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A comparison of RNA amplification techniques at sub-nanogram input concentration.

Lang JE, Magbanua MJ, Scott JH, Makrigiorgos GM, Wang G, Federman S, Esserman LJ, Park JW, Haqq CM - BMC Genomics (2009)

Bottom Line: Microarray filtering and data processing has an important effect on the correlation coefficient results generated by each method.Arrays derived from total RNA had higher Pearson's correlations than did arrays derived from amplified RNA when considering the entire unprocessed dataset, however, when considering a gene set of high signal intensity, the amplified arrays had superior correlation coefficients than did the total RNA arrays.RNA amplification and expression analysis at the sub-nanogram input level is both feasible and accurate if data processing is used to focus attention to high intensity genes for microarrays or if QPCR is used as a gold standard for validation.

View Article: PubMed Central - HTML - PubMed

Affiliation: Department of Surgery, UCSF Comprehensive Cancer Center, 1500 Divisadero Street, San Francisco, CA 94143, USA. jlang@azcc.arizona.edu

ABSTRACT

Background: Gene expression profiling of small numbers of cells requires high-fidelity amplification of sub-nanogram amounts of RNA. Several methods for RNA amplification are available; however, there has been little consideration of the accuracy of these methods when working with very low-input quantities of RNA as is often required with rare clinical samples. Starting with 250 picograms-3.3 nanograms of total RNA, we compared two linear amplification methods 1) modified T7 and 2) Arcturus RiboAmp HS and a logarithmic amplification, 3) Balanced PCR. Microarray data from each amplification method were validated against quantitative real-time PCR (QPCR) for 37 genes.

Results: For high intensity spots, mean Pearson correlations were quite acceptable for both total RNA and low-input quantities amplified with each of the 3 methods. Microarray filtering and data processing has an important effect on the correlation coefficient results generated by each method. Arrays derived from total RNA had higher Pearson's correlations than did arrays derived from amplified RNA when considering the entire unprocessed dataset, however, when considering a gene set of high signal intensity, the amplified arrays had superior correlation coefficients than did the total RNA arrays.

Conclusion: Gene expression arrays can be obtained with sub-nanogram input of total RNA. High intensity spots showed better correlation on array-array analysis than did unfiltered data, however, QPCR validated the accuracy of gene expression array profiling from low-input quantities of RNA with all 3 amplification techniques. RNA amplification and expression analysis at the sub-nanogram input level is both feasible and accurate if data processing is used to focus attention to high intensity genes for microarrays or if QPCR is used as a gold standard for validation.

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Study Design – BT474 and Stratagene Universal Human Pooled Reference RNA were used as the substrate for these experiments. 10 ug of total RNA from each were hybridized to microarrays and labeled "total RNA arrays". SAM analysis from these total RNA arrays were used to select QPCR genes in an unbiased fashion prior to performing any amplification reaction. Total RNA was serially diluted, amplified, and hybridized to cDNA microarrays. QPCR was performed on total RNA and amplified RNA. Statistical analyses included microarray vs microarray analysis as well as microarray vs QPCR analysis.
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Figure 1: Study Design – BT474 and Stratagene Universal Human Pooled Reference RNA were used as the substrate for these experiments. 10 ug of total RNA from each were hybridized to microarrays and labeled "total RNA arrays". SAM analysis from these total RNA arrays were used to select QPCR genes in an unbiased fashion prior to performing any amplification reaction. Total RNA was serially diluted, amplified, and hybridized to cDNA microarrays. QPCR was performed on total RNA and amplified RNA. Statistical analyses included microarray vs microarray analysis as well as microarray vs QPCR analysis.

Mentions: Figure 1 demonstrates a schematic of our study design. To validate our microarray results, we utilized quantitative RT-PCR (QPCR) for a panel of 37 genes. Unlike prior studies that used QPCR to validate expression of outliers – genes predominantly expressed in one RNA sample versus another – we selected QPCR primers to measure genes that are under-expressed, equivalent, or over-expressed in BT474 relative to StratRef total (unamplified) RNA (see Additional file 1 for graphical distribution of QPCR expression). Half of the genes were selected based on SAM[31] (Significance analysis of microarrays) analysis of total RNA BT474 expression relative to StratRef (2–4 fold under/over-expression) (Additional file 2 total RNA). The other half was selected to include genes that did not have a minimum of a 2-fold change in expression, which are genes considered to have low levels of expression on microarray analysis. Thus, our QPCR validation experiments were designed to determine whether fidelity of amplification was compromised without regard to the amplitude of the ratio of gene expression between two RNA samples. Our QPCR gene set covers a wide dynamic range of gene expression (including genes with equivalent expression in BT474 relative to StratRef) and was selected before amplifications were performed (see Additional file 3 for delta delta CTs).


A comparison of RNA amplification techniques at sub-nanogram input concentration.

