RNA-Seq analysis to capture the transcriptome landscape of a single cell

Current challenges in the bioinformatics of single cell genomics

Single cell genomics is a rapidly growing field with many new techniques emerging in the past few years. However, few bioinformatics tools specific for single cell genomics analysis are available. Single cell DNA/RNA sequencing data usually have low genome coverage and high amplification bias, which makes bioinformatics analysis challenging. Many current bioinformatics tools developed for bulk cell sequencing do not work well with single cell sequencing data. Here, we summarize current challenges in the bioinformatics analysis of single cell genomic DNA sequencing and single cell transcriptomes. These challenges include calling copy number variations, identifying mutated genes in tumor samples, reconstructing cell lineages, recovering low abundant transcripts, and improving the accuracy of quantitative analysis of transcripts. Development in single cell genomics bioinformatics analysis will promote the application of this technology to basic biology and medical research.

3' Untranslated regions mediate transcriptional interference between convergent genes both locally and ectopically in Saccharomyces cerevisiae

Paired sense and antisense (S/AS) genes located in cis represent a structural feature common to the genomes of both prokaryotes and eukaryotes, and produce partially complementary transcripts. We used published genome and transcriptome sequence data and found that over 20% of genes (645 pairs) in the budding yeast Saccharomyces cerevisiae genome are arranged in convergent pairs with overlapping 3'-UTRs. Using published microarray transcriptome data from the standard laboratory strain of S. cerevisiae, our analysis revealed that expression levels of convergent pairs are significantly negatively correlated across a broad range of environments. This implies an important role for convergent genes in the regulation of gene expression, which may compensate for the absence of RNA-dependent mechanisms such as micro RNAs in budding yeast. We selected four representative convergent gene pairs and used expression assays in wild type yeast and its genetically modified strains to explore the underlying patterns of gene expression. Results showed that convergent genes are reciprocally regulated in yeast populations and in single cells, whereby an increase in expression of one gene produces a decrease in the expression of the other, and vice-versa. Time course analysis of the cell cycle illustrated the functional significance of this relationship for the three pairs with relevant functional roles. Furthermore, a series of genetic modifications revealed that the 3'-UTR sequence plays an essential causal role in mediating transcriptional interference, which requires neither the sequence of the open reading frame nor the translation of fully functional proteins. More importantly, transcriptional interference persisted even when one of the convergent genes was expressed ectopically (in trans) and therefore does not depend on the cis arrangement of convergent genes; we conclude that the mechanism of transcriptional interference cannot be explained by the transcriptional collision model, which postulates a clash between simultaneous transcriptional processes occurring on opposite DNA strands.

Specific gene-regulation networks during the pre-implantation development of the pig embryo as revealed by deep sequencing

Background

Because few studies exist to describe the unique molecular network regulation behind pig pre-implantation embryonic development (PED), genetic engineering in the pig embryo is limited. Also, this lack of research has hindered derivation and application of porcine embryonic stem cells and porcine induced pluripotent stem cells (iPSCs).

Results

We identified and analyzed the genome wide transcriptomes of pig in vivo-derived and somatic cell nuclear transferred (SCNT) as well as mouse in vivo-derived pre-implantation embryos at different stages using mRNA deep sequencing. Comparison of the pig embryonic transcriptomes with those of mouse and human pre-implantation embryos revealed unique gene expression patterns during pig PED. Pig zygotic genome activation was confirmed to occur at the 4-cell stage via genome-wide gene expression analysis. This activation was delayed to the 8-cell stage in SCNT embryos. Specific gene expression analysis of the putative inner cell mass (ICM) and the trophectoderm (TE) revealed that pig and mouse pre-implantation embryos share regulatory networks during the first lineage segregation and primitive endoderm differentiation, but not during ectoderm commitment. Also, fatty acid metabolism appears to be a unique characteristic of pig pre-implantation embryonic development. In addition, the global gene expression patterns in the pig SCNT embryos were different from those in in vivo-derived pig embryos.

Conclusions

Our results provide a resource for pluripotent stem cell engineering and for understanding pig development.

Electronic supplementary material

The online version of this article (doi:10.1186/1471-2164-15-4) contains supplementary material, which is available to authorized users.

Full-length RNA-seq from single cells using Smart-seq2

Emerging methods for the accurate quantification of gene expression in individual cells hold promise for revealing the extent, function and origins of cell-to-cell variability. Different high-throughput methods for single-cell RNA-seq have been introduced that vary in coverage, sensitivity and multiplexing ability. We recently introduced Smart-seq for transcriptome analysis from single cells, and we subsequently optimized the method for improved sensitivity, accuracy and full-length coverage across transcripts. Here we present a detailed protocol for Smart-seq2 that allows the generation of full-length cDNA and sequencing libraries by using standard reagents. The entire protocol takes ∼2 d from cell picking to having a final library ready for sequencing; sequencing will require an additional 1-3 d depending on the strategy and sequencer. The current limitations are the lack of strand specificity and the inability to detect nonpolyadenylated (polyA(-)) RNA.

Quantitative assessment of single-cell RNA-sequencing methods

Interest in single-cell whole-transcriptome analysis is growing rapidly, especially for profiling rare or heterogeneous populations of cells. We compared commercially available single-cell RNA amplification methods with both microliter and nanoliter volumes, using sequence from bulk total RNA and multiplexed quantitative PCR as benchmarks to systematically evaluate the sensitivity and accuracy of various single-cell RNA-seq approaches. We show that single-cell RNA-seq can be used to perform accurate quantitative transcriptome measurement in individual cells with a relatively small number of sequencing reads and that sequencing large numbers of single cells can recapitulate bulk transcriptome complexity.

Quantitative dynamics of triacylglycerol accumulation in microalgae populations at single-cell resolution revealed by Raman microspectroscopy

BACKGROUND

Rapid, real-time and label-free measurement of the cellular contents of biofuel molecules such as triacylglycerol (TAG) in populations at single-cell resolution are important for bioprocess control and understanding of the population heterogeneity. Raman microspectroscopy can directly detect the changes of metabolite profile in a cell and thus can potentially serve these purposes.

