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The Environmental Microbiology Minimum Information (EMMI) Guidelines: qPCR and dPCR Quality and Reporting for Environmental Microbiology

  • Mark A. Borchardt*
    Mark A. Borchardt
    Environmentally Integrated Dairy Management Research Unit, USDA Agricultural Research Service, 2615 Yellowstone Drive, Marshfield, Wisconsin 54449, United States
    *Phone: 715-387-4943. Email: [email protected]
  • Alexandria B. Boehm
    Alexandria B. Boehm
    Department of Civil and Environmental Engineering, Stanford University, Stanford, California 94305, United States
  • Marc Salit
    Marc Salit
    Departments of Pathology and Bioengineering, Stanford University, Stanford, California 94305, United States
    Joint Initiative for Metrology in Biology, SLAC National Accelerator Laboratory, Menlo Park, California 94025, United States
    More by Marc Salit
  • Susan K. Spencer
    Susan K. Spencer
    Environmentally Integrated Dairy Management Research Unit, USDA Agricultural Research Service, 2615 Yellowstone Drive, Marshfield, Wisconsin 54449, United States
  • Krista R. Wigginton
    Krista R. Wigginton
    Department of Civil and Environmental Engineering, University of Michigan, Ann Arbor Michigan 48109, United States
  • , and 
  • Rachel T. Noble
    Rachel T. Noble
    Insitute for the Environment, University of North Carolina, Chapel Hill, North Carolina 27517, United States
Cite this: Environ. Sci. Technol. 2021, 55, 15, 10210–10223
Publication Date (Web):July 21, 2021
https://doi.org/10.1021/acs.est.1c01767

Copyright © 2021 The Authors. Published by American Chemical Society. This publication is licensed under

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Abstract

Real-time quantitative polymerase chain reaction (qPCR) and digital PCR (dPCR) methods have revolutionized environmental microbiology, yielding quantitative organism-specific data of nucleic acid targets in the environment. Such data are essential for characterizing interactions and processes of microbial communities, assessing microbial contaminants in the environment (water, air, fomites), and developing interventions (water treatment, surface disinfection, air purification) to curb infectious disease transmission. However, our review of recent qPCR and dPCR literature in our field of health-related environmental microbiology showed that many researchers are not reporting necessary and sufficient controls and methods, which would serve to strengthen their study results and conclusions. Here, we describe the application, utility, and interpretation of the suite of controls needed to make high quality qPCR and dPCR measurements of microorganisms in the environment. Our presentation is organized by the discrete steps and operations typical of this measurement process. We propose systematic terminology to minimize ambiguity and aid comparisons among studies. Example schemes for batching and combining controls for efficient work flow are demonstrated. We describe critical reporting elements for enhancing data credibility, and we provide an element checklist in the Supporting Information. Additionally, we present several key principles in metrology as context for laboratories to devise their own quality assurance and quality control reporting framework. Following the EMMI guidelines will improve comparability and reproducibility among qPCR and dPCR studies in environmental microbiology, better inform engineering and public health actions for preventing disease transmission through environmental pathways, and for the most pressing issues in the discipline, focus the weight of evidence in the direction toward solutions.

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Introduction

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Environmental microbiologists were quick to adopt real-time quantitative polymerase chain reaction (qPCR) and, more recently, digital polymerase chain reaction (dPCR) for measuring microbes in aquatic and terrestrial systems. Compared to traditional culture approaches, qPCR and dPCR provide (1) high analytical sensitivity (i.e., low limit of detection), by the power of nearly doubling the gene target every thermal cycle; (2) high analytical specificity, by targeting genes unique to the microbial taxon of interest; (3) high throughput, as the methods are amenable to analyzing multiple gene targets and multiple samples simultaneously; (4) high confidence in gene and, therefore, microbe identity, by within-assay hydrolysis probe or melt curve; and (5) high dynamic range on the order of 105, which while not unique to qPCR and dPCR, is readily accessible. Such ease and advantage has meant data derived from qPCR and dPCR methods now routinely inform public policy and public health in domains of recreational water quality criteria, (1) invasive species surveillance, (2) quantitative microbial risk assessment, (3) microbial source tracking, (4) epidemiological surveillance, (5,6) and treatment effectiveness for water, air, and surfaces. (7−11) The methods have also been applied to research questions in microbial ecology. (12,13)
In recognition of this widespread adoption of molecular technologies, we offer the Environmental Microbiology Minimum Information (EMMI) guidelines for consistent and systematic quality control of qPCR and dPCR data for environmental microbiology. The Minimum Information for Publication of Quantitative Real-Time PCR Experiments (the MIQE guidelines) published for qPCR (14) and dPCR (15,16) provide detailed information on the types of controls and information that should accompany data from PCR methods to support decision-making. The journal Clinical Chemistry requires authors to complete the MIQE checklist before manuscript submission; an incomplete list may result in manuscript rejection. (17) The International Organization for Standardization and United States Clinical Laboratory Improvement Amendments also specify controls and information to be included with every qPCR data report. (18−20) Taylor et al. (21) address the measurement uncertainty of qPCR and provide criteria for choosing between qPCR and dPCR methods. Absent from previous guidelines is how to implement and interpret quality controls, which can be particularly difficult with environmental samples given the field work and multistep processes involved. Here, we build upon previous published guidelines with an eye toward exploring the application, utility, and interpretation of the suite of quality controls for qPCR and dPCR analysis of environmental samples. Such understanding, we believe, will lead to more widespread adoption among environmental microbiologists of the guidelines necessary to ensure trustworthy and reproducible qPCR and dPCR data.
We completed a systematic review of the 100 most recent papers in the field of health-related environmental microbiology that used qPCR or dPCR for measuring pathogens or fecal source markers in water, air, or on surfaces. We compiled data on the most fundamental MIQE parameters (see Supporting Information systematic review methods and Table S1). The data show inconsistent application of quality control principles and insufficient reporting of experimental control results.
Of the 100 papers screened, only 33% reported that they assessed the efficiency of their sampling method for capturing the study microbe (i.e., sampling recovery control). Only 10% performed a sampling equipment negative control. Only 20 and 38 of the 100 studies reported doing nucleic acid extraction negative and positive controls, respectively, and only about half of those actually reported the results from the controls. About 40% of the studies reported testing for reverse transcription (RT) or PCR inhibition, and 72% and 61% of those reported the results, respectively. Only 46 studies reported they ran a no-template-control during PCR (i.e., PCR negative control) and only 13 of those 46 reported the results. Of the 86 studies that used an RT step, only 31 (36%) report using a RT negative control and 36 (42%) report using a RT positive control; of those that report using the controls, only about a third report the results. Reporting of details of qPCR standard curves that are suggested in the MIQE guidelines was similarly dismal. Only about a quarter of the papers that used qPCR reported methods for determining cycle of quantification values (Cq values), standard curve slopes, and R2 values. A recent review of environmental DNA (eDNA) studies showed a similar lapse in reporting quality controls. (22)
Omitting key information has consequences beyond making it difficult to substantiate a study’s findings. Insofar as the results include false-positives, false-negatives, and inaccurate concentration measurements, public resources and policy could be misdirected toward addressing a problem that does not exist or overlooking one that does. Engineering processes (e.g., water treatment) could be poorly evaluated, thus leading to the design of ineffective treatment strategies. From the researcher’s personal perspective, research time and funding is limited and not realizing a study’s full impact potential means lost opportunities, or worse, diminished credibility.
We have experienced firsthand from our own studies the problems that arise when qPCR/dPCR data have incomplete information on quality control. And serving as peer-reviewers, we witness the span in quality of qPCR/dPCR methods in our discipline and the opportunities lost to enhance our collective effectiveness and credibility. Here, our aim is to encourage consistent and transparent reporting of qPCR/dPCR assay performance, to initiate consensus on those elements that constitute the critical information required in every paper, and to promote understanding that all measurement results have bias and variability. Without clear quality criteria and expectations, environmental microbiology will not contribute with the significance and at the pace needed to address our most pressing environmental issues.

