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

Abstract

Deep mRNA sequencing (mRNAseq) is the state-of-the-art for whole transcriptome measurements. A key step is creating a library of cDNA sequencing fragments from RNA. This is generally done by random priming, creating multiple sequencing fragments along the length of each transcript. A 3' end-focused library approach cannot detect differential splicing, but has potentially higher throughput at lower cost (~10-fold lower), along with the ability to improve quantification by using transcript molecule counting with unique molecular identifiers (UMI) to correct for PCR bias. Here, we compare implementation of such a 3'-digital gene expression (3'-DGE) approach with "conventional" random primed mRNAseq, which has not yet been done.We find that while conventional mRNAseq detects ~15% more genes, the resulting lists of differentially expressed genes and therefore biological conclusions and gene signatures are highly concordant between the two techniques. We also find good quantitative agreement on the level of individual genes between the two techniques in terms of both read counts and fold change between two conditions. We conclude that for high-throughput applications, the potential cost savings associated with the 3'-DGE approach are a very reasonable tradeoff for modest reduction in sensitivity and inability to observe alternative splicing, and should enable much larger scale studies focused on not only differential expression analysis, but also quantitative transcriptome profiling. The computational scripts and programs, along with experimental standard operating procedures used in our pipeline presented here, are freely available on our website (www.dtoxs.org).

Sequencing-based quantitative mapping of the cellular small RNA landscape

“…The AQRNA-seq method is thus established to be the most accurate for application in miRNA profiling studies, with the recent miRNA method developed by Kim et al lacking evidence of quantitative rigor. 14 However, while random priming RNA-seq methods provide relatively accurate quantification of mRNAs, 46 AQRNA-seq is also applicable to longer RNA species such as mRNA and rRNA as a means to map RNA modifications and cleavage sites, as illustrated in RNA species to be reduced to an acceptable length for library generation. The introduction of this step allows for quantification of (1) all expressed copies of an RNA (all molecules with a 3'-end that maps to the end of the transcribed or fully mature sequence), (2) polymerase fall-off due to modifications, secondary structure, or polymerase detachment, and (3) fragmentation sites within the RNA molecules (3'-ends that map within the full-length sequence).…”

Proteomic cellular signatures of kinase inhibitor-induced cardiotoxicity: Mount Sinai DToxS LINCS Center Dataset

“…In this DToxS dataset, we've chosen the LFQ method in order to economically compare the proteomics signatures from over 300 samples that have been collected over an extended period of time, some with limited protein yields. The transcriptome of these samples have been previously assessed using the 3' digital gene expression RNA sequencing (RNAseq) 17 ; to ensure both proteomics and RNAseq data can be compared from the same drug-treated cells (see example use case below), we've optimized a method to extract proteins from the samples after RNA extraction (Fig. 1).…”

“…TRIzol lysates were mixed with chloroform according to manufacturer's instructions and the RNA-protein fractions were separated. From these organic partitions, RNA samples were processed, sequenced and analyzed as previously reported 17 .…”

“…The normalized spectral counts of identified proteins were processed by the edgeRbased computational pipeline to generate differentially expressed proteins (DEPs) associated with statistical significance for each drug treatment in each cell line, as described before 17 . hierarchically clustered based on the combined log2(fold changes) using Cluster 3.0 19,20 .…”

Gene-Specific Predictability of Protein Levels from mRNA Data in Humans

“…DToxS SOP A-1.0: Total RNA Isolation). RNA sequencing and analysis was performed as previously described (Xiong et al, 2017). Biological triplicates were performed for both cell lines, but one sample in the MCF10A data failed to be sequenced, leaving biological duplicates.…”