Strengthen Gene Expression Analysis With High-Precision PCR Workflows

PCR Assays Capture Gene Expression Insights Table of Contents Gene Expression Assays Through the Ages .................................................................................................................3 Tips and Tricks Addressing PCR Pain Points ................................................................................................................4 Delving into Gene Expression Data Analysis and Experimental Reproducibility .........................................................5 ddPCR Workflows for Gene Expression Analysis .........................................................................................................6 Gene Expression Assays Through the Ages Assays that enable researchers to measure gene expression have long been central as identifying both normal physiology and disease. In oncology, for instance, dysregulated oncogene or tumor-suppressor expression can drive malignant transformation, tumor growth, and metastasis.1 In neuroscience, changes in the expression of neurotransmitter receptors, ion channels, or growth factors influence processes such as synaptic plasticity, memory, and neurodegeneration.2 Reliable and scalable methods to quantify expression have become increasingly crucial across laboratories, and over the past three decades, PCR-based techniques have transformed how scientists analyze transcripts, as well as downstream translational applications.3 Background of PCR-Based Methods Developed in the early 1980s, conventional PCR, or end-point PCR, is often regarded as one of the earliest genetic technologies that researchers adopted for gene expression analysis.3 This approach allows scientists to amplify DNA or RNA-derived complementary DNA (cDNA) to visually detect expression, usually via gel electrophoresis. Conventional PCR provides greater sensitivity compared to microscopy-based hybridization methods, but it remains largely qualitative. Researchers can now use digital tools to compare signal intensities, but these results are typically semi-quantitative. The introduction of real-time quantitative PCR (qPCR) in the early 1990s opened the door for quantitative gene expression analysis.4 By incorporating fluorescent dyes or probes during amplification and measuring product accumulation during the exponential phase of amplification, qPCR provides cycle threshold values that help researchers calculate relative transcript abundance. qPCR’s combined sensitivity, specificity, and large dynamic range led to its widespread adoption for gene expression studies. Today, qPCR applications range from detecting © 2026 Bio-Rad Laboratories, Inc. minimal residual disease in cancer studies to monitoring genetic biomarkers in neurodegenerative conditions such as Alzheimer’s disease. Still, qPCR depends on standard curves, allowing only relative quantification, and it can be susceptible to variations in amplification efficiency. Digital PCR (dPCR) has emerged as a solution that provides highly sensitive absolute quantification of nucleic acids without the need for standard curves. Introduction of Digital PCR Scientists developed what we now refer to as dPCR before qPCR became widely adopted. Indeed, the concept was first described in the late 1980s as limiting dilution PCR, a method that combined end-point PCR and Poisson statistics to yield an absolute nucleic acid concentration measurement.4 In 1999, researchers at Johns Hopkins University coined the term 'digital PCR' to describe this method, whereby a sample is diluted and partitioned to the extent that single template molecules can be amplified individually. By partitioning a sample into thousands of tiny reactions, each ideally containing zero or one template molecule, dPCR allows researchers to directly count DNA molecules. After amplifica tion, the partitions are classified as positive or negative, and statistical modeling provides absolute copy numbers, no longer rely ing on external standards. Droplet Digital™ PCR (ddPCR™) technology represents a refinement of this approach.5 ddPCR technology generates tens of thousands of uniform droplets, dramatically increasing precision and sensitivity. ddPCR gene expression assays perform exceptionally well for small fold changes, such as as identifying expression differences that are 2-fold or lower, detecting low-abundance targets, or analyzing samples that are prone to containing PCR reaction inhibitors. For translational research workflows, this means detecting subtle gene