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The present study focused upon comprehensive analysis of the expression of stress-responsive genes at different time points in two indica rice varieties, IR-64 (moderately salt-sensitive) and Nonabokra (salt-tolerant), under salinity, dehydration and cold, and exogenous ABA treatment. The massively parallel signature sequencing and publicly-available microarray data indicated the physiological role of the concerned genes in different tissues. Gene expression through semi-quantitative reverse transcriptase-polymerase chain reaction followed by factorial analyses of variance identified that members of the antioxidant genes (OsCAT, OsSOD, OsGR2 and OsAPX8) showed early response and higher expression under different abiotic stresses in IR-64. The TFs and LEA family (TRAB1 and Osem), osmolyte (OsBADH1, OsP5CS) and polyamine (SAMDC) metabolic genes were more up regulated in the salt-tolerant cultivar, whereas the proline (ProDH) and polyamine (DAO and PAO) catabolic genes were significantly up regulated in the salt-sensitive one. The higher accumulation of TRAB1 transcript and protein in the tolerant cultivar during various stresses suggested it as an important component in stress signaling pathway, which participates in mediating cross-talk signaling between diverse metabolic pathways to withstand various forms of abiotic stress. Heat map data further corroborated differential transcript abundance in the two varieties, coinciding with the transcript profiling data. We envisage that the identified genes in this study, linked with tolerance against various abiotic stress factors, are the chief candidates involved in regulating the complexity of multiple stress responses in rice. The finding of the candidate genes in this study would be helpful in generating multiple abiotic stress tolerance in rice.

Rice growth and development is highly affected by the excessive concentration of the heavy metal cadmium (Cd). To elucidate the molecular basis of Cd tolerance, we carried out comprehensive transcriptome profiling of diverse groups of genes like those encoding antioxidative enzymes (APX, CAT, SOD, and GR), osmolyte-regulatory enzymes (P5CS, PDH, and BADH1), polyamine-regulatory enzymes (SAMDC, SPDS, SPMS, and DAO), transcription factors (TRAB-1 and WRKY-71), Osem and RbcS from leaf and root tissues under cadmium chloride (1.5 mM) stress at different time points (6, 12, and 24 h) of exposure in two indica rice varieties, that is, IR-64 (sensitive) and Nonabokra (tolerant). Analyses of massively parallel signature sequencing (MPSS) and publicly available microarray databases indicated the physiological role of the concerned genes and possible cross-talk between Cd and other abiotic stresses. Moreover, the pattern of expression, for a particular gene in leaves, varied in most cases from that in roots, suggesting a tissue-specific response. The higher accumulation of TRAB-1 transcript and protein in the tolerant cultivar Nonabokra in response to Cd stress suggested a role for TRAB-1 as a nodal component in Cd signaling pathway. Overall, the transcriptome analyses undertaken in the present study indicated that the genes, belonging to different functional classes and conferring Cd tolerance, were differentially regulated and mostly overrepresented under stress situations, thus providing novel insight into the functional basis of Cd tolerance in rice varieties and the importance of cross-talk of different signaling pathways. The present work will also pave the way in the future to select gene(s) for overexpression, so that rice can better tolerate Cd stress.

We present a tool to measure gene and protein expression levels in single cells with DNA-labeled antibodies and droplet microfluidics. Using the RNA expression and protein sequencing assay (REAP-seq), we quantified proteins with 82 barcoded antibodies and >20,000 genes in a single workflow. We used REAP-seq to assess the costimulatory effects of a CD27 agonist on human CD8+ lymphocytes and to identify and characterize an unknown cell type.

Single nucleus RNA-seq (sNuc-seq) profiles RNA from tissues that are preserved or cannot be dissociated, but does not provide the throughput required to analyse many cells from complex tissues. Here, we develop DroNc-seq, massively parallel sNuc-Seq with droplet technology. We profile 39,111 nuclei from mouse and human archived brain samples to demonstrate sensitive, efficient and unbiased classification of cell types, paving the way for systematic charting of cell atlases.

Isolength restriction site-associated DNA (isoRAD) sequencing is a very simple but powerful approach that was originally developed for genome-wide genotyping at minimal labor and cost, and it has recently extended its applicability to allow quantification of DNA methylation levels. The isoRAD method is distinct from other genotyping-by-sequencing (GBS) methods because of its use of special restriction enzymes to produce isolength tags (32-36 bp), and sequencing of these uniform tags can bring many benefits. However, the relatively short tags produced by the original protocol are mostly suited to single-end (SE) sequencing (36-50 bp), and therefore they cannot efficiently match the gradually increased sequencing capacity of next-generation sequencing (NGS) platforms. To address this issue, we describe an advanced protocol that allows the preparation of five concatenated isoRAD tags for Illumina paired-end (PE) sequencing (100-150 bp). The configuration of the five concatenated tags is highly flexible, and can be defined by users to work with a desired combination of samples and/or restriction enzymes to suit specific research purposes. In comparison with the original protocol, the advanced protocol has an additional digestion and ligation step, and library preparation can be completed in ∼8 h.

