RNA was isolated from human non-small cell lung cancer FFPE samples using the QIAGEN RNeasy FFPE Kit. Ribosomal RNA was depleted from the isolated RNA using the Ribo-Zero Gold rRNA Removal Kit from Illumina. Depleted rRNA samples were checked for ribosomal integrity numbers (RIN) using the Agilent Bioanyalizer. To construct an RNA-seq library using the QIAseq Kit, 10 ng of rRNA depleted FFPE RNA with a RIN of 2 was used. RNA-seq libraries were sequenced on the Illumina MiSeq instrument and reads were mapped to the GRCh38 human reference genome using CLC Genomics Workbench v10.
[A] Even exon coverage of FFPE RNA was observed. Normalized expression data (uniquely mapped read pairs to exons) for different transcript expression levels were plotted against the exon regions (transcript size normalization). Very low bias was detected across the gene body.
[B] A high proportion of protein coding RNA was mapped from FFPE samples. Highly degraded FFPE RNA samples show a high proportion of reads mapped to protein coding regions, which can be used to determine transcript expression, as well as lincRNA (long intergenic noncoding RNA).
mRNA was captured from different amounts of UHRR Reference RNA (Agilent) (100, 500, 1000 or 5000 ng) and used to generate QIAseq Stranded mRNA-seq libraries for sequencing on the Illumina MiSeq instrument. After the alignment of the RNA-seq NGS reads to the GRCh38 human reference genome using CLC Genomics Workbench, the expression values for annotated transcripts were calculated using the TPM (Transcripts Per Million) normalization method. Pearson correlation coefficients were calculated for technical replicates with similar or different RNA input amounts of UHRR RNA. QIAseq Stranded RNA-seq libraries show a high degree of correlation (R2 >0.99) between technical replicates, as well with different amounts of starting RNA.
Stranded RNA-seq libraries can be prepared in just 4–5 h using the QIAseq Stranded Total RNA and QIAseq Stranded mRNA Select Kits, with only 3 h of hands-on time. The compressed total protocol time allows subsequent library QC steps and start of an NGS run on any Illumina NGS instrument in just one working day.
UHRR RNA (Agilent) was used to prepare RNA-seq libraries with the QIAseq Stranded mRNA Select Kit or with a stranded RNA-seq kit from Supplier N. RNA-seq libraries were generated with 50 ng mRNA as starting material, sequenced on the Illumina MiSeq instrument and reads were aligned to the GRCh38 human reference genome. The RNA biotype distribution of uniquely aligned reads was analyzed with the HTSeq-count tool. Supplier N and the QIAseq Stranded RNA libraries show a very high fraction of reads, with more than 96% of reads mapped to protein coding regions. These results are indicative of the high efficiency and specificity of the QIAseq mRNA enrichment and library construction steps.
QIAseq Kits and kits from Supplier N were used to generate stranded RNA-seq libraries from 100 ng, 500 ng and 1000 ng of UHRR RNA (Agilent). These libraries were sequenced on the Illumina MiSeq instrument and reads were aligned to the GRCh38 human reference genome with CLC Genomics Workbench v10 with the RNA-seq plugin. PICARD tools allowed the analysis of duplication rates and estimated library yields, which are direct indicators of the library complexity.
[A] Duplication rates are used to measure the proportion of duplicated reads with identical start and end sequences, which are increased in NGS libraries with low-input amounts and higher numbers of PCR cycles. Compared to libraries from Supplier N, QIAseq Stranded RNA-seq libraries show significantly lower duplication rates, which is indicative of high library complexity. The unique protocol included with the QIAseq Stranded mRNA Select Kits results in more usable gene expression data.
[B] Estimated library yields are also indicators of library complexity. QIAseq Stranded RNA-seq libraries show significantly higher estimated library yields, demonstrating a more complex library and more usable NGS data in comparison to Supplier N. This suggests that deeper reads will results in more data from QIAseq Stranded RNA-seq libraries.