How Different Molecular Techniques Synergize in Gene Expression

RNA Sequencing (RNA-Seq)

RNA-Seq, the sequencing of all RNA transcripts in a sample – was realized with the development of NGS techniques in the 2000s (Kukurba - and Montgomery 2015). This method is essentially the same as DNA sequencing — genetic material is fragmented, sequencing libraries are prepared (and can be tagged with barcodes to facilitate multiplexing), and libraries are sequenced. The first step is to convert RNA from the sample(s) of interest into complementary DNA (cDNA), which is used as the template in the PCR reaction. Sequencing may follow either single-end or paired-end sequencing methods. Single-read sequencing is an affordable and rapid technique that sequences the cDNA fragments from just one end. Paired-end methods sequence from both ends and, while higher in cost, offer advantages in post sequencing data reconstruction.

Unlike PCR-based protocols (discussed later), RNA-Seq doesn’t require a priori knowledge of gene sequences. Instead of capturing information about predefined sets of genes, RNA-Seq provides a comprehensive, semi-quantitative, and unbiased view of all RNA transcripts in a sample. This makes it a great tool for identifying novel gene-disease associations, single nucleotide polymorphisms (SNPs), rare mutations, and even regulatory microRNAs (Whitley et al. 2016). It is also commonly used to simultaneously measure the expression of thousands of genes under a single condition or across multiple conditions using differential gene expression (Osabe et al. 2021).

One of the biggest advantages of RNA-Seq is that a prior knowledge of gene sequences isn’t required – this also leads to one of the biggest challenges associated with this method. Accurate sequencing annotation and data analysis have historically been a challenge for RNA-Seq data, especially if reference genomes don’t exist (Whitley et al. 2016). Several data analysis tools and databases have been or are being developed to address this issue and simplify analysis. Now researchers are optimizing an even more complex and challenging application for RNA-Seq: single-cell transcriptomics (Jovic et al. 2022).

Reverse Transcription Quantitative PCR (RT-qPCR)

RT-qPCR is one of the most common approaches used for gene expression analysis. It is a powerful PCR technique used for decades to measure gene expression. As with RNA-seq protocols, RNA is converted to cDNA, next, the use of fluorescent reporter molecules (either SYBR^® Green Dye, which binds to double-stranded DNA, or fluorescent probes) enables researchers to “see” the accumulation of the PCR product in real-time. Quantification is based on the number of amplification cycles it takes for the fluorescent signal to exceed a set threshold cycle (Ct), also known as the quantification cycle (Cq).

RT-qPCR is a great option for laboratories performing gene expression analysis because it is sensitive, easy to use, and has a low cost per sample (Sanders et al. 2014). However, the method requires close to perfect amplification efficiency to produce accurate results, requires optimization of every primer pair for every single target gene, and can only detect changes in gene expression greater than twofold (Taylor et al. 2017). Additionally, Cq values are only semi-quantitative, suffer from technical variance, and can be significantly impacted by the amplification efficiency of the PCR reaction (Campomenosi et al. 2016). Cq values also cannot be directly compared between different assays. The quantitative ability of Cq values can be increased by creating standard curves from control samples with known concentrations and comparing sample curves against the standard. However, the presence of different contaminants and inhibitors and different source DNA between the sample and control can impact the accuracy of quantification based on standard curves (Maet al. 2020, Taylor et al. 2017).

Digital PCR

In the early 90s, an alternative PCR technique was invented: digital PCR. Unlike qPCR, digital PCR partitions the PCR reaction into thousands of nanoreactions (Kojabad et al. 2021). Like qPCR, fluorescence is a proxy for the level of expression of the gene of interest in each nanoreaction. But, unlike qPCR, absolute quantification can be determined by applying Poisson statistics to determine the number of templates in each nanoreaction. Because of this, digital PCR does not require a standard curve for quantitation and is insensitive to the amplification efficiency.

The biggest advantage of digital PCR is its sensitivity, particularly when working with low-abundance targets and detecting rare alleles against abundant wild-type background (Kojabad et al. 2021). It is also robust to contaminants and inhibitors (Kojabad et al. 2021). Nevertheless, qPCR remains the industry standard due to its accessibility: instruments are affordable (Kojabad et al. 2021), and many people already have experience validating assays and publishing results. Its longstanding use means researchers have access to well-stocked supplies, protocols, and primary literature against which to design and compare their own studies, and the dynamic range of qPCR is larger than that of digital PCR (Ma et al. 2013). However, digital PCR is opening the door to investigate subtle gene expression changes that previously could not be examined with qPCR. Thus, the era of digital PCR has begun.