RESULTS

Single-cell Raman spectra (SCRS) of the unicellular oleaginous microalgae Nannochloropsis oceanica from the cultures under nitrogen depletion (TAG-producing condition) and nitrogen repletion (non-TAG-producing condition) were sampled at eight time points during the first 96 hours upon the onset of nitrogen depletion. Single N. oceanica cells were captured by a 532-nm laser and the SCRS were acquired by the same laser within one second per cell. Using chemometric methods, the SCRS were able to discriminate cells between nitrogen-replete and nitrogen-depleted conditions at as early as 6 hours with >93.3% accuracy, and among the eight time points under nitrogen depletion with >90.4% accuracy. Quantitative prediction of TAG content in single cells was achieved and validated via SCRS and liquid chromatography-mass spectrometry (LC-MS) analysis at population level. SCRS revealed the dynamics of heterogeneity in TAG production among cells in each isogenic population. A significant negative correlation between TAG content and lipid unsaturation degree in individual microalgae cells was observed.

CONCLUSIONS

Our results show that SCRS can serve as a label-free and non-invasive proxy for quantitatively tracking and screening cellular TAG content in real-time at single-cell level. Phenotypic comparison of single cells via SCRS should also help investigating the mechanisms of functional heterogeneity within a cellular population.

Next-generation sequencing applied to flower development: RNA-seq

Genome-wide study of gene expression, or transcriptome profiling, is critical for our understanding of biological functions, including developmental processes. Recent breakthroughs in high-throughput sequencing technologies have revolutionized gene expression profiling to study the transcriptome at the nucleotide level, which is known as RNA-seq. RNA-seq, also called "whole transcriptome shotgun sequencing," uses next-generation sequencing technologies to sequence cDNA in order to infer a sample's RNA content. Here we describe a detailed bench-ready protocol to generate RNA-seq libraries for high-throughput single-end or pair-end sequencing compatible with the Illumina sequencing platform.

From single-cell to cell-pool transcriptomes: stochasticity in gene expression and RNA splicing

Single-cell RNA-seq mammalian transcriptome studies are at an early stage in uncovering cell-to-cell variation in gene expression, transcript processing and editing, and regulatory module activity. Despite great progress recently, substantial challenges remain, including discriminating biological variation from technical noise. Here we apply the SMART-seq single-cell RNA-seq protocol to study the reference lymphoblastoid cell line GM12878. By using spike-in quantification standards, we estimate the absolute number of RNA molecules per cell for each gene and find significant variation in total mRNA content: between 50,000 and 300,000 transcripts per cell. We directly measure technical stochasticity by a pool/split design and find that there are significant differences in expression between individual cells, over and above technical variation. Specific gene coexpression modules were preferentially expressed in subsets of individual cells, including one enriched for mRNA processing and splicing factors. We assess cell-to-cell variation in alternative splicing and allelic bias and report evidence of significant differences in splice site usage that exceed splice variation in the pool/split comparison. Finally, we show that transcriptomes from small pools of 30–100 cells approach the information content and reproducibility of contemporary RNA-seq from large amounts of input material. Together, our results define an experimental and computational path forward for analyzing gene expression in rare cell types and cell states.

Cell-to-cell expression variability followed by signal reinforcement progressively segregates early mouse lineages

It is now recognized that extensive expression heterogeneities among cells precede the emergence of lineages in the early mammalian embryo. To establish a map of pluripotent epiblast (EPI) versus primitive endoderm (PrE) lineage segregation within the inner cell mass (ICM) of the mouse blastocyst, we characterised the gene expression profiles of individual ICM cells. Clustering analysis of the transcriptomes of 66 cells demonstrated that initially they are non-distinguishable. Early in the segregation, lineage-specific marker expression exhibited no apparent correlation, and a hierarchical relationship was established only in the late blastocyst. Fgf4 exhibited a bimodal expression at the earliest stage analysed, and in its absence, the differentiation of PrE and EPI was halted, indicating that Fgf4 drives, and is required for, ICM lineage segregation. These data lead us to propose a model where stochastic cell-to-cell expression heterogeneity followed by signal reinforcement underlies ICM lineage segregation by antagonistically separating equivalent cells.

RNA-sequencing from single nuclei

It has recently been established that synthesis of double-stranded cDNA can be done from a single cell for use in DNA sequencing. Global gene expression can be quantified from the number of reads mapping to each gene, and mutations and mRNA splicing variants determined from the sequence reads. Here we demonstrate that this method of transcriptomic analysis can be done using the extremely low levels of mRNA in a single nucleus, isolated from a mouse neural progenitor cell line and from dissected hippocampal tissue. This method is characterized by excellent coverage and technical reproducibility. On average, more than 16,000 of the 24,057 mouse protein-coding genes were detected from single nuclei, and the amount of gene-expression variation was similar when measured between single nuclei and single cells. Several major advantages of the method exist: first, nuclei, compared with whole cells, have the advantage of being easily isolated from complex tissues and organs, such as those in the CNS. Second, the method can be widely applied to eukaryotic species, including those of different kingdoms. The method also provides insight into regulatory mechanisms specific to the nucleus. Finally, the method enables dissection of regulatory events at the single-cell level; pooling of 10 nuclei or 10 cells obscures some of the variability measured in transcript levels, implying that single nuclei and cells will be extremely useful in revealing the physiological state and interconnectedness of gene regulation in a manner that avoids the masking inherent to conventional transcriptomics using bulk cells or tissues.

A microfluidic device to sort cells based on dynamic response to a stimulus

Single cell techniques permit the analysis of cellular properties that are obscured by studying the average behavior of cell populations. One way to determine how gene expression contributes to phenotypic differences among cells is to combine functional analysis with transcriptional profiling of single cells. Here we describe a microfluidic device for monitoring the responses of single cells to a ligand and then collecting cells of interest for transcriptional profiling or other assays. As a test, cells from the olfactory epithelium of zebrafish were screened by calcium imaging to identify sensory neurons that were responsive to the odorant L-lysine. Single cells were subsequently recovered for transcriptional profiling by qRT-PCR. Responsive cells all expressed TRPC2 but not OMP, consistent with known properties of amino-acid sensitive olfactory neurons. The device can be adapted for other areas in biology where there is a need to sort and analyze cells based on their signaling responses.

