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One-step RT-droplet digital PCR: a breakthrough in the quantification of waterborne RNA viruses

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Sours: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3892107/

One-Step RT-ddPCR Advanced Kit for Probes #1864022

Use this one-step reverse transcription digital PCR supermix to achieve improved efficiency, specificity, and sensitivity during precise RNA target quantification with Droplet Digital™ PCR (ddPCR™).

  • Absolute quantification by Droplet Digital PCR in a convenient single-reaction format
  • Contains all components required for hydrolysis probe–based RT-ddPCR except primers, probe(s), and template
  • Limits nonspecific PCR amplification
  • Optimized enzyme blend enables partitioning of RNA samples into droplets while keeping the enzymes inactive until the reverse transcription reaction is performed at 50°C
  • Contains RNase inhibitor to protect the RNA throughout the entire workflow
  • Absolute quantification
  • Gene expression analysis
  • Viral load detection
  • Pathogen detection
Number of 20 µl ReactionsCatalog Number
200186-4021
500186-4022

Bio-Rad offers additional consumables for completing your digital PCR workflow:

This digital PCR supermix requires the use of one or more of the following instruments and accessories:

Bio-Rad offers additional digital PCR supermixes including:

This ddPCR Supermix for Residual DNA Quantification is optimized for use with Droplet Generation Oil for Probes on the QX200™ Droplet Digital™ PCR System and QX200™ AutoDG™ Droplet Digital™ System.

Sours: https://www.bio-rad.com/en-es/sku/1864022-one-step-rt-ddpcr-advanced-kit-for-probes?ID=1864022
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Abstract

Infectious hematopoietic necrosis virus (IHNV) is an important pathogen of salmonid fishes. A validated universal reverse transcriptase quantitative PCR (RT-qPCR) assay that can quantify levels of IHNV in fish tissues has been previously reported. In the present study, we adapted the published set of IHNV primers and probe for use in a reverse-transcriptase droplet digital PCR (RT-ddPCR) assay for quantification of the virus in fish tissue samples. The RT-ddPCR and RT-qPCR assays detected 13 phylogenetically diverse IHNV strains, but neither assay produced detectable amplification when RNA from other fish viruses was used. The RT-ddPCR assay had a limit of detection (LOD) equating to 2.2 plaque forming units (PFU)/μl while the LOD for the RT-qPCR was 0.2 PFU/μl. Good agreement (69.4–100%) between assays was observed when used to detect IHNV RNA in cell culture supernatant and tissues from IHNV infected rainbow trout (Oncorhynchus mykiss) and arctic char (Salvelinus alpinus). Estimates of RNA copy number produced by the two assays were significantly correlated but the RT-qPCR consistently produced higher estimates than the RT-ddPCR. The analytical properties of the N gene RT-ddPCR test indicated that this method may be useful to assess IHNV RNA copy number for research and diagnostic purposes. Future work is needed to establish the within and between laboratory diagnostic performance of the RT-ddPCR assay.

Publication typeArticle
Publication SubtypeJournal Article
TitleAnalytical validation of a reverse transcriptase droplet digital PCR (RT-ddPCR) for quantitative detection of infectious hematopoietic necrosis virus
Series titleJournal of Virological Methods
DOI10.1016/j.jviromet.2017.03.010
Volume245
Year Published2017
LanguageEnglish
PublisherElsevier
Contributing office(s)Western Fisheries Research Center
Description8 p.
First page73
Last page80
Google Analytic MetricsMetrics page
Sours: https://pubs.er.usgs.gov/publication/70187485
Droplet Digital PCR Whole Cell DNA Workflow Tips for Success

Droplet Digital PCR versus qPCR for gene expression analysis with low abundant targets: from variable nonsense to publication quality data

Abstract

Quantitative PCR (qPCR) has become the gold standard technique to measure cDNA and gDNA levels but the resulting data can be highly variable, artifactual and non-reproducible without appropriate verification and validation of both samples and primers. The root cause of poor quality data is typically associated with inadequate dilution of residual protein and chemical contaminants that variably inhibit Taq polymerase and primer annealing. The most susceptible, frustrating and often most interesting samples are those containing low abundant targets with small expression differences of 2-fold or lower. Here, Droplet Digital PCR (ddPCR) and qPCR platforms were directly compared for gene expression analysis using low amounts of purified, synthetic DNA in well characterized samples under identical reaction conditions. We conclude that for sample/target combinations with low levels of nucleic acids (Cq ≥ 29) and/or variable amounts of chemical and protein contaminants, ddPCR technology will produce more precise, reproducible and statistically significant results required for publication quality data. A stepwise methodology is also described to choose between these complimentary technologies to obtain the best results for any experiment.

Introduction

Data from qPCR experiments are taken within each enzymatic reaction curve at the quantification cycle (Cq). Therefore, optimization is critical for each primer pair such that reaction efficiency is consistent between all samples and acceptable (between 90% to 110%) with sample contaminants diluted adequately to assure that all reactions and associated Cq values are within the efficient range of the respective standard curves1. Poorly optimized reactions can result in artifactual Cq values and misinterpreted data that are difficult or even impossible to reproduce2, 3. For absolute quantification, data analysis is further complicated by the different sources of DNA from which the samples and standard curves are derived with unique backgrounds and contaminants that can variably affect the activity of Taq polymerase giving misleading results4. The Minimum Information for the Publication of Quantitative Real-Time PCR Experiments (MIQE) guidelines and related articles published thereafter define a rigorous methodology for designing qPCR experiments that assures publication of reproducible and high quality data5,6,7. The consequence of ignoring MIQE-guided protocols has led to the retraction of multiple articles over the past several years and remains a major frustration in the scientific community8.

Droplet Digital PCR (ddPCR) is a recent technology that has become commercially available since 20119, 10. As with qPCR, ddPCR technology utilizes Taq polymerase in a standard PCR reaction to amplify a target DNA fragment from a complex sample using pre-validated primer or primer/probe assays. However, there are two distinct differences: 1) the partitioning of the PCR reaction into thousands of individual reaction vessels prior to amplification and 2) the acquisition of data at reaction end point. These factors offer the advantage of direct and independent quantification of DNA without standard curves giving more precise and reproducible data versus qPCR especially in the presence of sample contaminants that can partially inhibit Taq polymerase and/or primer annealing11,12,13. In addition, end-point measurement enables nucleic acid quantitation independently of the reaction efficiency, resulting in a positive-negative call for every droplet and greater amenability to multiplexed detection of target molecules14. Thereby, ddPCR technology can be used for extremely low-target quantitation from variably contaminated samples where the sample dilution requirements to assure consistent and acceptable reaction efficiency, primer annealing and Cq values for qPCR would likely lead to undetectable target levels11, 15.

In this study, synthetic DNA samples were used to directly compare and contrast qPCR with ddPCR technologies under common experimental conditions that can generate variable results in typical gene expression studies. In samples with low concentrations of nucleic acids and variable amounts of Taq inhibitors, ddPCR technology was shown to convert uninterpretable results generated from qPCR to highly quantitative and reproducible data.

Results

Experimental design to assess data quality between the qPCR and ddPCR acquisition platforms

Since the goal of the study was to directly compare the data quality between the qPCR and ddPCR platforms, care was taken to assure that the experimental design minimized all differences with the exception of the data acquisition platform (ie: qPCR versus ddPCR technology). A single reaction mix was therefore produced for each sample and for all experiments which was split (20 µL each) for data acquisition between platforms (see Materials and Methods) as similarly designed in a previous study13. Since qPCR is a sample interdependent technology where the relative quantity and normalized gene expression data rely on ΔCq values, the analysis from a single plate assures the best quality results by eliminating any bias from inter-plate variability16, 17. Therefore, all reactions for the study were pipetted into a single 96-well plate each for ddPCR and qPCR technologies.

