Quantitative Transcriptomics using Designed Primer-based Amplification
ABSTRACT We developed a novel Designed Primer-based RNA-sequencing strategy (DP-seq) that uses a defined set of heptamer primers to amplify the majority of expressed transcripts from limiting amounts of mRNA, while preserving their relative abundance. Our strategy reproducibly yielded high levels of amplification from as low as 50 picograms of mRNA while offering a dynamic range of over five orders of magnitude in RNA concentrations. We also demonstrated the potential of DP-seq to selectively suppress the amplification of the highly expressing ribosomal transcripts by more than 70% in our sequencing library. Using lineage segregation in embryonic stem cell cultures as a model of early mammalian embryogenesis, DP-seq revealed novel sets of low abundant transcripts, some corresponding to the identity of cellular progeny before they arise, reflecting the specification of cell fate prior to actual germ layer segregation.
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ABSTRACT: Abstract A significant challenge to the effective application of RNA-seq to the complete transcript analysis of low quantity and/or degraded samples is the amplification of minimal input RNA to enable sequencing library construction. Several strategies have been commercialized in order to facilitate this goal. However, each strategy has its own specific protocols and methodology, and each may introduce unique bias and in some cases show specific preference for a collection of sequences. Our wider investigation of human spermatozoal RNAs was able to reveal their complexity despite being generally characterized by low quantity and high fragmentation. In this study, the following four commercially available RNA-seq amplification and library protocols for the preparation of low quantity/highly fragmented samples, SMARTer™ Ultra Low RNA (SU) for Illumina® Sequencing, SeqPlex RNA Amplification (SP), Ovation® RNA-Seq System V2 (OR), and Ovation® RNA-Seq Formalin Fixed Paraffin Embedded System (FFPES) were assessed using human sperm RNAs. Further investigation analyzed the effects on the end results of two different library preparation methods, Encore NGS Multiplex System I (Enc) and Ovation Ultralow Library Systems (UL), that appeared best suited to this type of RNA, along with other potential confounding factors such as FFPE preservation. Our results indicate that for each library preparation protocol, the differences in the initial amount of input RNA and choice of RNA purification step do not generate marked differences in terms of RNA profiling. However, substantial disparity is introduced by individual amplification methods prior to library construction. These significant differences may be caused by the different priming methods or amplification strategies used in each of the four different protocols examined. The observation of intra-sample variation introduced by the choice of protocol highlights the role that external factors play in planning and subsequent reliable interpretation of results of any RNA-seq experiment.Systems Biology in Reproductive Medicine 07/2014; DOI:10.3109/19396368.2014.944318 · 1.70 Impact Factor
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ABSTRACT: Mouse epiblast stem cells (EpiSCs) display temporal differences in the upregulation of Mixl1 expression during the initial steps of in vitro differentiation, which can be correlated with their propensity for endoderm differentiation. EpiSCs that upregulated Mixl1 rapidly during differentiation responded robustly to both Activin A and Nodal in generating foregut endoderm and precursors of pancreatic and hepatic tissues. By contrast, EpiSCs that delayed Mixl1 upregulation responded less effectively to Nodal and showed an overall suboptimal outcome of directed differentiation. The enhancement in endoderm potency in Mixl1-early cells may be accounted for by a rapid exit from the progenitor state and the efficient response to the induction of differentiation by Nodal. EpiSCs that readily differentiate into the endoderm cells are marked by a distinctive expression fingerprint of transforming growth factor (TGF)-β signalling pathway genes and genes related to the endoderm lineage. Nodal appears to elicit responses that are associated with transition to a mesenchymal phenotype, whereas Activin A promotes gene expression associated with maintenance of an epithelial phenotype. We postulate that the formation of definitive endoderm (DE) in embryoid bodies follows a similar process to germ layer formation from the epiblast, requiring an initial de-epithelialization event and subsequent re-epithelialization. Our results show that priming EpiSCs with the appropriate form of TGF-β signalling at the formative phase of endoderm differentiation impacts on the further progression into mature DE-derived lineages, and that this is influenced by the initial characteristics of the cell population. Our study also highlights that Activin A, which is commonly used as an in vitro surrogate for Nodal in differentiation protocols, does not elicit the same downstream effects as Nodal, and therefore may not effectively mimic events that take place in the mouse embryo.Philosophical Transactions of The Royal Society B Biological Sciences 12/2014; 369(1657). DOI:10.1098/rstb.2013.0550 · 6.31 Impact Factor
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ABSTRACT: Time-dependent extracellular manipulations of human pluripotent stem cells can yield as much as 90% pure populations of cardiomyocytes. While the extracellular control of differentiation generally entails dynamic regulation of well-known pathways such as Wnt, BMP, and Nodal signaling, the underlying genetic networks are far more complex and are poorly understood. Notably, the identification of these networks holds promise for understanding heart disease and regeneration. The availability of genome-wide experimentation, such as RNA and DNA sequencing, as well as high throughput surveying with small molecule and small interfering RNA libraries, now enables us to map the genetic interactions underlying cardiac differentiation on a global scale. Initial studies demonstrate the complexity of the genetic regulation of cardiac differentiation, exposing unanticipated novel mechanisms. However, the large datasets generated tend to be overwhelming and systematic approaches are needed to process the vast amount of data to improve our mechanistic understanding of the complex biology. Systems biology methods spur high hopes for parsing vast amounts of data into genetic interaction models that can be verified experimentally and ultimately yield functional networks that expose the genetic connections underlying biological processes.For further resources related to this article, please visit the WIREs website.Conflict of interest: The authors have declared no conflicts of interest for this article.Wiley Interdisciplinary Reviews Systems Biology and Medicine 07/2014; 6(4). DOI:10.1002/wsbm.1268 · 3.01 Impact Factor