Sequencing and de novo analysis of the hemocytes transcriptome in Litopenaeus vannamei response to white spot syndrome virus infection

Background

White spot syndrome virus (WSSV) is a causative pathogen found in most shrimp farming areas of the world and causes large economic losses to the shrimp aquaculture. The mechanism underlying the molecular pathogenesis of the highly virulent WSSV remains unknown. To better understand the virus-host interactions at the molecular level, the transcriptome profiles in hemocytes of unchallenged and WSSV-challenged shrimp (Litopenaeus vannamei) were compared using a short-read deep sequencing method (Illumina).

Results

RNA-seq analysis generated more than 25.81 million clean pair end (PE) reads, which were assembled into 52,073 unigenes (mean size = 520 bp). Based on sequence similarity searches, 23,568 (45.3%) genes were identified, among which 6,562 and 7,822 unigenes were assigned to gene ontology (GO) categories and clusters of orthologous groups (COG), respectively. Searches in the Kyoto Encyclopedia of Genes and Genomes Pathway database (KEGG) mapped 14,941 (63.4%) unigenes to 240 KEGG pathways. Among all the annotated unigenes, 1,179 were associated with immune-related genes. Digital gene expression (DGE) analysis revealed that the host transcriptome profile was slightly changed in the early infection (5 hours post injection) of the virus, while large transcriptional differences were identified in the late infection (48 hpi) of WSSV. The differentially expressed genes mainly involved in pattern recognition genes and some immune response factors. The results indicated that antiviral immune mechanisms were probably involved in the recognition of pathogen-associated molecular patterns.

Conclusions

This study provided a global survey of host gene activities against virus infection in a non-model organism, pacific white shrimp. Results can contribute to the in-depth study of candidate genes in white shrimp, and help to improve the current understanding of host-pathogen interactions.

Non-biased and efficient global amplification of a single-cell cDNA library

Analysis of single-cell gene expression promises a more precise understanding of molecular mechanisms of a living system. Most techniques only allow studies of the expressions for limited numbers of gene species. When amplification of cDNA was carried out for analysing more genes, amplification biases were frequently reported. A non-biased and efficient global-amplification method, which uses a single-cell cDNA library immobilized on beads, was developed for analysing entire gene expressions for single cells. Every step in this analysis from reverse transcription to cDNA amplification was optimized. By removing degrading excess primers, the bias due to the digestion of cDNA was prevented. Since the residual reagents, which affect the efficiency of each subsequent reaction, could be removed by washing beads, the conditions for uniform and maximized amplification of cDNAs were achieved. The differences in the amplification rates for randomly selected eight genes were within 1.5-folds, which could be negligible for most of the applications of single-cell analysis. The global amplification gives a large amount of amplified cDNA (>100 μg) from a single cell (2-pg mRNA), and that amount is enough for downstream analysis. The proposed global-amplification method was used to analyse transcript ratios of multiple cDNA targets (from several copies to several thousand copies) quantitatively.

Accounting for technical noise in single-cell RNA-seq experiments

Single-cell RNA-seq can yield valuable insights about the variability within a population of seemingly homogeneous cells. We developed a quantitative statistical method to distinguish true biological variability from the high levels of technical noise in single-cell experiments. Our approach quantifies the statistical significance of observed cell-to-cell variability in expression strength on a gene-by-gene basis. We validate our approach using two independent data sets from Arabidopsis thaliana and Mus musculus.

The importance of tissue specificity for RNA-seq: highlighting the errors of composite structure extractions

Background

A composite biological structure, such as an insect head or abdomen, contains many internal structures with distinct functions. Composite structures are often used in RNA-seq studies, though it is unclear how expression of the same gene in different tissues and structures within the same structure affects the measurement (or even utility) of the resulting patterns of gene expression. Here we determine how complex composite tissue structure affects measures of gene expression using RNA-seq.

Results

We focus on two structures in the honey bee (the sting gland and digestive tract) both contained within one larger structure, the whole abdomen. For each of the three structures, we used RNA-seq to identify differentially expressed genes between two developmental stages, nurse bees and foragers. Based on RNA-seq for each structure-specific extraction, we found that RNA-seq with composite structures leads to many false negatives (genes strongly differentially expressed in particular structures which are not found to be differentially expressed within the composite structure). We also found a significant number of genes with one pattern of differential expression in the tissue-specific extraction, and the opposite in the composite extraction, suggesting multiple signals from such genes within the composite structure. We found these patterns for different classes of genes including transcription factors.

Conclusions

Many RNA-seq studies currently use composite extractions, and even whole insect extractions, when tissue and structure specific extractions are possible. This is due to the logistical difficultly of micro-dissection and unawareness of the potential errors associated with composite extractions. The present study suggests that RNA-seq studies of composite structures are prone to false negatives and difficult to interpret positive signals for genes with variable patterns of local expression. In general, our results suggest that RNA-seq on large composite structures should be avoided unless it is possible to demonstrate that the effects shown here do not exist for the genes of interest.

Single cell lineage analysis of mouse embryonic stem cells at the exit from pluripotency

Understanding how interactions between extracellular signalling pathways and transcription factor networks influence cellular decision making will be crucial for understanding mammalian embryogenesis and for generating specialised cell types in vitro. To this end, pluripotent mouse Embryonic Stem (mES) cells have proven to be a useful model system. However, understanding how transcription factors and signalling pathways affect decisions made by individual cells is confounded by the fact that measurements are generally made on groups of cells, whilst individual mES cells differentiate at different rates and towards different lineages, even in conditions that favour a particular lineage. Here we have used single-cell measurements of transcription factor expression and Wnt/β-catenin signalling activity to investigate their effects on lineage commitment decisions made by individual cells. We find that pluripotent mES cells exhibit differing degrees of heterogeneity in their expression of important regulators from pluripotency, depending on the signalling environment to which they are exposed. As mES cells differentiate, downregulation of Nanog and Oct4 primes cells for neural commitment, whilst loss of Sox2 expression primes cells for primitive streak commitment. Furthermore, we find that Wnt signalling acts through Nanog to direct cells towards a primitive streak fate, but that transcriptionally active β-catenin is associated with both neural and primitive streak commitment. These observations confirm and extend previous suggestions that pluripotency genes influence lineage commitment and demonstrate how their dynamic expression affects the direction of lineage commitment, whilst illustrating two ways in which the Wnt signalling pathway acts on this network during cell fate assignment.

