Quantifying gene expression.

Identifying those genes that are expressed and at what levels is an essential part of almost any biological inquiry at the cellular level. Techniques such as Northern blot have been in existence for decades to perform this task, but advances in molecular biology and bioinstrumentation have led to the development of a variety of new techniques with a range of sensitivities, throughputs and quantitative capabilities. This review focuses on the latter issue. For several commonly used gene expression techniques, the extent and range of quantitative applicability are reviewed, and approaches for maximizing the accuracy and precision of these measurements are discussed.

[1]  Thomas D. Schmittgen,et al.  Quantitative reverse transcription-polymerase chain reaction to study mRNA decay: comparison of endpoint and real-time methods. , 2000, Analytical biochemistry.

[2]  Martin L. Yarmush,et al.  Dynamics of gene expression in rat hepatocytes under stress. , 2000, Metabolic engineering.

[3]  A. Roda,et al.  Bio- and chemiluminescence in bioanalysis , 2000, Fresenius' journal of analytical chemistry.

[4]  C. Wittwer,et al.  Continuous fluorescence monitoring of rapid cycle DNA amplification. , 1997, BioTechniques.

[5]  Thomas D. Schmittgen,et al.  Effect of experimental treatment on housekeeping gene expression: validation by real-time, quantitative RT-PCR. , 2000, Journal of biochemical and biophysical methods.

[6]  B. Stillman,et al.  Expression microarray hybridization kinetics depend on length of the immobilized DNA but are independent of immobilization substrate. , 2001, Analytical biochemistry.

[7]  J. Warrington,et al.  Comparison of human adult and fetal expression and identification of 535 housekeeping/maintenance genes. , 2000, Physiological genomics.

[8]  G E Archer,et al.  Maximization of signal derived from cDNA microarrays. , 2001, BioTechniques.

[9]  Martin L. Yarmush,et al.  Rational selection and quantitative evaluation of antisense oligonucleotides. , 2001, Biochimica et biophysica acta.

[10]  O. Berthier‐Vergnes,et al.  Ribosomal 18S RNA prevails over glyceraldehyde-3-phosphate dehydrogenase and beta-actin genes as internal standard for quantitative comparison of mRNA levels in invasive and noninvasive human melanoma cell subpopulations. , 2001, Analytical biochemistry.

[11]  P. Sorger,et al.  Image metrics in the statistical analysis of DNA microarray data , 2001, Proceedings of the National Academy of Sciences of the United States of America.

[12]  C. K. Lee,et al.  Microarray profiling of gene expression in aging and its alteration by caloric restriction in mice. , 2001, The Journal of nutrition.

[13]  B. Korn,et al.  Normalization of array hybridization experiments in differential gene expression analysis. , 1999, Nucleic acids research.

[14]  M. Oh,et al.  Issues in cDNA microarray analysis: quality filtering, channel normalization, models of variations and assessment of gene effects. , 2001, Nucleic acids research.

[15]  U J Balis,et al.  The LightCycler: a microvolume multisample fluorimeter with rapid temperature control. , 1997, BioTechniques.

[16]  B. Gebhardt,et al.  The inherent quantitative capacity of the reverse transcription-polymerase chain reaction. , 1999, Analytical biochemistry.

[17]  L. Raeymaekers,et al.  Quantitative PCR: theoretical considerations with practical implications. , 1993, Analytical biochemistry.

[18]  M. Ringnér,et al.  Classification and diagnostic prediction of cancers using gene expression profiling and artificial neural networks , 2001, Nature Medicine.

[19]  G. Church,et al.  Microarray analysis of the transcriptional network controlled by the photoreceptor homeobox gene Crx , 2000, Current Biology.

[20]  M. Yarmush,et al.  Nucleic acid biotechnology. , 1999, Annual review of biomedical engineering.

[21]  D. Whitcombe,et al.  The elimination of primer-dimer accumulation in PCR. , 1997, Nucleic acids research.

[22]  K R Hess,et al.  Microarrays: handling the deluge of data and extracting reliable information. , 2001, Trends in biotechnology.

[23]  S. Walker,et al.  Quantitative RT-PCR : Pitfalls and Potential , 1999 .

[24]  S. Perrin,et al.  Analysis of cytokine mRNA and DNA: detection and quantitation by competitive polymerase chain reaction. , 1990, Proceedings of the National Academy of Sciences of the United States of America.

[25]  P. Meltzer,et al.  Disease fingerprinting with cDNA microarrays reveals distinct gene expression profiles in lethal type‐1 and type‐2 cytokine‐mediated inflammatory reactions , 2001, FASEB journal : official publication of the Federation of American Societies for Experimental Biology.

[26]  M. Holland,et al.  Rat liver transcript profiling in normal and disease states using a kinetic polymerase chain reaction assay. , 1997, Methods.

[27]  G. A. Whitmore,et al.  Importance of replication in microarray gene expression studies: statistical methods and evidence from repetitive cDNA hybridizations. , 2000, Proceedings of the National Academy of Sciences of the United States of America.

[28]  J. Mesirov,et al.  Molecular classification of cancer: class discovery and class prediction by gene expression monitoring. , 1999, Science.

[29]  M. Brown,et al.  mRNA quantification by real time TaqMan polymerase chain reaction: validation and comparison with RNase protection. , 1999, Analytical biochemistry.

[30]  Pierre Baldi,et al.  A Bayesian framework for the analysis of microarray expression data: regularized t -test and statistical inferences of gene changes , 2001, Bioinform..

[31]  J. Dhahbi,et al.  Genomic profiling of short- and long-term caloric restriction effects in the liver of aging mice , 2001, Proceedings of the National Academy of Sciences of the United States of America.

[32]  Russ B. Altman,et al.  Missing value estimation methods for DNA microarrays , 2001, Bioinform..

[33]  A. Pingoud,et al.  Comparison between Taq DNA polymerase and its Stoffel fragment for quantitative real-time PCR with hybridization probes. , 2001, BioTechniques.