IDENTIFICATION OF DIFFERENTIALLY EXPRESSED GENES BETWEEN FETAL AND ADULT MSC BY COMBINING SUPPRESSION SUBTRACTIVE HYBRIDIZATION AND GENE CHIPS

Yang Li, Li Tingyu, Zhou Yade

Children's Hospital, Chongqing University of Medical Science, Chongqing, China

 

Objective: To explore the availability of suppression subtractive hybridization (SSH )coupled to gene chips in analysis of differentially expressed genes and identify the genes expressed specifically or highly in fetal MSC comparing with adult MSC.

Methods: we constructed a SSH library between fetal MSC and normal adult MSC, which was used to make gene chips followed by comparing the relative expression level between these two tissues. mRNAs from culture fetal MSC and adult MSC were subjected to reverse transcription and assigned as tester and driver, respectively. After ligating with two individual adaptors, two groups of tester cDNA were denatured and hybridized with driver cDNAs. Then two hybridization mixtures were combined and re-hybridized with driver cDNA. The differentially expressed cDNAs were cloned into T-vector after PCR amplification and produced a SSH cDNA library following transforming the D50. competent cells. Our result shows the sizes of cDNA fragments inserted are mainly from 400bp to 600bp. We picked clones from the library to amplify cDNA fragments inserted by PCR. After purification, PCR products of 768 clones were printed on silanized glass slides automatically using arraying system. Then single-strand cDNAs were produced by reverse transcription of 2μg mRNAs from fetal and adult MSC with labeling by different fluoresceins, Cy3 and Cy5. After hybridization with gene chips, different fluorescent signals were obtained by confocal scanner, and analyzed by ImaGene software.

Results and Conclusion: Those clones with 2-fold difference in densities of two kinds of fluorescence were selected as differentially expressed clones. In all 768 clones, there were 302 clones higher in fetal MSC.82 clones highly expressed in fetal MSC were sequenced automatically. Sequences were classified into 3 groups by similarity after comparing with public database as follows, known sequences with similarity higher than 90%, homology sequences between 40% and 90% and unknown sequences less than 40%. These 82 ESTs include 5 unknown sequences, 4 homology sequences and 73 known sequences.

 

 
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