Creating cellular diversity through transcription factor competition.

The development of blood cells has long served as a model system to study the generation of diverse mature cells from multipotent progenitors. The article by Org et al (2015) reveals how transcription factor competition on primed DNA templates may contribute to embryonic blood cell specification during the early stages of mesoderm development. The study not only provides new insights into the functionality of the key haematopoietic transcription factor Scl/Tal1, but also provides a potentially widely applicable framework for transcription factor-mediated cell fate specification.Research in the author’s laboratory is supported by Cancer Research UK, BBSRC, MRC, Leukaemia and Lymphoma Research, Leukemia and Lymphoma Society, Microsoft Research and core support grants from the Wellcome Trust to the Cambridge Institute for Medical Research and Wellcome Trust–MRC Cambridge Stem Cell Institute.This is the accepted manuscript of a paper published in The EMBO Journal (Göttgens B, The EMBO Journal 2015, 34, 691-693, doi:10.15252/embj.201591017). The final version is available at http://dx.doi.org/10.15252/embj.20159101

Dissecting stem cell differentiation using single cell expression profiling.

Many assumptions about the way cells behave are based on analyses of populations. However, it is now widely recognized that even apparently pure populations can display a remarkable level of heterogeneity. This is particularly true in stem cell biology where it hinders our understanding of normal development and the development of strategies for regenerative medicine. Over the past decade technologies facilitating gene expression analysis at the single cell level have become widespread, providing access to rare cell populations and insights into population structure and function. Here we review the contributions of single cell biology to understanding stem cell differentiation so far, both as a new methodology for defining cell types and a tool for understanding the complexities of cellular decision-making.Research in the authors’ laboratories is supported by the Medical Research Council, Biotechnology and Biological Sciences Research Council, Bloodwise, the Leukemia and Lymphoma Society, a Wellcome Trust Strategic Award (Tracing Early Mammalian Lineage Decisions by Single Cell Genomics) and core support grants by the Wellcome Trust to the Cambridge Institute for Medical Research and Wellcome Trust - MRC Cambridge Stem Cell Institute.This is the author accepted manuscript. The final version is available from Elsevier via http://dx.doi.org/10.1016/j.ceb.2016.08.00

Protocol: precision engineering of plant gene loci by homologous recombination cloning in Escherichia coli.

Plant genome sequence data now provide opportunities to conduct molecular genetic studies at the level of the whole gene locus and above. Such studies will be greatly facilitated by adopting and developing further the new generation of genetic engineering tools, based on homologous recombination cloning in Escherichia coli, which are free from the constraints imposed by the availability of suitably positioned restriction sites. Here we describe the basis for homologous recombination cloning in E. coli, the available tools and resources, together with a protocol for long range cloning and manipulation of an Arabidopsis thaliana gene locus, to create constructs co-ordinately driven by locus-specific regulatory elements.RIGHTS : This article is licensed under the BioMed Central licence at http://www.biomedcentral.com/about/license which is similar to the 'Creative Commons Attribution Licence'. In brief you may : copy, distribute, and display the work; make derivative works; or make commercial use of the work - under the following conditions: the original author must be given credit; for any reuse or distribution, it must be made clear to others what the license terms of this work are

Evolution of candidate transcriptional regulatory motifs since the human-chimpanzee divergence.

BACKGROUND: Despite the recent completion of the chimpanzee genome project, few functionally significant sequence differences between humans and chimpanzees have thus far been identified. Alteration in transcriptional regulatory mechanisms represents an important platform for evolutionary change, suggesting that a significant proportion of functional human-chimpanzee sequence differences may affect regulatory elements. RESULTS: To explore this hypothesis, we performed genome-wide identification of conserved candidate transcription-factor binding sites that have evolved since the divergence of humans and chimpanzees. Analysis of candidate transcription-factor binding sites conserved between mouse and chimpanzee yet absent in human indicated that loss of candidate transcription-factor binding sites in the human lineage was not random but instead correlated with the biologic functions of associated genes. CONCLUSION: Our data support the notion that changes in transcriptional regulation have contributed to the recent evolution of humans. Moreover, genes associated with mutated candidate transcription-factor binding sites highlight potential pathways underlying human-chimpanzee divergence.RIGHTS : This article is licensed under the BioMed Central licence at http://www.biomedcentral.com/about/license which is similar to the 'Creative Commons Attribution Licence'. In brief you may : copy, distribute, and display the work; make derivative works; or make commercial use of the work - under the following conditions: the original author must be given credit; for any reuse or distribution, it must be made clear to others what the license terms of this work are

