scholarly journals The in vivo endothelial cell translatome is highly heterogeneous across vascular beds

2019 ◽  
Vol 116 (47) ◽  
pp. 23618-23624 ◽  
Author(s):  
Audrey C. A. Cleuren ◽  
Martijn A. van der Ent ◽  
Hui Jiang ◽  
Kristina L. Hunker ◽  
Andrew Yee ◽  
...  
Keyword(s):  
Gene Expression ◽  
Single Cell ◽  
Rna Sequencing ◽  
Expression Profiles ◽  
Affinity Purification ◽  
Specific Gene ◽  
Sequencing Analysis ◽  
Single Cell Rna Sequencing ◽  
Vascular Beds

Endothelial cells (ECs) are highly specialized across vascular beds. However, given their interspersed anatomic distribution, comprehensive characterization of the molecular basis for this heterogeneity in vivo has been limited. By applying endothelial-specific translating ribosome affinity purification (EC-TRAP) combined with high-throughput RNA sequencing analysis, we identified pan EC-enriched genes and tissue-specific EC transcripts, which include both established markers and genes previously unappreciated for their presence in ECs. In addition, EC-TRAP limits changes in gene expression after EC isolation and in vitro expansion, as well as rapid vascular bed-specific shifts in EC gene expression profiles as a result of the enzymatic tissue dissociation required to generate single-cell suspensions for fluorescence-activated cell sorting or single-cell RNA sequencing analysis. Comparison of our EC-TRAP with published single-cell RNA sequencing data further demonstrates considerably greater sensitivity of EC-TRAP for the detection of low abundant transcripts. Application of EC-TRAP to examine the in vivo host response to lipopolysaccharide (LPS) revealed the induction of gene expression programs associated with a native defense response, with marked differences across vascular beds. Furthermore, comparative analysis of whole-tissue and TRAP-selected mRNAs identified LPS-induced differences that would not have been detected by whole-tissue analysis alone. Together, these data provide a resource for the analysis of EC-specific gene expression programs across heterogeneous vascular beds under both physiologic and pathologic conditions.

scholarly journals The in vivo endothelial cell translatome is highly heterogeneous across vascular beds

2019 ◽  
Author(s):  
Audrey C.A. Cleuren ◽  
Martijn A. van der Ent ◽  
Hui Jiang ◽  
Kristina L. Hunker ◽  
Andrew Yee ◽  
...  
Keyword(s):  
Gene Expression ◽  
Endothelial Cells ◽  
Single Cell ◽  
Blood Vessels ◽  
Rna Sequencing ◽  
Specific Gene ◽  
Sequencing Analysis ◽  
Single Cell Rna Sequencing ◽  
Vascular Beds

AbstractEndothelial cells (ECs) are highly specialized across vascular beds. However, given their interspersed anatomic distribution, comprehensive characterization of the molecular basis for this heterogeneity in vivo has been limited. By applying endothelial-specific translating ribosome affinity purification (EC-TRAP) combined with high-throughput RNA sequencing analysis, we identified pan EC-enriched genes and tissue-specific EC transcripts, which include both established markers and genes previously unappreciated for their presence in ECs. In addition, EC-TRAP limits changes in gene expression following EC isolation and in vitro expansion, as well as rapid vascular bed-specific shifts in EC gene expression profiles as a result of the enzymatic tissue dissociation required to generate single cell suspensions for fluorescence-activated cell sorting (FACS) or single cell RNA sequencing analysis. Comparison of our EC-TRAP to published single cell RNA sequencing data further demonstrates considerably greater sensitivity of EC-TRAP for the detection of low abundant transcripts. Application of EC-TRAP to examine the in vivo host response to lipopolysaccharide (LPS) revealed the induction of gene expression programs associated with a native defense response, with marked differences across vascular beds. Furthermore, comparative analysis of whole tissue and TRAP-selected mRNAs identified LPS-induced differences that would not have been detected by whole tissue analysis alone. Together, these data provide a resource for the analysis of EC-specific gene expression programs across heterogeneous vascular beds under both physiologic and pathologic conditions.SignificanceEndothelial cells (ECs), which line all vertebrate blood vessels, are highly heterogeneous across different tissues. The present study uses a genetic approach to specifically tag mRNAs within ECs of the mouse, thereby allowing recovery and sequence analysis to evaluate the EC-specific gene expression program directly from intact organs. Our findings demonstrate marked heterogeneity in EC gene expression across different vascular beds under both normal and disease conditions, with a more accurate picture than can be achieved using other methods. The data generated in these studies advance our understanding of EC function in different blood vessels and provide a valuable resource for future studies.

