<b>Assessing microbial growth in drinking water using nucleic acid content and flow cytometry fingerprinting</b>

Summary: This study utilizes flow cytometry to evaluate the High Nucleic Acid (HNA) and Low Nucleic Acid (LNA) content of intact cells for monitoring bacterial dynamics in drinking water treatment and supply systems. Our findings indicate that chlorine and nutrients differently impact components of bacterial populations. HNA bacteria, characterized by high metabolic rates, quickly react to nutrient alterations, making them suitable indicators of growth under varying water treatment and supply conditions. Conversely, LNA bacteria adapt to environments with stable, slowly degradable organics, reflecting distinct physiological characteristics. Changes in water treatment and supply conditions, such as chlorine dosing and nutrient inputs, significantly impact the ratio between HNA and LNA. Flow cytometry fingerprinting combined with cluster analysis provides a more sensitive evaluation of water quality by capturing a broader range of microbial characteristics compared to using only HNA/LNA ratios. This work advocates for multi-parameter data analysis to advance monitoring techniques for water treatment and supply processes.研究摘要：本研究采用流式细胞术（flow cytometry）检测完整细胞的高核酸（High Nucleic Acid, HNA）与低核酸（Low Nucleic Acid, LNA）含量，以监测饮用水处理与输配系统中的细菌动态。研究结果显示，氯消毒剂与营养物质对细菌种群各组分的影响存在显著差异。高核酸细菌以高代谢速率为典型特征，可快速响应营养条件变化，因此适合作为不同水处理与输配工况下细菌生长的指示指标。与之相反，低核酸细菌可适配稳定且可缓慢降解的有机物环境，体现出独特的生理特性。水处理与输配条件的变化（如氯投加量与营养物质输入量）会显著改变HNA与LNA的比例。相较于仅采用HNA/LNA比例的检测方法，流式细胞术指纹图谱结合聚类分析可覆盖更广泛的微生物特征，从而实现更为灵敏的水质评价。本研究倡导采用多参数数据分析方法，以优化水处理与输配过程的监测技术。