Purpose of Controls—Two Questions Answered

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Environmental qPCR/dPCR results are the product of a multistep process. Biases and variation arise at each process step. Biases result in deviation from the true value, and variation gives rise to dispersion of the measurement results. There are well-developed strategies to use “controls” throughout the process to detect, mitigate, or minimize the biases and variation, and critical evaluation of measurement results requires knowledge of the steps taken to do this.
Figure 1A shows the general process steps and specific examples for analyzing environmental samples for microbial gene targets by qPCR or dPCR. Not all steps are required, depending on the method and gene target. Regardless of which steps and specific methods are performed, working with environmental matrices presents challenges in creating appropriate controls and understanding exactly what each control is indicating.

Figure 1

Figure 1. Process steps, controls, and reporting elements for qPCR or dPCR analyses of microbial gene targets in environmental samples. The specific process steps and controls that are performed will depend on the preanalytical and analytical methods chosen for a particular study. Colored arrows indicate controls and extend from the beginning of negative control assessment or positive control introduction (arrow bases) through the process steps that follow (arrow tips). Black brackets between bases of adjacent arrows indicate that portion of the process sequence that is assessed by comparing measurements of two controls. (A) Process sequence and example steps. (B) Examples of negative controls. (C) Examples of positive controls. (*Note that inhibition can be assessed by using controls or by dilution (see text).) Potential matrix effects on control measurement depend on the point of control introduction in the process sequence and whether the control is added to the sample (internal control) or assessed separately (external control). (C.1) Amount of positive control added is quantified by an independent method. (C.2) Amount of positive control added is quantified by a parallel process, where quantification of the added control has in common some of the same process steps used to quantify the amount of control recovered. Light-shaded arrows in C.2 indicate beginning points (arrow bases) for the parallel process of quantifying the added control, extending through the process steps that are in common. (D) Critical categories of quality assurance and control to be reported in study publication. Specific reporting elements are listed in Table 1.

Controls consist of two groups that address two general questions: (1) Is the analysis working? This question is answered by using positive controls to assess assay performance and ensure a negative result is not due to assay failure (i.e., false negatives). (2) Is there contamination? Negative controls (i.e., no-template controls) answer whether some part of the analytical process is contaminated with the gene target, and they ensure a positive result is not from some source other than the sample (i.e., false positives). Contamination can result from reagents, equipment (e.g., pipettors) or the actions of the process (e.g., not wearing gloves, uncapping a tube). Without positive and negative controls it is impossible to detect false negatives and false positives, and confidence in data quality is impossible.
Figure 1B and 1C shows examples of controls in relationship to each other along the sequence of process steps. A control applied at the end of the process, for example, a PCR negative control, provides information on just that last step. However, controls applied at steps “upstream” in the process must go through every “downstream” process step to obtain a result. For example, a sampling positive control, say an RNA virus concentrated by ultrafiltration, is assessing not only the sampling step but also the performance of filter elution, sample reduction, nucleic acid extraction, RT, and PCR. To isolate the performance of an “upstream” step the step’s controls must be compared to the controls for “downstream” steps. Continuing the example above, if the RNA virus positive control was also applied at the sample reduction step and the results of the sampling positive control and reduction positive control were compared, this would isolate the ultrafiltration and elution steps, providing performance information specific to those two steps (Figure 1C.1). Similarly, an RT negative control that was positive while the corresponding qPCR negative control remained negative would suggest contamination occurred at the RT step, perhaps in the master mix or pipets. This comparative approach for assessing the performance of “upstream” process steps is illustrated in Figure 1B and 1C.
Thus, interpretation of the positive and negative controls has two forms: (1) standalone, in which the control assesses the step at which it is applied and all steps “downstream” and (2) comparative, in which two controls are compared and the performance interpretation relates to the process steps not shared between the two.
One could argue that controls applied to “upstream” process steps also provide adequate control information for every “downstream” step. This is true if the “upstream” control is run with every “downstream” step, including with multiple batches of the same step (e.g., PCR batches). In other words, a separate control for every step may not be necessary, but every step (and batch) requires control information. Keep in mind that if an “upstream” control fails, the specific step that had been the culprit will be unknown unless the controls for the “downstream” steps are performed. Moreover, using multiple controls for specific steps and having the entire set “pass” adds further confidence that the sample results are indeed accurate.

Terminology

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Before discussing specific controls we must come to terms with terminology. In environmental microbiology qPCR and dPCR terms related to data quality are rife with synonyms and jargon. Of possible detriment is reader confusion and an uncritical acceptance of the data. The terms fall into two categories: (1) terms related to controls and (2) terms related to instrumentation and gene target quantification. The latter have been standardized in the qPCR and dPCR MIQE guidelines, (14−16) and we encourage environmental microbiologists to adopt these terms.
Terms for the various controls are more muddled and in need of a systematic approach. Recognizing that controls are applicable to specific process steps and that there are two basic control types, we suggest controls be prefixed with the step name followed by the type, positive or negative. For example, “extraction positive control” or “PCR negative control”. This approach applies also to field sampling where “equipment negative control” has clearer meaning than the jargon “equipment blank”, and the positive controls are named for the method used, for example, ”ultrafilter positive control”. Method performance can be assessed by the fraction of positive control recovered in which case it is necessary to indicate the process step or step sequence being assessed. Two examples are (1) process recovery control (performance of the entire process) and (2) filter recovery control (performance of just the filtering step). Some synonyms have roughly equal usage in the literature (e.g., “PCR negative control” and “PCR no-template control”) in which case it would aid comprehension to parenthetically give the synonym when first presenting the control. Some step names are unambiguous and have near universal usage, for example extraction and PCR. Others, like “secondary concentration”, vary by research team. Rather than stipulate specific step names, we urge environmental microbiologists to work actively toward standard names and consistent usage. Systematic naming of controls would reduce ambiguity, readily convey data quality, and aid comparisons among studies.