Excessive generation of reactive oxygen species (ROS) due to environmental stresses critically effects plant development and productivity. Plants efficiently detoxify ROS by both non-enzymatic and enzymatic mechanisms. Plant glutathione peroxidases (GPXs) are non-haeme thiol peroxidases that catalyze the reduction of H2O2 (or organic hydroperoxides) to water or the respective alcohols using reduced glutathione or thioredoxin. Genome-wide analysis of the known GPXs from rice and Arabidopsis genomes revealed their gene structure, conserved motifs, localization and tissue-specific and/or organ-specific expression profiles in response to various abiotic stresses. Among the eight genes that encoded GPX proteins from Arabidopsis, AtGPX3 showed two alternate spliced forms that spread over four chromosomes. Five genes encoded for rice GPX proteins, while OsGPX1 showed three spliced variants that were distributed on five chromosomes. Utilizing the publicly available microarray and massively parallel signature sequencing (MPSS) data, the GPXs revealed stress-responsive, tissue-specific and/or organ-specific expression profiles. Presence of important cis-regulatory elements analyzed in the GPX promoter sequences revealed their overlapping or specific responsiveness to different abiotic stresses. Co-expression data of Arabidopsis GPX genes suggested that various protein kinase family members and stress-responsive proteins co-expressed with the GPX proteins. Transcript profile of rice GPX genes by qRT-PCR validated their functional roles in signal transduction and stress pathways. Results revealed that plant GPXs play a crucial role in response to stress and significantly contribute towards their growth and development.

Cyclophilins are a set of ubiquitous proteins present in all subcellular compartments, involved in a wide variety of cellular processes. Comparative bioinformatics analysis of the rice and Arabidopsis genomes led us to identify novel putative cyclophilin gene family members in both the genomes not reported previously. We grouped cyclophilin members with similar molecular weight and subtypes together in the phylogenetic tree which indicated their co-evolution in rice and Arabidopsis. We also characterized a rice cyclophilin gene, OsCyp2-P (Os02g0121300), isolated from a salinity-tolerant landrace, Pokkali. Publicly available massively parallel signature sequencing (MPSS) and microarray data, besides our quantitative real time PCR (qRT-PCR) data suggest that transcript abundance of OsCyp2-P is regulated under different stress conditions in a developmental and organ specific manner. Ectopic expression of OsCyp2-P imparted multiple abiotic stress tolerance to transgenic tobacco plants as evidenced by higher root length, shoot length, chlorophyll content, and K(+)/Na(+) ratio under stress conditions. Transgenic plants also showed reduced lipid peroxidase content, electrolyte leakage, and superoxide content under stress conditions suggesting better ion homeostasis than WT plants. Localization studies confirmed that OsCyp2-P is localized in both cytosol and nucleus, indicating its possible interaction with several other proteins. The overall results suggest the explicit role of OsCyp2-P in bestowing multiple abiotic stress tolerance at the whole plant level. OsCyp2-P operates via reactive oxygen species (ROS) scavenging and ion homeostasis and thus is a promising candidate gene for enhancing multiple abiotic stress tolerance in crop plants.

RNA sequencing (RNAseq) samples the majority of expressed genes infrequently, owing to the large size, complex splicing and wide dynamic range of eukaryotic transcriptomes. This results in sparse sequencing coverage that can hinder robust isoform assembly and quantification. RNA capture sequencing (CaptureSeq) addresses this challenge by using oligonucleotide probes to capture selected genes or regions of interest for targeted sequencing. Targeted RNAseq provides enhanced coverage for sensitive gene discovery, robust transcript assembly and accurate gene quantification. Here we describe a detailed protocol for all stages of RNA CaptureSeq, from initial probe design considerations and capture of targeted genes to final assembly and quantification of captured transcripts. Initial probe design and final analysis can take less than 1 d, whereas the central experimental capture stage requires ∼7 d.

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.

Drought, salt and cold are the major abiotic stresses that limit the rice production. Identification of the key functional and regulatory genes in the abiotic stress signaling pathways is important for understanding the molecular basis of abiotic stress tolerance. In this study, we investigated the transcriptomes of rice leaves and roots under cold, drought, and salt stresses using the massively parallel signature sequencing (MPSS) and sequencing by synthesis (SBS) technologies. About 1.8 to 2.6 million individual signatures were obtained from the seven abiotic-stressed and control libraries of the japonica cultivar Nipponbare. A total of 102,630 and 1,414,788 distinct signatures were obtained from the MPSS and SBS libraries, respectively. Clustering analysis identified many up- and down-regulated genes specifically and commonly expressed in the cold, drought and salt-treated plant leaves and roots. Data mining revealed the expression patterns of key functional and regulatory genes that were involved in different abiotic stress signaling pathways. Highly conserved cis-regulatory elements in the promoter of the up-regulated genes were identified. Our comprehensive and deep survey of abiotic stress transcriptome of rice has provided candidate genes for further understanding the molecular basis of abiotic stress tolerance in rice.

