scholarly journals Integrated Single Cell Atlas of Endothelial Cells of the Human Lung

Circulation ◽  
2021 ◽  
Author(s):  
Jonas C. Schupp ◽  
Taylor S. Adams ◽  
Carlos Cosme Jr. ◽  
Micha Sam Brickman Raredon ◽  
Yifan Yuan ◽  
...  
Keyword(s):  
Endothelial Cells ◽  
Pulmonary Hypertension ◽  
Single Cell ◽  
Differential Expression ◽  
Human Lung ◽  
Differential Expression Analysis ◽  
Cell Types ◽  
Marker Genes ◽  
Lung Endothelium ◽  
Lung Endothelial Cells

Background: The cellular diversity of the lung endothelium has not been systematically characterized in humans. Here, we provide a reference atlas of human lung endothelial cells (ECs) to facilitate a better understanding of the phenotypic diversity and composition of cells comprising the lung endothelium. Methods: We reprocessed human control single cell RNA sequencing (scRNAseq) data from six datasets. EC populations were characterized through iterative clustering with subsequent differential expression analysis. Marker genes were validated by fluorescent microscopy and in situ hybridization. scRNAseq of primary lung ECs cultured in-vitro was performed. The signaling network between different lung cell types was studied. For cross species analysis or disease relevance, we applied the same methods to scRNAseq data obtained from mouse lungs or from human lungs with pulmonary hypertension. Results: Six lung scRNAseq datasets were reanalyzed and annotated to identify over 15,000 vascular EC cells from 73 individuals. Differential expression analysis of EC revealed signatures corresponding to endothelial lineage, including pan-endothelial, pan-vascular and subpopulation-specific marker gene sets. Beyond the broad cellular categories of lymphatic, capillary, arterial and venous ECs, we found previously indistinguishable subpopulations: among venous EC, we identified two previously indistinguishable populations, pulmonary-venous ECs (COL15A1neg) localized to the lung parenchyma and systemic-venous ECs (COL15A1pos) localized to the airways and the visceral pleura; among capillary EC, we confirmed their subclassification into recently discovered aerocytes characterized by EDNRB, SOSTDC1 and TBX2 and general capillary EC. We confirmed that all six endothelial cell types, including the systemic-venous EC and aerocytes, are present in mice and identified endothelial marker genes conserved in humans and mice. Ligand-receptor connectome analysis revealed important homeostatic crosstalk of EC with other lung resident cell types. scRNAseq of commercially available primary lung ECs demonstrated a loss of their native lung phenotype in culture. scRNAseq revealed that the endothelial diversity is maintained in pulmonary hypertension. Our manuscript is accompanied by an online data mining tool (www.LungEndothelialCellAtlas.com). Conclusions: Our integrated analysis provides the comprehensive and well-crafted reference atlas of lung endothelial cells in the normal lung and confirms and describes in detail previously unrecognized endothelial populations across a large number of humans and mice.

scholarly journals Integrated Single Cell Atlas of Endothelial Cells of the Human Lung

2020 ◽  
Author(s):  
Jonas C. Schupp ◽  
Taylor S. Adams ◽  
Carlos Cosme ◽  
Micha Sam Brickman Raredon ◽  
Norihito Omote ◽  
...  
Keyword(s):  
Endothelial Cells ◽  
Single Cell ◽  
Differential Expression ◽  
Human Lung ◽  
Differential Expression Analysis ◽  
Cell Types ◽  
Marker Genes ◽  
Health And Disease ◽  
Lung Endothelium ◽  
Lung Endothelial Cells

AbstractBackgroundDespite its importance in health and disease, the cellular diversity of the lung endothelium has not been systematically characterized in humans. Here we provide a reference atlas of human lung endothelial cells (ECs), to facilitate a better understanding of the phenotypic diversity and composition of cells comprising the lung endothelium, both in health and disease.MethodsWe reprocessed control single cell RNA sequencing (scRNAseq) data from five datasets of whole lungs that were used for the analysis of pan-endothelial markers, we later included a sixth dataset of sorted control EC for the vascular subpopulation analysis. EC populations were characterized through iterative clustering with subsequent differential expression analysis. Marker genes were validated by immunohistochemistry and in situ hybridization. Signaling network between different lung cell types was studied using connectomic analysis. For cross species analysis we applied the same methods to scRNAseq data obtained from mouse lungs.ResultsThe six lung scRNAseq datasets were reanalyzed and annotated to identify over 15,000 vascular EC cells from 73 individuals. Differential expression analysis of EC revealed signatures corresponding to endothelial lineage, including pan-endothelial, pan-vascular and subpopulation-specific marker gene sets. Beyond the broad cellular categories of lymphatic, capillary, arterial and venous ECs we found previously indistinguishable subpopulations; among venous EC we identified two previously indistinguishable populations, pulmonary-venous ECs (COL15A1neg) localized to the lung parenchyma and systemic-venous ECs (COL15A1pos) localized to the airways and the visceral pleura; among capillary EC we confirmed their subclassification into recently discovered aerocytes characterized by EDNRB, SOSTDC1 and TBX2 and general capillary EC. We confirmed that all six endothelial cell types, including the systemic-venous EC and aerocytes are present in mice and identified endothelial marker genes conserved in humans and mice. Ligand-Receptor connectome analysis revealed important homeostatic crosstalk of EC with other lung resident cell types. Our manuscript is accompanied by an online data mining tool (www.LungEndothelialCellAtlas.com).ConclusionOur integrated analysis provides the comprehensive and well-crafted reference atlas of lung endothelial cells in the normal lung and confirms and describes in detail previously unrecognized endothelial populations across a large number of humans and mice.

