Mol Biol Rep DOI 10.1007/s11033-014-3215-5
Valid application of western blotting Liuji Wu • Xiuli Hu • Haitao Tang Zanping Han • Yanhui Chen
•
Received: 8 July 2013 / Accepted: 28 January 2014 Ó Springer Science+Business Media Dordrecht 2014
Abstract Western blotting is a powerful and commonly used tool to identify and quantify a specific protein in a complex mixture. However, the systematic errors in the application of western blotting analysis are frequently to be found, which may compromise the interpretation of results. To make a valid application of western blotting, it is essential to begin with three independent biological replicates. Subsequently, a more reliable normalization method is in urgent need for western blotting analysis and using reference proteins is the currently preferred method of normalization. Additionally, identification of valid reference proteins is crucial for western blotting analysis and it should be examined carefully in relation to the cell or tissue types when using housekeeping proteins as internal standards. Keywords Western blotting Systematic errors Reference proteins Normalization Accuracy
L. Wu X. Hu Y. Chen (&) Henan Agricultural University and Synergetic Innovation Center of Henan Grain Crops, Zhengzhou 450002, China e-mail: [email protected]; [email protected] L. Wu Y. Chen Key Laboratory of Physiological Ecology and Genetic Improvement of Food Crops in Henan Province, Zhengzhou 450002, China H. Tang Crop Research Institute, Sichuan Academy of Agricultural Sciences, Chengdu 610066, China Z. Han College of Agronomy, Henan University of Science and Technology, Luoyang 471003, China
Introduction Western blotting (also known as immunoblotting) is a powerful and commonly used tool to identify and quantify a specific protein in a complex mixture. Originally conceived by Towbin et al. [1], the technique enables indirect detection of proteins immobilized on a nitrocellulose or PVDF membrane. Given the relatively low cost, sensitivity, and flexibility of western blotting, thousands of research labs around the world have employed its use to measure protein levels. Especially with the recent availability of commercially produced antibodies, western blotting is becoming a popular technique in the life sciences. However, the systematic errors in the application of western blotting analysis are continued to be found, which can compromise the interpretation of results. To ensure reproducible and accurate measurements of protein abundance in different cells types, the appended recommendation should be considered for western blotting analysis. We concentrate on the situation of comparing proteins levels in different sample types using relative quantification.
Three golden rules of western blotting Firstly, as mentioned for real-time quantitative RT-PCR by Udvardi et al. [2] and Rieu and Powers [3], an ideal experimental design should encompass at least three independent biological replicates of each treatment. Three biological replicates are based entirely on the fact that this is the minimum number required to carry out any useful statistical analysis. For each biological replicate, it is common to run at least two technical replicates of each immunoblot reaction to prevent the influence of gels. Each sample should provide material for both target proteins and
123
Mol Biol Rep
reference protein reactions, and are paired for each biological replicate. The difference of gels will lead to different experiment results during immunoblot reaction. So ideally, a full experiment should be analyzed on a single polyacrylamide gel, which includes all samples. However, an experiment with many treatments may require a design strategy for multiple polyacrylamide gels. To enable effective statistical comparison of treatments, a strategy termed ‘‘treatment maximization’’ can be used as a references [4]. Secondly, a robust normalization method is needed for western blotting analysis. The purpose of normalization is to remove the non-biological variation as much as possible. Contradictory results may be obtained when using this technique without a robust normalization strategy. To account for variations between samples in the amounts of proteins in the starting material and the efficiency of the quantification process, determinations of protein abundance by western blotting analysis should be normalized according to the total amounts of protein present in the samples. The use of reference proteins is currently the preferred method of normalization. Hence, the expression level of a target protein is described in terms of the ratios of the target protein level to the reference protein level in the sample. The reference should be a stably expressed protein whose abundance is strongly correlated to the total amount of protein present in each sample. However, relative change in the expression of a specific protein has been commonly measured on western blotting by calculating the ratio of the densitometric values of bands containing the protein between experimental samples and control [5]. It is generally assumed that this analysis provides an accurate determination of relative changes in a specific protein expression level if there is a linear relation between increasing amounts of that protein, as represented by bands on a western blot or a gel, and the densitometric measurements of these bands. However, Pitre et al. [5] provided direct evidence that this assumption is invalid because even in the presence of a linear relationship, densitometric ratios differ substantially from known actual ratios of protein amounts. It was recommended to use the purified form of the protein of interest to avoid the error, by using a standard curve to determine the actual amounts of the protein in control and experimental samples. When the protein investigated is unavailable in purified form, this error can be circumvented by the use of a ratio standard curve. Thirdly, identification of valid reference proteins is important for western blotting analysis. On the basis of our experience and the results of recent reports, the selection and use of appropriate reference proteins to normalize experimental results is necessary to ensure accuracy and reliability. On the other hand, the use of inappropriate
