Why your dPCR assay needs dMIQE guidelines

How dMIQE guidelines add credibility to your dPCR data

Shh. If you're very quiet, you can hear the collective groan of scientists around the world struggling to replicate an experiment from a paper written by another lab. The reproducibility problem is well known to the scientific community. Let's take just one of many examples. the Reproducibility Project: Cancer Biology (RPCB) initiative could only replicate 46% of the experimental outcomes from over 50 studies. And that took them more than a decade(1).  

There are plenty of reasons why, but a major one is lack of information. The replicators often had to contact the papers’ authors to gain crucial details on how to carry out the experiments. After all, protocols are like recipes. If you try baking your grandmother’s famous cake, but the instructions only tell you to add some milk and bake until ready, your attempt could end up resembling a fail from Hell’s Kitchen.

We can address this issue by establishing a checklist for method descriptions prior to publication. To promote consistency, help us better evaluate data, and to keep us out of Hell’s Lab, scientists working with the qPCR method established the Minimum Information for Publication of Quantitative Real-Time PCR Experiments (MIQE) guidelines back in 2009 (2). These guidelines were also adapted and published for dPCR in 2013, and updated in 2020 (3).

Purpose of dMIQE guidelines

The ultimate goal of the digital MIQE guidelines is to ensure published research is understood and can be reproduced. The latest dMIQE2020 support authors in designing, performing and reporting digital PCR experiments of high scientific integrity and credibility. The dMIQE checklist also facilitates replication of digital PCR (dPCR) protocols in already published studies. Lastly, the digital MIQE guidelines serve as an established standard for the technical quality of dPCR publications, beneficial for journal reviewers, editors and the broader scientific community. 

The dMIQE2020 checklist

The original dMIQE guidelines provided a list of both essential and desirable information for dPCR publications. This format was simplified in 2020 to include only the essential information needed to publish dPCR research. Authors can include this information in the main text or as supplemental information, preferably in a dMIQE2020 table format. What must be included in this dMIQE table?

Specimen

To adhere to dMIQE guidelines, you need to provide information on what specimen and sample you analyzed, how they were collected, and any special treatment you performed. Factors such as volume or mass of the specimen are pretty standard. But pay close attention to more variable parameters, such as sampling procedure, type of containers used, and handling and storage conditions, as these tend to vary across labs. 

Since physical linkage between targets can affect dPCR data, ensure you provide information on whether the original biological sample was fresh/frozen or FFPE. FFPE, for example, can introduce a large number of double stranded breaks in DNA and could affect linkage results.

Nucleic acid extraction

Nucleic acid extraction is an essential pre-analytical step where you purify and concentrate nucleic acids and remove inhibitors. In your dMIQE table, you should include the amount of sample extracted, and the amount of elution or dilution buffer used. Lysis and elution buffers should also be described, as some contain detergents that can interfere with the formation of droplets if performing droplet digital PCR (ddPCR).

Nucleic acid assessment and storage

Whenever you quantify your nucleic acids, you must detail your total nucleic acid measurement method, such as spectrophotometry or calibrated fluorimetry. If you assess fragment size or different amplicons, please also describe your gel electrophoresis method. You must also report on the storage conditions of your nucleic acids, including temperature, concentration, duration, buffer, pH, and whether you made any aliquots.

Nucleic acid modifications prior to dPCR

Please report on any modification of your extracted templates, such as fragmentation of genomic DNA, bisulfite treatment, dilution, or additional purification steps that take place before your dPCR reaction.

Reverse transcription

This step, along with nucleic acid extraction, is sensitive to potential biases and uncertainty, and could be problematic during replication attempts. To remain dMIQE-compliant, include details on the type and concentration of your reverse transcriptase, the amount of RNA, duration and temperature of the incubation period, primer concentration and priming strategy. In two-step RT-dPCR, include a description of the separate reverse transcription reaction, and disclose any details on dilution or additional purification steps performed following the reverse transcription step - but before adding the cDNA to the dPCR . Remember to properly define your controls.

Oligonucleotides design and target information

It is essential that you include information on the intended target, including the sequence accession number or official gene symbol, as well as the location of the amplicon. It is also necessary to provide the specific oligonucleotide sequences, and if that is not available, the amplicon context sequence.

dPCR protocol

Here, you need to disclose how you prepared the pre-reaction mixtures prior to partitioning, including components, volume, proportion and total nucleic acid concentration of the template added. In your experimental design, rather than pre-reaction volume, consider also the likely final reaction volume, calculated by the partition number multiplied by the partition volume. This is especially important for low-concentration targets where the dead volume (the difference between the reaction volume and pre-reaction volume) can be substantial. Report also on oligonucleotide concentrations, thermal cycling parameters, and any additional components or parameters of your dPCR setup. 

Assay validation

Remember to include any optimization attempts, including testing of different temperatures and oligonucleotide concentrations, along with a pass/fail assessment. Describe also how you evaluated analytical specificity and sensitivity, including your choice of control materials.

