dPCR and NGS: Better Together?

Often, as you start planning an experiment to tackle a burning question in your biomedical research, you encounter a number of methods that could help you find an answer. Here is a classic example. You want to detect this very interesting mutation, but how can you decide if you should use digital PCR (dPCR) or next-generation sequencing (NGS)? A good place to start is to gain a clear picture of how these two methods work.

dPCR in a snapshot

In dPCR, a PCR reaction mixture is divided into thousands of smaller reactions called partitions. The target DNAs are randomly distributed among the partitions, resulting in partitions with either zero or one or more copies. After endpoint PCR, the positive partitions containing a target emit a fluorescent signal, while the negative partitions remain dark. Based on the number of positive and negative partitions and using Poisson statistics, the exact number of target DNAs in the initial sample is determined¹.

NGS in 5 strokes or less

NGS offers massively parallel analysis with high throughput from multiple samples. Most NGS methods require a library preparation step, where DNA samples (or RNA samples, converted to dsDNA) are fragmented and adapters are attached to both ends. The NGS platform uses these adapters to read the DNA fragments one nucleotide at a time, producing raw sequencing data (primary analysis). A number of bioinformatics tools exist to carry out secondary and tertiary analyses².

dPCR and NGS: A comparison

Because they are based on different principles and due to their unique cost-benefit considerations, dPCR and NGS are often suited for particular applications. A summary of dPCR vs. NGS can be found in the table below:

	dPCR	NGS
Benefits	Effective for low target numbers Low limit of detection⁴ Quick results High sensitivity with resolution as low as 0.0005% compared to qPCR (1%) and most NGS assays (2%) for known mutations⁶ Low cost/sample Absolute quantification of mutant and wild-type copies	No prior knowledge of sequence information required High sensitivity for detection of novel genes and transcripts Can detect multiple genetic mutations at once Comprehensive detection of known and unknown mutations Single nucleotide resolution
Limitations	Can detect only specific gene mutations Requires specific assays for each gene⁵ Not useful in patients without specific markers⁵	Less cost-effective for fewer than 20 targets Requires bioinformatics software Longer turnaround times Lower sensitivity than dPCR⁶
Common applications	Screening of known variants Genotyping Serial monitoring of disease progression Rapid detection of common isolated pathogens³ Longitudinal monitoring, such as of viral load Resolution of rare mutants⁶	Studies with many targets or samples Screening of patients for targeted drug therapy Study of drug resistance mechanisms Diagnosis where classical methods fail to identify causative pathogens³

Hence, your starting material, experimental design and research aims will likely determine whether you should use NGS or dPCR.

Best of both worlds: using dPCR and NGS together

Precisely because of their differences, rather than looking at dPCR and NGS as competing methods, we could consider them as complementary methods instead. Both approaches offer highly sensitive and reliable variant detection.

For example, in liquid biopsy analysis, NGS can successfully read circulating tumor-derived DNA, providing a comprehensive profiling of ctDNA as a tool for cancer biomarker discovery, such as cancer-related somatic mutations, fusion genes, and copy number variations. Once the biomarker candidates have been identified by NGS, dPCR may be well suited for further validation, and potentially used for routine testing, such as tracking resistance levels to therapies over time⁷. This is due to dPCR technology’s strength and cost-benefit in analysing small numbers of known markers or mutations, and that it provides absolute rather than relative quantification of circulating DNA and is not sensitive to changes in overall DNA levels.

dPCR for NGS library quantification

Optimal sequencing requires accurate quantification of functional libraries. To achieve high-yield and high-quality sequencing, NGS sequencers operate within a relatively narrow range of library loading concentration. Underloading results in low yield, low read depth, and possible failure to detect SNPs or rare sequences. Overloading leads to overclustering, thus low yield of high quality reads. Either way, underestimation or overestimation of sequencing libraries leads to suboptimal use of sequencing capacity^6,8.
The most common methods for NGS library quantification are summarized in the table⁸ below.

Method	Instrument	Limit of quantification (for dsDNA 500-mer)	Quantification modality	Quantification reference	Quantification of functional libraries
Spectrophotometry-based	NanoDrop	2 ng (3.6 billion copies)	Mass / absolute	No standard necessary	Not possible
Fluorometry-based	Qubit, PicoGreen	0.3 fg – 1 ng (550 – 1.8 billion copies/rxn)	Mass / relative	Required: calibrated by mass	Not possible
Electrophoresis-based	Bioanalyzer, FragmentAnalyzer	2.5 ng (4.5 billion copies/rxn)	Mass / relative	Required: calibrated by mass	Not possible
qPCR-based	QIAquant	0.1 fg (180 copies/rxn)	Molecules / relative	Required: calibrated by mass	Possible
dPCR-based	QIAcuity	0.01 fg (12 copies/rxn)	Molecules / absolute	No standard necessary	Possible

The main role of dPCR in the NGS workflow is the accurate quantification of NGS libraries without any amplification or standard bias, resulting in efficient use of NGS platforms (e.g., Illumina). Some additional benefits of using QIAcuity dPCR for NGS library quantification include:

Uniform loading and subsequent sequencing of pooled libraries
Routine testing is possible with high-throughput and rapid turnaround times
Coverage of all Illumina library types with one assay
Provide information about library quality prior to sequencing on Illumina

Although different, dPCR and NGS could be closer than two coats of paint. Each method offers unique nuances, that when used together, can strengthen your data, and help grasp the bigger picture of your research quickly and effortlessly.

Interested in finding out more?

Watch our free webinar on using dPCR for NGS library quantification at your own convenience.

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References

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