Single-cell RNA-seq (scRNA-seq) allows researchers to define cell types on the basis of unsupervised clustering of the transcriptome. However, differences in experimental methods and computational ...analyses make it challenging to compare data across experiments. Here we present scmap (http://bioconductor.org/packages/scmap; web version at http://www.sanger.ac.uk/science/tools/scmap), a method for projecting cells from an scRNA-seq data set onto cell types or individual cells from other experiments.
Bacterial adhesion at the single-cell level Berne, Cecile; Ellison, Courtney K; Ducret, Adrien ...
Nature reviews. Microbiology,
10/2018, Letnik:
16, Številka:
10
Journal Article
Recenzirano
The formation of multicellular microbial communities, called biofilms, starts from the adhesion of a few planktonic cells to the surface. The transition from a free-living planktonic lifestyle to a ...sessile, attached state is a multifactorial process that is determined by biological, chemical and physical properties of the environment, the surface and the bacterial cell. The initial weak, reversible interactions between a bacterium and a surface strengthen to yield irreversible adhesion. In this Review, we summarize our understanding of the mechanisms governing bacterial adhesion at the single-cell level, including the physical forces experienced by a cell before reaching the surface, the first contact with a surface and the transition from reversible to permanent adhesion.
Single-cell gene expression analyses hold promise for characterizing cellular heterogeneity, but current methods compromise on either the coverage, the sensitivity or the throughput. Here, we ...introduce Smart-seq2 with improved reverse transcription, template switching and preamplification to increase both yield and length of cDNA libraries generated from individual cells. Smart-seq2 transcriptome libraries have improved detection, coverage, bias and accuracy compared to Smart-seq libraries and are generated with off-the-shelf reagents at lower cost.
Liver cirrhosis is a major cause of death worldwide and is characterized by extensive fibrosis. There are currently no effective antifibrotic therapies available. To obtain a better understanding of ...the cellular and molecular mechanisms involved in disease pathogenesis and enable the discovery of therapeutic targets, here we profile the transcriptomes of more than 100,000 single human cells, yielding molecular definitions for non-parenchymal cell types that are found in healthy and cirrhotic human liver. We identify a scar-associated TREM2
CD9
subpopulation of macrophages, which expands in liver fibrosis, differentiates from circulating monocytes and is pro-fibrogenic. We also define ACKR1
and PLVAP
endothelial cells that expand in cirrhosis, are topographically restricted to the fibrotic niche and enhance the transmigration of leucocytes. Multi-lineage modelling of ligand and receptor interactions between the scar-associated macrophages, endothelial cells and PDGFRα
collagen-producing mesenchymal cells reveals intra-scar activity of several pro-fibrogenic pathways including TNFRSF12A, PDGFR and NOTCH signalling. Our work dissects unanticipated aspects of the cellular and molecular basis of human organ fibrosis at a single-cell level, and provides a conceptual framework for the discovery of rational therapeutic targets in liver cirrhosis.
Single-cell analysis is a valuable tool for dissecting cellular heterogeneity in complex systems
. However, a comprehensive single-cell atlas has not been achieved for humans. Here we use single-cell ...mRNA sequencing to determine the cell-type composition of all major human organs and construct a scheme for the human cell landscape (HCL). We have uncovered a single-cell hierarchy for many tissues that have not been well characterized. We established a 'single-cell HCL analysis' pipeline that helps to define human cell identity. Finally, we performed a single-cell comparative analysis of landscapes from human and mouse to identify conserved genetic networks. We found that stem and progenitor cells exhibit strong transcriptomic stochasticity, whereas differentiated cells are more distinct. Our results provide a useful resource for the study of human biology.
Macrophages are the first cells of the nascent immune system to emerge during embryonic development. In mice, embryonic macrophages infiltrate developing organs, where they differentiate ...symbiotically into tissue-resident macrophages (TRMs)
. However, our understanding of the origins and specialization of macrophages in human embryos is limited. Here we isolated CD45
haematopoietic cells from human embryos at Carnegie stages 11 to 23 and subjected them to transcriptomic profiling by single-cell RNA sequencing, followed by functional characterization of a population of CD45
CD34
CD44
yolk sac-derived myeloid-biased progenitors (YSMPs) by single-cell culture. We also mapped macrophage heterogeneity across multiple anatomical sites and identified diverse subsets, including various types of embryonic TRM (in the head, liver, lung and skin). We further traced the specification trajectories of TRMs from either yolk sac-derived primitive macrophages or YSMP-derived embryonic liver monocytes using both transcriptomic and developmental staging information, with a focus on microglia. Finally, we evaluated the molecular similarities between embryonic TRMs and their adult counterparts. Our data represent a comprehensive characterization of the spatiotemporal dynamics of early macrophage development during human embryogenesis, providing a reference for future studies of the development and function of human TRMs.
Single-cell RNA-seq can yield valuable insights about the variability within a population of seemingly homogeneous cells. We developed a quantitative statistical method to distinguish true biological ...variability from the high levels of technical noise in single-cell experiments. Our approach quantifies the statistical significance of observed cell-to-cell variability in expression strength on a gene-by-gene basis. We validate our approach using two independent data sets from Arabidopsis thaliana and Mus musculus.
The high proportion of zeros in typical single-cell RNA sequencing datasets has led to widespread but inconsistent use of terminology such as dropout and missing data. Here, we argue that much of ...this terminology is unhelpful and confusing, and outline simple ideas to help to reduce confusion. These include: (1) observed single-cell RNA sequencing counts reflect both true gene expression levels and measurement error, and carefully distinguishing between these contributions helps to clarify thinking; and (2) method development should start with a Poisson measurement model, rather than more complex models, because it is simple and generally consistent with existing data. We outline how several existing methods can be viewed within this framework and highlight how these methods differ in their assumptions about expression variation. We also illustrate how our perspective helps to address questions of biological interest, such as whether messenger RNA expression levels are multimodal among cells.
Natural mitochondrial DNA (mtDNA) mutations enable the inference of clonal relationships among cells. mtDNA can be profiled along with measures of cell state, but has not yet been combined with the ...massively parallel approaches needed to tackle the complexity of human tissue. Here, we introduce a high-throughput, droplet-based mitochondrial single-cell assay for transposase-accessible chromatin with sequencing (scATAC-seq), a method that combines high-confidence mtDNA mutation calling in thousands of single cells with their concomitant high-quality accessible chromatin profile. This enables the inference of mtDNA heteroplasmy, clonal relationships, cell state and accessible chromatin variation in individual cells. We reveal single-cell variation in heteroplasmy of a pathologic mtDNA variant, which we associate with intra-individual chromatin variability and clonal evolution. We clonally trace thousands of cells from cancers, linking epigenomic variability to subclonal evolution, and infer cellular dynamics of differentiating hematopoietic cells in vitro and in vivo. Taken together, our approach enables the study of cellular population dynamics and clonal properties in vivo.
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