123
reference proteins may lead to relatively large errors in a significant proportion of samples [6, 7]. It is generally assumed that housekeeping genes are typically constitutive genes that are required for the maintenance of basic cellular function, and are expressed in all cells of an organism [8]. So, housekeeping proteins are usually used as reference proteins when comparing the relative expression levels of different samples. However, there is a growing body of evidence which demonstrates that the expression of a number of such housekeeping genes or proteins (such as glyceraldehyde-3phosphate dehydrogenase GAPDH, cyclophillin, b-actin, b-tubulin, and so on) although constant in some experimental conditions, vary considerably in other cases [9–11]. Some of the commonly used housekeeping genes (proteins) serve as inadequate internal standards for measuring gene expression levels as they are actually affected by a large number of factors, including drug and experimental treatment and conditions [12–14], cell cycle phase, differentiation [15, 16] or proliferation status [17], age, sex [18], and by stress conditions [19]. Variability between different tissues and/or pathological states has also been identified [20–26]. In terms of interpreting experimental results, such variation in these housekeeping genes could potentially obscure real differences in gene or protein expression between samples or indeed result in the description of ‘‘false-positive’’ differences. In fact, the origin of the term ‘‘housekeeping gene’’ remains obscure. Literature from 1976 used the term to describe specifically tRNA and rRNA [27]. The researchers therefore need to carefully assess whether a certain reference is stably expressed in the experimental system under study. Fortunately, for real-time RT-PCR, several algorithms and freely available software have been developed and can be used to address this problem [10, 28, 29]. However, it is well documented that there is often a poor correlation between mRNA and protein abundance [30, 31]. Some reasons were postulated for the discrepancy between the transcription and protein levels. The abundance of a protein integrates post-transcriptional and posttranslational processing, which modulates the quantity, temporal expression, localization, and efficiency of the final product in the cell. So the identified reference genes used for real-time PCR may not directly correlate to reference protein levels for western blotting analysis. In addition, in the postgenomic era, the important role of proteomics is becoming increasingly apparent as this type of approach is applied in genetic and physiological studies [32]. Like gene expression profiling, proteomics study provides a valuable approach to examine simultaneous changes and to classify temporal patterns of protein accumulation occurring in complex developmental processes. Exploration and confirmation of many upregulated or
Mol Biol Rep
downregulated proteins are also done using western blotting with normalization against ‘‘housekeeping proteins’’. To date, there is only one report of the identification and validation of reference proteins for western blotting analysis [11]. Based on the results, heat shock protein (HSP) can be applied as one of the reference proteins under a wide range of conditions. Although the potentially highly misleading effects of using inappropriate reference proteins for normalization are widely known, their full consideration on the western blotting analysis is lacking. Therefore, there is an urgent need to regard the systematic validation of reference proteins as an essential component of western blotting analysis for the accuracy of this powerful technique. In addition, the use of housekeeping proteins as internal standards should be examined carefully in relation to the cell or tissue types and the experimental conditions.
Conclusion In summary, inappropriate use of western blotting may lead to relatively large errors in a significant proportion of samples and have potentially highly misleading effects. To acquire reliable results with western blotting, it is crucial to begin with a proper statistical design incorporating at least three independent biological replicates. Then, a robust normalization method is needed for western blotting analysis and the use of reference proteins is currently the preferred method of normalization. Finally, identification of valid reference proteins is of great importance in western blotting analysis and it should be tested carefully according to the types of cell or tissue when the housekeeping proteins are used as internal standards. Acknowledgments We thank Erin DiCaprio for critical reading of the manuscript. This work was supported by the National Natural Science Foundation of China (No. 31101158) and Research Fund for the Doctoral Program of Higher Education (No. 30600272). Conflict of interest
There is no conflict of interest.
