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Optimization and analysis of a quantitative real-time PCR-based technique to determine microRNA expression in formalin-fixed paraffin-embedded samples.

Goswami RS, Waldron L, Machado J, Cervigne NK, Xu W, Reis PP, Bailey DJ, Jurisica I, Crump MR, Kamel-Reid S - BMC Biotechnol. (2010)

Bottom Line: By dividing the profiled miR into abundance strata of high (Ct<30), medium (30 < or = Ct < or = 35), and low (Ct>35), we show that reproducibility between technical replicates, equivalent dilutions, and FFPE vs. frozen samples is best in the high abundance stratum.Examining correlation coefficients between FFPE and fresh-frozen samples in terms of miR abundance reveals correlation coefficients of up to 0.32 (low abundance), 0.70 (medium abundance) and up to 0.97 (high abundance).Our study thus demonstrates the utility, reproducibility, and optimization steps needed in miR expression studies using FFPE samples on a high-throughput quantitative PCR-based miR platform, opening up a realm of research possibilities for retrospective studies.

View Article: PubMed Central - HTML - PubMed

Affiliation: Division of Applied Molecular Oncology, Ontario Cancer Institute, University Health Network, Toronto, ON, Canada.

ABSTRACT

Background: MicroRNAs (miRs) are non-coding RNA molecules involved in post-transcriptional regulation, with diverse functions in tissue development, differentiation, cell proliferation and apoptosis. miRs may be less prone to degradation during formalin fixation, facilitating miR expression studies in formalin-fixed paraffin-embedded (FFPE) tissue.

Results: Our study demonstrates that the TaqMan Human MicroRNA Array v1.0 (Early Access) platform is suitable for miR expression analysis in FFPE tissue with a high reproducibility (correlation coefficients of 0.95 between duplicates, p < 0.00001) and outlines the optimal performance conditions of this platform using clinical FFPE samples. We also outline a method of data analysis looking at differences in miR abundance between FFPE and fresh-frozen samples. By dividing the profiled miR into abundance strata of high (Ct<30), medium (30 < or = Ct < or = 35), and low (Ct>35), we show that reproducibility between technical replicates, equivalent dilutions, and FFPE vs. frozen samples is best in the high abundance stratum. We also demonstrate that the miR expression profiles of FFPE samples are comparable to those of fresh-frozen samples, with a correlation of up to 0.87 (p < 0.001), when examining all miRs, regardless of RNA extraction method used. Examining correlation coefficients between FFPE and fresh-frozen samples in terms of miR abundance reveals correlation coefficients of up to 0.32 (low abundance), 0.70 (medium abundance) and up to 0.97 (high abundance).

Conclusion: Our study thus demonstrates the utility, reproducibility, and optimization steps needed in miR expression studies using FFPE samples on a high-throughput quantitative PCR-based miR platform, opening up a realm of research possibilities for retrospective studies.

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Ct values according to different input RNA concentrations. A) Plot of the difference in Ct values vs. mean Ct between duplicate plates for varying input RNA concentrations. Red lines indicate divisions between high (Ct<30), medium (30≤Ct≤35) and low (Ct>35) abundance strata. The total number of miRs is shown within each stratum for each RNA concentration (e.g., 72 miRs have Ct values <30 at the 10 ng RNA dilution compared to 112 miRs at the 200 ng dilution). B) Boxplots depicting the median of the absolute difference in Cts between duplicate plates for each miR abundance stratum according to input RNA concentration.
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Figure 1: Ct values according to different input RNA concentrations. A) Plot of the difference in Ct values vs. mean Ct between duplicate plates for varying input RNA concentrations. Red lines indicate divisions between high (Ct<30), medium (30≤Ct≤35) and low (Ct>35) abundance strata. The total number of miRs is shown within each stratum for each RNA concentration (e.g., 72 miRs have Ct values <30 at the 10 ng RNA dilution compared to 112 miRs at the 200 ng dilution). B) Boxplots depicting the median of the absolute difference in Cts between duplicate plates for each miR abundance stratum according to input RNA concentration.

Mentions: In qRT-PCR, accurate and reproducible data are dependent on the Ct value. Low Ct values indicate the presence of higher template abundance, and are usually associated with a higher reproducibility and lower variability [22]. We thus stratified our data based on Ct values and divided miRs into three strata of high (Ct values <30), medium (Ct values from 30-35) and low abundance (Ct values >35). To establish these cutoffs for miR abundance, we plotted the absolute value of the difference between duplicate Ct measurements as a function of the duplicate mean, for all input RNA concentrations. Measurements where one or both of the duplicate Ct values are exactly 40 are removed. Using the R statistics package (R Development Core Team, 2008), we applied a cubic spline function to fit a smooth curve to the data (Additional file 1, Figure S1). miRs with Ct values <30 showed the least variability. In contrast, miRs with Ct values >35 were highly variable. We examined how the various RNA concentrations affected miR abundance and Ct reproducibility in each stratum. As seen in Figure 1, the high abundance stratum is associated with consistently high reproducibility between biological and technical replicates. Reproducibility decreases across the medium stratum, and is consistently poor in the low stratum. Results from this analysis also demonstrated a shift in the number of miRs from the low and medium abundance strata to the high abundance stratum with increasing concentrations of input RNA (Figure 1A).


