Merod Robin T, Warren Jennifer E, McCaslin Hope, Wuertz Stefan
Department of Civil and Environmental Engineering, University of California, Davis, Davis, CA 95616, USA.
Appl Environ Microbiol. 2007 Aug;73(15):4922-30. doi: 10.1128/AEM.00023-07. Epub 2007 Jun 1.
An increasing number of studies utilize confocal laser scanning microscopy (CLSM) for in situ visualization of biofilms and rely on the use of image analysis programs to extract quantitative descriptors of architecture. Recently, designed programs have begun incorporating procedures to automatically determine threshold values for three-dimensional CLSM image stacks. We have found that the automated threshold calculation is biased when a stack contains images lacking pixels of biological significance. Consequently, we have created the novel program Auto PHLIP-ML to resolve this bias by iteratively excluding extraneous images based on their area coverage of biomass. A procedure was developed to identify the optimal percent area coverage value used for extraneous image removal (PACVEIR). The optimal PACVEIR was defined to occur when the standard deviation of mean thickness, determined from replicate image stacks, was at a maximum, because it more accurately reflected inherent structural variation. Ten monoculture biofilms of either Ralstonia eutropha JMP228n::gfp or Acinetobacter sp. strain BD413 were tested to verify the routine. All biofilms exhibited an optimal PACVEIR between 0 and 1%. Prior to the exclusion of extraneous images, JMP228n::gfp appeared to develop more homogeneous biofilms than BD413. However, after the removal of extraneous images, JMP228n::gfp biofilms were found to form more heterogeneous biofilms. Similarly, JMP228n::gfp biofilms grown on glass surfaces vis-à-vis polyethylene membranes produced significantly different architectures after extraneous images had been removed but not when such images were included in threshold calculations. This study shows that the failure to remove extraneous images skewed a seemingly objective analysis of biofilm architecture and significantly altered statistically derived conclusions.
越来越多的研究利用共聚焦激光扫描显微镜（CLSM）对生物膜进行原位可视化，并依靠图像分析程序来提取结构的定量描述符。最近，设计的程序已开始纳入自动确定三维CLSM图像堆栈阈值的程序。我们发现，当堆栈中包含缺乏生物学意义像素的图像时，自动阈值计算会产生偏差。因此，我们创建了新颖的程序Auto PHLIP-ML，通过根据生物量的面积覆盖率迭代排除无关图像来解决此偏差。开发了一种程序来确定用于去除无关图像的最佳面积覆盖率值（PACVEIR）。当从重复图像堆栈确定的平均厚度标准偏差最大时，定义为最佳PACVEIR，因为它更准确地反映了固有的结构变化。测试了10个Ralstonia eutropha JMP228n::gfp或不动杆菌属BD413菌株的单培养生物膜以验证该程序。所有生物膜的最佳PACVEIR均在0％至1％之间。在排除无关图像之前，JMP228n::gfp似乎比BD413形成更均匀的生物膜。但是，在去除无关图像后，发现JMP228n::gfp生物膜形成了更多异质生物膜。同样，在去除无关图像后，在玻璃表面相对于聚乙烯膜上生长的JMP228n::gfp生物膜产生了明显不同的结构，但在阈值计算中包含此类图像时则没有。这项研究表明，未能去除无关图像会扭曲对生物膜结构看似客观的分析，并显著改变统计得出的结论。