References 1. Towbin H, Staehelin T, Gordon J (1979) Electrophoretic transfer of proteins from polyacrylamide gels to nitrocellulose sheets: procedure and some applications. Proc Natl Acad Sci U S A 76:4350–4354 2. Udvardi MK, Czechowski T, Scheible WR (2008) Eleven golden rules of quantitative RT-PCR. Plant Cell 20:1736–1737 3. Rieu I, Powers SJ (2009) Real-time quantitative RT-PCR: design, calculations, and statistics. Plant Cell 21:1031–1033 4. Hellemans J, Mortier G, De Paepe A et al (2007) qBase relative quantification framework and software for management and automated analysis of real-time quantitative PCR data. Genome Biol 8:R19
5. Pitre A, Pan Y, Pruett S et al (2007) On the use of ratio standard curves to accurately quantitate relative changes in protein levels by Western blot. Anal Biochem 361:305–307 6. Nolan T, Hands RE, Bustin SA (2006) Quantification of mRNA using real-time RT-PCR. Nat Protoc 1:1559–1582 7. Huggett J, Dheda K, Bustin S et al (2005) Real-time RT-PCR normalisation; strategies and considerations. Genes Immun 6:279–284 8. Eisenberg E, Levanon EY (2003) Human housekeeping genes are compact. Trends Genet 19:362–365 9. Ferguson RE, Carroll HP, Harris A et al (2005) Housekeeping proteins: a preliminary study illustrating some limitations as useful references in protein expression studies. Proteomics 5:566–571 10. Czechowski T, Stitt M, Altmann T et al (2005) Genome-wide identification and testing of superior reference genes for transcript normalization in Arabidopsis. Plant Physiol 139:5–17 11. Li X, Bai H, Wang X et al (2011) Identification and validation of rice reference proteins for western blotting. J Exp Bot 62:4763–4772 12. Oliveira JG, Prados RZ, Guedes AC et al (1999) The housekeeping gene glyceraldehyde-3-phosphate dehydrogenase is inappropriate as internal control in comparative studies between skin tissue and cultured skin fibroblasts using Northern blot analysis. Arch Dermatol Res 291:659–661 13. Schmittgen TD, Zakrajsek BA (2000) Effect of experimental treatment on housekeeping gene expression: validation by realtime, quantitative RT-PCR. J Biochem Biophys Methods 46:69–81 14. Murphy RM, Watt KK, Cameron-Smith D et al (2003) Effects of creatine supplementation on housekeeping genes in human skeletal muscle using real-time RT-PCR. Physiol Genomics 12:163–174 15. Murphy CL, Polak JM (2002) Differentiating embryonic stem cells: GAPDH, but neither HPRT nor beta-tubulin is suitable as an internal standard for measuring RNA levels. Tissue Eng 8:551–559 16. Steele BK, Meyers C, Ozbun MA (2002) Variable expression of some ‘‘housekeeping’’ genes during human keratinocyte differentiation. Anal Biochem 307:341–347 17. Hamalainen HK, Tubman JC, Vikman S et al (2001) Identification and validation of endogenous reference genes for expression profiling of T helper cell differentiation by quantitative real-time RT-PCR. Anal Biochem 299:63–70 18. Perrot-Sinal TS, Davis AM, McCarthy MM (2001) Developmental sex differences in glutamic acid decarboxylase (GAD(65)) and the housekeeping gene, GAPDH. Brain Res 922:201–208 19. Yamada H, Chen D, Monstein HJ et al (1997) Effects of fasting on the expression of gastrin, cholecystokinin, and somatostatin genes and of various housekeeping genes in the pancreas and upper digestive tract of rats. Biochem Biophys Res Commun 231:835–838 20. Blanquicett C, Johnson MR, Heslin M et al (2002) Housekeeping gene variability in normal and carcinomatous colorectal and liver tissues: applications in pharmacogenomic gene expression studies. Anal Biochem 303:209–214 21. Glare EM, Divjak M, Bailey MJ et al (2002) beta-Actin and GAPDH housekeeping gene expression in asthmatic airways is variable and not suitable for normalising mRNA levels. Thorax 57:765–770 22. Thellin O, Zorzi W, Lakaye B et al (1999) Housekeeping genes as internal standards: use and limits. J Biotechnol 75:291–295 23. Suzuki T, Higgins PJ, Crawford DR (2000) Control selection for RNA quantitation. Biotechniques 29:332–337 24. Zhong H, Simons JW (1999) Direct comparison of GAPDH, betaactin, cyclophilin, and 28S rRNA as internal standards for
123
Mol Biol Rep
25.
