DEVELOPMENT AND VALIDATION OF A SENSITIVE MOLECULAR ASSAY FOR QUANTIFICATION OF THE SALMON LOUSE Lepeophtheirus salmonis IN ZOOPLANKTON SAMPLES
Introduction
The salmon louse, Lepeophtheirus salmonis, is a major ectoparasite of farmed salmon, contributing to substantial production losses through tissue damage, impaired mucosal barrier function, and increased susceptibility to secondary infections. Adverse health outcomes and mortalities are increasing alongside the emergence of treatment resistance and adaptation; therefore, early outbreak detection is essential to facilitate timely control and to support proactive farm management. Conventional monitoring focuses on attached, mature sea lice stages, and the reliability of count estimates is limited by patchy distribution of parasite burden across both hosts and cages, requiring intensive sampling effort. Assessing the density of free-living, planktonic lice stage instead offers a more representative measure of infection risk. However, the larvae's sparse distribution amongst a diverse background zooplankton community, as well as their morphological similarity to other taxa, makes microscopic analysis a timely process requiring taxonomic expertise. Molecular processing therefore offers a highly specific, high-throughput, and scalable option for the monitoring of free-living sea lice. While species-specific molecular assays exist for L. salmonis, comparative studies suggest that they still lack the quantitative accuracy, precision, and sensitivity of visual methods (Bui et al., 2021). Therefore, this study aimed to optimise the molecular workflow by testing novel and existing marker types, amplification methods, and assessing the effects of subsampling depth, DNA extraction efficiency, and background zooplankton density on assay performance..
Methods
We compared existing mitochondrial markers (16S, COI) and novel nuclear high-copy repeat targets, alongside quantitative PCR (qPCR) and loop-mediated isothermal amplification (LAMP) approaches. Spiking experiments were conducted using zooplankton samples containing 0–30 L. salmonis larvae (copepodite and nauplius stages), with triplicate replicates per treatment. Samples were homogenised and subsampled (1–3 subsamples per replicate) prior to DNA extraction and target quantification.
Results
Target concentration varied non-linearly with lice abundance and DNA extraction yield (p < 0.001 for both spline terms). This initial model included a significant life stage × primer interaction driven by reduced COI signal in nauplii only. A predictive model was then developed to estimate lice abundance from average DNA concentration in the first round of spiked samples. Cross-validated performance indicated reasonable predictive ability with a mean R2 of 0.82 (SD = 0.11). This corresponded to a mean prediction error of approximately 3.1–3.5 lice (RMSE) across model configurations. Prediction error increased with abundance, with RMSE rising from 1.2 (1–5 lice) to 6.2 (>15 lice). Subsampling depth had no significant effect on model performance, although a minor, non-significant improvement in predictive accuracy was observed with increased subsampling (RMSE: 3.8 → 3.2), suggesting diminishing returns beyond a single subsample. Although extraction yield significantly influenced qPCR-derived target concentration, its inclusion did not improve predictive model performance, indicating that normalising samples to account for differential extraction efficiency would not improve estimates.
Figure 1. Correlation between qPCR-derived DNA concentration and L. salmonis abundance in zooplankton samples. 16S (Krolicka et al., 2022) and COI (McBeath et al., 2006) represent mitochondrial L. salmonis-specific molecular markers. DNA signal was significantly lower for nauplii using COI, but not for copepodites (Z = -5.812, p < 0.001).
Implications and ongoing work
While initial findings confirm a strong relationship between qPCR signal and lice abundance, the relative error of 41% at low densities (1–5 lice) could indicate reduced precision in absolute abundance estimates compared to visual methods. However, the method detected signal in all spiked samples (including n = 1), whereas traditional microscopy is prone to missing such low densities, suggesting improved sensitivity for early detection. Stage-dependent differences in primer performance highlight the importance of target selection when applying assays to field samples where stage composition is unknown. Development of a novel LAMP-based assay and tandem repeat primer targets for L. salmonis are ongoing. A second experimental phase will further validate and compare combinations of marker types and amplification methods under optimal extraction and subsampling conditions using independent samples spiked with realistic and mixed-stage larval densities. Molecular count estimates will be compared against conventional microscopy and automated imaging (PlanktoScope) to assess relative accuracy, sensitivity, and scalability. This work will deliver an integrated, validated molecular workflow for early sea lice quantification in zooplankton, with direct application to improving monitoring sensitivity and supporting proactive management in aquaculture systems.
Acknowledgments
This PhD project is funded by the Marine Alliance for Science and Technology for Scotland.
References
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