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. 2015 Jun 3:5:10438.
doi: 10.1038/srep10438.

A cross comparison of technologies for the detection of microRNAs in clinical FFPE samples of hepatoblastoma patients

Aniruddha Chatterjee  1 , Anna L Leichter  2 , Vicky Fan  3 , Peter Tsai  3 , Rachel V Purcell  4 , Michael J Sullivan  5 , Michael R Eccles  1
Affiliations

A cross comparison of technologies for the detection of microRNAs in clinical FFPE samples of hepatoblastoma patients

Aniruddha Chatterjee et al. Sci Rep. .

Erratum in

Abstract

Although formalin fixed paraffin embedded (FFPE) tissue is a major biological source in cancer research, it is challenging to work with due to macromolecular fragmentation and nucleic acid crosslinking. Therefore, it is important to characterise the quality of data that can be obtained from FFPE samples. We have compared three independent platforms (next generation sequencing, microarray and NanoString) for profiling microRNAs (miRNAs) using clinical FFPE samples from hepatoblastoma (HB) patients. The number of detected miRNAs ranged from 228 to 345 (median = 294) using the next generation sequencing platform, whereas 79 to 125 (median = 112) miRNAs were identified using microarrays in three HB samples, including technical replicates. NanoString identified 299 to 372 miRNAs in two samples. Between the platforms, we observed high reproducibility and significant levels of shared detection. However, for commonly detected miRNAs, a strong correlation between platforms was not observed. Analysis of 10 additional HB samples with NanoString identified significantly overlapping miRNA expression profiles, and an alternative pattern was identified in a poorly differentiated HB with an aggressive phenotype. This investigation serves as a roadmap for future studies investigating miRNA expression in clinical FFPE samples, and as a guideline for the selection of an appropriate platform.

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Figures

Figure 1
Figure 1. Overall study design and workflow.
The * symbol indicates where miRNAs with ≥10 reads were included for further comparative analysis between platforms.
Figure 2
Figure 2. The proportion of different types of RNA (except miRNA) detected within each sample by NGS platform.
Sequences were BLAST searched against NCBI Human RefSeq RNA database and the top hit of each query sequence was retrieved and summarised based on the type of RNA.
Figure 3
Figure 3. Matrix plot of relationship between different samples in NGS platform.
The log of read counts for the detected miRNAs (with a threshold of ≥10 reads for a miRNA) were calculated and plotted on the x and y axis.
Figure 4
Figure 4. Matrix plot of relationship between different samples in microarray platform.
The RMA values for the detected miRNAs were calculated and plotted on the x and y axis.
Figure 5
Figure 5. Venn diagrams of shared miRNAs between platforms a) S4 b) S5 c) S6.
Figure 6
Figure 6. Comparison of the relative abundance levels of the miRNAs detected by all three platforms
(a-c). Spearman’s ranked correlation (rho) was used to compare relative abundance of the common miRNAs between platforms.
Figure 7
Figure 7. Extended analysis of miRNAs in 10 additional samples with NanoString.
a) Number of miRNAs detected in each sample with high stringency from the nCounter platform. b) Hierarchical clustering of 50 miRNAs detected in all 12 samples using NanoString (method: complete linkage clustering, using a euclidean distance measurement). The heat map shows high (white/yellow) to low (red) expression of the miRNAs in the different samples. c) A zoomed in view of the sample clustering together with the clinical phenotype and features associated with each sample.
See this image and copyright information in PMC

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