mRNA enrichment protocols determine the quantification characteristics of external RNA spike-in controls in RNA-Seq studies

Qing, Tao; Yu, Ying; Du, Tingting; Shi, Leming

doi:10.1007/s11427-013-4437-9

Cited by 36 publications

(39 citation statements)

References 24 publications

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“…Alternatively, known quantities of exogenous DNA and RNA could be spiked into each sample prior to nucleic acid extraction in order to quantify the efficiencies of nucleic acid recovery and reverse transcription. This could potentially make it possible to quantify absolute transcriptome size, though Qing et al (2013) have documented large technical artifacts when using ERCC spike-in controls to estimate absolute transcript abundance in RNA-Seq experiments.…”

Section: Methods To Quantify Transcriptome Size and Gene Expression Pmentioning

confidence: 99%

“…For example, Illumina offers different RNA-Seq library kits that selectively target polyadenylated RNA, small RNA, or all nonribosomal RNA species. The various RNA pools differ dramatically in cellular abundance (Yang et al 2011;Qing et al 2013), and their abundances relative to each other can change across experimental conditions (Yang et al 2011;Aanes et al 2014). Thus, even transcriptome-normalized expression estimates will differ depending on the RNA pool to which they are normalized, as well as experimental context.…”

Section: Introductionmentioning

confidence: 99%

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Variation in transcriptome size: are we getting the message?

Coate

Doyle

2014

Chromosoma

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The number of RNA molecules per cell (transcriptome size) is highly variable, differing among and within cell types depending on cell size, stage of the cell cycle, ploidy level, age, disease state, and growth condition. Such variation has been observed at the level of total RNA, ribosomal RNA, messenger RNA (mRNA), and the polyadenylated fraction of mRNA, and these distinct RNA species can also vary in abundance with respect to each other. This variation in transcriptome size has been largely ignored or overlooked, and in fact, standard data normalization procedures for transcript profiling experiments implicitly assume that mRNA transcriptome size is constant. Consequently, variation in transcriptome size has important technical implications for such experiments, as well as profound biological implications for the affected cells and underlying genomes. Here, we review what is known about transcriptome size variation, explore how ignoring this variation introduces systematic bias into standard expression profiling experiments, and present examples of how such biases have led to erroneous conclusions in expression studies of sex chromosome dosage compensation, cancer, Rett syndrome, embryonic development, aging, and polyploidy. We also discuss how quantifying transcriptome size will help to elucidate the selective forces underlying patterns of gene and genome evolution and review the evidence that cells exert tight control over transcriptome size in order to maintain cell size homeostasis and to optimize chemical reactions within the cell, such that loss of control over transcriptome size is associated with cancer and aging. Thus, transcriptome size is an important phenotype in its own right. Finally, we discuss strategies for quantifying transcriptome size and individual gene dosage responses in order to account for and better understand this important biological phenomenon.

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Section: Methods To Quantify Transcriptome Size and Gene Expression Pmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Variation in transcriptome size: are we getting the message?

Coate

Doyle

2014

Chromosoma

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“…The efficiency of enrichment of the ERCC spike-in controls during library preparation has been shown to be dependent on the RNA purification protocols. For example, the ERCC controls are less efficiently enriched by poly(A) purification than by RiboZero enrichment of RNA (9). However, as long as the same RNA enrichment method is used for the samples being compared, this should not be a concern.…”

Section: What Exactly Is a Spike-in Control?mentioning

confidence: 99%

The Overlooked Fact: Fundamental Need for Spike-In Control for Virtually All Genome-Wide Analyses

Chen

Xia

et al. 2016

Molecular and Cellular Biology

165

161

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e Genome-wide analyses of changes in gene expression, transcription factor occupancy on DNA, histone modification patterns on chromatin, genomic copy number variation, and nucleosome positioning have become popular in many modern laboratories, yielding a wealth of information during health and disease states. However, most of these studies have overlooked an inherent normalization problem that must be corrected with spike-in controls. Here we describe the reason why spike-in controls are so important and explain how to appropriately design and use spike-in controls for normalization. We also suggest ways to retrospectively renormalize data sets that were wrongly interpreted due to omission of spike-in controls.

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“…Therefore, future improvements may include the direct indexing of RNA molecules during reverse transcription using barcoded cDNA synthesis primers. Special attention should also be devoted to RNA isolation and poly-A enrichment procedures, which are other potential sources of distortion (20,23).…”

Section: Discussionmentioning

confidence: 99%

Molecular indexing enables quantitative targeted RNA sequencing and reveals poor efficiencies in standard library preparations

Wilhelmy

et al. 2014

Proc. Natl. Acad. Sci. U.S.A.

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We present a simple molecular indexing method for quantitative targeted RNA sequencing, in which mRNAs of interest are selectively captured from complex cDNA libraries and sequenced to determine their absolute concentrations. cDNA fragments are individually labeled so that each molecule can be tracked from the original sample through the library preparation and sequencing process. Multiple copies of cDNA fragments of identical sequence become distinct through labeling, and replicate clones created during PCR amplification steps can be identified and assigned to their distinct parent molecules. Selective capture enables efficient use of sequencing for deep sampling and for the absolute quantitation of rare or transient transcripts that would otherwise escape detection by standard sequencing methods. We have also constructed a set of synthetic barcoded RNA molecules, which can be introduced as controls into the sample preparation mix and used to monitor the efficiency of library construction. The quantitative targeted sequencing revealed extremely low efficiency in standard library preparations, which were further confirmed by using synthetic barcoded RNA molecules. This finding shows that standard library preparation methods result in the loss of rare transcripts and highlights the need for monitoring library efficiency and for developing more efficient sample preparation methods.cDNA library | molecular barcoding | RNA-seq R NA sequencing (RNA-Seq) is a powerful method for the measurement of global gene expression (1, 2). As a discovery tool, the method has dramatically increased our knowledge of the transcriptome, providing new insights into transcript diversity, including the discovery of new structural variants such as alternative splicing, gene fusions or rearrangements, and lowexpressed molecules. As a profiling tool, the method is primarily challenged by the large dynamic range of expression levels of mRNAs in a library. Sequencing of millions to tens of millions of copies of high-abundance transcripts is required to detect rare transcripts of interest (3, 4). To compensate, increased numbers of reads are often used, despite the low efficiency of this strategy. Reports estimate that ∼40 million reads may be required for the reliable measurement of gene expression for transcripts of high and moderate abundance, and as many as 500 million reads may be required to cover the full sequence diversity of a complex transcript library (1,5,6). In routine use, sparse coverage is further compromised by the practice of multiplexing samples in a single RNA-Seq run, primarily driven by cost constraints when designing studies involving large numbers of samples, such as those required in clinical applications.To efficiently sample the rare or low-abundance isoforms of transcripts, capture methods have recently been used (7,8). These promising methods use a targeted strategy whereby genomic regions of interest are enriched through hybridization capture and amplification before sequence sampling. When applied to generate th...

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mRNA enrichment protocols determine the quantification characteristics of external RNA spike-in controls in RNA-Seq studies

Cited by 36 publications

References 24 publications

Variation in transcriptome size: are we getting the message?

Variation in transcriptome size: are we getting the message?

The Overlooked Fact: Fundamental Need for Spike-In Control for Virtually All Genome-Wide Analyses

Molecular indexing enables quantitative targeted RNA sequencing and reveals poor efficiencies in standard library preparations

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