Genome-scale technology driven advances to research into normal and malignant haematopoiesis.

Haematopoiesis or blood development has long served as a model system for adult stem cell biology. Moreover, when combined, the various cancers of the blood represent one of the commonest human malignancies. Large numbers of researchers have therefore dedicated their scientific careers to studying haematopoiesis for more than a century. Throughout this period, many new technologies have first been applied towards the study of blood cells, and the research fields of normal and malignant haematopoiesis have also been some of the earliest adopters of genome-scale technologies. This has resulted in significant new insights with implications ranging from basic biological mechanisms to patient diagnosis and prognosis and also produced lessons likely to be relevant for many other areas of biomedical research. This paper discusses the current state of play for a range of genome-scale applications within haemopoiesis research, including gene expression profiling, ChIP-sequencing, genomewide association analysis, and cancer genome sequencing. A concluding outlook section explores likely future areas of progress as well as potential technological and educational bottlenecks.Peer Reviewe

Machine learning predicts putative hematopoietic stem cells within large single-cell transcriptomics data sets.

Hematopoietic stem cells (HSCs) are an essential source and reservoir for normal hematopoiesis, and their function is compromised in many blood disorders. HSC research has benefitted from the recent development of single-cell molecular profiling technologies, where single-cell RNA sequencing (scRNA-seq) in particular has rapidly become an established method to profile HSCs and related hematopoietic populations. The classic definition of HSCs relies on transplantation assays, which have been used to validate HSC function for cell populations defined by flow cytometry. Flow cytometry information for single cells, however, is not available for many new high-throughput scRNA-seq methods, thus highlighting an urgent need for the establishment of alternative ways to pinpoint the likely HSCs within large scRNA-seq data sets. To address this, we tested a range of machine learning approaches and developed a tool, hscScore, to score single-cell transcriptomes from murine bone marrow based on their similarity to gene expression profiles of validated HSCs. We evaluated hscScore across scRNA-seq data from different laboratories, which allowed us to establish a robust method that functions across different technologies. To facilitate broad adoption of hscScore by the wider hematopoiesis community, we have made the trained model and example code freely available online. In summary, our method hscScore provides fast identification of mouse bone marrow HSCs from scRNA-seq measurements and represents a broadly useful tool for analysis of single-cell gene expression data.MRC, Wellcome Trust, Bloodwise, CRU

Single-cell transcriptional profiling: a window into embryonic cell-type specification.

During mammalian embryonic development, a single fertilized egg cell will proliferate and differentiate into all the cell lineages and cell types that eventually form the adult organism. Cell lineage diversification involves repeated cell fate choices that ultimately occur at the level of the individual cell rather than at the cell-population level. Improvements in single-cell technologies are transforming our understanding of mammalian development, not only by overcoming the limitations presented by the extremely low cell numbers of early embryos but also by enabling the study of cell fate specification in greater detail. In this Review, we first discuss recent advances in single-cell transcriptomics and imaging and provide a brief outline of current bioinformatics methods available to analyse the resulting data. We then discuss how these techniques have contributed to our understanding of pre-implantation and early post-implantation development and of in vitro pluripotency. Finally, we overview the current challenges facing single-cell research and highlight the latest advances and potential future avenues

Processing, visualising and reconstructing network models from single-cell data.