Single-cell RNA-Seq profiling of human preimplantation embryos and embryonic stem cells

Measuring gene expression in individual cells is crucial for understanding the gene regulatory network controlling human embryonic development. Here we apply single-cell RNA sequencing (RNA-Seq) analysis to 124 individual cells from human preimplantation embryos and human embryonic stem cells (hESCs) at different passages. The number of maternally expressed genes detected in our data set is 22,687, including 8,701 long noncoding RNAs (lncRNAs), which represents a significant increase from 9,735 maternal genes detected previously by cDNA microarray. We discovered 2,733 novel lncRNAs, many of which are expressed in specific developmental stages. To address the long-standing question whether gene expression signatures of human epiblast (EPI) and in vitro hESCs are the same, we found that EPI cells and primary hESC outgrowth have dramatically different transcriptomes, with 1,498 genes showing differential expression between them. This work provides a comprehensive framework of the transcriptome landscapes of human early embryos and hESCs.

The applications of single-cell genomics

We all start out as a single totipotent cell that is programmed to produce a multicellular organism. How do individual cells make those complex developmental switches? How do single cells within a tissue or organ differ, how do they coordinate their actions or go astray in a disease process? These are long-standing and fundamental questions in biology that are now becoming tractable because of advances in microfluidics, DNA amplification and DNA sequencing. Methods for studying single-cell transcriptomes (or at least the polyadenylated mRNA fraction of it) are by far the furthest ahead and reveal remarkable heterogeneity between morphologically identical cells. The analysis of genomic DNA variation is not far behind. The other 'omics' of single cells pose greater technological obstacles, but they are progressing and promise to yield highly integrated large data sets in the near future.

The details in the distributions: why and how to study phenotypic variability

Phenotypic variability is present even when genetic and environmental differences between cells are reduced to the greatest possible extent. For example, genetically identical bacteria display differing levels of resistance to antibiotics, clonal yeast populations demonstrate morphological and growth-rate heterogeneity, and mouse blastomeres from the same embryo have stochastic differences in gene expression. However, the distributions of phenotypes present among isogenic organisms are often overlooked; instead, many studies focus on population aggregates such as the mean. The details of these distributions are relevant to major questions in diverse fields, including the evolution of antimicrobial-drug and chemotherapy resistance. We review emerging experimental and statistical techniques that allow rigorous analysis of phenotypic variability and thereby may lead to advances across the biological sciences.

Genetic programs in human and mouse early embryos revealed by single-cell RNA sequencing

Mammalian pre-implantation development is a complex process involving dramatic changes in the transcriptional architecture. We report here a comprehensive analysis of transcriptome dynamics from oocyte to morula in both human and mouse embryos, using single-cell RNA sequencing. Based on single-nucleotide variants in human blastomere messenger RNAs and paternal-specific single-nucleotide polymorphisms, we identify novel stage-specific monoallelic expression patterns for a significant portion of polymorphic gene transcripts (25 to 53%). By weighted gene co-expression network analysis, we find that each developmental stage can be delineated concisely by a small number of functional modules of co-expressed genes. This result indicates a sequential order of transcriptional changes in pathways of cell cycle, gene regulation, translation and metabolism, acting in a step-wise fashion from cleavage to morula. Cross-species comparisons with mouse pre-implantation embryos reveal that the majority of human stage-specific modules (7 out of 9) are notably preserved, but developmental specificity and timing differ between human and mouse. Furthermore, we identify conserved key members (or hub genes) of the human and mouse networks. These genes represent novel candidates that are likely to be key in driving mammalian pre-implantation development. Together, the results provide a valuable resource to dissect gene regulatory mechanisms underlying progressive development of early mammalian embryos.

Methylmercury exposure increases lipocalin related (lpr) and decreases activated in blocked unfolded protein response (abu) genes and specific miRNAs in Caenorhabditis elegans

Methylmercury (MeHg) is a persistent environmental and dietary contaminant that causes serious adverse developmental and physiologic effects at multiple cellular levels. In order to understand more fully the consequences of MeHg exposure at the molecular level, we profiled gene and miRNA transcripts from the model organism Caenorhabditis elegans. Animals were exposed to MeHg (10 μM) from embryo to larval 4 (L4) stage and RNAs were isolated. RNA-seq analysis on the Illumina platform revealed 541 genes up- and 261 genes down-regulated at a cutoff of 2-fold change and false discovery rate-corrected significance q < 0.05. Among the up-regulated genes were those previously shown to increase under oxidative stress conditions including hsp-16.11 (2.5-fold), gst-35 (10.1-fold), and fmo-2 (58.5-fold). In addition, we observed up-regulation of 6 out of 7 lipocalin related (lpr) family genes and down regulation of 7 out of 15 activated in blocked unfolded protein response (abu) genes. Gene Ontology enrichment analysis highlighted the effect of genes related to development and organism growth. miRNA-seq analysis revealed 6-8 fold down regulation of mir-37-3p, mir-41-5p, mir-70-3p, and mir-75-3p. Our results demonstrate the effects of MeHg on specific transcripts encoding proteins in oxidative stress responses and in ER stress pathways. Pending confirmation of these transcript changes at protein levels, their association and dissociation characteristics with interaction partners, and integration of these signals, these findings indicate broad and dynamic mechanisms by which MeHg exerts its harmful effects.

