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Key Points Advances in DNA sequencing enable the analysis of the genomes and transcriptomes of single cells and will soon enable single-cell epigenomic and proteomic analyses. Single-cell genomic analysis can reveal genomic variability among individual cells, which can be used to reconstruct cellular ancestries in the form of a lineage tree. Single-cell transcriptome analysis can be used to study the functional states of individual cells and to infer and discover cell types in an unbiased manner. Future integrated single-cell analyses based on high-throughput sequencing will enable the simultaneous analysis of genomic, transcriptomic and epigenomic states of cells. Such data will reveal the ancestries of cells, their types, their current functional states and may be used to infer the types and functional states of their ancestors. Integrated single-cell analyses will shed light on fundamental questions of biology and medicine, including questions of the origin and development of cancer, the number of and relationship between human cell types, and the rate and structure of cell turnover in regenerating tissues. Technologies that are based on next-generation sequencing are increasingly being used to study individual cells. The authors discuss the application of this approach to single-cell genomics and transcriptomics, and explore the implications for both basic research and medicine. The unabated progress in next-generation sequencing technologies is fostering a wave of new genomics, epigenomics, transcriptomics and proteomics technologies. These sequencing-based technologies are increasingly being targeted to individual cells, which will allow many new and longstanding questions to be addressed. For example, single-cell genomics will help to uncover cell lineage relationships; single-cell transcriptomics will supplant the coarse notion of marker-based cell types; and single-cell epigenomics and proteomics will allow the functional states of individual cells to be analysed. These technologies will become integrated within a decade or so, enabling high-throughput, multi-dimensional analyses of individual cells that will produce detailed knowledge of the cell lineage trees of higher organisms, including humans. Such studies will have important implications for both basic biological research and medicine.

Three recent single-cell papers use novel CRISPR-Cas9-sgRNA genome editing methods to shed light on the zebrafish cell lineage tree.Three recent single-cell papers use novel CRISPR-Cas9-sgRNA genome editing methods to shed light on the zebrafish cell lineage tree.

When making decisions about funding and jobs the scientific community should recognise that most of the tools used to evaluate scientific excellence are biased in favour of established disciplines and against interdisciplinary research.

Advances in whole genome amplification (WGA) techniques enable understanding of the genomic sequence at a single cell level. Demand for single cell dedicated WGA kits (scWGA) has led to the development of several commercial kit. To this point, no robust comparison of all available kits was performed. Here, we benchmark an economical assay, comparing all commercially available scWGA kits. Our comparison is based on targeted sequencing of thousands of genomic loci, including highly mutable regions, from a large cohort of human single cells. Using this approach we have demonstrated the superiority of Ampli1 in genome coverage and of RepliG in reduced error rate. In summary, we show that no single kit is optimal across all categories, highlighting the need for a dedicated kit selection in accordance with experimental requirements.

Advances in single-cell (SC) genomics enable commensurate improvements in methods for uncovering lineage relations among individual cells, as determined by phylogenetic analysis of the somatic mutations harbored by each cell. Theoretically, complete and accurate knowledge of the genome of each cell of an individual can produce an extremely accurate cell lineage tree of that individual. However, the reality of SC genomics is that such complete and accurate knowledge would be wanting, in quality and in quantity, for the foreseeable future. In this paper we offer a framework for systematically exploring the feasibility of answering cell lineage questions based on SC somatic mutational analysis, as a function of SC genomics data quality and quantity. We take into consideration the current limitations of SC genomics in terms of mutation data quality, most notably amplification bias and allele dropouts (ADO), as well as cost, which puts practical limits on mutation data quantity obtained from each cell as well as on cell sample density. We do so by generating in silico cell lineage trees using a dedicated formal language, eSTG, and show how the ability to answer correctly a cell lineage question depends on the quality and quantity of the SC mutation data. The presented framework can serve as a baseline for the potential of current SC genomics to unravel cell lineage dynamics, as well as the potential contributions of future advancement, both biochemical and computational, for the task.

Doc number: 182 Abstract Background: Select cellulolytic bacteria produce multi-enzymatic cellulosome complexes that bind to the plant cell wall and catalyze its efficient degradation. The multi-modular interconnecting cellulosomal subunits comprise dockerin-containing enzymes that bind cohesively to cohesin-containing scaffoldins. The organization of the modules into functional polypeptides is achieved by intermodular linkers of different lengths and composition, which provide flexibility to the complex and determine its overall architecture. Results: Using a synthetic biology approach, we systematically investigated the spatial organization of the scaffoldin subunit and its effect on cellulose hydrolysis by designing a combinatorial library of recombinant trivalent designer scaffoldins, which contain a carbohydrate-binding module (CBM) and 3 divergent cohesin modules. The positions of the individual modules were shuffled into 24 different arrangements of chimaeric scaffoldins. This basic set was further extended into three sub-sets for each arrangement with intermodular linkers ranging from zero (no linkers), 5 (short linkers) and native linkers of 27-35 amino acids (long linkers). Of the 72 possible scaffoldins, 56 were successfully cloned and 45 of them expressed, representing 14 full sets of chimaeric scaffoldins. The resultant 42-component scaffoldin library was used to assemble designer cellulosomes, comprising three model C. thermocellum cellulases. Activities were examined using Avicel as a pure microcrystalline cellulose substrate and pretreated cellulose-enriched wheat straw as a model substrate derived from a native source. All scaffoldin combinations yielded active trivalent designer cellulosome assemblies on both substrates that exceeded the levels of the free enzyme systems. A preferred modular arrangement for the trivalent designer scaffoldin was not observed for the three enzymes used in this study, indicating that they could be integrated at any position in the designer cellulosome without significant effect on cellulose-degrading activity. Designer cellulosomes assembled with the long-linker scaffoldins achieved higher levels of activity, compared to those assembled with short-and no-linker scaffoldins. Conclusions: The results demonstrate the robustness of the cellulosome system. Long intermodular scaffoldin linkers are preferable, thus leading to enhanced degradation of cellulosic substrates, presumably due to the increased flexibility and spatial positioning of the attached enzymes in the complex. These findings provide a general basis for improved designer cellulosome systems as a platform for bioethanol production.

