Immunology and Infection
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Efficient SARS-CoV-2 Quantitative Reverse Transcriptase PCR Saliva Diagnostic Strategy utilizing Open-Source Pipetting Robots
Chapters
Summary February 11th, 2022
The protocol describes a SARS-CoV-2 diagnostic method that utilizes open-source automation to perform RT-qPCR molecular testing of saliva samples. This scalable approach can be applied to clinical public health surveillance as well as to increase the capacity of smaller university laboratories.
Transcript
We have developed a low cost, saliva based COVID 19 diagnostics test and workflow that is easy to implement and scale up for large scale community screening applications. Here we use open source liquid handling robots, standard thermocyclers and software to perform direct testing of saliva without extraction or stabilization buffers while still providing accurate diagnostic results. Because of the simple automated parallel workflows, the system can be scaled up to perform thousands of tests daily with minimal equipment, space, and personnel requirements.
In addition to Kylie King, Rachel Ham, our clinical lab supervisor and Austin Smothers our education coordinator, will help to demonstrate the procedure Begin by decontaminating the outside of the saliva collection tubes with 70%ethanol and transferring them to the laboratory for testing. Record the sample arrival by scanning each sample barcode into the daily intake spreadsheet. Heat treat the scan samples for a 30 minutes in a 95 degree Celsius oven and then remove the samples using heat resistant gloves.
Open the daily sample loading spreadsheets for each sample loading robot at the sample assignment station. Then assign 188 samples to each 384 well plate. Label trays with plate name, date and quarter number.
Scan the samples in order into the sample loading spreadsheet. At the sample loading station, line up two full sets of eight 3D printed racks, corresponding to the deck placement in the robot. Uncap quarter one tubes and place them in 3D printed racks, starting with position A1 in rack one.
Fill each rack from left to right and top to bottom. Continue this loading pattern in rack two, then proceed to racks four and five. Place loaded quarter one sample racks on decks one, two, four, five, seven, eight, 10, 11.
Place P20 tips on decks three and nine. To simplify the setup process, load materials from back to front into the robot. The master mix plates are produced on a dedicated robot and stored at four degrees Celsius.
One plate is used at a time and is labeled with plate name, and a sharp blade is used to cut a line in the foil around the control wells. Place the master mix plate on deck six and peel away the foil cover, leaving behind the small rectangle covering the control wells. Uncover the tip boxes and close the robot.
Initialize the custom Python operating protocol by clicking start run through the robot desktop application. Each quarter takes 24.5 minutes to load onto the plate. Set a timer as a reminder.
While the robot is running, uncap and load quarter two sample tubes into the second set of 3D printed racks. When the robot pauses, remove the quarter one racks and replace them with the quarter two racks, then click resume run in the desktop application. Recap quarter one sample tubes and store them in a four degree Celsius refrigerator while awaiting results.
Repeat this loading process for quarters three and four. Transfer the loaded plate to a bio safety cabinet. To minimize contamination, keep the plate covered during transfer.
Gather any repeat samples and assign them as the last samples in quarter four. Number the samples, scan the barcodes and enter the original sample location and the result into the sample loading spreadsheet. Transfer the repeat samples to the bio safety cabinet.
Do not load the repeat sample tubes into the robot loading racks. Pipette two microliters of each repeat sample to the correct wells. Use the designated pipette for adding patient samples.
Keep the control wells covered with foil while adding samples to minimize contamination. Peel away foil cover over control wells using forceps. Pipette two microliters of confirmed positive patient samples into wells M23 and M24.
Pipette two microliters of nuclease free water to wells N23 to N24 and two microliters of 200 copies per microliter mixed positive control to Wells O23 to O24. Leave wells P23 to P24 empty to monitor master mix batch quality. Cover the plate with an optically clear seal and use the applicator roller to adhere seal to all wells.
Vortex the plate at 2, 500 RPM for 30 seconds to mix thoroughly, then centrifuge the plate at 500 times G for one minute. Select the protocol program in the thermocycler software and save the protocol for future plates. Place the sealed plate in the thermocycler and run the protocol.
Export CT values and copy the values into the sample loading spreadsheet. For positive control, check if at least one positive control well produces CT values between 22 to 28 for both P1 and N1 probes. Alternatively, the known positive sample wells produce P1 and N1 CT values less than 33 on the P1 and N1 probes.
For negative control, check that there are no N1 or P1 CT values in either of the two negative control wells. Confirm that CT values have valid amplification curves before invalidating the plate. If P1 produces a result of CT less than 33, consider the well as valid and proceed to result from N1.If P1 produces a result of CT greater than or equals 33 or no CT value, consider the well as invalid.
If N1 produces a result of CT less than 33, assess the well as yes. If N1 does not produce a CT value, assess the well as no. If N1 produces a CT value greater than or equals 33, assess the well as no star.
Confirm that all N1 CT values are associated with a real amplification curve. If a CT value for N1 has no amplification curve, the well is no. Identify the repeat samples, label them with an internal sample number and sample type and return them to the loading workflow.
The representative images show the RTQ PCR detection of N1 or SARS CoV-2 synthetic RNA and P1 or HSRPP30 synthetic DNA. Standard curves were plotted with standard deviations to determine the range of accurate detection using this probe primer combination. The mean CT values obtained in respective dilutions were plotted against the estimated quantity of synthetic RNA and synthetic DNA.
In both cases, the linear curves showed good correlation coefficients across a wide range of gene copy concentrations. N1 CT values obtained from unique samples using both the robot and manual sample loading were transposed to determine the inter assay variability between the manual and robot loading. The linear relationship between the manual and automated methods produced a high correlation coefficient indicating that both the methods are functionally equivalent.
Intra assay variability was also determined using transposed replicative N1 CT values obtained from both the robot and manual sample loading. Evaluation of heat treatment methods for viscosity reduction in the saliva is shown here. SARS CoV-2 negative saliva was collected from a single source and aliquots were heat treated for either zero minutes, 30 minutes or 60 minutes at 95 degrees Celsius.
P1 CT values from technical replicates of each condition were plotted to determine the variability between the treatment methods. Both 30 minutes and 60 minutes heat treatment methods produced significantly decreased sample variability compared to no treatment control. There was no significant difference between 30 minute and 60 minute treatments.
Therefore the 30 minute heat treatment method was implemented to reduce the processing time. Use proper aseptic technique to minimize contamination of the testing plates, particularly when loading manual samples and controls. Avoid crossing over the plate with your hands or sleeves.
After clinical results are produced, samples can be disposed of in biohazard waste or they can be saved for further analysis, such as whole genome sequencing to determine specific strains. This technique allows broad scale community testing, which allows us to investigate the effects of testing coverage and the non-symptomatic spread of Covid 19.
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