scholarly journals A Single-Cell Transcriptome Atlas of the Mouse Glomerulus

2018 ◽  
Vol 29 (8) ◽  
pp. 2060-2068 ◽  
Author(s):  
Nikos Karaiskos ◽  
Mahdieh Rahmatollahi ◽  
Anastasiya Boltengagen ◽  
Haiyue Liu ◽  
Martin Hoehne ◽  
...  
Keyword(s):  
Gene Expression ◽  
Endothelial Cells ◽  
Single Cell ◽  
Mesangial Cells ◽  
Transcriptional Profiling ◽  
Wild Type Mouse ◽  
Cell Types ◽  
Cellular Heterogeneity ◽  
Marker Genes ◽  
Glomerular Cell

Background Three different cell types constitute the glomerular filter: mesangial cells, endothelial cells, and podocytes. However, to what extent cellular heterogeneity exists within healthy glomerular cell populations remains unknown.Methods We used nanodroplet-based highly parallel transcriptional profiling to characterize the cellular content of purified wild-type mouse glomeruli.Results Unsupervised clustering of nearly 13,000 single-cell transcriptomes identified the three known glomerular cell types. We provide a comprehensive online atlas of gene expression in glomerular cells that can be queried and visualized using an interactive and freely available database. Novel marker genes for all glomerular cell types were identified and supported by immunohistochemistry images obtained from the Human Protein Atlas. Subclustering of endothelial cells revealed a subset of endothelium that expressed marker genes related to endothelial proliferation. By comparison, the podocyte population appeared more homogeneous but contained three smaller, previously unknown subpopulations.Conclusions Our study comprehensively characterized gene expression in individual glomerular cells and sets the stage for the dissection of glomerular function at the single-cell level in health and disease.

scholarly journals Estimating and Correcting for Off-Target Cellular Contamination in Brain Cell Type Specific RNA-Seq Data

2021 ◽  
Vol 14 ◽  
Author(s):  
Jordan Sicherman ◽  
Dwight F. Newton ◽  
Paul Pavlidis ◽  
Etienne Sibille ◽  
Shreejoy J. Tripathy
Keyword(s):  
Single Cell ◽  
Differential Expression ◽  
Rna Sequencing ◽  
Target Cell ◽  
Differential Expression Analysis ◽  
Cell Types ◽  
Brain Cell ◽  
Cell Type ◽  
Cell Purification ◽  
Single Cell Type

Transcriptionally profiling minor cellular populations remains an ongoing challenge in molecular genomics. Single-cell RNA sequencing has provided valuable insights into a number of hypotheses, but practical and analytical challenges have limited its widespread adoption. A similar approach, which we term single-cell type RNA sequencing (sctRNA-seq), involves the enrichment and sequencing of a pool of cells, yielding cell type-level resolution transcriptomes. While this approach offers benefits in terms of mRNA sampling from targeted cell types, it is potentially affected by off-target contamination from surrounding cell types. Here, we leveraged single-cell sequencing datasets to apply a computational approach for estimating and controlling the amount of off-target cell type contamination in sctRNA-seq datasets. In datasets obtained using a number of technologies for cell purification, we found that most sctRNA-seq datasets tended to show some amount of off-target mRNA contamination from surrounding cells. However, using covariates for cellular contamination in downstream differential expression analyses increased the quality of our models for differential expression analysis in case/control comparisons and typically resulted in the discovery of more differentially expressed genes. In general, our method provides a flexible approach for detecting and controlling off-target cell type contamination in sctRNA-seq datasets.

