Single-cell multi-omics sequencing of mouse early embryos and embryonic stem cells.
Guo Fan,Li Lin,Li Jingyun,Wu Xinglong,Hu Boqiang,Zhu Ping,Wen Lu,Tang Fuchou
Cell research
Single-cell epigenome sequencing techniques have recently been developed. However, the combination of different layers of epigenome sequencing in an individual cell has not yet been achieved. Here, we developed a single-cell multi-omics sequencing technology (single-cell COOL-seq) that can analyze the chromatin state/nucleosome positioning, DNA methylation, copy number variation and ploidy simultaneously from the same individual mammalian cell. We used this method to analyze the reprogramming of the chromatin state and DNA methylation in mouse preimplantation embryos. We found that within < 12 h of fertilization, each individual cell undergoes global genome demethylation together with the rapid and global reprogramming of both maternal and paternal genomes to a highly opened chromatin state. This was followed by decreased openness after the late zygote stage. Furthermore, from the late zygote to the 4-cell stage, the residual DNA methylation is preferentially preserved on intergenic regions of the paternal alleles and intragenic regions of maternal alleles in each individual blastomere. However, chromatin accessibility is similar between paternal and maternal alleles in each individual cell from the late zygote to the blastocyst stage. The binding motifs of several pluripotency regulators are enriched at distal nucleosome depleted regions from as early as the 2-cell stage. This indicates that the cis-regulatory elements of such target genes have been primed to an open state from the 2-cell stage onward, long before pluripotency is eventually established in the ICM of the blastocyst. Genes may be classified into homogeneously open, homogeneously closed and divergent states based on the chromatin accessibility of their promoter regions among individual cells. This can be traced to step-wise transitions during preimplantation development. Our study offers the first single-cell and parental allele-specific analysis of the genome-scale chromatin state and DNA methylation dynamics at single-base resolution in early mouse embryos and provides new insights into the heterogeneous yet highly ordered features of epigenomic reprogramming during this process.
10.1038/cr.2017.82
Human Germline Cell Development: from the Perspective of Single-Cell Sequencing.
Wen Lu,Tang Fuchou
Molecular cell
Germline cells are the beginning of new individuals in multicellular animals, including humans. Our understanding of these cell types is limited by the difficulty of analyzing the precious and heterogeneous germline tissue samples. The rapid development of single-cell sequencing technologies provides a chance for comprehensive profiling of the omics dynamics of human germline development. In this review, we discuss progress in analyzing the development of human germline cells, including preimplantation and implantation embryos, fetal germ cells (FGCs), and adult spermatogenesis by single-cell transcriptome and epigenome sequencing technologies.
10.1016/j.molcel.2019.08.025
Tracing the temporal-spatial transcriptome landscapes of the human fetal digestive tract using single-cell RNA-sequencing.
Gao Shuai,Yan Liying,Wang Rui,Li Jingyun,Yong Jun,Zhou Xin,Wei Yuan,Wu Xinglong,Wang Xiaoye,Fan Xiaoying,Yan Jie,Zhi Xu,Gao Yun,Guo Hongshan,Jin Xiao,Wang Wendong,Mao Yunuo,Wang Fengchao,Wen Lu,Fu Wei,Ge Hao,Qiao Jie,Tang Fuchou
Nature cell biology
The development of the digestive tract is critical for proper food digestion and nutrient absorption. Here, we analyse the main organs of the digestive tract, including the oesophagus, stomach, small intestine and large intestine, from human embryos between 6 and 25 weeks of gestation as well as the large intestine from adults using single-cell RNA-seq analyses. In total, 5,227 individual cells are analysed and 40 cell types clearly identified. Their crucial biological features, including developmental processes, signalling pathways, cell cycle, nutrient digestion and absorption metabolism, and transcription factor networks, are systematically revealed. Moreover, the differentiation and maturation processes of the large intestine are thoroughly investigated by comparing the corresponding transcriptome profiles between embryonic and adult stages. Our work offers a rich resource for investigating the gene regulation networks of the human fetal digestive tract and adult large intestine at single-cell resolution.
10.1038/s41556-018-0105-4
Single-cell sequencing in stem cell biology.
