Silver nanoparticle-adjuvanted vaccine protects against lethal influenza infection through inducing BALT and IgA-mediated mucosal immunity.

Most of current influenza virus vaccines fail to develop a strong immunity at lung mucosae (site of viral entry) due to sub-optimal vaccination protocols (e.g. inactivated virus administered by parenteral injections). Mucosal immunity could be improved by using locally-delivered vaccines containing appropriate adjuvants. Here we show, in a mouse model, that inclusion of silver nanoparticles (AgNPs) in virus-inactivated flu vaccine resulted in reduction of viral loads and prevention of excessive lung inflammation following influenza infection. Concomitantly, AgNPs enhanced specific IgA secreting plasma cells and antibodies titers, a hallmark of successful mucosal immunity. Moreover, vaccination in the presence of AgNPs but not with gold nanoparticles, protected mice from lethal flu. Compared with other commercial adjuvants (squalene/oil-based emulsion) or silver salts, AgNPs stimulated stronger antigen specific IgA production with lower toxicity by promoting bronchus-associated lymphoid tissue (BALT) neogenesis, and acted as a bona fide mucosal adjuvant.

Open-CSAM, a new tool for semi-automated analysis of myofiber cross-sectional area in regenerating adult skeletal muscle.

Adult skeletal muscle is capable of complete regeneration after an acute injury. The main parameter studied to assess muscle regeneration efficacy is the cross-sectional area (CSA) of the myofibers as myofiber size correlates with muscle force. CSA analysis can be time-consuming and may trigger variability in the results when performed manually. This is why programs were developed to completely automate the analysis of the CSA, such as SMASH, MyoVision, or MuscleJ softwares. Although these softwares are efficient to measure CSA on normal or hypertrophic/atrophic muscle, they fail to efficiently measure CSA on regenerating muscles. We developed Open-CSAM, an ImageJ macro, to perform a high throughput semi-automated analysis of CSA on skeletal muscle from various experimental conditions. The macro allows the experimenter to adjust the analysis and correct the mistakes done by the automation, which is not possible with fully automated programs. We showed that Open-CSAM was more accurate to measure CSA in regenerating and dystrophic muscles as compared with SMASH, MyoVision, and MuscleJ softwares and that the inter-experimenter variability was negligible. We also showed that, to obtain a representative CSA measurement, it was necessary to analyze the whole muscle section and not randomly selected pictures, a process that was easily and accurately be performed using Open-CSAM. To conclude, we show here an easy and experimenter-controlled tool to measure CSA in muscles from any experimental condition, including regenerating muscle.