Deciphering the single-cell omic: innovative application for translational medicine

Traditional technologies to investigate system biology are limited by the detection of parameters resulting from the averages of large populations of cells, missing cells produced in small numbers, and attempting to uniform the heterogeneity. The advent of proteomics and genomics at a single-cell level has set the basis for an outstanding improvement in analytical technology and data acquisition. It has been well demonstrated that cellular heterogeneity is closely related to numerous stochastic transcriptional events leading to variations in patterns of expression among single genetically identical cells. The new-generation technology of single-cell analysis is able to better characterize a cell's population, identifying and differentiating outlier cells, in order to provide both a single-cell experiment and a corresponding bulk measurement, through the identification, quantification and characterization of all system biology aspects (genomics, transcriptomics, proteomics, metabolomics, degradomics and fluxomics). The movement of omics into single-cell analysis represents a significant and outstanding shift.

Transcriptome characterization by RNA-Seq reveals the involvement of the complement components in noise-traumatized rat cochleae

Acoustic trauma, a leading cause of sensorineural hearing loss in adults, induces a complex degenerative process in the cochlea. Although previous investigations have identified multiple stress pathways, a comprehensive analysis of cochlear responses to acoustic injury is still lacking. In the current study, we used the next-generation RNA-sequencing (RNA-Seq) technique to sequence the whole transcriptome of the normal and noise-traumatized cochlear sensory epithelia (CSE). CSE tissues were collected from rat inner ears 1d after the rats were exposed to a 120-dB (sound pressure level) noise for 2 h. The RNA-Seq generated over 176 million sequence reads for the normal CSE and over 164 million reads for the noise-traumatized CSE. Alignment of these sequences with the rat Rn4 genome revealed the expression of over 17,000 gene transcripts in the CSE, over 2000 of which were exclusively expressed in either the normal or noise-traumatized CSE. Seventy-eight gene transcripts were differentially expressed (70 upregulated and 8 downregulated) after acoustic trauma. Many of the differentially expressed genes are related to the innate immune system. Further expression analyses using quantitative real time PCR confirmed the constitutive expression of multiple complement genes in the normal organ of Corti and the changes in the expression levels of the complement factor I (Cfi) and complement component 1, s subcomponent (C1s) after acoustic trauma. Moreover, protein expression analysis revealed strong expression of Cfi and C1s proteins in the organ of Corti. Importantly, these proteins exhibited expression changes following acoustic trauma. Collectively, the results of the current investigation suggest the involvement of the complement components in cochlear responses to acoustic trauma.

Prdm14 promotes germline fate and naive pluripotency by repressing FGF signalling and DNA methylation

The transcription factor Prdm14 is a critical regulator of pluripotency and germline development. This study shows that Prdm14 represses DNA methylation and FGF signaling, thereby promoting both germ cell fate and naive pluripotency in ESC.

Primordial germ cells (PGCs) and somatic cells originate from postimplantation epiblast cells in mice. As pluripotency is lost upon differentiation of somatic lineages, a naive epigenome and the pluripotency network are re-established during PGC development. Here we demonstrate that Prdm14 contributes not only to PGC specification, but also to naive pluripotency in embryonic stem (ES) cells by repressing the DNA methylation machinery and fibroblast growth factor (FGF) signalling. This indicates a critical role for Prdm14 in programming PGCs and promoting pluripotency in ES cells.

Quartz-Seq: a highly reproducible and sensitive single-cell RNA sequencing method, reveals non-genetic gene-expression heterogeneity

Development of a highly reproducible and sensitive single-cell RNA sequencing (RNA-seq) method would facilitate the understanding of the biological roles and underlying mechanisms of non-genetic cellular heterogeneity. In this study, we report a novel single-cell RNA-seq method called Quartz-Seq that has a simpler protocol and higher reproducibility and sensitivity than existing methods. We show that single-cell Quartz-Seq can quantitatively detect various kinds of non-genetic cellular heterogeneity, and can detect 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 in the same cell-cycle phase.

Purification of transcripts and metabolites from Drosophila heads

For the last decade, we have tried to understand the molecular and cellular mechanisms of neuronal degeneration using Drosophila as a model organism. Although fruit flies provide obvious experimental advantages, research on neurodegenerative diseases has mostly relied on traditional techniques, including genetic interaction, histology, immunofluorescence, and protein biochemistry. These techniques are effective for mechanistic, hypothesis-driven studies, which lead to a detailed understanding of the role of single genes in well-defined biological problems. However, neurodegenerative diseases are highly complex and affect multiple cellular organelles and processes over time. The advent of new technologies and the omics age provides a unique opportunity to understand the global cellular perturbations underlying complex diseases. Flexible model organisms such as Drosophila are ideal for adapting these new technologies because of their strong annotation and high tractability. One challenge with these small animals, though, is the purification of enough informational molecules (DNA, mRNA, protein, metabolites) from highly relevant tissues such as fly brains. Other challenges consist of collecting large numbers of flies for experimental replicates (critical for statistical robustness) and developing consistent procedures for the purification of high-quality biological material. Here, we describe the procedures for collecting thousands of fly heads and the extraction of transcripts and metabolites to understand how global changes in gene expression and metabolism contribute to neurodegenerative diseases. These procedures are easily scalable and can be applied to the study of proteomic and epigenomic contributions to disease.

Neuronal subtypes are specified by the level of neurod expression in the zebrafish lateral line

During neural development, even in seemingly homogeneous cell populations, individual neurons acquire diverse morphology and behavior, and even adjacent neurons can establish synaptic connections with distinct targets. Although diversity among individual neurons is essential for a functional nervous system, the underlying molecular mechanism of establishing heterogeneity in a population of neuronal precursors has been poorly addressed at the single-cell level. We focused on the development of the zebrafish posterior lateral line (PLL) and revealed a molecular mechanism that differentiates a homogenous neuronal population in the ganglion into the two types of neurons, leaders and followers. We developed a method to analyze gene expression levels in leaders and followers at the single-cell resolution, and found that leaders expressed significantly higher levels of neurod compared with followers. Furthermore, neurod expression was found to be heterogeneous among neurons before the onset of phenotypic differentiation of leaders and followers, and neurod overexpression in single PLL neurons promoted differentiation into leaders. These results suggest that the quantity, rather than quality (i.e., the ON/OFF states), of neurod expression directly or indirectly determines the two subtypes of PLL neurons.