Wen Lu,Tang Fuchou
Genome biology
Cell-to-cell variation and heterogeneity are fundamental and intrinsic characteristics of stem cell populations, but these differences are masked when bulk cells are used for omic analysis. Single-cell sequencing technologies serve as powerful tools to dissect cellular heterogeneity comprehensively and to identify distinct phenotypic cell types, even within a 'homogeneous' stem cell population. These technologies, including single-cell genome, epigenome, and transcriptome sequencing technologies, have been developing rapidly in recent years. The application of these methods to different types of stem cells, including pluripotent stem cells and tissue-specific stem cells, has led to exciting new findings in the stem cell field. In this review, we discuss the recent progress as well as future perspectives in the methodologies and applications of single-cell omic sequencing technologies.
10.1186/s13059-016-0941-0
A comparison of automatic cell identification methods for single-cell RNA sequencing data.
Genome biology
BACKGROUND:Single-cell transcriptomics is rapidly advancing our understanding of the cellular composition of complex tissues and organisms. A major limitation in most analysis pipelines is the reliance on manual annotations to determine cell identities, which are time-consuming and irreproducible. The exponential growth in the number of cells and samples has prompted the adaptation and development of supervised classification methods for automatic cell identification. RESULTS:Here, we benchmarked 22 classification methods that automatically assign cell identities including single-cell-specific and general-purpose classifiers. The performance of the methods is evaluated using 27 publicly available single-cell RNA sequencing datasets of different sizes, technologies, species, and levels of complexity. We use 2 experimental setups to evaluate the performance of each method for within dataset predictions (intra-dataset) and across datasets (inter-dataset) based on accuracy, percentage of unclassified cells, and computation time. We further evaluate the methods' sensitivity to the input features, number of cells per population, and their performance across different annotation levels and datasets. We find that most classifiers perform well on a variety of datasets with decreased accuracy for complex datasets with overlapping classes or deep annotations. The general-purpose support vector machine classifier has overall the best performance across the different experiments. CONCLUSIONS:We present a comprehensive evaluation of automatic cell identification methods for single-cell RNA sequencing data. All the code used for the evaluation is available on GitHub ( https://github.com/tabdelaal/scRNAseq_Benchmark ). Additionally, we provide a Snakemake workflow to facilitate the benchmarking and to support the extension of new methods and new datasets.
10.1186/s13059-019-1795-z
Developmental Heterogeneity of Microglia and Brain Myeloid Cells Revealed by Deep Single-Cell RNA Sequencing.
Li Qingyun,Cheng Zuolin,Zhou Lu,Darmanis Spyros,Neff Norma F,Okamoto Jennifer,Gulati Gunsagar,Bennett Mariko L,Sun Lu O,Clarke Laura E,Marschallinger Julia,Yu Guoqiang,Quake Stephen R,Wyss-Coray Tony,Barres Ben A
Neuron
Microglia are increasingly recognized for their major contributions during brain development and neurodegenerative disease. It is currently unknown whether these functions are carried out by subsets of microglia during different stages of development and adulthood or within specific brain regions. Here, we performed deep single-cell RNA sequencing (scRNA-seq) of microglia and related myeloid cells sorted from various regions of embryonic, early postnatal, and adult mouse brains. We found that the majority of adult microglia expressing homeostatic genes are remarkably similar in transcriptomes, regardless of brain region. By contrast, early postnatal microglia are more heterogeneous. We discovered a proliferative-region-associated microglia (PAM) subset, mainly found in developing white matter, that shares a characteristic gene signature with degenerative disease-associated microglia (DAM). Such PAM have amoeboid morphology, are metabolically active, and phagocytose newly formed oligodendrocytes. This scRNA-seq atlas will be a valuable resource for dissecting innate immune functions in health and disease.
10.1016/j.neuron.2018.12.006
Single-cell RNA sequencing for the study of development, physiology and disease.