Glutathione S-transferases (GSTs; EC 2.5.1.18) are members of an isozyme family and catalyze the conjugation of the reduced tripeptide glutathione to a variety of hydrophobic and electrophilic substrates. Though members of different classes of the GST superfamily have been identified and characterized from many plant species including rice, no detailed information is available for the Lambda class gene family in rice. In this study, a genome-wide analysis was carried out to investigate expression patterns of three Lambda class GST members of rice including OsGSTL1, OsGSTL2 and OsGSTL3 in seedlings, at different growth and developmental stages as well as in response to various biotic and abiotic stresses. Expression analysis using microarray datasets and quantitative real-time reverse transcriptase polymerase chain reaction suggests that this gene family express differentially in various tissues, in response to hormones and during different biotic and abiotic stresses including heavy metals, cold, drought and salt stress. Massively Parallel Signature Sequencing (MPSS) analysis also showed differential expression of OsGSTLs during plant growth and development and under different stresses. Out of three members, maximum expression of OsGSTL2 was observed for the MPSS libraries in comparison to other members. We conclude that members of rice Lambda class GST family play an important role in plant growth and development and in combating different biotic and abiotic stresses.

QuantSeq provides an easy protocol to generate highly strand-specific next-generation sequencing (NGS) libraries close to the 3′ end of polyadenylated RNAs within 4.5 h. Only one fragment per transcript is generated, directly linking the number of reads mapping to a gene to its expression. QuantSeq reduces data analysis time and enables a higher level of multiplexing per run. QuantSeq is the RNA sample preparation method for accurate and affordable gene expression measurement.

Allelic differences in expression are important genetic factors contributing to quantitative trait variation in various organisms. However, the extent of genome-wide allele-specific expression by different modes of gene regulation has not been well characterized in plants. In this study we developed a new methodology for allele-specific expression analysis by applying Massively Parallel Signature Sequencing (MPSS™), an open ended and sequencing based mRNA profiling technology. This methodology enabled a genome-wide evaluation of cis- and trans-effects on allelic expression in six meristem stages of the maize hybrid. Summarization of data from nearly 400 pairs of MPSS allelic signature tags showed that 60% of the genes in the hybrid meristems exhibited differential allelic expression. Because both alleles are subjected to the same trans-acting factors in the hybrid, the data suggest the abundance of cis-regulatory differences in the genome. Comparing the same allele expressed in the hybrid versus its inbred parents showed that 40% of the genes were differentially expressed, suggesting different trans-acting effects present in different genotypes. Such trans-acting effects may result in gene expression in the hybrid different from allelic additive expression. With this approach we quantified gene expression in the hybrid relative to its inbred parents at the allele-specific level. As compared to measuring total transcript levels, this study provides a new level of understanding of different modes of gene regulation in the hybrid and the molecular basis of heterosis.

Identification of all expressed transcripts in a sequenced genome is essential both for genome analysis and for realization of the goals of systems biology. We used the transcriptional profiling technology called 'massively parallel signature sequencing' to develop a comprehensive expression atlas of rice (Oryza sativa cv Nipponbare). We sequenced 46,971,553 mRNA transcripts from 22 libraries, and 2,953,855 small RNAs from 3 libraries. The data demonstrate widespread transcription throughout the genome, including sense expression of at least 25,500 annotated genes and antisense expression of nearly 9,000 annotated genes. An additional set of approximately 15,000 mRNA signatures mapped to unannotated genomic regions. The majority of the small RNA data represented lower abundance short interfering RNAs that match repetitive sequences, intergenic regions and genes. Among these, numerous clusters of highly regulated small RNAs were readily observed. We developed a genome browser (http://mpss.udel.edu/rice) for public access to the transcriptional profiling data for this important crop.

Large-scale sequencing of short mRNA-derived tags can establish the qualitative and quantitative characteristics of a complex transcriptome. We sequenced 12,304,362 tags from five diverse libraries of Arabidopsis thaliana using massively parallel signature sequencing (MPSS). A total of 48,572 distinct signatures, each representing a different transcript, were expressed at significant levels. These signatures were compared to the annotation of the A. thaliana genomic sequence; in the five libraries, this comparison yielded between 17,353 and 18,361 genes with sense expression, and between 5,487 and 8,729 genes with antisense expression. An additional 6,691 MPSS signatures mapped to unannotated regions of the genome. Expression was demonstrated for 1,168 genes for which expression data were previously unknown. Alternative polyadenylation was observed for more than 25% of A. thaliana genes transcribed in these libraries. The MPSS expression data suggest that the A. thaliana transcriptome is complex and contains many as-yet uncharacterized variants of normal coding transcripts.

