Quantitative miRNA expression analysis using fluidigm microfluidics dynamic arrays

Abstract

Background: MicroRNAs (miRNAs) represent a growing class of small non-coding RNAs that are important regulators of gene expression in both plants and animals. Studies have shown that miRNAs play a critical role in human cancer and they can influence the level of cell proliferation and apoptosis by modulating gene expression. Currently, methods for the detection and measurement of miRNA expression include small and moderate-throughput technologies, such as standard quantitative PCR and microarray based analysis. However, these methods have several limitations when used in large clinical studies where a high-throughput and highly quantitative technology needed for the efficient characterization of a large number of miRNA transcripts in clinical samples. Furthermore, archival formalin fixed, paraffin embedded (FFPE) samples are increasingly becoming the primary resource for gene expression studies because fresh frozen (FF) samples are often difficult to obtain and requires special storage conditions. In this study, we evaluated the miRNA expression levels in FFPE and FF samples as well as several lung cancer cell lines employing a high throughput qPCR-based microfluidic technology. The results were compared to standard qPCR and hybridization-based microarray platforms using the same samples.

Results: We demonstrated highly correlated Ct values between multiplex and singleplex RT reactions in standard qPCR assays for miRNA expression using total RNA from A549 (R = 0.98; p < 0.0001) and H1299 (R = 0.95; p < 0.0001) lung cancer cell lines. The Ct values generated by the microfluidic technology (Fluidigm 48.48 dynamic array systems) resulted in a left-shift toward lower Ct values compared to those observed by ABI 7900 HT (mean difference, 3.79), suggesting that the microfluidic technology exhibited a greater sensitivity. In addition, we show that as little as 10 ng total RNA can be used to reliably detect all 48 or 96 tested miRNAs using a 96-multiplexing RT reaction in both FFPE and FF samples. Finally, we compared miRNA expression measurements in both FFPE and FF samples by qPCR using the 96.96 dynamic array and Affymetrix microarrays. Fold change comparisons for comparable genes between the two platforms indicated that the overall correlation was R = 0.60. The maximum fold change detected by the Affymetrix microarray was 3.5 compared to 13 by the 96.96 dynamic array.

Conclusion: The qPCR-array based microfluidic dynamic array platform can be used in conjunction with multiplexed RT reactions for miRNA gene expression profiling. We showed that this approach is highly reproducible and the results correlate closely with the existing singleplex qPCR platform at a throughput that is 5 to 20 times higher and a sample and reagent usage that was approximately 50-100 times lower than conventional assays. We established optimal conditions for using the Fluidigm microfluidic technology for rapid, cost effective, and customizable arrays for miRNA expression profiling and validation.

Figure 1

Correlation scatter plots of Ct values for qPCR using multiplexed or single-plexed RT reactions. Eleven different miRNA primers were used in single-plex (Y-axis) or 11-plex (X-axis) for reverse transcriptions using A549 (A) and H1299 (B) lung cancer cell lines. The qPCR was done with respected TaqMan probes using the ABI 7900HT instrument.

Figure 2

Ct value comparisons using the 48.48 dynamic array and ABI 7900 HT. cDNAs were synthesized using 96-plexed primer set and 100 ng total RNA from FF normal lung (A) and FF tumor lung (B) samples. Bars represent the means of Ct values from replicates for the indicated miRNA targets. Open bars: ABI 7900 HT, closed bars: 48.48 dynamic array systems.

Figure 3

Correlation between matched FFPE and FF samples in qPCR by ABI vs 48.48 dynamic array platforms. cDNAs were synthesized using 96-plex primer sets and RNA from both normal lung (A and C) and lung tumor samples (B and D). qPCR reactions were carried out individually for 16 miRNA targets (A and B) by ABI 7900 HT, and for 48 miRNA targets by the 48.48 dynamic array (C and D). Each plot displays mean values calculated from triplicate samples.

Figure 4

Effect of in put RNA concentrations on Ct values. A total of 48 miRNAs were tested using different input amounts of total RNA from FFPE and FF samples for RT and qPCR by the 48.48 dynamic array systems. The Ct values were plotted using the average of the duplicated measurements and the error bar for values on Y-axis. Correlation scatter plots represent the correlation of Ct values for 100 ng RNA (X-axis) and the Ct values for the same FF (A and B) or FFPE (C and D) sample at 10 ng to100 ng concentrations (Y-axis).

Figure 5

miRNA expression measurements by Fluidigm dynamic array and Affymetrix microarray. The raw intensity values of microarray data were transformed to log₂ values for comparison to PCR Ct values generated by the qPCR platform. Gene expression differences between FF and FFPE were compared against 59 shared genes. Fold differences by the dynamic array (log₂) were calculated by ΔΔCt method; ΔCt = (target miRNA log₂ values-hsa-mir-16 log₂ value), ΔΔCt = (fresh frozen ΔCt-FFPE ΔCt).