Potter S Steven
Nature reviews. Nephrology
An ongoing technological revolution is continually improving our ability to carry out very high-resolution studies of gene expression patterns. Current technology enables the global gene expression profiles of single cells to be defined, facilitating dissection of heterogeneity in cell populations that was previously hidden. In contrast to gene expression studies that use bulk RNA samples and provide only a virtual average of the diverse constituent cells, single-cell studies enable the molecular distinction of all cell types within a complex population mix, such as a tumour or developing organ. For instance, single-cell gene expression profiling has contributed to improved understanding of how histologically identical, adjacent cells make different differentiation decisions during development. Beyond development, single-cell gene expression studies have enabled the characteristics of previously known cell types to be more fully defined and facilitated the identification of novel categories of cells, contributing to improvements in our understanding of both normal and disease-related physiological processes and leading to the identification of new treatment approaches. Although limitations remain to be overcome, technology for the analysis of single-cell gene expression patterns is improving rapidly and beginning to provide a detailed atlas of the gene expression patterns of all cell types in the human body.
10.1038/s41581-018-0021-7
Deep sequencing reveals cell-type-specific patterns of single-cell transcriptome variation.
Dueck Hannah,Khaladkar Mugdha,Kim Tae Kyung,Spaethling Jennifer M,Francis Chantal,Suresh Sangita,Fisher Stephen A,Seale Patrick,Beck Sheryl G,Bartfai Tamas,Kuhn Bernhard,Eberwine James,Kim Junhyong
Genome biology
BACKGROUND:Differentiation of metazoan cells requires execution of different gene expression programs but recent single-cell transcriptome profiling has revealed considerable variation within cells of seeming identical phenotype. This brings into question the relationship between transcriptome states and cell phenotypes. Additionally, single-cell transcriptomics presents unique analysis challenges that need to be addressed to answer this question. RESULTS:We present high quality deep read-depth single-cell RNA sequencing for 91 cells from five mouse tissues and 18 cells from two rat tissues, along with 30 control samples of bulk RNA diluted to single-cell levels. We find that transcriptomes differ globally across tissues with regard to the number of genes expressed, the average expression patterns, and within-cell-type variation patterns. We develop methods to filter genes for reliable quantification and to calibrate biological variation. All cell types include genes with high variability in expression, in a tissue-specific manner. We also find evidence that single-cell variability of neuronal genes in mice is correlated with that in rats consistent with the hypothesis that levels of variation may be conserved. CONCLUSIONS:Single-cell RNA-sequencing data provide a unique view of transcriptome function; however, careful analysis is required in order to use single-cell RNA-sequencing measurements for this purpose. Technical variation must be considered in single-cell RNA-sequencing studies of expression variation. For a subset of genes, biological variability within each cell type appears to be regulated in order to perform dynamic functions, rather than solely molecular noise.
10.1186/s13059-015-0683-4
Integration of Flow Cytometry and Single Cell Sequencing.
Andreyev Dmitry S,Zybailov Boris L
Trends in biotechnology
Integrating cytometric analysis of cells, mitochondria, and other polynucleotide-containing biological particles with high-throughput single particle sequencing would provide an ultimate bioanalytical tool, simultaneously assessing phenotype, functionality, genome, and transcriptome of each particle in a large population. Here, we describe how such integration could be performed by adapting existing, well-established technologies.
10.1016/j.tibtech.2019.09.002
Whole-organism clone tracing using single-cell sequencing.
Alemany Anna,Florescu Maria,Baron Chloé S,Peterson-Maduro Josi,van Oudenaarden Alexander
Nature
Embryonic development is a crucial period in the life of a multicellular organism, during which limited sets of embryonic progenitors produce all cells in the adult body. Determining which fate these progenitors acquire in adult tissues requires the simultaneous measurement of clonal history and cell identity at single-cell resolution, which has been a major challenge. Clonal history has traditionally been investigated by microscopically tracking cells during development, monitoring the heritable expression of genetically encoded fluorescent proteins and, more recently, using next-generation sequencing technologies that exploit somatic mutations, microsatellite instability, transposon tagging, viral barcoding, CRISPR-Cas9 genome editing and Cre-loxP recombination. Single-cell transcriptomics provides a powerful platform for unbiased cell-type classification. Here we present ScarTrace, a single-cell sequencing strategy that enables the simultaneous quantification of clonal history and cell type for thousands of cells obtained from different organs of the adult zebrafish. Using ScarTrace, we show that a small set of multipotent embryonic progenitors generate all haematopoietic cells in the kidney marrow, and that many progenitors produce specific cell types in the eyes and brain. In addition, we study when embryonic progenitors commit to the left or right eye. ScarTrace reveals that epidermal and mesenchymal cells in the caudal fin arise from the same progenitors, and that osteoblast-restricted precursors can produce mesenchymal cells during regeneration. Furthermore, we identify resident immune cells in the fin with a distinct clonal origin from other blood cell types. We envision that similar approaches will have major applications in other experimental systems, in which the matching of embryonic clonal origin to adult cell type will ultimately allow reconstruction of how the adult body is built from a single cell.