scholarly journals SCDC: bulk gene expression deconvolution by multiple single-cell RNA sequencing references

2020 ◽  
Author(s):  
Meichen Dong ◽  
Aatish Thennavan ◽  
Eugene Urrutia ◽  
Yun Li ◽  
Charles M Perou ◽  
...  
Keyword(s):  
Gene Expression ◽  
Single Cell ◽  
Rna Sequencing ◽  
Expression Profiles ◽  
Gene Expression Profiles ◽  
Specific Gene ◽  
Rna Seq ◽  
Cell Type ◽  
Mixed Cell ◽  
Single Cell Rna Sequencing

Abstract Recent advances in single-cell RNA sequencing (scRNA-seq) enable characterization of transcriptomic profiles with single-cell resolution and circumvent averaging artifacts associated with traditional bulk RNA sequencing (RNA-seq) data. Here, we propose SCDC, a deconvolution method for bulk RNA-seq that leverages cell-type specific gene expression profiles from multiple scRNA-seq reference datasets. SCDC adopts an ENSEMBLE method to integrate deconvolution results from different scRNA-seq datasets that are produced in different laboratories and at different times, implicitly addressing the problem of batch-effect confounding. SCDC is benchmarked against existing methods using both in silico generated pseudo-bulk samples and experimentally mixed cell lines, whose known cell-type compositions serve as ground truths. We show that SCDC outperforms existing methods with improved accuracy of cell-type decomposition under both settings. To illustrate how the ENSEMBLE framework performs in complex tissues under different scenarios, we further apply our method to a human pancreatic islet dataset and a mouse mammary gland dataset. SCDC returns results that are more consistent with experimental designs and that reproduce more significant associations between cell-type proportions and measured phenotypes.

scholarly journals SCDC: Bulk Gene Expression Deconvolution by Multiple Single-Cell RNA Sequencing References

2019 ◽  
Author(s):  
Meichen Dong ◽  
Aatish Thennavan ◽  
Eugene Urrutia ◽  
Yun Li ◽  
Charles M. Perou ◽  
...  
Keyword(s):  
Gene Expression ◽  
Single Cell ◽  
Rna Sequencing ◽  
Expression Profiles ◽  
Gene Expression Profiles ◽  
Specific Gene ◽  
Rna Seq ◽  
Cell Type ◽  
Mixed Cell ◽  
Single Cell Rna Sequencing

AbstractRecent advances in single-cell RNA sequencing (scRNA-seq) enable characterization of transcriptomic profiles with single-cell resolution and circumvent averaging artifacts associated with traditional bulk RNA sequencing (RNA-seq) data. Here, we propose SCDC, a deconvolution method for bulk RNA-seq that leverages cell-type specific gene expression profiles from multiple scRNA-seq reference datasets. SCDC adopts an ENSEMBLE method to integrate deconvolution results from different scRNA-seq datasets that are produced in different laboratories and at different times, implicitly addressing the problem of batch-effect confounding. SCDC is benchmarked against existing methods using both in silico generated pseudo-bulk samples and experimentally mixed cell lines, whose known cell-type compositions serve as ground truths. We show that SCDC outperforms existing methods with improved accuracy of cell-type decomposition under both settings. To illustrate how the ENSEMBLE framework performs in complex tissues under different scenarios, we further apply our method to a human pancreatic islet dataset and a mouse mammary gland dataset. SCDC returns results that are more consistent with experimental designs and that reproduce more significant associations between cell-type proportions and measured phenotypes.

scholarly journals A molecular cell atlas of the human lung from single cell RNA sequencing

2019 ◽  
Author(s):  
Kyle J. Travaglini ◽  
Ahmad N. Nabhan ◽  
Lolita Penland ◽  
Rahul Sinha ◽  
Astrid Gillich ◽  
...  
Keyword(s):  
Gene Expression ◽  
Single Cell ◽  
Rna Sequencing ◽  
Immune Cell ◽  
Human Lung ◽  
Expression Profiles ◽  
Cell Types ◽  
Specific Gene ◽  
Cell Trafficking ◽  
Single Cell Rna Sequencing

