Optimizing RT-qPCR Assays for Multiplex Gene Expression Analysis

Optimizing RT-qPCR Assays for Multiplex Gene Expression Analysis Srikanth Perike, Andrew Prantner, and Chelsea Pratt Abstract Regulation of gene expression is a fundamental mechanism for cellular response to environmental stressors, such as toxins or pathogens. RT-qPCR remains the gold standard for quantifying these changes due to its sensitivity and broad dynamic range. However, the accuracy of RT-qPCR is highly dependent on technical variables such as the reference gene stability and amplification efficiency. This study aimed to provide practical guidance for experimental design and data analysis for gene expression analysis, specifically evaluating the impact of transitioning from singleplex to multiplex formats on data reliability. We utilized PrimePCR arrays to empirically select stable reference genes (ACTB and PGK1) and compared singleplex and multiplex performance for two target genes (GADD45A and BCL2) following cisplatin treatment of cultured HeLa cells. The findings offer a framework for implementing singleplex and multiplex gene expression assays that enable high-fidelity and reproducible quantification. Introduction Regulation of gene expression is a fundamental biological mechanism, enabling cells to respond to environmental changes, such as fluctuations in nutrient availability, temperature, toxins, or pathogen exposure. Quantifying these patterns is essential for deciphering molecular drivers of a broad spectrum of diseases, from cancer to diabetes. Detecting differentially expressed genes helps reveal novel biomarkers and diagnostic signatures required for developing targeted therapies. Due to its ease of access, broad dynamic range, affordability, and suitability for automation, quantitative real-time PCR (qPCR) remains the gold standard for detecting gene expression differences. This approach often involves the conversion of RNA into complementary DNA (cDNA) via reverse transcription (RT), followed by real-time fluorescent monitoring of the target sequence amplification. The importance of normalization Gene expression analysis by RT-qPCR presents several technical challenges. Firstly, the accuracy of the quantification cycle (Cq) value, which serves as the basis for calculating template concentration, depends on near-perfect amplification efficiency. This efficiency is sensitive to technical variability, so primer pair optimization is critical. To account for variability in RNA quantity, quality, and reverse transcription efficiency across samples and experiments, data must be normalized against reference genes, producing ΔCq values. However, the validity of this step relies on selecting appropriate reference genes that are stably expressed across all samples and conditions. Classic choices like GAPDH or TBP can vary across experimental conditions and developmental stages, compromising the normalization process. Additionally, using multiple reference genes creates a more robust normalization factor. By minimizing the impact of variability or instability in any single gene, this approach provides reproducible gene expression results and ensures that observed changes reflect true biological differences rather than artifacts of unstable controls. REAL-TIME PCR Visit bio-rad.com/multiplexqPCR for more information. Bulletin 3990 Ver AHeLa cells Treatment RNA extraction cDNA synthesis Real-time PCR Cell analysis Relative gene expression is then typically determined using the ΔΔCq method, which compares normalized ΔCq values between experimental and control samples to calculate fold changes in gene expression. This method assumes nearperfect amplification efficiency of both the target and reference genes. Verifying these efficiencies using standard curves is therefore a critical prerequisite for accurate quantification. Since qPCR is sensitive to technical variability, standardized methods are essential to enhance cross-study comparisons and overall data reliability. To promote broader standardization in gene expression workflows, this study provides practical guidance for experimental design and data analysis. We specifically evaluated how transitioning from singleplex to multiplex formats affects performance by comparing both formats within a four-target RT-qPCR assay. The panel included two cisplatin-responsive target genes (GADD45A and BCL2) and two reference genes selected using PrimePCR arrays (ACTB and PGK1). Materials and methods HeLa cells were cultured in Dulbecco’s Modified Eagle Medium (DMEM; Thermo Fisher Scientific Inc., catalog #11995065) supplemented with 10% fetal bovine serum (Hyclone, GE Life Sciences, catalog #SH30070.03) and 1% Penicillin/Streptomycin Solution (Thermo Fisher, #15140122). The cells were washed with phosphate buffered saline (PBS), trypsinized, then centrifuged and resuspended in complete cell culture medium. Cell counts were performed using a TC20 Automated Cell Counter (Bio-Rad Laboratories, Inc., #1450102). Cells were seeded at 5 x 105 cells/mL into 10 cm2 dishes and incubated at 37 °C in a humidified atmosphere with 5% CO2 (Figure 1). After 24 hours, experimental groups were treated with 10 µM cisplatin (Sigma Aldrich, catalog #232120) for 24 hours; controls received PBS supplemented with 140 mM NaCl. RNA extraction Total RNA was extracted from the HeLa cells using PureZOL RNA Isolation Reagent (Bio-Rad, #7326890). To do this, 1 ml of PureZOL Reagent was added to cells cultured in a 10 cm² dish, followed by a 5-minute incubation. Following lysis, RNA was separated from the aqueous fraction using chloroform, precipitated with isopropyl alcohol, and washed with ethanol per the manufacturer’s instructions. The resulting RNA pellet was resuspended in nuclease-free water, and the RNA concentration was measured using a NanoDrop One UV-Vis spectrophotometer (Thermo Fisher, #ND-ONE-W). DNase digestion and cDNA synthesis The iScript gDNA Clear cDNA Synthesis Kit (Bio-Rad, #1725034) was used to eliminate genomic DNA (gDNA) and synthesize cDNA from the HeLa cell RNA. Specifically, 1 µg of total RNA was diluted in nuclease-free water and subjected to iScript DNase digestion per manufacturer guidelines. cDNA synthesis of the RNA template was performed using the iScript Reverse Transcription Supermix for RT-qPCR (Bio-Rad, #1708840) in a PTC Tempo Deepwell Thermal Cycler (BioRad, #12015392). The mixture was incubated at 25 °C for 5 min, followed by reverse transcription at 46 °C for 20 min and 95 °C for 1 min. The resulting cDNA was further diluted fivefold in TE buffer (10 mM Tris, 0.1 mM EDTA, pH 8.0). Figure 1. Schematic representation of the gene expression analysis workflow. Cisplatin PureZOL™ iScript™ CFX Opus 96 CFX Maestro Software © 2026 Bio-Rad Laboratories, Inc. 2 Bulletin 3990 Ver A Optimizing RT-qPCR Assays for Multiplex Gene Expression AnalysisQuantitative real-time PCR Reference gene analysis To estimate reference gene expression stability values and determine the optimal genes for normalization, we first identified human reference genes with minimal expression variation in response to cisplatin treatment compared to untreated controls. For this, we used a validated, predesigned 96-well PrimePCR Reference Genes H96 qPCR array (Bio-Rad, #10025898). Each 20 µL PCR reaction contained 1 µL of the diluted cDNA, SsoAdvanced Universal SYBR Green Supermix (Bio-Rad, #1725270), and nuclease-free water. qPCR was performed in triplicate for both the treated and untreated samples using a CFX Opus 96 Real-Time PCR System (BioRad, #12011319) and the PrimePCR cycling protocol (Table 2). The reference genes identified were ACTB and PGK1. Singleplex and multiplex assay design Gene expression was quantified using singleplex and multiplex qPCR. We targeted two genes of interest (BCL2 and GADD45A) with established roles in the cisplatin response, normalized against the reference genes (ACTB and PGK1).1,2 Assays were executed on a CFX Opus 96 Real-Time PCR System in 20 µL total volumes containing cDNA template, nuclease-free water, and either SsoAdvanced Universal SYBR Green Supermix for singleplex PCR or iQ Multiplex Powermix (#1725849) for multiplex reactions. All primers and probes were sourced from Bio-Rad (Table 1). qPCR was performed in triplicate for each biological replicate to ensure technical reproducibility. Cycling conditions and detection The assays followed PrimePCR cycling protocols (Table 2). Fluorescence was detected in the FAM channel for singleplex assays and in the FAM, HEX, Cy5, and Texas Red channels for multiplex assays. Data analysis and statistics Data were analyzed using the Gene Study feature in CFX Maestro Software v2.3 (Bio-Rad, #12013758). Reference gene expression stability values were first estimated. For the singleplex and multiplex assays, three biological replicates, each with three technical replicates for each target, were included. Reference genes were evaluated for all the biological replicates to ensure minimal variation between the cisplatin-treated and untreated samples. Normalized gene expression variations were calculated using the ΔΔCq method for both the singleplex and multiplex qPCR assays. Statistical significance was determined by Student’s t-test (P < 0.05), and genes were considered differentially expressed if their change in the treated samples was greater than ±2-fold of the transcript levels in the untreated samples. Table 1. List of genes and predesigned PrimePCR assay used in singleplex and multiplex gene expression assays. Cy5, cyanine5; FAM, 5(6)-carboxyfluorescein; HEX, hexachlorofluorescein Gene Fluorophore detection Assay ID Assay format ACTB (reference) SYBR® qHsaCED0036269 Singleplex Cy5 qHsaCEP0036280 Multiplex BCL2 SYBR® qHsaCED0057245 Singleplex FAM qHsaCEP0058350 Multiplex PGK1 (reference) SYBR® qHsaCED0042912 Singleplex HEX qHsaCEP0050174 Multiplex GADD45A SYBR® qHsaCED0036441 Singleplex Texas