scholarly journals Finding cell-specific expression patterns in the early Ciona embryo with single-cell RNA-seq

2017 ◽  
Author(s):  
Garth R. Ilsley ◽  
Ritsuko Suyama ◽  
Takeshi Noda ◽  
Nori Satoh ◽  
Nicholas M. Luscombe
Keyword(s):  
Gene Expression ◽  
Single Cell ◽  
Expression Patterns ◽  
Cell Types ◽  
Specific Gene ◽  
Rna Seq ◽  
Cell Stage ◽  
Specific Expression ◽  
Temporal Gene Expression

AbstractSingle-cell RNA-seq has been established as a reliable and accessible technique enabling new types of analyses, such as identifying cell types and studying spatial and temporal gene expression variation and change at single-cell resolution. Recently, single-cell RNA-seq has been applied to developing embryos, which offers great potential for finding and characterising genes controlling the course of development along with their expression patterns. In this study, we applied single-cell RNA-seq to the 16-cell stage of the Ciona embryo, a marine chordate and performed a computational search for cell-specific gene expression patterns. We recovered many known expression patterns from our single-cell RNA-seq data and despite extensive previous screens, we succeeded in finding new cell-specific patterns, which we validated by in situ and single-cell qPCR.

scholarly journals Single cell RNA-seq study of wild type and Hox9,10,11 mutant developing uterus

2018 ◽  
Author(s):  
Michael L. Mucenski ◽  
Robert Mahoney ◽  
Mike Adam ◽  
Andrew S. Potter ◽  
S. Steven Potter
Keyword(s):  
Gene Expression ◽  
Single Cell ◽  
Hox Genes ◽  
Expression Patterns ◽  
Wild Type Mouse ◽  
Cell Types ◽  
Specific Gene ◽  
Rna Seq ◽  
Wild Type ◽  
Mouse Uterus

AbstractThe uterus is a remarkable organ that must guard against infections while maintaining the ability to support growth of a fetus without rejection. The Hoxa10 and Hoxa11 genes have previously been shown to play essential roles in uterus development and function. In this report we show that the Hoxc9,10,11 genes play a redundant role in the formation of uterine glands. In addition, we use single cell RNA-seq to create a high resolution gene expression atlas of the developing wild type mouse uterus. Cell types and subtypes are defined, for example dividing endothelial cells into arterial, venous, capillary, and lymphatic, while epithelial cells separate into luminal and glandular subtypes. Further, a surprising heterogeneity of stromal and myocyte cell types are identified. Transcription factor codes and ligand/receptor interactions are characterized. We also used single cell RNA-seq to globally define the altered gene expression patterns in all developing uterus cell types for two Hox mutants, with 8 or 9 mutant Hox genes. The mutants show a striking disruption of Wnt signaling as well as the Cxcl12/Cxcr4 ligand/receptor axis.Summary statementA single cell RNA-seq study of the developing mouse uterus defines cellular heterogeneities, lineage specific gene expression programs and perturbed pathways in Hox9,10,11 mutants.

scholarly journals Comprehensive characterization of tissue-specific chromatin accessibility in L2 Caenorhabditis elegans nematodes

2020 ◽  
Author(s):  
Timothy J. Durham ◽  
Riza M. Daza ◽  
Louis Gevirtzman ◽  
Darren A. Cusanovich ◽  
William Stafford Noble ◽  
...  
Keyword(s):  
Gene Expression ◽  
Single Cell ◽  
Expression Patterns ◽  
Cell Types ◽  
Chromatin Accessibility ◽  
Gene Expression Patterns ◽  
Rna Seq ◽  
Cell Type ◽  
Tissue Specific ◽  
C Elegans

