Quantitative transcriptomics using designed primer-based amplification

Advancing Breast Cancer Heterogeneity Analysis: Insights from Genomics, Transcriptomics and Proteomics at Bulk and Single-Cell Levels

Breast cancer continues to pose a significant healthcare challenge worldwide for its inherent molecular heterogeneity. This review offers an in-depth assessment of the molecular profiling undertaken to understand this heterogeneity, focusing on multi-omics strategies applied both in traditional bulk and single-cell levels. Genomic investigations have profoundly informed our comprehension of breast cancer, enabling its categorization into six intrinsic molecular subtypes. Beyond genomics, transcriptomics has rendered deeper insights into the gene expression landscape of breast cancer cells. It has also facilitated the formulation of more precise predictive and prognostic models, thereby enriching the field of personalized medicine in breast cancer. The comparison between traditional and single-cell transcriptomics has identified unique gene expression patterns and facilitated the understanding of cell-to-cell variability. Proteomics provides further insights into breast cancer subtypes by illuminating intricate protein expression patterns and their post-translational modifications. The adoption of single-cell proteomics has been instrumental in this regard, revealing the complex dynamics of protein regulation and interaction. Despite these advancements, this review underscores the need for a holistic integration of multiple 'omics' strategies to fully decipher breast cancer heterogeneity. Such integration not only ensures a comprehensive understanding of breast cancer's molecular complexities, but also promotes the development of personalized treatment strategies.

Non-coding RNAs in human health and disease: potential function as biomarkers and therapeutic targets

Human diseases have been a critical threat from the beginning of human history. Knowing the origin, course of action and treatment of any disease state is essential. A microscopic approach to the molecular field is a more coherent and accurate way to explore the mechanism, progression, and therapy with the introduction and evolution of technology than a macroscopic approach. Non-coding RNAs (ncRNAs) play increasingly important roles in detecting, developing, and treating all abnormalities related to physiology, pathology, genetics, epigenetics, cancer, and developmental diseases. Noncoding RNAs are becoming increasingly crucial as powerful, multipurpose regulators of all biological processes. Parallel to this, a rising amount of scientific information has revealed links between abnormal noncoding RNA expression and human disorders. Numerous non-coding transcripts with unknown functions have been found in addition to advancements in RNA-sequencing methods. Non-coding linear RNAs come in a variety of forms, including circular RNAs with a continuous closed loop (circRNA), long non-coding RNAs (lncRNA), and microRNAs (miRNA). This comprises specific information on their biogenesis, mode of action, physiological function, and significance concerning disease (such as cancer or cardiovascular diseases and others). This study review focuses on non-coding RNA as specific biomarkers and novel therapeutic targets.

The impact of single-cell genomics on the field of mycobacterial infection

Genome sequencing projects of humans and other organisms reinforced that the complexity of biological systems is largely attributed to the tight regulation of gene expression at the epigenome and RNA levels. As a consequence, plenty of technological developments arose to increase the sequencing resolution to the cell dimension creating the single-cell genomics research field. Single-cell RNA sequencing (scRNA-seq) is leading the advances in this topic and comprises a vast array of different methodologies. scRNA-seq and its variants are more and more used in life science and biomedical research since they provide unbiased transcriptomic sequencing of large populations of individual cells. These methods go beyond the previous “bulk” methodologies and sculpt the biological understanding of cellular heterogeneity and dynamic transcriptomic states of cellular populations in immunology, oncology, and developmental biology fields. Despite the large burden caused by mycobacterial infections, advances in this field obtained via single-cell genomics had been comparatively modest. Nonetheless, seminal research publications using single-cell transcriptomics to study host cells infected by mycobacteria have become recently available. Here, we review these works summarizing the most impactful findings and emphasizing the different and recent single-cell methodologies used, potential issues, and problems. In addition, we aim at providing insights into current research gaps and potential future developments related to the use of single-cell genomics to study mycobacterial infection.

