Low-Input Transcript Profiling With Enhanced Sensitivity Using A Highly Efficient, Low-Bias And Strand-Specific Rna-Seq Library Preparation Method

RNA-seq has become the most popular method for transcriptome analysis and is widely used to study gene expression, detect mutations, fusion transcripts, alternative splicing, and post-transcriptional modifications. It is becoming the method of choice to detect alterations in diseases, especially cancer, to provide insights on the various molecular pathways perturbed by changes in the transcriptome and study their implications. As RNA-seq is being adopted in molecular diagnostics and biomarker identifications, the need for good quality, reproducible library preparation methods using very low amounts of RNA input, or precious clinical samples, is increasing. To address these challenges, we have developed a strand-specific RNA-seq library preparation method that retains information about which strand of DNA is transcribed, from as low as 5 ng total RNA input. Strand specificity is important for correct annotation of genes, identification of antisense transcripts with potential regulatory roles, and accurate determination of gene expression levels in the presence of antisense transcripts. Enhanced sensitivity to detect transcripts with even coverage across their length offers a non-biased approach for accurate quantification of transcript levels. Methods: Enriched poly-A mRNA or ribo-depleted RNA (Universal Human Reference RNA) was used to make libraries with our strand specific library preparation method. Library quality and quantity were analyzed on an Agilent Bioanalyzer, pooled at equimolar ratio and sequenced on Illumina’s Nextseq 500. Paired end reads were mapped to a human reference genome (hg19) using Hisat2 and sequencing metrics were calculated using Picard9s RNA-seq Metrics and RSeqC tools. Transcript abundance was measured using Salmon and the Ensembl GRCh38 CDS sequences. Results: Libraries prepared with our streamlined method using inputs that range from 5ng to 1ug show greater than 98% directionality at all input levels. GC content analysis, gene body coverage and gene expression correlation are similar for all inputs tested (5ng to 1ug), even though input amounts vary by over three orders of magnitude. These consistent results are recapitulated with the spiked-in ERCC controls at all inputs. Conclusions: Our library preparation method is streamlined and can be used for a wide range of input RNA without any major modifications to the protocol, making it an easy to follow, convenient method for RNA-seq library preparation. In addition, our method has increased sensitivity and specificity, especially for low-abundance transcripts, reduced PCR duplicates and sequence bias, delivering high quality strand-specific data even for low input RNA. Finally, our method is compatible with both poly A-tail enriched and ribosomal RNA depleted samples, and is amenable to large-scale library construction and automation. Citation Format: Keerthana Krishnan, Erbay Yigit, Mehmet Karaca, Deyra Rodriguez, Bradley Langhorst, Timur Shtatland, Daniela Munafo, Pingfang Liu, Lynne Apone, Vaishnavi Panchapakesa, Karen Duggan, Christine Sumner, Christine Rozzi, Fiona Stewart, Laurie Mazzola, Joanna Bybee, Danielle Rivizzigno, Eileen T. Dimalanta, Theodore B. Davis. Low-input transcript profiling with enhanced sensitivity using a highly efficient, low-bias and strand-specific RNA-Seq library preparation method [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2017; 2017 Apr 1-5; Washington, DC. Philadelphia (PA): AACR; Cancer Res 2017;77(13 Suppl):Abstract nr 5406. doi:10.1158/1538-7445.AM2017-5406