AbstractRecently developed single cell technologies allow researchers to characterize cell states at ever greater resolution and scale. C. elegans is a particularly tractable system for studying development, and recent single cell RNA-seq studies characterized the gene expression patterns for nearly every cell type in the embryo and at the second larval stage (L2). Gene expression patterns are useful for learning about gene function and give insight into the biochemical state of different cell types; however, in order to understand these cell types, we must also determine how these gene expression levels are regulated. We present the first single cell ATAC-seq study in C. elegans. We collected data in L2 larvae to match the available single cell RNA-seq data set, and we identify tissue-specific chromatin accessibility patterns that align well with existing data, including the L2 single cell RNA-seq results. Using a novel implementation of the latent Dirichlet allocation algorithm, we leverage the single-cell resolution of the sci-ATAC-seq data to identify accessible loci at the level of individual cell types, providing new maps of putative cell type-specific gene regulatory sites, with promise for better understanding of cellular differentiation and gene regulation in the worm.

scholarly journals Single-Cell RNA-Seq of Mouse Decidual Leukocytes Reveals Intriguing Gestational Changes in the Immune Cell Landscape and Effects of SRC-1/-2 Double-Deficiency

2021 ◽  
Vol 5 (Supplement_1) ◽  
pp. A753-A754
Author(s):  
Jingfei Chen ◽  
Jialei Duan ◽  
Alina Montalbano ◽  
Boxun Li ◽  
Gary Hon ◽  
...  
Keyword(s):  
Gene Expression ◽  
Single Cell ◽  
Immune Cell ◽  
Expression Patterns ◽  
Critical Role ◽  
Fetal Lung ◽  
Specific Gene ◽  
Rna Seq ◽  
Cell Clusters ◽  
Pregnant Mice

Abstract Single-cell RNA-seq of Mouse Decidual Leukocytes Reveals Intriguing Gestational Changes in the Immune Cell Landscape and Effects of SRC-1/-2 Double-DeficiencyOur previous findings suggest that the fetus signals the mother when it is ready to be born through secretion of surfactant components/immune modulators, surfactant protein A (SP-A) and platelet-activating factor (PAF) by the fetal lung into amniotic fluid (AF). This occurs with increased proinflammatory cytokine expression by fetal AF macrophages (Mϕ), increased myometrial NF-κB activation and contractile (CAP) gene expression. Steroid receptor coactivator (Src)-1 and Src-2 are critical for developmental induction SP-A and PAF by the fetal lung. The finding that pregnant wild-type (WT) mice carrying Src-1 and Src-2 double-deficient fetuses (Src-1/-2d/d) manifested a marked delay (~38h) in parturition, further suggests that signals for parturition arise from the fetus and that Src-1 and Src-2 serve a critical role. Infiltrating leukocytes at the maternal-fetal interface (MFI)/decidua are known to play a central role in pregnancy maintenance and parturition timing. However, there is limited knowledge regarding gestational changes in immune cell composition toward term. To analyze gestational changes in the composition of immune cells within decidua and effects of Src-1/-2d/d, we conducted single-cell RNA-seq of ~17,000 decidual leukocytes (CD45+) from pregnant mice at 15.5 and 18.5 dpc carrying WT or Src-1/-2d/d fetuses. Unsupervised clustering identified 22 distinct decidual immune cell clusters, comprised of Mϕ, B cells, natural killer cells, neutrophils, dendritic cells and monocytes. Significant differences in cell type composition and transcriptional profiles were found across study groups (WT @ 15.5 dpc vs. 18.5 dpc; Src-1/-2d/dvs. WT @15.5 dpc and 18.5 dpc). Interestingly, in deciduas of pregnant mice carrying WT fetuses, Mϕ and B cell clusters markedly increased between 15.5 and 18.5 dpc, whereas, neutrophils declined; however, these gestational changes did not occur in pregnant mice carrying Src-1/-2d/d fetuses. Differential gene expression and gene ontology enrichment analyses revealed specific gene expression patterns distinguishing these immune cell subtypes to uncover their putative functions. These findings highlight the complexity and dynamics of the decidual immune cell landscape during the transition from myometrial quiescence to contractility, and the fetal-maternal interactions leading to parturition. Support: NIH grants R01-HL050022 (CRM) and P01-HD087150 (CRM), Burroughs Wellcome Preterm Birth Grant #1019823 (CRM).

scholarly journals Cell Dissociation from Butterfly Pupal Wing Tissues for Single-Cell RNA Sequencing