Moderate expression of Wnt signaling genes is essential for porcine parthenogenetic embryo development

Parthenogenetic embryos are invariably lost in mid-gestation, possibly due to the lack of the paternal genome and the consequent induction of aberrant gene expression. Wnt signaling is essential for embryonic development; however, the studies of this pathway in porcine parthenogenetic embryos have been limited. Here, the role of Wnt signaling in porcine parthenogenetic embryos was studied. In vivo embryos were used as controls. Single cell quantitative real-time PCR showed that Wnt signaling was down-regulated in porcine parthenogenetic embryos. Furthermore, immunofluorescence staining and real-time PCR demonstrated that porcine parthenogenetic embryo development was largely unaffected by the inhibition of Wnt signaling with IWP-2, but blastocyst hatching and trophectoderm development was blocked. In addition, parthenogenetic blastocyst hatching was improved by the activation of Wnt signaling by BIO. However, the developmental competency of porcine embryos, including blastocyst hatching, was impaired and apoptosis was induced upon the excessive activation of Wnt signaling. These findings constitute novel evidence that Wnt signaling is important for porcine pre-implantation development and that its down-regulation may lead to the low hatching rate of porcine parthenogenetic blastocysts.

Stochastic profiling of transcriptional regulatory heterogeneities in tissues, tumors and cultured cells

Single-cell variations in gene and protein expression are important during development and disease. Such cell-to-cell heterogeneities can be directly inspected one cell at a time, but global methods are usually not sensitive enough to work with the starting material of a single cell. Here we provide a detailed protocol for stochastic profiling, a method that infers single-cell regulatory heterogeneities by repeatedly sampling small collections of cells selected at random. Repeated stochastic sampling is performed by laser-capture microdissection or limiting dilution, followed by careful exponential cDNA amplification, hybridization to microarrays and statistical analysis. Stochastic profiling surveys the transcriptome for programs that are heterogeneously regulated among cellular subpopulations in their native tissue context. The protocol is readily optimized for specific biological applications and takes about 1 week to complete.

A simple add-on microfluidic appliance for accurately sorting small populations of cells with high fidelity

Current advances in single cell sequencing, gene expression and proteomics require the isolation of single cells, frequently from a very small source population. In this work we describe the design and characterization of a manually operated microfluidic cell sorter that 1) can accurately sort single or small groups of cells from very small cell populations with minimal losses, 2) that is easy to operate and that can be used in any laboratory that has a basic fluorescent microscope and syringe pump, 3) that can be assembled within minutes, 4) that can sort cells in very short time (minutes) with minimum cell stress, 5) that is cheap and reusable. This microfluidic sorter is made from hard plastic material (PMMA) into which microchannels are directly milled with hydraulic diameter of 70 μm. Inlet and outlet reservoirs are drilled through the chip. Sorting occurs through hydrodynamic switching ensuring low hydrodynamic shear stresses, which were modeled or experimentally confirmed to be below the cell damage threshold. Manually operated, the maximum sorting frequencies were approximately 10 cells per minute. Experiments verified that cell sorting operations could be achieved in as little as 15 minutes, including the assembly and testing of the sorter. In only one out of 10 sorting experiments the sorted cells were contaminated with another cell type. This microfluidic cell sorter represents an important capability for protocols requiring fast isolation of single cells from small number of rare cell populations.

Single-neuron transcriptome and methylome sequencing for epigenomic analysis of aging

Enormous heterogeneity in transcription and signaling is the feature that slows down progress in our understanding of the mechanisms of normal aging and age-related diseases. This is critical for neurobiology of aging where the enormous diversity of neuronal populations presents a significant challenge in experimental design. Here, we introduce Aplysia as a model for genomic analysis of aging at the single-cell level and provide protocols for integrated transcriptome and methylome profiling of individually identified neurons during the aging process. These single-cell RNA-seq and DNA methylation assays (methyl-capture/methyl enrichment) are compatible with all major next generation sequencing platforms (we used Roche/454 and SOLiD/Life Technologies as illustrative examples) and can be used to integrate an epigenetic signature with transcriptional output. The described sequencing library construction protocol provides both quantitative and directional information from transcriptional profiling of individual cells. Our results also confirm that different copies of DNA in polyploid Aplysia neurons behave similarly with respect to their DNA methylation.

Single-cell semiconductor sequencing

RNA-seq or transcriptome analysis of individual cells and small-cell populations is essential for virtually any biomedical field. It is especially critical for developmental, aging, and cancer biology as well as neuroscience where the enormous heterogeneity of cells present a significant methodological and conceptual challenge. Here we present two methods that allow for fast and cost-efficient transcriptome sequencing from ultra-small amounts of tissue or even from individual cells using semiconductor sequencing technology (Ion Torrent, Life Technologies). The first method is a reduced representation sequencing which maximizes capture of RNAs and preserves transcripts' directionality. The second, a template-switch protocol, is designed for small mammalian neurons. Both protocols, from cell/tissue isolation to final sequence data, take up to 4 days. The efficiency of these protocols has been validated with single hippocampal neurons and various invertebrate tissues including individually identified neurons within a simpler memory-forming circuit of Aplysia californica and early (1-, 2-, 4-, 8-cells) embryonic and developmental stages from basal metazoans.

ENU mutagenesis reveals that Notchless homolog 1 (Drosophila) affects Cdkn1a and several members of the Wnt pathway during murine pre-implantation development

Background

Our interests lie in determining the genes and genetic pathways that are important for establishing and maintaining maternal-fetal interactions during pregnancy. Mutation analysis targeted to a 34 Mb domain flanked by Trp53 and Wnt3 demonstrates that this region of mouse chromosome 11 contains a large number of essential genes. Two mutant alleles (l11Jus1 and l11Jus4), which fall into the same complementation group, survive through implantation but fail prior to gastrulation.