26.
27.
28.
quantifying RNA levels under hypoxia. Biochem Biophys Res Commun 259:523–526 Wu YY, Rees JL (2000) Variation in epidermal housekeeping gene expression in different pathological states. Acta Derm Venereol 80:2–3 Perfetti V, Manenti G, Dragani TA (1991) Expression of housekeeping genes in Hodgkin’s disease lymph nodes. Leukemia 5:1110–1112 Rifkind RA, Marks PA, Bank A et al (1976) Erythroid differentiation and the cell cycle: some implications from murine foetal and erythroleukemic cells. Ann Immunol (Paris) 127: 887–893 Vandesompele J, De Preter K, Pattyn F et al (2002) Accurate normalization of real-time quantitative RT-PCR data by geometric
123
29. 30.
31.
32.
averaging of multiple internal control genes. Genome Biol 3(7): RESEARCH0034 Dong B, Zhang P, Chen X et al (2011) Predicting housekeeping genes based on Fourier analysis. PLoS One 6:e21012 Chan Z, Wang Q, Xu X et al (2008) Functions of defense-related proteins and dehydrogenases in resistance response induced by salicylic acid in sweet cherry fruits at different maturity stages. Proteomics 8:4791–4807 Bohmer M, Schroeder JI (2011) Quantitative transcriptomic analysis of abscisic acid-induced and reactive oxygen speciesdependent expression changes and proteomic profiling in Arabidopsis suspension cells. Plant J 67:105–118 Pandey A, Mann M (2000) Proteomics to study genes and genomes. Nature 405:837–846
Valid application of western blotting Liuji Wu • Xiuli Hu • Haitao Tang Zanping Han • Yanhui Chen
•
Received: 8 July 2013 / Accepted: 28 January 2014 Ó Springer Science+Business Media Dordrecht 2014
Abstract Western blotting is a powerful and commonly used tool to identify and quantify a specific protein in a complex mixture. However, the systematic errors in the application of western blotting analysis are frequently to be found, which may compromise the interpretation of results. To make a valid application of western blotting, it is essential to begin with three independent biological replicates. Subsequently, a more reliable normalization method is in urgent need for western blotting analysis and using reference proteins is the currently preferred method of normalization. Additionally, identification of valid reference proteins is crucial for western blotting analysis and it should be examined carefully in relation to the cell or tissue types when using housekeeping proteins as internal standards. Keywords Western blotting Systematic errors Reference proteins Normalization Accuracy
L. Wu X. Hu Y. Chen (&) Henan Agricultural University and Synergetic Innovation Center of Henan Grain Crops, Zhengzhou 450002, China e-mail: [email protected]; [email protected] L. Wu Y. Chen Key Laboratory of Physiological Ecology and Genetic Improvement of Food Crops in Henan Province, Zhengzhou 450002, China H. Tang Crop Research Institute, Sichuan Academy of Agricultural Sciences, Chengdu 610066, China Z. Han College of Agronomy, Henan University of Science and Technology, Luoyang 471003, China
Introduction Western blotting (also known as immunoblotting) is a powerful and commonly used tool to identify and quantify a specific protein in a complex mixture. Originally conceived by Towbin et al. [1], the technique enables indirect detection of proteins immobilized on a nitrocellulose or PVDF membrane. Given the relatively low cost, sensitivity, and flexibility of western blotting, thousands of research labs around the world have employed its use to measure protein levels. Especially with the recent availability of commercially produced antibodies, western blotting is becoming a popular technique in the life sciences. However, the systematic errors in the application of western blotting analysis are continued to be found, which can compromise the interpretation of results. To ensure reproducible and accurate measurements of protein abundance in different cells types, the appended recommendation should be considered for western blotting analysis. We concentrate on the situation of comparing proteins levels in different sample types using relative quantification.