10.1038/nature25969
Single-cell RNA sequencing to explore immune cell heterogeneity.
Papalexi Efthymia,Satija Rahul
Nature reviews. Immunology
Advances in single-cell RNA sequencing (scRNA-seq) have allowed for comprehensive analysis of the immune system. In this Review, we briefly describe the available scRNA-seq technologies together with their corresponding strengths and weaknesses. We discuss in depth how scRNA-seq can be used to deconvolve immune system heterogeneity by identifying novel distinct immune cell subsets in health and disease, characterizing stochastic heterogeneity within a cell population and building developmental 'trajectories' for immune cells. Finally, we discuss future directions of the field and present integrated approaches to complement molecular information from a single cell with studies of the environment, epigenetic state and cell lineage.
10.1038/nri.2017.76
Comparative Analysis of Single-Cell RNA Sequencing Methods.
Ziegenhain Christoph,Vieth Beate,Parekh Swati,Reinius Björn,Guillaumet-Adkins Amy,Smets Martha,Leonhardt Heinrich,Heyn Holger,Hellmann Ines,Enard Wolfgang
Molecular cell
Single-cell RNA sequencing (scRNA-seq) offers new possibilities to address biological and medical questions. However, systematic comparisons of the performance of diverse scRNA-seq protocols are lacking. We generated data from 583 mouse embryonic stem cells to evaluate six prominent scRNA-seq methods: CEL-seq2, Drop-seq, MARS-seq, SCRB-seq, Smart-seq, and Smart-seq2. While Smart-seq2 detected the most genes per cell and across cells, CEL-seq2, Drop-seq, MARS-seq, and SCRB-seq quantified mRNA levels with less amplification noise due to the use of unique molecular identifiers (UMIs). Power simulations at different sequencing depths showed that Drop-seq is more cost-efficient for transcriptome quantification of large numbers of cells, while MARS-seq, SCRB-seq, and Smart-seq2 are more efficient when analyzing fewer cells. Our quantitative comparison offers the basis for an informed choice among six prominent scRNA-seq methods, and it provides a framework for benchmarking further improvements of scRNA-seq protocols.
10.1016/j.molcel.2017.01.023
Mapping human pluripotent stem cell differentiation pathways using high throughput single-cell RNA-sequencing.
Genome biology
BACKGROUND:Human pluripotent stem cells (hPSCs) provide powerful models for studying cellular differentiations and unlimited sources of cells for regenerative medicine. However, a comprehensive single-cell level differentiation roadmap for hPSCs has not been achieved. RESULTS:We use high throughput single-cell RNA-sequencing (scRNA-seq), based on optimized microfluidic circuits, to profile early differentiation lineages in the human embryoid body system. We present a cellular-state landscape for hPSC early differentiation that covers multiple cellular lineages, including neural, muscle, endothelial, stromal, liver, and epithelial cells. Through pseudotime analysis, we construct the developmental trajectories of these progenitor cells and reveal the gene expression dynamics in the process of cell differentiation. We further reprogram primed H9 cells into naïve-like H9 cells to study the cellular-state transition process. We find that genes related to hemogenic endothelium development are enriched in naïve-like H9. Functionally, naïve-like H9 show higher potency for differentiation into hematopoietic lineages than primed cells. CONCLUSIONS:Our single-cell analysis reveals the cellular-state landscape of hPSC early differentiation, offering new insights that can be harnessed for optimization of differentiation protocols.
10.1186/s13059-018-1426-0