scholarly journals Characterization of Gene Expression Patterns among Artificially Developed Cancer Stem Cells Using Spherical Self-Organizing Map

2016 ◽  
Vol 15 ◽  
pp. CIN.S39839 ◽  
Author(s):  
Akimasa Seno ◽  
Tomonari Kasai ◽  
Masashi Ikeda ◽  
Arun Vaidyanath ◽  
Junko Masuda ◽  
...  
Keyword(s):  
Gene Expression ◽  
Stem Cells ◽  
Cancer Stem Cells ◽  
Human Cancer ◽  
Expression Patterns ◽  
Embryonic Stem ◽  
Culture Conditions ◽  
Self Organizing Map ◽  
Cancer Tissues ◽  
Self Organizing

We performed gene expression microarray analysis coupled with spherical self-organizing map (sSOM) for artificially developed cancer stem cells (CSCs). The CSCs were developed from human induced pluripotent stem cells (hiPSCs) with the conditioned media of cancer cell lines, whereas the CSCs were induced from primary cell culture of human cancer tissues with defined factors ( OCT3/4, SOX2, and KLF4). These cells commonly expressed human embryonic stem cell (hESC)/hiPSC-specific genes ( POU5F1, SOX2, NANOG, LIN28, and SALL4) at a level equivalent to those of control hiPSC 201B7. The sSOM with unsupervised method demonstrated that the CSCs could be divided into three groups based on their culture conditions and original cancer tissues. Furthermore, with supervised method, sSOM nominated TMED9, RNASE1, NGFR, ST3GAL1, TNS4, BTG2, SLC16A3, CD177, CES1, GDF15, STMN2, FAM20A, NPPB, CD99, MYL7, PRSS23, AHNAK, and LOC152573 genes commonly upregulating among the CSCs compared to hiPSC, suggesting the gene signature of the CSCs.

scholarly journals Impact of AHR Ligand TCDD on Human Embryonic Stem Cells and Early Differentiation

2020 ◽  
Vol 21 (23) ◽  
pp. 9052
Author(s):  
Indrek Teino ◽  
Antti Matvere ◽  
Martin Pook ◽  
Inge Varik ◽  
Laura Pajusaar ◽  
...  
Keyword(s):  
Gene Expression ◽  
Stem Cells ◽  
Embryonic Stem Cells ◽  
Human Embryonic Stem Cells ◽  
Target Genes ◽  
Expression Patterns ◽  
Embryonic Stem ◽  
Stem Cell Differentiation ◽  
Cell Types ◽  
Early Differentiation

Aryl hydrocarbon receptor (AHR) is a ligand-activated transcription factor, which mediates the effects of a variety of environmental stimuli in multiple tissues. Recent advances in AHR biology have underlined its importance in cells with high developmental potency, including pluripotent stem cells. Nonetheless, there is little data on AHR expression and its role during the initial stages of stem cell differentiation. The purpose of this study was to investigate the temporal pattern of AHR expression during directed differentiation of human embryonic stem cells (hESC) into neural progenitor, early mesoderm and definitive endoderm cells. Additionally, we investigated the effect of the AHR agonist 2,3,7,8-tetrachlorodibenzo-p-dioxin (TCDD) on the gene expression profile in hESCs and differentiated cells by RNA-seq, accompanied by identification of AHR binding sites by ChIP-seq and epigenetic landscape analysis by ATAC-seq. We showed that AHR is differentially regulated in distinct lineages. We provided evidence that TCDD alters gene expression patterns in hESCs and during early differentiation. Additionally, we identified novel potential AHR target genes, which expand our understanding on the role of this protein in different cell types.

scholarly journals MicroRNA and gene expression patterns in the differentiation of human embryonic stem cells

2009 ◽  
Vol 7 (1) ◽  
pp. 20 ◽  
Author(s):  
Jiaqiang Ren ◽  
Ping Jin ◽  
Ena Wang ◽  
Francesco M Marincola ◽  
David F Stroncek
Keyword(s):  
Gene Expression ◽  
Stem Cells ◽  
Embryonic Stem Cells ◽  
Human Embryonic Stem Cells ◽  
Expression Patterns ◽  
Embryonic Stem ◽  
Gene Expression Patterns