Results

Through a positional cloning strategy, we discovered that these homozygous mutant alleles contain non-conservative missense mutations in the Notchless homolog 1 (Drosophila) (Nle1) gene. NLE1 is a member of the large WD40-repeat protein family, and is thought to signal via the canonical NOTCH pathway in vertebrates. However, the phenotype of the Nle1 mutant mice is much more severe than single Notch receptor mutations or even in animals in which NOTCH signaling is blocked. To test the hypothesis that NLE1 functions in multiple signaling pathways during pre-implantation development, we examined expression of multiple Notch downstream target genes, as well as select members of the Wnt pathway in wild-type and mutant embryos. We did not detect altered expression of any primary members of the Notch pathway or in Notch downstream target genes. However, our data reveal that Cdkn1a, a NOTCH target, was upregulated in Nle1 mutants, while several members of the Wnt pathway are downregulated. In addition, we found that Nle1 mutant embryos undergo caspase-mediated apoptosis as hatched blastocysts, but not as morulae or blastocysts.

Conclusions

Taken together, these results uncover potential novel functions for NLE1 in the WNT and CDKN1A pathways during embryonic development in mammals.

Mechanistic and structural insight into the functional dichotomy between IL-2 and IL-15

Interleukin 15 (IL-15) and IL-2 have distinct immunological functions even though both signal through the receptor subunit IL-2Rβ and the common γ-chain (γ(c)). Here we found that in the structure of the IL-15-IL-15Rα-IL-2Rβ-γ(c) quaternary complex, IL-15 binds to IL-2Rβ and γ(c) in a heterodimer nearly indistinguishable from that of the IL-2-IL-2Rα-IL-2Rβ-γ(c) complex, despite their different receptor-binding chemistries. IL-15Rα substantially increased the affinity of IL-15 for IL-2Rβ, and this allostery was required for IL-15 trans signaling. Consistent with their identical IL-2Rβ-γ(c) dimer geometries, IL-2 and IL-15 showed similar signaling properties in lymphocytes, with any differences resulting from disparate receptor affinities. Thus, IL-15 and IL-2 induced similar signals, and the cytokine specificity of IL-2Rα versus IL-15Rα determined cellular responsiveness. Our results provide new insights for the development of specific immunotherapeutics based on IL-15 or IL-2.

Methods, Challenges and Potentials of Single Cell RNA-seq

RNA-sequencing (RNA-seq) has become the tool of choice for transcriptomics. Several recent studies demonstrate its successful adaption to single cell analysis. This allows new biological insights into cell differentiation, cell-to-cell variation and gene regulation, and how these aspects depend on each other. Here, I review the current single cell RNA-seq (scRNA-seq) efforts and discuss experimental protocols, challenges and potentials.

Investigating transcriptional states at single-cell-resolution

Gene expression analysis at single-cell-resolution is a powerful tool for uncovering individual cell differences within heterogeneous cell populations and complex tissues, which can provide invaluable insights into the extent of gene expression variability. Multi-dimensional information of gene expression at the level of the individual cell can help to identify distinct and rare molecular cell 'states' within populations and aid in unravelling genetic regulatory circuits. Gene expression analysis at the single-cell-level will also enhance our understanding of the molecular basis of aberrant cell states and disease development and holds great promise for the advancement of personalized medicine. We present approaches that provide large-scale views of gene expression at the level of the individual cell.

SERE: single-parameter quality control and sample comparison for RNA-Seq

Background

Assessing the reliability of experimental replicates (or global alterations corresponding to different experimental conditions) is a critical step in analyzing RNA-Seq data. Pearson’s correlation coefficient r has been widely used in the RNA-Seq field even though its statistical characteristics may be poorly suited to the task.

Results

Here we present a single-parameter test procedure for count data, the Simple Error Ratio Estimate (SERE), that can determine whether two RNA-Seq libraries are faithful replicates or globally different. Benchmarking shows that the interpretation of SERE is unambiguous regardless of the total read count or the range of expression differences among bins (exons or genes), a score of 1 indicating faithful replication (i.e., samples are affected only by Poisson variation of individual counts), a score of 0 indicating data duplication, and scores >1 corresponding to true global differences between RNA-Seq libraries. On the contrary the interpretation of Pearson’s r is generally ambiguous and highly dependent on sequencing depth and the range of expression levels inherent to the sample (difference between lowest and highest bin count). Cohen’s simple Kappa results are also ambiguous and are highly dependent on the choice of bins. For quantifying global sample differences SERE performs similarly to a measure based on the negative binomial distribution yet is simpler to compute.

Conclusions

SERE can therefore serve as a straightforward and reliable statistical procedure for the global assessment of pairs or large groups of RNA-Seq datasets by a single statistical parameter.

Technical considerations for functional sequencing assays

Next-generation sequencing technologies need careful design of experiments and evaluation of results to meet field requirements. Here we discuss technical considerations for these high-throughput assays, together with criteria to assess the quality of the results and the necessary validation.

A simple and novel method for RNA-seq library preparation of single cell cDNA analysis by hyperactive Tn5 transposase

BACKGROUND

Deep sequencing of single cell-derived cDNAs offers novel insights into oncogenesis and embryogenesis. However, traditional library preparation for RNA-seq analysis requires multiple steps with consequent sample loss and stochastic variation at each step significantly affecting output. Thus, a simpler and better protocol is desirable. The recently developed hyperactive Tn5-mediated library preparation, which brings high quality libraries, is likely one of the solutions.

RESULTS AND CONCLUSIONS

Here, we tested the applicability of hyperactive Tn5-mediated library preparation to deep sequencing of single cell cDNA, optimized the protocol, and compared it with the conventional method based on sonication. This new technique does not require any expensive or special equipment, which secures wider availability. A library was constructed from only 100 ng of cDNA, which enables the saving of precious specimens. Only a few steps of robust enzymatic reaction resulted in saved time, enabling more specimens to be prepared at once, and with a more reproducible size distribution among the different specimens. The obtained RNA-seq results were comparable to the conventional method. Thus, this Tn5-mediated preparation is applicable for anyone who aims to carry out deep sequencing for single cell cDNAs.

