Studying Production of Respiratory Epithelial Aerosols and Droplets (SPREAD)

Jessica Resnick, TERRENCE GARCIA, Benjamin Alvarez, Natalie Sebeck, Michael Schuit, JHU/APL

     Abstract Number: 683
     Working Group: Bioaerosols

Abstract
Although the COVID-19 pandemic has receded, pathogen transmission by the aerosol route remains a serious and ongoing hazard. Because of this, there is a need for a capability to evaluate and quantify the aerosol transmission risk of existing and emerging pathogens, including rapid screening of novel variants, assessment of the potential for zoonotic spillover events, and evaluation of host and environmental factors that may affect the likelihood and timing of transmission or the effectiveness of interventions. Current methods used to predict transmissibility of novel pathogens are costly, use bulk cultured material, and often require the use of research animals.

We have developed a prototype method for generating aerosols directly from the apical surface of air-liquid interface (ALI) respiratory epithelial cell cultures, intended to mimic naturally occurring aerosol generation from breathing, coughing, and other respiratory maneuvers. Preliminary data suggest that it is feasible to generate aerosols from ALI cultures in sizes relevant to real-world respiratory particles while maintaining the viability and integrity of the cultures themselves. This method is being linked with a process to deposit aerosols onto the surfaces of ALI cultures to create a completely in vitro respiratory aerosol transmission cycle, reducing the need for animal studies and allowing manipulations of host and environmental variables to investigate transmission potential/ risk. This system is intended to be both broadly applicable (e.g. assessment of inter- and intra- species transmission) and able to be narrowly targeted (e.g. isolation of cells from individuals for a "personalized medicine" aerosol risk assessment).

This novel approach for studying respiratory aerosol transmission cycles has the potential to significantly accelerate the characterization of both emerging pathogens and novel variants of existing pathogens. This capability will enable crucial studies to aid in decision support and modeling as well as allow for quicker, near real-time threat characterization.