expression shifts and rare transcripts in limited samples, such as circulating tumor cells or patient-derived neurons. Multiplexing and the Evolution of Gene Expression Analysis Along with the evolution of PCR-based gene expression methods, the ability to study multiple genes simultaneously has advanced through multiplexing. The multi plex ddPCR approach benefits from the partitioning strategy, which minimizes the risk of cross-reactivity or spectral overlap. This enables reliable detection of multiple transcripts in the same reaction, saving sample and reagents while increasing experimental power. Taken together, the trajectory of PCR technology shows how innovations have steadily improved sensitivity, precision, and scope. Conventional PCR made detection routine, qPCR turned quantification into a reliable process, and ddPCR technology has become the modern-day gold standard for expression analysis. For oncology and neuroscience, where rare transcripts or subtle changes can have substantial biological implications, ddPCR platforms advance analysis, providing absolute measurements with the robustness required for discovery and clinical research. 1. Creighton CJ (2023). Gene expression profiles in cancers and their therapeutic implications. Cancer J 29, 9–14. https://journals.lww. com/journalppo/abstract/2023/01000/ gene_expression_profiles_in_cancers_and_ their.3.aspx 2. Baratta AM et al. (2022). Advancements in genomic and behavioral neuroscience analysis for the study of normal and pathological brain function. Front Mol Neurosci 15, 905328. https://www.frontiersin.org/journals/ molecular-neuroscience/articles/10.3389/ fnmol.2022.905328/full 3. Zhu H et al. (2020). PCR past, present and future. Biotechniques 69, 317–325. https:// www.tandfonline.com/doi/full/10.2144/btn2020-0057 4. Morley AA (2014). Digital PCR: A brief history. Biomol Detect Quantif 1, 1–2. https:// www.sciencedirect.com/science/article/pii/ S2214753514000023?via%3Dihub 5. Introduction to digital PCR. Bio-Rad, accessed March 20, 2026. https://www.bio-rad.com/enus/life-science/learning-center/introductionto-digital-pcr 3 Bulletin 3933 Ver AGene Expression Ebook Tips and Tricks Addressing PCR Pain Points The fields of cell therapy and gene ther apy have existed for a long time, first as independent areas of study and more recently as a com bined one. Throughout this history, a number of key techniques have facilitated cell and gene therapy strategies in the laboratory and the clinic. As technologies such as CRISPR are making gene editing and manipulation easier and more accessible, cell and gene therapy is well poised to help shape therapeutics in the 21st century. PCR-based applications have expanded into diverse fields, including clinical research such as diagnostics and therapeutic development. However, with increasing implementation in realworld scenarios, the risk of inaccurate or inconsistent real-time quan titative PCR (qPCR) results is becoming more pressing.1 Gene expression tests that are sensitive and specific and also address common qPCR pitfalls are an approachable solution for scientists pursuing translational breakthroughs. Common Challenges in qPCR Workflows Researchers carefully design and validate primers and probes, meticulously prepare high-quality standards, and include appropriate controls to measure gene expression with precision.1 Although qPCR is a staple method that has revolutionized gene expression analysis, it requires careful preparation of standard curves, and errors in preparing or quantifying standards can cascade into inaccurate results. Amplification efficiency is a common culprit in the troubleshooting steps of qPCR workflows, as it can vary between templates based on factors such as GC content, DNA secondary structure, and the presence of inhibitors diagnostic avenues, reproducibility is particularly essential. Because ddPCR assays produce absolute copy numbers directly, they enable reliable comparisons across experiments. Adopting ddPCR Technology for Flexible Applications in different samples.2 These issues become particularly problematic when studying rare transcripts, where low signal can be obscured by background noise. Multiplexing adds another layer of complexity, as designing multiple probes that amplify with similar efficiency and do not interfere with one another requires thorough optimization.1 Moreover, small differences in reagent batches, thermocycler calibration, or handling practices can cause variability between experiments or laboratories, making reproducibility a persistent