Red qHsaCEP0039165 Multiplex Table 2. PrimePCR cycling protocols Reference gene analysis and singleplex Multiplex 1 95 °C for 2 min 95 °C for 3 min 2 40 cycles of 95 °C for 5 sec 40 cycles of 95 °C for 10 sec 3 60 °C for 30 sec 60 °C for 45 sec 4 Melt curve from 65 to 95 °C in 0.5 °C increments © 2026 Bio-Rad Laboratories, Inc. 3 Bulletin 3990 Ver A Optimizing RT-qPCR Assays for Multiplex Gene Expression AnalysisBulletin 3990 Ver A 06/2026 Website bio-rad.com USA 1 800 424 6723 Australia 61 2 9914 2800 Austria 00 800 00 24 67 23 Belgium 00 800 00 24 67 23 Brazil 55 11 3065 7550 Canada 1 800 361 1808 China 86 21 6169 8500 Czech Republic 00 800 00 24 67 23 Denmark 00 800 00 24 67 23 Finland 00 800 00 24 67 23 France 00 800 00 24 67 23 Germany 00 800 00 24 67 23 Greece 30 210 7774396 Hong Kong 852 2789 3300 Hungary 00 800 00 24 67 23 India 91 124 4029300 Israel 000 800 00 24 67 23 Italy 00 800 00 24 67 23 Japan 81 3 6361 7000 Korea 82 080 007 7373 Luxembourg 00 800 00 24 67 23 Mexico 52 55 5488 7670 The Netherlands 00 800 00 24 67 23 New Zealand 64 9 415 2280 Norway 00 800 00 24 67 23 Poland 00 800 00 24 67 23 Portugal 00 800 00 24 67 23 Russian Federation 7 495 721 14 04 Singapore 65 6415-3170 South Africa 27 21 531 7504 Spain 00 800 00 24 67 23 Sweden 00 800 00 24 67 23 Switzerland 00 800 00 24 67 23 Taiwan 886 2 2578 7189 Thailand 662 651 8311 United Arab Emirates 971 4 818 7300 United Kingdom 00 800 00 24 67 23 Life Science Group Bio-Rad Laboratories, Inc. Results Gene reference selection using PrimePCR arrays To ensure normalization accuracy, 14 candidate genes were screened using a validated, predesigned 96-well PrimePCR reference gene panel. PGK1 and ACTB emerged as the most stable targets based on their consistency across treated and control samples (Table 3). By using these stable genes rather than classic choices that may fluctuate under drug-induced stress, we ensured that the observed fold-changes reflect true biological responses. Table 3. Stability values of reference genes for samples treated with cisplatin for 24 hours or untreated. Gene Name Evaluation Ave M Value Stability (Ln(1/Avg/AvgM)) Treatment PGK1 Ideal 31.82 36.2 ± Cisplatin at 24h ACTB Ideal 52.41 8.36 ± Cisplatin at 24h Singleplex and multiplex qPCR assays for gene expression analysis To compare singleplex and multiplex formats, all four targets were assessed using highly similar primer sequences. The PrimePCR assay format provided a distinct advantage, as both singleplex and multiplex assays were designed with identical annealing temperatures and validated amplification efficiency. As a recommended best practice, all assays were first validated in singleplex format to confirm amplification efficiency and specificity. While the ΔΔCq method assumes near 100% amplification efficiencies for target and reference genes, algorithms such as Bio-Rad’s CFX Maestro Software allow users to input gene-specific efficiency values into the Pfaffl equation. This accounts for deviations from perfect efficiency and improves result accuracy.3 Both singleplex and multiplex assays showed nearly identical fold-change results: GADD45A showed significant upregulation (>2-fold, Cq value < 30), while BCL2 expression didn’t differ from the control (Figure 2). This confirms that multiplexing does not compromise the accuracy of gene expression quantification when using wet-lab validated assays, reagents, and systems. Singleplex qPCR assays Multiplex qPCR assays Relative Normalized Expression Relative Normalized Expression BCL2 GADD45A 4 3 2 1 0 BCL2 GADD45A 4 3 2 1 0 Control 24h Cisplatin 24h *(P<0.050) Figure 2. Average target gene levels between cisplatin-treated samples and control samples for singleplex and multiplex qPCR. Conclusion This study demonstrates that a standardized approach to RTqPCR—from empirical reference gene selection using PrimePCR assays to stringent efficiency validation—ensures reliable data across assay formats. The consistent results for GADD45A and BCL2 show that transitioning to multiplexing does not require extensive troubleshooting when best practices are followed and commercially wet lab validated assays are used. References 1. Michaud WA, Nichols AC, Mroz EA, et al. Bcl-2 blocks cisplatin-induced apoptosis and predicts poor outcome following chemoradiation treatment in advanced oropharyngeal squamous cell carcinoma. Clin Cancer Res. 2009;15(5):1645–1654. doi: 10.1158/1078-0432.ccr-08-2581 2. Liu J, Jiang G, Mao P, et al. Down-regulation of GADD45A enhances chemosensitivity in melanoma. Sci Rep. 2018;8:4111. doi: 10.1038/s41598- 018-22484-6 3. Pfaffl MW. A new mathematical model for relative quantification in real-time RT-PCR. Nucleic Acids Res. 2001;29(9):e45. doi: 10.1093/nar/29.9.e45 Bio-Rad is a trademark of Bio-Rad Laboratories, Inc. All trademarks used herein are the property of their respective owner. © 2026 Bio-Rad Laboratories, Inc. Optimizing RT-qPCR Assays for Multiplex Gene Expression Analysis