2020 ◽  
Vol 3 (4) ◽  
pp. 72
Author(s):  
Anupama Prakash ◽  
Antónia Monteiro
Keyword(s):  
Gene Expression ◽  
Single Cell ◽  
Rna Sequencing ◽  
Single Cells ◽  
Expression Patterns ◽  
Cell Types ◽  
Rna Seq ◽  
Bicyclus Anynana ◽  
Single Cell Sequencing ◽  
Pupal Wing

Butterflies are well known for their beautiful wings and have been great systems to understand the ecology, evolution, genetics, and development of patterning and coloration. These color patterns are mosaics on the wing created by the tiling of individual units called scales, which develop from single cells. Traditionally, bulk RNA sequencing (RNA-seq) has been used extensively to identify the loci involved in wing color development and pattern formation. RNA-seq provides an averaged gene expression landscape of the entire wing tissue or of small dissected wing regions under consideration. However, to understand the gene expression patterns of the units of color, which are the scales, and to identify different scale cell types within a wing that produce different colors and scale structures, it is necessary to study single cells. This has recently been facilitated by the advent of single-cell sequencing. Here, we provide a detailed protocol for the dissociation of cells from Bicyclus anynana pupal wings to obtain a viable single-cell suspension for downstream single-cell sequencing. We outline our experimental design and the use of fluorescence-activated cell sorting (FACS) to obtain putative scale-building and socket cells based on size. Finally, we discuss some of the current challenges of this technique in studying single-cell scale development and suggest future avenues to address these challenges.

scholarly journals High-density spatial transcriptomics arrays for in situ tissue profiling

2019 ◽  
Author(s):  
Sanja Vickovic ◽  
Gökcen Eraslan ◽  
Johanna Klughammer ◽  
Linnea Stenbeck ◽  
Fredrik Salmén ◽  
...  
Keyword(s):  
Single Cell ◽  
Spatial Organization ◽  
Expression Patterns ◽  
Cell Types ◽  
High Density ◽  
Rna Seq ◽  
Spatial Profiling ◽  
Tissue Profiling ◽  
Molecular Profiles

AbstractTissue function relies on the precise spatial organization of cells characterized by distinct molecular profiles. Single-cell RNA-Seq captures molecular profiles but not spatial organization. Conversely, spatial profiling assays either lack global transcriptome information or are not at the single-cell level. Here, we develop High-Density Spatial Transcriptomics (HDST), a method for RNA-seq at high spatial resolution. Spatially barcoded reverse transcription oligonucleotides are coupled to beads that are then randomly deposited in individual wells on a slide. The barcoded beads are decoded and coupled to a specific spatial address. We then capture and spatially in situ label RNA from the same histological tissue sections placed on the bead array slide. HDST recovers hundreds of thousands of transcript-coupled barcodes per experiment at 2 μm resolution. We demonstrate HDST in the mouse brain, use it to resolve spatial expression patterns and cell types, and show how to combine it with histological stains to relate expression patterns to tissue architecture and anatomy. HDST opens the way to 2D spatial analysis of tissues at high resolution.

scholarly journals CellMeSH: Probabilistic Cell-Type Identification Using Indexed Literature

2020 ◽  
Author(s):  
Shunfu Mao ◽  
Yue Zhang ◽  
Georg Seelig ◽  
Sreeram Kannan
Keyword(s):  
Gene Expression ◽  
Single Cell ◽  
Probabilistic Method ◽  
Expression Patterns ◽  
Cell Types ◽  
Cellular Systems ◽  
Biological Knowledge ◽  
Specific Gene ◽  
Cell Type ◽  
Link Type

AbstractSingle-cell RNA sequencing (scRNA-seq) is widely used for analyzing gene expression in multi-cellular systems and provides unprecedented access to cellular heterogeneity. scRNA-seq experiments aim to identify and quantify all cell types present in a sample. Measured single-cell transcriptomes are grouped by similarity and the resulting clusters are mapped to cell types based on cluster-specific gene expression patterns. While the process of generating clusters has become largely automated, annotation remains a laborious ad-hoc effort that requires expert biological knowledge. Here, we introduce CellMeSH - a new automated approach to identifying cell types based on prior literature. CellMeSH combines a database of gene-cell type associations with a probabilistic method for database querying. The database is constructed by automatically linking gene and cell type information from millions of publications using existing indexed literature resources. Compared to manually constructed databases, CellMeSH is more comprehensive and scales automatically. The probabilistic query method enables reliable information retrieval even though the gene-cell type associations extracted from the literature are necessarily noisy. CellMeSH achieves up to 60% top-1 accuracy and 90% top-3 accuracy in annotating the cell types on a human dataset, and up to 58.8% top-1 accuracy and 88.2% top-3 accuracy on three mouse datasets, which is consistently better than existing approaches.AvailabilityWeb server: https://uncurl.cs.washington.edu/db_query and API: https://github.com/shunfumao/cellmesh