Toward uncharted territory of cellular heterogeneity: advances and applications of single-cell RNA-seq

Among single-cell analysis technologies, single-cell RNA-seq (scRNA-seq) has been one of the front runners in technical inventions. Since its induction, scRNA-seq has been well received and undergone many fast-paced technical improvements in cDNA synthesis and amplification, processing and alignment of next generation sequencing reads, differentially expressed gene calling, cell clustering, subpopulation identification, and developmental trajectory prediction. scRNA-seq has been exponentially applied to study global transcriptional profiles in all cell types in humans and animal models, healthy or with diseases, including cancer. Accumulative novel subtypes and rare subpopulations have been discovered as potential underlying mechanisms of stochasticity, differentiation, proliferation, tumorigenesis, and aging. scRNA-seq has gradually revealed the uncharted territory of cellular heterogeneity in transcriptomes and developed novel therapeutic approaches for biomedical applications. This review of the advancement of scRNA-seq methods provides an exploratory guide of the quickly evolving technical landscape and insights of focused features and strengths in each prominent area of progress.

Towards a comprehensive pipeline to identify and functionally annotate long noncoding RNA (lncRNA)

Long noncoding RNAs (lncRNAs) are implicated in various genetic diseases and cancer, attributed to their critical role in gene regulation. They are a divergent group of RNAs and are easily differentiated from other types with unique characteristics, functions, and mechanisms of action. In this review, we provide a list of some of the prominent data repositories containing lncRNAs, their interactome, and predicted and validated disease associations. Next, we discuss various wet-lab experiments formulated to obtain the data for these repositories. We also provide a critical review of in silico methods available for the identification purpose and suggest techniques to further improve their performance. The bulk of the methods currently focus on distinguishing lncRNA transcripts from the coding ones. Functional annotation of these transcripts still remains a grey area and more efforts are needed in that space. Finally, we provide details of current progress, discuss impediments, and illustrate a roadmap for developing a generalized computational pipeline for comprehensive annotation of lncRNAs, which is essential to accelerate research in this area.

Principles and innovative technologies for decrypting noncoding RNAs: from discovery and functional prediction to clinical application

Noncoding RNAs (ncRNAs) are a large segment of the transcriptome that do not have apparent protein-coding roles, but they have been verified to play important roles in diverse biological processes, including disease pathogenesis. With the development of innovative technologies, an increasing number of novel ncRNAs have been uncovered; information about their prominent tissue-specific expression patterns, various interaction networks, and subcellular locations will undoubtedly enhance our understanding of their potential functions. Here, we summarized the principles and innovative methods for identifications of novel ncRNAs that have potential functional roles in cancer biology. Moreover, this review also provides alternative ncRNA databases based on high-throughput sequencing or experimental validation, and it briefly describes the current strategy for the clinical translation of cancer-associated ncRNAs to be used in diagnosis.

Non-coding RNAs in cancer: platforms and strategies for investigating the genomic "dark matter"

The discovery of the role of non-coding RNAs (ncRNAs) in the onset and progression of malignancies is a promising frontier of cancer genetics. It is clear that ncRNAs are candidates for therapeutic intervention, since they may act as biomarkers or key regulators of cancer gene network. Recently, profiling and sequencing of ncRNAs disclosed deep deregulation in human cancers mostly due to aberrant mechanisms of ncRNAs biogenesis, such as amplification, deletion, abnormal epigenetic or transcriptional regulation. Although dysregulated ncRNAs may promote hallmarks of cancer as oncogenes or antagonize them as tumor suppressors, the mechanisms behind these events remain to be clarified. The development of new bioinformatic tools as well as novel molecular technologies is a challenging opportunity to disclose the role of the "dark matter" of the genome. In this review, we focus on currently available platforms, computational analyses and experimental strategies to investigate ncRNAs in cancer. We highlight the differences among experimental approaches aimed to dissect miRNAs and lncRNAs, which are the most studied ncRNAs. These two classes indeed need different investigation taking into account their intrinsic characteristics, such as length, structures and also the interacting molecules. Finally, we discuss the relevance of ncRNAs in clinical practice by considering promises and challenges behind the bench to bedside translation.

Single-cell sequencing of miRNAs: A modified technology

The recent development of next-generation sequencing technologies has offered valuable insights into individual cells. This technology is centered on the characterization of single cells for epigenomics, genomics, and transcriptomics. Ever since the first report appeared in 2009, the single-cell RNA-sequencing saga started to explore deeper into the mechanics intrigued within a single cell. microRNA (miRNA) has been increasingly recognized as an essential molecule triggering an additional layer for gene regulation. Therefore, single-cell sequencing of miRNAs is crucial to explore the logical riddles surrounding the epigenomics, genomics, and transcriptomics of an individual cell. Scientists from the Vienna Biocenter Campus have lately performed single-cell sequencing of miRNAs in the fly, Drosophila, and nematode, Caenorhabditis elegans. In this review, we present the latest scientific explorations supported by all-inclusive data on this novel subject matter of next-generation sequencing.

