Editing Recombinase Polymerase Amplification (section)

== Discussion ==
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In order to obtain meaningful quantification data using the MATLAB algorithm, the user must select appropriate input values when prompted. After initiating each script in Sections 5 and 6, all input variables are automatically solicited in the command window and outputs are automatically generated. In Section 5.7 the user is prompted to select a slope threshold. The value of the slope threshold affects the square of the correlation coefficient (r2) of the fit. When using raw fluorescence data exported from a thermal cycler, values between 2.0 and 5.0 tend to yield a high r2 coefficient. In Section 5.8 the user must designate the number of standard deviations above the background to set the positive threshold. To score a sample as positive or negative, the script automatically determines the difference Δsample between the maximum and minimum fluorescence for each sample using the raw fluorescence data exported from the thermal cycler. It calculates the average difference Δbackground and standard deviation σbackground for all no-target control samples. A sample is considered positive if Δsample is more than z × σbackground above Δbackground. In Section 5.10 the user decides whether to use the default threshold or set a new threshold. If the user wishes to set a new threshold, determine the new threshold experimentally by performing 3 experiments each containing 12 RPA reactions without any HIV-1 DNA present. Set the threshold at the average increase in fluorescence intensity from these experiments plus 3 standard deviations. After completing Section 6, the script JoVE_qRPA_validation_and_quantification.m automatically returns the estimated DNA concentration for each qRPA reaction (in log10 copies). If the script determines that no HIV-1 DNA was present in the sample, the estimated concentration is listed as either “Negative for HIV” or “Invalid,” depending on whether the fluorescent signal for the internal positive control exceeded the threshold (z × σbackground + Δbackground). If the user is validating the standard curve with known sample concentrations, the script will also return an additional table similar to that ofTable 1.

In order to develop a real-time RPA assay that provides accurate quantification, experimental consistency is crucial. For example, use the same primer and probe aliquots for both the standard curve and validation experiments. Also avoid freeze-thaw cycles by storing the primer and probe aliquots at 4 °C between experiments rather than -20 °C. The template aliquots used for the standard curve and validation experiments are stored in the same manner. RPA enzyme pellets and reagents from the same lot are used according to the manufacturer recommendations. Lastly, because RPA lacks true ‘cycles’ to precisely control the rate of amplification, standardization of user steps is absolutely imperative. When assembling reactions, the user must always follow the same steps in the same order, spending approximately the same amount of time on each step. Reactions must always be mixed gently with a new pipette tip, and bubbles must always be eliminated. Before amplification, reactions must be held at a consistent temperature, and the thermal cycler or microscope software must always be prepared before loading reactions to avoid any amplification at sub-optimal temperatures that could influence quantification. Any variation in the initial reaction conditions may lead to inconsistency in experimental outcomes.

When using a microscope to collect data, additional variables must be controlled to minimize variation in fluorescence intensity. All reactions must be placed in the same region on the stage warmer, and the microscope must be focused on the same region of the well for every sample. Even if these practices are followed, fluorescence data collected on a microscope may exhibit variability due to local bright spots that naturally form during RPA reactions, bubble formation within the reaction chambers, or photobleaching resulting from repeated exposure to [[excitation]] light. The influence of these variables is evident in fluorescence data collected on the microscope (Figures 4A and 4B), which demonstrate baseline variability, peaks, and troughs. These features are absent from the fluorescence data collected on the thermal cycler (Figures 3A and 3B). Ultimately, data collection on the microscope is for proof-of-principle purposes only and the final assay will be implemented on a field-operable fluorescence reader with more precise geometry and software control that minimizes these variables.

Another important aspect of the qRPA assay development process is consistency in data processing. The protocol described in the methods section uses scripts to process raw fluorescence data (stored in a spreadsheet file) collected from a thermal cycler or microscope. All experiments used to build the standard curve must be formatted identically. When using a thermal cycler to collect data, the same plate layout must be used, and data from wells that do not contain RPA reactions must not be exported. When using a microscope to collect data, the format of the data must match the format of the data automatically exported from the thermal cycler. For example, the no-target-control data must be in cells C2:C61, and data for increasing template concentrations must be in cells D2:D61, E2:E61,etc. If there are multiple replicates of each concentration in an experiment, the 2nd replicate [[dilution series]] must be ordered left to right from no target control (NTC) to highest concentration and saved in the columns immediately to the right of the 1st replicate dilution series. For example, in the plate layout used in Section 1.2 with 2 replicates for each sample, fluorescence data for the 1st replicate of each sample in the dilution series must be saved in cells C2:H61 and fluorescence data for the 2nd replicate of each sample in the dilution series must be saved in cells I2:N61. For the plate layout used in Section 1.2, this is the default formatting when exporting data from the thermal cycler software to a spreadsheet.

Representative data provided from HIV-1 qRPA experiments demonstrate proof-of-concept support that RPA may be used for quantification of nucleic acid concentration in unknown samples. Clinically useful HIV-1 viral load tests have a clinical range of at least 4 orders of magnitude, a precision of 0.5 log10 copies, and a limit-of-detection of at least 200 copies19,20. The HIV-1 DNA assay described meets these criteria and is most accurate at low concentrations, as shown in Table 1. Therefore, with the inclusion of a reverse transcriptase step, these results suggest that an HIV-1 RT-RPA assay may have the potential to measure HIV-1 viral load in clinical samples. When developing a qRPA assay, adjusting the algorithm parameters may tune the sensitivity and linear dynamic range depending on clinical needs.Figure 6 shows that adjusting z (a parameter that determines the threshold for positive samples) can influence the sensitivity and accuracy at low and high target concentrations. Furthermore, it may be possible to increase the resolution and accuracy of quantification by incubating reactions at a lower temperature or using less magnesium acetate, thereby decreasing the rate of amplification.

This proof of concept qRPA assay can be used to quantify the concentration of samples containing HIV-1 DNA. The qRPA assay described in this manuscript includes detailed instructions on how to assemble real-time RPA reactions, develop and screen an IPC, and process raw fluorescence data to build a standard curve that can be used to quantify unknown samples. With the detailed instructions included, this protocol may be adapted to quantify DNA concentration in a wide variety of samples.
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