Assessment of primer efficiency, linear dynamic range and precision of ddPCR and qPCR technologies for low target concentration

For qPCR, the primers gave good reaction efficiencies (between 90% and 110%) for low concentration samples diluted in water between 27 and 32 cycles (Fig. 1A inset) with a single melt curve peak (Fig. 1B) and a relative fold decrease of approximately 2-fold between each 1/2 dilution (Fig. 1C -“Avg”) with low variability between replicates (<15% CV). The ddPCR data generated from the identical reaction mixtures revealed good separation between the negative and positive droplets with few interface droplets (Fig. 1D) supporting good primer specificity and reaction efficiency. The absolute concentrations of DNA from ddPCR technology correlated with the 2-fold dilution factor used between the samples (Fig. 1E -“Fold”).

Assessment of primer efficiency, linear dynamic range and precision of ddPCR and qPCR platforms for low target concentration. Five reactions of 45 µL were prepared in triplicate from 1/2 serial dilutions of synthetic DNA in nuclease-free water with primers and ddPCR EvaGreen supermix. Each reaction mix was split for quantification in qPCR and ddPCR (20 µL for each platform). Amplification traces (A), standard curve (A inset), melt analysis (B) and tabulated relative fold difference (ΔCq) results (C) for qPCR. The ddPCR amplitude plot (D) and tabulated absolute concentration data (E). NTC: No Template Control; Dilution: Dilution factor of DNA samples; Rep: Replicate number; Avg: Average of the replicates; Std. Dev.: Standard Deviation between the replicates; % CV: % Coefficient of Variance (Std. Dev./Avg*100).

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Effect of consistent sample contamination between identical DNA dilutions for ddPCR and qPCR technologies

Since the most common contaminant in a qPCR reaction is the reverse transcription (RT) mix from which the cDNA is synthesized, reaction mixtures containing a 1/2 dilution series of low concentration synthetic DNA supplemented with either 4 µL or 5 µL of RT mix were split between the qPCR (Fig. 2A–F) and ddPCR (Fig. 2G–J) platforms. For qPCR, the reaction efficiency was approximately 89.6% and 67.1% with 4 µL (Fig. 2B) and 5 µL (Fig. 2D) of RT mix respectively resulting in an approximate 2 Cq shift in the amplification curves (compare Fig. 2A – 30 to 34 Cq with 2 C – 31 to 36 Cq). This correlated to a perceived four-fold reduction in average relative quantity at each DNA dilution with increased RT mix (compare “Avg” between Fig. 2E and F at each dilution).

Effect of consistent sample contamination between identical DNA dilutions for ddPCR and qPCR technologies. Four reactions of 50 µL were prepared in triplicate from 1/2 serial dilutions of synthetic DNA in a background of either 10 µL (A) or 12.5 µL (B) of 1X reverse transcription (RT) mix with primers and ddPCR EvaGreen supermix. Each reaction mix was split for quantification in qPCR and ddPCR (20 µL for each platform containing either 4 µL or 5 µL of contaminating RT mix) and run on a single plate for each platform. Amplification traces (A and C), standard curves (B and D) and tabulated relative fold difference (ΔCq) results (E and F) were generated for qPCR. The ddPCR amplitude plots (G and I) and tabulated absolute concentration data (H and J) from the same reactions supplemented with 4 uL (A,B,E,G,H) and 5 uL (C,D,F,I,J) of RT mix were produced. NTC: No Template Control; Dilution: Dilution factor of DNA samples; Rep: Replicate number; Avg: Average of the replicates; Std. Dev.: Standard Deviation between the replicates; % CV: % Coefficient of Variance (Std. Dev./Avg*100).

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For ddPCR technology, the number of interface droplets between the positive (blue) and negative (black) amplitudes increased with the amount of RT contamination (compare Fig. 1D (none), Fig. 2G (4 µL) and Fig. 2I (5 µL)). However, the average absolute concentration was very similar for each respective dilution (compare “Avg” for the same dilution between Figs 1E, 2H and 2J).

Effect of inconsistent sample contamination for the combined RT-contaminated samples at each DNA dilution for ddPCR and qPCR technologies without normalization

To assess the effect of inconsistent contamination on qPCR and ddPCR, the data from the 4 µL and 5 µL RT-contaminated samples (Fig. 2) were combined at each dilution of synthetic DNA without normalization (Fig. 3). Since all samples for both levels of contamination were quantified from the same plate, the relative quantification method was used to assess the results. The combined results for each dilution of DNA and RT mix were assessed (Fig. 3A (4 µL - blue and 5 µL - red) and Fig. 3B (compare Rep 1 to 3 at each level of RT mix and “%CV” for the combined results). The separation between negative and positive droplets and associated quantitative data for the identical samples were also gathered using ddPCR technology (Fig. 3C and D).

Effect of inconsistent sample contamination for the combined RT-contaminated samples at each DNA dilution for ddPCR and qPCR platforms without normalization. The reproducibility of the data between the combined reactions containing differing amounts of (RT) mix (Fig. 2) was examined for qPCR and ddPCR technologies. Amplification traces (A) and box plot (A inset) from the combined qPCR reactions supplemented with 4 uL and 5 uL of RT mix (blue and red traces and graphed points respectively) and tabulated relative fold difference (ΔCq) was produced (B). The amplitude plot and tabulated absolute concentration data from the combined ddPCR reactions supplemented with 4 uL and 5 uL of RT mix (C) and tabulated absolute concentration data (D) was generated. NTC: No Template Control; Dilution: Dilution factor of DNA samples; Rep: Replicate number; Avg: Average of the replicates; Std. Dev.: Standard Deviation between the replicates; % CV: % Coefficient of Variance (Std. Dev./Avg*100); p-value: based on a Student t-test of the Avg between each dilution.

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Inconsistent contamination levels on ddPCR and qPCR data with reference gene normalization

A consistent amount of synthetic DNA was introduced to reactions under the identical conditions and contaminant levels used for the diluted samples to mimic a reference gene (Fig. 4). The data from four of the replicates (ie: two from each of the 4 µL and 5 µL of RT mix contaminated samples) were combined at each dilution of synthetic DNA for both ddPCR technology without normalization and qPCR with normalization (Fig. 4A and C respectively -4 µL (blue) and 5 µL (red) and Fig. 4B and D). The full cohort of six samples was also quantified using qPCR with normalization (Fig. 4E and F).

Reference gene normalization can improve qPCR precision depending on contaminant levels and number of replicate samples. The reproducibility of the data between the combined reactions at each 1/2 dilution of DNA (Fig. 2) was compared between ddPCR and qPCR technologies with normalization to the same synthetic target amplified from a consistent amount of DNA at each level of RT contaminant. Box plot (A) from four combined ddPCR reactions supplemented with 4 uL and 5 uL of RT mix and tabulated absolute concentration data from the combined ddPCR reactions (B). Box plot (C) from qPCR data generated from the same four reactions as described in A with normalization to the same target at a consistent DNA concentration between the RT contaminated samples (E inset) and tabulated normalized relative fold difference (ΔΔCq) (D). Box plot (E) from qPCR data generated from all six reactions as described in Fig. 2 with normalization to the same target at a consistent DNA concentration between the RT contaminated samples (E inset) and tabulated normalized relative fold difference (ΔΔCq) (F). Blue and Red traces and graphed data points from qPCR and ddPCR reactions supplemented with 4 uL and 5 uL of RT mix respectively. NTC: No Template Control; Dilution: Dilution factor of DNA samples; Rep: Replicate number; Avg: Average of the replicates; Std. Dev.: Standard Deviation between the replicates; % CV: % Coefficient of Variance (Std. Dev./Avg*100); p-value: based on a Student t-test of the Avg between each dilution.