scholarly journals A United Statistical Framework for Single Cell and Bulk Sequencing Data

2017 ◽  
Author(s):  
Lingxue Zhu ◽  
Jing Lei ◽  
Bernie Devlin ◽  
Kathryn Roeder
Keyword(s):  
Gene Expression ◽  
Single Cell ◽  
Cell Types ◽  
Accurate Estimation ◽  
Specific Gene ◽  
Rna Seq ◽  
Cell Type ◽  
Cell Type Specific ◽  
Different Cell Types ◽  
Cell Data

Recent advances in technology have enabled the measurement of RNA levels for individual cells. Compared to traditional tissue-level bulk RNA-seq data, single cell sequencing yields valuable insights about gene expression profiles for different cell types, which is potentially critical for understanding many complex human diseases. However, developing quantitative tools for such data remains challenging because of high levels of technical noise, especially the “dropout” events. A “dropout” happens when the RNA for a gene fails to be amplified prior to sequencing, producing a “false” zero in the observed data. In this paper, we propose a Unified RNA-Sequencing Model (URSM) for both single cell and bulk RNA-seq data, formulated as a hierarchical model. URSM borrows the strength from both data sources and carefully models the dropouts in single cell data, leading to a more accurate estimation of cell type specific gene expression profile. In addition, URSM naturally provides inference on the dropout entries in single cell data that need to be imputed for downstream analyses, as well as the mixing proportions of different cell types in bulk samples. We adopt an empirical Bayes approach, where parameters are estimated using the EM algorithm and approximate inference is obtained by Gibbs sampling. Simulation results illustrate that URSM outperforms existing approaches both in correcting for dropouts in single cell data, as well as in deconvolving bulk samples. We also demonstrate an application to gene expression data on fetal brains, where our model successfully imputes the dropout genes and reveals cell type specific expression patterns.

scholarly journals Identifying cell types from single-cell data based on similarities and dissimilarities between cells

2021 ◽  
Vol 22 (S3) ◽  
Author(s):  
Yuanyuan Li ◽  
Ping Luo ◽  
Yi Lu ◽  
Fang-Xiang Wu
Keyword(s):  
Gene Expression ◽  
Single Cell ◽  
Spectral Clustering ◽  
Incidence Matrix ◽  
Expression Patterns ◽  
Cell Types ◽  
Clustering Method ◽  
Different Types ◽  
Cell Data ◽  
Spectral Clustering Method

Abstract Background With the development of the technology of single-cell sequence, revealing homogeneity and heterogeneity between cells has become a new area of computational systems biology research. However, the clustering of cell types becomes more complex with the mutual penetration between different types of cells and the instability of gene expression. One way of overcoming this problem is to group similar, related single cells together by the means of various clustering analysis methods. Although some methods such as spectral clustering can do well in the identification of cell types, they only consider the similarities between cells and ignore the influence of dissimilarities on clustering results. This methodology may limit the performance of most of the conventional clustering algorithms for the identification of clusters, it needs to develop special methods for high-dimensional sparse categorical data. Results Inspired by the phenomenon that same type cells have similar gene expression patterns, but different types of cells evoke dissimilar gene expression patterns, we improve the existing spectral clustering method for clustering single-cell data that is based on both similarities and dissimilarities between cells. The method first measures the similarity/dissimilarity among cells, then constructs the incidence matrix by fusing similarity matrix with dissimilarity matrix, and, finally, uses the eigenvalues of the incidence matrix to perform dimensionality reduction and employs the K-means algorithm in the low dimensional space to achieve clustering. The proposed improved spectral clustering method is compared with the conventional spectral clustering method in recognizing cell types on several real single-cell RNA-seq datasets. Conclusions In summary, we show that adding intercellular dissimilarity can effectively improve accuracy and achieve robustness and that improved spectral clustering method outperforms the traditional spectral clustering method in grouping cells.