Three golden rules of western blotting Firstly, as mentioned for real-time quantitative RT-PCR by Udvardi et al. [2] and Rieu and Powers [3], an ideal experimental design should encompass at least three independent biological replicates of each treatment. Three biological replicates are based entirely on the fact that this is the minimum number required to carry out any useful statistical analysis. For each biological replicate, it is common to run at least two technical replicates of each immunoblot reaction to prevent the influence of gels. Each sample should provide material for both target proteins and
123
Mol Biol Rep
reference protein reactions, and are paired for each biological replicate. The difference of gels will lead to different experiment results during immunoblot reaction. So ideally, a full experiment should be analyzed on a single polyacrylamide gel, which includes all samples. However, an experiment with many treatments may require a design strategy for multiple polyacrylamide gels. To enable effective statistical comparison of treatments, a strategy termed ‘‘treatment maximization’’ can be used as a references [4]. Secondly, a robust normalization method is needed for western blotting analysis. The purpose of normalization is to remove the non-biological variation as much as possible. Contradictory results may be obtained when using this technique without a robust normalization strategy. To account for variations between samples in the amounts of proteins in the starting material and the efficiency of the quantification process, determinations of protein abundance by western blotting analysis should be normalized according to the total amounts of protein present in the samples. The use of reference proteins is currently the preferred method of normalization. Hence, the expression level of a target protein is described in terms of the ratios of the target protein level to the reference protein level in the sample. The reference should be a stably expressed protein whose abundance is strongly correlated to the total amount of protein present in each sample. However, relative change in the expression of a specific protein has been commonly measured on western blotting by calculating the ratio of the densitometric values of bands containing the protein between experimental samples and control [5]. It is generally assumed that this analysis provides an accurate determination of relative changes in a specific protein expression level if there is a linear relation between increasing amounts of that protein, as represented by bands on a western blot or a gel, and the densitometric measurements of these bands. However, Pitre et al. [5] provided direct evidence that this assumption is invalid because even in the presence of a linear relationship, densitometric ratios differ substantially from known actual ratios of protein amounts. It was recommended to use the purified form of the protein of interest to avoid the error, by using a standard curve to determine the actual amounts of the protein in control and experimental samples. When the protein investigated is unavailable in purified form, this error can be circumvented by the use of a ratio standard curve. Thirdly, identification of valid reference proteins is important for western blotting analysis. On the basis of our experience and the results of recent reports, the selection and use of appropriate reference proteins to normalize experimental results is necessary to ensure accuracy and reliability. On the other hand, the use of inappropriate
123
reference proteins may lead to relatively large errors in a significant proportion of samples [6, 7]. It is generally assumed that housekeeping genes are typically constitutive genes that are required for the maintenance of basic cellular function, and are expressed in all cells of an organism [8]. So, housekeeping proteins are usually used as reference proteins when comparing the relative expression levels of different samples. However, there is a growing body of evidence which demonstrates that the expression of a number of such housekeeping genes or proteins (such as glyceraldehyde-3phosphate dehydrogenase GAPDH, cyclophillin, b-actin, b-tubulin, and so on) although constant in some experimental conditions, vary considerably in other cases [9–11]. Some of the commonly used housekeeping genes (proteins) serve as inadequate internal standards for measuring gene expression levels as they are actually affected by a large number of factors, including drug and experimental treatment and conditions [12–14], cell cycle phase, differentiation [15, 16] or proliferation status [17], age, sex [18], and by