New single-cell technologies readily permit gene expression profiling of thousands of cells at single-cell resolution. In this review, we will discuss methods for visualisation and interpretation of single-cell gene expression data, and the computational analysis needed to go from raw data to predictive executable models of gene regulatory network function. We will focus primarily on single-cell real-time quantitative PCR and RNA-sequencing data, but much of what we cover will also be relevant to other platforms, such as the mass cytometry technology for high-dimensional single-cell proteomics.S.W is supported by a Microsoft Research PhD Scholarship.This is the author accepted manuscript. The final version is available from Nature Publishing Group via http://dx.doi.org/10.1038/icb.2015.10

Dysregulation of haematopoietic stem cell regulatory programs in acute myeloid leukaemia.

Haematopoietic stem cells (HSC) are situated at the apex of the haematopoietic differentiation hierarchy, ensuring the life-long supply of mature haematopoietic cells and forming a reservoir to replenish the haematopoietic system in case of emergency such as acute blood loss. To maintain a balanced production of all mature lineages and at the same time secure a stem cell reservoir, intricate regulatory programs have evolved to control multi-lineage differentiation and self-renewal in haematopoietic stem and progenitor cells (HSPCs). Leukaemogenic mutations commonly disrupt these regulatory programs causing a block in differentiation with simultaneous enhancement of proliferation. Here, we briefly summarize key aspects of HSPC regulatory programs, and then focus on their disruption by leukaemogenic fusion genes containing the mixed lineage leukaemia (MLL) gene. Using MLL as an example, we explore important questions of wider significance that are still under debate, including the importance of cell of origin, to what extent leukaemia oncogenes impose specific regulatory programs and the relevance of leukaemia stem cells for disease development and prognosis. Finally, we suggest that disruption of stem cell regulatory programs is likely to play an important role in many other pathologies including ageing-associated regenerative failure

A new RNASeq-based reference transcriptome for sugar beet and its application in transcriptome-scale analysis of vernalization and gibberellin responses.

BACKGROUND: Sugar beet (Beta vulgaris sp. vulgaris) crops account for about 30% of world sugar. Sugar yield is compromised by reproductive growth hence crops must remain vegetative until harvest. Prolonged exposure to cold temperature (vernalization) in the range 6 °C to 12 °C induces reproductive growth, leading to bolting (rapid elongation of the main stem) and flowering. Spring cultivation of crops in cool temperate climates makes them vulnerable to vernalization and hence bolting, which is initiated in the apical shoot meristem in processes involving interaction between gibberellin (GA) hormones and vernalization. The underlying mechanisms are unknown and genome scale next generation sequencing approaches now offer comprehensive strategies to investigate them; enabling the identification of novel targets for bolting control in sugar beet crops. In this study, we demonstrate the application of an mRNA-Seq based strategy for this purpose. RESULTS: There is no sugar beet reference genome, or public expression array platforms. We therefore used RNA-Seq to generate the first reference transcriptome. We next performed digital gene expression profiling using shoot apex mRNA from two sugar beet cultivars with and without applied GA, and also a vernalized cultivar with and without applied GA. Subsequent bioinformatics analyses identified transcriptional changes associated with genotypic difference and experimental treatments. Analysis of expression profiles in response to vernalization and GA treatment suggested previously unsuspected roles for a RAV1-like AP2/B3 domain protein in vernalization and efflux transporters in the GA response. CONCLUSIONS: Next generation RNA-Seq enabled the generation of the first reference transcriptome for sugar beet and the study of global transcriptional responses in the shoot apex to vernalization and GA treatment, without the need for a reference genome or established array platforms. Comprehensive bioinformatic analysis identified transcriptional programmes associated with different sugar beet genotypes as well as biological treatments; thus providing important new opportunities for basic scientists and sugar beet breeders. Transcriptome-scale identification of agronomically important traits as used in this study should be widely applicable to all crop plants where genomic resources are limiting.RIGHTS : This article is licensed under the BioMed Central licence at http://www.biomedcentral.com/about/license which is similar to the 'Creative Commons Attribution Licence'. In brief you may : copy, distribute, and display the work; make derivative works; or make commercial use of the work - under the following conditions: the original author must be given credit; for any reuse or distribution, it must be made clear to others what the license terms of this work are