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Accurate normalization of real-time quantitative RT-PCR data by geometric averaging of multiple internal control genes

BACKGROUND: Gene-expression analysis is increasingly important in biological research, with real-time reverse transcription PCR (RT-PCR) becoming the method of choice for high-throughput and accurate expression profiling of selected genes. Given the increased sensitivity, reproducibility and large d...

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Autores principales: Vandesompele, Jo, De Preter, Katleen, Pattyn, Filip, Poppe, Bruce, Van Roy, Nadine, De Paepe, Anne, Speleman, Frank
Formato: Texto
Lenguaje:English
Publicado: BioMed Central 2002
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC126239/
https://www.ncbi.nlm.nih.gov/pubmed/12184808
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author Vandesompele, Jo
De Preter, Katleen
Pattyn, Filip
Poppe, Bruce
Van Roy, Nadine
De Paepe, Anne
Speleman, Frank
author_facet Vandesompele, Jo
De Preter, Katleen
Pattyn, Filip
Poppe, Bruce
Van Roy, Nadine
De Paepe, Anne
Speleman, Frank
author_sort Vandesompele, Jo
collection PubMed
description BACKGROUND: Gene-expression analysis is increasingly important in biological research, with real-time reverse transcription PCR (RT-PCR) becoming the method of choice for high-throughput and accurate expression profiling of selected genes. Given the increased sensitivity, reproducibility and large dynamic range of this methodology, the requirements for a proper internal control gene for normalization have become increasingly stringent. Although housekeeping gene expression has been reported to vary considerably, no systematic survey has properly determined the errors related to the common practice of using only one control gene, nor presented an adequate way of working around this problem. RESULTS: We outline a robust and innovative strategy to identify the most stably expressed control genes in a given set of tissues, and to determine the minimum number of genes required to calculate a reliable normalization factor. We have evaluated ten housekeeping genes from different abundance and functional classes in various human tissues, and demonstrated that the conventional use of a single gene for normalization leads to relatively large errors in a significant proportion of samples tested. The geometric mean of multiple carefully selected housekeeping genes was validated as an accurate normalization factor by analyzing publicly available microarray data. CONCLUSIONS: The normalization strategy presented here is a prerequisite for accurate RT-PCR expression profiling, which, among other things, opens up the possibility of studying the biological relevance of small expression differences.
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spelling pubmed-1262392002-09-25 Accurate normalization of real-time quantitative RT-PCR data by geometric averaging of multiple internal control genes Vandesompele, Jo De Preter, Katleen Pattyn, Filip Poppe, Bruce Van Roy, Nadine De Paepe, Anne Speleman, Frank Genome Biol Research BACKGROUND: Gene-expression analysis is increasingly important in biological research, with real-time reverse transcription PCR (RT-PCR) becoming the method of choice for high-throughput and accurate expression profiling of selected genes. Given the increased sensitivity, reproducibility and large dynamic range of this methodology, the requirements for a proper internal control gene for normalization have become increasingly stringent. Although housekeeping gene expression has been reported to vary considerably, no systematic survey has properly determined the errors related to the common practice of using only one control gene, nor presented an adequate way of working around this problem. RESULTS: We outline a robust and innovative strategy to identify the most stably expressed control genes in a given set of tissues, and to determine the minimum number of genes required to calculate a reliable normalization factor. We have evaluated ten housekeeping genes from different abundance and functional classes in various human tissues, and demonstrated that the conventional use of a single gene for normalization leads to relatively large errors in a significant proportion of samples tested. The geometric mean of multiple carefully selected housekeeping genes was validated as an accurate normalization factor by analyzing publicly available microarray data. CONCLUSIONS: The normalization strategy presented here is a prerequisite for accurate RT-PCR expression profiling, which, among other things, opens up the possibility of studying the biological relevance of small expression differences. BioMed Central 2002 2002-06-18 /pmc/articles/PMC126239/ /pubmed/12184808 Text en Copyright © 2002 Vandesompele et al., licensee BioMed Central Ltd
spellingShingle Research
Vandesompele, Jo
De Preter, Katleen
Pattyn, Filip
Poppe, Bruce
Van Roy, Nadine
De Paepe, Anne
Speleman, Frank
Accurate normalization of real-time quantitative RT-PCR data by geometric averaging of multiple internal control genes
title Accurate normalization of real-time quantitative RT-PCR data by geometric averaging of multiple internal control genes
title_full Accurate normalization of real-time quantitative RT-PCR data by geometric averaging of multiple internal control genes
title_fullStr Accurate normalization of real-time quantitative RT-PCR data by geometric averaging of multiple internal control genes
title_full_unstemmed Accurate normalization of real-time quantitative RT-PCR data by geometric averaging of multiple internal control genes
title_short Accurate normalization of real-time quantitative RT-PCR data by geometric averaging of multiple internal control genes
title_sort accurate normalization of real-time quantitative rt-pcr data by geometric averaging of multiple internal control genes
topic Research
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC126239/
https://www.ncbi.nlm.nih.gov/pubmed/12184808
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