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

2021 ◽  
Vol 17 (7) ◽  
Author(s):  
Sergio Triana ◽  
Megan L Stanifer ◽  
Camila Metz‐Zumaran ◽  
Mohammed Shahraz ◽  
Markus Mukenhirn ◽  
...  
Keyword(s):  
Immune Response ◽  
Viral Infection ◽  
Single Cell ◽  
Cell Types ◽  
Intestinal Cell

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.

Single-cell messenger RNA sequencing reveals rare intestinal cell types

Nature ◽  
2015 ◽  
Vol 525 (7568) ◽  
pp. 251-255 ◽  
Author(s):  
Dominic Grün ◽  
Anna Lyubimova ◽  
Lennart Kester ◽  
Kay Wiebrands ◽  
Onur Basak ◽  
...  
Keyword(s):  
Single Cell ◽  
Rna Sequencing ◽  
Messenger Rna ◽  
Cell Types ◽  
Intestinal Cell

scholarly journals SARS-CoV-2 activates lung epithelia cell proinflammatory signaling and leads to immune dysregulation in COVID-19 patients by single-cell sequencing

2020 ◽  
Author(s):  
Huarong Chen ◽  
Weixin Liu ◽  
Dabin Liu ◽  
Liuyang Zhao ◽  
Jun Yu
Keyword(s):  
Immune Response ◽  
Epithelial Cells ◽  
Nk Cells ◽  
Single Cell ◽  
Immune Cells ◽  
Cell Types ◽  
Lung Epithelial Cells ◽  
Healthy Controls ◽  
Inflammatory Signaling ◽  
Lung Epithelial

Objective: The outbreak of Coronavirus disease 2019 (COVID-19) caused by SARS-CoV-2 infection has become a global health emergency. We aim to decipher SARS-CoV-2 infected cell types, the consequent host immune response and their interplay in the lung of COVID-19 patients. Design: We analyzed single-cell RNA sequencing (scRNA-seq) data of lung samples from 17 subjects (6 severe COVID-19 patients, 3 mild patients who recovered and 8 healthy controls). The expression of SARS-CoV-2 receptors (ACE2 and TMPRSS2) was examined among different cell types in the lung. The immune cells infiltration patterns, their gene expression profiles, and the interplay of immune cells and SARS-CoV-2 target cells were further investigated. Results: Compared to healthy controls, the overall ACE2 (receptor of SARS-CoV-2) expression was significantly higher in lung epithelial cells of COVID-19 patients, in particular in ciliated cell, club cell and basal cell. Comparative transcriptome analysis of these lung epithelial cells of COVID-19 patients and healthy controls identified that SARS-CoV-2 infection activated pro-inflammatory signaling including interferon pathway and cytokine signaling. Moreover, we identified dysregulation of immune response in patients with COVID-19. In severe COVID-19 patients, significantly higher neutrophil, but lower T and NK cells in lung were observed along with markedly increased cytokines (CCL2, CCL3, CCL4, CCL7, CCL3L1 and CCL4L2) compared with healthy controls as well as mild patients who recovered. The cytotoxic phenotypes were shown in lung T and NK cells of severe patients as evidenced by enhanced IFNγ, Granulysin, Granzyme B and Perforin expression. Moreover, SARS-CoV-2 infection altered the community interplay of lung epithelial cells and immune cells: the interaction between epithelial cells with macrophage, T and NK cell was stronger, but their interaction with neutrophils was lost in COVID-19 patients compared to healthy controls. Conclusions: SARS-CoV-2 infection activates pro-inflammatory signaling in lung epithelial cells expressing ACE2 and causes dysregulation of immune response to release more pro-inflammatory cytokines. Moreover, SARS-CoV-2 infection breaks the interplay of lung epithelial cells and immune cells.

scholarly journals An intestinal cell type in zebrafish is the nexus for the SARS-CoV-2 receptor and the Renin-Angiotensin-Aldosterone System that contributes to COVID-19 comorbidities

2020 ◽  
Author(s):  
John H. Postlethwait ◽  
Dylan R. Farnsworth ◽  
Adam C. Miller
Keyword(s):  
Single Cell ◽  
Disease Model ◽  
Cell Types ◽  
Intestinal Cell ◽  
Ang Ii ◽  
Cell Type ◽  
Renin Angiotensin Aldosterone System ◽  
Renin Angiotensin ◽  
Obesity And Diabetes ◽  
Genomic Analyses

