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. 2014 Jul;16(4):440-51.
doi: 10.1016/j.jmoldx.2014.03.004. Epub 2014 May 9.

cDNA hybrid capture improves transcriptome analysis on low-input and archived samples

Christopher R Cabanski  1 , Vincent Magrini  2 , Malachi Griffith  2 , Obi L Griffith  1 , Sean McGrath  3 , Jin Zhang  1 , Jason Walker  3 , Amy Ly  3 , Ryan Demeter  3 , Robert S Fulton  3 , Winnie W Pong  4 , David H Gutmann  5 , Ramaswamy Govindan  1 , Elaine R Mardis  6 , Christopher A Maher  7
Affiliations

cDNA hybrid capture improves transcriptome analysis on low-input and archived samples

Christopher R Cabanski et al. J Mol Diagn. 2014 Jul.

Abstract

The use of massively parallel sequencing for studying RNA expression has greatly enhanced our understanding of the transcriptome through the myriad ways these data can be characterized. In particular, clinical samples provide important insights about RNA expression in health and disease, yet these studies can be complicated by RNA degradation that results from the use of formalin as a clinical preservative and by the limited amounts of RNA often available from these precious samples. In this study we describe the combined use of RNA sequencing with an exome capture selection step to enhance the yield of on-exon sequencing read data when compared with RNA sequencing alone. In particular, the exome capture step preserves the dynamic range of expression, permitting differential comparisons and validation of expressed mutations from limited and FFPE preserved samples, while reducing the data generation requirement. We conclude that cDNA hybrid capture has the potential to significantly improve transcriptome analysis from low-yield FFPE material.

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Figures

Figure 1
Figure 1
cDNA-Capture sequencing of FF lung adenocarcinomas. A: Total reads generated (light blue) and aligned (dark blue) for both RNA-Seq and cDNA-Capture across four FF lung adenocarcinomas (LUC4, LUC6, LUC13, and LUC20). B: Percentage of reads that aligned uniquely to the target regions for RNA-Seq and cDNA-Capture. C: Distribution of read alignments relative to genomic features, including coding, intergenic, intronic, ribosomal, and UTRs. D: Normalized coverage across transcripts for LUC4, LUC6, LUC13, and LUC20 from 5′ (left) to 3′ (right). E: Frequency of genes expressed at increasing coverage depth (1×, 2×, 10×, 20×, 50×, 100×, and 500×) using RNA-Seq and cDNA-Capture.
Figure 2
Figure 2
Comparison of cDNA-Capture and RNA-Seq using FF lung tumors. A: Scatterplot of LUC13 gene expression values measured by RNA-Seq and cDNA-Capture. Gene expression is measured as log2(FPKM + 1). B: Percentage of reads aligning to exon-exon junctions from RNA-Seq (gray bars) and cDNA-Capture (black bars). C: Percentage of reads spanning a splice junction that aligned to the highest 1% of expressed genes from RNA-Seq and cDNA-Capture.
Figure 3
Figure 3
Validation of expressed SNVs in LUC4 FF tissue using RNA-Seq and cDNA-Capture. Scatterplots highlight the VAF of Tier 1 SNVs discovered by whole genome sequencing relative to VAF supported by RNA-Seq (A) and cDNA-Capture (B) in LUC4 using FF tissue. Each protein-coding gene harboring an SNV is colored based on its normalized expression level [0 FPKM (yellow) to 10 + FPKM (red)]. C: Correlation of expressed SNVs detected by cDNA-Capture or RNA-Seq and their corresponding normalized expression values. SNVs are color-coded based on whether they are found by both approaches (green), neither approach (black), cDNA-Capture only (yellow), or RNA-Seq only (red).
Figure 4
Figure 4
Comparison of cDNA-Capture and RNA-Seq using archived material. A: Total reads generated and aligned from RNA-Seq and cDNA-Capture on FFPE material in lung adenocarcinomas LUC6 and LUC7. B: Percentage of reads that aligned uniquely to the target regions for RNA-Seq and cDNA-Capture. C: Distribution of read alignments relative to genomic features, including coding, intergenic, intronic, ribosomal, and UTRs. D: Normalized coverage across transcripts. E: Dynamic range of gene coverage at varying depths.
Figure 5
Figure 5
Comparison of cDNA-Capture and RNA-Seq using FF and archived material. Scatterplots comparing LUC6 (A) and LUC7 (B) gene expression values calculated from FFPE material using cDNA-Capture and RNA-Seq. The least-squares regression line is shown in gray and the 45° line in black. Gene expression is measured as log2(FPKM + 1). Correlation of LUC6 (C) and LUC7 (D) gene expression values measured from FFPE and FF material using cDNA-Capture.
Figure 6
Figure 6
Validation of expressed Tier 1 SNVs using FFPE material. Scatterplots highlight concordance between expressed Tier 1 SNVs using cDNA-Capture and RNA-Seq on FFPE and FF material. The x and y axes indicate the normalized expression level (FPKM) of the genes harboring the SNVs. Only Tier 1 SNVs with at least one read supporting the variant are displayed. Circles indicate SNVs supported by both RNA-Seq and cDNA-Capture, squares for SNVs supported by only RNA-Seq, and diamonds for SNVs supported only by cDNA-Capture. For SNVs detected by both RNA-Seq and cDNA-Capture, the color indicates the mean VAF between cDNA-Capture and RNA-Seq. Otherwise, the color indicates the VAF of the approach validating the SNV.
Figure 7
Figure 7
Differential gene expression analysis using cDNA-Capture on low-input libraries. A: Spearman rank correlation of edgeR P values between varying levels of RNA input. B and C: Venn diagrams show the overlap between the down- (B) and up-regulated (C) genes among the 60, 50, 10, and 2 ng input amounts. The total number of differentially expressed genes is shown under the RNA input. These plots suggest that the reliability of discovering differentially expressed genes diminishes for RNA inputs <10 ng.
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