Massively parallel sequencing of cDNA has enabled deep and efficient probing of transcriptomes. Current approaches for transcript reconstruction from such data often rely on aligning reads to a reference genome, and are thus unsuitable for samples with a partial or missing reference genome. Here we present the Trinity method for de novo assembly of full-length transcripts and evaluate it on samples from fission yeast, mouse and whitefly, whose reference genome is not yet available. By efficiently constructing and analyzing sets of de Bruijn graphs, Trinity fully reconstructs a large fraction of transcripts, including alternatively spliced isoforms and transcripts from recently duplicated genes. Compared with other de novo transcriptome assemblers, Trinity recovers more full-length transcripts across a broad range of expression levels, with a sensitivity similar to methods that rely on genome alignments. Our approach provides a unified solution for transcriptome reconstruction in any sample, especially in the absence of a reference genome.

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Full-length transcriptome assembly from RNA-Seq data without a reference genome.

We have mapped and quantified mouse transcriptomes by deeply sequencing them and recording how frequently each gene is represented in the sequence sample (RNA-Seq). This provides a digital measure of the presence and prevalence of transcripts from known and previously unknown genes. We report reference measurements composed of 41–52 million mapped 25-base-pair reads for poly(A)-selected RNA from adult mouse brain, liver and skeletal muscle tissues. We used RNA standards to quantify transcript prevalence and to test the linear range of transcript detection, which spanned five orders of magnitude. Although >90% of uniquely mapped reads fell within known exons, the remaining data suggest new and revised gene models, including changed or additional promoters, exons and 3′ untranscribed regions, as well as new candidate microRNA precursors. RNA splice events, which are not readily measured by standard gene expression microarray or serial analysis of gene expression methods, were detected directly by mapping splice-crossing sequence reads. We observed 1.45 × 10 5 distinct splices, and alternative splices were prominent, with 3,500 different genes expressing one or more alternate internal splices. The mRNA population specifies a cell’s identity and helps to govern its present and future activities. This has made transcriptome analysis a general phenotyping method, with expression microarrays of many kinds in routine use. Here we explore the possibility that transcriptome analysis, transcript discovery and transcript refinement can be done effectively in large and complex mammalian genomes by ultra-high-throughput sequencing. Expression microarrays are currently the most widely used methodology for transcriptome analysis, although some limitations persist. These include hybridization and cross-hybridization artifacts 1–3 , dye-based detection issues and design constraints that preclude or seriously limit the detection of RNA splice patterns and previously unmapped genes. These issues have made it difficult for standard array designs to provide full sequence comprehensiveness (coverage of all possible genes, including unknown ones, in large genomes) or transcriptome comprehensiveness (reliable detection of all RNAs of all prevalence classes, including the least abundant ones that are physiologically relevant). Other

/pdf/mapping-and-quantifying-mammalian-transcriptomes-by-rna-seq-1egh5oivst.pdf

Mapping and quantifying mammalian transcriptomes by RNA-Seq.

RNA-Seq is a recently developed approach to transcriptome profiling that uses deep-sequencing technologies. Studies using this method have already altered our view of the extent and complexity of eukaryotic transcriptomes. RNA-Seq also provides a far more precise measurement of levels of transcripts and their isoforms than other methods. This article describes the RNA-Seq approach, the challenges associated with its application, and the advances made so far in characterizing several eukaryote transcriptomes.

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RNA-Seq: a revolutionary tool for transcriptomics

Chromatin Modifications and Their Function

Several reactive oxygen species (ROS) are continuously produced in plants as byproducts of aerobic metabolism. Depending on the nature of the ROS species, some are highly toxic and rapidly detoxified by various cellular enzymatic and nonenzymatic mechanisms. Whereas plants are surfeited with mechanisms to combat increased ROS levels during abiotic stress conditions, in other circumstances plants appear to purposefully generate ROS as signaling molecules to control various processes including pathogen defense, programmed cell death, and stomatal behavior. This review describes the mechanisms of ROS generation and removal in plants during development and under biotic and abiotic stress conditions. New insights into the complexity and roles that ROS play in plants have come from genetic analyses of ROS detoxifying and signaling mutants. Considering recent ROS-induced genome-wide expression analyses, the possible functions and mechanisms for ROS sensing and signaling in plants are compared with those in animals and yeast.

