Optimized Cannabis Microbial Testing: Combined Use of Extraction Methods and Pathogen Detection Tests Using Quantitative Polymerase Chain Reaction: Page 4 of 4

August 22, 2019
Figure 8: (a) STEC E. coli qPCR dilution curves. (b) qPCR efficiency (E).
Figure 8 (click to enlarge): (a) STEC E. coli qPCR dilution curves. (b) qPCR efficiency (E).
Figure 9
Figure 9 (click to enlarge): Comparative growth of Aspergillus species and other fungal monocultures on 3M petrifilm compared to the Cq determined by PathoSEEK qPCR. “Expected” is the inferred CFU count from the Cq measurement using the formula CFU/g = 10[(42.185 - Cq Value)/3.6916]. We show the discrepancy and potential for underreporting of Aspergillus spp. by culture-based methods.
Abstract / Synopsis: 

With the introduction of legal cannabis available for sale in the U.S., providing safe, high-quality products defines the industry standard. As part of compliance testing, each state has different requirements for detection of microbial species in cannabis products. An ideal test is one that can be performed quickly with small amounts of cannabis product, is specific for the microbes required, can differentiate between live and dead microbes, and can be automated for high sample throughput. Medicinal Genomics developed a novel quantitative polymerase chain reaction (qPCR)-based test, in a 96-well plate format, that relies on fluorescence to detect amplified deoxyribonucleic acid (DNA). Fluorescence detection indicates the presence of microbial contamination on cannabis. This quantitative PCR method has been adapted for multiple matrices such as flower, leaf, concentrates, and an array of non-flower marijuana infused products (MIP). This review introduces the qPCR assay and compares its performance to culture-based methods.

Conversion of Cq to CFU Equations

Correlation of qPCR results with plating live species will result in an equation that enables conversion of Cq/g to CFU/g as given in equation 1:
CFU/g = 10 * [(42.185 – Cq Value)/3.691]

Molecular methods often leverage amplification of ribosomal DNA, internal transcribed spacers (ITS) regions (3,6). As a result, these PCR products can detect unculturable organisms and organisms that clump and distort CFU/g enumeration such as Aspergillus species (Figure 9).  (See upper right for Figure 9, click to enlarge.) Aspergillus spp. demonstrate logarithmically slower growth at room temperature than most other yeast. You can see in this comparison to the qPCR PathoSEEK assay results, the CFU plated and the growth on plating media does not appear linear and significantly underestimates Aspergillus spp. growth. However, the plated CFU does compare accurately to the qPCR quantitative cycle (Cq) result for other species.


Accurate methods are required to assess exposure risk across diverse cannabis samples and matrices. Traditional testing methods include culture on nonspecific media, such that many microbial organisms can grow, resulting in false positive results. Further, certain organisms do not grow on media, and their apparent culture and growth are not representative of true organism density, thus causing under-reporting or false negatives. qPCR is being repurposed for its ability to detect small amounts of specific pathogenic microbial organisms potentially present in these myriad sample presentations. To meet this new demand, Medicinal Genomics (MGC) has partnered with Agilent to employ their PCR technology on the Agilent qPCR system. This chemistry includes the SenSATIVAx DNA purification kit and the PathoSEEK qPCR reagents, which include the appropriate positive and negative controls.

  1. C. De Bekker, G.J.  van Veluw, A. Vinck, L.A Wiebenga, and H.A. Wosten, Applied and Environmental Microbiology 77(4),1263–7 (2011). PubMed PMID: 21169437. PubMed Central PMCID: 3067247.
  2. K.J. McKernan, J. Spangler, and L. Zhang et. al., F1000 Research. 4(1422) https://doi.org/10.12688/f1000research.7507.2
  3. Medicinal Genomics, “PathogINDICAtor qPCR microbial detection Assay on the AriaMX Real-Time PCR System optional decontamination step,” Document EUD-00021 1.4.  (2017). (Medicinal Genomics Corporation).
  4. K.J. McKernan, et. al., PLoS One 9(5)e, 96492 (2014).
  5. K.J. McKernan, J. Spangler, and Y. Helbert et. al.,“Metagenomic analysis of medicinal Cannabis samples; pathogenic bacteria, toxigenic fungi, and beneficial microbes grow in culture-based yeast and mold tests,” F1000Res. 5(2471) https://doi.org/10.12688/f1000research.7507.2.
  6. S.D. Leppanen and H. Ebling, “Optimized Cannabis Microbial Testing: Combined Use of Medicinal Genomics Extraction Methods with the AriaMx qPCR Instrument,” Application Note 5994-0430EN, Agilent Technologies (2018).

Scott Leppanen is the Senior Field Applications Scientist and Anthony Macherone is the Senior Scientist for Agilent Technologies, Inc.  Heather Ebling is the Senior Applications and Support Manager for Medicinal Genomics. Direct correspondence to: [email protected] com and [email protected].

How to Cite This Article

S.D. Leppanen, H. Ebling, and A. Macherone, Cannabis Science and Technology 2(4), 69-76 (2019).

Editor's Note:

The print version of this article mistakenly left off Figure 7a and Table IV. Those elements are included in the correct version found here.