schnack Nielsen, Sara Rose Newell Jensen, Kasper Thorsen, Rasmus Røge
{"title":"GeoMx和RNAscope:它们在福尔马林固定乳腺癌组织中空间mRNA表达谱的效用的比较评估。","authors":"Christian Thomsen, Birgit Truumees, Søren Nielsen, Boye Schnack Nielsen, Sara Rose Newell Jensen, Kasper Thorsen, Rasmus Røge","doi":"10.1002/cyto.a.70020","DOIUrl":null,"url":null,"abstract":"<p>mRNA expression analysis in formalin-fixed tissue is essential for many biomarker and tumor microenvironment studies. NanoString GeoMx Digital Spatial Profiler is a recent technique that offers high-plex spatial transcriptomics (up to 18,000 genes), while RNAscope (1–4 plex) is a well-established mRNA in situ hybridization method. This study compares quantitative expression estimates obtained by GeoMX and RNAscope. Serial sections from two TMAs containing mammary cancers, including triple-negative (<i>n</i> = 48) and varying ER/HER2 status (<i>n</i> = 45), were analyzed using GeoMx Cancer Transcriptomics Atlas (CTA) covering approximately 1,800 genes and RNAscope probes for GATA3, SOX10, and PD-L1 mRNAs. Expression was quantified as counts/cell (GeoMx, geometric mean of five probes per gene) and average dots/cell (RNAscope, QuPath). Positivity was defined as above the limit of quantification (GeoMx) or > 0.1 dots/cell (RNAscope). High correlation was observed for GATA3 (<i>R</i> = 0.87) and SOX10 (<i>R</i> = 0.77) between methods. PD-L1 expression was high in only one core, precluding correlation analysis. RNAscope demonstrated higher sensitivity and a broader dynamic range than GeoMx. In conclusion, GeoMx CTA and RNAscope exhibit a strong correlation. GeoMx enabled highly multiplexed gene expression analysis, whereas RNAscope provided better sensitivity and single-cell resolution. The choice of method should be guided by study objectives.</p>","PeriodicalId":11068,"journal":{"name":"Cytometry Part A","volume":"109 2","pages":"147-154"},"PeriodicalIF":2.1000,"publicationDate":"2026-03-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/cyto.a.70020","citationCount":"0","resultStr":"{\"title\":\"GeoMx and RNAscope: A Comparative Assessment of Their Utility for Spatial mRNA Expression Profiling in Formalin-Fixed Breast Cancer Tissue\",\"authors\":\"Christian Thomsen, Birgit Truumees, Søren Nielsen, Boye Schnack Nielsen, Sara Rose Newell Jensen, Kasper Thorsen, Rasmus Røge\",\"doi\":\"10.1002/cyto.a.70020\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p>mRNA expression analysis in formalin-fixed tissue is essential for many biomarker and tumor microenvironment studies. NanoString GeoMx Digital Spatial Profiler is a recent technique that offers high-plex spatial transcriptomics (up to 18,000 genes), while RNAscope (1–4 plex) is a well-established mRNA in situ hybridization method. This study compares quantitative expression estimates obtained by GeoMX and RNAscope. Serial sections from two TMAs containing mammary cancers, including triple-negative (<i>n</i> = 48) and varying ER/HER2 status (<i>n</i> = 45), were analyzed using GeoMx Cancer Transcriptomics Atlas (CTA) covering approximately 1,800 genes and RNAscope probes for GATA3, SOX10, and PD-L1 mRNAs. Expression was quantified as counts/cell (GeoMx, geometric mean of five probes per gene) and average dots/cell (RNAscope, QuPath). Positivity was defined as above the limit of quantification (GeoMx) or > 0.1 dots/cell (RNAscope). High correlation was observed for GATA3 (<i>R</i> = 0.87) and SOX10 (<i>R</i> = 0.77) between methods. PD-L1 expression was high in only one core, precluding correlation analysis. RNAscope demonstrated higher sensitivity and a broader dynamic range than GeoMx. In conclusion, GeoMx CTA and RNAscope exhibit a strong correlation. GeoMx enabled highly multiplexed gene expression analysis, whereas RNAscope provided better sensitivity and single-cell resolution. 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GeoMx and RNAscope: A Comparative Assessment of Their Utility for Spatial mRNA Expression Profiling in Formalin-Fixed Breast Cancer Tissue
mRNA expression analysis in formalin-fixed tissue is essential for many biomarker and tumor microenvironment studies. NanoString GeoMx Digital Spatial Profiler is a recent technique that offers high-plex spatial transcriptomics (up to 18,000 genes), while RNAscope (1–4 plex) is a well-established mRNA in situ hybridization method. This study compares quantitative expression estimates obtained by GeoMX and RNAscope. Serial sections from two TMAs containing mammary cancers, including triple-negative (n = 48) and varying ER/HER2 status (n = 45), were analyzed using GeoMx Cancer Transcriptomics Atlas (CTA) covering approximately 1,800 genes and RNAscope probes for GATA3, SOX10, and PD-L1 mRNAs. Expression was quantified as counts/cell (GeoMx, geometric mean of five probes per gene) and average dots/cell (RNAscope, QuPath). Positivity was defined as above the limit of quantification (GeoMx) or > 0.1 dots/cell (RNAscope). High correlation was observed for GATA3 (R = 0.87) and SOX10 (R = 0.77) between methods. PD-L1 expression was high in only one core, precluding correlation analysis. RNAscope demonstrated higher sensitivity and a broader dynamic range than GeoMx. In conclusion, GeoMx CTA and RNAscope exhibit a strong correlation. GeoMx enabled highly multiplexed gene expression analysis, whereas RNAscope provided better sensitivity and single-cell resolution. The choice of method should be guided by study objectives.
期刊介绍:
Cytometry Part A, the journal of quantitative single-cell analysis, features original research reports and reviews of innovative scientific studies employing quantitative single-cell measurement, separation, manipulation, and modeling techniques, as well as original articles on mechanisms of molecular and cellular functions obtained by cytometry techniques.
The journal welcomes submissions from multiple research fields that fully embrace the study of the cytome:
Biomedical Instrumentation Engineering
Biophotonics
Bioinformatics
Cell Biology
Computational Biology
Data Science
Immunology
Parasitology
Microbiology
Neuroscience
Cancer
Stem Cells
Tissue Regeneration.