scholarly journals Different Isolation Methods Alter the Gene Expression Profiling of Adipose Derived Stem Cells

2014 ◽  
Vol 11 (4) ◽  
pp. 391-403 ◽  
Author(s):  
Nareshwaran Gnanasegaran ◽  
Vijayendran Govindasamy ◽  
Sabri Musa ◽  
Noor Hayaty Abu Kasim
Keyword(s):  
Gene Expression ◽  
Stem Cells ◽  
Gene Expression Profiling ◽  
Expression Profiling ◽  
Adipose Derived Stem Cells ◽  
Isolation Methods

Gene Expression Profiling of Neural Stem Cells and Identification of Regulators of Neural Differentiation During Cortical Development

Stem Cells ◽  
2011 ◽  
Vol 29 (11) ◽  
pp. 1817-1828 ◽  
Author(s):  
Toshiyuki Ohtsuka ◽  
Hiromi Shimojo ◽  
Mitsuhiro Matsunaga ◽  
Naoki Watanabe ◽  
Kohei Kometani ◽  
...  
Keyword(s):  
Gene Expression ◽  
Stem Cells ◽  
Neural Stem Cells ◽  
Gene Expression Profiling ◽  
Expression Profiling ◽  
Neural Differentiation ◽  
Cortical Development

A microfluidic platform enables comprehensive gene expression profiling of mouse retinal stem cells

Lab on a Chip ◽  
2021 ◽  
Author(s):  
Shana O Kelley ◽  
Mahmoud Labib ◽  
Brenda Coles ◽  
Mahla Poudineh ◽  
Brendan Innes ◽  
...  
Keyword(s):  
Gene Expression ◽  
Stem Cells ◽  
Stem Cell ◽  
Visual Impairment ◽  
Gene Expression Profiling ◽  
Replacement Therapy ◽  
Retinal Degeneration ◽  
Expression Profiling ◽  
Cell Replacement Therapy ◽  
Cell Replacement

Loss of photoreceptors due to retinal degeneration is a major cause of untreatable visual impairment and blindness. Cell replacement therapy, using retinal stem cell (RSC)-derived photoreceptors, holds promise for reconstituting...

Gene expression profiling of bone marrow mesenchymal stem cells from Osteogenesis Imperfecta patients during osteoblast differentiation

2017 ◽  
Vol 60 (6) ◽  
pp. 326-334 ◽  
Author(s):  
Carla Martins Kaneto ◽  
Patrícia S. Pereira Lima ◽  
Karen Lima Prata ◽  
Jane Lima dos Santos ◽  
João Monteiro de Pina Neto ◽  
...  
Keyword(s):  
Gene Expression ◽  
Stem Cells ◽  
Bone Marrow ◽  
Mesenchymal Stem Cells ◽  
Osteogenesis Imperfecta ◽  
Gene Expression Profiling ◽  
Expression Profiling ◽  
Osteoblast Differentiation ◽  
Marrow Mesenchymal Stem Cells

Gene Expression Profiling of Stem Cells by Microarray

2007 ◽  
pp. 149-161 ◽  
Author(s):  
Timothy McDaniel ◽  
Shawn Baker ◽  
David Barker ◽  
Roy Williams ◽  
Franz-Josef Mueller
Keyword(s):  
Gene Expression ◽  
Stem Cells ◽  
Gene Expression Profiling ◽  
Expression Profiling

Gene expression profiling of amniotic fluid mesenchymal stem cells of monozygotic twins discordant for trisomy 21

Gene ◽  
2020 ◽  
Vol 738 ◽  
pp. 144461
Author(s):  
Shu-Han You ◽  
Yun-Shien Lee ◽  
Yu-Jen Chang ◽  
Chiao-Yun Lin ◽  
Tzu-Hao Wang ◽  
...  
Keyword(s):  
Gene Expression ◽  
Stem Cells ◽  
Mesenchymal Stem Cells ◽  
Amniotic Fluid ◽  
Gene Expression Profiling ◽  
Expression Profiling ◽  
Trisomy 21 ◽  
Monozygotic Twins

Cardiomyogenic gene expression profiling of differentiating human embryonic stem cells

2008 ◽  
Vol 134 (1-2) ◽  
pp. 162-170 ◽  
Author(s):  
Jane Synnergren ◽  
Sudeshna Adak ◽  
Mikael C.O. Englund ◽  
Theresa L. Giesler ◽  
Karin Noaksson ◽  
...  
Keyword(s):  
Gene Expression ◽  
Stem Cells ◽  
Embryonic Stem Cells ◽  
Gene Expression Profiling ◽  
Human Embryonic Stem Cells ◽  
Expression Profiling ◽  
Embryonic Stem

scholarly journals Differential gene expression profiling of human bone marrow-derived mesenchymal stem cells during adipogenic development

