Quantitative RNA Assessment and Long Term Stability in the FFPE Tumor Samples using Digital Spatial Profiler
Background: Long-term storage of tissue slides has been reported to induce reduced biomarker (e.g. proteins and messenger RNA) detection. This study aimed to evaluate the impact of long-term storage time (0, 16, 24 and 36 weeks) and treatment conditions (non-paraffin and paraffin dipping) at 4°C on RNA quality in formalin-fixed and paraffin-embedded (FFPE) tissue sections. Materials and methods: NanoString GeoMx Digital Spatial Profiling (DSP), a novel platform that allows spatial profiling, was used to profile RNA in human bladder cancer FFPE tissue sections. Results: We observed excellent consistency of quantitative DSP RNA counts of all targets between two different treatment conditions (R > 0.97, Pearson correlation) at each time point and among all four different storage time points (R > 0.96, Pearson correlation) within each treatment condition. No significant difference was observed in the percentage of target genes with sufficient signal across two different treatment conditions at any time point (0 week, P = 0.96; 16 weeks, P = 0.76; 24 weeks, P = 0.96; 36 weeks, P = 0.76, Kolmogorov-Smirnov test) and across all four different storage time points (P > 0.05, Kolmogorov-Smirnov test) in either treatment condition. Conclusion: Although both treatment conditions provided similar results in terms of count reproducibility and signal preservation, we recommend paraffin dipping to generate reproducible RNA results and optimize sample storage. Technology behind the NanoString GeoMx DSP platform shows a robust and reproducible RNA signal from multiple targets in the FFPE tissue sections stored at 4°C for at least up to 36 weeks. Keywords: GeoMx Digital Spatial Profiler; RNA signal preservation; formalin-fixed paraffin-embedded slides; long-term stability.
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基因水平:PCR Array、RT-PCR、PCR、单细胞测序
蛋白水平:MSD、Luminex、CBA、Elispot、Antibody Array、ELISA、Sengenics
细胞水平:细胞染色、细胞分选、细胞培养、细胞功能
组织水平:空间多组学、多重荧光免疫组化、免疫组化、免疫荧光
数据分析:流式数据分析、组化数据分析、多因子数据分析
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