Tumour heterogeneity and metastasis at single-cell resolution

Tumours comprise a heterogeneous collection of cells with distinct genetic and phenotypic properties that can differentially promote progression, metastasis and drug resistance. Emerging single-cell technologies provide a new opportunity to profile individual cells within tumours and investigate what roles they play in these processes. This Review discusses key technological considerations for single-cell studies in cancer, new findings using single-cell technologies and critical open questions for future applications.

Global and targeted approaches to single-cell transcriptome characterization

Analysing transcriptomes of cell populations is a standard molecular biology approach to understand how cells function. Recent methodological development has allowed performing similar experiments on single cells. This has opened up the possibility to examine samples with limited cell number, such as cells of the early embryo, and to obtain an understanding of heterogeneity within populations such as blood cell types or neurons. There are two major approaches for single-cell transcriptome analysis: quantitative reverse transcription PCR (RT-qPCR) on a limited number of genes of interest, or more global approaches targeting entire transcriptomes using RNA sequencing. RT-qPCR is sensitive, fast and arguably more straightforward, while whole-transcriptome approaches offer an unbiased perspective on a cell's expression status.

Characterization of Human Salivary Extracellular RNA by Next-generation Sequencing

BACKGROUND

It was recently discovered that abundant and stable extracellular RNA (exRNA) species exist in bodily fluids. Saliva is an emerging biofluid for biomarker development for noninvasive detection and screening of local and systemic diseases. Use of RNA-Sequencing (RNA-Seq) to profile exRNA is rapidly growing; however, no single preparation and analysis protocol can be used for all biofluids. Specifically, RNA-Seq of saliva is particularly challenging owing to high abundance of bacterial contents and low abundance of salivary exRNA. Given the laborious procedures needed for RNA-Seq library construction, sequencing, data storage, and data analysis, saliva-specific and optimized protocols are essential.

METHODS

We compared different RNA isolation methods and library construction kits for long and small RNA sequencing. The role of ribosomal RNA (rRNA) depletion also was evaluated.

RESULTS

The miRNeasy Micro Kit (Qiagen) showed the highest total RNA yield (70.8 ng/mL cell-free saliva) and best small RNA recovery, and the NEBNext library preparation kits resulted in the highest number of detected human genes [5649-6813 at 1 reads per kilobase RNA per million mapped (RPKM)] and small RNAs [482-696 microRNAs (miRNAs) and 190-214 other small RNAs]. The proportion of human RNA-Seq reads was much higher in rRNA-depleted saliva samples (41%) than in samples without rRNA depletion (14%). In addition, the transfer RNA (tRNA)-derived RNA fragments (tRFs), a novel class of small RNAs, were highly abundant in human saliva, specifically tRF-4 (4%) and tRF-5 (15.25%).

CONCLUSIONS

Our results may help in selection of the best adapted methods of RNA isolation and small and long RNA library constructions for salivary exRNA studies.

Capturing the 'ome': the expanding molecular toolbox for RNA and DNA library construction

All sequencing experiments and most functional genomics screens rely on the generation of libraries to comprehensively capture pools of targeted sequences. In the past decade especially, driven by the progress in the field of massively parallel sequencing, numerous studies have comprehensively assessed the impact of particular manipulations on library complexity and quality, and characterized the activities and specificities of several key enzymes used in library construction. Fortunately, careful protocol design and reagent choice can substantially mitigate many of these biases, and enable reliable representation of sequences in libraries. This review aims to guide the reader through the vast expanse of literature on the subject to promote informed library generation, independent of the application.

Robust transcriptional signatures for low-input RNA samples based on relative expression orderings

Background

It is often difficult to obtain sufficient quantity of RNA molecules for gene expression profiling under many practical situations. Amplification from low-input samples may induce artificial signals.

