Microbiome-based profiles of airborne bacteria to support microbial risk assessment in cleanroom environments

Maintaining aseptic conditions is essential for cell product processing, as sterilization cannot be applied to living cells. Conventional environmental monitoring relies on particle counts and culture-based colony-forming unit measurements. These indicators fail to capture much of the diversity and provenance of airborne microbes because many taxa are nonculturable or require growth conditions not supported by standard culture media. Therefore, comprehensive DNA-based microbiome analysis is critical for evaluating microbial risks that conventional methods may overlook; however, such studies remain limited in cleanroom settings. This study aimed to comprehensively visualize the structure of airborne microbial communities in cleanroom environments and clarify microbial risks that cannot be fully captured by particle counts or culture-based methods.

We collected airborne bacterial DNA from cleanrooms with environmental Grades B, C, and D using a high-volume air sampler. The DNA was extracted and analyzed via 16S rRNA gene amplicon sequencing targeting the V3-V4 regions. Bioinformatic analysis was performed using the QIIME2 pipeline, and microbial diversity was assessed using alpha and beta diversity indices. Abundant taxa were categorized based on their likely origin (environment- or skin-derived), and their distributions were examined in relation to facility management practices.

Analysis revealed the consistent detection of skin-associated bacteria, such as Cutibacterium and Corynebacterium, and environmental bacteria, including Bacillus and Paracoccus, across all cleanroom grades. Alpha- and beta-diversities exhibited no significant differences among the grades. However, temporary and irregular increases in skin-derived bacteria indicated operator-related non-persistent contamination. This interpretation was supported by skewness and kurtosis analyses, which indicated occasional but noticeable shifts in microbial abundance, particularly in high-grade cleanroom environments.

This study demonstrates the limitations of conventional culture-based monitoring and underscores the value of DNA-based approaches for characterizing airborne microbial communities in cleanrooms. The detection of temporary increases in skin-associated bacteria indicates that operator-related contamination can occur even under stringent environmental conditions. These findings support the development of integrated monitoring strategies that can capture both the composition and temporal fluctuations of airborne microbiota to enhance microbial risk assessment.