Data analysis

In this section, you should describe your experimental design, along with positive and negative controls, as they can be used for assay quality control and threshold setting. Include examples of plots where false positive signals are considered acceptable. Also provide information on replication of biological samples, controls, or entire experimental processes. 

Include a comprehensive description of how you compiled, normalized, rescaled and analyzed your dPCR results. For example, report on the average number and range of measured partitions, as well as lambda (λ) of the respective targets. Describe your statistical methods, the rationale behind them, and have related data available upon request. 

Application-dependent considerations for your dPCR assay

Depending on your application, some of the key parameters you need to consider for your dPCR experiments will vary. You can gain general optimization tips for your dPCR assay in our technical note, whereas below are suggestions on how you can improve your dPCR performance according to your specific application.

Gene expression and digital RT-PCR

Because of the additional step of reverse transcription, quantification of RNA by any type of PCR is at a greater risk of variation and bias. There are several steps you can take to mitigate this risk:

• Use one-step mixes, which decrease the number of pipetting steps from sample to result
• Increase the number of replicates per sample
• Control for bias in cDNA production (affected by both choice of enzyme and assay), for example by introducing calibration to ensure reproducible results
• Check if reverse transcriptase possesses RNase H activity or not: RNAse H positive enzymes will digest the RNA template in an RNA-cDNA heteroduplex, increasing the chance that one RNA transcript will only lead to one molecule of cDNA; in RNAse H negative mixes, the same transcript may be read more than once, causing an artificial increase in the number of molecules

Rare event detection

For challenging rare mutation detection studies, one of the key elements to success is the probe design. While partitioning of the reaction substantially helps in reducing competition between products, the probes still need to discriminate based on a single nucleotide difference. Try finding related literature for tips, or if your mutation of interest is frequently studied, check if commercial dPCR assays are available for your dPCR application.

Copy number variation (CNV)

One key aspect to successful CNV analysis with dPCR is ensuring the targets are physically independent. You can often achieve this by using restriction digestion prior to adding the sample to the PCR mix or in the PCR mix itself. It is also important to choose a reference gene whose copy number will remain constant in your dPCR assay. Choosing a reference in a different chromosome is recommended. In cases where the extent of the genomic anomalies is unknown, for example several polyploidic chromosomes or regions, the use of more than one reference per target can increase the confidence of the CNV analysis. Bear in mind there are also validated and commercially available assays to determine CNVs.

Next-generation sequencing (NGS) library quantification

You can use digital PCR to quantify NGS libraries, and even more, to assess the quality of the libraries. By co-detecting two different probes, you can evaluate the ligation of the 5’ and 3’ adapters in Illumina libraries where the presence of high amplitude clusters indicates the proportion of adapter dimers with no inserts. You can also use DNA-binding dyes in dPCR to assess insert sizes in NGS libraries. 

Learn more on the topic in our recent blog post on dPCR and NGS or webinar on using dPCR for NGS library quantification. 

Is your dPCR instrument dMIQE-compliant?

The primary goal of the dMIQE guidelines is to improve the reproducibility of a digital PCR experiment. Besides your protocol, your dPCR system and accessories should adhere to these guidelines as well.

For example, the QIAcuity nanoplates with partitions of equal and defined volume meet the requirement for concentration calculations based on Poisson distribution. You can achieve even higher accuracy and precision if you also apply the volume precision factors in your calculations. The format of the nanoplates makes an automated reaction setup possible, which lowers the risk of user error or contamination.

The QIAcuity Software Suite enables you to analyze your dPCR results by imaging, which can be repeated and confirmed multiple times, also with varying imaging conditions. Advanced user management allows you to assign permissions to various users per defined roles, reducing the risk of erroneous changes to the run and analysis parameters. An audit trail permits traceability of all steps performed on the dPCR instrument.

Make sure your reagents also adhere to dMIQE guidelines. The QIAcuity dPCR kits are subjected to stringent quality control with guaranteed lot-to-lot consistency to improve the reproducibility of data from the QIAcuity system both from user to user and lab to lab.

Following dMIQE guidelines might seem like a lot of work, but pays off much better than trying to replicate results with low success rates. If we all adhere to these guidelines, then maybe we'll hear a sigh of relief instead of a groan of frustration the next time our colleagues successfully replicate that very tricky experiment. Then we can all celebrate with a slice of grandma's cake.

References

1. Nature Editorial. Replicating scientific results is tough – but essential. Nature, December 15, 2021.
2. Bustin SA et al. The MIQE guidelines: minimum information for publication of quantitative real-time PCR experiments. 2019; Clin Chem 55(4):611-622.
3. The dMIQE Group and Hugget JF. The Digital MIQE Guidelines Update: Minimum Information for Publication of Quantitative Digital PCR Experiments for 2020. 2020; Clin Chem 66(8):1012-1029.