Metabolomics,Unknown,Transcriptomics,Genomics,Proteomics

Dataset Information

0

Expression data from healthy and malignant (chronic lymphocytic leukemia, CLL) human B-lymphocytes after B-cell receptor stimulation


ABSTRACT: Three different cell populations (6 healthy B-lymphocytes, 6 leukemic CLL B-lymphocyte of indolent form and 5 leukemic CLL B-lymphocyte of aggressive form) were stimulated in vitro with an anti-IgM antibody, activating the B-cell receptor (BCR). We analyzed the gene expression at 4 time points (60, 90, 210 and 390 minutes). Each gene expression measurement is performed both in stimulated cells and in control unstimulated cells. For one aggressive CLL case, we silenced expression of DUSP1 by transfecting DUSP1-specific RNAi and, as a control, transfected cells with a non-targeting RNAi. We then stimulated the BCR of these cells and analyzed the gene expression at the same time points in stimulated cells and in control unstimulated cells. B-cells were negatively selected from healthy donors and previously untreated CLL patients. BCR stimulated and unstimulated control B-cells were treated at four time points after stimulation for total RNA extraction and hybridization on Affymetrix microarrays.

ORGANISM(S): Homo sapiens

SUBMITTER: Laurent Vallat 

PROVIDER: E-GEOD-39411 | biostudies-arrayexpress |

REPOSITORIES: biostudies-arrayexpress

altmetric image

Publications

Reverse-engineering the genetic circuitry of a cancer cell with predicted intervention in chronic lymphocytic leukemia.

Vallat Laurent L   Kemper Corey A CA   Jung Nicolas N   Maumy-Bertrand Myriam M   Bertrand Frédéric F   Meyer Nicolas N   Pocheville Arnaud A   Fisher John W JW   Gribben John G JG   Bahram Seiamak S  

Proceedings of the National Academy of Sciences of the United States of America 20121224 2


Cellular behavior is sustained by genetic programs that are progressively disrupted in pathological conditions--notably, cancer. High-throughput gene expression profiling has been used to infer statistical models describing these cellular programs, and development is now needed to guide orientated modulation of these systems. Here we develop a regression-based model to reverse-engineer a temporal genetic program, based on relevant patterns of gene expression after cell stimulation. This method i  ...[more]

Similar Datasets

2013-01-04 | GSE39411 | GEO
2014-01-01 | E-GEOD-52774 | biostudies-arrayexpress
2014-01-01 | E-GEOD-52775 | biostudies-arrayexpress
2011-06-30 | E-GEOD-30105 | biostudies-arrayexpress
2011-08-17 | E-GEOD-31360 | biostudies-arrayexpress
2011-06-30 | E-GEOD-30106 | biostudies-arrayexpress
2013-08-08 | E-GEOD-48268 | biostudies-arrayexpress
2014-01-01 | GSE52775 | GEO
2014-01-01 | GSE52774 | GEO
2023-03-11 | PXD027131 | Pride