CURVE-BASED CLUSTERING OF TIME COURSE GENE EXPRESSION DATA USING SELF-ORGANIZING MAPS

2009 ◽  
Vol 07 (04) ◽  
pp. 645-661 ◽  
Author(s):  
XIN CHEN
Keyword(s):  
Gene Expression ◽  
Gene Expression Data ◽  
Regulatory Networks ◽  
Time Course ◽  
Clustering Algorithm ◽  
Expression Patterns ◽  
Self Organizing Map ◽  
Expression Data ◽  
Wide Range ◽  
Self Organizing

There is an increasing interest in clustering time course gene expression data to investigate a wide range of biological processes. However, developing a clustering algorithm ideal for time course gene express data is still challenging. As timing is an important factor in defining true clusters, a clustering algorithm shall explore expression correlations between time points in order to achieve a high clustering accuracy. Moreover, inter-cluster gene relationships are often desired in order to facilitate the computational inference of biological pathways and regulatory networks. In this paper, a new clustering algorithm called CurveSOM is developed to offer both features above. It first presents each gene by a cubic smoothing spline fitted to the time course expression profile, and then groups genes into clusters by applying a self-organizing map-based clustering on the resulting splines. CurveSOM has been tested on three well-studied yeast cell cycle datasets, and compared with four popular programs including Cluster 3.0, GENECLUSTER, MCLUST, and SSClust. The results show that CurveSOM is a very promising tool for the exploratory analysis of time course expression data, as it is not only able to group genes into clusters with high accuracy but also able to find true time-shifted correlations of expression patterns across clusters.

Characterization of differentiation specific gene expression in embryonic stem cells under serum-reduced culture conditions in the EST

2008 ◽  
Vol 26 (1) ◽  
pp. 61-62
Author(s):  
Burkhard Flick ◽  
Friedericke Rolle ◽  
Patrick Scholz ◽  
Marcus Wehland ◽  
Stephan Klug
Keyword(s):  
Gene Expression ◽  
Stem Cells ◽  
Embryonic Stem Cells ◽  
Embryonic Stem ◽  
Culture Conditions ◽  
Specific Gene ◽  
Specific Gene Expression

scholarly journals Changes in the Expression of Mitochondrial Morphology-Related Genes during the Differentiation of Murine Embryonic Stem Cells

2020 ◽  
Vol 2020 ◽  
pp. 1-12 ◽  
Author(s):  
Jeong Eon Lee ◽  
Bong Jong Seo ◽  
Min Ji Han ◽  
Yean Ju Hong ◽  
Kwonho Hong ◽  
...  
Keyword(s):  
Gene Expression ◽  
Stem Cells ◽  
Embryonic Stem Cells ◽  
Mitochondrial Fission ◽  
Expression Patterns ◽  
Embryonic Stem ◽  
Cell Types ◽  
Mitochondrial Fusion ◽  
Mitochondrial Morphology ◽  
Stems Cells

During embryonic development, cells undergo changes in gene expression, signaling pathway activation/inactivation, metabolism, and intracellular organelle structures, which are mediated by mitochondria. Mitochondria continuously switch their morphology between elongated tubular and fragmented globular via mitochondrial fusion and fission. Mitochondrial fusion is mediated by proteins encoded by Mfn1, Mfn2, and Opa1, whereas mitochondrial fission is mediated by proteins encoded by Fis1 and Dnm1L. Here, we investigated the expression patterns of mitochondria-related genes during the differentiation of mouse embryonic stem cells (ESCs). Pluripotent ESCs maintain stemness in the presence of leukemia inhibitory factor (LIF) via the JAK-STAT3 pathway but lose pluripotency and differentiate in response to the withdrawal of LIF. We analyzed the expression levels of mitochondrial fusion- and fission-related genes during the differentiation of ESCs. We hypothesized that mitochondrial fusion genes would be overexpressed while the fission genes would be downregulated during the differentiation of ESCs. Though the mitochondria exhibited an elongated morphology in ESCs differentiating in response to LIF withdrawal, only the expression of Mfn2 was increased and that of Dnm1L was decreased as expected, the other exceptions being Mfn1, Opa1, and Fis1. Next, by comparing gene expression and mitochondrial morphology, we proposed an index that could precisely represent mitochondrial changes during the differentiation of pluripotent stem cells by analyzing the expression ratios of three fusion- and two fission-related genes. Surprisingly, increased Mfn2/Dnm1L ratio was correlated with elongation of mitochondria during the differentiation of ESCs. Moreover, application of this index to other specialized cell types revealed that neural stems cells (NSCs) and mouse embryonic fibroblasts (MEFs) showed increased Mfn2/Dnm1L ratio compared to ESCs. Thus, we suggest that the Mfn2/Dnm1L ratio could reflect changes in mitochondrial morphology according to the extent of differentiation.

