scholarly journals Profiling Glioblastoma Cases with an Expression of DCX, OLIG2 and NES

2021 ◽  
Vol 22 (24) ◽  
pp. 13217
Author(s):  
Adrian Odrzywolski ◽  
Bożena Jarosz ◽  
Michał Kiełbus ◽  
Ilona Telejko ◽  
Dominik Ziemianek ◽  
...  
Keyword(s):  
Single Cell ◽  
Expression Patterns ◽  
Survival Rates ◽  
Glioma Cells ◽  
Intratumor Heterogeneity ◽  
Small Subset ◽  
Rna Seq ◽  
Multiple Factors ◽  
Factors Influencing ◽  
Tumour Margin

Glioblastoma (GBM) remains the leading cause of cancer-related deaths with the lowest five-year survival rates among all of the human cancers. Multiple factors contribute to its poor outcome, including intratumor heterogeneity, along with migratory and invasive capacities of tumour cells. Over the last several years Doublecortin (DCX) has been one of the debatable factors influencing GBM cells’ migration. To resolve DCX’s ambiguous role in GBM cells’ migration, we set to analyse the expression patterns of DCX along with Nestin (NES) and Oligodendrocyte lineage transcription factor 2 (OLIG2) in 17 cases of GBM, using immunohistochemistry, followed by an analysis of single-cell RNA-seq data. Our results showed that only a small subset of DCX positive (DCX+) cells was present in the tumour. Moreover, no particular pattern emerged when analysing DCX+ cells relative position to the tumour margin. By looking into single-cell RNA-seq data, the majority of DCX+ cells were classified as non-cancerous, with a small subset of cells that could be regarded as glioma stem cells. In conclusion, our findings support the notion that glioma cells express DCX; however, there is no clear evidence to prove that DCX participates in GBM cell migration.

scholarly journals Clustering Single-Cell RNA-Seq Data with Regularized Gaussian Graphical Model

Genes ◽  
2021 ◽  
Vol 12 (2) ◽  
pp. 311
Author(s):  
Zhenqiu Liu
Keyword(s):  
Single Cell ◽  
Free Parameter ◽  
Graphical Model ◽  
Expression Patterns ◽  
Information Criterion ◽  
Log P ◽  
Rna Seq ◽  
Clustering Methods ◽  
Wide Range ◽  
Free Parameters

Single-cell RNA-seq (scRNA-seq) is a powerful tool to measure the expression patterns of individual cells and discover heterogeneity and functional diversity among cell populations. Due to variability, it is challenging to analyze such data efficiently. Many clustering methods have been developed using at least one free parameter. Different choices for free parameters may lead to substantially different visualizations and clusters. Tuning free parameters is also time consuming. Thus there is need for a simple, robust, and efficient clustering method. In this paper, we propose a new regularized Gaussian graphical clustering (RGGC) method for scRNA-seq data. RGGC is based on high-order (partial) correlations and subspace learning, and is robust over a wide-range of a regularized parameter λ. Therefore, we can simply set λ=2 or λ=log(p) for AIC (Akaike information criterion) or BIC (Bayesian information criterion) without cross-validation. Cell subpopulations are discovered by the Louvain community detection algorithm that determines the number of clusters automatically. There is no free parameter to be tuned with RGGC. When evaluated with simulated and benchmark scRNA-seq data sets against widely used methods, RGGC is computationally efficient and one of the top performers. It can detect inter-sample cell heterogeneity, when applied to glioblastoma scRNA-seq data.

scholarly journals Finding cell-specific expression patterns in the early Ciona embryo with single-cell RNA-seq

2020 ◽  
Vol 10 (1) ◽  
Author(s):  
Garth R. Ilsley ◽  
Ritsuko Suyama ◽  
Takeshi Noda ◽  
Nori Satoh ◽  
Nicholas M. Luscombe
Keyword(s):  
Single Cell ◽  
Expression Patterns ◽  
Rna Seq ◽  
Specific Expression

scholarly journals Comprehensive characterization of tissue-specific chromatin accessibility in L2 Caenorhabditis elegans nematodes