A survey of tissue-specific genomic imprinting in mammals

In mammals, most somatic cells contain two copies of each autosomal gene, one inherited from each parent. When a gene is expressed, both parental alleles are usually transcribed. However, a subset of genes is subject to the epigenetic silencing of one of the parental copies by genomic imprinting. In this review, we explore the evidence for variability in genomic imprinting between different tissue and cell types. We also consider why the imprinting of particular genes may be restricted to, or lost in, specific tissues and discuss the potential for high-throughput sequencing technologies in facilitating the characterisation of tissue-specific imprinting and assaying the potentially functional variations in epigenetic marks.

Single-neuron RNA-Seq: technical feasibility and reproducibility

Understanding brain function involves improved knowledge about how the genome specifies such a large diversity of neuronal types. Transcriptome analysis of single neurons has been previously described using gene expression microarrays. Using high-throughput transcriptome sequencing (RNA-Seq), we have developed a method to perform single-neuron RNA-Seq. Following electrophysiology recording from an individual neuron, total RNA was extracted by aspirating the cellular contents into a fine glass electrode tip. The mRNAs were reverse transcribed and amplified to construct a single-neuron cDNA library, and subsequently subjected to high-throughput sequencing. This approach was applied to both individual neurons cultured from embryonic mouse hippocampus, as well as neocortical neurons from live brain slices. We found that the average pairwise Spearman’s rank correlation coefficient of gene expression level expressed as RPKM (reads per kilobase of transcript per million mapped reads) was 0.51 between five cultured neuronal cells, whereas the same measure between three cortical layer 5 neurons in situ was 0.25. The data suggest that there may be greater heterogeneity of the cortical neurons, as compared to neurons in vitro. The results demonstrate the technical feasibility and reproducibility of RNA-Seq in capturing a part of the transcriptome landscape of single neurons, and confirmed that morphologically identical neurons, even from the same region, have distinct gene expression patterns.

A systematic comparison and evaluation of high density exon arrays and RNA-seq technology used to unravel the peripheral blood transcriptome of sickle cell disease

Background

Transcriptomic studies in clinical research are essential tools for deciphering the functional elements of the genome and unraveling underlying disease mechanisms. Various technologies have been developed to deduce and quantify the transcriptome including hybridization and sequencing-based approaches. Recently, high density exon microarrays have been successfully employed for detecting differentially expressed genes and alternative splicing events for biomarker discovery and disease diagnostics. The field of transcriptomics is currently being revolutionized by high throughput DNA sequencing methodologies to map, characterize, and quantify the transcriptome.

Methods

In an effort to understand the merits and limitations of each of these tools, we undertook a study of the transcriptome in sickle cell disease, a monogenic disease comparing the Affymetrix Human Exon 1.0 ST microarray (Exon array) and Illumina’s deep sequencing technology (RNA-seq) on whole blood clinical specimens.

Results

Analysis indicated a strong concordance (R = 0.64) between Exon array and RNA-seq data at both gene level and exon level transcript expression. The magnitude of differential expression was found to be generally higher in RNA-seq than in the Exon microarrays. We also demonstrate for the first time the ability of RNA-seq technology to discover novel transcript variants and differential expression in previously unannotated genomic regions in sickle cell disease. In addition to detecting expression level changes, RNA-seq technology was also able to identify sequence variation in the expressed transcripts.

Conclusions

Our findings suggest that microarrays remain useful and accurate for transcriptomic analysis of clinical samples with low input requirements, while RNA-seq technology complements and extends microarray measurements for novel discoveries.

Transcriptome profiling of the demosponge Amphimedon queenslandica reveals genome-wide events that accompany major life cycle transitions

Background

The biphasic life cycle with pelagic larva and benthic adult stages is widely observed in the animal kingdom, including the Porifera (sponges), which are the earliest branching metazoans. The demosponge, Amphimedon queenslandica, undergoes metamorphosis from a free-swimming larva into a sessile adult that bears no morphological resemblance to other animals. While the genome of A. queenslandica contains an extensive repertoire of genes very similar to that of complex bilaterians, it is as yet unclear how this is drawn upon to coordinate changing morphological features and ecological demands throughout the sponge life cycle.

Results

To identify genome-wide events that accompany the pelagobenthic transition in A. queenslandica, we compared global gene expression profiles at four key developmental stages by sequencing the poly(A) transcriptome using SOLiD technology. Large-scale changes in transcription were observed as sponge larvae settled on the benthos and began metamorphosis. Although previous systematics suggest that the only clear homology between Porifera and other animals is in the embryonic and larval stages, we observed extensive use of genes involved in metazoan-associated cellular processes throughout the sponge life cycle. Sponge-specific transcripts are not over-represented in the morphologically distinct adult; rather, many genes that encode typical metazoan features, such as cell adhesion and immunity, are upregulated. Our analysis further revealed gene families with candidate roles in competence, settlement, and metamorphosis in the sponge, including transcription factors, G-protein coupled receptors and other signaling molecules.

Conclusions

This first genome-wide study of the developmental transcriptome in an early branching metazoan highlights major transcriptional events that accompany the pelagobenthic transition and point to a network of regulatory mechanisms that coordinate changes in morphology with shifting environmental demands. Metazoan developmental and structural gene orthologs are well-integrated into the expression profiles at every stage of sponge development, including the adult. The utilization of genes involved in metazoan-associated processes throughout sponge development emphasizes the potential of the genome of the last common ancestor of animals to generate phenotypic complexity.

The challenge of gene expression profiling in heterogeneous clinical samples

Almost all samples used in tumor biology, such as tissues and bodily fluids, are heterogeneous, i.e., consist of different cell types. Evaluating the degree of heterogeneity in samples can increase our knowledge on processes such as clonal selection and metastasis. In addition, generating expression profiles from specific sub populations of cells can reveal their distinct functions. Tissue heterogeneity also poses a challenge, as it can confound the interpretation of gene expression data. This chapter will (1) give a brief overview on how heterogeneity may influence gene expression profiling data and (2) describe the methods that are currently available to assess transcriptional biomarkers in a heterogeneous cell population.