The innovation of the eukaryote cytoskeleton enabled phagocytosis, intracellular transport and cytokinesis, and is responsible for diverse eukaryotic morphologies. Still, the relationship between phenotypic innovations in the cytoskeleton and their underlying genotype is poorly understood. To explore the genetic mechanism of morphological evolution of the eukaryotic cytoskeleton we provide the first single cell transcriptomes from uncultivable, free-living unicellular eukaryotes: the radiolarian species Lithomelissa setosa and Sticholonche zanclea. Analysis of the genetic components of the cytoskeleton and mapping of the evolution of these to a revised phylogeny of Rhizaria reveals lineage-specific gene duplications and neo-functionalization of α and β tubulin in Retaria, actin in Retaria and Endomyxa, and Arp2/3 complex genes in Chlorarachniophyta. We show how genetic innovations have shaped cytoskeletal structures in Rhizaria, and how single cell transcriptomics can be applied for resolving deep phylogenies and studying gene evolution of uncultivable protist species.
SPADE is a density-based algorithm that can be used to organize data from heterogeneous populations of single cells into a two-dimensional tree representation. It can be applied to flow and mass cytometry data sets as well as to single-cell RNA-seq data.
Nat Methods. 2016 Jan 11. doi: 10.1038/nmeth.3728. [Epub ahead of print]
"We report scM&T-seq, a method for parallel single-cell genome-wide methylome and transcriptome sequencing that allows for the discovery of associations between transcriptional and epigenetic variation. Profiling of 61 mouse embryonic stem cells confirmed known links between DNA methylation and transcription. Notably, the method revealed previously unrecognized associations between heterogeneously methylated distal regulatory elements and transcription of key pluripotency genes."
The mammalian cerebral cortex supports cognitive functions such as sensorimotor integration, memory, and social behaviors. Normal brain function relies on a diverse set of differentiated cell types, including neurons, glia, and vasculature. Here, we have used large-scale single-cell RNA-seq to classify cells in the mouse somatosensory cortex and hippocampal CA1 region. We found 47 molecularly distinct subclasses, comprising all known major cell types in the cortex. We identified numerous marker genes, which allowed alignment with known cell types, morphology, and location. We found a layer I interneuron expressing Pax6 and a distinct postmitotic oligodendrocyte subclass marked by Itpr2. Across the diversity of cortical cell types, transcription factors formed a complex, layered regulatory code, suggesting a mechanism for the maintenance of adult cell type identity.
Phenotypically identical cells can dramatically vary with respect to behavior during their lifespan and this variation is reflected in their molecular composition such as the transcriptomic landscape. Single-cell transcriptomics using next-generation transcript sequencing (RNA-seq) is now emerging as a powerful tool to profile cell-to-cell variability on a genomic scale. Its application has already greatly impacted our conceptual understanding of diverse biological processes with broad implications for both basic and clinical research. Different single-cell RNA-seq protocols have been introduced and are reviewed here—each one with its own strengths and current limitations. We further provide an overview of the biological questions single-cell RNA-seq has been used to address, the major findings obtained from such studies, and current challenges and expected future developments in this booming field.
Population genomic analyses have demonstrated power to address major questions in evolutionary and molecular microbiology. Collecting populations of genomes is hindered in many microbial species by the absence of a cost effective and practical method to collect ample quantities of sufficiently-pure genomic DNA for next-generation sequencing. Here we present a simple method to amplify genomes of a target microbial species present in a complex, natural sample. The Selective Whole Genome Amplification (SWGA) technique amplifies target genomes using nucleotide sequence motifs that are common in the target microbe genome, but rare in the background genomes, to prime the highly-processive phi29 polymerase. SWGA thus selectively amplifies the target genome from samples in which it originally represented a minor fraction of the total DNA. The post-SWGA samples are enriched in target genomic DNA which are ideal for population resequencing. We demonstrate the efficacy of SWGA using both laboratory prepared mixtures of cultured microbes as well as a natural host-microbe association. Targeted amplification of Borrelia burgdorferi mixed with Escherichia coli at genome ratios of 1:2000 resulted in greater than 105 fold amplification of the target genomes with less than 6.7 fold amplification of the background. SWGA treated genomic extracts from Wolbachia pipientis-infectedDrosophila melanogaster resulted in up to 70% of high-throughput re-sequencing reads mapping to the W. pipientis genome. By contrast, 2-9% of sequencing reads were derived from W. pipientis without prior amplification. The SWGA technique results in high sequencing coverage at a fraction of the sequencing effort, thus allowing population genomic studies at affordable costs.