Results

We compared the expression measurements of low-input mRNA samples, from 25 pg to 1000 pg mRNA, which were amplified and profiled by Smart-seq, DP-seq and CEL-seq techniques using the Illumina HiSeq 2000 platform, with those of the paired high-input (50 ng) mRNA samples. Even with 1000 pg mRNA input, we found that thousands of genes had at least 2 folds-change of expression levels in the low-input samples compared with the corresponding paired high-input samples. Consequently, a transcriptional signature based on quantitative expression values and determined from high-input RNA samples cannot be applied to low-input samples, and vice versa. In contrast, the within-sample relative expression orderings (REOs) of approximately 90% of all the gene pairs in the high-input samples were maintained in the paired low-input samples with 1000 pg input mRNA molecules. Similar results were observed in the low-input total RNA samples amplified and profiled by the Whole-Genome DASL technique using the Illumina HumanRef-8 v3.0 platform. As a proof of principle, we developed REOs-based signatures from high-input RNA samples for discriminating cancer tissues and showed that they can be robustly applied to low-input RNA samples.

Conclusions

REOs-based signatures determined from the high-input RNA samples can be robustly applied to samples profiled with the low-input RNA samples, as low as the 1000 pg and 250 pg input samples but no longer stable in samples with less than 250 pg RNA input to a certain degree.

Electronic supplementary material

The online version of this article (10.1186/s12864-017-4280-7) contains supplementary material, which is available to authorized users.

Single-cell RNA sequencing: Technical advancements and biological applications

Cells are the basic building blocks of organisms and each cell is unique. Single-cell RNA sequencing has emerged as an indispensable tool to dissect the cellular heterogeneity and decompose tissues into cell types and/or cell states, which offers enormous potential for de novo discovery. Single-cell transcriptomic atlases provide unprecedented resolution to reveal complex cellular events and deepen our understanding of biological systems. In this review, we summarize and compare single-cell RNA sequencing technologies, that were developed since 2009, to facilitate a well-informed choice of method. The applications of these methods in different biological contexts are also discussed. We anticipate an ever-increasing role of single-cell RNA sequencing in biology with further improvement in providing spatial information and coupling to other cellular modalities. In the future, such biological findings will greatly benefit medical research.

Development of a Data-Independent Targeted Metabolomics Method for Relative Quantification Using Liquid Chromatography Coupled with Tandem Mass Spectrometry

Quantitative metabolomics approaches can significantly improve the repeatability and reliability of metabolomics investigations but face critical technical challenges, owing to the vast number of unknown endogenous metabolites and the lack of authentic standards. The present study contributes to the development of a novel method known as "data-independent targeted quantitative metabolomics" (DITQM), which was used to investigate the label-free quantitative metabolomics of multiple known and unknown metabolites in biofluid samples. This approach initially involved the acquisition of MS/MS data for all metabolites in biosamples using a sequentially stepped targeted MS/MS (sst-MS/MS) method, in which multiple product ion scans were performed by selecting all ions in the targeted mass ranges as the precursor ions. Subsequently, scheduled multiple reaction monitoring (MRM) by LC-MS/MS of the metabolome was established for 1658 characteristic ion pairs of 1324 metabolites. For sensitive and accurate quantification of these metabolites, mixed calibration curves were generated using sequentially diluted standard reference plasma samples using established MRM methods. Relative concentrations of all metabolites in each sample were calculated without using individual authentic standards. To evaluate the reliability and applicability of this new method, the performance of DITQM was validated by comparison to absolute quantification of 12 acylcarnitines using authentic standards and traditional metabolomics analysis for lung cancer. The results proved that the DITQM protocol is more reliable and can significantly improve clustering effects and repeatability in biomarker discovery. In this study, we established a novel methodology to standardize and quantify large-scale metabolome, providing a new choice for metabolomics research and its clinical applications.

RNA-Seq methods for transcriptome analysis

Deep sequencing has been revolutionizing biology and medicine in recent years, providing single base-level precision for our understanding of nucleic acid sequences in high throughput fashion. Sequencing of RNA, or RNA-Seq, is now a common method to analyze gene expression and to uncover novel RNA species. Aspects of RNA biogenesis and metabolism can be interrogated with specialized methods for cDNA library preparation. In this study, we review current RNA-Seq methods for general analysis of gene expression and several specific applications, including isoform and gene fusion detection, digital gene expression profiling, targeted sequencing and single-cell analysis. In addition, we discuss approaches to examine aspects of RNA in the cell, technical challenges of existing RNA-Seq methods, and future directions. WIREs RNA 2017, 8:e1364. doi: 10.1002/wrna.1364 For further resources related to this article, please visit the WIREs website.