stress conditions [19]. Variability between different tissues and/or pathological states has also been identified [20–26]. In terms of interpreting experimental results, such variation in these housekeeping genes could potentially obscure real differences in gene or protein expression between samples or indeed result in the description of ‘‘false-positive’’ differences. In fact, the origin of the term ‘‘housekeeping gene’’ remains obscure. Literature from 1976 used the term to describe specifically tRNA and rRNA [27]. The researchers therefore need to carefully assess whether a certain reference is stably expressed in the experimental system under study. Fortunately, for real-time RT-PCR, several algorithms and freely available software have been developed and can be used to address this problem [10, 28, 29]. However, it is well documented that there is often a poor correlation between mRNA and protein abundance [30, 31]. Some reasons were postulated for the discrepancy between the transcription and protein levels. The abundance of a protein integrates post-transcriptional and posttranslational processing, which modulates the quantity, temporal expression, localization, and efficiency of the final product in the cell. So the identified reference genes used for real-time PCR may not directly correlate to reference protein levels for western blotting analysis. In addition, in the postgenomic era, the important role of proteomics is becoming increasingly apparent as this type of approach is applied in genetic and physiological studies [32]. Like gene expression profiling, proteomics study provides a valuable approach to examine simultaneous changes and to classify temporal patterns of protein accumulation occurring in complex developmental processes. Exploration and confirmation of many upregulated or
Mol Biol Rep
downregulated proteins are also done using western blotting with normalization against ‘‘housekeeping proteins’’. To date, there is only one report of the identification and validation of reference proteins for western blotting analysis [11]. Based on the results, heat shock protein (HSP) can be applied as one of the reference proteins under a wide range of conditions. Although the potentially highly misleading effects of using inappropriate reference proteins for normalization are widely known, their full consideration on the western blotting analysis is lacking. Therefore, there is an urgent need to regard the systematic validation of reference proteins as an essential component of western blotting analysis for the accuracy of this powerful technique. In addition, the use of housekeeping proteins as internal standards should be examined carefully in relation to the cell or tissue types and the experimental conditions.
Conclusion In summary, inappropriate use of western blotting may lead to relatively large errors in a significant proportion of samples and have potentially highly misleading effects. To acquire reliable results with western blotting, it is crucial to begin with a proper statistical design incorporating at least three independent biological replicates. Then, a robust normalization method is needed for western blotting analysis and the use of reference proteins is currently the preferred method of normalization. Finally, identification of valid reference proteins is of great importance in western blotting analysis and it should be tested carefully according to the types of cell or tissue when the housekeeping proteins are used as internal standards. Acknowledgments We thank Erin DiCaprio for critical reading of the manuscript. This work was supported by the National Natural Science Foundation of China (No. 31101158) and Research Fund for the Doctoral Program of Higher Education (No. 30600272). Conflict of interest
There is no conflict of interest.
References 1. Towbin H, Staehelin T, Gordon J (1979) Electrophoretic transfer of proteins from polyacrylamide gels to nitrocellulose sheets: procedure and some applications. Proc Natl Acad Sci U S A 76:4350–4354 2. Udvardi MK, Czechowski T, Scheible WR (2008) Eleven golden rules of quantitative RT-PCR. Plant Cell 20:1736–1737 3. Rieu I, Powers SJ (2009) Real-time quantitative RT-PCR: design, calculations, and statistics. Plant Cell 21:1031–1033 4. Hellemans J, Mortier G, De Paepe A et al (2007) qBase relative quantification framework and software for management and automated analysis of real-time quantitative PCR data. Genome Biol 8:R19
