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Multiplex cDNA quantification method that facilitates the standardization of gene expression data.

Gotoh O, Murakami Y, Suyama A - Nucleic Acids Res. (2011)

Bottom Line: Therefore, valid comparisons of the microarray data require standardized platforms, internal and/or external controls and complicated normalizations.These requirements impose limitations on the extensive comparison of gene expression data.Here, we report an effective approach to removing the unfavorable limitations by measuring the absolute amounts of gene expression levels on common DNA microarrays.

View Article: PubMed Central - PubMed

Affiliation: Department of Life Sciences and Institute of Physics, Graduate School of Arts and Sciences, The University of Tokyo, 3-8-1 Komaba, Meguro-ku, Tokyo 153-8902, Japan.

ABSTRACT
Microarray-based gene expression measurement is one of the major methods for transcriptome analysis. However, current microarray data are substantially affected by microarray platforms and RNA references because of the microarray method can provide merely the relative amounts of gene expression levels. Therefore, valid comparisons of the microarray data require standardized platforms, internal and/or external controls and complicated normalizations. These requirements impose limitations on the extensive comparison of gene expression data. Here, we report an effective approach to removing the unfavorable limitations by measuring the absolute amounts of gene expression levels on common DNA microarrays. We have developed a multiplex cDNA quantification method called GEP-DEAN (Gene expression profiling by DCN-encoding-based analysis). The method was validated by using chemically synthesized DNA strands of known quantities and cDNA samples prepared from mouse liver, demonstrating that the absolute amounts of cDNA strands were successfully measured with a sensitivity of 18 zmol in a highly multiplexed manner in 7 h.

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Dependence of the Cy5/Cy3 ratio on DNA quantity. The quantities (concentrations) of DNA strands in the serial dilution samples were (a) 1–100 amol (0.033–3.3 pM), (b) 0.1–10 amol (3.3 fM–0.33 pM), (c) 0.01–1 amol (0.33 to 33 fM), (d) 1–100 zmol (33 aM–3.3 fM) and (e) 0.1–10 zmol (3.3 aM–0.33 fM). Those of DNA strands in the reference mixtures were uniform: (a) 10 amol (0.33 pM), (b) 1 amol (33 fM), (c) 0.1 amol (3.3 fM), (d) 10 zmol (0.33 fM) and (e) 1 zmol (33 aM). The slopes of the regression lines in a, b and c were 0.99, 0.93 and 0.76, respectively. The R2-values were 0.99, 0.98 and 0.92, respectively. The solid curves in d and e are the ones fitted to the model in Equation (2). The horizontal solid lines represent background levels. The dashed lines represent a signal-to-noise ratio of 3. (f) The quantification error in a–e. The shaded area is below the lower limit of detection. The horizontal dashed line shows the level of no quantification error.
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Figure 2: Dependence of the Cy5/Cy3 ratio on DNA quantity. The quantities (concentrations) of DNA strands in the serial dilution samples were (a) 1–100 amol (0.033–3.3 pM), (b) 0.1–10 amol (3.3 fM–0.33 pM), (c) 0.01–1 amol (0.33 to 33 fM), (d) 1–100 zmol (33 aM–3.3 fM) and (e) 0.1–10 zmol (3.3 aM–0.33 fM). Those of DNA strands in the reference mixtures were uniform: (a) 10 amol (0.33 pM), (b) 1 amol (33 fM), (c) 0.1 amol (3.3 fM), (d) 10 zmol (0.33 fM) and (e) 1 zmol (33 aM). The slopes of the regression lines in a, b and c were 0.99, 0.93 and 0.76, respectively. The R2-values were 0.99, 0.98 and 0.92, respectively. The solid curves in d and e are the ones fitted to the model in Equation (2). The horizontal solid lines represent background levels. The dashed lines represent a signal-to-noise ratio of 3. (f) The quantification error in a–e. The shaded area is below the lower limit of detection. The horizontal dashed line shows the level of no quantification error.

Mentions: As the quantity of sample DNA was reduced from 100 amol (3.3 pM) to 0.1 amol (3.3 fM), the Cy5/Cy3 ratio decreased proportionally (Figure 2a and b). The slope of the linear regression and the R2-value for the log–log plot in Figure 2a were 0.99 and 0.99, respectively, which means that the Cy5/Cy3 ratio was proportional to and 99% of the variance in the Cy5/Cy3 ratio could be explained by the proportional relation. In Figure 2b, the slope and the R2-value were slightly decreased to 0.93 and 0.98, respectively, still indicating the proportional relation.Figure 2.


