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qPCR testing for yeast, mold, and bacteria counts: how reliable is it?
While this method is great for detection of certain target species, the quantification of those species is where it gets tricky.

For years, qPCR has been a widely used but somewhat controversial method for screening cannabis and cannabis products for yeast, mold, and other microbiological contaminants. With more regulated cannabis being produced, tested, and sold than ever before, the debate over qPCR’s reliability has gotten greater attention. Michigan regulators recently put out a temporary ban on the testing method, asking their labs to incubate samples for 72 hours minimum, switch to either a non-molecular automated system method or a plating-based method, or pair their qPCR method with either of those methods.

This came after a group of Michigan labs sounded the alarm that cannabis products in the state could potentially be contaminated with mold or yeast. The qPCR method used to screen for yeast and mold had returned too many false negatives, they said.

In this blog, we will discuss the issue of qPCR that our lead microbiologist Michael Esposito outlined in his white paper “Broad Species qPCR Found Inappropriate for Total Yeast and Mold Counts.” For a more detailed and scientific explanation, you can read that white paper here.

While the qPCR method can work well for detection of a small number of target species, the quantification of those species is where it gets tricky. At MCR Labs, our microbiology team has experienced similar issues with the method. We performed our own experiments on qPCR’s ability to count the bacteria E. Coli, and the method failed to produce accurate results. So what is the issue with a qPCR method? To better understand, we should first discuss what exactly qPCR is.

What is qPCR?

qPCR stands for Quantitative Polymerase Chain Reaction. It is essentially a way to copy small segments of DNA in a fast, inexpensive, and observable way. When analyzing DNA, it’s important to have large amounts for analysis. It is hard to see one single strand of DNA. However when you have multiple copies of that DNA it is easier to detect. For example, when you are looking at sand it is easier to visualize the sand in a large quantity rather than grain by grain. During qPCR you are taking a handful of sand and multiplying into a beach worth of sand. 

A qPCR instrument produces a data point called a Cq value. The Cq is the chronological point where you can see the “sand” begin to build up. Once you have created your “beach,” your qPCR instrument has the ability to guess how much “sand” you had in your hand based on how early or late that Cq is reached. This technology is extremely valuable in the laboratory and clinical areas of science. It gives scientists the ability to detect a small handful of specific types of “sand”, though you may not be able to count the amount of “sand” in that handful. If you are more concerned with whether or not your handful of sand has some grains that could hurt you, opposed to how many grains you had, this technology is perfect. This means we can detect and see the presence of individual species, however, it wont tell us exactly how much of each species is present. 

So what are the limitations with qPCR?

As awesome as this sounds, it is important to remember that with this kind of technology, there is no “one size fits all.” There are so many factors that influence the output of the equipment used – the Cq – making this method unreliable for counting organisms. 

Primers are one of these factors. When you are running qPCR, you need a starting set of target DNA and primers. Those primers lead specific enzymes to the desired DNA to be copied. DNA varies between different species, just like your DNA is different from a dog’s or a frog’s. So if we are trying to copy fungal DNA and bacterial DNA from different species all in one test, we would need different primers to isolate that gene. This means we would have to run separate tests with different primers or compromise the tests ability to copy the gene into multiple copies of that gene, impacting the Cq result. 

Even if you are familiar with the species you’re working with, it’s hard to correlate the data generated by qPCR (Cq value) into colony forming unit (CFU) value. This means it’s not possible to determine the amount of organisms present based on your starting DNA. While it is beneficial to receive results in 24 hours, because there is no correlation between Cq value and CFU value, this application is not able to be refined to produce accurate results in good conscience. 

Here is an image of one of our Yeast/Mold samples containing Mold colonies.

Here at MCR, we value our clients and their customers. Our main mission is protecting the health and safety of cannabis consumers. So when it comes to counting microbes, we do not want to create room for error. We want to ensure our clients’ products are safe for their consumers. We perform qPCR testing for shiga-toxin producing E. coli (STEC) and Salmonella, but not for aerobic count, coliform count, Enterobacteriaceae, or yeast/mold, because we trust the technology for detection, but not for counting. The most probable number and plating methods are the standard methods used in similar industries and regulatory agencies, so we have chosen to go with these technologies knowing they have been thoroughly researched and are trusted. We run qPCR for STEC and Salmonella detection due to the ability of primers to specifically target those pathogenic genes and detect their presence. This does not rely on any sort of counting of the organisms themselves. 

We are hopeful that Michigan’s investigation into qPCR testing for yeast and mold counts encourages others to follow suit, and methods are validated and refined to ensure the industry is taking action to put protecting consumer health and safety as the number one priority.

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