Multi-Gene Validation of Differential Gene Expression Using the
RT2Profiler™ PCR Array, a Low-Density qPCR Array
Ray Blanchard; Hongguang Pan; Yanyang Sun; Jie Wang; Siulan Lee; Xiaomei Bao; Savita Prabhakar; Bill Wang; Jingping Yang
SuperArray Bioscience Corporation, Frederick, MD
Using the RT²Profiler™ PCR Array to Identify Tumor-Specific Genes
Performance of RT²Profiler™ PCR Array
Figure 1: The RT Profiler™ PCR Array Shows High Reproducibility of Ct
Values on Replicate Plates
Figure 4: Identify Differentially Expressed Genes Between Breast Tumor and
Normal Breast Using the Cancer PathwayFinder™ RT2Profiler™ PCR Array
∆∆Ct (GOI) = average ∆Ct (Expt) – average ∆Ct (Cntrl)
The GOI fold-change from control to experimental group = 2 ^ (-∆∆Ct).
The significance of the fold-change in gene expression between the two groups
is evaluated by the student t-test and displayed on a volcano plot.
Coefficiency of Variation Rang
Figure 2: Validation of Ct reproducibility Across Time and Investigator
Ave Raw Ct
To identify breast tumor specific genes, total RNA from normal breast tissue and the first of two unmatched breast tumor
samples (BioChain Institute, Inc.) were characterized on the RT2Profiler™ Cancer PathwayFinder™ PCR Array (APH-033A).
The scatter plot (Panel A) depicts a log transformation plot of the relative expression level of each gene (2^(-∆Ct)) between
breast tumor and normal breast. The pink lines indicate the 3-fold change in gene expression threshold. The volcano plot (panel
B) depicts the fold changes versus the p-values from the t-test. Panel C plots the fold changes of each gene as a z-axis
displacement from the xy-plane representing the 96-well layout of the PCR Array. A total of 33 genes in the Cancer
PathwayFinder™ PCR Array demonstrate at least a three-fold difference in gene expression between normal breast tissue and
the breast tumor, as listed in panel D. Seven of these genes (colored red in panel A) represent cellular adhesion molecules. The
results suggest that changes in the expression of genes involved in cellular interactions played an important role in the
transformation of this and perhaps other breast tumors. To further confirm this hypothesis and to analyze the expression of other
adhesion-related genes, a second breast tumor sample was characterized on the Extracellular Matrix and Adhesion Molecules
PCR Array (APH-013A) and comparable results were obtained (data not shown).
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Validation of RT2Profiler™ PCR array reproducibility at different times and in the hands of different investigators is demonstrated
by the high level of correlation between replicate plate Ct values. The MAQC brain reference RNA sample was reverse transcribed
and run on four replicate APH-002 (Human Drug Metabolism PCR Array) plates three months apart and by different investigators.
The average correlation coefficient for Ct values between replicate runs was 0.995 ± 0.001 and 0.998 ± 0.000 for experiment 1 and 2,
Figure 3: Cross Instrument Platform Validation of the RT2Profiler™ PCR Array
A. Sample A ave Ct
B. Sample B ave Ct
C. B/A Fold change (log2)
RT²Profiler™ PCR array is easy-to-use and requires minimal hands-on time.
The protocol takes only two hours per sample to perform from start to finish
(from cDNA to raw data). With its combination of pathway-specific gene lists,
gene-specific PCR primers, and master mixes specific for individual
instrumentation, the PCR Array system delivers superior system convenience
and cost effectiveness.
Data Analysis Method
Fold-changes in gene expression are calculated using the ∆∆Ct method.
For each gene of interest (GOI):
∆Ct (Cntrl Rep 1) = Ct (GOI) – avg Ct (HK genes)
∆Ct (Cntrl Rep 2) = Ct (GOI) – avg Ct (HK genes)
∆Ct (Cntrl Rep 3) = Ct (GOI) – avg Ct (HK genes)…
∆Ct (Expt Rep 1) = Ct (GOI) – avg Ct (HK genes)
∆Ct (Expt Rep 2) = Ct (GOI) – avg Ct (HK genes)
∆Ct (Expt Rep 3) = Ct (GOI) – avg Ct (HK genes)…
Two different RNA samples were each characterized in technical triplicates on a PCR Array. Panel A plots the average Ct values for
each gene in each sample. The y-axis bars in the plot depict one average standard deviation about each calculated average Ct value.
The table in Panel B summarizes the results in Panel A by listing the average standard deviation for different Ct value ranges as well
as the percentage of genes in each group (percent frequency). Panel C charts the frequency of genes exhibiting a coefficient of
variation in Ct value determination within a given range while the insert shows the %CV for each individual assay on the plate.
