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Recent molecular studies have shown that, even when derived from a seemingly homogenous population, individual cells can exhibit substantial differences in gene expression, protein levels and phenotypic output1, 2, 3, 4, 5, with important functional consequences4, 5. Existing studies of cellular heterogeneity, however, have typically measured only a few pre-selected RNAs1, 2 or proteins5, 6 simultaneously, because genomic profiling methods3 could not be applied to single cells until very recently7, 8, 9, 10. Here we use single-cell RNA sequencing to investigate heterogeneity in the response of mouse bone-marrow-derived dendritic cells (BMDCs) to lipopolysaccharide. We find extensive, and previously unobserved, bimodal variation in messenger RNA abundance and splicing patterns, which we validate by RNA-fluorescence in situ hybridization for select transcripts. In particular, hundreds of key immune genes are bimodally expressed across cells, surprisingly even for genes that are very highly expressed at the population average. Moreover, splicing patterns demonstrate previously unobserved levels of heterogeneity between cells. Some of the observed bimodality can be attributed to closely related, yet distinct, known maturity states of BMDCs; other portions reflect differences in the usage of key regulatory circuits. For example, we identify a module of 137 highly variable, yet co-regulated, antiviral response genes. Using cells from knockout mice, we show that variability in this module may be propagated through an interferon feedback circuit, involving the transcriptional regulators Stat2 and Irf7. Our study demonstrates the power and promise of single-cell genomics in uncovering functional diversity between cells and in deciphering cell states and circuits., National Institutes of Health (U.S.) (NIH Postdoctoral Fellowship (1F32HD075541-01)), Charles H. Hood Foundation (Postdoctoral Fellowship), National Institutes of Health (U.S.) (NIH grant U54 AI057159), National Institutes of Health (U.S.) (NIH New Innovator Award (DP2 OD002230)), National Institutes of Health (U.S.) (NIH CEGS Award (1P50HG006193-01)), National Institutes of Health (U.S.) (NIH Pioneer Awards (5DP1OD003893-03)), National Institutes of Health (U.S.) (NIH Pioneer Awards (DP1OD003958-01)), Broad Institute of MIT and Harvard, Broad Institute of MIT and Harvard (Klarman Cell Observatory)

available in PMC 2014 January 01, RNA-seq is an effective method for studying the transcriptome, but it can be difficult to apply to scarce or degraded RNA from fixed clinical samples, rare cell populations or cadavers. Recent studies have proposed several methods for RNA-seq of low-quality and/or low-quantity samples, but the relative merits of these methods have not been systematically analyzed. Here we compare five such methods using metrics relevant to transcriptome annotation, transcript discovery and gene expression. Using a single human RNA sample, we constructed and sequenced ten libraries with these methods and compared them against two control libraries. We found that the RNase H method performed best for chemically fragmented, low-quality RNA, and we confirmed this through analysis of actual degraded samples. RNase H can even effectively replace oligo(dT)-based methods for standard RNA-seq. SMART and NuGEN had distinct strengths for measuring low-quantity RNA. Our analysis allows biologists to select the most suitable methods and provides a benchmark for future method development., National Institutes of Health (U.S.) (Pioneer Award DP1-OD003958-01), National Human Genome Research Institute (U.S.) (NHGRI) 1P01HG005062-01), National Human Genome Research Institute (U.S.) (NHGRI Center of Excellence in Genome Science Award 1P50HG006193-01), Howard Hughes Medical Institute (Investigator), Merkin Family Foundation for Stem Cell Research, Broad Institute of MIT and Harvard (Klarman Cell Observatory), National Human Genome Research Institute (U.S.) (NHGRI grant HG03067), Fonds voor Wetenschappelijk Onderzoek--Vlaanderen

