scholarly journals Identification of ZEB2 as an Immune-Associated Gene in Endometrial Carcinoma and Associated with Macrophage Infiltration by Bioinformatic Analysis

2021 ◽  
Vol 2021 ◽  
pp. 1-12
Author(s):  
Yuanyuan Zhu ◽  
Xinchao Lin ◽  
Yuanlong Zang ◽  
Qiaohui Yang
Keyword(s):  
Endometrial Carcinoma ◽  
Single Cell ◽  
Immune Cells ◽  
Prognostic Significance ◽  
Group Analysis ◽  
Disease Free Survival ◽  
Rna Seq ◽  
Mesenchymal Transition ◽  
Cell Transcriptome ◽  
Single Cell Transcriptome

Endometrial cancer (EC) is the most common gynecological tumor arising from the endometrium. In this study, we use a published single-cell transcriptome profile of endometrial carcinoma (EC) to reveal the composition of immune cells and found an immunosuppressive environment since the presence of macrophage subtype M2 and exhausted CD8+ T cell markers. We focused on ZEB2 (Zinc finger E-box binding homeobox 2), a well-known player in epithelial to mesenchymal transition process, and we showed that ZEB2 is exclusively expressed in immune cells in single-cell transcriptome and, at the same time, downregulated in TCGA-UCEC (The Cancer Genome Atlas—Uterine Corpus Endometrial Carcinoma) bulk RNA-seq data and negatively associated with tumor purity. Loss of ZEB2 protein in EC in normal endometrium and EC samples was validated in samples using immunohistochemistry (IHC) from HPA (Human Protein Atlas) database. Furthermore, we found ZEB2 was associated with immune infiltrations especially for macrophage using TIMER 2.0. Interestingly, ZEB2 prognostic significance differed under various macrophage and Th2 helper cell content using Kaplan–Meier Plotter analysis. More importantly, we showed that over 11% EC patients have somatic mutations of ZEB2 in EC samples collected from cBioportal and they have a lower body weight, earlier diagnosis age, and better overall survival and disease-free survival status compared with the unaltered group. Analysis in TIMER2.0 suggested that ZEB2 mutation would possibly change the composition of tumor-infiltrating lymphocytes. Taken together, by combining the results from single-cell data, bulk TCGA RNA-seq, and other online bioinformatic tools, we provided evidence that ZEB2 might have a unique role in the immune environment of EC. These results would provide a better insight into the pathogenetic process and ZEB2 might further be used an immunotherapeutic target of EC.

scholarly journals A single cell transcriptome atlas of the developing zebrafish hindbrain

2019 ◽  
Author(s):  
Monica Tambalo ◽  
Richard Mitter ◽  
David G. Wilkinson
Keyword(s):  
Single Cell ◽  
Neuronal Differentiation ◽  
Cell Types ◽  
Spatial Patterning ◽  
Rna Seq ◽  
Valuable Resource ◽  
Dorsoventral Axis ◽  
Single Cell Rna Sequencing ◽  
Cell Transcriptome ◽  
Single Cell Transcriptome

AbstractSegmentation of the vertebrate hindbrain leads to the formation of rhombomeres, each with a distinct anteroposterior identity. Specialised boundary cells form at segment borders that act as a source or regulator of neuronal differentiation. In zebrafish, there is spatial patterning of neurogenesis in which non-neurogenic zones form at bounderies and segment centres, in part mediated by Fgf20 signaling. To further understand the control of neurogenesis, we have carried out single cell RNA sequencing of the zebrafish hindbrain at three different stages of patterning. Analyses of the data reveal known and novel markers of distinct hindbrain segments, of cell types along the dorsoventral axis, and of the transition of progenitors to neuronal differentiation. We find major shifts in the transcriptome of progenitors and of differentiating cells between the different stages analysed. Supervised clustering with markers of boundary cells and segment centres, together with RNA-seq analysis of Fgf-regulated genes, has revealed new candidate regulators of cell differentiation in the hindbrain. These data provide a valuable resource for functional investigations of the patterning of neurogenesis and the transition of progenitors to neuronal differentiation.

scholarly journals Single-Cell Transcriptomics-Based Study of Transcriptional Regulatory Features in the Mouse Brain Vasculature

