Abstract 5410: Optimization and evaluation of SMART-Seq v4 kit for low input RNA sequencing
Authors: Dan Su, Thomas Halsey, Martin Buchkovich, Jason G. Powers, Steven Abbott, Jenny Shu, John Pufky, Michell Chang, Victor Weigman, Patrick Hurban
Journal: Cancer Research (2017)

Abstract Recent advances in library preparation methodology from limiting amounts of total RNA have facilitated the characterization of rare cell-types in various biological systems. The SMART-Seq v4 Ultra Low Input RNA Kit incorporates a number of workflow improvements, including fewer purifications to increase yield, a locked nucleic acid (LNA) template-switching oligo to enhance stability, and an improved polymerase designed to reduce amplification bias of GC-rich regions. Certain workflow challenges remain however, including variability in library insert size, which can lead to less predictable sequencing results. We have modified the SMART-Seq v4 method to incorporate library size selection between 250-650 bp, and have characterized analytical performance of the modified procedure. A total of 63 libraries were generated using 10 pg - 10 ng of Universal Human Reference RNA (UHRR), Human Brain Reference RNA (HBRR), and total RNA isolated from multiple human tissues, including lung, colon, spleen, adrenal tumor, heart, and lung tumor. To facilitate the comparison between libraries, we normalized library read counts by down-sampling to 24 million reads prior to further processing with an internally-developed sequence analysis pipeline (RNAv9_rsem). The values for ERCC Limit of Detection ranged between 3-50 copies with a mean of 22 copies, which confirmed the high absolute sensitivity of SMART-seq v4 workflow. The total number of genes detected (RPKM, or reads per kilobase per million mapped &amp;gt;=3) varied as a function of the amount of input RNA, with as many as 19,500 genes detected. Gene detection was highly consistent across replicates utilizing 100 pg-10 ng input RNA, with a precipitous decline in genes detected using 10 pg of RNA input. This is due to less reliable detection of low abundance genes, which in turn leads to increased discordance in detected genes between replicates, possibly due to increased sampling error. Nonetheless, when genes are commonly detected, their normalized read counts are highly correlated at any given RNA input, with correlation coefficients (r) ranging from 0.977 to 0.993. Compared to previous versions of the SMARTer method, the modified SMART-Seq v4 procedure implemented at Q2 Solutions - EA Genomics significantly improved the percentage of reads aligned to the transcriptome, as well as the total number of genes detected, all with reduced technical variability. Our study demonstrates an improved strategy for expression profiling via RNA sequencing from limiting amounts of RNA. Citation Format: Dan Su, Thomas Halsey, Martin Buchkovich, Jason G. Powers, Steven Abbott, Jenny Shu, John Pufky, Michell Chang, Victor Weigman, Patrick Hurban. Optimization and evaluation of SMART-Seq v4 kit for low input RNA sequencing [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 5410. doi:10.1158/1538-7445.AM2017-5410