2020 ◽  
Author(s):  
Timothy J. Durham ◽  
Riza M. Daza ◽  
Louis Gevirtzman ◽  
Darren A. Cusanovich ◽  
William Stafford Noble ◽  
...  
Keyword(s):  
Gene Expression ◽  
Single Cell ◽  
Expression Patterns ◽  
Cell Types ◽  
Chromatin Accessibility ◽  
Gene Expression Patterns ◽  
Rna Seq ◽  
Cell Type ◽  
Tissue Specific ◽  
C Elegans

AbstractRecently developed single cell technologies allow researchers to characterize cell states at ever greater resolution and scale. C. elegans is a particularly tractable system for studying development, and recent single cell RNA-seq studies characterized the gene expression patterns for nearly every cell type in the embryo and at the second larval stage (L2). Gene expression patterns are useful for learning about gene function and give insight into the biochemical state of different cell types; however, in order to understand these cell types, we must also determine how these gene expression levels are regulated. We present the first single cell ATAC-seq study in C. elegans. We collected data in L2 larvae to match the available single cell RNA-seq data set, and we identify tissue-specific chromatin accessibility patterns that align well with existing data, including the L2 single cell RNA-seq results. Using a novel implementation of the latent Dirichlet allocation algorithm, we leverage the single-cell resolution of the sci-ATAC-seq data to identify accessible loci at the level of individual cell types, providing new maps of putative cell type-specific gene regulatory sites, with promise for better understanding of cellular differentiation and gene regulation in the worm.

scholarly journals SCPattern: A statistical approach to identify and classify expression changes in single cell RNA-seq experiments with ordered conditions

2016 ◽  
Author(s):  
Ning Leng ◽  
Li-Fang Chu ◽  
Jeea Choi ◽  
Christina Kendziorski ◽  
James A. Thomson ◽  
...  
Keyword(s):  
Single Cell ◽  
Cell Fate ◽  
Empirical Bayes ◽  
Time Course ◽  
Expression Patterns ◽  
R Package ◽  
Embryonic Cell ◽  
Rna Seq ◽  
Cell Fate Decisions ◽  
Probability Estimates

AbstractMotivationWith the development of single cell RNA-seq (scRNA-seq) technology, scRNA-seq experiments with ordered conditions (e.g. time-course) are becoming common. Methods developed for analyzing ordered bulk RNA-seq experiments are not applicable to scRNA-seq, since their distributional assumptions are often violated by additional heterogeneities prevalent in scRNA-seq. Here we present SC-Pattern - an empirical Bayes model to characterize genes with expression changes in ordered scRNA-seq experiments. SCPattern utilizes the non-parametrical Kolmogorov-Smirnov statistic, thus it has the flexibility to identify genes with a wide variety of types of changes. Additionally, the Bayes framework allows SCPattern to classify genes into expression patterns with probability estimates.ResultsSimulation results show that SCPattern is well powered for identifying genes with expression changes while the false discovery rate is well controlled. SCPattern is also able to accurately classify these dynamic genes into directional expression patterns. Applied to a scRNA-seq time course dataset studying human embryonic cell differentiation, SCPattern detected a group of important genes that are involved in mesendoderm and definitive endoderm cell fate decisions, positional patterning, and cell cycle.Availability and ImplementationThe SCPattern is implemented as an R package along with a user-friendly graphical interface, which are available at:https://github.com/lengning/SCPatternContact:[email protected]

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 SMARTer single cell total RNA sequencing

2018 ◽  
Author(s):  
Verboom Karen ◽  
Everaert Celine ◽  
Bolduc Nathalie ◽  
Livak J. Kenneth ◽  
Yigit Nurten ◽  
...  
Keyword(s):  
Single Cell ◽  
Rna Sequencing ◽  
Expression Patterns ◽  
Transcript Level ◽  
Cellular Heterogeneity ◽  
Circular Rnas ◽  
Rna Seq ◽  
Sequencing Data ◽  
Total Rna ◽  
Sequencing Experiment

AbstractSingle cell RNA sequencing methods have been increasingly used to understand cellular heterogeneity. Nevertheless, most of these methods suffer from one or more limitations, such as focusing only on polyadenylated RNA, sequencing of only the 3’ end of the transcript, an exuberant fraction of reads mapping to ribosomal RNA, and the unstranded nature of the sequencing data. Here, we developed a novel single cell strand-specific total RNA library preparation method addressing all the aforementioned shortcomings. Our method was validated on a microfluidics system using three different cancer cell lines undergoing a chemical or genetic perturbation. We demonstrate that our total RNA-seq method detects an equal or higher number of genes compared to classic polyA[+] RNA-seq, including novel and non-polyadenylated genes. The obtained RNA expression patterns also recapitulate the expected biological signal. Inherent to total RNA-seq, our method is also able to detect circular RNAs. Taken together, SMARTer single cell total RNA sequencing is very well suited for any single cell sequencing experiment in which transcript level information is needed beyond polyadenylated genes.