Background Recent studies in budding yeast have shown that antisense transcription occurs at many loci. However, the functional role of antisense transcripts has been demonstrated only in a few cases and it has been suggested that most antisense transcripts may result from promiscuous bi-directional transcription in a dense genome. Results Here, we use strand-specific RNA sequencing to study anti-sense transcription in Saccharomyces cerevisiae. We detect 1,103 putative antisense transcripts expressed in mid-log phase growth, ranging from 39 short transcripts covering only the 3' UTR of sense genes to 145 long transcripts covering the entire sense open reading frame. Many of these antisense transcripts overlap sense genes that are repressed in mid-log phase and are important in stationary phase, stress response, or meiosis. We validate the differential regulation of 67 antisense transcripts and their sense targets in relevant conditions, including nutrient limitation and environmental stresses. Moreover, we show that several antisense transcripts and, in some cases, their differential expression have been conserved across five species of yeast spanning 150 million years of evolution. Divergence in the regulation of antisense transcripts to two respiratory genes coincides with the evolution of respiro-fermentation. Conclusions Our work provides support for a global and conserved role for antisense transcription in yeast gene regulation., Canadian Friends of the Hebrew University, Howard Hughes Medical Institute, Human Frontier Science Program (Strasbourg, France), Burroughs Wellcome Fund (Career Award at the Scientific Interface), National Institutes of Health (U.S.). Pioneer Award, Broad Institute of MIT and Harvard, U.S.-Israel Binational Science Foundation (BSF), National Human Genome Research Institute (U.S.), Alfred P. Sloan Foundation

This article is part of the supplement: Beyond the Genome: The true gene count, human evolution and disease genomics, RNA-seq provides insights at multiple levels into the transcription of the genome as it yields sequence, splicing and expression-level information. We have been developing and comparing a wide range of RNA-seq methods for their ability to annotate transcribed genomic regions, identify differences between normal and cancer states, and quantify mRNA expression levels. We will present results in two areas: (i) strand-specific RNA-seq; and (ii) RNA-seq starting from total RNA. Strand-specific RNA-seq is a powerful tool for novel transcript discovery and genome annotation because it enables the identification of the strand of origin for non-coding RNA and anti-sense RNA, as well as defining the ends of adjacent or overlapping transcripts transcribed in different directions. Using the well-annotated Saccharomyces cerevisiae transcriptome as a benchmark, we directly compared seven library construction protocols, including both published and our own novel methods. We found marked differences in strand-specificity, library complexity, evenness and continuity of coverage, agreement with known annotations, and accuracy for expression profiling. Weighing the performance and ease of conducting each method, we identified the dUTP second strand marking [1] and the Illumina RNA ligation methods as the leading protocols, with the former benefitting from the current availability of paired-end sequencing. Our analysis provides a comprehensive benchmark, and our computational pipeline is applicable for assessment of future protocols in other organisms. RNA-seq methods that do not require the purification of mRNA will be valuable for several applications, including samples with low input amounts and/or partial degradation. In these experiments, it is necessary to reduce the fraction of sequencing reads derived from ribosomal RNA. We will present results from multiple approaches, including the use of Not-So-Random (NSR) primers for reverse transcription [2] and the NuGEN Ovation RNA-Seq

available in PMC 2010 November 2., RNA-Seq provides an unbiased way to study a transcriptome, including both coding and noncoding genes. To date, most RNA-Seq studies have critically depended on existing annotations, and thus focused on expression levels and variation in known transcripts. Here, we present Scripture, a method to reconstruct the transcriptome of a mammalian cell using only RNA-Seq reads and the genome sequence. We apply it to mouse embryonic stem cells, neuronal precursor cells, and lung fibroblasts to accurately reconstruct the full-length gene structures for the vast majority of known expressed genes. We identify substantial variation in protein-coding genes, including thousands of novel 5′-start sites, 3′-ends, and internal coding exons. We then determine the gene structures of over a thousand lincRNA and antisense loci. Our results open the way to direct experimental manipulation of thousands of non-coding RNAs, and demonstrate the power of ab initio reconstruction to render a comprehensive picture of mammalian transcriptomes., Merkin Family Foundation for Stem Cell Research, Howard Hughes Medical Institute, National Human Genome Research Institute (U.S.), Burroughs Wellcome Fund, Broad Institute