5. Pitre A, Pan Y, Pruett S et al (2007) On the use of ratio standard curves to accurately quantitate relative changes in protein levels by Western blot. Anal Biochem 361:305–307 6. Nolan T, Hands RE, Bustin SA (2006) Quantification of mRNA using real-time RT-PCR. Nat Protoc 1:1559–1582 7. Huggett J, Dheda K, Bustin S et al (2005) Real-time RT-PCR normalisation; strategies and considerations. Genes Immun 6:279–284 8. Eisenberg E, Levanon EY (2003) Human housekeeping genes are compact. Trends Genet 19:362–365 9. Ferguson RE, Carroll HP, Harris A et al (2005) Housekeeping proteins: a preliminary study illustrating some limitations as useful references in protein expression studies. Proteomics 5:566–571 10. Czechowski T, Stitt M, Altmann T et al (2005) Genome-wide identification and testing of superior reference genes for transcript normalization in Arabidopsis. Plant Physiol 139:5–17 11. Li X, Bai H, Wang X et al (2011) Identification and validation of rice reference proteins for western blotting. J Exp Bot 62:4763–4772 12. Oliveira JG, Prados RZ, Guedes AC et al (1999) The housekeeping gene glyceraldehyde-3-phosphate dehydrogenase is inappropriate as internal control in comparative studies between skin tissue and cultured skin fibroblasts using Northern blot analysis. Arch Dermatol Res 291:659–661 13. Schmittgen TD, Zakrajsek BA (2000) Effect of experimental treatment on housekeeping gene expression: validation by realtime, quantitative RT-PCR. J Biochem Biophys Methods 46:69–81 14. Murphy RM, Watt KK, Cameron-Smith D et al (2003) Effects of creatine supplementation on housekeeping genes in human skeletal muscle using real-time RT-PCR. Physiol Genomics 12:163–174 15. Murphy CL, Polak JM (2002) Differentiating embryonic stem cells: GAPDH, but neither HPRT nor beta-tubulin is suitable as an internal standard for measuring RNA levels. Tissue Eng 8:551–559 16. Steele BK, Meyers C, Ozbun MA (2002) Variable expression of some ‘‘housekeeping’’ genes during human keratinocyte differentiation. Anal Biochem 307:341–347 17. Hamalainen HK, Tubman JC, Vikman S et al (2001) Identification and validation of endogenous reference genes for expression profiling of T helper cell differentiation by quantitative real-time RT-PCR. Anal Biochem 299:63–70 18. Perrot-Sinal TS, Davis AM, McCarthy MM (2001) Developmental sex differences in glutamic acid decarboxylase (GAD(65)) and the housekeeping gene, GAPDH. Brain Res 922:201–208 19. Yamada H, Chen D, Monstein HJ et al (1997) Effects of fasting on the expression of gastrin, cholecystokinin, and somatostatin genes and of various housekeeping genes in the pancreas and upper digestive tract of rats. Biochem Biophys Res Commun 231:835–838 20. Blanquicett C, Johnson MR, Heslin M et al (2002) Housekeeping gene variability in normal and carcinomatous colorectal and liver tissues: applications in pharmacogenomic gene expression studies. Anal Biochem 303:209–214 21. Glare EM, Divjak M, Bailey MJ et al (2002) beta-Actin and GAPDH housekeeping gene expression in asthmatic airways is variable and not suitable for normalising mRNA levels. Thorax 57:765–770 22. Thellin O, Zorzi W, Lakaye B et al (1999) Housekeeping genes as internal standards: use and limits. J Biotechnol 75:291–295 23. Suzuki T, Higgins PJ, Crawford DR (2000) Control selection for RNA quantitation. Biotechniques 29:332–337 24. Zhong H, Simons JW (1999) Direct comparison of GAPDH, betaactin, cyclophilin, and 28S rRNA as internal standards for
123
Mol Biol Rep
25.
26.
27.
28.
quantifying RNA levels under hypoxia. Biochem Biophys Res Commun 259:523–526 Wu YY, Rees JL (2000) Variation in epidermal housekeeping gene expression in different pathological states. Acta Derm Venereol 80:2–3 Perfetti V, Manenti G, Dragani TA (1991) Expression of housekeeping genes in Hodgkin’s disease lymph nodes. Leukemia 5:1110–1112 Rifkind RA, Marks PA, Bank A et al (1976) Erythroid differentiation and the cell cycle: some implications from murine foetal and erythroleukemic cells. Ann Immunol (Paris) 127: 887–893 Vandesompele J, De Preter K, Pattyn F et al (2002) Accurate normalization of real-time quantitative RT-PCR data by geometric
123
29. 30.
31.
32.
averaging of multiple internal control genes. Genome Biol 3(7): RESEARCH0034 Dong B, Zhang P, Chen X et al (2011) Predicting housekeeping genes based on Fourier analysis. PLoS One 6:e21012 Chan Z, Wang Q, Xu X et al (2008) Functions of defense-related proteins and dehydrogenases in resistance response induced by salicylic acid in sweet cherry fruits at different maturity stages. Proteomics 8:4791–4807 Bohmer M, Schroeder JI (2011) Quantitative transcriptomic analysis of abscisic acid-induced and reactive oxygen speciesdependent expression changes and proteomic profiling in Arabidopsis suspension cells. Plant J 67:105–118 Pandey A, Mann M (2000) Proteomics to study genes and genomes. Nature 405:837–846