2021 ◽  
Vol 2021 ◽  
pp. 1-15
Author(s):  
Wei-Wei Lin ◽  
Lin-Tao Xu ◽  
Yi-Sheng Chen ◽  
Ken Go ◽  
Chenyu Sun ◽  
...  
Keyword(s):  
Single Cell ◽  
Mouse Brain ◽  
Regulatory Network ◽  
Regulatory Networks ◽  
Transcriptional Regulatory Network ◽  
Rna Seq ◽  
Brain Vasculature ◽  
Transcriptional Regulatory ◽  
Cell Transcriptome ◽  
Single Cell Transcriptome

Background. The critical role of vascular health on brain function has received much attention in recent years. At the single-cell level, studies on the developmental processes of cerebral vascular growth are still relatively few. Techniques for constructing gene regulatory networks (GRNs) based on single-cell transcriptome expression data have made significant progress in recent years. Herein, we constructed a single-cell transcriptional regulatory network of mouse cerebrovascular cells. Methods. The single-cell RNA-seq dataset of mouse brain vessels was downloaded from GEO (GSE98816). This cell clustering was annotated separately using singleR and CellMarker. We then used a modified version of the SCENIC method to construct GRNs. Next, we used a mouse version of SEEK to assess whether genes in the regulon were coexpressed. Finally, regulatory module analysis was performed to complete the cell type relationship quantification. Results. Single-cell RNA-seq data were used to analyze the heterogeneity of mouse cerebrovascular cells, whereby four cell types including endothelial cells, fibroblasts, microglia, and oligodendrocytes were defined. These subpopulations of cells and marker genes together characterize the molecular profile of mouse cerebrovascular cells. Through these signatures, key transcriptional regulators that maintain cell identity were identified. Our findings identified genes like Lmo2, which play an important role in endothelial cells. The same cell type, for instance, fibroblasts, was found to have different regulatory networks, which may influence the functional characteristics of local tissues. Conclusions. In this study, a transcriptional regulatory network based on single-cell analysis was constructed. Additionally, the study identified and profiled mouse cerebrovascular cells using single-cell transcriptome data as well as defined TFs that affect the regulatory network of the mouse brain vasculature.

scholarly journals Single-cell transcriptome analysis reveals the dynamics of human immune cells during early fetal skin development

Cell Reports ◽  
2021 ◽  
Vol 36 (6) ◽  
pp. 109524
Author(s):  
Yingping Xu ◽  
Jun Zhang ◽  
Yongfei Hu ◽  
Xuefei Li ◽  
Lihua Sun ◽  
...  
Keyword(s):  
Single Cell ◽  
Immune Cells ◽  
Transcriptome Analysis ◽  
Fetal Skin ◽  
Skin Development ◽  
Human Immune ◽  
Cell Transcriptome ◽  
Single Cell Transcriptome

scholarly journals scRNABatchQC: multi-samples quality control for single cell RNA-seq data

2019 ◽  
Vol 35 (24) ◽  
pp. 5306-5308
Author(s):  
Qi Liu ◽  
Quanhu Sheng ◽  
Jie Ping ◽  
Marisol Adelina Ramirez ◽  
Ken S Lau ◽  
...  
Keyword(s):  
Single Cell ◽  
R Package ◽  
Supplementary Information ◽  
Rna Seq ◽  
Technical Artifact ◽  
Multiple Sample ◽  
Systematic Biases ◽  
Cell Transcriptome ◽  
Single Cell Transcriptome ◽  
Spurious Results

Abstract Summary Single cell RNA sequencing is a revolutionary technique to characterize inter-cellular transcriptomics heterogeneity. However, the data are noise-prone because gene expression is often driven by both technical artifacts and genuine biological variations. Proper disentanglement of these two effects is critical to prevent spurious results. While several tools exist to detect and remove low-quality cells in one single cell RNA-seq dataset, there is lack of approach to examining consistency between sample sets and detecting systematic biases, batch effects and outliers. We present scRNABatchQC, an R package to compare multiple sample sets simultaneously over numerous technical and biological features, which gives valuable hints to distinguish technical artifact from biological variations. scRNABatchQC helps identify and systematically characterize sources of variability in single cell transcriptome data. The examination of consistency across datasets allows visual detection of biases and outliers. Availability and implementation scRNABatchQC is freely available at https://github.com/liuqivandy/scRNABatchQC as an R package. Supplementary information Supplementary data are available at Bioinformatics online.

scholarly journals What has single-cell RNA-seq taught us about mammalian spermatogenesis?

