Dataset features

Specifications


Application: Gene expression microarray analysis
Number of samples: 30
Release date: Dec 23 2009
Last update date: Aug 10 2018
Access: Public
Diseases: Hodgkin Disease, Myelodysplastic Syndromes, Neoplasms
Genes: CD34, CD9, CXCR4, GATA2
Dataset link Gene expression profiling of myelodysplastic CD34+ hematopoietic stem cells treated in vitro with decitabine

Experimental Protocol


Bone marrow mononuclear cells were obtained by Ficoll gradient centrifugation from 4 patients with intermediate-2/high risk Myelodysplastic Syndromes (MDS) (two with normal and two with abnormal karyotype) and from two patients with untreated early-stage Hodgkin’s lymphoma (HL). CD34+ cells were freshly isolated using immunomagnetic beads (Minimacs, Milteny Biotec GmbH, Germany), according to the manufacturer’s instructions. CD34+ cells were cultured in 24-well-plates with a pool of growth factors, including 10 ng/ml each of Stem Cell Factor, Flt3 ligand, IL3 and Thrombopoietin (Sigma Aldrich), for 24 hours. Decitabine (Sigma Aldrich) was then added to a final concentration of 1 µM for the following 72 hours, while the corresponding amount of solvent was added for the mock treated plates. Three biological replicates were prepared for each patient (for both decitabine- and mock-treated). At the end of the treatment cells were harvested and total RNA was extracted by RNeasy Mini Kit (Qiagen) according to the manufacturer’s instructions. The GeneChip® Two-Cycle Target Labeling and Control Reagents kit (Affymetrix) was used to amplify and label RNA, according to the manufacturer’s instructions. Biotinilated cRNA were then hybridized on Affymetrix GeneChips HG-U133A. We used 5 arrays for each patient: three replicates for decitabine treated CD34+ cells and two replicates for mock treated cells. Data were analyzed by the software Bioconductor (www.bioconductor.org and www.bioinformatica.unito.it) through the program RGui version 2.5.0, package oneChannelGUI. Data were then normalized by GCRMA. The Bayesian method Linear Model Analysis of Microarray (Limma) was used to identify differentially expressed genes, by sorting data with p value 1. Contrasts were analyzed for diagnosis (MDS vs HL), karyotype (normal vs abnormal) and treatment (decitabine vs mock).

Repositories


GEO

GSE19610

BioProject

PRJNA122447

Download


Contact


Francesco D'Alò
Francesco D'Alò