Negative Controls

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Every PCR lab experiences contamination; not every lab knows it. Having the proper negative controls in place to detect false-positives when they happen is essential. Figure 1B shows examples of negative controls and how the comparative approach will test for contamination of specific process steps. Confidence that false positives are absent is greatest when all negative controls are truly negative (i.e., no Cq value for qPCR and no positive droplets for dPCR). This should be the goal of every qPCR and dPCR practitioner. However, when negative controls reveal contamination several actions can be taken. The most prudent is to exclude the affected data, clean the suspected contamination source, and repeat the analyses. Sometimes only that data corresponding to the discovered contaminant must be excluded, and the data on the other targets for the same sample, whose negative controls were fine, can be accepted. Some laboratories and some methods (23) allow low level contamination and establish a “cut-off” concentration above which the results are deemed true positives. This may be appropriate when working with some environmental matrices (e.g., wastewater) or targets (e.g., 16S rRNA gene) where the target concentration is many orders of magnitude higher than background contamination. However, in many environmental matrices concentrations of study targets are usually low and difficult to discern from contamination.
An important consideration is the number of negative controls required to detect contamination. This can be readily calculated for a specified probability of detection by assuming the contaminating gene target is Poisson distributed. Then, from the probability mass function of the Poisson distribution the following equation can be derived: (24)
(1)
where n = the number of replicate negative controls, P = the desired probability of detection, and λ = the target contaminant concentration in the negative control (gene copies per reaction). For example, to be 95% certain that contamination at the level of one gene copy per reaction is absent, three replicate negative controls are required. To be 99% certain five replicates are needed and so on. The specified probability refers to the probability of detecting the contaminant in the reaction, which is replicated at the level of the process step being checked. For example, conducting two extraction negative controls within a batch of samples being extracted means there will be two reactions (n = 2 in terms of eq 1), and rearranging eq 1 to solve for P yields an 86% probability of detecting one copy of contaminant per reaction. Rather than follow a prescribed number of negative controls, using the Poisson function allows researchers to determine the contaminant copy number they wish to avoid and the probability of detecting it. In practice, performing negative controls for multiple process steps and multiple analysis batches (i.e., well plates) provides de facto replication such that if there is a contamination problem it will eventually show up.

Positive Controls

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Internal versus External Controls

Positive controls assess whether a specific process step or sequence of steps is working as expected ,and they fall into one of two types, internal or external. (Examples of positive controls are shown in Figure 1C.) An internal control is added directly into the sample, either during sample collection or at a later process step. To avoid having the recovery measurement confounded by any study target present in the sample, the internal control is usually a unique sequence (25) or different microbe (26,27) (cf., Li et al. (28)), although the same target can be used if the control amplicon is altered to be distinguishable from wildtype. (29,30) In contrast, an external control can be the same as the microbial target under study. However, because they are identical, the control is assessed in a process parallel to that of the study target, in which the control is added to a nonsample matrix or to a companion sample in which the amount of study microbe present has been already accounted for. Thus, an internal positive control provides sample-specific information than can be obtained for every sample, but being different, the control may not perfectly represent the study microbe. (31) Whereas an external positive control can assess a process step by and of itself, but as it cannot be directly added into the sample, the measured performance may not adequately represent any effects contributed by the environmental matrix. The choice of which positive control type to use depends on the study objectives, experimental conditions, and the process steps that need assessment. For example, if an extraction method has not been previously validated, the extraction positive control may need to be the study microbe itself, which means it cannot be added to the sample and would be performed as an external control. Another example, to account for variable environmental conditions suspected to affect sampling performance, it may be helpful to add the sampling positive control during every sampling event as an internal control.

Independent versus Parallel Measurement of Controls

Two approaches are typically used to measure the amount of positive control added to process steps: (1) measure the added control amount with a method that is independent and separate from the process steps being assessed (Figure 1C.1) or (2) measure the added control amount using some of the same process steps that will be used to measure the amount of control recovered (Figure 1C.2).
The first approach, independent quantification of process performance (losses and recoveries), requires that the measurements of the amount added and amount recovered of the positive control do not have measurement steps in common. This is accomplished by measuring the control amount added by an independent “benchmark” method, for example, microscopy or flow cytometry. Knowing the number of target genes per unit microbe counted (e.g., number of cells or oocysts), the count can be converted to gene copies to be compared with the qPCR or dPCR output. Alternatively, the positive control can be purchased premeasured from a vendor, for example salmon testes DNA (32) or quantified virions (e.g., American Type Culture Collection). The fraction of control recovered is the amount recovered (PCR output) divided by the amount added measured by the benchmark method.
The steps assessed by this approach depends on where in the process sequence the positive control is added. This is demonstrated by the arrows in Figure 1C.1 where the length from the arrow base (positive control is added) to the arrow tip (positive control is recovered and measured) encompasses the process steps under assessment. For example, the steps assessed for the sampling positive control in Figure 1C.1 are environmental sampling, sample treatment, sample reduction, nucleic acid extraction, reverse transcription (if the target is RNA), and PCR. Similarly, an independently quantified RNA extraction positive control added at the extraction step means three steps are assessed: extraction, RT, and PCR (Figure 1C.1).
Each process step results in recovery or loss of the positive control such that the fraction of the amount recovered at the end of the process (i.e., percent recovery) can be represented by eq 2:
(2)
where F represents the fraction of positive control (or study target) recovered at each step indicated by the following subscripts:
  • S = sampling concentration

  • ST = sample treatment

  • SR = sample reduction

  • E = nucleic acid extraction

  • RT = reverse transcription (for an RNA target)