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The reference gene data for qPCR shifted from an average of 28.5 to 30.0 Cq values between the two levels of RT mix contamination corresponding to an approximate 2.8 fold (ie: 2(30–28.5)) or 280% contamination effect on the data (Fig. 4E -inset -4 µL (blue) and 5 µL (red)). Conversely, the ddPCR results showed very little variability between the two levels of contamination for the same samples with average concentrations of 34.0 and 32.0 copies per µL (data not shown) at the two levels of contaminant giving a 5.9% (ie: (1-32/34)*100) difference and hence no significant effect of contamination.

Discussion

qPCR and ddPCR technologies give comparable results for identical samples with low levels of contamination

The data generated from standard curves of 1/2 serially diluted DNA in water gave the expected results with excellent precision, reaction efficiency and similar dynamic range for both ddPCR and qPCR platforms (Fig. 1). Thus, for samples that contain very low or no levels of background contaminants, ddPCR and qPCR technologies give similar performance for absolute and relative quantification. However, in the case of miRNA and mRNA, the components of the reverse transcription (RT) reaction can also act as inhibitors of Taq polymerase, altering reaction efficiency and the associated Cq values to give artifactual qPCR data if not adequately diluted (Fig. 2)18.

Consistent sample contamination within a set of conditions gives comparable data quality between qPCR and ddPCR platforms

Since qPCR data are calculated from Cq values that are measured from individual amplification curves, any changes in the chemical and/or protein contaminants altering Taq polymerase enzymatic activity and primer binding will change the Cq values between the samples independent of the absolute amount of DNA18,19,20. The most susceptible samples include those with high levels of impurities and low target concentrations because these samples cannot be adequately diluted to eliminate the effect of contamination on data quality1, 21.

The effect of the contamination was evidenced by the poor separation between the amplification curves and reaction efficiency well below 90% for the 5 µL of RT contaminant (Fig. 2C and D) resulting in an approximate 2 Cq shift at each dilution between the two levels of RT mix (compare Fig. 2A and B with 2C and D). Although the 1/2 dilution series from 4 µL of RT contaminant produced the predicted 2-fold reduction in average relative quantity with good precision (Fig. 2E - “Fold” and “% CV”) there was generally higher variability and less precision at the 5 µL level of contaminant particularly at the 1/16 dilution which decreased by 3-fold from the 1/8 dilution. In contrast, the ddPCR platform gave a consistent absolute concentration for each dilution of DNA between the two levels of RT mix contaminant with good precision and the expected 2-fold difference (compare Fig. 2H and J-“Avg”, “% CV” and “Fold”).

Thus, presuming a given set of samples contains the same level of background contamination, qPCR and ddPCR technologies produce similar results for gene expression even when contaminant levels are high enough to affect reaction efficiency. However, there are frequently subtle differences in the level of impurities between samples even under highly rigorous RNA extraction procedures that can alter Taq activity, imparting gross variability on the resulting data18, 19.

Variable contamination between samples gives high variability and inconsistent data for qPCR whereas ddPCR results remain consistent and reproducible

Combining the data from Fig. 2 gave six replicate samples for each dilution containing the identical amount of DNA but variable levels of RT mix contaminant (Fig. 3). For qPCR, the amplification curves and resulting Cq values were highly variable within each dilution between the 4 µL and 5 µL of RT contaminant (Fig. 3A - 4 µL (blue) and 5 µL (red)). Thus, the relative quantity between each replicate differed by approximately 4-fold giving two distinct clusters of triplicate data points at each dilution (Fig. 3A inset -(4 µL (blue) and 5 µL (red)) and Fig. 3B - compare each of the three replicates “Rep” for each dilution between the RT contaminant levels). Although the average relative quantity (Fig. 3B - “Avg”) gave a consistent 2-fold difference between each 1/2 dilution (“Fold”), the variability was very high ranging from 60% to 87% (“% CV”) with no statistically significant differences between each dilution for qPCR (“p-value” ranging from 0.0831 to 0.1492 and log transformed from 0.0801 to 0.1841).

For ddPCR technology, the combined results at the two levels of contaminant gave consistent separation between negative and positive droplets and minimal interface droplets at each DNA dilution (Fig. 3C). The absolute concentrations were highly reproducible between the replicates for each DNA and contaminant level (Fig. 3C - “Rep”) with an average concentration (“Avg”) that was precisely decreasing by 2-fold (“Fold”) as expected. More importantly, the variance between the replicates was much lower than with qPCR (between 7% and 30% for ddPCR technology versus 60% to 87% for qPCR (compare Fig. 3B and D - “% CV”) with strong statistically significant differences (Fig. 3D - p-value < 0.0002) between each dilution.

Reference gene normalization can improve qPCR precision depending on contaminant levels and number of replicate samples

In order to mimic the perfect reference gene, the same synthetic DNA sample used for the 1/2 dilutions throughout this study was introduced at the same concentration into reaction mixtures containing the two different levels of RT mix on the same plate as the 1/2 dilutions. Since many labs performing cell-based assays use smaller sample sets (<5 biological replicates), a subset of four samples per DNA dilution consisting of the first two replicates (ie: Fig. 3B and D - Rep 1 and 2) from each of the 4 µL and 5 µL levels of RT-mix contaminant were analyzed (Fig. 4A–D). The ddPCR absolute concentration data remained accurate and precise without normalization as shown by the tight clustering of the four data points around each dilution (Fig. 4A - 4 µL (blue) and 5 µL (red)) with a precise 1/2 fold decrease, low % CV and high statistically significant differences (p-value ≤ 0.0024) (Fig. 4B). However, the normalized relative qPCR results from the identical four samples gave variable clustering at each dilution (Fig. 4C -4 µL (blue) and 5 µL (red)) with no statistically significant differences between 1/4 and 1/8 DNA dilutions (p-value of 0.0961) and between the 1/2 and 1/4 and 1/8 dilutions with log transformed data (p-value of 0.0572 and 0.1029 respectively) (Fig. 4D).

When the full set of three replicates from each DNA dilution and RT-mix background (ie: six samples total per dilution) were tabulated, the normalized relative expression values between the different levels of RT-mix were much tighter (varying by about 1.5-fold between the two levels of contaminant) than those from the non-normalized data which varied by about 4-fold (compare Fig. 3A inset with 4E (4 µL (blue) and 5 µL (red)) and Fig. 4F with 3B - “Rep” within each dilution). Furthermore, the normalized data for qPCR gave more precise and statistically significant data for the average of the six replicates between each dilution for the identical samples (compare Fig. 3B with 4F - “% CV” and “p-value”). This underlines the effect of reference gene normalization where the 2.8 fold variance in reference gene expression between the two levels of contaminant (Fig. 4E - inset - 4 µL (blue) and 5 µL (red)) effectively normalized the 4-fold (non-normalized data – Fig. 3A and B) to a much tighter 1.5 fold (Fig. 4E and F).

