scholarly journals Quantitative single-cell interactomes in normal and virus-infected mouse lungs

2020 ◽  
Author(s):  
Margo P Cain ◽  
Belinda J Hernandez ◽  
Jichao Chen
Keyword(s):  
Single Cell ◽  
Infected Mouse ◽  
Sendai Virus ◽  
Cell Lineage ◽  
Cell Types ◽  
Rna Seq ◽  
Receptor Interactions ◽  
Cost Efficient ◽  
Intuitive Method ◽  
Mouse Lungs

ABSTRACTMammalian organs consist of diverse, intermixed cell types that signal to each other via ligand-receptor interactions – an interactome – to ensure development, homeostasis, and injury-repair. Dissecting such intercellular interactions is facilitated by rapidly growing single-cell RNA-seq (scRNA-seq) data; however, existing computational methods are often not sufficiently quantitative nor readily adaptable by bench scientists without advanced programming skills. Here we describe a quantitative intuitive algorithm, coupled with an optimized experimental protocol, to construct and compare interactomes in control and Sendai virus-infected mouse lungs. A minimum of 90 cells per cell type compensates for the known gene dropout issue in scRNA-seq and achieves comparable sensitivity to bulk RNA-seq. Cell lineage normalization after cell sorting allows cost-efficient representation of cell types of interest. A numeric representation of ligand-receptor interactions identifies, as outliers, known and potentially new interactions as well as changes upon viral infection. Our experimental and computational approaches can be generalized to other organs and human samples.Summary statementAn intuitive method to construct quantitative ligand-receptor interactomes using single-cell RNA-seq data and its application to normal and Sendai virus-infected mouse lungs.

scholarly journals Understanding the Adult Mammalian Heart at Single-Cell RNA-Seq Resolution

2021 ◽  
Vol 9 ◽  
Author(s):  
Ernesto Marín-Sedeño ◽  
Xabier Martínez de Morentin ◽  
Jose M. Pérez-Pomares ◽  
David Gómez-Cabrero ◽  
Adrián Ruiz-Villalba
Keyword(s):  
Single Cell ◽  
Single Gene ◽  
Cell Lineage ◽  
Cell Types ◽  
Lineage Tracing ◽  
Cardiac Cell ◽  
Rna Seq ◽  
Cardiovascular Research ◽  
Cell Diversity ◽  
Unbiased Manner

During the last decade, extensive efforts have been made to comprehend cardiac cell genetic and functional diversity. Such knowledge allows for the definition of the cardiac cellular interactome as a reasonable strategy to increase our understanding of the normal and pathologic heart. Previous experimental approaches including cell lineage tracing, flow cytometry, and bulk RNA-Seq have often tackled the analysis of cardiac cell diversity as based on the assumption that cell types can be identified by the expression of a single gene. More recently, however, the emergence of single-cell RNA-Seq technology has led us to explore the diversity of individual cells, enabling the cardiovascular research community to redefine cardiac cell subpopulations and identify relevant ones, and even novel cell types, through their cell-specific transcriptomic signatures in an unbiased manner. These findings are changing our understanding of cell composition and in consequence the identification of potential therapeutic targets for different cardiac diseases. In this review, we provide an overview of the continuously changing cardiac cellular landscape, traveling from the pre-single-cell RNA-Seq times to the single cell-RNA-Seq revolution, and discuss the utilities and limitations of this technology.

scholarly journals Single-cell transcriptome analysis of the zebrafish embryonic trunk

2021 ◽  
Author(s):  
Sanjeeva S Metikala ◽  
Satish Casie Chetty ◽  
Saulius Sumanas
Keyword(s):  
Single Cell ◽  
Molecular Mechanisms ◽  
Cell Lineage ◽  
Cell Types ◽  
Rna Seq ◽  
Cell Lineages ◽  
Distinct Cell ◽  
Cell Transcriptome ◽  
Single Cell Transcriptome ◽  
Different Cell Types

During embryonic development, cells differentiate into a variety of distinct cell types and subtypes with diverse transcriptional profiles. To date, transcriptomic signatures of different cell lineages that arise during development have been only partially characterized. Here we used single-cell RNA-seq to perform transcriptomic analysis of over 20,000 cells disaggregated from the trunk region of zebrafish embryos at the 30 hpf stage. Transcriptional signatures of 27 different cell types and subtypes were identified and annotated during this analysis. This dataset will be a useful resource for many researchers in the fields of developmental and cellular biology and facilitate the understanding of molecular mechanisms that regulate cell lineage choices during development.