Lang JE, Magbanua MJ, Scott JH, Makrigiorgos GM, Wang G, Federman S, Esserman LJ, Park JW, Haqq CM - BMC Genomics (2009)

Study Design – BT474 and Stratagene Universal Human Pooled Reference RNA were used as the substrate for these experiments. 10 ug of total RNA from each were hybridized to microarrays and labeled "total RNA arrays". SAM analysis from these total RNA arrays were used to select QPCR genes in an unbiased fashion prior to performing any amplification reaction. Total RNA was serially diluted, amplified, and hybridized to cDNA microarrays. QPCR was performed on total RNA and amplified RNA. Statistical analyses included microarray vs microarray analysis as well as microarray vs QPCR analysis.
© Copyright Policy - open-access
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC2724417&req=5

Figure 1: Study Design – BT474 and Stratagene Universal Human Pooled Reference RNA were used as the substrate for these experiments. 10 ug of total RNA from each were hybridized to microarrays and labeled "total RNA arrays". SAM analysis from these total RNA arrays were used to select QPCR genes in an unbiased fashion prior to performing any amplification reaction. Total RNA was serially diluted, amplified, and hybridized to cDNA microarrays. QPCR was performed on total RNA and amplified RNA. Statistical analyses included microarray vs microarray analysis as well as microarray vs QPCR analysis.
Mentions: Figure 1 demonstrates a schematic of our study design. To validate our microarray results, we utilized quantitative RT-PCR (QPCR) for a panel of 37 genes. Unlike prior studies that used QPCR to validate expression of outliers – genes predominantly expressed in one RNA sample versus another – we selected QPCR primers to measure genes that are under-expressed, equivalent, or over-expressed in BT474 relative to StratRef total (unamplified) RNA (see Additional file 1 for graphical distribution of QPCR expression). Half of the genes were selected based on SAM[31] (Significance analysis of microarrays) analysis of total RNA BT474 expression relative to StratRef (2–4 fold under/over-expression) (Additional file 2 total RNA). The other half was selected to include genes that did not have a minimum of a 2-fold change in expression, which are genes considered to have low levels of expression on microarray analysis. Thus, our QPCR validation experiments were designed to determine whether fidelity of amplification was compromised without regard to the amplitude of the ratio of gene expression between two RNA samples. Our QPCR gene set covers a wide dynamic range of gene expression (including genes with equivalent expression in BT474 relative to StratRef) and was selected before amplifications were performed (see Additional file 3 for delta delta CTs).

Bottom Line: Microarray filtering and data processing has an important effect on the correlation coefficient results generated by each method.Arrays derived from total RNA had higher Pearson's correlations than did arrays derived from amplified RNA when considering the entire unprocessed dataset, however, when considering a gene set of high signal intensity, the amplified arrays had superior correlation coefficients than did the total RNA arrays.RNA amplification and expression analysis at the sub-nanogram input level is both feasible and accurate if data processing is used to focus attention to high intensity genes for microarrays or if QPCR is used as a gold standard for validation.

View Article: PubMed Central - HTML - PubMed

Affiliation: Department of Surgery, UCSF Comprehensive Cancer Center, 1500 Divisadero Street, San Francisco, CA 94143, USA. jlang@azcc.arizona.edu

ABSTRACT

Background: Gene expression profiling of small numbers of cells requires high-fidelity amplification of sub-nanogram amounts of RNA. Several methods for RNA amplification are available; however, there has been little consideration of the accuracy of these methods when working with very low-input quantities of RNA as is often required with rare clinical samples. Starting with 250 picograms-3.3 nanograms of total RNA, we compared two linear amplification methods 1) modified T7 and 2) Arcturus RiboAmp HS and a logarithmic amplification, 3) Balanced PCR. Microarray data from each amplification method were validated against quantitative real-time PCR (QPCR) for 37 genes.

Results: For high intensity spots, mean Pearson correlations were quite acceptable for both total RNA and low-input quantities amplified with each of the 3 methods. Microarray filtering and data processing has an important effect on the correlation coefficient results generated by each method. Arrays derived from total RNA had higher Pearson's correlations than did arrays derived from amplified RNA when considering the entire unprocessed dataset, however, when considering a gene set of high signal intensity, the amplified arrays had superior correlation coefficients than did the total RNA arrays.

Conclusion: Gene expression arrays can be obtained with sub-nanogram input of total RNA. High intensity spots showed better correlation on array-array analysis than did unfiltered data, however, QPCR validated the accuracy of gene expression array profiling from low-input quantities of RNA with all 3 amplification techniques. RNA amplification and expression analysis at the sub-nanogram input level is both feasible and accurate if data processing is used to focus attention to high intensity genes for microarrays or if QPCR is used as a gold standard for validation.

Show MeSH
Related in: MedlinePlus