A Comparison of mRNA Sequencing with Random Primed and 3′-Directed Libraries

Abstract

Creating a cDNA library for deep mRNA sequencing (mRNAseq) is generally done by random priming, creating multiple sequencing fragments along each transcript. A 3′-end-focused library approach cannot detect differential splicing, but has potentially higher throughput at a lower cost, along with the ability to improve quantification by using transcript molecule counting with unique molecular identifiers (UMI) that correct PCR bias. Here, we compare an implementation of such a 3′-digital gene expression (3′-DGE) approach with “conventional” random primed mRNAseq. Given our particular datasets on cultured human cardiomyocyte cell lines, we find that, while conventional mRNAseq detects ~15% more genes and needs ~500,000 fewer reads per sample for equivalent statistical power, the resulting differentially expressed genes, biological conclusions, and gene signatures are highly concordant between two techniques. We also find good quantitative agreement at the level of individual genes between two techniques for both read counts and fold changes between given conditions. We conclude that, for high-throughput applications, the potential cost savings associated with 3′-DGE approach are likely a reasonable tradeoff for modest reduction in sensitivity and inability to observe alternative splicing, and should enable many larger scale studies focusing on not only differential expression analysis, but also quantitative transcriptome profiling.

Lasy-Seq: a high-throughput library preparation method for RNA-Seq and its application in the analysis of plant responses to fluctuating temperatures

“…4B). A similar tendency was observed in a previous study on comparison of 3′mRNA-Seq and a conventional RNA-Seq 33 . For genes with relatively low expression, higher expression was detected by the conventional method than by Lasy-Seq, which might have been caused by the difference in distribution of reads within gene bodies between RNA-Seq with oligo-dT RT primer (3′mRNA-Seq, including Lasy-seq) and random RT-primer (conventional method) ( Fig.…”

“…4C). A previous study reported that www.nature.com/scientificreports www.nature.com/scientificreports/ the number of detected genes and the differentially expressed genes (DEGs) became larger in the conventional method with random RT primer than in the RNA-Seq with oligo-dT RT primer (3′mRNA-Seq) 33 . Also, in our comparison of the two methods, a larger number of genes and DEGs between light and dark conditions were detected in the conventional method than in Lasy-Seq (Fig.…”

A mechanistic pan-cancer pathway model informed by multi-omics data interprets stochastic cell fate responses to drugs and mitogens

“…Total RNA was extracted using TRIzol (Life Technologies, Cat: 15596018) per manufacturer instructions (detailed SOP at www.dtoxs.org ; DToxS SOP A– 1.0: Total RNA Isolation). RNA sequencing and analysis was performed as previously described [ 83 ]. We prepared RNA-seq libraries by adapting a single-cell RNA-seq technique—Single Cell RNA Barcoding and Sequencing method (SCRB-seq; [ 84 , 85 ])—to extract total RNA from cells (detailed SOP at www.dtoxs.org ; DToxS SOP A– 6.0: High-throughput mRNA Seq Library Construction for 3' Digital Gene Expression (DGE)).…”

Systems Pharmacology: Defining the Interactions of Drug Combinations

“…Combinatorial therapies have been suggested as a crucial approach to addressing such intratumor heterogeneity and associated treatment response (30,31,83). Emerging single-cell analytical tools, such as single-cell transcriptomics, are thus of great interest for their potential to support development of combinatorial therapies (84)(85)(86). For instance, single-cell phosphoproteomic profiling allowed the characterization of signaling dynamics and the selection and validation of combination therapies for a glioblastoma in vivo model (86).…”