AbstractAlthough single cell RNA sequencing studies have begun providing compendia of cell expression profiles, it has proven more difficult to systematically identify and localize all molecular cell types in individual organs to create a full molecular cell atlas. Here we describe droplet- and plate-based single cell RNA sequencing applied to ∼75,000 human lung and blood cells, combined with a multi-pronged cell annotation approach, which have allowed us to define the gene expression profiles and anatomical locations of 58 cell populations in the human lung, including 41 of 45 previously known cell types or subtypes and 14 new ones. This comprehensive molecular atlas elucidates the biochemical functions of lung cell types and the cell-selective transcription factors and optimal markers for making and monitoring them; defines the cell targets of circulating hormones and predicts local signaling interactions including sources and targets of chemokines in immune cell trafficking and expression changes on lung homing; and identifies the cell types directly affected by lung disease genes and respiratory viruses. Comparison to mouse identified 17 molecular types that appear to have been gained or lost during lung evolution and others whose expression profiles have been substantially altered, revealing extensive plasticity of cell types and cell-type-specific gene expression during organ evolution including expression switches between cell types. This atlas provides the molecular foundation for investigating how lung cell identities, functions, and interactions are achieved in development and tissue engineering and altered in disease and evolution.

scholarly journals Microbial single-cell RNA sequencing by split-pool barcoding

Science ◽  
2020 ◽  
Vol 371 (6531) ◽  
pp. eaba5257 ◽  
Author(s):  
Anna Kuchina ◽  
Leandra M. Brettner ◽  
Luana Paleologu ◽  
Charles M. Roco ◽  
Alexander B. Rosenberg ◽  
...  
Keyword(s):  
Gene Expression ◽  
Single Cell ◽  
Rna Sequencing ◽  
High Throughput ◽  
Single Cell Analysis ◽  
Expression Profiles ◽  
Gene Expression Profiles ◽  
Growth Stages ◽  
High Throughput Analysis ◽  
Single Cell Rna Sequencing

Single-cell RNA sequencing (scRNA-seq) has become an essential tool for characterizing gene expression in eukaryotes, but current methods are incompatible with bacteria. Here, we introduce microSPLiT (microbial split-pool ligation transcriptomics), a high-throughput scRNA-seq method for Gram-negative and Gram-positive bacteria that can resolve heterogeneous transcriptional states. We applied microSPLiT to >25,000 Bacillus subtilis cells sampled at different growth stages, creating an atlas of changes in metabolism and lifestyle. We retrieved detailed gene expression profiles associated with known, but rare, states such as competence and prophage induction and also identified unexpected gene expression states, including the heterogeneous activation of a niche metabolic pathway in a subpopulation of cells. MicroSPLiT paves the way to high-throughput analysis of gene expression in bacterial communities that are otherwise not amenable to single-cell analysis, such as natural microbiota.

scholarly journals PRIME: a probabilistic imputation method to reduce dropout effects in single-cell RNA sequencing

2020 ◽  
Vol 36 (13) ◽  
pp. 4021-4029
Author(s):  
Hyundoo Jeong ◽  
Zhandong Liu
Keyword(s):  
Gene Expression ◽  
Single Cell ◽  
Rna Sequencing ◽  
Expression Profiles ◽  
Expression Patterns ◽  
Imputation Method ◽  
Supplementary Information ◽  
Single Cell Sequencing ◽  
Depth Analysis ◽  
Single Cell Rna Sequencing

Abstract Summary Single-cell RNA sequencing technology provides a novel means to analyze the transcriptomic profiles of individual cells. The technique is vulnerable, however, to a type of noise called dropout effects, which lead to zero-inflated distributions in the transcriptome profile and reduce the reliability of the results. Single-cell RNA sequencing data, therefore, need to be carefully processed before in-depth analysis. Here, we describe a novel imputation method that reduces dropout effects in single-cell sequencing. We construct a cell correspondence network and adjust gene expression estimates based on transcriptome profiles for the local subnetwork of cells of the same type. We comprehensively evaluated this method, called PRIME (PRobabilistic IMputation to reduce dropout effects in Expression profiles of single-cell sequencing), on synthetic and eight real single-cell sequencing datasets and verified that it improves the quality of visualization and accuracy of clustering analysis and can discover gene expression patterns hidden by noise. Availability and implementation The source code for the proposed method is freely available at https://github.com/hyundoo/PRIME. Supplementary information Supplementary data are available at Bioinformatics online.