  • PCR = qPCR or dPCR

We decomposed the process into fractional recovery terms (eq 2) to provide a conceptual framework for understanding how positive controls can be used in a comparative approach for isolating the performance of specific process steps. For example, the sampling positive control and sample reduction positive control are compared to isolate the performance of only the sampling and treatment step sequence. The former is added at the beginning of sampling and the latter is added at the beginning of sample reduction (Figure 1C.1). Following from eq 2, the fraction of sample reduction positive control recovered is the product of all fractions recovered from sample reduction to PCR (FSR→PCR):
(3)
Therefore, the “difference” in the two controls attributed to the sampling and sample treatment steps (FS→ST) is
(4)
The terms FSR × FE × FRT × FPCR are common between numerator and denominator and mathematically cancel, leaving FS × FST, the separate recovery performance of the sampling and treatment step sequence. In practice, it is not necessary to multiply individual step recovery fractions. The fraction recovered for the performance steps under assessment can be obtained by collapsing eq 4 to
(5)
where FS→PCR is the fraction recovered across all steps from sampling to PCR (measured by adding a positive control at the sampling step) and FSR→PCR is the fraction recovered across all steps from sample reduction to PCR (measured by adding a positive control at the sample reduction step).
For example, if the fraction recovered of the sampling positive control (FS→PCR) is 60% and the fraction recovered of the sample reduction positive control (FSR→PCR) is 80%, then the fraction recovered by sampling and treatment (FS→ST) is 60/80 = 75%.
The second approach for quantifying a positive control is in effect a parallel measurement, as it relies on some of the same process steps and final qPCR/dPCR analysis as the measurement of the recovered control (Figure 1C.2). Typically the control amount to be added is suspended in a matrix like AE buffer or culture media and quantification begins at the extraction step. Quantifying the sampling positive control in this manner, as the example shown in Figure 1C.2, is conceptually the same as the comparative approach for independently quantified positive controls. The process steps that are in common between the parallel measured added control and recovered control mathematically cancel, leaving the recovered fraction to represent those steps unique to the recovered microbe. For the sampling positive control example in Figure 1C.2, the sampling, treatment, and reduction steps are unique; therefore, the recovered amount indicates the performance of these three steps. When the goal is to determine the fraction of positive control recovered, this relative approach will work, even without knowing the true quantities of added or recovered gene copies. The caveat is that the steps used to measure the added control amount cannot be isolated and assessed (in our example, extraction, RT, and PCR). Identical steps in the numerator and denominator for the calculation of recovered fraction would always yield 100%, assuming no matrix effects (see below). To assess these “downstream” steps by themselves or assess the entire process sequence of which the “downstream” steps are integral, an independently quantified positive control is required.
Understanding the subtleties of positive controls gives the capability to isolate and assess the performance of any step or sequence of steps by devising comparative combinations of controls. This is a straightforward exercise with independently quantified controls (Figure 1C.1). With parallel quantified positive controls, which step or sequence is isolated for assessment results from manipulating the two starting points: (1) the step at which quantification begins of the amount of control to be added and (2) the “upstream” step at which control is added to be recovered at the end of the process (Figure 1C.2). The manipulation creates the sequence of steps not in common between the added and recovered controls and, therefore, the steps under assessment. All of this presents a cautionary corollary; comparing the performance of process methods (e.g., sampling methods for waterborne pathogens), especially between studies, requires knowledge of how the positive controls were quantified, where positive controls were added to the process and which controls were compared to isolate the performance of specific steps.

Matrix Effects on Measurements of Positive Controls

A potential source of variability in measuring the recovery of positive controls and thereby making judgments on method performance is the effect of sample matrix. Specific constituents of the matrix may diminish or even enhance the efficiency of the PCR; these effects may not necessarily stem from direct inhibition of polymerase or reverse transcriptase enzymes. (33) As a sample moves through the process steps from collection through processing to preparation for PCR measurement, it becomes a mixture of naturally occurring compounds, additives, and reagents from three matrices: environmental, preanalytical steps, and analytical steps (Figure 1C). An identical quantity of positive control measured in analytical matrix (e.g., PCR master mix) versus environmental matrix (e.g., concentrated groundwater) may not give the same result due to the different chemistries of the reaction mixtures. This is the reason in even uninhibited samples the fraction of recovered sampling positive control can vary substantially, depending on whether the amount of control added is measured in a simple matrix or a facsimile of the environmental matrix. (34,35) Consequently, eq 4 is idealized as it does not account for differences in matrices between numerator and denominator process steps. For example, the observed sampling recovery fraction (FS,obs) is a function of the idealized sampling recovery fraction (FS) and environmental matrix effects:
(6)
where M represents matrix effects in general and subscripts EV, P, and A specify environmental, preanalytical, and analytical matrix effects, respectively.
Thus, for the positive control comparison in eq 6 the fraction recovered represents the loss from sampling and the effects of the environmental matrix. Overall, the variability contributed by matrix effects may be minor compared to the measurement variability contributed by other factors (e.g., extraction efficiency) and usually matrix effects are ignored. However, researchers should be cognizant of this source of bias. If the bias is significant, a facsimile of the sample matrix can be created, for example, by processing a companion sample, in which then the amount of control to be added is measured. (34,35)

Process Steps and Their Controls

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Environmental Sampling Controls

Controls for sampling the environment (e.g., water, sewage, soil, air) must be designed to answer the questions posed earlier: “Does the sampling method work?” and “Is the sampling equipment contaminated?” The former question is answered by adding a sampling positive control to the sample while it is being collected (internal) (26) or to a companion sample collected independently (external). (7) Both control types are carried out in a similar fashion. A measured quantity of control microbe is seeded into the environmental matrix under study (e.g., river water), the sampling process and “downstream” steps are performed with the seeded volume, and the quantity of microbe recovered (numerator) is expressed as a percentage of that seeded (denominator). Correcting measured study microbe concentrations for processing losses can be as simple as dividing by the average percent recovery, (36,37) or as involved as defining the statistical distribution of measured recoveries and using a Monte Carlo procedure for random draws of recoveries to be applied to each sample. (31) The degree to which these corrections contribute bias in estimating concentrations has been questioned. (38)
The sampling negative control evaluates equipment contamination by simulating sampling with sterile matrix (e.g., autoclaved water) run through the equipment, ideally in the field. The sampling negative control is carried through the process and should be negative for the gene target.
None of these approaches for assessing performance and contamination of field sampling are ideal. Conducting external sampling positive controls that require a separate collection effort to correspond with every sample is usually not practical or affordable. Adding an internal positive control to large sample volumes (>10 L) becomes cumbersome in the field. And as discussed earlier, if the environmental matrix affects quantification and the added and recovered control amounts are measured in different matrices, the recovery percentage can be inaccurate. Lastly, how well a sampling negative control represents actual field sampling is questionable, and again, assessing equipment contamination for every sample is not practical.
What is an environmental microbiologist to do? Do what is necessary to provide confidence in the data. Show the sampling method works for the study objectives at hand, and demonstrate the sampling equipment is unlikely to introduce contaminant target sufficient to bias the sample. It may be sufficient to cite previous validation studies of the method if the sampling conditions of the present study are similar. Consider when key factors affecting sampling performance might change and measure performance to address the ranges of those factors in these circumstances. For example, if large variations in turbidity are observed across samples from a river, assess performance under low and high turbidity conditions. Focus on the process steps that could impede successful completion of the study objectives or affect the study findings. Importantly, be transparent in addressing these issues and when necessary include performance data when reporting sample results.