Background Xylogen, a chimeric arabinogalactan protein containing a non-specific lipid transfer protein domain, can promote xylem cell differentiation. No comprehensive study has been carried out on the XYLP gene family in rice. As a first step in research on this gene family and as a useful strategy in general, a genome-wide analysis of the OsXYLP gene family is thus needed. Results In this study, we identified 21 XYLP genes from the rice genome and comprehensively analyzed their protein structures, phylogenetic relationships, chromosomal locations, and gene duplication status. Our results indicate that gene duplication has played major roles in the expansion of the OsXYLP gene family. We used expressed sequence tag, microarray, massively parallel signature sequencing, and quantitative real-time PCR data to analyze OsXYLP gene expression during various developmental stages and under abiotic stress conditions. We found that many OsXYLP genes are abundantly expressed in vascular tissues and seeds, with some genes regulated under hormonal or abiotic stresses. In addition, we identified knockout mutants of OsXYLP7 and OsXYLP16 and discovered that the mutant xylp7 has a defect in stem height. Conclusions We analyzed expression profiles of 21 XYLP genes and characterized the structures and evolutionary relationships of their proteins. Our results demonstrate that the rice XYLP gene family may play roles in plant vascular system development and hormone signaling. Among the 21 detected OsXYLPs, 19 are newly identified genes encoding arabinogalactan proteins. Our results provide comprehensive insights that will assist future research on the biological functions of the rice XYLP gene family. Electronic supplementary material The online version of this article (doi:10.1186/s12870-014-0299-y) contains supplementary material, which is available to authorized users.

We describe a novel sequencing approach that combines non-gel-based signature sequencing with in vitro cloning of millions of templates on separate 5 microm diameter microbeads. After constructing a microbead library of DNA templates by in vitro cloning, we assembled a planar array of a million template-containing microbeads in a flow cell at a density greater than 3x10(6) microbeads/cm2. Sequences of the free ends of the cloned templates on each microbead were then simultaneously analyzed using a fluorescence-based signature sequencing method that does not require DNA fragment separation. Signature sequences of 16-20 bases were obtained by repeated cycles of enzymatic cleavage with a type IIs restriction endonuclease, adaptor ligation, and sequence interrogation by encoded hybridization probes. The approach was validated by sequencing over 269,000 signatures from two cDNA libraries constructed from a fully sequenced strain of Saccharomyces cerevisiae, and by measuring gene expression levels in the human cell line THP-1. The approach provides an unprecedented depth of analysis permitting application of powerful statistical techniques for discovery of functional relationships among genes, whether known or unknown beforehand, or whether expressed at high or very low levels.

Transcriptome sequencing is a term that encompasses experiments including mRNA transcript-expression analysis (full-length mRNA, expressed sequence tags (ESTs) and ditags), novel gene discovery, gene space identification in novel genomes, assembly of full-length genes, single-nucleotide polymorphism (SNP), insertion-deletion and splice-variant discovery, as well as analyses of allele-specific expression and chromosomal rearrangement. The combination of long, accurate reads and high throughput makes 454 Sequencing™ analysis on the Genome Sequencer FLX ideally suited to detailed transcriptome investigation.

Using a cell as an RNA sequencing chip enables spatial analyses of the transcriptome at subcellular resolution.

Background Although the complete genome sequence and annotation of Arabidopsis were released at the end of year 2000, it is still a great challenge to understand the function of each gene in the Arabidopsis genome. One way to understand the function of genes on a genome-wide scale is expression profiling by microarrays. However, the expression level of many genes in Arabidopsis genome cannot be detected by microarray experiments. In addition, there are many more novel genes that have been discovered by experiments or predicted by new gene prediction programs. Another way to understand the function of individual genes is to investigate their in vivo expression patterns by reporter constructs in transgenic plants which can provide basic information on the patterns of gene expression. Results A high throughput pipeline was developed to generate promoter-reporter (GFP) transgenic lines for Arabidopsis genes expressed at very low levels and to examine their expression patterns in vivo. The promoter region from a total of 627 non- or low-expressed genes in Arabidopsis based on Arabidopsis annotation release 5 were amplified and cloned into a Gateway vector. A total of 353 promoter-reporter (GFP) constructs were successfully transferred into Agrobacterium (GV3101) by triparental mating and subsequently used for Arabidopsis transformation. Kanamycin-resistant transgenic lines were obtained from 266 constructs and among them positive GFP expression was detected from 150 constructs. Of these 150 constructs, multiple transgenic lines exhibiting consistent expression patterns were obtained for 112 constructs. A total 81 different regions of expression were discovered during our screening of positive transgenic plants and assigned Plant Ontology (PO) codes. Conclusions Many of the genes tested for which expression data were lacking previously are indeed expressed in Arabidopsis during the developmental stages screened. More importantly, our study provides plant researchers with another resource of gene expression information in Arabidopsis. The results of this study are captured in a MySQL database and can be searched at http://www.jcvi.org/arabidopsis/qpcr/index.shtml. Transgenic seeds and constructs are also available for the research community.