Long non-coding RNAs (lncRNAs) comprise a diverse class of transcripts that structurally resemble mRNAs but do not encode proteins. Recent genome-wide studies in human and mouse have annotated lncRNAs expressed in cell lines and adult tissues, but a systematic analysis of lncRNAs expressed during vertebrate embryogenesis has been elusive. To identify lncRNAs with potential functions in vertebrate embryogenesis, we performed a time series of RNA-Seq experiments at eight stages during early zebrafish development. We reconstructed 56,535 high-confidence transcripts in 28,912 loci, recovering the vast majority of expressed RefSeq transcripts, while identifying thousands of novel isoforms and expressed loci. We defined a stringent set of 1,133 non-coding multi-exonic transcripts expressed during embryogenesis. These include long intergenic ncRNAs (lincRNAs), intronic overlapping lncRNAs, exonic antisense overlapping lncRNAs, and precursors for small RNAs (sRNAs). Zebrafish lncRNAs share many of the characteristics of their mammalian counterparts: relatively short length, low exon number, low expression, and conservation levels comparable to introns. Subsets of lncRNAs carry chromatin signatures characteristic of genes with developmental functions. The temporal expression profile of lncRNAs revealed two novel properties: lncRNAs are expressed in narrower time windows than protein-coding genes and are specifically enriched in early-stage embryos. In addition, several lncRNAs show tissue-specific expression and distinct subcellular localization patterns. Integrative computational analyses associated individual lncRNAs with specific pathways and functions, ranging from cell cycle regulation to morphogenesis. Our study provides the first systematic identification of lncRNAs in a vertebrate embryo and forms the foundation for future genetic, genomic and evolutionary studies.

Strand-specific, massively parallel cDNA sequencing (RNA-seq) is a powerful tool for transcript discovery, genome annotation and expression profiling. There are multiple published methods for strand-specific RNA-seq, but no consensus exists as to how to choose between them. Here we developed a comprehensive computational pipeline to compare library quality metrics from any RNA-seq method. Using the well-annotated Saccharomyces cerevisiae transcriptome as a benchmark, we compared seven library-construction protocols, including both published and our own methods. We found marked differences in strand specificity, library complexity, evenness and continuity of coverage, agreement with known annotations and accuracy for expression profiling. Weighing each method's performance and ease, we identified the dUTP second-strand marking and the Illumina RNA ligation methods as the leading protocols, with the former benefitting from the current availability of paired-end sequencing. Our analysis provides a comprehensive benchmark, and our computational pipeline is applicable for assessment of future protocols in other organisms., Howard Hughes Medical Institute, United States-Israel Binational Science Foundation

Long noncoding RNAs (lncRNAs) comprise a diverse class of transcripts that structurally resemble mRNAs but do not encode proteins. Recent genome-wide studies in humans and the mouse have annotated lncRNAs expressed in cell lines and adult tissues, but a systematic analysis of lncRNAs expressed during vertebrate embryogenesis has been elusive. To identify lncRNAs with potential functions in vertebrate embryogenesis, we performed a time-series of RNA-seq experiments at eight stages during early zebrafish development. We reconstructed 56,535 high-confidence transcripts in 28,912 loci, recovering the vast majority of expressed RefSeq transcripts while identifying thousands of novel isoforms and expressed loci. We defined a stringent set of 1133 noncoding multi-exonic transcripts expressed during embryogenesis. These include long intergenic ncRNAs (lincRNAs), intronic overlapping lncRNAs, exonic antisense overlapping lncRNAs, and precursors for small RNAs (sRNAs). Zebrafish lncRNAs share many of the characteristics of their mammalian counterparts: relatively short length, low exon number, low expression, and conservation levels comparable to that of introns. Subsets of lncRNAs carry chromatin signatures characteristic of genes with developmental functions. The temporal expression profile of lncRNAs revealed two novel properties: lncRNAs are expressed in narrower time windows than are protein-coding genes and are specifically enriched in early-stage embryos. In addition, several lncRNAs show tissue-specific expression and distinct subcellular localization patterns. Integrative computational analyses associated individual lncRNAs with specific pathways and functions, ranging from cell cycle regulation to morphogenesis. Our study provides the first systematic identification of lncRNAs in a vertebrate embryo and forms the foundation for future genetic, genomic, and evolutionary studies., National Human Genome Research Institute (U.S.) (Grant 1RO1HG005111-01)