pain point.1,2 Strategies and Solutions for PCR Pain Points By eliminating the need for standard curves and relying instead on absolute quantification through droplet partitioning, Droplet Digital™ PCR (ddPCR™) circumvents one of the most common pain points in qPCR: relative quantification. Even when amplification efficiencies vary within a ddPCR assay, the binary classification of droplets as positive or negative makes the system more tolerant to differences that would confound qPCR analyses. This robustness becomes especially important for low-abundance transcripts, where ddPCR consistently excels in precision and sensitivity.3 Another source of frustration in gene expression analysis is the reproducibility of results across laboratories and over time. Differences in how scientists choose and calibrate qPCR standards can create inconsistency between datasets that disrupt how gene expression changes are interpreted. In clinically relevant research fields such as cancer and neuroscience, where monitoring changes in gene expression may guide therapeutic decisions or provide new For researchers who are well-versed in performing qPCR assays, incorporating ddPCR solutions into their workflows is straightforward. The amplification reaction is similar, and each ddPCR partition contains the core reagents used in qPCR to generate a fluorescent signal in response to the presence of a target sequence.2,3 Scientists can readily adapt assays from qPCR to ddPCR technology and achieve consistent and reliable gene expression analysis results. Ultimately, qPCR remains a powerful and widely used tool, but many of its challenges persist despite best practices. ddPCR technology directly addresses these limitations with workflows that are approachable for researchers already familiar with qPCR. When dPCR is paired with carefully designed assays, validated reagents, and compatible instruments, it not only simplifies workflows but also enhances specificity and sensitivity. Refernces 1. Bustin SA et al. (2025). MIQE 2.0: Revision of the minimum information for publication of quantitative real-time PCR experiments guidelines. Clin Chem 71, 634–651. https://academic.oup.com/clinchem/ article/71/6/634/8119148 2. Hays A et al. (2022). Best practices in qPCR and dPCR validation in regulated bioanalytical laboratories. AAPS 24, 36. https://link.springer. com/article/10.1208/s12248-022-00686-1 3. dMIQE Group et al. (2020). The Digital MIQE Guidelines Update: Minimum information for publication of quantitative digital PCR experiments for 2020. Clin Chem 66, 10121029. https://academic.oup.com/clinchem/ article/66/8/1012/5880117 © 2026 Bio-Rad Laboratories, Inc. 4 Bulletin 3933 Ver AGene Expression Ebook Delving into Gene Expression Data Analysis and Experimental Reproducibility Standardized frameworks for designing, executing, and reporting gene expression assays, including real-time quan titative PCR (qPCR) and digital PCR (dPCR), enhance transparency and reproducibility, helping researchers ensure their data are reliable and can be independently verified. The minimum information for publication of quantitative real-time PCR experiments guidelines (MIQE) and digital MIQE guidelines (dMIQE) include detailed checklists and standardized terminology recommendations for qPCR and dPCR experiments and publications. Sample handling Assay design and validation Data analysis MIQE 2.0 Guideline Focus Points The latest qPCR guidelines, i.e., MIQE 2.0, emphasize the importance of efficiencycorrected data analysis, appropriate statistical methods, consistent reporting of technical replicates, and inclusion of positive and negative controls.1 They encourage the use of digital resources and data repositories for sharing experimental details, raw fluorescence data, and analysis scripts to foster collaboration, independent result verification, and research output transparency. The Importance of Normalization Researchers must account for data analysis variables such as standard curves and reference genes early in their expression assays. Ideally, target standards can be accurately quantified for standard curve consistency, for example by dPCR.1 In gene expression assays based on both qPCR and dPCR, normalization using reference genes supports precise measurements and helps correct technical variability, but inconsistencies in how normalization is performed can introduce additional errors.1 ddPCR gene expression probe assays with relevant and complimentary reference assays help researchers achieve maximum specificity and transcript