Multiplex cDNA quantification method that facilitates the standardization of gene expression data.

Gotoh O, Murakami Y, Suyama A - Nucleic Acids Res. (2011)

Dependence of the Cy5/Cy3 ratio on DNA quantity. The quantities (concentrations) of DNA strands in the serial dilution samples were (a) 1–100 amol (0.033–3.3 pM), (b) 0.1–10 amol (3.3 fM–0.33 pM), (c) 0.01–1 amol (0.33 to 33 fM), (d) 1–100 zmol (33 aM–3.3 fM) and (e) 0.1–10 zmol (3.3 aM–0.33 fM). Those of DNA strands in the reference mixtures were uniform: (a) 10 amol (0.33 pM), (b) 1 amol (33 fM), (c) 0.1 amol (3.3 fM), (d) 10 zmol (0.33 fM) and (e) 1 zmol (33 aM). The slopes of the regression lines in a, b and c were 0.99, 0.93 and 0.76, respectively. The R2-values were 0.99, 0.98 and 0.92, respectively. The solid curves in d and e are the ones fitted to the model in Equation (2). The horizontal solid lines represent background levels. The dashed lines represent a signal-to-noise ratio of 3. (f) The quantification error in a–e. The shaded area is below the lower limit of detection. The horizontal dashed line shows the level of no quantification error.
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Figure 2: Dependence of the Cy5/Cy3 ratio on DNA quantity. The quantities (concentrations) of DNA strands in the serial dilution samples were (a) 1–100 amol (0.033–3.3 pM), (b) 0.1–10 amol (3.3 fM–0.33 pM), (c) 0.01–1 amol (0.33 to 33 fM), (d) 1–100 zmol (33 aM–3.3 fM) and (e) 0.1–10 zmol (3.3 aM–0.33 fM). Those of DNA strands in the reference mixtures were uniform: (a) 10 amol (0.33 pM), (b) 1 amol (33 fM), (c) 0.1 amol (3.3 fM), (d) 10 zmol (0.33 fM) and (e) 1 zmol (33 aM). The slopes of the regression lines in a, b and c were 0.99, 0.93 and 0.76, respectively. The R2-values were 0.99, 0.98 and 0.92, respectively. The solid curves in d and e are the ones fitted to the model in Equation (2). The horizontal solid lines represent background levels. The dashed lines represent a signal-to-noise ratio of 3. (f) The quantification error in a–e. The shaded area is below the lower limit of detection. The horizontal dashed line shows the level of no quantification error.
Mentions: As the quantity of sample DNA was reduced from 100 amol (3.3 pM) to 0.1 amol (3.3 fM), the Cy5/Cy3 ratio decreased proportionally (Figure 2a and b). The slope of the linear regression and the R2-value for the log–log plot in Figure 2a were 0.99 and 0.99, respectively, which means that the Cy5/Cy3 ratio was proportional to and 99% of the variance in the Cy5/Cy3 ratio could be explained by the proportional relation. In Figure 2b, the slope and the R2-value were slightly decreased to 0.93 and 0.98, respectively, still indicating the proportional relation.Figure 2.

Bottom Line: Therefore, valid comparisons of the microarray data require standardized platforms, internal and/or external controls and complicated normalizations.These requirements impose limitations on the extensive comparison of gene expression data.Here, we report an effective approach to removing the unfavorable limitations by measuring the absolute amounts of gene expression levels on common DNA microarrays.

View Article: PubMed Central - PubMed

Affiliation: Department of Life Sciences and Institute of Physics, Graduate School of Arts and Sciences, The University of Tokyo, 3-8-1 Komaba, Meguro-ku, Tokyo 153-8902, Japan.

ABSTRACT
Microarray-based gene expression measurement is one of the major methods for transcriptome analysis. However, current microarray data are substantially affected by microarray platforms and RNA references because of the microarray method can provide merely the relative amounts of gene expression levels. Therefore, valid comparisons of the microarray data require standardized platforms, internal and/or external controls and complicated normalizations. These requirements impose limitations on the extensive comparison of gene expression data. Here, we report an effective approach to removing the unfavorable limitations by measuring the absolute amounts of gene expression levels on common DNA microarrays. We have developed a multiplex cDNA quantification method called GEP-DEAN (Gene expression profiling by DCN-encoding-based analysis). The method was validated by using chemically synthesized DNA strands of known quantities and cDNA samples prepared from mouse liver, demonstrating that the absolute amounts of cDNA strands were successfully measured with a sensitivity of 18 zmol in a highly multiplexed manner in 7 h.

Show MeSH
Related in: MedlinePlus