Next-generation sequencing (also known as massively parallel sequencing) technologies are revolutionising our ability to characterise cancers at the genomic, transcriptomic and epigenetic levels. Cataloguing all mutations, copy number aberrations and somatic rearrangements in an entire cancer genome at base pair resolution can now be performed in a matter of weeks. Furthermore, massively parallel sequencing can be used as a means for unbiased transcriptomic analysis of mRNAs, small RNAs and noncoding RNAs, genome-wide methylation assays and high-throughput chromatin immunoprecipitation assays. Here, I discuss the potential impact of this technology on breast cancer research and the challenges that come with this technological breakthrough.

Background MicroRNAs (miRNAs), small non-coding RNAs of 19 to 25 nt, play important roles in gene regulation in both animals and plants. In the last few years, the oligonucleotide microarray is one high-throughput and robust method for detecting miRNA expression. However, the approach is restricted to detecting the expression of known miRNAs. Second-generation sequencing is an inexpensive and high-throughput sequencing method. This new method is a promising tool with high sensitivity and specificity and can be used to measure the abundance of small-RNA sequences in a sample. Hence, the expression profiling of miRNAs can involve use of sequencing rather than an oligonucleotide array. Additionally, this method can be adopted to discover novel miRNAs. Results This work presents a systematic approach, miRExpress, for extracting miRNA expression profiles from sequencing reads obtained by second-generation sequencing technology. A stand-alone software package is implemented for generating miRNA expression profiles from high-throughput sequencing of RNA without the need for sequenced genomes. The software is also a database-supported, efficient and flexible tool for investigating miRNA regulation. Moreover, we demonstrate the utility of miRExpress in extracting miRNA expression profiles from two Illumina data sets constructed for the human and a plant species. Conclusion We develop miRExpress, which is a database-supported, efficient and flexible tool for detecting miRNA expression profile. The analysis of two Illumina data sets constructed from human and plant demonstrate the effectiveness of miRExpress to obtain miRNA expression profiles and show the usability in finding novel miRNAs.

Background The tissue specificity of gene expression has been linked to a number of significant outcomes including level of expression, and differential rates of polymorphism, evolution and disease association. Recent studies have also shown the importance of exploring differential gene connectivity and sequence conservation in the identification of disease-associated genes. However, no study relates gene interactions with tissue specificity and disease association. Methods We adopted an a priori approach making as few assumptions as possible to analyse the interplay among gene-gene interactions with tissue specificity and its subsequent likelihood of association with disease. We mined three large datasets comprising expression data drawn from massively parallel signature sequencing across 32 tissues, describing a set of 55,606 true positive interactions for 7,197 genes, and microarray expression results generated during the profiling of systemic inflammation, from which 126,543 interactions among 7,090 genes were reported. Results Amongst the myriad of complex relationships identified between expression, disease, connectivity and tissue specificity, some interesting patterns emerged. These include elevated rates of expression and network connectivity in housekeeping and disease-associated tissue-specific genes. We found that disease-associated genes are more likely to show tissue specific expression and most frequently interact with other disease genes. Using the thresholds defined in these observations, we develop a guilt-by-association algorithm and discover a group of 112 non-disease annotated genes that predominantly interact with disease-associated genes, impacting on disease outcomes. Conclusion We conclude that parameters such as tissue specificity and network connectivity can be used in combination to identify a group of genes, not previously confirmed as disease causing, that are involved in interactions with disease causing genes. Our guilt-by-association algorithm should be useful for the discovery of additional modifiers of genetic diseases, and more generally, for the ability to associate genes of unknown function to clusters of genes with defined functions allowing for novel biological inference that can be subsequently validated.