Bioinformatics approach to spatially resolved transcriptomics

2021 ◽  
Author(s):  
Ivan Krešimir Lukić
Keyword(s):  
Gene Expression ◽  
Single Cell ◽  
Expression Patterns ◽  
Spatial Dimension ◽  
Medical Diagnostics ◽  
Regions Of Interest ◽  
Rna Seq ◽  
Neoplastic Tissue ◽  
Spatially Resolved ◽  
Cell Assays

Spatially resolved transcriptomics encompasses a growing number of methods developed to enable gene expression profiling of individual cells within a tissue. Different technologies are available and they vary with respect to: the method used to define regions of interest, the method used to assess gene expression, and resolution. Since techniques based on next-generation sequencing are the most prevalent, and provide single-cell resolution, many bioinformatics tools for spatially resolved data are shared with single-cell RNA-seq. The analysis pipelines diverge at the level of quantification matrix, downstream of which spatial techniques require specific tools to answer key biological questions. Those questions include: (i) cell type classification; (ii) detection of genes with specific spatial distribution; (iii) identification of novel tissue regions based on gene expression patterns; (iv) cell–cell interactions. On the other hand, analysis of spatially resolved data is burdened by several specific challenges. Defining regions of interest, e.g. neoplastic tissue, often calls for manual annotation of images, which then poses a bottleneck in the pipeline. Another specific issue is the third spatial dimension and the need to expand the analysis beyond a single slice. Despite the problems, it can be predicted that the popularity of spatial techniques will keep growing until they replace single-cell assays (which will remain limited to specific cases, like blood). As soon as the computational protocol reach the maturity (e.g. bulk RNA-seq), one can foresee the expansion of spatial techniques beyond basic or translational research, even into routine medical diagnostics.

scholarly journals Analysis of allelic expression patterns in clonal somatic cells by single-cell RNA–seq

2016 ◽  
Vol 48 (11) ◽  
pp. 1430-1435 ◽  
Author(s):  
Björn Reinius ◽  
Jeff E Mold ◽  
Daniel Ramsköld ◽  
Qiaolin Deng ◽  
Per Johnsson ◽  
...  
Keyword(s):  
Single Cell ◽  
Expression Patterns ◽  
Somatic Cells ◽  
Allelic Expression ◽  
Rna Seq

scholarly journals Prediction of single-cell mechanisms for disease progression in hypertrophic remodelling by a trans-omics approach

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Momoko Hamano ◽  
Seitaro Nomura ◽  
Midori Iida ◽  
Issei Komuro ◽  
Yoshihiro Yamanishi
Keyword(s):  
Heart Failure ◽  
Single Cell ◽  
Large Scale ◽  
Molecular Mechanisms ◽  
Expression Patterns ◽  
Marker Genes ◽  
Specific Gene ◽  
Rna Seq ◽  
Aortic Constriction ◽  
Multiple Risk Factors

AbstractHeart failure is a heterogeneous disease with multiple risk factors and various pathophysiological types, which makes it difficult to understand the molecular mechanisms involved. In this study, we proposed a trans-omics approach for predicting molecular pathological mechanisms of heart failure and identifying marker genes to distinguish heterogeneous phenotypes, by integrating multiple omics data including single-cell RNA-seq, ChIP-seq, and gene interactome data. We detected a significant increase in the expression level of natriuretic peptide A (Nppa), after stress loading with transverse aortic constriction (TAC), and showed that cardiomyocytes with high Nppa expression displayed specific gene expression patterns. Multiple NADH ubiquinone complex family, which are associated with the mitochondrial electron transport system, were negatively correlated with Nppa expression during the early stages of cardiac hypertrophy. Large-scale ChIP-seq data analysis showed that Nkx2-5 and Gtf2b were transcription factors characteristic of high-Nppa-expressing cardiomyocytes. Nppa expression levels may, therefore, represent a useful diagnostic marker for heart failure.

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