Literature DB >> 7758958

High-density cDNA filter analysis: a novel approach for large-scale, quantitative analysis of gene expression.

N Zhao1, H Hashida, N Takahashi, Y Misumi, Y Sakaki.   

Abstract

In order to analyze the expression profiles of a large number of genes in the tissues (or cells) of interest, and to identify the genes preferentially expressed in the tissues, we have developed a large-scale gene expression analysis system. It is based on the hybridization of the mRNAs from the tissues with a high-density cDNA filter followed by the quantitative measurement of the amount of the hybridized mRNA on each cDNA spot. By employing a high-performance bioimaging analyzer, the system allowed us to compare the expression profiles of thousands of genes (cDNAs) simultaneously with a sensitivity comparable to conventional Northern blotting analysis. By this system (called high-density cDNA filter analysis or HDCFA), the expression profiles of 2505 cloned human brain cDNAs (genes) were monitored. Through the comparison of the expression profiles of these cDNAs in the adult brain, fetal brain and adult liver, about one half of these brain cDNAs (1239 clones) were identified as the candidates which were expressed preferentially in the brain. Among these, 408 and 288 clones were found to be preferentially expressed in the adult and fetal brain, respectively. The results have shown that the system may be widely applicable for analysis of the gene expression profiles of various tissues on a large scale.

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Year:  1995        PMID: 7758958     DOI: 10.1016/0378-1119(95)00023-y

Source DB:  PubMed          Journal:  Gene        ISSN: 0378-1119            Impact factor:   3.688


  10 in total

1.  Identification of iron-responsive, differential gene expression in the cyanobacterium Synechocystis sp. strain PCC 6803 with a customized amplification library.

Authors:  A K Singh; L A Sherman
Journal:  J Bacteriol       Date:  2000-06       Impact factor: 3.490

2.  Correlation between apoptosis microarray gene expression profiling and histopathological lymph node lesions.

Authors:  J P Dales; J Plumas; F Palmerini; E Devilard; T Defrance; A Lajmanovich; V Pradel; F Birg; L Xerri
Journal:  Mol Pathol       Date:  2001-02

3.  Expression profiling to understand actions of NMDA/glutamate receptor antagonists in rat brain.

Authors:  Petri Törönen; Marcus Storvik; Anni-Maija Lindén; Outi Kontkane; Markéta Marvanová; Merja Lakso; Eero Castrén; Garry Wong
Journal:  Neurochem Res       Date:  2002-10       Impact factor: 3.996

Review 4.  Gene expression profiling with DNA microarrays: advancing our understanding of psychiatric disorders.

Authors:  Julie Pongrac; Frank A Middleton; David A Lewis; Pat Levitt; Károly Mirnics
Journal:  Neurochem Res       Date:  2002-10       Impact factor: 3.996

5.  Identification of transforming growth factor-beta- regulated genes in caenorhabditis elegans by differential hybridization of arrayed cDNAs.

Authors:  M Mochii; S Yoshida; K Morita; Y Kohara; N Ueno
Journal:  Proc Natl Acad Sci U S A       Date:  1999-12-21       Impact factor: 11.205

6.  Cryptococcus neoformans differential gene expression detected in vitro and in vivo with green fluorescent protein.

Authors:  M del Poeta; D L Toffaletti; T H Rude; S D Sparks; J Heitman; J R Perfect
Journal:  Infect Immun       Date:  1999-04       Impact factor: 3.441

7.  Gene expression profile in prion protein-deficient fibroblasts in culture.

Authors:  J Satoh; Y Kuroda; S Katamine
Journal:  Am J Pathol       Date:  2000-07       Impact factor: 4.307

8.  The Genexpress IMAGE knowledge base of the human brain transcriptome: a prototype integrated resource for functional and computational genomics.

Authors:  G Piétu; R Mariage-Samson; N A Fayein; C Matingou; E Eveno; R Houlgatte; C Decraene; Y Vandenbrouck; F Tahi; M D Devignes; U Wirkner; W Ansorge; D Cox; T Nagase; N Nomura; C Auffray
Journal:  Genome Res       Date:  1999-02       Impact factor: 9.043

Review 9.  A systematic molecular genetic approach to study mammalian germline development.

Authors:  K Abe; M S Ko; G R MacGregor
Journal:  Int J Dev Biol       Date:  1998       Impact factor: 2.203

10.  Quantitative comparison of EST libraries requires compensation for systematic biases in cDNA generation.

Authors:  Donglin Liu; Joel H Graber
Journal:  BMC Bioinformatics       Date:  2006-02-17       Impact factor: 3.169

  10 in total

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