available in PMC 2011 November 01., Cellular RNA levels are determined by the interplay of RNA production, processing and degradation. However, because most studies of RNA regulation do not distinguish the separate contributions of these processes, little is known about how they are temporally integrated. Here we combine metabolic labeling of RNA at high temporal resolution with advanced RNA quantification and computational modeling to estimate RNA transcription and degradation rates during the response of mouse dendritic cells to lipopolysaccharide. We find that changes in transcription rates determine the majority of temporal changes in RNA levels, but that changes in degradation rates are important for shaping sharp 'peaked' responses. We used sequencing of the newly transcribed RNA population to estimate temporally constant RNA processing and degradation rates genome wide. Degradation rates vary significantly between genes and contribute to the observed differences in the dynamic response. Certain transcripts, including those encoding cytokines and transcription factors, mature faster. Our study provides a quantitative approach to study the integrative process of RNA regulation., Human Frontier Science Program (Strasbourg, France), Howard Hughes Medical Institute, Burroughs Wellcome Fund (Career Award at the Scientific Interface)

Deep sequencing technologies have the potential to transform the study of highly variable viral pathogens by providing a rapid and cost-effective approach to sensitively characterize rapidly evolving viral quasispecies. Here, we report on a high-throughput whole HIV-1 genome deep sequencing platform that combines 454 pyrosequencing with novel assembly and variant detection algorithms. In one subject we combined these genetic data with detailed immunological analyses to comprehensively evaluate viral evolution and immune escape during the acute phase of HIV-1 infection. The majority of early, low frequency mutations represented viral adaptation to host CD8+ T cell responses, evidence of strong immune selection pressure occurring during the early decline from peak viremia. CD8+ T cell responses capable of recognizing these low frequency escape variants coincided with the selection and evolution of more effective secondary HLA-anchor escape mutations. Frequent, and in some cases rapid, reversion of transmitted mutations was also observed across the viral genome. When located within restricted CD8 epitopes these low frequency reverting mutations were sufficient to prime de novo responses to these epitopes, again illustrating the capacity of the immune response to recognize and respond to low frequency variants. More importantly, rapid viral escape from the most immunodominant CD8+ T cell responses coincided with plateauing of the initial viral load decline in this subject, suggestive of a potential link between maintenance of effective, dominant CD8 responses and the degree of early viremia reduction. We conclude that the early control of HIV-1 replication by immunodominant CD8+ T cell responses may be substantially influenced by rapid, low frequency viral adaptations not detected by conventional sequencing approaches, which warrants further investigation. These data support the critical need for vaccine-induced CD8+ T cell responses to target more highly constrained re

Global studies of transcript structure and abundance in cancer cells enable the systematic discovery of aberrations that contribute to carcinogenesis, including gene fusions, alternative splice isoforms, and somatic mutations. We developed a systematic approach to characterize the spectrum of cancer-associated mRNA alterations through integration of transcriptomic and structural genomic data, and we applied this approach to generate new insights into melanoma biology. Using paired-end massively parallel sequencing of cDNA (RNA-seq) together with analyses of high-resolution chromosomal copy number data, we identified 11 novel melanoma gene fusions produced by underlying genomic rearrangements, as well as 12 novel readthrough transcripts. We mapped these chimeric transcripts to base-pair resolution and traced them to their genomic origins using matched chromosomal copy number information. We also used these data to discover and validate base-pair mutations that accumulated in these melanomas, revealing a surprisingly high rate of somatic mutation and lending support to the notion that point mutations constitute the major driver of melanoma progression. Taken together, these results may indicate new avenues for target discovery in melanoma, while also providing a template for large-scale transcriptome studies across many tumor types.