Although the p-values obtained for qPCR with normalization were below 0.05 for the six samples (Fig. 4F), the preference by many labs is to achieve higher levels of reproducibility such that the reported p-values are at or below 0.00122. The qPCR results could not meet this more stringent criterion even with normalization for the small sample set (Fig. 4D – all p-values > 0.01) and for the large sample set (Fig. 4F – most p-values > 0.008). Whereas the ddPCR data for both small and large sample sets gave p-values either well below or close to 0.001 for all dilutions without normalization (Figs 3D and 4B).

Reference gene normalization was not required in this experiment for ddPCR technology because there was virtually no effect of contamination and a measured amount of DNA was applied to each sample (see results section) but this does not mean that reference genes are never required for this application. The protein and chemical contaminants that vary between RNA extracts from biological samples can not only partially inhibit the qPCR reaction but also the reverse transcriptase leading to variable levels of input cDNA in the ddPCR reaction23. Hence, reference gene normalization is always recommended for gene expression analysis with ddPCR technology.

Criteria for selecting between qPCR and ddPCR platforms for absolute quantification, gene expression or miRNA analysis

Given the large diversity of samples used in life sciences, some general criteria can be applied to selecting the appropriate technology for gene expression analysis as follows:

  1. (1)

    Always begin a study by testing each primer pair in a 1/10 diluted, pooled cDNA or gDNA sample (using individual samples from each treatment group in the study set) in duplicate with qPCR under optimized thermocycling conditions and a no template control for each target.

  2. (2)

    Presuming good, sigmoidal amplification curves are obtained with single melt curve peaks for each primer pair, run the duplicate samples from the qPCR reactions on a gel to assess the molecular weight and identity of the amplicon and sequence if necessary.

  3. (3)

    All targets must then be assessed for reaction efficiency with a standard curve using the same pooled gDNA or cDNA sample from point 1 serially diluted based on the Cq value obtained from point 2 starting from the concentrated sample plus seven dilutions7.

  4. (4)

    Any target that passes point 2 but fails point 3 should be ported to ddPCR technology for optimization13. Individual samples for any target that passes points 2 and 3 should be diluted appropriately according to the associated standard curves7 and quantified using qPCR but if the resulting data give high variability and poor p-values, these targets may also be good candidates for ddPCR technology but should also first be optimized13.

  5. (5)

    Primers that fail optimization using ddPCR technology should either be redesigned or the experimental design and samples should be re-assessed and undergo troubleshooting7.

qPCR is an excellent tool for the detection of 2 fold or greater expression differences with good statistical significance above about 100 copies (<30 Cqs) in applications such as gene expression and/or miRNA analysis and absolute DNA quantitation. Although reference gene normalization serves the important purpose of correcting for differential loading of cDNA between samples, it may not reduce contaminant dependent data variability observed with qPCR (Figs 3 and 4) which can only be addressed through sample dilution7. Thus quantification of low expressing/abundant targets using qPCR is challenging because sample dilution is not possible to adequately minimize the effect of contaminants while maintaining quantifiable levels of cDNA. ddPCR technology permits quantification of these samples with excellent precision.

Methods

Primers, template and Reverse Transcription Mix

Primers and template were sourced from the EvaGreen ddPCR™ Demonstration Kit (Bio-Rad: 186–4029). Primers were diluted according to the kit instructions and template was diluted by approximately 400 fold to achieve the starting concentration used in each experiment. The iScript™ Reverse Transcription (RT) Supermix, 100 × 20 µl rxns (Bio-Rad: 170–8841) was diluted to 1X with corresponding volumes transferred to each reaction (see Results and Figures).

ddPCR and qPCR reactions

50 µL reaction mixtures containing RT mix, primers, template and QX200™ ddPCR™ EvaGreen Supermix (Bio-Rad: 186–4034) were divided 20 µL each between ddPCR (QX200 Droplet Digital PCR (ddPCR™) System – Bio-Rad) and qPCR (The CFX96™ Touch System – Bio-Rad) platforms for quantification. For ddPCR technology, droplet generation and transfer of emulsified samples to PCR plates was performed according to manufacturer’s instructions (Instruction Manual, QX200™ Droplet Generator – Bio-Rad).

The cycling protocol was identical for both platforms with a 95 °C enzyme activation step for 5 minutes followed by 40 cycles of a two-step cycling protocol (95 °C for 30 seconds and 60 °C for 1 minute). For ddPCR technology, the ramp rate between these steps was slowed to 2 °C/second while for qPCR, the maximum ramp rate was employed. Post cycling, a standard melt curve protocol was applied for qPCR (a single step of 94 °C for 10 seconds followed by a melt curve from 65 °C to 95 °C with a plate read at 0.5 °C increments after a dwell time of 5 seconds at each temperature). For ddPCR technology the post-cycling protocol was in accordance with the kit instructions (Bio-Rad – 186–4034).

ddPCR and qPCR plate set up and data processing

Each reaction mixture (see previous section) was split between one plate each for ddPCR (ddPCR™ 96-Well Plates: Bio-Rad - 12001925) and qPCR (Hard-Shell® 96-Well PCR Plate: Bio-Rad - HSP9601). The qPCR plate was sealed (Microseal® ‘B’ PCR Plate Sealing Film: Bio-Rad - MSB1001) and run in qPCR. The ddPCR plate was sealed with a foil heat seal (Bio-Rad - 181–4040) and the PX1™ PCR Plate Sealer (Bio-Rad - 181–4000).

For qPCR, the relative quantity and normalized expression data were processed using CFX Manager (v.3.1). For ddPCR technology, the absolute quantity of DNA per sample (copies/µL) was processed using QuantaSoft (v.1.7.4). For both ddPCR and qPCR, the data were exported to Microsoft EXCEL for further statistical analysis using the Analyze IT plugin (Analyse-it for Microsoft Excel (v.2.20) Analyse-it Software, Ltd. http://www.analyse-it.com/; 2009).

Data and Statistical Analysis

All statistically analyzed data conformed to a normal distribution as determined using a Shapiro-Wilk test. A Student’s t test was then used to assess the statistical significance between the two-fold dilutions of DNA for each experiment. The %CV (Standard Deviation/Mean *100) was also used to assess variability. Finally, since it is generally accepted that qPCR derived gene expression data are log transformed for statistical analysis24, the log transformed normalized gene expression and relative quantity data from each biological replicate were also normality tested (Shapiro-Wilk) and then Student’s t tested for statistical significance.

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Author information

Author notes
  1. Sean C. Taylor and Genevieve Laperriere contributed equally to this work.

Affiliations

  1. Bio-Rad Laboratories, Inc., Hercules, CA, 94547, USA

    Sean C. Taylor

  2. Department of Chemistry, Biochemistry and Physics, Université du Québec à Trois-Rivières, 3351 boul. des Forges, Trois-Rivières, QC, G9A 5H7, Canada

    Genevieve Laperriere & Hugo Germain

Contributions

The authors have made significant contributions to this work. S.C.T. wrote the main manuscript text and all authors reviewed the manuscript.

Corresponding author

Correspondence to Sean C. Taylor.

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Taylor, S.C., Laperriere, G. & Germain, H. Droplet Digital PCR versus qPCR for gene expression analysis with low abundant targets: from variable nonsense to publication quality data. Sci Rep7, 2409 (2017). https://doi.org/10.1038/s41598-017-02217-x

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Ddpcr rt

Development of a reverse transcription droplet digital PCR (RT-ddPCR) assay for sensitive detection of simian immunodeficiency virus (SIV)

Virology Journalvolume 18, Article number: 35 (2021) Cite this article

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Abstract

Background

Simian immunodeficiency virus (SIV)-infected rhesus macaques constitute an excellent model of human HIV infection. Sensitive detection of SIV RNA in cell and tissue samples from infected animals subjected to treatment regimens becomes especially critical in determining which therapeutic attempts are successful, and consequently, which interventions should be prioritized in HIV cure research.