Background Oligoarrays have become an accessible technique for exploring the transcriptome, but it is presently unclear how absolute transcript data from this technique compare to the data achieved with tag-based quantitative techniques, such as massively parallel signature sequencing (MPSS) and serial analysis of gene expression (SAGE). By use of the TransCount method we calculated absolute transcript concentrations from spotted oligoarray intensities, enabling direct comparisons with tag counts obtained with MPSS and SAGE. The tag counts were converted to number of transcripts per cell by assuming that the sum of all transcripts in a single cell was 5·105. Our aim was to investigate whether the less resource demanding and more widespread oligoarray technique could provide data that were correlated to and had the same absolute scale as those obtained with MPSS and SAGE. Results A number of 1,777 unique transcripts were detected in common for the three technologies and served as the basis for our analyses. The correlations involving the oligoarray data were not weaker than, but, similar to the correlation between the MPSS and SAGE data, both when the entire concentration range was considered and at high concentrations. The data sets were more strongly correlated at high transcript concentrations than at low concentrations. On an absolute scale, the number of transcripts per cell and gene was generally higher based on oligoarrays than on MPSS and SAGE, and ranged from 1.6 to 9,705 for the 1,777 overlapping genes. The MPSS data were on same scale as the SAGE data, ranging from 0.5 to 3,180 (MPSS) and 9 to1,268 (SAGE) transcripts per cell and gene. The sum of all transcripts per cell for these genes was 3.8·105 (oligoarrays), 1.1·105 (MPSS) and 7.6·104 (SAGE), whereas the corresponding sum for all detected transcripts was 1.1·106 (oligoarrays), 2.8·105 (MPSS) and 3.8·105 (SAGE). Conclusion The oligoarrays and TransCount provide quantitative transcript concentrations that are correlated to MPSS and SAGE data, but, the absolute scale of the measurements differs across the technologies. The discrepancy questions whether the sum of all transcripts within a single cell might be higher than the number of 5·105 suggested in the literature and used to convert tag counts to transcripts per cell. If so, this may explain the apparent higher transcript detection efficiency of the oligoarrays, and has to be clarified before absolute transcript concentrations can be interchanged across the technologies. The ability to obtain transcript concentrations from oligoarrays opens up the possibility of efficient generation of universal transcript databases with low resource demands.

Background High-throughput systems for gene expression profiling have been developed and have matured rapidly through the past decade. Broadly, these can be divided into two categories: hybridization-based and sequencing-based approaches. With data from different technologies being accumulated, concerns and challenges are raised about the level of agreement across technologies. As part of an ongoing large-scale cross-platform data comparison framework, we report here a comparison based on identical samples between one-dye DNA microarray platforms and MPSS (Massively Parallel Signature Sequencing). Results The DNA microarray platforms generally provided highly correlated data, while moderate correlations between microarrays and MPSS were obtained. Disagreements between the two types of technologies can be attributed to limitations inherent to both technologies. The variation found between pooled biological replicates underlines the importance of exercising caution in identification of differential expression, especially for the purposes of biomarker discovery. Conclusion Based on different principles, hybridization-based and sequencing-based technologies should be considered complementary to each other, rather than competitive alternatives for measuring gene expression, and currently, both are important tools for transcriptome profiling.

A ligation-based sequencing method reveals distant exon connections within long RNA molecules.

INTRODUCTION: Diverse microarray and sequencing technologies have been widely used to characterise the molecular changes in malignant epithelial cells in breast cancers. Such gene expression studies to identify markers and targets in tumour cells are, however, compromised by the cellular heterogeneity of solid breast tumours and by the lack of appropriate counterparts representing normal breast epithelial cells. METHODS: Malignant neoplastic epithelial cells from primary breast cancers and luminal and myoepithelial cells isolated from normal human breast tissue were isolated by immunomagnetic separation methods. Pools of RNA from highly enriched preparations of these cell types were subjected to expression profiling using massively parallel signature sequencing (MPSS) and four different genome wide microarray platforms. Functional related transcripts of the differential tumour epithelial transcriptome were used for gene set enrichment analysis to identify enrichment of luminal and myoepithelial type genes. Clinical pathological validation of a small number of genes was performed on tissue microarrays. RESULTS: MPSS identified 6,553 differentially expressed genes between the pool of normal luminal cells and that of primary tumours substantially enriched for epithelial cells, of which 98% were represented and 60% were confirmed by microarray profiling. Significant expression level changes between these two samples detected only by microarray technology were shown by 4,149 transcripts, resulting in a combined differential tumour epithelial transcriptome of 8,051 genes. Microarray gene signatures identified a comprehensive list of 907 and 955 transcripts whose expression differed between luminal epithelial cells and myoepithelial cells, respectively. Functional annotation and gene set enrichment analysis highlighted a group of genes related to skeletal development that were associated with the myoepithelial/basal cells and upregulated in the tumour sample. One of the most highly overexpressed genes in this category, that encoding periostin, was analysed immunohistochemically on breast cancer tissue microarrays and its expression in neoplastic cells correlated with poor outcome in a cohort of poor prognosis estrogen receptor-positive tumours. CONCLUSION: Using highly enriched cell populations in combination with multiplatform gene expression profiling studies, a comprehensive analysis of molecular changes between the normal and malignant breast tissue was established. This study provides a basis for the identification of novel and potentially important targets for diagnosis, prognosis and therapy in breast cancer.