scholarly journals Pancreatic cancer is marked by complement-high blood monocytes and tumor-associated macrophages

2021 ◽  
Vol 4 (6) ◽  
pp. e202000935
Author(s):  
Samantha B Kemp ◽  
Nina G Steele ◽  
Eileen S Carpenter ◽  
Katelyn L Donahue ◽  
Grace G Bushnell ◽  
...  
Keyword(s):  
Single Cell ◽  
Pancreatic Ductal Adenocarcinoma ◽  
Rna Sequencing ◽  
Gene Expression Signature ◽  
Ductal Adenocarcinoma ◽  
Specific Gene ◽  
Sequencing Analysis ◽  
Tumor Associated Macrophages ◽  
Primary Tumors ◽  
Single Cell Rna Sequencing

Pancreatic ductal adenocarcinoma (PDA) is accompanied by reprogramming of the local microenvironment, but changes at distal sites are poorly understood. We implanted biomaterial scaffolds, which act as an artificial premetastatic niche, into immunocompetent tumor-bearing and control mice, and identified a unique tumor-specific gene expression signature that includes high expression of C1qa, C1qb, Trem2, and Chil3. Single-cell RNA sequencing mapped these genes to two distinct macrophage populations in the scaffolds, one marked by elevated C1qa, C1qb, and Trem2, the other with high Chil3, Ly6c2 and Plac8. In mice, expression of these genes in the corresponding populations was elevated in tumor-associated macrophages compared with macrophages in the normal pancreas. We then analyzed single-cell RNA sequencing from patient samples, and determined expression of C1QA, C1QB, and TREM2 is elevated in human macrophages in primary tumors and liver metastases. Single-cell sequencing analysis of patient blood revealed a substantial enrichment of the same gene signature in monocytes. Taken together, our study identifies two distinct tumor-associated macrophage and monocyte populations that reflects systemic immune changes in pancreatic ductal adenocarcinoma patients.

scholarly journals Single-Cell RNA Sequencing Elucidates the Structure and Organization of Microbial Communities

2021 ◽  
Vol 12 ◽  
Author(s):  
Melanie A. Brennan ◽  
Adam Z. Rosenthal
Keyword(s):  
Single Cell ◽  
Rna Sequencing ◽  
Large Scale ◽  
Expression Profiles ◽  
Human Life ◽  
Bacterial Species ◽  
Population Heterogeneity ◽  
Specific Gene ◽  
Valuable Insight ◽  
Single Cell Rna Sequencing

Clonal bacterial populations exhibit various forms of heterogeneity, including co-occurrence of cells with different morphological traits, biochemical properties, and gene expression profiles. This heterogeneity is prevalent in a variety of environments. For example, the productivity of large-scale industrial fermentations and virulence of infectious diseases are shaped by cell population heterogeneity and have a direct impact on human life. Due to the need and importance to better understand this heterogeneity, multiple methods of examining single-cell heterogeneity have been developed. Traditionally, fluorescent reporters or probes are used to examine a specific gene of interest, providing a useful but inherently biased approach. In contrast, single-cell RNA sequencing (scRNA-seq) is an agnostic approach to examine heterogeneity and has been successfully applied to eukaryotic cells. Unfortunately, current extensively utilized methods of eukaryotic scRNA-seq present difficulties when applied to bacteria. Specifically, bacteria have a cell wall which makes eukaryotic lysis methods incompatible, bacterial mRNA has a shorter half-life and lower copy numbers, and isolating an individual bacterial species from a mixed community is difficult. Recent work has demonstrated that these technical hurdles can be overcome, providing valuable insight into factors influencing microbial heterogeneity. This perspective describes the emerging microbial scRNA-seq toolkit. We outline the benefit of these new tools in elucidating numerous scientific questions in microbiological studies and offer insight about the possible rules that govern the segregation of traits in individual microbial cells.

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