Exploring MIQE-Like Standards for qPCR-Based Molecular Detection of eDNA

In the previous blog, we briefly touched on the MIQE standards – guidelines published to promote minimal standards of reporting qPCR data from clinical trials,  used to detect small relative changes in gene expression within cells or tissues, or to quantify the amount of pathogen(s). As much of contemporary environmental DNA work is conducted using qPCR approaches, it is only sensible that similar standards be developed for eDNA work, that should – it will be argued – extend beyond what is acceptable for clinical applications.

This is somewhat of a reverse of convention in scientific circles, as it is normally the case that clinical applications need an elevated burden of evidence to support any conclusions drawn from experimental data. However, because of the ecology of eDNA, its distribution in natural systems with myriad potential sources of generation and decay, allied to the hierarchical nature of its sampling, I believe eDNA presents a special case whereby more rigorous standards should be applied than clinical settings so that confidence in our results are trustworthy.

What are the chief differences in using qPCR to detect changes in gene expression or viral load, for instance, with using qPCR to detect eDNA? Figure outlines the workflow for A) performing a gene expression qPCR analysis alongside B) a generic eDNA workflow. In A) an investigator has a sample of tissue, within which there is guaranteed nucleic acid content. Imagine that they want to test for the expression levels of a gene that is hypothesized to play a positive role in alleviating stress in plants subject to an environmental stressor. In such studies, expression levels are compared against so-called reference genes, which are always expressed, so that any changes in the target gene can be compared after normalization of expression levels. Although plants in a control plot should show little expression of the target gene, it would still be expected to be found, albeit at reduced levels, in the plant tissue subject to nucleic acid extractions (in this case, messenger RNA, which is converted to DNA (complementary DNA or cDNA)) in a process called reverse transcription, so that the target is amenable to DNA-based qPCR. As such, one expects a lot of cDNA, if it were to be visualized using standard laboratory gel techniques. There will always be much more – and stable – expression of the reference gene’s mRNA, and by extension cDNA. In short: there is a reliable source of cDNA, which forms the template for the qPCR assays for both the target gene and the reference gene(s).

Generic workflows for conducting A) gene expression qPCR and B) eDNA qPCR

Consider then, environmental DNA. Leaving aside the complex ecology of eDNA to one side (subject to another blog post but see excellent review by Barnes and Turner [2]), the distribution of eDNA in a water body is largely ephemeral and at much lower concentrations, with no guarantee that sampling will entrain DNA molecules or cellular debris into sampling tubes or onto filter papers. There is a significant source of observational error at this stage, which is largely absent in gene expression studies, although both share procedural errors that can impact downstream qPCR success. Depending on factors including the volume of water sampled and pore size of the filters, the amount of total eDNA collected may vary substantially, although if visualized on a gel, is less likely to contain as much target as tissue-extracted mRNA turned cDNA.

Performing qPCR for either A) or B) requires taking an amount of total cDNA or total eDNA for use as template for the reactions. The probability of subsampling this extract and not getting detection is higher for eDNA, due to the extra rarity of the target in both the water sample – which may not have been collected at all – and by the relative rarity of the target molecule in the soup of total eDNA molecules.  For eDNA we have two uncertainties in sampling due to the two sampling events – of the water body and of the total eDNA – that act to increase underlying statistical error.

Bottom-line: eDNA needs to adopt more sensitive and, arguably, more specific assays than clinical applications. More specific? Well, whilst many genes evolve during evolution by way of duplication and subsequent divergence, these events are localized to gene families so whilst when developing a qPCR assay for a gene with a number of evolutionary similar orthologs and paralogs, the nucleotide divergence between these genes and all others in the genome of a single tissue type is huge, a dn thus non-specific amplification of a non-target gene attenuated. However, when using an eDNA marker, that same stretch of DNA (e.g., COI locus) is present in all non-targets, as it has been inherited by a common ancestor. However, evolution will increase the number of nucleotide differences between species as generations pass. However, more recently diverged taxa may not display enough between species variation to design an appropriate assay – but see the previous blog for an in-depth treatment of assay design and target specificity.

In the next blog, I shall discuss exactly how we estimate LOD, LOQ – further adopting MIQE standards – and how we optimize an assay to befit a rigorous, repeatable and reproducible eDNA assay. To do so, not only do we need to optimize the target, but also identify and countenance factors that input variance into the system, most nefariously PCR inhibitors. I shall describe how we use MIQE-like guidance and synthetic internal positive control elements to determine the reliability of eDNA results. We shall also discuss in the future, how the ecology of eDNA and assay performance in pilot trials can be used to optimize detection (and the potential quantification) of targeted eDNA detection studies.

[1] Bustin et al. (2009). The MIQE guidelines: minimum information for publication of qPCR experiments. Clinical Chemistry 55, doi.org/41373/clinch.2008.112797.

[2] Barnes and Turner (2016). The ecology of environmental DNA and implications for conservation genetics. Conservation Genetics, 17, 1-17.

[3] Doi et al. (2017). Environmental DNA analysis for estimating the abundance and biomass of stream fish. Freshwater Biology, 62, doi.org/10.1111/fwb.12846.

 [4] Nevers et al. (2018). Environmental DNA (eDNA): a tool for quantifying the abundant but elusive round goby (Neogobius melanostomus). PLoS One, 13, doi.org/10.1371/journal.pone.0191720.

[5] Evans et al. (2016). Quantification of mesocosm fish and amphibian species diversity via eDNA metabarcoding. Molecular Ecology Resources, 16, doi/10.1111/1755-0998.12433.

[6] Hunter et al. (2016). Detection limits of quantitative and digital PCR assays and their influence in presence-absence surveys of eDNA. Molecular Ecology Resources, 17, doi/10.1111/1755-0998.12619.

[7] Forootan et al. (2017). Methods to determine limit of detection and limit of quantification in quantitative real-time PCR (qPCR). Biomolecular Detection Quantification, 12, 1-6.

***cDNA vs eDNA (diagram of how each is made and detected)? – largely deterministic range of signal vs. stochastic ephemeral signal; # orthologs/paralogs intragenomically vs. interspecifically; targets are constant within cells vs. ephemeral and temporal-spatial of species distributions and predictors of shedding rate and eDNA decomposition in ecosystems. ***