REACTIVE OXYGEN SPECIES: Metabolism, Oxidative Stress, and Signal Transduction

The effects of structural variants on phenotypic diversity and evolution are poorly understood. We recently described the genetic and phenotypic variation among fission yeast strains and showed that genome-wide association studies are informative in this model. Here we extend this work by systematically identifying structural variations and investigating their consequences. We establish a curated catalog of copy number variants (CNVs) and rearrangements, including inversions and translocations. We find that SVs frequently vary within clonal populations and are weakly tagged by SNPs, consistent with rapid turnover. We show that CNVs produce measurable effects on gene expression both within and outside the duplicated regions. CNVs contribute to quantitative traits such as cell shape, cell growth under diverse conditions, sugar utilization in winemaking and antibiotic resistance, whereas rearrangements are strongly associated with reproductive isolation. Collectively, these findings have broad implications for evolution and for our understanding of quantitative traits and complex human diseases.

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Transient structural variations alter gene expression and quantitative traits in Schizosaccharomyces pombe.

Genetically identical cells are known to differ in many physiological parameters such as growth rate and drug tolerance. Metabolic specialization is believed to be a cause of such phenotypic heterogeneity, but detection of metabolically divergent subpopulations remains technically challenging. We developed a proteomics-based technology, termed differential isotope labelling by amino acids (DILAC), that can detect producer and consumer subpopulations of a particular amino acid within an isogenic cell population by monitoring peptides with multiple occurrences of the amino acid. We reveal that young, morphologically undifferentiated yeast colonies contain subpopulations of lysine producers and consumers that emerge due to nutrient gradients. Deconvoluting their proteomes using DILAC, we find evidence for in situ cross-feeding where rapidly growing cells ferment and provide the more slowly growing, respiring cells with ethanol. Finally, by combining DILAC with fluorescence-activated cell sorting, we show that the metabolic subpopulations diverge phenotypically, as exemplified by a different tolerance to the antifungal drug amphotericin B. Overall, DILAC captures previously unnoticed metabolic heterogeneity and provides experimental evidence for the role of metabolic specialization and cross-feeding interactions as a source of phenotypic heterogeneity in isogenic cell populations. 

/pdf/metabolic-heterogeneity-and-cross-feeding-within-isogenic-3bgwqidl.pdf

Metabolic heterogeneity and cross-feeding within isogenic yeast populations captured by DILAC

Cell-to-cell variability is central for microbial populations and contributes to cell function, stress adaptation and drug resistance. Gene-expression heterogeneity underpins this variability, but has been challenging to study genome-wide. Here, we report an integrated approach for imaging of individual fission yeast cells followed by single-cell RNA sequencing (scRNA-seq) and novel Bayesian normalisation. We analyse >2000 single cells and >700 matching RNA controls in various environmental conditions and identify sets of highly variable genes. Combining scRNA-seq with cell-size measurements provides unique insights into genes regulated during cell growth and division in single cells, including genes whose expression does not scale with cell size. We further analyse the heterogeneity and dynamics of gene expression during adaptive and acute responses to changing environments. Entry into stationary phase is preceded by a gradual, synchronised adaptation in gene regulation, followed by highly variable gene expression when growth decreases. Conversely, a sudden and acute heat-shock leads to a stronger and coordinated response and adaptation across cells. This analysis reveals that the extent and dynamics of global gene-expression heterogeneity is regulated in response to different physiological conditions within populations of a unicellular eukaryote. In summary, this works illustrates the potential of combined transcriptomics and imaging analysis in single cells to provide comprehensive and unbiased mechanistic understanding of cell-to-cell variability in microbial communities.

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Single-cell phenotyping and RNA sequencing reveal novel patterns of gene expression heterogeneity and regulation during growth and stress adaptation in a unicellular eukaryote

http://www.cell.com/article/S0960982223005286/pdf

Jürg Bähler

Papers

Transient structural variations alter gene expression and quantitative traits in Schizosaccharomyces pombe.

Metabolic heterogeneity and cross-feeding within isogenic yeast populations captured by DILAC

Single-cell phenotyping and RNA sequencing reveal novel patterns of gene expression heterogeneity and regulation during growth and stress adaptation in a unicellular eukaryote

Optimization of energy production and central carbon metabolism in a non-respiring eukaryote

Getting the most from public microarray data