Introns are key regulators of eukaryotic gene expression and present a potentially powerful tool for the design of synthetic eukaryotic gene expression systems. However, intronic control over gene expression is governed by a multitude of complex, incompletely understood, regulatory mechanisms. Despite this lack of detailed mechanistic understanding, here we show how a relatively simple model enables accurate and predictable tuning of synthetic gene expression system in yeast using several predictive intron features such as transcript folding and sequence motifs. Using only natural Saccharomyces cerevisiae introns as regulators, we demonstrate fine and accurate control over gene expression spanning a 100 fold expression range. These results broaden the engineering toolbox of synthetic gene expression systems and provide a framework in which precise and robust tuning of gene expression is accomplished.

Trinucleotide hereditary diseases such as Huntington disease and Friedreich ataxia are cureless diseases associated with inheriting an abnormally large number of DNA trinucleotide repeats in a gene. The genes associated with different diseases are unrelated and harbor a trinucleotide repeat in different functional regions; therefore, it is striking that many of these diseases have similar correlations between their genotype, namely the number of inherited repeats and age of onset and progression phenotype. These correlations remain unexplained despite more than a decade of research. Although mechanisms have been proposed for several trinucleotide diseases, none of the proposals, being disease-specific, can account for the commonalities among these diseases. Here, we propose a universal mechanism in which length-dependent somatic repeat expansion occurs during the patient's lifetime toward a pathological threshold. Our mechanism uniformly explains for the first time to our knowledge the genotype-phenotype correlations common to trinucleotide disease and is well-supported by both experimental and clinical data. In addition, mathematical analysis of the mechanism provides simple explanations to a wide range of phenomena such as the exponential decrease of the age-of-onset curve, similar onset but faster progression in patients with Huntington disease with homozygous versus heterozygous mutation, and correlation of age of onset with length of the short allele but not with the long allele in Friedreich ataxia. If our proposed universal mechanism proves to be the core component of the actual mechanisms of specific trinucleotide diseases, it would open the search for a uniform treatment for all these diseases, possibly by delaying the somatic expansion process.

Fundamental aspects of embryonic and post-natal development, including maintenance of the mammalian female germline, are largely unknown. Here we employ a retrospective, phylogenetic-based method for reconstructing cell lineage trees utilizing somatic mutations accumulated in microsatellites, to study female germline dynamics in mice. Reconstructed cell lineage trees can be used to estimate lineage relationships between different cell types, as well as cell depth (number of cell divisions since the zygote). We show that, in the reconstructed mouse cell lineage trees, oocytes form clusters that are separate from hematopoietic and mesenchymal stem cells, both in young and old mice, indicating that these populations belong to distinct lineages. Furthermore, while cumulus cells sampled from different ovarian follicles are distinctly clustered on the reconstructed trees, oocytes from the left and right ovaries are not, suggesting a mixing of their progenitor pools. We also observed an increase in oocyte depth with mouse age, which can be explained either by depth-guided selection of oocytes for ovulation or by post-natal renewal. Overall, our study sheds light on substantial novel aspects of female germline preservation and development.

The unique properties of DNA make it a fundamental building block in the fields of supramolecular chemistry, nanotechnology, nano-circuits, molecular switches, molecular devices, and molecular computing. In our recently introduced autonomous molecular automaton, DNA molecules serve as input, output, and software, and the hardware consists of DNA restriction and ligation enzymes using ATP as fuel. In addition to information, DNA stores energy, available on hybridization of complementary strands or hydrolysis of its phosphodiester backbone. Here we show that a single DNA molecule can provide both the input data and all of the necessary fuel for a molecular automaton. Each computational step of the automaton consists of a reversible software molecule/input molecule hybridization followed by an irreversible software-directed cleavage of the input molecule, which drives the computation forward by increasing entropy and releasing heat. The cleavage uses a hitherto unknown capability of the restriction enzyme FokI, which serves as the hardware, to operate on a noncovalent software/input hybrid. In the previous automaton, software/input ligation consumed one software molecule and two ATP molecules per step. As ligation is not performed in this automaton, a fixed amount of software and hardware molecules can, in principle, process any input molecule of any length without external energy supply. Our experiments demonstrate 3 × 1012automata per μ l performing 6.6 × 1010transitions per second per μ l with transition fidelity of 99.9%, dissipating about$5 \\times 10^{-9}\\>W/\\mu l$as heat at ambient temperature.