scholarly journals Approximate distance correlation for selecting highly interrelated genes across datasets

2021 ◽  
Vol 17 (11) ◽  
pp. e1009548
Author(s):  
Qunlun Shen ◽  
Shihua Zhang
Keyword(s):  
Single Cell ◽  
Large Scale ◽  
Target Gene ◽  
Mononuclear Cells ◽  
Statistical Significance ◽  
Cell Lineage ◽  
Cell Types ◽  
Rna Seq ◽  
Peripheral Blood Mononuclear ◽  
Distance Correlation

With the rapid accumulation of biological omics datasets, decoding the underlying relationships of cross-dataset genes becomes an important issue. Previous studies have attempted to identify differentially expressed genes across datasets. However, it is hard for them to detect interrelated ones. Moreover, existing correlation-based algorithms can only measure the relationship between genes within a single dataset or two multi-modal datasets from the same samples. It is still unclear how to quantify the strength of association of the same gene across two biological datasets with different samples. To this end, we propose Approximate Distance Correlation (ADC) to select interrelated genes with statistical significance across two different biological datasets. ADC first obtains the k most correlated genes for each target gene as its approximate observations, and then calculates the distance correlation (DC) for the target gene across two datasets. ADC repeats this process for all genes and then performs the Benjamini-Hochberg adjustment to control the false discovery rate. We demonstrate the effectiveness of ADC with simulation data and four real applications to select highly interrelated genes across two datasets. These four applications including 21 cancer RNA-seq datasets of different tissues; six single-cell RNA-seq (scRNA-seq) datasets of mouse hematopoietic cells across six different cell types along the hematopoietic cell lineage; five scRNA-seq datasets of pancreatic islet cells across five different technologies; coupled single-cell ATAC-seq (scATAC-seq) and scRNA-seq data of peripheral blood mononuclear cells (PBMC). Extensive results demonstrate that ADC is a powerful tool to uncover interrelated genes with strong biological implications and is scalable to large-scale datasets. Moreover, the number of such genes can serve as a metric to measure the similarity between two datasets, which could characterize the relative difference of diverse cell types and technologies.

scholarly journals Single-cell transcriptome analysis of the zebrafish embryonic trunk

PLoS ONE ◽  
2021 ◽  
Vol 16 (7) ◽  
pp. e0254024
Author(s):  
Sanjeeva Metikala ◽  
Satish Casie Chetty ◽  
Saulius Sumanas
Keyword(s):  
Single Cell ◽  
Molecular Mechanisms ◽  
Cell Lineage ◽  
Cell Types ◽  
Rna Seq ◽  
Cell Lineages ◽  
Distinct Cell ◽  
Cell Transcriptome ◽  
Single Cell Transcriptome ◽  
Different Cell Types

During embryonic development, cells differentiate into a variety of distinct cell types and subtypes with diverse transcriptional profiles. To date, transcriptomic signatures of different cell lineages that arise during development have been only partially characterized. Here we used single-cell RNA-seq to perform transcriptomic analysis of over 20,000 cells disaggregated from the trunk region of zebrafish embryos at the 30 hpf stage. Transcriptional signatures of 27 different cell types and subtypes were identified and annotated during this analysis. This dataset will be a useful resource for many researchers in the fields of developmental and cellular biology and facilitate the understanding of molecular mechanisms that regulate cell lineage choices during development.

scholarly journals talklr uncovers ligand-receptor mediated intercellular crosstalk

2020 ◽  
Author(s):  
Yuliang Wang
Keyword(s):  
Single Cell ◽  
Holistic Approach ◽  
Cell Types ◽  
R Package ◽  
Tissue Homeostasis ◽  
Rna Seq ◽  
Receptor Interactions ◽  
Multiple Cell ◽  
Potential Ligand ◽  
Different Cell Types