coverage. Reliable ddPCR Quantification Similar to qPCR, specific dMIQE guidelines have been developed to ensure unbiased and reproducible dPCR experiments, the most recent being dMIQE2020.2 These guidelines also emphasize data analysis and provide detailed guidance on calibration, normalization, and controls. Data analysis considerations that can help account for technical variability and accuracy include choosing appropriate partitioning strategies, automated threshold setting, and sharing raw data. Software programs that help researchers perform all necessary calibrations and standardizations limit variability and promote consistency while allowing for automatic adjustments that account for reaction efficiency and multiple reference comparisons. References 1. Bustin SA et al. (2025). MIQE 2.0: Revision of the minimum information for publication of quantitative real-time PCR experiments guidelines. Clin Chem 71, 634–651. https://academic.oup.com/clinchem/article/71/6/634/8119148 2. dMIQE Group et al. (2020). The Digital MIQE Guidelines Update: Minimum information for publication of quantitative digital PCR experiments for 2020. Clin Chem 66, 1012–1029. https://academic.oup.com/clinchem/article/66/8/1012/5880117 © 2026 Bio-Rad Laboratories, Inc. 5 Bulletin 3933 Ver AGene Expression Ebook ddPCR Workflows for Gene Expression Analysis Similar to real-time quan titative PCR (qPCR), assays based on Droplet Digital™ PCR (ddPCR™) technology can be preceded by reverse transcription (RT), which converts RNA into complementary DNA (cDNA) and enables gene expression analysis, directly quantifying RNA levels.1,2 Unique technical and ease-ofapplication strengths contribute to RT-ddPCR technology emerging as an exceptional and flexible tool for RNA quantification. Two-Step Versus One-Step RT-ddPCR Analysis Two main RT strategies are available: one-step and two-step RT-ddPCR approachs.3 In a two-step workflow, RNA is first reverse transcribed into cDNA, which can then be stored and used for multiple downstream assays. Following RT, scientists partition the ddPCR mixture that contains cDNA, primers, probes, and polymerase into tens of thousands of nanoliter-sized droplets. Each droplet functions as an independent reaction chamber for amplification and quantification. This provides flexibility and allows the same cDNA preparation to be interrogated for different transcripts, though it requires more handling and introduces additional opportunities for variability. In a one-step RT-ddPCR workflow, RT and PCR amplification occur in the same droplet. This approach streamlines the process, reduces the risk of contamination, and shortens turnaround time, making it particularly useful in clinical or high-throughput research environments. © 2026 Bio-Rad Laboratories, Inc. During thermal cycling for either method, if a droplet contains a target molecule, it will yield a positive fluorescent signal, whereas droplets without template are negative. Following amplification, researchers analyze the ratio of positives to total droplets via Poisson statistical calculations to derive absolute transcript copy numbers. The inherent partitioning strategy of any ddPCR workflow, including both onestep and two-step RT-ddPCR methods, minimizes competition among templates and enables quantifiable and highly sensitive transcript detection. It also contributes to RT-ddPCR quantification being less affected by amplification efficiencies, as poorly amplified partitions can still be detected, thus exhibiting higher tolerance to reaction inhibitors that can hinder RT-qPCR experiments.4 RT-ddPCR Applications In neuroscience, where researchers often contend with scarce tissue samples and subtle transcript changes, the RT-ddPCR approach offers greater detection power compared to conventional PCR, in which the presence of or fluctuations in lowabundance transcripts may be undetectable relative to more abundant housekeeping transcripts. Partitioning allows scientists to analyze each droplet independently, so no signal goes uncounted, offering high accuracy and efficiency.5 Researchers have applied RT-ddPCR technology to analyze serum microRNA (miRNA) levels with potential relevance to neurological conditions such as 6 multiple sclerosis (MS), the progression of which is conventionally studied via markers in cerebrospinal fluid, a sample that is challenging to collect and limited in availability.6 The RTddPCR method enabled scientists to validate candidates and measure serum concentration of specific miRNAs with precision, demonstrating higher levels