scholarly journals genesorteR: Feature Ranking in Clustered Single Cell Data

2019 ◽  
Author(s):  
Mahmoud M Ibrahim ◽  
Rafael Kramann
Keyword(s):  
Single Cell ◽  
Differential Expression ◽  
Expression Analysis ◽  
Differential Expression Analysis ◽  
Large Cell ◽  
R Package ◽  
Marker Genes ◽  
Data Sets ◽  
Cell Type ◽  
Cell Data

ABSTRACTMarker genes identified in single cell experiments are expected to be highly specific to a certain cell type and highly expressed in that cell type. Detecting a gene by differential expression analysis does not necessarily satisfy those two conditions and is typically computationally expensive for large cell numbers.Here we present genesorteR, an R package that ranks features in single cell data in a manner consistent with the expected definition of marker genes in experimental biology research. We benchmark genesorteR using various data sets and show that it is distinctly more accurate in large single cell data sets compared to other methods. genesorteR is orders of magnitude faster than current implementations of differential expression analysis methods, can operate on data containing millions of cells and is applicable to both single cell RNA-Seq and single cell ATAC-Seq data.genesorteR is available at https://github.com/mahmoudibrahim/genesorteR.

scholarly journals A Markov Random Field Model for Network-based Differential Expression Analysis of Single-cell RNA-seq Data

2020 ◽  
Author(s):  
Hongyu Li ◽  
Zhichao Xu ◽  
Taylor Adams ◽  
Naftali Kaminski ◽  
Hongyu Zhao
Keyword(s):  
Random Field ◽  
Single Cell ◽  
Differential Expression ◽  
Markov Random Field ◽  
Statistical Power ◽  
Differential Expression Analysis ◽  
Cell Types ◽  
Cell Type ◽  
Markov Random ◽  
Cell Type Specific

Abstract Background: Recent development of single cell sequencing technologies has made it possible to identify genes with different expression (DE) levels at the cell type level between different groups of samples. However, the often-low sample size of single cell data limits the statistical power to identify DE genes. In this article, we propose to borrow information through known biological networks. Results: We develop MRFscRNAseq, which is based on a Markov Random Field (MRF) model to appropriately accommodate gene network information as well as dependencies among cell types to identify cell-type specific DE genes. We implement an Expectation-Maximization (EM) algorithm with mean field-like approximation to estimate model parameters and a Gibbs sampler to infer DE status. Simulation study shows that our method has better power to detect cell-type specific DE genes than conventional methods while appropriately controlling type I error rate. The usefulness of our method is demonstrated through its application to study the pathogenesis and biological processes of idiopathic pulmonary fibrosis (IPF) using a single-cell RNA-sequencing (scRNA-seq) data set, which contains 18,150 protein-coding genes across 38 cell types on lung tissues from 32 IPF patients and 28 normal controls.Conclusions: The proposed MRF model is implemented in the R package MRFscRNAseq available on GitHub. By utilizing gene-gene and cell-cell networks, our method provides differential expression analysis for scRNA-seq data with increased statistical power.

scholarly journals A single-cell atlas of human fetal lung development between 14 and 19 weeks of gestation

2021 ◽  
Author(s):  
Laurent Renesme ◽  
Flore Lesage ◽  
David Cook ◽  
Shumei Zhong ◽  
Satu M Hanninen ◽  
...  
Keyword(s):  
Single Cell ◽  
Lung Development ◽  
Molecular Mechanisms ◽  
Human Lung ◽  
Cell Communication ◽  
Cell Types ◽  
Developmental Trajectory ◽  
Marker Genes ◽  
Different Cell Types ◽  
Pseudoglandular Stage

Rationale: Human lung development has been mainly described in morphologic studies and the potential underlying molecular mechanisms were extrapolated from animal models. Therefore, there is a need to gather knowledge from native human lung tissue. In this study we describe changes at a single-cell level in human fetal lungs during the pseudoglandular stage. Methods: We report the cellular composition, cell trajectories and cell-to-cell communication in developing human lungs with single-nuclei RNA sequencing (snRNA-seq) on 23,251 nuclei isolated from nine human fetuses with gestational ages between 14 to 19 weeks of gestation. Results: We identified nine different cell types, including a rare pulmonary neuroendocrine cells population. For each cell type, marker genes are reported, and selected marker genes are used for spatial validation with fluorescent RNA in situ hybridization. Enrichment and developmental trajectory analysis provide insight into molecular mechanisms and signaling pathways within individual cell clusters according to gestational age. Lastly, ligand-receptor analysis highlights determinants of cell-to-cell communication among the different cell types through the pseudoglandular stage, including general developmental pathways (NOTCH and TGFB), as well as more specific pathways involved in vasculogenesis, neurogenesis, and immune system regulation. Conclusion: These findings provide a clinically relevant background for research hypotheses generation in projects studying normal or impaired lung development and help to develop and validate surrogate models to study human lung development, such as human lung organoids.