Whole genome amplification methods facilitate the detection and characterization of microbial communities in low biomass environments. We examined the extent to which the actual community structure is reliably revealed and factors contributing to bias. One widely used [multiple displacement amplification (MDA)] and one new primer-free method [primase-based whole genome amplification (pWGA)] were compared using a polymerase chain reaction (PCR)-based method as control. Pyrosequencing of an environmental sample and principal component analysis revealed that MDA impacted community profiles more strongly than pWGA and indicated that this related to species GC content, although an influence of DNA integrity could not be excluded. Subsequently, biases by species GC content, DNA integrity and fragment size were separately analysed using defined mixtures of DNA from various species. We found significantly less amplification of species with the highest GC content for MDA-based templates and, to a lesser extent, for pWGA. DNA fragmentation also interfered severely: species with more fragmented DNA were less amplified with MDA and pWGA. pWGA was unable to amplify low molecular weight DNA (< 1.5 kb), whereas MDA was inefficient. We conclude that pWGA is the most promising method for characterization of microbial communities in low-biomass environments and for currently planned astrobiological missions to Mars.
Single cell genomics (SCG) uncovers hereditary information at the most basic level of biological organization. It is emerging as a powerful complement to cultivation-based and microbial community-focused research approaches. SCG has been instrumental in identifying metabolic features, evolutionary histories and inter-organismal interactions of the uncultured microbial groups that dominate many environments and biogeochemical cycles. The SCG approach also holds great promise in microbial microevolution studies and industrial bioprospecting. Methods for SCG consist of a series of integrated processes, beginning with the collection and preservation of environmental samples, followed by physical separation, lysis and whole genome amplification of individual cells, and culminating in genomic sequencing and the inference of encoded biological features.
High-throughput single-cell transcriptomics offers an unbiased approach for understanding the extent, basis and function of gene expression variation between seemingly identical cells. Here we sequence single-cell RNA-seq libraries prepared from over 1,700 primary mouse bone-marrow-derived dendritic cells spanning several experimental conditions. We find substantial variation between identically stimulated dendritic cells, in both the fraction of cells detectably expressing a given messenger RNA and the transcript’s level within expressing cells. Distinct gene modules are characterized by different temporal heterogeneity profiles. In particular, a ‘core’ module of antiviral genes is expressed very early by a few ‘precocious’ cells in response to uniform stimulation with a pathogenic component, but is later activated in all cells. By stimulating cells individually in sealed microfluidic chambers, analysing dendritic cells from knockout mice, and modulating secretion and extracellular signalling, we show that this response is coordinated by interferon-mediated paracrine signalling from these precocious cells. Notably, preventing cell-to-cell communication also substantially reduces variability between cells in the expression of an early-induced ‘peaked’ inflammatory module, suggesting that paracrine signalling additionally represses part of the inflammatory program. Our study highlights the importance of cell-to-cell communication in controlling cellular heterogeneity and reveals general strategies that multicellular populations can use to establish complex dynamic responses.
The otocyst harbors progenitors for most cell types of the mature inner ear. Developmental lineage analyses and gene expression studies suggest that distinct progenitor populations are compartmentalized to discrete axial domains in the early otocyst. Here, we conducted highly parallel quantitative RT-PCR measurements on 382 individual cells from the developing otocyst and neuroblast lineages to assay 96 genes representing established otic markers, signaling-pathway-associated transcripts, and novel otic-specific genes. By applying multivariate cluster, principal component, and network analyses to the data matrix, we were able to readily distinguish the delaminating neuroblasts and to describe progressive states of gene expression in this population at single-cell resolution. It further established a three-dimensional model of the otocyst in which each individual cell can be precisely mapped into spatial expression domains. Our bioinformatic modeling revealed spatial dynamics of different signaling pathways active during early neuroblast development and prosensory domain specification.
SOURCE March, 2016 The field of single-cell genomics is advancing rapidly and is generating many new insights into complex biological systems, ranging from the diversity of microbial ecosystems to the genomics of human cancer. In this Review, we provide an overview of the current state of the field of single-cell genome sequencing. First, we focus on the technical challenges of making measurements that start from a single molecule of DNA, and then explore how some of these recent methodological advancements have enabled the discovery of unexpected new biology. Areas highlighted include the application of single-cell genomics to interrogate microbial dark matter and to evaluate the pathogenic roles of genetic mosaicism in multicellular organisms, with a focus on cancer. We then attempt to predict advances we expect to see in the next few years. left Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Cell theory provided an entirely new framework for understanding
Authors: Surendra Kumar, Anders K. Krabberød, Ralf S. Neumann, Katerina Michalickova, Sen Zhao, Xiaoli Zhang and Kamran Shalchian-Tabrizi
We present a pipeline named BIR (Blast, Identify and Realign) developed for phylogenomic analyses. BIR is intended for the identification of gene sequences applicable for phylogenomic inference. The pipeline allows users to apply their own manually curated sequence alignments (seed) in search for homologous genes in sequence databases and available genomes. BIR automatically adds the identified sequences from these databases to the seed alignments and reconstruct a phylogenetic tree from each. The BIR pipeline is an efficient tool for the identification of orthologous gene copies because it expands user-defined sequence alignments and conducts massive parallel phylogenetic reconstruction. The application is also particularly useful for large-scale sequencing projects that require management of a large number of single-gene alignments for gene comparison, functional annotation, and evolutionary analyses.
