scholarly journals Interrogation of the microenvironmental landscape in spinal ependymomas reveals dual functions of tumor-associated macrophages

2021 ◽  
Vol 12 (1) ◽  
Author(s):  
Qianqian Zhang ◽  
Sijin Cheng ◽  
Yongzhi Wang ◽  
Mengdi Wang ◽  
Yufeng Lu ◽  
...  
Keyword(s):  
Tumor Progression ◽  
Single Cell ◽  
Stromal Cells ◽  
High Capacity ◽  
Cellular Heterogeneity ◽  
Sequencing Data ◽  
Tumor Associated Macrophage ◽  
Spinal Ependymoma ◽  
Sequencing Data Analysis ◽  
Dual Functions

AbstractSpinal ependymomas are the most common spinal cord tumors in adults, but their intratumoral cellular heterogeneity has been less studied, and how spinal microglia are involved in tumor progression is still unknown. Here, our single-cell RNA-sequencing analyses of three spinal ependymoma subtypes dissect the microenvironmental landscape of spinal ependymomas and reveal tumor-associated macrophage (TAM) subsets with distinct functional phenotypes. CCL2+ TAMs are related to the immune response and exhibit a high capacity for apoptosis, while CD44+ TAMs are associated with tumor angiogenesis. By combining these results with those of single-cell ATAC-sequencing data analysis, we reveal that TEAD1 and EGR3 play roles in regulating the functional diversity of TAMs. We further identify diverse characteristics of both malignant cells and TAMs that might underlie the different malignant degrees of each subtype. Finally, assessment of cell-cell interactions reveal that stromal cells act as extracellular factors that mediate TAM diversity. Overall, our results reveal dual functions of TAMs in tumor progression, providing valuable insights for TAM-targeting immunotherapy.

How to Get Started with Single Cell RNA Sequencing Data Analysis

2021 ◽  
pp. ASN.2020121742 ◽  
Author(s):  
Michael S. Balzer ◽  
Ziyuan Ma ◽  
Jianfu Zhou ◽  
Amin Abedini ◽  
Katalin Susztak
Keyword(s):  
Single Cell ◽  
Single Cell Analysis ◽  
Cell Analysis ◽  
Epigenetic Changes ◽  
Sequencing Data ◽  
Single Experiment ◽  
Gene And Protein Expression ◽  
Single Cell Rna Sequencing ◽  
Analytical Tools ◽  
Sequencing Data Analysis

Over the last 5 years, single cell methods have enabled the monitoring of gene and protein expression, genetic, and epigenetic changes in thousands of individual cells in a single experiment. With the improved measurement and the decreasing cost of the reactions and sequencing, the size of these datasets is increasing rapidly. The critical bottleneck remains the analysis of the wealth of information generated by single cell experiments. In this review, we give a simplified overview of the analysis pipelines, as they are typically used in the field today. We aim to enable researchers starting out in single cell analysis to gain an overview of challenges and the most commonly used analytical tools. In addition, we hope to empower others to gain an understanding of how typical readouts from single cell datasets are presented in the published literature.

scholarly journals Spectacle: An interactive resource for ocular single-cell RNA sequencing data analysis

2020 ◽  
Vol 200 ◽  
pp. 108204 ◽  
Author(s):  
Andrew P. Voigt ◽  
S. Scott Whitmore ◽  
Nicholas D. Lessing ◽  
Adam P. DeLuca ◽  
Budd A. Tucker ◽  
...  
Keyword(s):  
Data Analysis ◽  
Single Cell ◽  
Rna Sequencing ◽  
Sequencing Data ◽  
Single Cell Rna Sequencing ◽  
Sequencing Data Analysis

scholarly journals rCASC: reproducible classification analysis of single-cell sequencing data

GigaScience ◽  
2019 ◽  
Vol 8 (9) ◽  
Author(s):  
Luca Alessandrì ◽  
Francesca Cordero ◽  
Marco Beccuti ◽  
Maddalena Arigoni ◽  
Martina Olivero ◽  
...  
Keyword(s):  
Single Cell ◽  
Rna Sequencing ◽  
Cellular Heterogeneity ◽  
Cell Subpopulation ◽  
Integrated Analysis ◽  
Specific Gene ◽  
Sequencing Data ◽  
Single Cell Sequencing ◽  
Cell Stability ◽  
Reproducible Analysis

Abstract Background Single-cell RNA sequencing is essential for investigating cellular heterogeneity and highlighting cell subpopulation-specific signatures. Single-cell sequencing applications have spread from conventional RNA sequencing to epigenomics, e.g., ATAC-seq. Many related algorithms and tools have been developed, but few computational workflows provide analysis flexibility while also achieving functional (i.e., information about the data and the tools used are saved as metadata) and computational reproducibility (i.e., a real image of the computational environment used to generate the data is stored) through a user-friendly environment. Findings rCASC is a modular workflow providing an integrated analysis environment (from count generation to cell subpopulation identification) exploiting Docker containerization to achieve both functional and computational reproducibility in data analysis. Hence, rCASC provides preprocessing tools to remove low-quality cells and/or specific bias, e.g., cell cycle. Subpopulation discovery can instead be achieved using different clustering techniques based on different distance metrics. Cluster quality is then estimated through the new metric "cell stability score" (CSS), which describes the stability of a cell in a cluster as a consequence of a perturbation induced by removing a random set of cells from the cell population. CSS provides better cluster robustness information than the silhouette metric. Moreover, rCASC's tools can identify cluster-specific gene signatures. Conclusions rCASC is a modular workflow with new features that could help researchers define cell subpopulations and detect subpopulation-specific markers. It uses Docker for ease of installation and to achieve a computation-reproducible analysis. A Java GUI is provided to welcome users without computational skills in R.

scholarly journals A 3D gene expression atlas of the floral meristem based on spatial reconstruction of single nucleus RNA sequencing data

2021 ◽  
Author(s):  
Manuel Neumann ◽  
Xiaocai Xu ◽  
Cezary Smaczniak ◽  
Julia Schumacher ◽  
Wenhao Yan ◽  
...  
Keyword(s):  
Gene Expression ◽  
Single Cell ◽  
Expression Patterns ◽  
Floral Meristem ◽  
Cellular Heterogeneity ◽  
Sequencing Data ◽  
Gene Expression Atlas ◽  
Spatial Reconstruction ◽  
Expression Atlas ◽  
3D Gene

Identity and functions of plant cells are influenced by their precise cellular location within the plant body. Cellular heterogeneity in growth and differentiation trajectories results in organ patterning. Therefore, assessing this heterogeneity at molecular scale is a major question in developmental biology. Single-cell transcriptomics (scRNA-seq) allows to characterize and quantify gene expression heterogeneity in developing organs at unprecedented resolution. However, the original physical location of the cell is lost during the scRNA-seq procedure. To recover the original location of cells is essential to link gene activity with cellular function and morphology. Here, we reconstruct genome-wide gene expression patterns of individual cells in a floral meristem by combining single-nuclei RNA-seq with 3D spatial reconstruction. By this, gene expression differences among meristematic domains giving rise to different tissue and organ types can be determined. As a proof of principle, the data are used to trace the initiation of vascular identity within the floral meristem. Our work demonstrates the power of spatially reconstructed single cell transcriptome atlases to understand plant morphogenesis. The floral meristem 3D gene expression atlas can be accessed at http://threed-flower-meristem.herokuapp.com

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