Characterization of the Atmospheric Microbiome in a Semi-Rural Area of Central Europe Using Flow Cytometry

The abundance, characterization, and identification of biological aerosol particles (bioaerosols) are important for understanding their impact on the Earth system in terms of biogeochemical cycles of phosphorus and nitrogen, cloud formation, precipitation, and human health. Bioaerosols consist of all airborne prokaryotes or eukaryotes ranging in size from a few nanometers to hundreds of micrometers. In this study, a flow cytometry protocol was optimized in order to quantify and characterize the biogenic particles collected from a semi-rural site in central Europe (Payern, Switzerland). Samples collection (n = 39) was performed using a high-volume wet-cyclone over a period of 5 months (April to August 2024). Specifically, a live/dead protocol for atmospheric samples was optimized using two nucleic acid stains: Syto13 to stain all live cells and propidium iodide to stain all dead cells. The simultaneous use of the dyes and the subsequent application of an automated clustering algorithm after acquisition (FlowSOM, Bioconductor - FlowSOM) allowed us to identify populations characterized by a high nucleic acid (HNA) content (e.g., fungal spores and protists) and a low nucleic acid (LNA) content (e.g., bacterial cells and dead protists). Preliminary results showed that the average concentration of bioaerosols was 2.25x104 ± 2.99x104 microorganisms m-3. The HNA population was dominant during the sampling period (detected in 79% of the samples) while the LNA population dominated the bioaerosols fraction on rainy days. The intact population dominated the bioaerosol fraction (92.6 ± 12.3%) compared to the dead population (7.4 ± 12.3%). A significant high correlation was found between the LNA and the dead populations (rspearman = 0.88), indicating that the dead population is a component of the LNA population (rspearman = 0.50 with the HNA population). The populations quantified by flow cytometry will be identified taxonomically using Oxford Nanopore sequencing. The results will be discussed in detail.