AbstractSingle cell RNA-seq measures the transcriptomes of many cell types across diverse conditions. However, an emerging challenge is to uncover how different cell types communicate with each other to maintain tissue homeostasis, and how inter-cellular communications are perturbed in diseases. To address this problem, we developed talklr, an information theory-based approach to uncover potential ligand-receptor interactions involved in tissue homeostasis and diseases. Compared to existing approaches that analyze changes in each gene in each cell type separately, talklr uses a holistic approach to simultaneously consider expression changes in both ligands and receptors across multiple cell types and conditions. talklr outperformed existing approaches in identifying ligand-receptor interactions, including those known to be important for tissue-specific functions and diseases across diverse datasets. talklr can reveal important signaling events in many biological problems in an unbiased way, and will be a valuable tool in single cell RNA-seq analysis. talklr is available as both an interactive website and an R package.

scholarly journals treeclimbR pinpoints the data-dependent resolution of hierarchical hypotheses

2021 ◽  
Vol 22 (1) ◽  
Author(s):  
Ruizhu Huang ◽  
Charlotte Soneson ◽  
Pierre-Luc Germain ◽  
Thomas S.B. Schmidt ◽  
Christian Von Mering ◽  
...  
Keyword(s):  
Single Cell ◽  
Synthetic Data ◽  
Cell Types ◽  
Data Driven ◽  
Rna Seq ◽  
Hierarchical Trees

AbstracttreeclimbR is for analyzing hierarchical trees of entities, such as phylogenies or cell types, at different resolutions. It proposes multiple candidates that capture the latent signal and pinpoints branches or leaves that contain features of interest, in a data-driven way. It outperforms currently available methods on synthetic data, and we highlight the approach on various applications, including microbiome and microRNA surveys as well as single-cell cytometry and RNA-seq datasets. With the emergence of various multi-resolution genomic datasets, treeclimbR provides a thorough inspection on entities across resolutions and gives additional flexibility to uncover biological associations.

scholarly journals Single-cell analysis reveals cell communication triggered by macrophages associated with the reduction and exhaustion of CD8+ T cells in COVID-19

2021 ◽  
Vol 19 (1) ◽  
Author(s):  
Lei He ◽  
Quan Zhang ◽  
Yue Zhang ◽  
Yixian Fan ◽  
Fahu Yuan ◽  
...  
Keyword(s):  
T Cells ◽  
T Cell ◽  
Single Cell ◽  
Cell Communication ◽  
Cell Types ◽  
Trial Registration ◽  
T Cell Subsets ◽  
Disease Group ◽  
Communication Analysis ◽  
Receptor Interactions

Abstract Background The coronavirus disease 2019 (COVID-19) outbreak caused by severe acute respiratory syndrome coronavirus 2 (SARS-Cov-2) has become an ongoing pandemic. Understanding the respiratory immune microenvironment which is composed of multiple cell types, together with cell communication based on ligand–receptor interactions is important for developing vaccines, probing COVID-19 pathogenesis, and improving pandemic control measures. Methods A total of 102 consecutive hospitalized patients with confirmed COVID-19 were enrolled in this study. Clinical information, routine laboratory tests, and flow cytometry analysis data with different conditions were collected and assessed for predictive value in COVID-19 patients. Next, we analyzed public single-cell RNA-sequencing (scRNA-seq) data from bronchoalveolar lavage fluid, which offers the closest available view of immune cell heterogeneity as encountered in patients with varying severity of COVID-19. A weighting algorithm was used to calculate ligand–receptor interactions, revealing the communication potentially associated with outcomes across cell types. Finally, serum cytokines including IL6, IL1β, IL10, CXCL10, TNFα, GALECTIN-1, and IGF1 derived from patients were measured. Results Of the 102 COVID-19 patients, 42 cases (41.2%) were categorized as severe. Multivariate logistic regression analysis demonstrated that AST, D-dimer, BUN, and WBC were considered as independent risk factors for the severity of COVID-19. T cell numbers including total T cells, CD4+ and CD8+ T cells in the severe disease group were significantly lower than those in the moderate disease group. The risk model containing the above mentioned inflammatory damage parameters, and the counts of T cells, with AUROCs ranged from 0.78 to 0.87. To investigate the molecular mechanism at the cellular level, we analyzed the published scRNA-seq data and found that macrophages displayed specific functional diversity after SARS-Cov-2 infection, and the metabolic pathway activities in the identified macrophage subtypes were influenced by hypoxia status. Importantly, we described ligand–receptor interactions that are related to COVID-19 serverity involving macrophages and T cell subsets by communication analysis. Conclusions Our study showed that macrophages driving ligand–receptor crosstalk contributed to the reduction and exhaustion of CD8+ T cells. The identified crucial cytokine panel, including IL6, IL1β, IL10, CXCL10, IGF1, and GALECTIN-1, may offer the selective targets to improve the efficacy of COVID-19 therapy. Trial registration: This is a retrospective observational study without a trial registration number.