of three transcripts that are significantly linked to progression from relapsing remitting MS to secondary progressive disease. In translational studies, in which semi-quantitative hybridization-based methods are often standard, the ability to reliably quantify slight variations in transcripts holds great promise for improving diagnostic, prognostic, and therapeutic opportunities. For example, in oncology studies, scientists have implemented RT-ddPCR technology to measure transcripts associated with HER2 expression in human breast tissue samples.3 HER2 is an established therapeutic target in several cancer types, and determining expression of its gene within tumor samples is a clinical routine that currently relies on a combination of immunohistochemistry (IHC) to evaluate HER2 protein expression and in situ hybridization (ISH) to assess HER2 gene status.7 Although this approach allows pathologists to distinguish between HER2-positive and -negative tumors with good interobserver reproducibility and consistency, it is semi-quantitative and may not be as robust for classifying emerging clinically relevant subtypes, such as HER2-low cancers.8 Researchers have demonstrated that Bulletin 3933 Ver AGene Expression Ebook RT-ddPCR technology can rank order breast cancer samples by HER2 status, in agreement with current standard methods but with the added bene fit of yielding accurate and precise quantitative gene expression data.3 Simplified and Streamlined Gene Expression Analysis Beyond technical strengths, RT-ddPCR assays benefit researchers through streamlined experimental processes, simplified workflows, and reduced complexity. Dedicated kits for one-step reverse transcription, instruments that enable simplified droplet generation workflows and intuitive analysis software lower the barrier for adoption. One-step RT-ddPCR technology also reduces sample handling, leading to faster turnaround times. ddPCR gene expression assays are available as probe or EvaGreen® Assays, with protocols for use in one-step or twostep RT-ddPCR protocols. The removal of the need for standard curves further simplifies data analysis, reducing both workload and potential sources of error. Taken together, the streamlined workflows, reproducibility, and sensitivity of the RT-ddPCR approach make it an indispensable tool for modern gene expression research. References 1. Bustin SA et al. (2025). MIQE 2.0: Revision of the minimum information for publication of quantitative real-time PCR experiments guidelines. Clin Chem 71, 634–651. https://academic.oup.com/clinchem/ article/71/6/634/8119148 2. dMIQE Group et al. (2020). The Digital MIQE Guidelines Update: Minimum information for publication of quantitative digital PCR experiments for 2020. Clin Chem 66, 10121029. https://academic.oup.com/clinchem/ article/66/8/1012/5880117 3. Droplet Digital PCR Applications Guide. (2016). Bio-Rad Bulletin 6407. 4. Kojabad AA et al. (2021). Droplet digital PCR of viral DNA/RNA, current progress, challenges, and future perspectives. J Med Virol 93, 4182–4197. https://onlinelibrary.wiley.com/ doi/10.1002/jmv.26846 5. Hindson BJ et al. (2011). High-throughput droplet digital PCR system for absolute quantitation of DNA copy number. Anal Chem 83, 8604–8610. https://pubs.acs.org/ doi/10.1021/ac202028g 6. Agostini S et al. (2024). Serum miR-34a-5p, miR103a-3p, and miR-376a-3p as possible biomarkers of conversion from relapsing-remitting to secondary progressive multiple sclerosis. Neurobiol Dis 200, 106648. https://www.sciencedirect.com/science/ article/pii/S0969996124002481 7. Schmidt G et al. (2025). HER2-low status as a distinct breast cancer subtype: myth or truth? Analysis of the WSG trials WSG-ADAPT-HR+/HER2-, WSG-PlanB, and WSG-ADAPT-TN. Breast Cancer Res 27, 22. https:// link.springer.com/article/10.1186/s13058-025-01969-z 8. Nielsen K et al. (2023). High inter-laboratory variability in the assessment of HER2-low breast cancer: A national registry study on 50,714 Danish patients. Breast Cancer Res 25, 139. https://link.springer.com/ article/10.1186/s13058-023-01739-9 Bulletin 3933 Ver A © 2026 Bio-Rad Laboratories, Inc. 7 Gene Expression Ebook Visit bio-rad.com/ddPCR for more information. Bio-Rad, ddPCR, Droplet Digital, and Droplet Digital PCR are trademarks of Bio-Rad Laboratories, Inc. in certain jurisdictions. All trademarks used herein are the property of their respective owner. © 2026 Bio-Rad Laboratories, Inc. EvaGreen is a trademark of Biotium, Inc. Bio-Rad Laboratories, Inc. 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