Accurate Profiling of Gene Expression and Alternative Polyadenylation with Whole Transcriptome Termini Site Sequencing (WTTS-Seq)

Construction of next-generation sequencing (NGS) libraries involves RNA manipulation, which often creates noisy, biased, and artifactual data that contribute to errors in transcriptome analysis. In this study, a total of 19 whole transcriptome termini site sequencing (WTTS-seq) and seven RNA sequencing (RNA-seq) libraries were prepared from Xenopus tropicalis adult and embryo samples to determine the most effective library preparation method to maximize transcriptomics investigation. We strongly suggest that appropriate primers/adaptors are designed to inhibit amplification detours and that PCR overamplification is minimized to maximize transcriptome coverage. Furthermore, genome annotation must be improved so that missing data can be recovered. In addition, a complete understanding of sequencing platforms is critical to limit the formation of false-positive results. Technically, the WTTS-seq method enriches both poly(A)+ RNA and complementary DNA, adds 5′- and 3′-adaptors in one step, pursues strand sequencing and mapping, and profiles both gene expression and alternative polyadenylation (APA). Although RNA-seq is cost prohibitive, tends to produce false-positive results, and fails to detect APA diversity and dynamics, its combination with WTTS-seq is necessary to validate transcriptome-wide APA.

Nuclear RNA-seq of single neurons reveals molecular signatures of activation

Single-cell sequencing methods have emerged as powerful tools for identification of heterogeneous cell types within defined brain regions. Application of single-cell techniques to study the transcriptome of activated neurons can offer insight into molecular dynamics associated with differential neuronal responses to a given experience. Through evaluation of common whole-cell and single-nuclei RNA-sequencing (snRNA-seq) methods, here we show that snRNA-seq faithfully recapitulates transcriptional patterns associated with experience-driven induction of activity, including immediate early genes (IEGs) such as Fos, Arc and Egr1. SnRNA-seq of mouse dentate granule cells reveals large-scale changes in the activated neuronal transcriptome after brief novel environment exposure, including induction of MAPK pathway genes. In addition, we observe a continuum of activation states, revealing a pseudotemporal pattern of activation from gene expression alone. In summary, snRNA-seq of activated neurons enables the examination of gene expression beyond IEGs, allowing for novel insights into neuronal activation patterns in vivo.

The molecular dynamics associated with neuronal activation patterns in vivo are unclear. Lacar et al. perform single-nuclei RNA-sequencing of hippocampal neurons from mice exposed to a novel environment, and identify large-scale transcriptome changes in individual neurons associated with the experience.

Single-cell transcriptome sequencing: recent advances and remaining challenges

Single-cell RNA-sequencing methods are now robust and economically practical and are becoming a powerful tool for high-throughput, high-resolution transcriptomic analysis of cell states and dynamics. Single-cell approaches circumvent the averaging artifacts associated with traditional bulk population data, yielding new insights into the cellular diversity underlying superficially homogeneous populations. Thus far, single-cell RNA-sequencing has already shown great effectiveness in unraveling complex cell populations, reconstructing developmental trajectories, and modeling transcriptional dynamics. Ongoing technical improvements to single-cell RNA-sequencing throughput and sensitivity, the development of more sophisticated analytical frameworks for single-cell data, and an increasing array of complementary single-cell assays all promise to expand the usefulness and potential applications of single-cell transcriptomic profiling.

Discovery and functional analysis of lncRNAs: Methodologies to investigate an uncharacterized transcriptome

It is known that more than 70% of mammalian genomes are transcribed, yet the vast majority of transcripts do not code for proteins. Are these noncoding transcripts merely transcriptional noise, or do they serve a biological purpose? Recent developments in genomic analysis technologies, especially sequencing methods, have allowed researchers to create a large atlas of transcriptomes, study subcellular localization, and investigate potential interactions with proteins for a growing number of transcripts. Here, we review the current methodologies available for discovering and investigating functions of long noncoding RNAs (lncRNAs), which require a wide variety of applications to study their potential biological roles. This article is part of a Special Issue entitled: Clues to long noncoding RNA taxonomy1, edited by Dr. Tetsuro Hirose and Dr. Shinichi Nakagawa.