How RNA Seq, qPCR, and Digital PCR Complement Each Other

Comprehensive profiling of gene expression requires the use of multiple techniques to achieve reliable and reproducible results. The methods discussed in this article nicely complement one another by addressing the drawbacks associated with one or more of the other methods. One of the most common and informative approaches to a multi-technique approach to gene expression analysis is to screen for changes in large groups of genes and then use complementary techniques that target smaller gene subsets to confirm or refute the results of a broad screen.

RNA-Seq Is an Ideal Screening Method to Profile the Transcriptome

RNA-Seq is an attractive and increasingly popular approach to comprehensively analyze transcriptome changes and profile genome-wide gene expression levels, making it a great screening protocol. Technological advances have also made this approach useful for analyzing gene expression at the tissue level or even down to the single-cell level, which can facilitate the discovery of heterogeneous gene expression patterns within a complex cellular population. And, as we discussed above, RNA-Seq captures information about all RNA transcripts in a sample so that it can identify important roles for noncoding RNAs in disease etiology. Many research groups are thus using RNA-Seq as a screening approach to identify gene-disease associations.

Digital PCR and RT-qPCR Can Readily Confirm a Gene Expression Screen

PCR-based techniques, unlike RNA-Seq, require a priori knowledge of gene sequences. This makes them great choices for confirming the results of RNA-Seq screening studies, where a smaller number of candidate genes are identified. In addition, digital PCR and qPCR offer a more limited size of targets to detect in a multiplexed format compared to RNA-Seq. These technologies are much faster, with results in hours and not days, lower in cost (by four to tenfold) (Campomenosi et al. 2016), and far easier to analyze. So, when rapid confirmation of a gene expression technique is needed, these orthogonal techniques are commonly accessed by many researchers due to the quick and accurate confirmation that they provide.

RT-qPCR is the low-cost industry standard offering a broad dynamic range that can be used to confirm the results of any RNA-Seq screening experiment. However, the sensitivity of digital PCR makes it an especially good choice for verifying low-abundance targets, such as allelic or structural variants. Additionally, digital PCR is highly tolerant of PCR inhibitors commonly found in biological or environmental samples. Digital PCR technology has continually developed to provide greater multiplexing capabilities, simplified protocols with automation to eliminate manual steps, and more compact benchtop systems, encouraging its use for a broader number of research applications.

Use cases for RNA-Seq, qPCR, and digital PCR individually and in combination vary widely based on the research stage and the therapeutic modality chosen. We discuss such use cases in great detail in our article, Benefits of Complementary Methods in Gene Expression.

Conclusions

Three of the most common techniques used in gene expression analysis are RNA-Seq, RT-qPCR, and digital PCR. Each approach has its advantages and disadvantages, and because one approach cannot address all needs, implementing a fit-for-purpose combination of techniques optimized for the specific research stage and application can yield more optimal outcomes, save time and money, and shorten the time to scientific discovery. By assessing your lab’s specific requirements for sensitivity, specificity, gene variant type, and ultimate therapeutic application, you can select the right combination of protocols to help you discover and develop therapeutics that work.

References

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Jovic D et al. (2022). Single-cell RNA sequencing technologies and applications: A brief overview. Clin Transl Med 12, e694.  

Kojabad AA et al. (2021). Droplet digital PCR of viral DNA/RNA, current progress, challenges, and future perspectives. J Med Virol 93, 4,182–4,197.  

Kukurba KR and Montgomery SB (2015). RNA sequencing and analysis. Cold Spring Harb Protoc 11, 951– 969.  

Ma H et al. (2020). qPCR and qRT-PCR analysis: Regulatory points to consider when conducting biodistribution and vector shedding studies. Mol Ther Methods Clin Dev 20, 152–168.  

Ma J et al. (2013). Quantification of plasma miRNAs by digital PCR for cancer diagnosis. Biomark Insights 8, 127–136.  

Osabe T et al. (2021). Differential gene expression analysis using a model-based gene clustering algorithm for RNA-seq data. BMC Bioinformatics 22, 511.  

Sanders R et al. (2014). Considerations for accurate gene expression measurement by reverse transcription quantitative PCR when analysing clinical samples. Anal Bioanal Chem 406, 6,471– 6,483.  

Taylor SC et al. (2017). Droplet Digital PCR versus qPCR for gene expression analysis with low abundant targets: From variable nonsense to publication quality data. Sci Rep 7, 2,409.  

Whitley SK et al. (2016). Research techniques made simple: Methodology and clinical applications of RNA sequencing. J Invest Dermatol 136, e77–e82.

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