scholarly journals Neoplastic human embryonic stem cells as a model of radiation resistance of human cancer stem cells

Oncotarget ◽  
2015 ◽  
Vol 6 (26) ◽  
pp. 22258-22269 ◽  
Author(s):  
Steve Dingwall ◽  
Jung Bok Lee ◽  
Borhane Guezguez ◽  
Aline Fiebig ◽  
Jamie McNicol ◽  
...  
Keyword(s):  
Stem Cells ◽  
Embryonic Stem Cells ◽  
Cancer Stem Cells ◽  
Human Embryonic Stem Cells ◽  
Radiation Resistance ◽  
Human Cancer ◽  
Embryonic Stem

scholarly journals Systems level approach reveals the correlation of endoderm differentiation of mouse embryonic stem cells with specific microstructural cues of fibrin gels

2014 ◽  
Vol 11 (95) ◽  
pp. 20140009 ◽  
Author(s):  
Keith Task ◽  
Antonio D'Amore ◽  
Satish Singh ◽  
Joe Candiello ◽  
Maria Jaramillo ◽  
...  
Keyword(s):  
Gene Expression ◽  
Stem Cells ◽  
Embryonic Stem Cells ◽  
Expression Patterns ◽  
Embryonic Stem ◽  
Mouse Embryonic Stem Cells ◽  
Complex Nature ◽  
Specific Substrate ◽  
Fibrin Gels ◽  
Differentiated Cells

Stem cells receive numerous cues from their associated substrate that help to govern their behaviour. However, identification of influential substrate characteristics poses difficulties because of their complex nature. In this study, we developed an integrated experimental and systems level modelling approach to investigate and identify specific substrate features influencing differentiation of mouse embryonic stem cells (mESCs) on a model fibrous substrate, fibrin. We synthesized a range of fibrin gels by varying fibrinogen and thrombin concentrations, which led to a range of substrate stiffness and microstructure. mESCs were cultured on each of these gels, and characterization of the differentiated cells revealed a strong influence of substrate modulation on gene expression patterning. To identify specific substrate features influencing differentiation, the substrate microstructure was quantified by image analysis and correlated with stem cell gene expression patterns using a statistical model. Significant correlations were observed between differentiation and microstructure features, specifically fibre alignment. Furthermore, this relationship occurred in a lineage-specific manner towards endoderm. This systems level approach allows for identification of specific substrate features from a complex material which are influential to cellular behaviour. Such analysis may be effective in guiding the design of scaffolds with specific properties for tissue engineering applications.

Metabolic flux analysis of embryonic stem cells using three distinct differentiation protocols and comparison to gene expression patterns

2010 ◽  
Vol 26 (5) ◽  
pp. 1222-1229 ◽  
Author(s):  
Darío E. Sepúlveda ◽  
Barbara A. Andrews ◽  
Eleftherios Terry Papoutsakis ◽  
Juan A. Asenjo
Keyword(s):  
Gene Expression ◽  
Stem Cells ◽  
Embryonic Stem Cells ◽  
Metabolic Flux Analysis ◽  
Metabolic Flux ◽  
Expression Patterns ◽  
Embryonic Stem ◽  
Gene Expression Patterns ◽  
Flux Analysis
Sign in / Sign up

Export Citation Format

Share Document