Literature DB >> 15329382

RNA expression microarrays (REMs), a high-throughput method to measure differences in gene expression in diverse biological samples.

Charles E Rogler1, Tatyana Tchaikovskaya, Raquel Norel, Aldo Massimi, Christopher Plescia, Eugeny Rubashevsky, Paul Siebert, Leslie E Rogler.   

Abstract

We have developed RNA expression microarrays (REMs), in which each spot on a glass support is composed of a population of cDNAs synthesized from a cell or tissue sample. We used simultaneous hybridization with test and reference (housekeeping) genes to calculate an expression ratio based on normalization with the endogenous reference gene. A test REM containing artificial mixtures of liver cDNA and dilutions of the bacterial LysA gene cDNA demonstrated the feasibility of detecting transcripts at a sensitivity of four copies of LysA mRNA per liver cell equivalent. Furthermore, LysA cDNA detection varied linearly across a standard curve that matched the sensitivity of quantitative real-time PCR. In REMs with real samples, we detected organ-specific expression of albumin, Hnf-4 and Igfbp-1, in a set of mouse organ cDNA populations and c-Myc expression in tumor samples in paired tumor/normal tissue cDNA samples. REMs extend the use of classic microarrays in that a single REM can contain cDNAs from hundreds to thousands of cell or tissue samples each representing a specific physiological or pathophysiological state. REMs will extend the analysis of valuable samples by providing a common broad based platform for their analysis and will promote research aimed at defining gene functions, by broadening our understanding of their expression patterns in health and disease.

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Year:  2004        PMID: 15329382      PMCID: PMC516075          DOI: 10.1093/nar/gnh116

Source DB:  PubMed          Journal:  Nucleic Acids Res        ISSN: 0305-1048            Impact factor:   16.971


  35 in total

1.  Genomic expression analysis implicates Wnt signaling pathway and extracellular matrix alterations in hepatic specification and differentiation of murine hepatic stem cells.

Authors:  C Plescia; C Rogler; L Rogler
Journal:  Differentiation       Date:  2001-10       Impact factor: 3.880

2.  Analysis of relative gene expression data using real-time quantitative PCR and the 2(-Delta Delta C(T)) Method.

Authors:  K J Livak; T D Schmittgen
Journal:  Methods       Date:  2001-12       Impact factor: 3.608

3.  Gene expression profiling of clear cell renal cell carcinoma: gene identification and prognostic classification.

Authors:  M Takahashi; D R Rhodes; K A Furge; H Kanayama ; S Kagawa; B B Haab; B T Teh
Journal:  Proc Natl Acad Sci U S A       Date:  2001-08-07       Impact factor: 11.205

4.  A genomewide oscillation in transcription gates DNA replication and cell cycle.

Authors:  Robert R Klevecz; James Bolen; Gerald Forrest; Douglas B Murray
Journal:  Proc Natl Acad Sci U S A       Date:  2004-01-20       Impact factor: 11.205

5.  Accurate and reproducible gene expression profiles from laser capture microdissection, transcript amplification, and high density oligonucleotide microarray analysis.

Authors:  Veronica Luzzi; Mamatha Mahadevappa; Rajiv Raja; Janet A Warrington; Mark A Watson
Journal:  J Mol Diagn       Date:  2003-02       Impact factor: 5.568

6.  Amplification of mRNA populations using aRNA generated from immobilized oligo(dT)-T7 primed cDNA.

Authors:  J Eberwine
Journal:  Biotechniques       Date:  1996-04       Impact factor: 1.993

7.  Quantitative monitoring of gene expression patterns with a complementary DNA microarray.

Authors:  M Schena; D Shalon; R W Davis; P O Brown
Journal:  Science       Date:  1995-10-20       Impact factor: 47.728

8.  Determination of nucleic acid sequence homologies and relative concentrations by a dot hybridization procedure.

Authors:  F C Kafatos; C W Jones; A Efstratiadis
Journal:  Nucleic Acids Res       Date:  1979-11-24       Impact factor: 16.971

9.  Expression profiling identifies the cytoskeletal organizer ezrin and the developmental homeoprotein Six-1 as key metastatic regulators.

Authors:  Yanlin Yu; Javed Khan; Chand Khanna; Lee Helman; Paul S Meltzer; Glenn Merlino
Journal:  Nat Med       Date:  2004-01-04       Impact factor: 53.440

10.  Analysis of the progesterone displacement of its human serum albumin binding site by beta-estradiol using biochromatographic approaches: effect of two salt modifiers.

Authors:  C André; Y Jacquot; T T Truong; M Thomassin; J F Robert; Y C Guillaume
Journal:  J Chromatogr B Analyt Technol Biomed Life Sci       Date:  2003-11-05       Impact factor: 3.205

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  4 in total

1.  [Identification of potential hub genes of Alzheimer's disease by weighted gene co-expression network analysis].

Authors:  J Xue; J Liu; M Geng; J Yue; H He; J Fan
Journal:  Nan Fang Yi Ke Da Xue Xue Bao       Date:  2021-12-20

2.  Preservation of ranking order in the expression of human Housekeeping genes.

Authors:  Grace T W Shaw; Edward S C Shih; Chun-Houh Chen; Ming-Jing Hwang
Journal:  PLoS One       Date:  2011-12-22       Impact factor: 3.240

3.  DNA microarrays on a dendron-modified surface improve significantly the detection of single nucleotide variations in the p53 gene.

Authors:  Soon Jin Oh; Jimin Ju; Byung Chul Kim; Eunsil Ko; Bong Jin Hong; Jae-Gahb Park; Joon Won Park; Kwan Yong Choi
Journal:  Nucleic Acids Res       Date:  2005-06-06       Impact factor: 16.971

4.  Assessment of gene expression in many samples using vertical arrays.

Authors:  Rosa Ana Risques; Gaelle Rondeau; Martin Judex; Michael McClelland; John Welsh
Journal:  Nucleic Acids Res       Date:  2008-05-12       Impact factor: 16.971

  4 in total

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