ABSTRACTPeople with underlying conditions, including hypertension, obesity, and diabetes, are especially susceptible to negative outcomes after infection with the coronavirus SARS-CoV-2. These COVID-19 comorbidities are exacerbated by the Renin-Angiotensin-Aldosterone System (RAAS), which normally protects from rapidly dropping blood pressure or dehydration via the peptide Angiotensin II (Ang II) produced by the enzyme Ace. The Ace paralog Ace2 degrades Ang II, thus counteracting its chronic effects. Ace2 is also the SARS-CoV-2 receptor. Ace, the coronavirus, and COVID-19 comorbidities all regulate Ace2, but we don’t yet understand how. To exploit zebrafish (Danio rerio) as a disease model to understand mechanisms regulating the RAAS and its relationship to COVID-19 comorbidities, we must first identify zebrafish orthologs and co-orthologs of human RAAS genes, and second, understand where and when these genes are expressed in specific cells in zebrafish development. To achieve these goals, we conducted genomic analyses and investigated single cell transcriptomes. Results showed that most human RAAS genes have an ortholog in zebrafish and some have two or more co-orthologs. Results further identified a specific intestinal cell type in zebrafish larvae as the site of expression for key RAAS components, including Ace, Ace2, the coronavirus co-receptor Slc6a19, and the Angiotensin-related peptide cleaving enzymes Anpep and Enpep. Results also identified specific vascular cell subtypes as expressing Ang II receptors, apelin, and apelin receptor genes. These results identify specific genes and cell types to exploit zebrafish as a disease model for understanding the mechanisms leading to COVID-19 comorbidities.SUMMARY STATEMENTGenomic analyses identify zebrafish orthologs of the Renin-Angiotensin-Aldosterone System that contribute to COVID-19 comorbidities and single-cell transcriptomics show that they act in a specialized intestinal cell type.

scholarly journals Single-cell Transcriptome Mapping Identifies Common and Cell-type Specific Genes Affected by Acute Delta9-tetrahydrocannabinol in Humans

2019 ◽  
Author(s):  
Ying Hu ◽  
Mohini Ranganathan ◽  
Chang Shu ◽  
Xiaoyu Liang ◽  
Suhas Ganesh ◽  
...  
Keyword(s):  
Gene Expression ◽  
Immune Response ◽  
Single Cell ◽  
Immune Cells ◽  
Cell Types ◽  
Gene Set Enrichment Analysis ◽  
Cell Type ◽  
Cell Type Specific ◽  
Cell Transcriptome ◽  
Single Cell Transcriptome

AbstractDelta 9-tetrahydrocannabinol (THC), the principal psychoactive constituent of cannabis, is also known to modulate immune response in peripheral cells. The mechanisms of THC’s effects on gene expression in human immune cells remains poorly understood. Combining a within-subject design with single cell transcriptome mapping, we report that administration of THC acutely alters gene expression in 15,973 human blood immune cells. Controlled for high inter-individual transcriptomic variability, we identified 294 transcriptome-wide significant genes among eight cell types including 69 common genes and 225 cell-type specific genes affected by acute THC administration, including those genes involving not only in immune response, cytokine production, but signal transduction, and cell proliferation and apoptosis. We revealed distinct transcriptomic sub-clusters affected by THC in major immune cell types where THC perturbed cell type-specific intracellular gene expression correlations. Gene set enrichment analysis further supports the findings of THC’s common and cell-type specific effects on immune response and cell toxicity. We found that THC alters the correlation of cannabinoid receptor gene, CNR2, with other genes in B cells, in which CNR2 showed the highest level of expression. This comprehensive cell-specific transcriptomic profiling identified novel genes regulated by THC and provides important insights into THC’s acute effects on immune function that may have important medical implications.

scholarly journals Toward a Consensus in the Repertoire of Hemocytes Identified in Drosophila

2021 ◽  
Vol 9 ◽  
Author(s):  
Pierre B. Cattenoz ◽  
Sara Monticelli ◽  
Alexia Pavlidaki ◽  
Angela Giangrande
Keyword(s):  
Immune Response ◽  
Molecular Markers ◽  
Single Cell ◽  
Immune Cells ◽  
Cell Types ◽  
Molecular Signature ◽  
Peripheral Tissues ◽  
Comparative Analyses ◽  
Analytical Approaches

The catalog of the Drosophila immune cells was until recently limited to three major cell types, based on morphology, function and few molecular markers. Three recent single cell studies highlight the presence of several subgroups, revealing a large diversity in the molecular signature of the larval immune cells. Since these studies rely on somewhat different experimental and analytical approaches, we here compare the datasets and identify eight common, robust subgroups associated to distinct functions such as proliferation, immune response, phagocytosis or secretion. Similar comparative analyses with datasets from different stages and tissues disclose the presence of larval immune cells resembling embryonic hemocyte progenitors and the expression of specific properties in larval immune cells associated with peripheral tissues.

Prioritization of cell types responsive to biological perturbations in single-cell data with Augur

2021 ◽  
Author(s):  
Jordan W. Squair ◽  
Michael A. Skinnider ◽  
Matthieu Gautier ◽  
Leonard J. Foster ◽  
Grégoire Courtine
Keyword(s):  
Single Cell ◽  
Cell Types ◽  
Cell Data

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.

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