National Biofilms Innovation Centre, Institute of Life Sciences, University of Southampton, Southampton, SO17 1BJ, UK.
Biomedical Imaging Unit, University of Southampton, Southampton, SO16 6YD, UK.
Sci Rep. 2019 Dec 12;9(1):19002. doi: 10.1038/s41598-019-55567-z.
Non-surface attached bacterial aggregates are frequently found in clinical settings associated with chronic infections. Current methods quantifying the extent to which a suspended bacterial population is aggregated mainly rely on: (1) cell size distribution curves that are difficult to be compared numerically among large-scale samples; (2) the average size/proportion of aggregates in a population that do not specify the aggregation patterns. Here we introduce a novel application of Gini coefficient, herein named Aggregation Coefficient (AC), to quantify the aggregation levels of cystic fibrosis Pseudomonas aeruginosa (CF-PA) isolates in vitro using 3D micrographs, Fiji and MATLAB. Different aggregation patterns of five strains were compared statistically using the numerical AC indexes, which correlated well with the size distribution curves plotted by different biovolumes of aggregates. To test the sensitivity of AC, aggregates of the same strains were treated with nitric oxide (NO), a dispersal agent that reduces the biomass of surface attached biofilms. Strains unresponsive to NO were reflected by comparable AC indexes, while those undergoing dispersal showed a significant reduction in AC index, mirroring the changes in average aggregate sizes and proportions. Therefore, AC provides simpler and more descriptive numerical outputs for measuring different aggregation patterns compared to current approaches.
非附着表面的细菌聚集体经常在与慢性感染相关的临床环境中被发现。目前,定量评估悬浮细菌群体聚集程度的方法主要依赖于:(1)细胞大小分布曲线,在大规模样本中难以进行数值比较;(2)群体中聚集物的平均大小/比例,而没有指定聚集模式。在这里,我们引入基尼系数的一种新应用,称为聚集系数(AC),使用 3D 显微照片、Fiji 和 MATLAB 来定量体外囊性纤维化铜绿假单胞菌(CF-PA)分离株的聚集水平。使用数值 AC 指数对五种菌株的不同聚集模式进行了统计学比较,该指数与通过不同聚集物生物量绘制的大小分布曲线很好地相关。为了测试 AC 的灵敏度,用一氧化氮(NO)处理相同菌株的聚集体,NO 是一种分散剂,可减少附着生物膜的生物量。对 NO 无反应的菌株反映出可比的 AC 指数,而那些发生分散的菌株的 AC 指数显著降低,反映了平均聚集体大小和比例的变化。因此,与当前方法相比,AC 为测量不同聚集模式提供了更简单和更具描述性的数值输出。