scholarly journals Molecular characteristics and spatial distribution of adult human corneal cell subtypes

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Ann J. Ligocki ◽  
Wen Fury ◽  
Christian Gutierrez ◽  
Christina Adler ◽  
Tao Yang ◽  
...  
Keyword(s):  
Single Cell ◽  
Rna Sequencing ◽  
Cross Sections ◽  
Cell Types ◽  
Marker Genes ◽  
Molecular Characteristics ◽  
Transcriptional Level ◽  
Human Cornea ◽  
Adult Human ◽  
And Migration

AbstractBulk RNA sequencing of a tissue captures the gene expression profile from all cell types combined. Single-cell RNA sequencing identifies discrete cell-signatures based on transcriptomic identities. Six adult human corneas were processed for single-cell RNAseq and 16 cell clusters were bioinformatically identified. Based on their transcriptomic signatures and RNAscope results using representative cluster marker genes on human cornea cross-sections, these clusters were confirmed to be stromal keratocytes, endothelium, several subtypes of corneal epithelium, conjunctival epithelium, and supportive cells in the limbal stem cell niche. The complexity of the epithelial cell layer was captured by eight distinct corneal clusters and three conjunctival clusters. These were further characterized by enriched biological pathways and molecular characteristics which revealed novel groupings related to development, function, and location within the epithelial layer. Moreover, epithelial subtypes were found to reflect their initial generation in the limbal region, differentiation, and migration through to mature epithelial cells. The single-cell map of the human cornea deepens the knowledge of the cellular subsets of the cornea on a whole genome transcriptional level. This information can be applied to better understand normal corneal biology, serve as a reference to understand corneal disease pathology, and provide potential insights into therapeutic approaches.

There are no A1 and A2 astrocytes in brain

2021 ◽  
Author(s):  
Shuang-qi Gao
Keyword(s):  
Endothelial Cells ◽  
Single Cell ◽  
Rna Sequencing ◽  
Cell Sorting ◽  
Reactive Astrocytes ◽  
Marker Genes ◽  
Set Up ◽  
The Brain

Abstract Objectives The subsets of astrocytes in the brain have not been fully elucidated. Using bulk RNA sequencing, reactive astrocytes were divided into A1 versus A2. However, using single-cell RNAseq (ScRNAseq), astrocytes were divided into over two subsets. Our aim was to set up the correspondence between the fluorescent-activated cell sorting (FACS)-bulk RNAseq and ScRNAseq data. Results We found that most of reactive astrocytes (RAs) marker genes were expressed in endothelial cells but not in astrocytes, suggesting those marker genes are not suitable for astrocytic activation. The absence of A1 and A2 astrocytes in the brain.

scholarly journals JIND: Joint Integration and Discrimination for Automated Single-Cell Annotation

2020 ◽  
Author(s):  
Mohit Goyal ◽  
Guillermo Serrano ◽  
Ilan Shomorony ◽  
Mikel Hernaez ◽  
Idoia Ochoa
Keyword(s):  
Single Cell ◽  
Cell Types ◽  
Marker Genes ◽  
Specific Marker ◽  
Rna Seq ◽  
Batch Effects ◽  
Cell Type ◽  
Latent Space ◽  
Cell Type Specific ◽  
Low Dimensional

AbstractSingle-cell RNA-seq is a powerful tool in the study of the cellular composition of different tissues and organisms. A key step in the analysis pipeline is the annotation of cell-types based on the expression of specific marker genes. Since manual annotation is labor-intensive and does not scale to large datasets, several methods for automated cell-type annotation have been proposed based on supervised learning. However, these methods generally require feature extraction and batch alignment prior to classification, and their performance may become unreliable in the presence of cell-types with very similar transcriptomic profiles, such as differentiating cells. We propose JIND, a framework for automated cell-type identification based on neural networks that directly learns a low-dimensional representation (latent code) in which cell-types can be reliably determined. To account for batch effects, JIND performs a novel asymmetric alignment in which the transcriptomic profile of unseen cells is mapped onto the previously learned latent space, hence avoiding the need of retraining the model whenever a new dataset becomes available. JIND also learns cell-type-specific confidence thresholds to identify and reject cells that cannot be reliably classified. We show on datasets with and without batch effects that JIND classifies cells more accurately than previously proposed methods while rejecting only a small proportion of cells. Moreover, JIND batch alignment is parallelizable, being more than five or six times faster than Seurat integration. Availability: https://github.com/mohit1997/JIND.

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