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.