scholarly journals Enhancing droplet-based single-nucleus RNA-seq resolution using the semi-supervised machine learning classifier DIEM

2019 ◽  
Author(s):  
Marcus Alvarez ◽  
Elior Rahmani ◽  
Brandon Jew ◽  
Kristina M. Garske ◽  
Zong Miao ◽  
...  
Keyword(s):  
Gene Expression ◽  
Single Cell ◽  
Cell Types ◽  
Supervised Machine Learning ◽  
Data Sets ◽  
Rna Seq ◽  
Novel Approach ◽  
Single Nucleus ◽  
Downstream Analysis

AbstractSingle-nucleus RNA sequencing (snRNA-seq) measures gene expression in individual nuclei instead of cells, allowing for unbiased cell type characterization in solid tissues. Contrary to single-cell RNA seq (scRNA-seq), we observe that snRNA-seq is commonly subject to contamination by high amounts of extranuclear background RNA, which can lead to identification of spurious cell types in downstream clustering analyses if overlooked. We present a novel approach to remove debris-contaminated droplets in snRNA-seq experiments, called Debris Identification using Expectation Maximization (DIEM). Our likelihood-based approach models the gene expression distribution of debris and cell types, which are estimated using EM. We evaluated DIEM using three snRNA-seq data sets: 1) human differentiating preadipocytes in vitro, 2) fresh mouse brain tissue, and 3) human frozen adipose tissue (AT) from six individuals. All three data sets showed various degrees of extranuclear RNA contamination. We observed that existing methods fail to account for contaminated droplets and led to spurious cell types. When compared to filtering using these state of the art methods, DIEM better removed droplets containing high levels of extranuclear RNA and led to higher quality clusters. Although DIEM was designed for snRNA-seq data, we also successfully applied DIEM to single-cell data. To conclude, our novel method DIEM removes debris-contaminated droplets from single-cell-based data fast and effectively, leading to cleaner downstream analysis. Our code is freely available for use at https://github.com/marcalva/diem.

scholarly journals Enabling out-of-clinic human immunity studies via single-cell profiling of capillary blood

2020 ◽  
Author(s):  
Tatyana Dobreva ◽  
David Brown ◽  
Jong Hwee Park ◽  
Matt Thomson
Keyword(s):  
Gene Expression ◽  
Immune System ◽  
Environmental Factors ◽  
Single Cell ◽  
Immune Cell ◽  
Cell Types ◽  
Capillary Blood ◽  
Specific Gene ◽  
Human Immune ◽  
Over Time

AbstractAn individual’s immune system is driven by both genetic and environmental factors that vary over time. To better understand the temporal and inter-individual variability of gene expression within distinct immune cell types, we developed a platform that leverages multiplexed single-cell sequencing and out-of-clinic capillary blood extraction to enable simplified, cost-effective profiling of the human immune system across people and time at single-cell resolution. Using the platform, we detect widespread differences in cell type-specific gene expression between subjects that are stable over multiple days.SummaryIncreasing evidence implicates the immune system in an overwhelming number of diseases, and distinct cell types play specific roles in their pathogenesis.1,2 Studies of peripheral blood have uncovered a wealth of associations between gene expression, environmental factors, disease risk, and therapeutic efficacy.4 For example, in rheumatoid arthritis, multiple mechanistic paths have been found that lead to disease, and gene expression of specific immune cell types can be used as a predictor of therapeutic non-response.12 Furthermore, vaccines, drugs, and chemotherapy have been shown to yield different efficacy based on time of administration, and such findings have been linked to the time-dependence of gene expression in downstream pathways.21,22,23 However, human immune studies of gene expression between individuals and across time remain limited to a few cell types or time points per subject, constraining our understanding of how networks of heterogeneous cells making up each individual’s immune system respond to adverse events and change over time.

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