scholarly journals Annotating cell types in human single-cell RNA-seq data with CellO

2021 ◽  
Vol 2 (3) ◽  
pp. 100705
Author(s):  
Matthew N. Bernstein ◽  
Colin N. Dewey
Keyword(s):  
Single Cell ◽  
Cell Types ◽  
Rna Seq

scholarly journals Systematic comparison of high-throughput single-cell RNA-seq methods for immune cell profiling

BMC Genomics ◽  
2021 ◽  
Vol 22 (1) ◽  
Author(s):  
Tracy M. Yamawaki ◽  
Daniel R. Lu ◽  
Daniel C. Ellwanger ◽  
Dev Bhatt ◽  
Paolo Manzanillo ◽  
...  
Keyword(s):  
Single Cell ◽  
High Throughput ◽  
Immune Cell ◽  
Cell Types ◽  
Data Interpretation ◽  
Detection Sensitivity ◽  
Rna Seq ◽  
Cell Recovery

Abstract Background Elucidation of immune populations with single-cell RNA-seq has greatly benefited the field of immunology by deepening the characterization of immune heterogeneity and leading to the discovery of new subtypes. However, single-cell methods inherently suffer from limitations in the recovery of complete transcriptomes due to the prevalence of cellular and transcriptional dropout events. This issue is often compounded by limited sample availability and limited prior knowledge of heterogeneity, which can confound data interpretation. Results Here, we systematically benchmarked seven high-throughput single-cell RNA-seq methods. We prepared 21 libraries under identical conditions of a defined mixture of two human and two murine lymphocyte cell lines, simulating heterogeneity across immune-cell types and cell sizes. We evaluated methods by their cell recovery rate, library efficiency, sensitivity, and ability to recover expression signatures for each cell type. We observed higher mRNA detection sensitivity with the 10x Genomics 5′ v1 and 3′ v3 methods. We demonstrate that these methods have fewer dropout events, which facilitates the identification of differentially-expressed genes and improves the concordance of single-cell profiles to immune bulk RNA-seq signatures. Conclusion Overall, our characterization of immune cell mixtures provides useful metrics, which can guide selection of a high-throughput single-cell RNA-seq method for profiling more complex immune-cell heterogeneity usually found in vivo.

scholarly journals Single-cell transcriptomics reveals immune response of intestinal cell types to viral infection

2020 ◽  
Author(s):  
Sergio Triana ◽  
Megan L. Stanifer ◽  
Mohammed Shahraz ◽  
Markus Mukenhirn ◽  
Carmon Kee ◽  
...  
Keyword(s):  
Immune Response ◽  
Single Cell ◽  
Cell Lineage ◽  
Enteric Virus ◽  
Cell Types ◽  
Intestinal Cell ◽  
Intestinal Epithelial ◽  
Proliferating Cells ◽  
Interferon Stimulated Genes ◽  
Human Intestinal Epithelium

AbstractHuman intestinal epithelial cells form a primary barrier protecting us from pathogens, yet only limited knowledge is available about individual contribution of each cell type to mounting an immune response against infection. Here, we developed a pipeline combining single-cell RNA-Seq and highly-multiplex RNA imaging and applied it to human intestinal organoids infected with human astrovirus, a model human enteric virus. We found that interferon controls the infection and that astrovirus infects all major cell types and lineages with a preferential infection of proliferating cells. Intriguingly, each intestinal epithelial cell lineage has a unique basal expression of interferon-stimulated genes and, upon astrovirus infection, undergoes an antiviral transcriptional reprogramming by upregulating distinct sets of interferon-stimulated genes. These findings suggest that in the human intestinal epithelium, each cell lineage plays a unique role in resolving virus infection. Our pipeline can be applicable to other organoids and viruses, opening new avenues to unravel roles of individual cell types in viral pathogenesis.

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