Background In order to compare the gene expression profiles of human embryonic stem cell (hESC) lines and their differentiated progeny and to monitor feeder contaminations, we have examined gene expression in seven hESC lines and human fibroblast feeder cells using Illumina® bead arrays that contain probes for 24,131 transcript probes. Results A total of 48 different samples (including duplicates) grown in multiple laboratories under different conditions were analyzed and pairwise comparisons were performed in all groups. Hierarchical clustering showed that blinded duplicates were correctly identified as the closest related samples. hESC lines clustered together irrespective of the laboratory in which they were maintained. hESCs could be readily distinguished from embryoid bodies (EB) differentiated from them and the karyotypically abnormal hESC line BG01V. The embryonal carcinoma (EC) line NTera2 is a useful model for evaluating characteristics of hESCs. Expression of subsets of individual genes was validated by comparing with published databases, MPSS (Massively Parallel Signature Sequencing) libraries, and parallel analysis by microarray and RT-PCR. Conclusion we show that Illumina's bead array platform is a reliable, reproducible and robust method for developing base global profiles of cells and identifying similarities and differences in large number of samples.

Background Dinoflagellates are one of the most important classes of marine and freshwater algae, notable both for their functional diversity and ecological significance. They occur naturally as free-living cells, as endosymbionts of marine invertebrates and are well known for their involvement in "red tides". Dinoflagellates are also notable for their unusual genome content and structure, which suggests that the organization and regulation of dinoflagellate genes may be very different from that of most eukaryotes. To investigate the content and regulation of the dinoflagellate genome, we performed a global analysis of the transcriptome of the toxic dinoflagellate Alexandrium fundyense under nitrate- and phosphate-limited conditions using Massively Parallel Signature Sequencing (MPSS). Results Data from the two MPSS libraries showed that the number of unique signatures found in A. fundyense cells is similar to that of humans and Arabidopsis thaliana, two eukaryotes that have been extensively analyzed using this method. The general distribution, abundance and expression patterns of the A. fundyense signatures were also quite similar to other eukaryotes, and at least 10% of the A. fundyense signatures were differentially expressed between the two conditions. RACE amplification and sequencing of a subset of signatures showed that multiple signatures arose from sequence variants of a single gene. Single signatures also mapped to different sequence variants of the same gene. Conclusion The MPSS data presented here provide a quantitative view of the transcriptome and its regulation in these unusual single-celled eukaryotes. The observed signature abundance and distribution in Alexandrium is similar to that of other eukaryotes that have been analyzed using MPSS. Results of signature mapping via RACE indicate that many signatures result from sequence variants of individual genes. These data add to the growing body of evidence for widespread gene duplication in dinoflagellates, which would contribute to the transcriptional complexity of these organisms. The MPSS data also demonstrate that a significant number of dinoflagellate mRNAs are transcriptionally regulated, indicating that dinoflagellates commonly employ transcriptional gene regulation along with the post-transcriptional regulation that has been well documented in these organisms.

Background The identification of molecular pathways of differentiation of embryonic stem cells (hESC) is critical for the development of stem cell based medical therapies. In order to identify biomarkers and potential regulators of the process of differentiation, a high quality microarray containing 16,659 seventy base pair oligonucleotides was used to compare gene expression profiles of undifferentiated hESC lines and differentiating embryoid bodies. Results Previously identified "stemness" genes in undifferentiated hESC lines showed down modulation in differentiated cells while expression of several genes was induced as cells differentiated. In addition, a subset of 194 genes showed overexpression of greater than ≥ 3 folds in human embryoid bodies (hEB). These included 37 novel and 157 known genes. Gene expression was validated by a variety of techniques including another large scale array, reverse transcription polymerase chain reaction, focused cDNA microarrays, massively parallel signature sequencing (MPSS) analysis and immunocytochemisty. Several novel hEB specific expressed sequence tags (ESTs) were mapped to the human genome database and their expression profile characterized. A hierarchical clustering analysis clearly depicted a distinct difference in gene expression profile among undifferentiated and differentiated hESC and confirmed that microarray analysis could readily distinguish them. Conclusion These results present a detailed characterization of a unique set of genes, which can be used to assess the hESC differentiation.