Optimization and analysis of a quantitative real-time PCR-based technique to determine microRNA expression in formalin-fixed paraffin-embedded samples.

Goswami RS, Waldron L, Machado J, Cervigne NK, Xu W, Reis PP, Bailey DJ, Jurisica I, Crump MR, Kamel-Reid S - BMC Biotechnol. (2010)

Ct values according to different input RNA concentrations. A) Plot of the difference in Ct values vs. mean Ct between duplicate plates for varying input RNA concentrations. Red lines indicate divisions between high (Ct<30), medium (30≤Ct≤35) and low (Ct>35) abundance strata. The total number of miRs is shown within each stratum for each RNA concentration (e.g., 72 miRs have Ct values <30 at the 10 ng RNA dilution compared to 112 miRs at the 200 ng dilution). B) Boxplots depicting the median of the absolute difference in Cts between duplicate plates for each miR abundance stratum according to input RNA concentration.
© Copyright Policy - open-access
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC2902407&req=5

Figure 1: Ct values according to different input RNA concentrations. A) Plot of the difference in Ct values vs. mean Ct between duplicate plates for varying input RNA concentrations. Red lines indicate divisions between high (Ct<30), medium (30≤Ct≤35) and low (Ct>35) abundance strata. The total number of miRs is shown within each stratum for each RNA concentration (e.g., 72 miRs have Ct values <30 at the 10 ng RNA dilution compared to 112 miRs at the 200 ng dilution). B) Boxplots depicting the median of the absolute difference in Cts between duplicate plates for each miR abundance stratum according to input RNA concentration.
Mentions: In qRT-PCR, accurate and reproducible data are dependent on the Ct value. Low Ct values indicate the presence of higher template abundance, and are usually associated with a higher reproducibility and lower variability [22]. We thus stratified our data based on Ct values and divided miRs into three strata of high (Ct values <30), medium (Ct values from 30-35) and low abundance (Ct values >35). To establish these cutoffs for miR abundance, we plotted the absolute value of the difference between duplicate Ct measurements as a function of the duplicate mean, for all input RNA concentrations. Measurements where one or both of the duplicate Ct values are exactly 40 are removed. Using the R statistics package (R Development Core Team, 2008), we applied a cubic spline function to fit a smooth curve to the data (Additional file 1, Figure S1). miRs with Ct values <30 showed the least variability. In contrast, miRs with Ct values >35 were highly variable. We examined how the various RNA concentrations affected miR abundance and Ct reproducibility in each stratum. As seen in Figure 1, the high abundance stratum is associated with consistently high reproducibility between biological and technical replicates. Reproducibility decreases across the medium stratum, and is consistently poor in the low stratum. Results from this analysis also demonstrated a shift in the number of miRs from the low and medium abundance strata to the high abundance stratum with increasing concentrations of input RNA (Figure 1A).

Bottom Line: By dividing the profiled miR into abundance strata of high (Ct<30), medium (30 < or = Ct < or = 35), and low (Ct>35), we show that reproducibility between technical replicates, equivalent dilutions, and FFPE vs. frozen samples is best in the high abundance stratum.Examining correlation coefficients between FFPE and fresh-frozen samples in terms of miR abundance reveals correlation coefficients of up to 0.32 (low abundance), 0.70 (medium abundance) and up to 0.97 (high abundance).Our study thus demonstrates the utility, reproducibility, and optimization steps needed in miR expression studies using FFPE samples on a high-throughput quantitative PCR-based miR platform, opening up a realm of research possibilities for retrospective studies.

View Article: PubMed Central - HTML - PubMed

Affiliation: Division of Applied Molecular Oncology, Ontario Cancer Institute, University Health Network, Toronto, ON, Canada.

ABSTRACT

Background: MicroRNAs (miRs) are non-coding RNA molecules involved in post-transcriptional regulation, with diverse functions in tissue development, differentiation, cell proliferation and apoptosis. miRs may be less prone to degradation during formalin fixation, facilitating miR expression studies in formalin-fixed paraffin-embedded (FFPE) tissue.

Results: Our study demonstrates that the TaqMan Human MicroRNA Array v1.0 (Early Access) platform is suitable for miR expression analysis in FFPE tissue with a high reproducibility (correlation coefficients of 0.95 between duplicates, p < 0.00001) and outlines the optimal performance conditions of this platform using clinical FFPE samples. We also outline a method of data analysis looking at differences in miR abundance between FFPE and fresh-frozen samples. By dividing the profiled miR into abundance strata of high (Ct<30), medium (30 < or = Ct < or = 35), and low (Ct>35), we show that reproducibility between technical replicates, equivalent dilutions, and FFPE vs. frozen samples is best in the high abundance stratum. We also demonstrate that the miR expression profiles of FFPE samples are comparable to those of fresh-frozen samples, with a correlation of up to 0.87 (p < 0.001), when examining all miRs, regardless of RNA extraction method used. Examining correlation coefficients between FFPE and fresh-frozen samples in terms of miR abundance reveals correlation coefficients of up to 0.32 (low abundance), 0.70 (medium abundance) and up to 0.97 (high abundance).

Conclusion: Our study thus demonstrates the utility, reproducibility, and optimization steps needed in miR expression studies using FFPE samples on a high-throughput quantitative PCR-based miR platform, opening up a realm of research possibilities for retrospective studies.

Show MeSH