The technology and biology of single-cell RNA sequencing

The differences between individual cells can have profound functional consequences, in both unicellular and multicellular organisms. Recently developed single-cell mRNA-sequencing methods enable unbiased, high-throughput, and high-resolution transcriptomic analysis of individual cells. This provides an additional dimension to transcriptomic information relative to traditional methods that profile bulk populations of cells. Already, single-cell RNA-sequencing methods have revealed new biology in terms of the composition of tissues, the dynamics of transcription, and the regulatory relationships between genes. Rapid technological developments at the level of cell capture, phenotyping, molecular biology, and bioinformatics promise an exciting future with numerous biological and medical applications.

Differential response of epiblast stem cells to Nodal and Activin signalling: a paradigm of early endoderm development in the embryo

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.

Identification of therapeutic targets for glioblastoma by network analysis

Glioblastoma can originate from terminally differentiated astrocytes and neurons, which can dedifferentiate to a stem cell-like state upon transformation. In this study, we confirmed that transformed dedifferentiated astrocytes and neurons acquired a stem/progenitor cell state, although they still retained gene expression memory from their parental cell. Transcriptional network analysis on these cells identified upregulated genes in three main pathways: Wnt signaling, cell cycle and focal adhesion with the gene Spp1, also known as osteopontin (OPN) serving as a key common node connecting these three pathways. Inhibition of OPN blocked the formation of neurospheres, affected the proliferative capacity of transformed neurons and reduced the expression levels of neural stem cell markers. Specific inhibition of OPN in both murine and human glioma tumors prolonged mice survival. We conclude that OPN is an important player in dedifferentiation of cells during tumor formation, hence its inhibition can be a therapeutic target for glioblastoma.

A comparison of sperm RNA-seq methods

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.

An interplay between extracellular signalling and the dynamics of the exit from pluripotency drives cell fate decisions in mouse ES cells

Embryonic Stem cells derived from the epiblast tissue of the mammalian blastocyst retain the capability to differentiate into any adult cell type and are able to self-renew indefinitely under appropriate culture conditions. Despite the large amount of knowledge that we have accumulated to date about the regulation and control of self-renewal, efficient directed differentiation into specific tissues remains elusive. In this work, we have analysed in a systematic manner the interaction between the dynamics of loss of pluripotency and Activin/Nodal, BMP4 and Wnt signalling in fate assignment during the early stages of differentiation of mouse ES cells in culture. During the initial period of differentiation, cells exit from pluripotency and enter an Epi-like state. Following this transient stage, and under the influence of Activin/Nodal and BMP signalling, cells face a fate choice between differentiating into neuroectoderm and contributing to Primitive Streak fates. We find that Wnt signalling does not suppress neural development as previously thought and that it aids both fates in a context dependent manner. Our results suggest that as cells exit pluripotency they are endowed with a primary neuroectodermal fate and that the potency to become endomesodermal rises with time. We suggest that this situation translates into a “race for fates” in which the neuroectodermal fate has an advantage.

Library construction for next-generation sequencing: overviews and challenges

High-throughput sequencing, also known as next-generation sequencing (NGS), has revolutionized genomic research. In recent years, NGS technology has steadily improved, with costs dropping and the number and range of sequencing applications increasing exponentially. Here, we examine the critical role of sequencing library quality and consider important challenges when preparing NGS libraries from DNA and RNA sources. Factors such as the quantity and physical characteristics of the RNA or DNA source material as well as the desired application (i.e., genome sequencing, targeted sequencing, RNA-seq, ChIP-seq, RIP-seq, and methylation) are addressed in the context of preparing high quality sequencing libraries. In addition, the current methods for preparing NGS libraries from single cells are also discussed.

Technical variations in low-input RNA-seq methodologies

Recent advances in RNA-seq methodologies from limiting amounts of mRNA have facilitated the characterization of rare cell-types in various biological systems. So far, however, technical variations in these methods have not been adequately characterized, vis-à-vis sensitivity, starting with reduced levels of mRNA. Here, we generated sequencing libraries from limiting amounts of mRNA using three amplification-based methods, viz. Smart-seq, DP-seq and CEL-seq, and demonstrated significant technical variations in these libraries. Reduction in mRNA levels led to inefficient amplification of the majority of low to moderately expressed transcripts. Furthermore, noise in primer hybridization and/or enzyme incorporation was magnified during the amplification step resulting in significant distortions in fold changes of the transcripts. Consequently, the majority of the differentially expressed transcripts identified were either high-expressed and/or exhibited high fold changes. High technical variations ultimately masked subtle biological differences mandating the development of improved amplification-based strategies for quantitative transcriptomics from limiting amounts of mRNA.