Background Gene expression profiling among different tissues is of paramount interest in various areas of biomedical research. We have developed a novel method (DADA, Digital Analysis of cDNA Abundance), that calculates the relative abundance of genes in cDNA libraries. Results DADA is based upon multiple restriction fragment length analysis of pools of clones from cDNA libraries and the identification of gene-specific restriction fingerprints in the resulting complex fragment mixtures. A specific cDNA cloning vector had to be constructed that governed missing or incomplete cDNA inserts which would generate misleading fingerprints in standard cloning vectors. Double stranded cDNA was synthesized using an anchored oligo dT primer, uni-directionally inserted into the DADA vector and cDNA libraries were constructed in E. coli. The cDNA fingerprints were generated in a PCR-free procedure that allows for parallel plasmid preparation, labeling, restriction digest and fragment separation of pools of 96 colonies each. This multiplexing significantly enhanced the throughput in comparison to sequence-based methods (e.g. EST approach). The data of the fragment mixtures were integrated into a relational database system and queried with fingerprints experimentally produced by analyzing single colonies. Due to limited predictability of the position of DNA fragments on the polyacrylamid gels of a given size, fingerprints derived solely from cDNA sequences were not accurate enough to be used for the analysis. We applied DADA to the analysis of gene expression profiles in a model for impaired wound healing (treatment of mice with dexamethasone). Conclusions The method proved to be capable of identifying pharmacologically relevant target genes that had not been identified by other standard methods routinely used to find differentially expressed genes. Due to the above mentioned limited predictability of the fingerprints, the method was yet tested only with a limited number of experimentally determined fingerprints and was able to detect differences in gene expression of transcripts representing 0.05% of the total mRNA population (e.g. medium abundant gene transcripts).

Understanding the spatial organization of gene expression with single nucleotide resolution requires localizing the sequences of expressed RNA transcripts within a cell in situ. Here we describe fluorescent in situ RNA sequencing (FISSEQ), in which stably cross-linked cDNA amplicons are sequenced within a biological sample. Using 30-base reads from 8,742 genes in situ, we examined RNA expression and localization in human primary fibroblasts using a simulated wound healing assay. FISSEQ is compatible with tissue sections and whole mount embryos, and reduces the limitations of optical resolution and noisy signals on single molecule detection. Our platform enables massively parallel detection of genetic elements, including gene transcripts and molecular barcodes, and can be used to investigate cellular phenotype, gene regulation, and environment in situ.

University of Texas researchers have developed a high throughput method for sequencing individual B cell immunoglobulin receptors that keeps the link between their paired heavy and light variable chains intact. The next step is modifications to increase throughput and accuracy.

Natural antisense transcripts (NAT) are a class of endogenous coding or non-protein-coding RNAs with sequence complementarity to other transcripts. Several lines of evidence have shown that cis- and trans-NATs may participate in a broad range of gene regulatory events. Genome-wide identification of cis-NATs in human, mouse and rice has revealed their widespread occurrence in eukaryotes. However, little is known about cis-NATs in the model plant Arabidopsis thaliana. We developed a new computational method to predict and identify cis-encoded NATs in Arabidopsis and found 1,340 potential NAT pairs. The expression of both sense and antisense transcripts of 957 NAT pairs was confirmed using Arabidopsis full-length cDNAs and public massively parallel signature sequencing (MPSS) data. Three known or putative Arabidopsis imprinted genes have cis-antisense transcripts. Sequences and the genomic arrangement of two Arabidopsis NAT pairs are conserved in rice. We combined information from full-length cDNAs and Arabidopsis genome annotation in our NAT prediction work and reported cis-NAT pairs that could not otherwise be identified by using one of the two datasets only. Analysis of MPSS data suggested that for most Arabidopsis cis-NAT pairs, there is predominant expression of one of the two transcripts in a tissue-specific manner.

HIV-1-specific sequences could easily be amplified from the DNA of peripheral blood lymphocytes or from the viral RNA present in the plasma. A detailed characterization of the resulting amplicons requires sequencing. Here we have shown that the sequencing strategy depends on the variability of the region of interest. The higher the variability of a distinct region the more a cloning step is necessary prior to sequencing to obtain reliable information. Conversely, direct sequencing analysis is usually sufficient to record changes of conserved viral sequences, e.g. during therapy with reverse transcriptase inhibitors.

Background An important challenge for transcript counting methods such as Serial Analysis of Gene Expression (SAGE), "Digital Northern" or Massively Parallel Signature Sequencing (MPSS), is to carry out statistical analyses that account for the within-class variability, i.e., variability due to the intrinsic biological differences among sampled individuals of the same class, and not only variability due to technical sampling error.

Background A class of eukaryotic non-coding RNAs termed microRNAs (miRNAs) interact with target mRNAs by sequence complementarity to regulate their expression. The low abundance of some miRNAs and their time- and tissue-specific expression patterns make experimental miRNA identification difficult. We present here a computational method for genome-wide prediction of Arabidopsis thaliana microRNAs and their target mRNAs. This method uses characteristic features of known plant miRNAs as criteria to search for miRNAs conserved between Arabidopsis and Oryza sativa. Extensive sequence complementarity between miRNAs and their target mRNAs is used to predict miRNA-regulated Arabidopsis transcripts.