Turn occasional testing into routine monitoring—before small problems become big losses

Preventing honey bee population decline through virus surveillance

Honey bees underpin pollination for crops and ecosystems, but their health has been under sustained pressure for decades from a messy combination of stressors including parasites, pathogens, pesticides, habitat alteration and more. Among the many stressors, the parasitic mite Varroa destructor, and the RNA virus Deformed wing virus (DWV), have been recurring headlines in colony losses. The challenge is that viral infections can be asymptomatic, so timely, routine testing is essential for tracking colony health. In that sense, surveillance doesn’t just document decline—it can help unmask threats early enough for action.

Here’s the bottleneck: virus screening starts with nucleic acid extraction, with the manual, multistep nature of common workflows being slow and labor-intensive. Even when there is a strong motivation to monitor colonies regularly, the many handling steps in traditional manual extraction methods create opportunities for variability, especially when comparing colonies, apiaries, or seasons. Because qPCR-based surveillance depends on clean nucleic acid input, any friction at the extraction stage ripples downstream: fewer colonies tested, fewer timepoints collected, and fewer chances to detect infections before they become problems.

Nikulin et al. (2024, PLOS ONE) addressed this challenge head-on, presenting a workflow designed for the real world: rapid, semi-automated, high-throughput screening of honey bee colonies for viral load—up to 96 samples at a time—with performance comparable to standard labor-intensive, multistep manual extraction approaches.

Making the RNA extraction process consistent and scalable

While automation offers reduced hands-on time and improved run-to-run consistency, total RNA extraction from honey bees still relies primarily on manual spin columns rather than automated magnetic bead protocols. Nikulin et al. describe adapting magnetic bead-based extraction methods, commonly used in biomedical workflows, to honey bee viral RNA extraction and directly comparing results to a manual spin-column approach. 

The authors found that, despite differences in RNA yield and purity, both methods produced comparable read length and quality distributions for downstream sequencing. With sequencing quality holding up across the two methods, the magnetic bead approach offers a practical path to consistency and scalability. It enabled faster viral load screening at 96 samples per run and produced a greater overall volume of sequencing data, reinforcing its suitability for high-throughput workflows where speed, reproducibility, and scale are essential.

Do the results hold up? RT-qPCR says yes!

A reduction in hands-on time only helps if the answers remain trustworthy. To validate the quality of RNA extracted by the spin-column (NucleoSpin Virus kit) and magnetic bead-based (NucleoMag Virus kit) methods, the authors quantified viral load for two common variants, DWV-A and DWV-B, using RT-qPCR. The two extraction approaches showed agreement for both DWV strains across Ct values and viral genome copies per colony (figure below). Although there were modest differences in signal strength, the overall correlation remained high, supporting the magnetic bead workflow as a scalable alternative for surveillance.

Correlation between the two methods of extraction by Ct value and log10 viral genome copies per colony. (A) R² = 0.9988 for viral genome copies, R² = 0.9992 for Ct values for DWV-A; (B) R² = 0.9985 for viral genome copies, R² = 0.9976 for Ct values for DWV-B. (Figure reused from Nikulin et al. 2024, PLOS ONE under a CC BY 4.0 license)

Pairing qPCR for scale with sequencing for deeper insight

RT-qPCR is powerful when you know exactly what your targets are, but sequencing reveals the broader genomic context. In this study, sequencing was used to confirm findings and assess genome-wide coverage. One practical takeaway—the authors observed cases where RT-qPCR and sequencing didn’t perfectly align. This underscores a known limitation of targeted assays—measurements reflect only the genomic region queried and miss recombination events. Sequencing, by contrast, interrogates the full genome, enabling deeper characterization and helping identify recombinant DWV strains.
For labs building surveillance programs, a method combining qPCR for routine monitoring with sequencing for confirmation and strain-level insight could be a pragmatic, high-information workflow.

Conclusion

This study’s key contribution is a rapid, semi-automated, high-throughput workflow that respects both sides of the equation—it maintains measurement confidence while making screening realistic at the scale that surveillance demands. Just as importantly, the approach is designed not just for honey bee surveillance, it also solves challenges that many labs face: large sample sets, limited time, and a premium on consistency. By reducing manual steps and standardizing RNA extraction across large batches, the authors propose an approach that is rapid, uniform, and lower risk for cross-contamination—exactly what is needed for big screening surveys. 

Reference

Nikulin, S. L. et al. A semi-automated and high-throughput approach for the detection of honey bee viruses in bee samples. PLOS ONE 19 (3): e0297623 (2024).

Takara products used in this exciting study

NucleoSpin Virus kit, NucleoMag Virus kit, PrimeSTAR GXL polymerase