Controls for Preanalytical Steps

Unlike field sampling where controls can sometimes be challenging to employ, the controls for procedures in the laboratory are practical and imperative. Previous publications have provided guidelines on the necessary controls, but these have tended to focus on gene expression or clinical specimens. (14,19,21) Here, we suggest the minimum laboratory controls and supporting information needed for high quality qPCR and dPCR measurements of microbes in the environment.
Some environmental sampling methods require a subsequent treatment step. For example, swabbing a fomite for viruses may require the adhered viruses to be eluted from the swab using a buffer. Sampling for waterborne pathogens by ultrafiltration requires an elution step. With these methods, the eluent itself is shown to be free of contamination using an elution negative control. The control is simply the constituted eluent solution carried through each downstream step of the process. Microbiologists would never consider using constituted culture media without a sterility test; the same careful mindset applies to eluent. A treatment step positive control can be performed, but usually the performance of the treatment step is assessed in combination with the environmental sampling step.
The sample reduction step increases the target concentration in the volume being processed (e.g., collected wastewater, filter eluate). Many of the reduction methods use reagents and supplies that are sterile from manufacturing (e.g., polyethylene glycol, commercial molecular weight cutoff filters). Nevertheless, the processing actions at the lab bench may contaminate the sample so negative controls are still necessary. For methods requiring elution, it is reasonable to perform the reduction procedure on the elution negative control and assess contamination of both steps (elution and reduction) combined. If needed, a separate reduction step negative control can be performed. One might argue that negative controls for treatment and reduction are unnecessary because contaminating solutions, supplies or actions would make all samples target-positive, raising suspicion. However, at low contamination concentrations the stochastic distribution of the contaminant target can result in a mixed pattern of positive and negative samples that may appear realistic. It is best to perform negative controls to provide more confidence that contamination is not the cause of positive samples. The reduction step positive control can be performed separately or in combination with sampling and treatment.
Only 20% and 38% of the 100 publications, we reviewed reported they conducted negative and positive controls, respectively, for the nucleic acid extraction step. Even fewer reported the results of their controls. This means researchers cannot tell, for at least 80% of the papers, whether samples positive for the target were the result of contamination during extraction. Failed negative extraction controls (evidence of contamination) are not uncommon in our laboratories, in which case the extraction step has to be repeated.
Extraction positive controls, the types and interpretation, are highly variable among environmental microbiology laboratories. Some use an intact microbe because an oligonucleotide will not represent the extraction inefficiencies that might occur when nucleic acid is internalized in a cell, capsid, or cyst. Matching the control nucleic acid type to that of the study microbe (RNA or DNA) helps ensure that it meaningfully represents the performance of interest. Unless there is evidence that the study microbe will present extraction difficulties, we believe it is acceptable to use surrogate microbes as positive controls. One benefit of this approach is cross-contamination is minimized as the laboratory isolate of the study microbe serving as the control is not extracted in proximity or at the same time as the samples. Moreover, if extraction efficiency is expected to vary with every sample, the surrogate can be added directly to the sample aliquot to be extracted (i.e., internal control) and still be distinguishable from the study microbe. For surrogates there are many options; our laboratories use bovine herpes virus for DNA extractions and bovine respiratory syncytial virus or murine hepatitis virus for RNA extractions. Salmon testes DNA has been used, (39,40) as well as other sources of DNA, (41,42) and there are commercial, easy-to-use products designed to evaluate extraction efficiency.
Measuring extraction efficiency can represent different aspects of the extraction process, depending on how the positive control is quantified before it is added. Quantifying the positive control with a parallel process, when the control is placed into a simple matrix like TE buffer, extracted, quantified by qPCR or dPCR, and then compared to the recovered control that had undergone the same extraction and PCR methods, the only difference is the matrix. Thus, the calculated efficiency shows the degree to which extraction is affected by the sample matrix. Quantifying the positive control with an independent method, one that does not share the same extraction procedure as the sample, would yield an efficiency that represents not only the effects of the sample matrix, but also the recoveries and losses of the extraction process, for example, lysis, silica binding, nucleic acid washing, and elution. Another approach is to benchmark against a different extraction method (e.g., heat extraction) that is demonstrated or documented to have insignificant or well-understood losses. The only in-common steps would then be qPCR or dPCR (and RT step if needed); comparison of the results yields the extraction recovery (Figure 1C.2). The advantage of using commercially available vendor-quantified DNA for an extraction positive control is that the expected amount to be recovered is a straightforward calculation. On the other hand, naked DNA may not adequately represent the target being studied, and its use could underestimate losses in extraction.
If extraction efficiency is not quantified, confidence is still needed that the extraction step did not fail. One simple approach is to compare the positive control’s Cq value (or positive droplet count) with the expected value obtained from previous extractions. The Levey–Jennings plot is an excellent means for tracking over time the deviance of positive controls from expected values. (43)

Controls for RT and PCR Steps

The positive and negative controls for the final process steps, PCR and RT (if performed separately), are perhaps the easiest to interpret as there are no more “downstream” steps to alter the outcome. The negative controls can be master mix with sterile water or buffer substituted for nucleic acid extract. These, as well as the negative controls for the other steps, must be tested for every target in the study; testing for only select targets may miss contamination events.
Synthesized oligonucleotides that match the target’s strand length and complementary sequences of the primers and hydrolysis probe can be used as PCR or RT positive controls. One option for designing positive controls is to rearrange the bases of the noncomplementary regions while keeping the GC content the same as the target sequence. This differentiates the amplicons of wildtype target from synthesized positive control, which, in the event of contamination, can be useful for tracking the source.
Reaction efficiencies for reverse transcription and PCR can be less than 100%, and in some cases it might be necessary to account for efficiency losses and adjust the final target concentration to a more accurate value. Reverse transcription efficiency depends on sample and enzyme types, priming method, and RNA concentration. (44) Differences in PCR efficiencies between standards and samples may also be a source of significant measurement error (45)—depending on the nature of the positive control and matrix, this effect might be missed.

