Detection of specific Atlantic salmon antibodies against salmonid alphavirus using a bead-based immunoassay

Salmonid alphavirus (SAV) is the etiological cause of pancreas disease (PD) in Atlantic salmon (Salmo salar). Several vaccines against SAV are in use, but PD still cause significant mortality and concern in European aquaculture, raising the need for optimal tools to monitor SAV immunity. To monitor and control the distribution of PD in Norway, all salmonid farms are regularly screened for SAV by RT-qPCR. While the direct detection of SAV is helpful in the early stages of infection, serological methods could bring additional information on acquired SAV immunity in the later stages. Traditionally, SAV antibodies are monitored in neutralization assays, but they are time-consuming and cumbersome, thus alternative assays are warranted. Enzyme-linked immunosorbent assays (ELISAs) have not yet been successfully used for anti-SAV antibody detection in aquaculture. We aimed to develop a bead-based immunoassay for SAV-specific antibodies. By using detergent-treated SAV particles as antigens, we detected SAV-specific antibodies in plasma collected from both a SAV challenge trial and a field outbreak of PD. Increased levels of SAV-specific antibodies were seen after most fish had become negative for viral RNA. The bead-based assay is time saving compared to virus neutralization assays, and suitable for non-lethal testing due to low sample size requirements. We conclude that the bead-based immunoassay for SAV antibody detection is a promising diagnostic tool to complement SAV screening in aquaculture.

Protein Multiplexed Immunoassay Analysis with R

Plasma samples from 177 control and type 2 diabetes patients collected at three Australian hospitals are screened for 14 analytes using six custom-made multiplex kits across 60 96-well plates. In total 354 samples were collected from the patients, representing one baseline and one end point sample from each patient. R methods and source code for analyzing the analyte fluorescence response obtained from these samples by Luminex Bio-Plex^® xMap multiplexed immunoassay technology are disclosed. Techniques and R procedures for reading Bio-Plex^® result files for statistical analysis and data visualization are also presented. The need for technical replicates and the number of technical replicates are addressed as well as plate layout design strategies. Multinomial regression is used to determine plate to sample covariate balance. Methods for matching clinical covariate information to Bio-Plex^® results and vice versa are given. As well as methods for measuring and inspecting the quality of the fluorescence responses are presented. Both fixed and mixed-effect approaches for immunoassay statistical differential analysis are presented and discussed. A random effect approach to outlier analysis and detection is also shown. The bioinformatics R methodology present here provides a foundation for rigorous and reproducible analysis of the fluorescence response obtained from multiplexed immunoassays.