2019 ◽  
Vol 101 (3) ◽  
pp. 617-634 ◽  
Author(s):  
Shinnosuke Suzuki ◽  
Victoria D Diaz ◽  
Brian P Hermann
Keyword(s):  
Single Cell ◽  
Cell Types ◽  
Molecular Heterogeneity ◽  
Rna Seq ◽  
Technical Requirements ◽  
Male Gametes ◽  
The Veil ◽  
Cell Transcriptome ◽  
Future Utility ◽  
Single Cell Transcriptome

Abstract Mammalian spermatogenesis is a complex developmental program that transforms mitotic testicular germ cells (spermatogonia) into mature male gametes (sperm) for production of offspring. For decades, it has been known that this several-weeks-long process involves a series of highly ordered and morphologically recognizable cellular changes as spermatogonia proliferate, spermatocytes undertake meiosis, and spermatids develop condensed nuclei, acrosomes, and flagella. Yet, much of the underlying molecular logic driving these processes has remained opaque because conventional characterization strategies often aggregated groups of cells to meet technical requirements or due to limited capability for cell selection. Recently, a cornucopia of single-cell transcriptome studies has begun to lift the veil on the full compendium of gene expression phenotypes and changes underlying spermatogenic development. These datasets have revealed the previously obscured molecular heterogeneity among and between varied spermatogenic cell types and are reinvigorating investigation of testicular biology. This review describes the extent of available single-cell RNA-seq profiles of spermatogenic and testicular somatic cells, how those data were produced and evaluated, their present value for advancing knowledge of spermatogenesis, and their potential future utility at both the benchtop and bedside.

scholarly journals DSAVE: Detection of misclassified cells in single-cell RNA-Seq data

PLoS ONE ◽  
2020 ◽  
Vol 15 (12) ◽  
pp. e0243360
Author(s):  
Johan Gustafsson ◽  
Jonathan Robinson ◽  
Juan S. Inda-Díaz ◽  
Elias Björnson ◽  
Rebecka Jörnsten ◽  
...  
Keyword(s):  
Gene Expression ◽  
Single Cell ◽  
Cell Types ◽  
Rna Seq ◽  
Cell Type ◽  
Log Likelihood ◽  
Single Cell Rna Sequencing ◽  
Cell Transcriptome ◽  
Average Gene ◽  
Single Cell Transcriptome

Single-cell RNA sequencing has become a valuable tool for investigating cell types in complex tissues, where clustering of cells enables the identification and comparison of cell populations. Although many studies have sought to develop and compare different clustering approaches, a deeper investigation into the properties of the resulting populations is lacking. Specifically, the presence of misclassified cells can influence downstream analyses, highlighting the need to assess subpopulation purity and to detect such cells. We developed DSAVE (Down-SAmpling based Variation Estimation), a method to evaluate the purity of single-cell transcriptome clusters and to identify misclassified cells. The method utilizes down-sampling to eliminate differences in sampling noise and uses a log-likelihood based metric to help identify misclassified cells. In addition, DSAVE estimates the number of cells needed in a population to achieve a stable average gene expression profile within a certain gene expression range. We show that DSAVE can be used to find potentially misclassified cells that are not detectable by similar tools and reveal the cause of their divergence from the other cells, such as differing cell state or cell type. With the growing use of single-cell RNA-seq, we foresee that DSAVE will be an increasingly useful tool for comparing and purifying subpopulations in single-cell RNA-Seq datasets.

scholarly journals Single Cell Transcriptome-Based Dissection of Lineage Fate Decisions in Myelopoiesis

Blood ◽  
2014 ◽  
Vol 124 (21) ◽  
pp. 1395-1395
Author(s):  
Andre Olsson ◽  
H. Leighton Grimes ◽  
Virendra K Chaudhri ◽  
Philip Dexheimer ◽  
Bruce J Aronow ◽  
...  
Keyword(s):  
Single Cell ◽  
Cell Fate ◽  
Molecular Mechanisms ◽  
Conflicts Of Interest ◽  
Expression Patterns ◽  
Rna Seq ◽  
Cell Transcriptome ◽  
Single Cell Transcriptome ◽  
Cell Data ◽  
Insight Into