Results

In this report, we describe the design and testing of a Raindance ddPCR platform-based, sensitive SIV reverse transcription droplet digital PCR (RT-ddPCR) assay by exploring the combinations of various priming conditions and reverse transcriptases, and testing one-step vs. two-step procedures, to eliminate background signal(s) and enable detection and quantification of low level target signals.

Conclusions

Similar reaction conditions and assay validation procedures can be explored for potential development of additional assays for other applications that require sensitive detection of low-level targets in RNA samples.

Background

Quantitative reverse transcription PCR (qRT-PCR) has established itself to be the benchmark for RNA target detection and quantification. The technology yields accurate quantitative data, and significantly simplifies quality control and assay standardization. One of the most prevalent applications of qRT-PCR is for the detection of viral loads [1,2,3], in which qRT-PCR-enabled data on various causative infectious agents [4] have helped to study disease processes and delineate connections between unique viral sequences and clinical signs and symptoms [5]. Various qRT-PCR assays have been developed to detect and quantify negative-strand RNA viruses (including the measles and mumps viruses, and various viruses that target the respiratory tract) [6, 7], positive-strand RNA viruses (including rhino-, entero- and coronaviruses such as SARS-CoV-2) [8,9,10], double-stranded RNA viruses (such as human rotaviruses) [11], and retroviruses (HIV, HTLV, and related viruses in animal models, such as simian immunodeficiency virus (SIV)) [12,13,14,15,16]. In most cases, qRT-PCR assays are more sensitive than traditional methods such as viral culture [17], and have led to a significant increase in the accuracy and clinical relevance of patient testing. Several assays have been critical for identifying, isolating and treating patients and defining the viral epidemiology during some of the recent and ongoing pandemics [18, 19].

Despite its tremendous utility, qRT-PCR has several limitations. First, quantitation relies on external reference/standard material, and independent, accurate determination of the reference/standard is critical to successful quantitation by qRT-PCR. Second, extreme sequence heterogeneity exists in some viruses, which can interfere with primer and/or probe binding to target sequences and consequently lead to under-quantification and even non-recognition of targets with significant sequence divergence such as newly identified subtypes and clades. Template sequence heterogeneity also often limits the design of qRT-PCR assays, which requires placement of primer and probe sequences in highly conserved regions. Third, in some diseases, tissue sites have been found to serve as better predictors of disease outcome and indicators of treatment efficacy [20], highlighting the value of sensitive detection of viruses in tissues from infected individuals or animal models. However, nucleic acid extracted from tissues can present challenges during qRT-PCR quantification due to co-purified inhibitors and significant amounts of background nucleic acid (i.e. compared to nucleic acid extracted from plasma), which can both contribute to quantitation inhibition. Consequently, sensitivity limitations of many tissue qRT-PCR assays derive mainly from how much input nucleic acid is allowed in each reaction before inhibition occurs.

Digital PCR conceptually takes a different approach to measure the number of target nucleic acid molecules in a sample. Instead of relying on PCR threshold cycle (Ct) values and standard curves as in real-time PCR (qPCR), digital PCR partitions reactants in each PCR reaction into up to millions of mini-reactions. These reactions are thermocycled to PCR endpoint, the numbers of positive and negative reactions are scored, and the target copy number in the original sample is calculated based on Poisson statistics. Compared to qPCR, digital PCR allows direct absolute quantitation of analyte without the need for external standards or calibration curves, and is therefore not influenced by inaccuracy during reference/standard quantitation. As digital PCR quantitation relies on detecting end point PCR products, this method is less susceptible than Ct-dependent qRT-PCR to inefficient amplification, which can occur due to primer and/or probe mismatches caused by sequence heterogeneity, or inhibitors in samples. Additional advantages include higher quantification precision, especially at lower target template copy numbers, as well as greater multiplexing ability because of digital PCR’s unique amplitude or ratio-based higher order multiplexing [21]. Digital PCR has also been widely used in virus/pathogen analysis [22, 23], in addition to cancer mutation detection [24,25,26,27], GMO screening [28], gene and miRNA expression testing, copy number variation (CNV) determination [29,30,31], as well as nucleic acid reference standard and NGS library quantification [32].

Traditionally, digital PCR presented two limitations compared to qPCR. (1) Limited dynamic range. In digital PCR, dynamic range by definition is determined by partition number that is available for each sample, as ideally each reaction compartment contains at most one target molecule. On chip- and array-based platforms (i.e. Fluidigm BioMark HD, QuantStudio 3D Digital and JN MedSys Clarity and Clarity Plus) partition number per sample is in the range of 10,000 to 45,000, while on a couple of oil emulsion droplet digital (i.e. ddPCR) platforms (i.e. Stilla Naica System and BioRad QX100/200 instruments), partition number per sample is on the order of 20,000 to 30,000. These relatively low partition numbers consequently necessitate dilution of many input samples to achieve accurate measurements [33]. The Raindance ddPCR platform, which is used in the current report, partitions each sample into 10 million droplets (i.e. 6 log dynamic range), making the platform compare favorably to the quantification dynamic range achieved on qPCR systems. (2) Limited nucleic acid input in each reaction. Overloading each reaction with nucleic acids above certain threshold amount in some ddPCR platforms [34] was reported to cause significant droplet deformation, decline of droplet number, and quantitation inhibition (i.e. fewer target-containing droplets reach the required fluorescent intensity at the end of thermocycling). We recently reported [22] that on the Raindance ddPCR platform, at least 8 million mammalian cell equivalent genome can be included in each reaction without causing droplets integrity or numbers to drop. In addition, 4 million mammalian cell equivalent genome was included in each reaction without introducing viral target quantitation inhibition. The Raindance ddPCR platform, therefore, drastically improves the nucleic acid input quantity in each reaction.

Simian immunodeficiency virus (SIV)-infected rhesus macaques constitute an excellent model of human HIV infection [35,36,37,38,39,40,41]. Many aspects of infected monkeys such as viral infection, pathogenesis and response to cure strategies closely mirror those of infected humans. Sensitive detection of SIV RNA in cell and tissue samples from infected animals subjected to treatment regimens becomes especially critical in determining which attempts are successful, and consequently, which interventions should be prioritized. In the current report, we describe the development and validation of a SIV RT-ddPCR assay through exploration of combinations of various priming conditions and reverse transcriptases, and testing one-step vs. two-step procedures. The SIV RT-ddPCR assay described here is able to detect low level (i.e. single digit level) viral nucleic acid [22], making it ideally suited for the detection of rare events (such as in the context of antiretroviral therapy in HIV cure studies). Similar reaction conditions and assay validation procedures can be explored for potential development of additional assays for applications that require sensitive detection of low quantity target(s) from RNA samples derived from cell or tissue sources.

Results

One-step RT-ddPCR

In the process of developing a ddPCR assay for quantifying SIV RNA, we first tested the option of one-step reverse transcription ddPCR (RT-ddPCR). The “one-step” nomenclature by definition refers to the RT step being performed in the same reaction compartment (i.e. tube, microwell, or droplet) as the PCR step. One-step qRT-PCR has been commonly used in gene quantitation due to advantages such as: (1) Simpler workflow, during which no transfer or procedural manipulation is required once the RT step is initiated, which reduces both risk of contamination and sample-to-sample variability due to reduced number of handling steps. (2) Reduced 3′ and 5′ biases introduced by random oligomers and oligo-dT primers. (3) Single RNA molecules being converted to cDNA without competition and entire cDNA sample being used as template for the PCR step. Both can contribute to enhanced detection sensitivity. (4) Automation potential. The fast and simple procedure allows rapid processing of multiple samples and enables easy automation.