BMC Genomics ◽  
2011 ◽  
Vol 12 (1) ◽  
pp. 461 ◽  
Author(s):  
Adriane Menssen ◽  
Thomas Häupl ◽  
Michael Sittinger ◽  
Bruno Delorme ◽  
Pierre Charbord ◽  
...  
Keyword(s):  
Gene Expression ◽  
Stem Cells ◽  
Bone Marrow ◽  
Mesenchymal Stem Cells ◽  
Gene Expression Profiling ◽  
Differential Gene Expression ◽  
Expression Profiling ◽  
Human Bone ◽  
Human Bone Marrow ◽  
Differential Gene

Gene Expression Profiling Reveals Fundamental Differences between Leukemic Stem Cells (Lin-CD34+CD38−) and Mature Blasts (Lin−CD34+CD38+) of Acute Myeloid Leukaemia (AML) Patients.

Blood ◽  
2005 ◽  
Vol 106 (11) ◽  
pp. 1377-1377
Author(s):  
Kazem Zibara ◽  
Daniel Pearce ◽  
David Taussig ◽  
Spyros Skoulakis ◽  
Simon Tomlinson ◽  
...  
Keyword(s):  
Gene Expression ◽  
Stem Cells ◽  
Gene Expression Profiling ◽  
Expression Profiling ◽  
Expression Profiles ◽  
Gene Expression Profiles ◽  
Cell Populations ◽  
Leukemic Stem Cells ◽  
Significant Differential Expression ◽  
New Genes

Abstract The identification of LSC has important implications for future research as well as for the development of novel therapies. The phenotypic description of LSC now enables their purification and should facilitate the identification of genes that are preferentially expressed in these cells compared to normal HSC. However, gene-expression profiling is usually conducted on mononuclear cells of AML patients from either peripheral blood and/or bone marrow. These samples contain a mixture of blasts cells, normal hematopoietic cells and limited number of leukemic stem cells. Thus, this results in a composite profile that obscure differences between LSC and blasts cells with low proliferative potential. The aim of this study was to compare the gene expression profile of highly purified LSC versus leukemic blasts in order to identify genes that might have important roles in driving the leukemia. For this purpose, we analyzed the gene expression profiles of highly purified LSCs (Lin−CD34+CD38−) and more mature blast cells (Lin−CD34+CD38+) isolated from 7 adult AML patients. All samples were previously tested for the ability of the Lin−CD34+CD38− cells but not the Lin−CD34+CD38+ fraction to engraft using the non-obese diabetic/severe combined immuno-deficiency (NOD-SCID) repopulation assay. Affymetrix microarrays (U133A chip), containing 22,283 genes, were used for the analysis. Comparison of Lin-CD34+CD38- cell population to the Lin−CD34+CD38+ cell fraction showed 5421 genes to be expressed in both fractions. Comparative analysis of gene-expression profiles showed statistically significant differential expression of 133 genes between the 2 cell populations. Most of the genes were downregulated in the LSC-enriched fraction, compared to the more differentiated fraction. Gene ontology was used to determine the categories of the up-regulated transcripts. These transcripts, which are selectively expressed, include a number of known genes (e.g., receptors, signalling genes, proliferation and cell cycle genes and transcription factors). These genes play important roles in differentiation, self-renewal, migration and adhesion of HSCs. Among the genes showing the highest differences in expression levels were the following: ribonucleotide reductase M2 polypeptide, thymidylate synthetase, ZW10 interactor, cathepsin G, azurocidin 1, topoisomerase II, CDC20, nucleolar and spindle associated protein 1, Rac GTPase activating protein 1, leukocyte immunoglobulin-like receptor, proliferating cell nuclear antigen, myeloperoxidase, cyclin A1 (RRM2, TYMS, ZWINT, CTSG, AZU1, TOP2A, CDC20, NUSAP1, RACGAP1, LILRB2, PCNA, MPO, CCNA1). Some transcripts detected have not been implicated in HSC functions, and others have unknown function so far. This work identifies new genes that might play a role in leukemogenesis and cancer stem cells. It also leads to a better description and understanding of the molecular phenotypes of these 2 cell populations. Hence, in addition to being a more efficient way to further understand the biology of LSC, this should also provide a more efficient way of identifying new therapeutics and diagnostic targets.

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