Droplet Digital polymerase chain reaction (ddPCR) uses emulsion chemistry to partition nucleic acid samples into approximately 20,000 oil-encapsulated nanodroplets to produce data that surpasses the precision of other molecular methods with equivalent or much higher sensitivity.

Numerous circumstances can lead to a weakened immune system, making the body more susceptible to viral infection and knowing whether the infection is developing resistance to certain drugs is key to providing optimal treatment, as such resistance can often develop from a single, spontaneous point mutation in the viral ribonucleic acid (RNA).

Scientists at Central University Hospital and Laval University (Quebec City, QC, Canada) working with their colleagues compared the percent mutation abundance between ddPCR and reverse transcription quantitative PCR (RT-qPCR) platforms, on nucleic acid extracts of patient samples were collected over time. Influenza A (H1N1) pdm09 virus infection was diagnosed on January 5, 2011, in a 31 month-old boy with medulloblastoma who received consolidation chemotherapy in preparation of an autologous bone marrow transplantation.

The amplification process for the RT-qPCR was performed in a LightCycler 480 real-time thermocycler (Roche Applied Science, Mannheim, Germany). For all reactions split between RT-qPCR and ddPCR, the amplification process was performed in a LightCycler 480 real-time thermocycler using the optimized ddPCR cycling protocol. For the ddPCR 20 µL of each reaction mix was converted to droplets with the QX200 droplet generator (Bio-Rad; Hercules, CA, USA). Droplet-partitioned samples were then transferred to a 96-well plate, sealed and cycled in a Bio-Rad C1000 deep well Thermocycler.

The scientists used a mixture of mutant and wild-type viral RNA to show that ddPCR technology markedly increased the sensitivity by more than 30-fold and precision by more than 10-fold, for both inter- and intra-assay variability of mutation abundance quantification when compared to RT-qPCR. Because ddPCR is based on absolute quantification, it can remove much of the variability intrinsic to RT-qPCR, which relies on relative quantification. The investigators discovered a statistically significant correlation between two independent ddPCR datasets that was not found with RT-qPCR, allowing for accurate identification of a residual mutant viral population.

Guy Boivin, MD, MSc, FRCPC, an associate professor of microbiology and senior author of the study said, “Influenza research, especially translational research, needs rapid and reproducible methods. We plan to use Droplet Digital PCR to follow the fate of immunocompromised patients on antiviral therapy.” The study was published in the November 2015 issue of the Journal of Virological Methods.

Related Links:
Central University Hospital and Laval University
Roche Applied Science 
Bio-Rad Laboratories