For one-step RT-ddPCR, the SuperScript III One-Step RT-PCR System with Platinum Taq DNA Polymerase was used to perform both the cDNA synthesis and PCR amplification steps. More specifically, the one-step reaction mastermix was combined with the enzyme mix, gene-specific primers, probe(s), test or control RNA sample or reference standard, and droplet stabilizer. The mixture was dropletized, and incubated on a thermocycler with adjustable ramp speed (a slower ramp speed was required for ddPCR as it benefits equilibrating temperature exposure across the droplet population because heat transfers more slowly in emulsified samples than in bulk PCR reaction). The end-point PCR products were then analyzed on the Raindance Sense instrument for droplet counts and fluorescent intensity reading. The data thus generated were in turn analyzed with the RainDrop Analyst software to generate graph and statistical data.

In previous ddPCR tests [22; also see below], assays incorporating minor groove binder (MGB) modified detection probes gave the tightest clusters and clean background in target area (i.e. where positive droplets are located on the plotting space), and were therefore adopted for the PCR stage of the one-step procedure. Four different assay/sample combinations were tested, including SIV assay plus SIV standard spike (Fig. 1a, b), SIV assay plus SIV standard spiked in Rhesus macaque RNA background (Fig. 1c, d), SIV and CCR5 assays plus SIV standard spike (Fig. 1e, f), and SIV and CCR5 assays plus SIV standard spiked in Rhesus macaque RNA background (Fig. 1g, h). One main issue associated with the one-step procedure was background signals in target region when there was no target input, which prevented the utility of the one-step procedure in quantifying low level viruses.

One step RT-ddPCR test. One step RT-ddPCR was performed as described in “Materials and Methods” using the SuperScript III One-Step RT-PCR System with Platinum Taq DNA Polymerase. Assay/sample combinations tested were: SIV single plex assay with buffer (a) or SIV RNA standard spike (b), SIV single plex assay with 1 μg Rhesus macaque RNA background (c) or SIV RNA standard spiked in 1 μg Rhesus macaque RNA background (d), SIV and CCR5 duplex assay with buffer (e) or SIV RNA standard spike (f), and SIV and CCR5 duplex assay with 1 μg Rhesus macaque RNA background (g) or with SIV RNA standard spiked in 1 μg Rhesus macaque RNA background (h). Detailed experimental conditions are listed in Table 1. Note that in all panels there were background signals in putative SIV target signal region. Quantitation was not done due to background signals

Full size image

Full size table

Two-step RT-ddPCR

We then tested the two-step RT-ddPCR option. This method separates the RT step and the PCR step in two different reaction vessels. The advantages associated with two-step RT-PCR are: (1) The RT and PCR steps being performed separately, allowing both steps to be optimized to ensure efficient and accurate amplification, i.e. added flexibility regarding choice of primers/priming methods and a wide variety of RT and PCR enzymes. (2) Use of oligo-dT primers or random oligomers for reverse transcription enabling cDNA from a single reverse transcription to be used in several PCRs for analysis of multiple targets, and increased sensitivity for some target templates. (3) Precious RNA samples being transcribed into more stable cDNA without delay for long-term storage and later use.

For two-step RT-ddPCR, a reverse transcriptase combined with a priming method was used to perform the cDNA synthesis step in bulk. The RT enzyme was mixed with other reaction components, including dNTPs, buffer components, random hexamers or gene-specific primer, and test or control RNA sample or reference standard. The mixture was incubated allowing the RT reaction to complete, and then supplemented with components required for the PCR step (including TaqMan genotyping mastermix, primers and MGB-based probe(s)) and the droplet stabilizer. (For the ddPCR step, we previously compared various probe systems and their combinations with different mastermix conditions. MGB probe assays in TaqMan genotyping mastermix, among all the combinations tested, gave the best result in that the signal cluster was tight, there was no background signal in target signal region for no template controls, and ddPCR reads agreed well with input template quantity [22]) This final mixture was dropletized, and incubated in a thermocycler at a ramp speed of 0.5 °C/s. The end-point PCR products were then analyzed on the Raindance Sense instruments for droplet counts and fluorescent intensity reading, and data analyzed with the RainDrop Analyst software to generate graphical and statistical data.

We tested different cDNA cocktail combinations involving M-MLV and SSIII as RT enzymes, random hexamers and sequence specific-primer as priming methods using a low SIV RNA template input range (i.e. 10 copies of SIV RNA standard), with a major emphasis of identifying conditions that would enable detection of low-levels of viral RNA, typical of those encountered in subjects on prolonged cART suppression. We found that 200U M-MLV and gene specific priming performed well as the target signal background was clean, there was distinct target positive signal cluster, and the ddPCR reading agreed with template input (Fig. 2a, b; Additional File 1: Fig. 1 and Table 1). 200U of SSIII in each RT reaction gave a similar ddPCR readout after the PCR step, however, there was a low background in the SIV target signal region of no template control reactions (Fig. 2c, d). Random hexamers, combined with M-MLV or SSIII (200U in each reaction) yielded significantly fewer ddPCR signal counts compared to template input (Fig. 2e–h), with background signals continuing to be an issue when SSIII was used as the RT enzyme. The background signal issue persisted when the quantity of SSIII was reduced to 20U in each RT reaction, regardless of the priming methods used (Fig. 3).

Two step RT-ddPCR test. The following condition combinations were tested during the reverse transcription step of RT-ddPCR: M-MLV RT with gene specific priming (a background; b SIV RNA standard spike); SSIII RT with gene specific priming (c background; d SIV RNA standard spike); M-MLV RT with random hexamer priming (e background; f SIV RNA standard spike); SSIII RT with random hexamer priming (g background; h SIV RNA standard spike). Each reaction contained 1 μg Rhesus macaque RNA background. The ddPCR step of all reactions was performed with SIV and CCR5 duplex MGB probe assays. Detailed experimental conditions are listed in Table 1

Full size image

Two step RT-ddPCR test with reduced SSIII RT amount. SSIII RT enzyme was reduced to 20 U in each RT reaction. The condition combinations were: SSIII RT with gene specific priming (a buffer background; b SIV RNA standard spike); SSIII RT with random hexamer priming (c buffer background; d SIV RNA standard spike). The ddPCR step of all reactions was performed with MGB probe assays. Detailed experimental conditions are listed in Table 1. Note that in control reactions (a, c) there were background signals in putative SIV target signal region. Quantitation was not done due to background signals. Random hexamer priming yielded significantly fewer ddPCR signal counts compared to gene-specific priming (compare d to b)

Full size image

Superscript IV (SSIV) test

We proceeded to test another reverse transcriptase, SSIV, in the two-step procedure, as it was previously shown to overcome reaction inhibition from sources such as alcohols, salts, detergents, heparin, hematin, bile salts, formalin, which are typically found in sample preparation reagents, cells and tissues and FFPE samples. This reverse transcriptase can potentially benefit RNA quantification analysis of samples originating from a variety of sources, especially cells and tissues. We showed that SSIV was compatible with the two-step RT-ddPCR protocol and low copy viral signal was successfully identified without background issues when SSIV was used as the reverse transcriptase in the procedure (Fig. 4a, b).