RT and PCR Inhibition

Inhibition of RT or PCR or both results from compounds intrinsic to environmental samples (e.g., humic acids) or reagents added during sample processing (e.g., BSA, ethanol). (46,47) When inhibitors are present the target quantity will be underestimated, or worse, the reactions will be so inefficient the target is not detected. Two approaches are generally employed to assess inhibition: (1) the study target concentration measured by qPCR or dPCR is compared in diluted and undiluted nucleic acid extract. Because dilution can mitigate inhibition, the target concentration may appear higher in the diluted extract (after correcting for dilution); (47) (2) a control gene or oligonucleotide is added into sample extract and the measured quantity is compared to the expected quantity. If the sample extract is inhibitory, the added control will have a lower calculated quantity than expected and the dilution necessary to mitigate inhibition can be estimated. (48) The expected amount of the added control can be measured by an independent method, in which case the observed inhibition reflects the combined effects of inhibitory compounds introduced by environmental, preanalytical, and analytical matrices (Figure 1C.1) Alternatively, the expected amount can be measured in parallel in a noninhibitory matrix using the same RT and PCR assays as for the recovered control. The observed inhibition will then reflect the effects of the environmental and preanalytical matrices (Figure 1C.2). Inhibition assessment using an added control also can also be accomplished by modeling and comparing the control’s sigmoidal amplification curve in different matrices. (49) When two-step RT-PCR is employed, a separate assessment of RT inhibition is necessary by the dilution or added control approach.
The benefit of the dilution-first approach, without an added control, is that inhibition is evaluated for the same PCR assay used to measure the target. The downside is that the dilutions, established a priori, could over- or under-shoot the optimal dilution for minimizing inhibition while not yielding a false negative for the target. Testing multiple dilutions requires multiple PCRs per sample, which can become costly and cumbersome, especially if a study has multiple targets. The benefit of using an added control is that inhibition is quantified, minimizing the risk of diluting the target to nondetection. However, because the control must be distinguishable from the target, requiring its own additional PCR assay, the inhibition level measured may not represent the target’s PCR. (50,51) Alternatively, an internal inhibition control can be designed that amplifies with the same primers as for the target but differs in the probe sequence that is detected on a separate fluorescence channel in a duplex reaction. (30,39)

Efficiently Maximizing Quality Control

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Cost, practicality, and time compose the usual litany for why controls were not included with PCR analyses of environmental samples. Keeping in mind the principal questions of “Is this step working? Is this step resulting in signal from contamination?” makes it easy to determine which steps require controls, when a single control will suffice for multiple steps, and when a single control will suffice for multiple samples grouped into a single batch. With some thoughtful planning a laboratory can design a set of informative controls while not sacrificing efficiency.
Controls can be devised that assess two or more sequential steps of the process, and only if an issue arises are step-specific controls necessary to isolate the problem. For example, the RNA extraction positive control can be assessed only after reverse transcription, therefore one control can serve as a check on two steps, extraction and reverse transcription. If the combined positive control fails, separate positive controls for each step can be run to identify which one failed. Similarly, the qPCR positive control can serve double-duty, as a check on reaction performance and as the calibration curve reference control. Saving time and money by omission is also possible. If there is no reason to suspect that sampling bottles or filters are contaminated, equipment negative controls can be skipped perhaps other than a one-time check. If qPCR calibration curves for a specific target have shown minimal variability and the analytical process remains consistent, one calibration curve may suffice for multiple qPCR batches, and only a single point reference control need be run with every batch. Reducing the number of laboratory manipulations with standards has the added benefit of creating fewer opportunities for samples to become contaminated.
Perhaps the greatest efficiencies are achieved by batching samples in a manner where a single control covers the processing step of multiple samples. Figure 2 shows a hypothetical set of 18 samples and three batching scenarios of process steps that differ in the total number of required controls. An eluent solution of sufficient volume to elute multiple samples only needs to be checked once for contamination, whereas many small independently created volumes of eluent would require an equal number of negative controls. Likewise, maximizing the number of samples included in the batches of each processing step (in Figure 2, extraction, RT, and PCR) minimizes the number of controls. Of course, the number of samples cannot exceed what is practical to handle. Scenario B is the most efficient in the Figure 2 example. Scenario C is not atypical of many environmental laboratories where a new PCR master mix is needed each time a small number of samples are analyzed, generating a large number of positive and negative controls. Central to batching efficiency is choosing a method conducive to archiving samples. Otherwise samples must be processed as they come into the lab, or stored only briefly, potentially reducing the efficiency of processing and controls.

Figure 2

Figure 2. Three hypothetical scenarios that illustrate efficient and nonefficient batching of samples. The example portrays 18 incoming sample filters that are eluted as a single batch and then divided into three extraction batches. Each extraction batch undergoes different batching schemes for RT and PCR steps. Positive and negative controls are checks on process steps performed in batches. Therefore, increasing the number of samples per batch decreases the total number of controls required. Rounded rectangles represent batches. Control replicates are not shown. The process steps shown are for illustration purposes only; the actual types of steps and controls will depend on the methods chosen.

Critical Reporting Elements for Data Credibility

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Table 1 lists the quality control and assurance elements that we propose should be reported in publications with qPCR or dPCR analyses of environmental samples. Many elements are the same as those stipulated in the seminal MIQE papers for qPCR and dPCR, (14−16) papers that should be on every PCR practitioner’s lab bench. Other elements in Table 1 are unique to environmental microbiology (e.g., sample treatment and reduction, equivalent sample volume analyzed), pertain to issues that can be more problematic with environmental samples (e.g, inhibition), or elements that in our field tend to be overlooked (e.g., accounting for failed negative controls). Furthermore, the EMMI guidelines emphasize the value and interpretation of process controls. For as much as qPCR and dPCR environmental data are hard-earned, it would be counterproductive to diminish data credibility by not reporting critical elements of a study’s methods, controls, and analysis (Figure 1D).
Table 1. Reporting Elements for Publishing Credible qPCR or dPCR Analyses of Microbial Targets in Environmental Samplesa
a

Checklist of reporting elements is provided in Supporting Information.

b

Controls may be designed to provide information for multiple steps, minimizing the number of step-specific controls.

c

Indicate the results of negative controls, which process steps were assessed, and for negative controls that failed, how the associated data were handled.

d

Indicate positive control type (e.g., oligo, plasmid, etc.), internal or external, how the control was quantified, which process steps were assessed, and the results of controls.

