Dataset Information

RollFISH achieves robust quantification of single-molecule RNA biomarkers in paraffin-embedded tumor tissue samples.

ABSTRACT: Single-molecule RNA fluorescence in situ hybridization (smFISH) represents a promising approach to quantify the expression of clinically useful biomarkers in tumor samples. However, routine application of smFISH to formalin-fixed, paraffin-embedded (FFPE) samples is challenging due to the low signal intensity and high background noise. Here we present RollFISH, a method combining the specificity of smFISH with the signal boosting of rolling circle amplification. We apply RollFISH to quantify widely used breast cancer biomarkers in cell lines and FFPE samples. Thanks to the high signal-to-noise ratio, we can visualize selected biomarkers at low magnification (20?×?) across entire tissue sections, and thus assess their spatial heterogeneity. Lastly, we apply RollFISH to quantify HER2 mRNA in 150 samples on a single tissue microarray, achieving a sensitivity and specificity of detection of HER2-positive samples of ~90%. RollFISH is a robust method for quantifying the expression and intratumor heterogeneity of biomarkers in FFPE tissues.

SUBMITTER: Wu C

PROVIDER: S-EPMC6262000 | biostudies-literature | 2018

REPOSITORIES: biostudies-literature

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RollFISH achieves robust quantification of single-molecule RNA biomarkers in paraffin-embedded tumor tissue samples.

Wu Chenglin C Simonetti Michele M Rossell Carla C Mignardi Marco M Mirzazadeh Reza R Annaratone Laura L Marchiò Caterina C Sapino Anna A Bienko Magda M Crosetto Nicola N Nilsson Mats M

Communications biology 20181128

Single-molecule RNA fluorescence in situ hybridization (smFISH) represents a promising approach to quantify the expression of clinically useful biomarkers in tumor samples. However, routine application of smFISH to formalin-fixed, paraffin-embedded (FFPE) samples is challenging due to the low signal intensity and high background noise. Here we present RollFISH, a method combining the specificity of smFISH with the signal boosting of rolling circle amplification. We apply RollFISH to quantify wid ...[more]

PMID: 30511022

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Project description:BackgroundWe have developed a gene expression assay (Whole-Genome DASL), capable of generating whole-genome gene expression profiles from degraded samples such as formalin-fixed, paraffin-embedded (FFPE) specimens.Methodology/principal findingsWe demonstrated a similar level of sensitivity in gene detection between matched fresh-frozen (FF) and FFPE samples, with the number and overlap of probes detected in the FFPE samples being approximately 88% and 95% of that in the corresponding FF samples, respectively; 74% of the differentially expressed probes overlapped between the FF and FFPE pairs. The WG-DASL assay is also able to detect 1.3-1.5 and 1.5-2 -fold changes in intact and FFPE samples, respectively. The dynamic range for the assay is approximately 3 logs. Comparing the WG-DASL assay with an in vitro transcription-based labeling method yielded fold-change correlations of R(2) approximately 0.83, while fold-change comparisons with quantitative RT-PCR assays yielded R(2) approximately 0.86 and R(2) approximately 0.55 for intact and FFPE samples, respectively. Additionally, the WG-DASL assay yielded high self-correlations (R(2)>0.98) with low intact RNA inputs ranging from 1 ng to 100 ng; reproducible expression profiles were also obtained with 250 pg total RNA (R(2) approximately 0.92), with approximately 71% of the probes detected in 100 ng total RNA also detected at the 250 pg level. When FFPE samples were assayed, 1 ng total RNA yielded self-correlations of R(2) approximately 0.80, while still maintaining a correlation of R(2) approximately 0.75 with standard FFPE inputs (200 ng).Conclusions/significanceTaken together, these results show that WG-DASL assay provides a reliable platform for genome-wide expression profiling in archived materials. It also possesses utility within clinical settings where only limited quantities of samples may be available (e.g. microdissected material) or when minimally invasive procedures are performed (e.g. biopsied specimens).

Project description:RNA-sequencing (RNA-seq) provides a comprehensive quantification of transcriptomic activities in biological samples. Formalin-Fixed Paraffin-Embedded (FFPE) samples are collected as part of routine clinical procedure, and are the most widely available biological sample format in medical research and patient care. Normalization is an essential step in RNA-seq data analysis. A number of normalization methods, though developed for RNA-seq data from fresh frozen (FF) samples, can be used with FFPE samples as well. The only extant normalization method specifically designed for FFPE RNA-seq data, MIXnorm, which has been shown to outperform the normalization methods, but at the cost of a complex mixture model and a high computational burden. It is therefore important to adapt MIXnorm for simplicity and computational efficiency while maintaining superior performance. Furthermore, it is critical to develop an integrated tool that performs commonly used normalization methods for both FF and FFPE RNA-seq data. We developed a new normalization method for FFPE RNA-seq data, named SMIXnorm, based on a simplified two-component mixture model compared to MIXnorm to facilitate computation. The expression levels of expressed genes are modeled by normal distributions without truncation, and those of non-expressed genes are modeled by zero-inflated Poisson distributions. The maximum likelihood estimates of the model parameters are obtained by a nested Expectation-Maximization algorithm with a less complicated latent variable structure, and closed-form updates are available within each iteration. Real data applications and simulation studies show that SMIXnorm greatly reduces computing time compared to MIXnorm, without sacrificing the performance. More importantly, we developed a web-based tool, RNA-seq Normalization (RSeqNorm), that offers a simple workflow to compute normalized RNA-seq data for both FFPE and FF samples. It includes SMIXnorm and MIXnorm for FFPE RNA-seq data, together with five commonly used normalization methods for FF RNA-seq data. Users can easily upload a raw RNA-seq count matrix and select one of the seven normalization methods to produce a downloadable normalized expression matrix for any downstream analysis. The R package is available at https://github.com/S-YIN/RSEQNORM. The web-based tool, RSeqNorm is available at http://lce.biohpc.swmed.edu/rseqnorm with no restriction to use or redistribute.

Dataset Information

RollFISH achieves robust quantification of single-molecule RNA biomarkers in paraffin-embedded tumor tissue samples.

Publications

RollFISH achieves robust quantification of single-molecule RNA biomarkers in paraffin-embedded tumor tissue samples.

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