SSIV test. The condition combination was SSIV with gene specific priming (a 1 μg Rhesus macaque RNA background; b SIV RNA standard spiked in 1 μg Rhesus macaque RNA background). The ddPCR step of both reactions was performed with MGB probe assays. Detailed experimental conditions are listed in Table 1

Full size image

Discussions

In this report we described the design and testing of a SIV RT-ddPCR assay through testing combinations of different priming conditions, reverse transcriptases, and various procedures, to eliminate background signal(s) and ensure detection and quantification of low-level target signals. We recently reported that the validated assay can detect single digit level SIV RNA in each reaction [22], making it ideally suited for applications involving detection of rare events such as required in many HIV reservoir/cure studies in which viral nucleic acid level is tremendously suppressed. This RT-ddPCR assay was also subjected to additional validation and testing [22]. The linear dynamic range of the assay was at least up to 1 million copies (test upper limit) of viral nucleic acid per reaction [22]. The lower limit of detection for the assay under the M-MLV and gene-specific priming combination condition was determined to be 7 copies per reaction, and the sensitivity and specificity of the assay were determined to be 93% and 100%, respectively.

The reverse transcription step, in which RNA is converted to a DNA template by a reverse transcriptase, can potentially introduce variability and ambiguity through different factors (such as RNA quality and inhibitors) to the final quantitation data in RT-PCR procedures. Endogenous, copurified inhibitors and reagents introduced during sample procurement or extraction often present issues as they can inhibit the reverse transcription step, the PCR step, or both. We recently described using the SuperScript IV reverse transcriptase to overcome severe inhibition at the reverse transcription step. Combining this with Raindance ddPCR system at the PCR step could allow quantifying the SIV viral target in RNA samples that demonstrated more than 99.99% inhibition in qRT-PCR procedure [22]. Therefore, combining a high processivity RT with Raindance ddPCR can potentially expand the repertoire of analyzable tissue RNA samples without the need to remove inhibitors, which is especially important in scenarios where the identity of the inhibitor is unknown.

Different priming methods at the reverse transcription step can lead to different cDNA yield and priming specificity. For example, random priming gives the highest cDNA yield due to the fact that priming initiates from multiple points along the template. However, the majority of cDNA generated through random priming will be from ribosomal RNA and may compete with low level targets. Target/gene-specific priming leads to the most specific cDNA. Consistent with this, we observed that when gene-specific priming was used in the 2-step procedure, there was general agreement between the ddPCR reading and signal input, while random hexamer priming usually yielded significantly fewer ddPCR signal counts compared to template input. It therefore appears that for HIV cure/reservoir studies where the viral detection target signals are at low levels, gene-specific priming should be the preferred priming method during RT-ddPCR analysis.

Quantitation of the SIV RNA reference standard used in this study was based on A260 measurements and the calculated extinction coefficient for the transcript sequence, and validation by terminal dilution qRT-PCR. In Fig. 2b, d, f and h, 10 copies (based on prior quantification) of SIV RNA template were used as input in each reaction. Based on ddPCR results in Fig. 2b, f, the prior quantification under-quantifies the SIV RNA template by about 29% (10 copies vs. 14 copies), assuming the conditions in 2b and 2f allow detecting all target signals present, although another possible contributing factor is the serial dilution step. This highlights the vulnerability of qRT-PCR analysis to inaccuracies introduced during steps such as the quantification and serial dilution of the external calibrator molecules. Nevertheless, valid comparisons can still be made regarding the relative performance of the assays under various RT-ddPCR conditions by comparing the ddPCR signal counts obtained under these conditions.

Conclusions

Combining gene-specific priming with suitable reverse transcriptases, and using the Raindance ddPCR platform at the signal detection step allowed development of a sensitive SIV RT-ddPCR assay that has many potential applications of interest to HIV reservoir/cure studies. Similar conditions can be explored on the Raindance ddPCR system to enable potential development and validation of additional assays for applications that require sensitive detection of low amount target(s).

Methods

RNA extraction and qPCR quantification of SIV viral RNA

RNA isolation and qRT-PCR quantification of tissue-derived SIV RNA followed procedures and conditions as described previously [42,43,44,45] and are briefly described here.

Small specimens (i.e. cell pellets or tissue specimens of ≤ 200 mg) were homogenized in 1 mL of TriReagent (Molecular Research Center) in 2 mL homogenization tubes (P000918-LYSK0-A, Bertin Instruments) containing ceramic (zirconium oxide) beads as grinding material using a Precellys tissue homogenizer (Bertin Instruments) according to the reagent manufacturer’s recommendations. Tissue specimens of 200 mg to 2 g quantity range, or 400 mg to 4 g quantity range, were processed in 7 mL (P000935-LYSK0-A, Bertin Instruments) or 15 mL (P000947-LYSK0-A, Bertin Instruments) homogenization tubes containing ceramic (zirconium oxide) beads as grinding material, respectively. For large tissue samples, total RNA was prepared from 1 mL of TriReagent suspension, with residual suspension being archived at − 80 °C for potential additional analysis. All ensuing procedures are based on 1 mL TriReagent homogenate.

RNA isolation from the homogenate included the following steps: (1) Phase separation. The homogenate was stored for 5 min at room temperature to allow complete dissociation of nucleoprotein complexes. The homogenate was spun at 13,000g for 1 min, and the top lipid layer removed with a pipette. For phase separation, 0.1 mL 1-bromo-3-chloropropane (BCP) was added to the homogenate. The sample was vortexed vigorously for 15 s. The resulting mixture was incubated at room temperature for 15 min and centrifuged at 14,000g for 15 min at 4 °C. Following centrifugation, RNA remained exclusively in the colorless upper aqueous phase. (2) RNA precipitation and wash. The aqueous phase was transferred to a fresh tube containing 240 µg glycogen (Roche 34990920). 0.5 mL of isopropanol was added to the aqueous phase. The mixture was vortexed for 5 s, incubated at room temperature for 5–10 min then centrifuged at 21,000g for 10 min at 25 °C. The supernatant was removed and the RNA pellet was washed by vortexing with 0.5 mL of 70% ethanol. The RNA pellet was stored at − 20 °C in ethanol overnight, and was washed a second time with 0.5 mL 70% ethanol. The ethanol wash was then decanted and the RNA pellet allowed to air-dry for 5 min. Recovered RNA was dissolved in 240 µL of 10 mM Tris, pH 8.0 for replicate testing in qRT-PCR protocol.

For qRT-PCR quantification of SIV in tissue-derived RNA, reaction conditions and thermal profiles followed those for the plasma and isolated cell assays as described previously [43, 44] with two exceptions: (1) In the reverse transcription step, the ‘nested’ reverse primer (SIVnestR01) [46], as opposed to random hexamers, was used to prime cDNA synthesis specifically for SIV sequence to avoid generation of non-specific targets and enhance the sensitivity of detection of SIV; (2) 1.25 units of PlatinumTaq (Invitrogen), rather than TaqGold, were used in the amplification steps. For RNA determination, 12 (10 + 2 format) or 6 (5 + 1 format) replicate reactions were tested per sample including a spike of RNA internal control sequence standard [47] (1000 copies per reaction) in two of the 12 reactions (10 + 2 format) or one of the 6 reactions (5 + 1 format) to assess overall amplification efficiency and potential inhibition of the PCR.