Information on positive controls should include control type (e.g., oligo, microorganism), whether it was an internal or external control, how the control was quantified, which process steps were assessed, and whether performance criteria (if any) were acceptable.
Information on negative controls should include which steps were assessed, the results of the controls, and for those negative controls that were positive, how the associated data were handled. For example, a common approach in qPCR is to accept sample data with Cq values below a “cutoff” value that is sufficiently different than the Cq values of the failed negative controls. A 5 cycle difference (i.e., approximately 30-fold copy number difference) has been recommended to separate potential false positives from true positives. (52) Others have recommended setting a limit on the number of negative controls that are allowed positive before sample data are excluded. (23) Regardless of the approach, the key for reporting credible data is to be transparent. Report when negative controls failed, how the corresponding data were handled, and make the argument convincingly that the data do not include false-positives. When the “cutoff” approach is used with qPCR, publications should report the degree of separation in Cq values that was applied for distinguishing false positives from true positives. With dPCR, sufficient fluorescence separation between negative controls and positive samples can be demonstrated graphically.
Inhibition controls for PCR and RT (if two-step RT-PCR) should be described in sufficient detail such that readers of the publication are convinced that the data are sound. This includes reporting the number of samples tested and found inhibited (only 26% of the studies we reviewed did this). As the degree of inhibition can vary by sample, (48,53) ideally every sample should be tested, or at the very least, justification given for why this was not done.
qPCR and dPCR instrumentation can readily churn-out quantitative data, but to understand the limitations and potential biases behind the numbers, specific method and performance elements for the reactions and analysis are necessary. Reaction conditions and chemistry, primers and probes, target gene and amplicon, and instrumentation should be described. It is difficult to provide too much detail. Quality of qPCR calibration curves should be conveyed by reporting the slope (equivalent to the PCR efficiency), goodness of fit, the number of standards, and the lowest standard concentration measured. dPCR requires reporting the number and volume of partitions measured and the target copies per partition. Of importance for dPCR, reporting the method for establishing the fluorescence threshold: (1) manual settings, (54) (2) standard deviation approach, (55,56) or (3) droplet separation analyses conducted by absolute negative and positive droplet quantification by target. (57,58) The latter two approaches are gaining utility for reproducible threshold setting for ddPCR across multiple targets, which takes advantage of the multiplexing capabilities of ddPCR, and promotes consistency across batches and reagent lots.
Reporting the number of technical replicates per sample and how they were analyzed to obtain the sample-level result informs estimates of precision. Poor precision is inherent to the methods at low target concentrations for qPCR and at both low and high target concentrations for dPCR. (14,15) At best qPCR can detect a 1.25- to 1.5-fold difference in target quantities, (59) and dPCR can detect less than 1.2-fold differences. (60) To counter poor precision, the qPCR or dPCR measurements can be replicated and summarized with a measure of central tendency (e.g., mean). Another approach is to define the limit of quantification (LOQ) and exclude samples outside the LOQ’s lower or upper limits (i.e., work only with those samples measured in the range where precision is acceptable). The LOQ is defined by the user based on what they find to be an acceptable level of variance or accuracy of the measurement. (61) As such, it behooves the researcher to report how the LOQ was defined.
Depending on a study’s objectives, the researchers may wish to calculate the limit of detection (LOD), a concept of considerable confusion with environmental qPCR and dPCR data. The LOD is a probabilistic parameter (not the lowest concentration measured) and is usually estimated as that target concentration at which there is a 95% chance of detection. LOD can be estimated several ways. (24,62) Concentrations measured below the 95% LOD should not be instinctively classified as nondetections; they were indeed detected, but at a likelihood less than 95%.
Conversion of the qPCR or dPCR instrument measure to a target concentration per sample unit is susceptible to error, given the large number of process steps involving sample aliquots, dilutions, and reductions. While not listed in Table 1 as a reporting element, we strongly encourage researchers to report the computation in sufficient detail that it can be evaluated and reproduced. Dimensional analysis is a convenient approach for ensuring all factors have been accounted for by the cancellation of like-units and like-steps leading to the final result. Examples of dimensional analysis for qPCR and dPCR analyses of samples with dimensions of mass and volume are presented in Supporting Information.
Calibration curves for qPCR require standards that should be fully described. The type, provenance, source, concentrations, and quantification method are key attributes to list. The importance of the calibration process and the applicability of standards for accurate qPCR measurements has been, for the most part, conveniently ignored in environmental microbiology. Critical considerations include (1) the goodness of match between the calibration standards presented to the instrument to build the calibration model and the samples analyzed (e.g., matrix effects); (2) the stability of the calibration relationship and the measurement process, which determines the necessity and frequency of recalibration; (3) the valid range of the calibration relationship (typically bounded by the “low” and “high” standards); and (4) the comparability of calibration standards within and among studies. When the same calibration standards are used among studies, their results can be compared, even possibly aggregated (permitting meaningful meta-analysis and data reuse); such data interoperability allows better understanding of how target concentrations vary across time and space due to factors other than measurement methods. This is often made practical with the concept of metrological traceability, where the comparability of standards is derived from a chain of comparisons to a shared reference. This property is commutable, and ultimately lands at the highest order reference sample. Results that are traceable to a common standard can be compared to each other. At this time, there is not a set of shared standards for qPCR.

Metrological Principles for Producing Credible Data

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The number of controls and reporting elements described in this Critical Review may seem overwhelming and prescriptive. That is not our intent. While Table 1 provides a detailed list of reporting elements, three principles of metrology can guide environmental microbiologists on what they choose to report: fit-to-purpose, transparency, and harmonization.
Fit-to-purpose refers to the systematic accumulation of objective evidence sufficient to convince skeptical readers that the results are valid. When critical evidence of potential sources of bias or variability are missing, thorough evaluation is impossible, weakening support for the study conclusions. The systematic decomposition of the measurement process presented in Figure 1A is coupled with the systematic enumeration of controls in Figure 1B and 1C to provide a framework for developing evidence of fitness-to-purpose. This framework is not prescriptive but is intended to enable the scientist to build a rationale for compiling evidence and a basis for the critical reader to evaluate it. Table 1 offers a checklist and a suggestion for how that evidence is reported, but not all of it may be required or appropriate for a particular study. For example, during development of the measurement process exhaustive use of positive and negative controls may be appropriate. When the developed method is established and deployed, a more parsimonious approach may be perfectly fit-to-purpose. Adopting a fit-to-purpose mindset and having awareness of the critical points in the measurement process that skeptics might question will help researchers devise appropriate controls and reporting elements. (63) To bring to bear the Carl Sagan standard that “extraordinary claims require extraordinary evidence”, for environmental qPCR and dPCR data even ordinary claims need ordinary evidence.
Transparency in clearly describing methods and reporting the results of controls allows for qPCR and dPCR measurements to have greater external validity and thus comparability. As a discipline, we need to accept that bias and variability are inherent to all measurements, but understanding the level of error and its impact on study conclusions is not possible when critical information is missing. As peer reviewers of each other’s papers, we must encourage and call for transparency in reporting and not penalize authors for providing it.
Harmonization refers to the process of working toward the comparability of measurement results across laboratories using different methods and even different calibration standards. Reporting the results of controls offers the possibility of harmonization and permits critical and quantitative evaluation of results. Furthermore, the results of measurements of the controls makes possible the identification of the sources of bias and variability, enabling improvement and optimization. Harmonization of environmental qPCR and dPCR measurement methods may occur organically by our collective transparency. A key example is the concept of the equivalent sample volume or mass analyzed; that is, after processing steps the fraction of the original sample amount collected that is ultimately added to the PCR reaction and analyzed. When this parameter is not reported it is very difficult to compare results across laboratories and achieve harmonization.

