Over the years, we at Ambion receive a number of requests for information about the best way to isolate and ribo-deplete prokaryotic total RNA for next-gen sequencing. I just wanted to give a brief overview and some suggestions on RNA bacterial sequencing using the Ion platform (specifically, the PGM™). After trying several methods to extract and purify total RNA from E. coli dh10b, we found that lysing and extracting using TRIzol® Reagent and isolating/purifying with the mirVana™ miRNA isolation glass-fiber filter results in the most complete recovery of total RNA. Specifically, we followed the TRIzol® Reagent protocol and homogenized using trizol then phase separated with chloroform. We then transferred the aqueous phase containing the RNA into a new RNase-free 1.5mL microcentrifuge tube and followed the mirVana™ miRNA Isolation Kit protocol starting with the Total RNA Isolation Procedure section and eluted with 100uL of Elution Solution. This method is able to recover both large and small RNA molecules including miRNA, siRNA, snRNA, and other small RNA transcripts of yet unknown functions. The total RNA went through DNase I digestion to remove any genomic DNA contamination. Before proceeding to rRNA removal we checked the quality of the total RNA on an Agilent 2100 Bioanalyzer using the RNA 6000 Nano kit (see Figure 1). It is important that the RNA be high quality for maximum rRNA removal efficiency.
Figure 1. Agilent 2100 Bioanalyzer electropherogram of total RNA using TRIzol® Reagent for lysis/extraction and mirVana™ miRNA isolation glass-fiber filter for purification.
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A highly reproducible and sensitive single-cell RNA-Seq method will facilitate the understanding of the biological roles and underlying mechanisms of the non-genetic cellular heterogeneity. Now, researchers from the RIKEN Center have developed a novel single-cell RNA-Seq method called Quartz-Seq that has a simpler protocol and higher reproducibility and sensitivity compared to previously developed methods. They demonstrate that single-cell Quartz-Seq quantitatively detects various kind of non-genetic cellular heterogeneity. The method detects different cell types and different cell-cycle phases of a single-cell type. Moreover, this method can comprehensively reveal gene expression heterogeneity between single-cells of the same cell type at the same cell-cycle phase.
Demonstration of new single-cell RNA-seq method, Quartz-Seq – http://single.cellcomplex.org/
- Sasagawa Y, Nikaido I et al. (2013) Quartz-Seq: a highly reproducible and sensitive single-cell RNA-Seq reveals non-genetic gene expression heterogeneity. Genome Biology 14:R31. [abstract]
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The cost of DNA sequencing has undergone a dramatical reduction in the past decade. As a result, sequencing technologies have been increasingly applied to genomic research. RNA-Seq is becoming a common technique for surveying gene expression based on DNA sequencing. As it is not clear how increased sequencing capacity has affected measurement accuracy of mRNA, we sought to investigate that relationship.
Researchers at the University of Texas MD Anderson Cancer Center have empirically evaluated the accuracy of repeated gene expression measurements using RNA-Seq. They identifed library preparation steps prior to DNA sequencing as the main source of error in this process. Studying three datasets, they show that the accuracy indeed improves with the sequencing depth. However, the rate of improvement as a function of sequence reads is generally slower than predicted by the binomial distribution. They therefore used the beta-binomial distribution to model the overdispersion. The overdispersion parameters they introduced depend explicitly on the number of reads so that the resulting statistical uncertainty is consistent with the empirical data that measurement accuracy increases with the sequencing depth. The overdispersion parameters were determined by maximizing the likelihood. They show that their modified beta-binomial model had lower false discovery rate than the binomial or the pure beta-binomial models.
- Cai G, Li H, Lu Y, Huang X, Lee J, Müller P, Ji Y, Liang S. (2012) Accuracy of RNA-Seq and its dependence on sequencing depth. BMC Bioinformatics 13 Suppl 13, S5. [article]
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Degradome sequencing for identification of miRNA targets in plants
MicroRNAs (miRNAs) are endogenous regulators of a broad range of physiological processes and act by either degrading mRNA or blocking its translation. Mature miRNAs function within large complexes to negatively regulate specific target mRNAs. Plant miRNAs generally interact with their targets through perfect or near-perfect complementarity and direct mRNA target degradation.
In plants, miRNAs not only post-transcriptionally regulate their own targets but also interact with each other in regulatory networks to affect many aspects of development, such as growth, development and responses to biotic and abiotic stresses. Hundreds of miRNAs have been identified in higher plants by direct cloning or more recently by next-gen sequencing. To determine the function of these miRNAs we must first identify their targets.
Originally, plant miRNA targets have been studied via computational prediction, which is based on either perfect or near-perfect sequence complementarity between miRNA and the target mRNA or sequence conservation among different species. However, target prediction is very challenging, especially when a high level of mismatches exists in miRNA:target pairing.
Recently, a new method called degradome sequencing, which combines high-throughput RNA sequencing with bioinformatic tools, has-been successfully established to screen for miRNA targets in plants. Using degradome sequencing, many of the previously validated and predicted targets of miRNAs have been verified indicating that it is an efficient strategy to identify smRNA targets on a large scale in plants.
Degradome sequencing reveals miRNA targets by globally identifying the remnants of small RNA-directed target cleavage by sequencing the 5′ ends of uncapped RNAs. Sequencing reads are mapped to mRNAs and the 5′ terminal nucleotide of miRNA-cleaved mRNA fragments corresponds to the nucleotide that is complementary to the 10th nucleotide of the miRNA. Therefore, the cleaved RNA targets have distinct peaks in the degradome sequence reads at the predicted cleavage site relative to other regions of the transcript. Confirmed miRNA targets are presented in the form of target plots (t-plots).
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High-throughput sequencing of cDNA libraries (RNA-Seq) has proven to be a highly effective approach for studying bacterial transcriptomes. A central challenge in designing RNA-Seq-based experiments is estimating a priori the number of reads per sample needed to detect and quantify thousands of individual transcripts with a large dynamic range of abundance.
Now, researchers at The Broad Institute have conducted a systematic examination of how changes in the number of RNA-Seq reads per sample influences both profiling of a single bacterial transcriptome and the comparison of gene expression among samples. Their findings suggest that the number of reads typically produced in a single lane of the Illumina HiSeq sequencer far exceeds the number needed to saturate the annotated transcriptomes of diverse bacteria growing in monoculture. Moreover, as sequencing depth increases, so too does the detection of cDNAs that likely correspond to spurious transcripts or genomic DNA contamination. Finally, even when dozens of barcoded individual cDNA libraries are sequenced in a single lane, the vast majority of transcripts in each sample can be detected and numerous genes differentially expressed between samples can be identified.
- Haas BJ, Chin M, Nusbaum C, Birren BW, Livny J. (2012) How deep is deep enough for RNA-Seq profiling of bacterial transcriptomes? BMC Genomics 13(1):734. [Epub ahead of print]. [abstract]
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