Abstract In spite of tremendous advances in the analysis of hematopoietic progenitors and transcription factors that give rise to different lineages, molecular insight into the mechanisms that underlie cell fate choice at the level of individual cells is lacking. We utilized single-cell RNA sequencing of murine granulocyte-monocyte progenitors (GMPs) to analyze the molecular basis of cell fate choice. Over 200 libraries were generated with average read depths of 4 million per library and an expressed gene call of over 3,800 genes with FPKM >3. Our data reveal a varied but coherent spectrum of gene expression patterns in individual murine GMPs. The majority of cells could be clustered into ones expressing either granulocytic or monocytic genes, suggesting that they were primed for lineage determination. A minority of GMPs expressed a mixed-lineage pattern of genes. The single-cell data suggested an antagonistic transcription factor circuit involving Gfi1 and IRF8 that was validated with both loss- and gain-of-function experiments in GMPs. Our data highlight the utility of single cell RNA-Seq analysis to reveal molecular mechanisms controlling lineage fate decisions in hematopoiesis. Disclosures No relevant conflicts of interest to declare.

scholarly journals Targeting individual cells by barcode in pooled sequence libraries

2017 ◽  
Author(s):  
Navpreet Ranu ◽  
Alexandra-Chloé Villani ◽  
Nir Hacohen ◽  
Paul C. Blainey
Keyword(s):  
Single Cell ◽  
Mononuclear Cells ◽  
Single Cells ◽  
Dna Barcodes ◽  
Rna Seq ◽  
Enrichment Method ◽  
Peripheral Blood Mononuclear ◽  
Efficient Sampling ◽  
Cell Transcriptome ◽  
Single Cell Transcriptome

There is rising interest in applying single-cell transcriptome analysis and other single-cell sequencing methods to resolve differences between cells. Pooled processing of thousands of single cells is now routinely practiced by introducing cell-specific DNA barcodes early in cell processing protocols1-5. However, researchers must sequence a large number of cells to sample rare subpopulations6-8, even when fluorescence-activated cell sorting (FACS) is used to pre-enrich rare cell populations. Here, a new molecular enrichment method is used in conjunction with FACS enrichment to enable efficient sampling of rare dendritic cell (DC) populations, including the recently identified AXL+SIGLEC6+ (AS DCs) subset7, within a 10X Genomics single-cell RNA-Seq library. DC populations collectively represent 1-2% of total peripheral blood mononuclear cells (PBMC), with AS DC representing only 1-3% of human blood DCs and 0.01-0.06% of total PBMCs.

scholarly journals Single cell transcriptome research in human placenta

Reproduction ◽  
2020 ◽  
Vol 160 (6) ◽  
pp. R155-R167
Author(s):  
Hui Li ◽  
Qianhui Huang ◽  
Yu Liu ◽  
Lana X Garmire
Keyword(s):  
Single Cell ◽  
Human Placenta ◽  
Pregnancy Complications ◽  
Cell Types ◽  
Rna Seq ◽  
Structural Components ◽  
The Public ◽  
Cell Transcriptome ◽  
Areas Of Interest ◽  
Single Cell Transcriptome

Human placenta is a complex and heterogeneous organ interfacing between the mother and the fetus that supports fetal development. Alterations to placental structural components are associated with various pregnancy complications. To reveal the heterogeneity among various placenta cell types in normal and diseased placentas, as well as elucidate molecular interactions within a population of placental cells, a new genomics technology called single cell RNA-seq (or scRNA-seq) has been employed in the last couple of years. Here we review the principles of scRNA-seq technology, and summarize the recent human placenta studies at scRNA-seq level across gestational ages as well as in pregnancy complications, such as preterm birth and preeclampsia. We list the computational analysis platforms and resources available for the public use. Lastly, we discuss the future areas of interest for placenta single cell studies, as well as the data analytics needed to accomplish them.

Sign in / Sign up

Export Citation Format

Share Document