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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.

Show MeSH
Clustering heat-maps and pair-wise correlations for equivalent samples. The pair-wise correlations between equivalent samples are shown within A) low abundance stratum: The low abundance miRs have a low correlation coefficient (range: 0.16-0.31) regardless of the concentration and dilution factor used. B) medium abundance stratum, showing higher correlation coefficients (range: 0.75-0.86) compared to the low abundance stratum. C) high abundance stratum: In this stratum we detect the highest correlation coefficients (range: 0.98-0.99) between samples. Note that the scales are different for each abundance stratum, reflecting the respective correlation coefficients.
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Figure 2: Clustering heat-maps and pair-wise correlations for equivalent samples. The pair-wise correlations between equivalent samples are shown within A) low abundance stratum: The low abundance miRs have a low correlation coefficient (range: 0.16-0.31) regardless of the concentration and dilution factor used. B) medium abundance stratum, showing higher correlation coefficients (range: 0.75-0.86) compared to the low abundance stratum. C) high abundance stratum: In this stratum we detect the highest correlation coefficients (range: 0.98-0.99) between samples. Note that the scales are different for each abundance stratum, reflecting the respective correlation coefficients.

Mentions: The concordance rate was highly dependent on transcript abundance, as seen in the correlation heat-maps produced from these data (Figures 2A-C). We found a similar correlation between equivalent samples, with no statistically significant differences among the pair-wise correlations. As would be expected for equivalent samples, there were no significant differences in the distribution of miRs among the abundance strata by the chi-square test (χ2 = 3.3, df = 6, p = 0.77) (Additional file 4, Table S2).


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)

Clustering heat-maps and pair-wise correlations for equivalent samples. The pair-wise correlations between equivalent samples are shown within A) low abundance stratum: The low abundance miRs have a low correlation coefficient (range: 0.16-0.31) regardless of the concentration and dilution factor used. B) medium abundance stratum, showing higher correlation coefficients (range: 0.75-0.86) compared to the low abundance stratum. C) high abundance stratum: In this stratum we detect the highest correlation coefficients (range: 0.98-0.99) between samples. Note that the scales are different for each abundance stratum, reflecting the respective correlation coefficients.
© Copyright Policy - open-access
Related In: Results  -  Collection

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

Figure 2: Clustering heat-maps and pair-wise correlations for equivalent samples. The pair-wise correlations between equivalent samples are shown within A) low abundance stratum: The low abundance miRs have a low correlation coefficient (range: 0.16-0.31) regardless of the concentration and dilution factor used. B) medium abundance stratum, showing higher correlation coefficients (range: 0.75-0.86) compared to the low abundance stratum. C) high abundance stratum: In this stratum we detect the highest correlation coefficients (range: 0.98-0.99) between samples. Note that the scales are different for each abundance stratum, reflecting the respective correlation coefficients.
Mentions: The concordance rate was highly dependent on transcript abundance, as seen in the correlation heat-maps produced from these data (Figures 2A-C). We found a similar correlation between equivalent samples, with no statistically significant differences among the pair-wise correlations. As would be expected for equivalent samples, there were no significant differences in the distribution of miRs among the abundance strata by the chi-square test (χ2 = 3.3, df = 6, p = 0.77) (Additional file 4, Table S2).

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