Seq by poly (A) capture, ribosomal RNA depletion, and DNA mi
RNA sequencing (RNA-Seq) is often used for transcriptome profiling as well as the identification of novel transcripts and alternative splicing events. Typically, RNA-Seq libraries are prepared from total RNA using poly(A) enrichment of the mRNA (mRNA-Seq) to remove ribosomal RNA (rRNA), however, this method fails to capture non-poly(A) transcripts or partially degraded mRNAs. Hence, a mRNA-Seq protocol will not be compatible for use with RNAs coming from Formalin-Fixed and Paraffin-Embedded (FFPE) samples.
Results
To address the desire to perform RNA-Seq on FFPE materials, we evaluated two different library preparation protocols that could be compatible for use with small RNA fragments. We obtained paired Fresh Frozen (FF) and FFPE RNAs from multiple tumors and subjected these to different gene expression profiling methods. We tested 11 human breast tumor samples using: (a) FF RNAs by microarray, mRNA-Seq, Ribo-Zero-Seq and DSN-Seq (Duplex-Specific Nuclease) and (b) FFPE RNAs by Ribo-Zero-Seq and DSN-Seq. We also performed these different RNA-Seq protocols using 10 TCGA tumors as a validation set.
The data from paired RNA samples showed high concordance in transcript quantification across all protocols and between FF and FFPE RNAs. In both FF and FFPE, Ribo-Zero-Seq removed rRNA with comparable efficiency as mRNA-Seq, and it provided an equivalent or less biased coverage on gene 3′ ends. Compared to mRNA-Seq where 69% of bases were mapped to the transcriptome, DSN-Seq and Ribo-Zero-Seq contained significantly fewer reads mapping to the transcriptome (20-30%); in these RNA-Seq protocols, many if not most reads mapped to intronic regions. Approximately 14 million reads in mRNA-Seq and 45–65 million reads in Ribo-Zero-Seq or DSN-Seq were required to achieve the same gene detection levels as a standard Agilent DNA microarray.
Conclusions
Our results demonstrate that compared to mRNA-Seq and microarrays, Ribo-Zero-Seq provides equivalent rRNA removal efficiency, coverage uniformity, genome-based mapped reads, and consistently high quality quantification of transcripts. Moreover, Ribo-Zero-Seq and DSN-Seq have consistent transcript quantification using FFPE RNAs, suggesting that RNA-Seq can be used with FFPE-derived RNAs for gene expression profiling.
RNA sequencing
FFPE
RNA depletion
Ribo-zero
Gene expression
Microarray
The development of massively parallel sequencing for use in gene expression profiling is known as RNA-sequencing (RNA-Seq). RNA-Seq has had an enormous impact on gene expression studies. Compared to hybridization-based technologies like DNA microarrays, it provides consistent quantification and manifests its superiority in terms of the dynamic range, sampling depth, and has independence from pre-existing sequence information [, ]. RNA-Seq can be used for traditional transcriptome profiling [, ], identification of novel transcripts [], identification of expressed SNPs[, ], alternative splicing, and for the detection of gene fusion events [–].
To allow for mRNA/gene detection, highly abundant ribosomal RNAs (rRNAs) must be removed from total RNA before sequencing. One standard solution is to enrich for the polyadenylated (poly(A)) RNA transcripts (so called mRNA-Seq) with oligo (dT) primers, similar to how DNA microarrays are primed; however, this method eliminates all non-poly(A) RNAs in addition to rRNAs. Recent studies suggested that certain non-polyA RNAs, either non-coding or protein coding, are functionally important [–]. Moreover, mRNA-Seq poorly captures partially degraded mRNAs, hence it is not an optimal method to use when the starting materials are from Formalin-Fixed and Paraffin-Embedded (FFPE) samples, because the RNAs from FFPE are degraded to a small average size []. To overcome these challenges, several rRNA depletion protocols have been developed. The Ribo-Zero method removes rRNA through hybridization capture of rRNA followed by binding to magnetic beads for subtraction. Another method involves Duplex-Specific Nuclease (DSN) degradation by the C0t-kinetics-based normalization method to deplete abundant sequences that reanneal quickly, such as those derived from the highly abundant rRNAs and tRNAs []. In this study, we examined rRNA-depleted libraries from total RNA of fresh-frozen (FF) and FFPE samples sequenced by mRNA-Seq, Ribo-Zero-Seq and DSN-Seq and compared these results across methods and with conventional DNA microarrays.
To rigorously evaluate the feasibility of reproducible gene expression profiling using RNA from clinically relevant FFPE materials, we collected FFPE and fresh-frozen (FF) tumor RNAs for matched sets of tumors from two different sources (UNC and TCGA). Most tumors were subjected to gene expression profiling using six different methods that included: 1) Agilent DNA microarrays using FF RNA, 2) mRNA-Seq using FF RNA, 3) Ribo-Zero-Seq using FF RNA, 4) DSN-Seq using FF RNA, 5) Ribo-Zero-Seq using FFPE RNA, and 6) DSN-Seq using FFPE RNA; see Figure for a comparison of each RNA-Seq protocol and the number of samples tested for each protocol. Analytical comparisons were focused on several features including rRNA depletion efficiency, genome alignment profile, transcriptome coverage, transcript quantification accuracy and reproducibility, gene expression patterns and differential gene expression, as well as coverage of annotated genes at different sequencing depths.
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