Final Thoughts

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We developed the graphic of process steps, controls, matrices, and critical reporting categories (Figure 1) as an aid for each laboratory to develop their own reporting framework for controls and quality assurance elements. This is particularly important for University laboratories where training of new students is an essential part of the pipeline in the education of new environmental microbiologists. In addition, we provide in Supporting Information a checklist of reporting elements. Our wish is that Figure 1 and the checklist are of such help that they earn the honor of being taped to a lab refrigerator door.
In a random survey of attendees of the European Calcified Tissue Society’s Congress in 2017, 72% believed qPCR is simple, but, surprisingly, eight years after their introduction only 6% were aware of the MIQE guidelines. (64) Environmental microbiologists would likely have not fared much better; in our random survey of 100 papers, only 13% abided by the MIQE guideline of reporting results of PCR negative controls. Perhaps we should be excused in this era of chasing publication numbers and, besides, our journals do not require MIQE compliance for publication. (And the authors herein are not without MIQE infractions.)
Now may be the time, though, to appeal to our better angels. Why? Because the benefits are shared by all. Following the EMMI and MIQE guidelines improves comparability and reproducibility among research studies, better informs crucial engineering and public health decisions, enhances the translation and relevance of research findings, and for our most pressing problems, strengthens the weight of evidence in the direction toward solutions. And, there are personal benefits. Producing credible high-quality qPCR and dPCR data enhances researcher reputation and success. Lastly, as much of the effort in pursuing and building scientific knowledge relies on public goodwill, we environmental microbiologists need to meet our part of the bargain by striving to produce the best possible scientific information.

Supporting Information

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The Supporting Information is available free of charge at https://pubs.acs.org/doi/10.1021/acs.est.1c01767.

  • Systematic review methods; information extracted from 100 most recent papers in health-related environmental microbiology that used qPCR and dPCR; dimensional analysis examples of unit conversions for qPCR or dPCR from instrument output to target concentration in sample; and checklist of reporting elements for publishing credible qPCR or dPCR analyses of microbial targets in environmental samples (PDF)

Terms & Conditions

Most electronic Supporting Information files are available without a subscription to ACS Web Editions. Such files may be downloaded by article for research use (if there is a public use license linked to the relevant article, that license may permit other uses). Permission may be obtained from ACS for other uses through requests via the RightsLink permission system: http://pubs.acs.org/page/copyright/permissions.html.

Author Information

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  • Corresponding Author
  • Authors
    • Alexandria B. Boehm - Department of Civil and Environmental Engineering, Stanford University, Stanford, California 94305, United StatesOrcidhttps://orcid.org/0000-0002-8162-5090
    • Marc Salit - Departments of Pathology and Bioengineering, Stanford University, Stanford, California 94305, United StatesJoint Initiative for Metrology in Biology, SLAC National Accelerator Laboratory, Menlo Park, California 94025, United States
    • Susan K. Spencer - Environmentally Integrated Dairy Management Research Unit, USDA Agricultural Research Service, 2615 Yellowstone Drive, Marshfield, Wisconsin 54449, United States
    • Krista R. Wigginton - Department of Civil and Environmental Engineering, University of Michigan, Ann Arbor Michigan 48109, United StatesOrcidhttps://orcid.org/0000-0001-6665-5112
    • Rachel T. Noble - Insitute for the Environment, University of North Carolina, Chapel Hill, North Carolina 27517, United States
  • Notes
    The authors declare no competing financial interest.

Acknowledgments

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We thank Denene Blackwood for assistance with the literature systematic review and initial contributions, Joel Stokdyk for editorial comments, Aaron Firnstahl and Katherine Graham for reviewing portions of the manuscript, and Tucker Burch for assistance with mathematical derivations. We thank also Stephen Bustin and Jim Huggett for early conversations that led to the development of the EMMI guidelines. K.R.W. and A.B.B. were supported by NSF RAPID (CBET-2023057) and a gift from an anonymous donor. M.S. was supported by the DOE Office of Science through the National Virtual Biotechnology Laboratory, a consortium of DOE national laboratories focused on response to COVID-19, with funding provided by the Coronavirus CARES Act.

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  • Abstract

    Figure 1

    Figure 1. Process steps, controls, and reporting elements for qPCR or dPCR analyses of microbial gene targets in environmental samples. The specific process steps and controls that are performed will depend on the preanalytical and analytical methods chosen for a particular study. Colored arrows indicate controls and extend from the beginning of negative control assessment or positive control introduction (arrow bases) through the process steps that follow (arrow tips). Black brackets between bases of adjacent arrows indicate that portion of the process sequence that is assessed by comparing measurements of two controls. (A) Process sequence and example steps. (B) Examples of negative controls. (C) Examples of positive controls. (*Note that inhibition can be assessed by using controls or by dilution (see text).) Potential matrix effects on control measurement depend on the point of control introduction in the process sequence and whether the control is added to the sample (internal control) or assessed separately (external control). (C.1) Amount of positive control added is quantified by an independent method. (C.2) Amount of positive control added is quantified by a parallel process, where quantification of the added control has in common some of the same process steps used to quantify the amount of control recovered. Light-shaded arrows in C.2 indicate beginning points (arrow bases) for the parallel process of quantifying the added control, extending through the process steps that are in common. (D) Critical categories of quality assurance and control to be reported in study publication. Specific reporting elements are listed in Table 1.

    Figure 2

    Figure 2. Three hypothetical scenarios that illustrate efficient and nonefficient batching of samples. The example portrays 18 incoming sample filters that are eluted as a single batch and then divided into three extraction batches. Each extraction batch undergoes different batching schemes for RT and PCR steps. Positive and negative controls are checks on process steps performed in batches. Therefore, increasing the number of samples per batch decreases the total number of controls required. Rounded rectangles represent batches. Control replicates are not shown. The process steps shown are for illustration purposes only; the actual types of steps and controls will depend on the methods chosen.

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    • Systematic review methods; information extracted from 100 most recent papers in health-related environmental microbiology that used qPCR and dPCR; dimensional analysis examples of unit conversions for qPCR or dPCR from instrument output to target concentration in sample; and checklist of reporting elements for publishing credible qPCR or dPCR analyses of microbial targets in environmental samples (PDF)


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