Single-cell genomics and single-cell transcriptomics have emerged as powerful tools to study the biology of single cells at a genome-wide scale. However, a major challenge is to sequence both genomic DNA and mRNA from the same cell, which would allow direct comparison of genomic variation and transcriptome heterogeneity. We describe a quasilinear amplification strategy to quantify genomic DNA and mRNA from the same cell without physically separating the nucleic acids before amplification. We show that the efficiency of our integrated approach is similar to existing methods for single-cell sequencing of either genomic DNA or mRNA. Further, we find that genes with high cell-to-cell variability in transcript numbers generally have lower genomic copy numbers, and vice versa, suggesting that copy number variations may drive variability in gene expression among individual cells. Applications of our integrated sequencing approach could range from gaining insights into cancer evolution and heterogeneity to understanding the transcriptional consequences of copy number variations in healthy and diseased tissues.
Intensively developed in the last few years, single-cell sequencing technologies now present numerous advantages over traditional sequencing methods for solving the problems of biological heterogeneity and low quantities of available biological materials. The application of single-cell sequencing technologies has profoundly changed our understanding of a series of biological phenomena, including gene transcription, embryo development, and carcinogenesis. However, before single-cell sequencing technologies can be used extensively, researchers face the serious challenge of overcoming inherent issues of high amplification bias, low accuracy and reproducibility. Here, we simply summarize the techniques used for single-cell isolation, and review the current technologies used in single-cell genomic, transcriptomic, and epigenomic sequencing. We discuss the merits, defects, and scope of application of single-cell sequencing technologies and then speculate on the direction of future developments.
Large-scale surveys of single-cell gene expression have the potential to reveal rare cell populations and lineage relationships but require efficient methods for cell capture and mRNA sequencing1, 2, 3, 4. Although cellular barcoding strategies allow parallel sequencing of single cells at ultra-low depths5, the limitations of shallow sequencing have not been investigated directly. By capturing 301 single cells from 11 populations using microfluidics and analyzing single-cell transcriptomes across downsampled sequencing depths, we demonstrate that shallow single-cell mRNA sequencing (~50,000 reads per cell) is sufficient for unbiased cell-type classification and biomarker identification. In the developing cortex, we identify diverse cell types, including multiple progenitor and neuronal subtypes, and we identify EGR1 and FOS as previously unreported candidate targets of Notch signaling in human but not mouse radial glia. Our strategy establishes an efficient method for unbiased analysis and comparison of cell populations from heterogeneous tissue by microfluidic single-cell capture and low-coverage sequencing of many cells.
The vast majority of microbial species remain uncultivated and, until recently, about half of all known bacterial phyla were identified only from their 16S ribosomal RNA gene sequence. With the advent of single-cell sequencing, genomes of uncultivated species are rapidly filling in unsequenced branches of the microbial phylogenetic tree. The wealth of new insights gained from these previously inaccessible groups is providing a deeper understanding of their basic biology, taxonomy and evolution, as well as their diverse roles in environmental ecosystems and human health.
Genome sequencing studies have shown that human malignancies often bear mutations in four or more driver genes1, but it is difficult to recapitulate this degree of genetic complexity in mouse models using conventional breeding. Here we use the CRISPR-Cas9 system of genome editing2, 3,4 to overcome this limitation. By delivering combinations of small guide RNAs (sgRNAs) and Cas9 with a lentiviral vector, we modified up to five genes in a single mouse hematopoietic stem cell (HSC), leading to clonal outgrowth and myeloid malignancy. We thereby generated models of acute myeloid leukemia (AML) with cooperating mutations in genes encoding epigenetic modifiers, transcription factors and mediators of cytokine signaling, recapitulating the combinations of mutations observed in patients. Our results suggest that lentivirus-delivered sgRNA:Cas9 genome editing should be useful to engineer a broad array of in vivo cancer models that better reflect the complexity of human disease.
Human cancers are complex ecosystems composed of cells with distinct phenotypes, genotypes, and epigenetic states, but current models do not adequately reflect tumor composition in patients. We used single-cell RNA sequencing (RNA-seq) to profile 430 cells from five primary glioblastomas, which we found to be inherently variable in their expression of diverse transcriptional programs related to oncogenic signaling, proliferation, complement/immune response, and hypoxia. We also observed a continuum of stemness-related expression states that enabled us to identify putative regulators of stemness in vivo. Finally, we show that established glioblastoma subtype classifiers are variably expressed across individual cells within a tumor and demonstrate the potential prognostic implications of such intratumoral heterogeneity. Thus, we reveal previously unappreciated heterogeneity in diverse regulatory programs central to glioblastoma biology, prognosis, and therapy.
AbstractDegraded and low template DNA is often analyzed in forensic genetics laboratories. Reliable analysis of degraded and low template DNA is of great importance, because its results impact the quality and reliability of expert testimonies.
Physical cues exist across all biological scales, from the geometries of molecules to the shapes of complex organisms. While their roles have been identified across a range of scales, i.e. the arrangements of biomolecules and the form and function of tissues, less is known in some intermediate lengths. Parti
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