Multicolor microRNA FISH effectively differentiates tumor types
Neil Renwick, Pavol Cekan, Paul A. Masry, Sean E. McGeary, Jason B. Miller, Markus Hafner, Zhen Li, Aleksandra Mihailovic, Pavel Morozov, Miguel Brown, Tasos Gogakos, Mehrpouya B. Mobin, Einar L. Snorrason, Harriet E. Feilotter, Xiao Zhang, Clifford S. Perlis, Hong Wu, Mayte Suárez-Fariñas, Huichen Feng, Masahiro Shuda, Patrick S. Moore, Victor A. Tron, Yuan Chang, Thomas Tuschl
Neil Renwick, Pavol Cekan, Paul A. Masry, Sean E. McGeary, Jason B. Miller, Markus Hafner, Zhen Li, Aleksandra Mihailovic, Pavel Morozov, Miguel Brown, Tasos Gogakos, Mehrpouya B. Mobin, Einar L. Snorrason, Harriet E. Feilotter, Xiao Zhang, Clifford S. Perlis, Hong Wu, Mayte Suárez-Fariñas, Huichen Feng, Masahiro Shuda, Patrick S. Moore, Victor A. Tron, Yuan Chang, Thomas Tuschl
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Technical Advance

Multicolor microRNA FISH effectively differentiates tumor types

Abstract

MicroRNAs (miRNAs) are excellent tumor biomarkers because of their cell-type specificity and abundance. However, many miRNA detection methods, such as real-time PCR, obliterate valuable visuospatial information in tissue samples. To enable miRNA visualization in formalin-fixed paraffin-embedded (FFPE) tissues, we developed multicolor miRNA FISH. As a proof of concept, we used this method to differentiate two skin tumors, basal cell carcinoma (BCC) and Merkel cell carcinoma (MCC), with overlapping histologic features but distinct cellular origins. Using sequencing-based miRNA profiling and discriminant analysis, we identified the tumor-specific miRNAs miR-205 and miR-375 in BCC and MCC, respectively. We addressed three major shortcomings in miRNA FISH, identifying optimal conditions for miRNA fixation and ribosomal RNA (rRNA) retention using model compounds and high-pressure liquid chromatography (HPLC) analyses, enhancing signal amplification and detection by increasing probe-hapten linker lengths, and improving probe specificity using shortened probes with minimal rRNA sequence complementarity. We validated our method on 4 BCC and 12 MCC tumors. Amplified miR-205 and miR-375 signals were normalized against directly detectable reference rRNA signals. Tumors were classified using predefined cutoff values, and all were correctly identified in blinded analysis. Our study establishes a reliable miRNA FISH technique for parallel visualization of differentially expressed miRNAs in FFPE tumor tissues.

Authors

Neil Renwick, Pavol Cekan, Paul A. Masry, Sean E. McGeary, Jason B. Miller, Markus Hafner, Zhen Li, Aleksandra Mihailovic, Pavel Morozov, Miguel Brown, Tasos Gogakos, Mehrpouya B. Mobin, Einar L. Snorrason, Harriet E. Feilotter, Xiao Zhang, Clifford S. Perlis, Hong Wu, Mayte Suárez-Fariñas, Huichen Feng, Masahiro Shuda, Patrick S. Moore, Victor A. Tron, Yuan Chang, Thomas Tuschl

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Figure 1

Unsupervised hierarchical clustering of miRNA expression profiles.

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Unsupervised hierarchical clustering of miRNA expression profiles. Unsup...
Unsupervised hierarchical clustering was performed using log2 relative frequencies (RF) of miRNA precursor cluster sequence reads for the given cell line and FFPE tissue samples; MCV status is also indicated where available. miRNA precursor clusters were selected from the top 85% expressed miRNA precursor clusters across all samples. The number of members per precursor cluster is indicated in parentheses following the miRNA gene name; precursor clusters are named according to Farazi et al. (30). miR-205 and miR-375 expression values for all samples are indicated by red and green arrows, respectively.