Reverse transcriptases

Three reverse transcriptases were tested in this study. These include: (1) Moloney Murine Leukemia Virus Reverse Transcriptase (M-MLV) (28025013, ThermoFisher Scientific), a recombinant DNA polymerase that lacks DNA endonuclease activity and has a lower RNase H activity. M-MLV has an optimal activity at 37 °C. (2) SuperScript III Reverse Transcriptase (SSIII) (18080093, ThermoFisher Scientific), generated by introducing several mutations into M-MLV to further reduce RNase H activity and increase half-life. SSIII has an optimal activity at 50 °C. Compared to M-MLV, SuperScript III reverse transcriptase was found to produce higher cDNA yields, improved cDNA lengths and enhanced efficiency on GC-rich target RNAs. (3) SuperScript IV Reverse Transcriptase (SSIV) (18090010, ThermoFisher Scientific), an enzyme developed particularly for challenging samples such as poorly purified RNA that contains inhibitors, RNA from formalin-fixed, paraffin-embedded (FFPE) samples, and unpurified RNA. The enzyme demonstrates low variability especially at low amount of input RNA and has the highest thermostability (100% activity up to 56 °C and 90% activity at 60 °C) among the 3 reverse transcriptases.

One-step RT-ddPCR

For one-step RT-ddPCR, the following RT reaction was prepared in a volume of 50 µL (all concentrations indicate final concentration): 25 µL of 2× reaction buffer and 2 µL of SuperScript III RT/Platinum Taq Mix from the SuperScript III One-Step RT-PCR System with Platinum Taq DNA Polymerase (12574018, ThermoFisher Scientific, Waltham, MA), SIVnestR01 (2 µM), SGag ddPCR forward and reverse primers (600 nM each), SGag ddPCR probe (200 nM) [22, 48], RNA sample, reference standard or buffer, 1xddPCR stabilizer (Raindance) and H2O. For duplex SIV and CCR5 one-step RT-ddPCR, the mixture also contains RCCR5 forward and reverse primers (600 nM each) [22, 48] and RCCR5ProbeMGB (200 nM) [22, 48].

The mixture was then dropletized on Raindance source instrument according to the manufacturer’s instructions. Droplet integrity was monitored by visually examining a portion of the droplets in each lane as they moved through the device during dropletization. Total droplet count data for each sample after dropletization was retrieved from the Source instrument as an independent measure of droplet generation success.

One step RT-ddPCR thermocycling was performed on a Bio-Rad C1000 Touch Thermal Cycler with the following PCR cycling conditions: 50 °C 30 min, 94 °C 7 min (these constitute the RT segments) followed by 38× (94 °C 14 s, 60 °C 30 s, 68 °C 30 s, 94 °C 1 s), 94 °C 14 s, 60 °C 30 s, 68 °C 5 min 30 s, 98 °C 10 min, 4 °C hold.

After thermocycling, droplet fluorescence detection was performed on the RainDrop Sense instrument following the manufacturer’s instruction. At the end of Sense instrument reading, total droplet count data for each sample was retrieved from the Sense instrument.

Two-step RT-ddPCR

The RT step of the two-step RT-ddPCR was performed in a total volume of 15 µL composed of the following: 5 mM MgCl2, 500 nM of each dNTP, 1 mM DTT, 2 µM of SIVNestR01 [46] or 5 µg random hexamers (ThermoFisher), 1× PCR II buffer (ThermoFisher) with 0.2% Tween, 10 U RNaseOUT, M-MLV, SSIII or SSIV (ThermoFisher) reverse transcriptase (amount varies), RNA sample (or reference standard or buffer) and H2O. The PCR thermocycling programs for the RT step are: For M-MLV: 25 °C 15 min; 37 °C 60 min; 90 °C 30 min; 25 °C 30 min; 4 °C hold. For SSIII: 25 °C 15 min; 50 °C 50 min; 85 °C 5 min; 25 °C 30 min; 4 °C hold. For SSIV, 25 °C 15 min; 50 °C 10 min; 95 °C 10 min; 25 °C 30 min; 4 °C hold.

After the RT step, the reverse transcription product was directly combined with the following reagents to yield a total mixture volume of 50 µL (the following are final concentrations of reagents in the ddPCR reaction): 1× TaqMan genotyping mastermix, SGag ddPCR forward and reverse primers (600 nM each), SGag ddPCR probe (200 nM), 1× reaction stabilizer (RainDance) and water. In duplex SIV/RCCR5 RT-ddPCR, RCCR5 ddPCR forward and reverse primers (600 nM each) and RCCR5 ddPCR probe (200 nM) were also included in the mixture.

Dropletization was performed on the Raindance sense instrument according to the manufacturer’s instructions and as described above. End-point PCR thermocycling was performed on a Bio-Rad C1000 Touch Thermal Cycler with the following PCR cycling conditions: 95 °C 7 min; 40 cycles of (95 °C, 15 s; 60 °C, 1 min with a ramp rate of 0.5 °C/s); 98 °C 10 min; 4 °C hold. Sense reading and data analysis were performed as described above.

ddPCR data analysis

Data from Sense runs were analyzed using RainDrop Analyst software to calculate the template copy number by modeling as a Poisson distribution. The formula used for singleplex Poisson modeling was:

$${\text{Copies}}\,{\text{per}}\,{\text{droplet}} = - \ln \left( {1 - p} \right)$$

where p = fraction of positive droplets.

For duplex assay Poisson modeling, the following definition and formula were used:

$$\begin{aligned} & A^{ - } B^{ - } = N \times e^{ - A\% } \times e^{ - B\% } \\ & A^{ + } B^{ + } = N \times \left( {1 - e^{ - A\% } } \right) \times \left( {1 - e^{ - B\% } } \right) \\ & A^{ + } B^{ - } = \, N \times \left( {1 - e^{ - A\% } } \right) \times e^{ - B\% } \\ & A^{ - } B^{ + } = \, N \times e^{ - A\% } \times \left( {1 - e^{ - B\% } } \right) \\ & A\% = - \ln \frac{{1 + \frac{{A^{ - } B^{ + } - A^{ + } B^{ - } }}{N} + \sqrt {\left( {1 + \frac{{A^{ - } B^{ + } - A^{ + } B^{ - } }}{N}} \right)^{2} - \frac{{4A^{ - } B^{ + } }}{N}} }}{2} \\ & B\% = - \ln \frac{{1 + \frac{{A^{ + } B^{ - } - A^{ - } B^{ + } }}{N} + \sqrt {\left( {1 + \frac{{A^{ + } B^{ - } - A^{ - } B^{ + } }}{N}} \right)^{2} - \frac{{4A^{ + } B^{ - } }}{N}} }}{2} \\ \end{aligned}$$

where AB refers to the number of droplets that contain neither target, AB+ refers to the number of droplets that contain target B only, A+ B refers to the number of droplets that contain target A only, and A+ B+ refers to the number of droplets that contain both targets. N = total number of droplet events.

Availability of data and materials

All data generated or analysed during this study are included in this published article and its supplementary information files.

Abbreviations

American Association for Accreditation of Laboratory Animal Care

Copy number variation

Threshold cycle

Droplet digital PCR

Reverse transcriptase

Formalin-fixed, paraffin-embedded

Genetically modified organism

Human immunodeficiency virus

Human T-cell leukaemia virus

Minor groove binder

MicroRNA

Moloney Murine Leukemia Virus Reverse Transcriptase

Next generation sequencing

Quantitative (real-time) PCR

Quantitative reverse transcription PCR

Reverse transcription droplet digital PCR

Severe acute respiratory syndrome coronavirus 2

Simian immunodeficiency virus

SuperScript III reverse transcriptase

SuperScript IV reverse transcriptase

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Sours: https://virologyj.biomedcentral.com/articles/10.1186/s12985-021-01503-5
Digital PCR Principle \u0026 Advantages

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