RT²Profiler™ PCR array is a 96-well plate
containing gene-specific primer sets for a
thoroughly researched set of 84 genes relevant to
a specific biological pathway or disease state plus
five housekeeping genes and technical controls.
A single sample is applied to all the wells of each
plate so that replicates have their own plate.
RT²Profiler™ PCR array control elements include:
1) A panel of 5 common housekeeping genes (HK1-HK5)
The Cts for all 5 are averaged to produce a plate normalizing factor
2) A genomic DNA contamination control (GDC)
Primers detect a region of non-mRNA coding gDNA
3) Triplicate reverse transcription control (RTC) elements
Primers detect an external RNA control sequence from SuperArray’s
RT2 PCR Array First Strand Kit (C-02)
4) Triplicate positive PCR control (PPC) elements
The pre-dispensed external DNA template and primers in these wells
produce a defined Ct under proper PCR conditions
Average Ct value
What is the RT²Profiler™ PCR Array?
Coefficiency of Variation
Average Ct value
Validation of the RT²Profiler™ PCR Array platform can be demonstrated by its high level of
reproducibility on several levels. SuperArray’s experimentally verified primer design algorithm
and specially formulated master mix of the RT² SYBR® Green PCR system insure uniform
annealing temperatures and high amplification efficiencies for optimal performance of every
assay on the array plate. The individual PCR assays, which generate gene expression data using
∆∆Ct calculations, produce a standard deviation of 0.24 cycles and a CV of 0.92% between Ct
values in replicate runs on the same cDNA. Collectively, the correlation coefficient for Ct values
between replicate runs was 0.995 ± 0.001 (n=6) and 0.998 ± 0.000 (n=6) on two separate tests.
Further tests provide cross-platform validation on three different real-time thermal cyclers with
correlation coefficients averaging 0.991. A biological demonstration of the PCR Arrays’
performance identifies human breast tumor-specific genes from cancerous and normal tissues
using the Cancer PathwayFinder™ RT²Profiler™ PCR Array. These results identified 33 genes
with a statistically significant 3-fold or greater change in mRNA levels and indicated that cell
adhesion molecules are the most dramatically affected pathway.
Biological validation of gene expression results from high-density microarrays is an essential
part of any expression profiling experiment and reverse transcription, real-time PCR (RTqPCR) has become the standard for this process. Unfortunately, the speed of this process has
been hindered by the low throughput of traditional RT-qPCR using individual gene assays and
standard curves. Additionally, the use of RT-qPCR multi-gene panels to achieve higher
throughput has been hampered by the requirement for very tight control of assay reaction
conditions across a wide range of genes in order to produce consistent results across expression
system and instrument platforms. The SuperArray RT²Profiler™ PCR Array accelerates
microarray validation by RT-qPCR through the combination of optimized and validated SYBR®
Green PCR assays in a multi-gene array plate format.
Overall, the RT²Profiler™ PCR Array provides the high reproducibility, specificity, sensitivity
and wide linear dynamic range expected of real-time PCR with the multi-gene advantages of a
low-density array. As demonstrated by the validation data in this report, the RT²Profiler™
PCR Array provides highly reliable SYBR® Green qPCR gene expression analysis. In both preand custom designed options the RT²Profiler™ PCR Array offers a powerful solution for
accelerating the pace of microarray data validation.
Wide linear dynamic range, high amplification efficiency, reproducibility and specificity
expected of real-time PCR
Replicate Ct value measurements within 0.24 cycle average standard deviation
High degree of correlation, R2 ≥ 0.995, between Ct values obtained months apart by
High degree of correlation, R2 ≥ 0.988, between Ct values obtained on different models of
real-time PCR instruments
High degree of correlation, R2 ≥ 0.973 between fold-changes results obtained from
different real-time PCR instruments
Cross platform validation of measurements and results were performed using the two MAQC reference RNAs analyzed on the Human
Drug Metabolism RT2Profiler™ PCR array (APH-002) using three different real-time PCR thermal cycler models. Five replicate
arrays were run for each sample on each model instrument and the average Ct value for each assay is compared in panels A and B. The
results show a high correlation across instruments at the level of instrument measurement values. Panel C shows the cross platform
comparison of fold change results obtained from these data with an average correlation coefficient of 0.991.
Thirty-three genes from the Cancer PathwayFinder™ PCR Array demonstrated at least a
3-fold change in gene expression between a human breast tumor and normal breast tissue
Pathway-focused PCR arrays offer a simple and reliable method to identify and monitor
gene expression changes within a defined group of genes