Image Segmentation of Bacterial Biofilms to Study Pathogen-Surface Interactions

Biofilms are structured communities of microbial cells encased in a self-produced extracellular polymeric substance (EPS) matrix, which adhere to biotic or abiotic surfaces and are ubiquitous in natural, industrial, and clinical settings. Biofilms play critical roles in various ecosystems and pose significant challenges in healthcare due to their resistance to antibiotics. Understanding biofilms is complex due to their heterogeneous nature and dynamic behavior. Biofilms exhibit spatial and temporal variations in their structure, composition, and function. These variations are heavily influenced by environmental conditions, microbial species involved, and the physical and chemical properties of the surfaces they colonize. With the biofilm formation occurring in multiple stages a deeper understanding of the fundamental mechanisms for attachment and the subsequent cascade of physical and chemical events that promote biofilm growth and propagation is essential for developing materials that resist biofilm formation and enhance antimicrobial therapies across various fields. However, the properties that can be collected change dynamically from small to large area biofilm imaging. While small area biofilm studies reveal details of individual cell structures, large area biofilm studies can help in studying dynamic changes in biofilms which have a potential for commercial application.Atomic Force Microscopy (AFM) is a valuable tool in biofilm research, offering detailed structural and mechanical insights. It enables imaging of cell structures, interactions, and mechanical properties of biofilms with nanoscale resolution. By scanning a sharp probe over the biofilm surface and measuring the forces between the probe and the sample, AFM can achieve nanometer-scale resolution, revealing detailed structural features of biofilms. Higher-resolution imaging techniques like AFM are crucial for studying individual cell structures, fine features, and appendages such as flagella and pili. These advanced imaging methods provide detailed insights into cellular morphology and interactions that are often not visible with lower-resolution techniques. However, AFM has its own limitations. AFM has a slow scanning process and typically has small scanning ranges of up to 100 μm × 100 μm limiting its ability to capture dynamic changes in biofilms across macroscopic scales. Currently, we have used an automated scanning platform to image large macro-scale (~mm) biofilms with nanoscale resolution, which equates to a large amount of data to be processed manually. Therefore, automated methods of image processing and analysis are necessary, to capture the growth patterns of biofilms.In this study, we integrate AFM with multiple cutting edge AI techniques and developing methods for AFM imaging will enhance the ability to study biofilms comprehensively, leading to significant advancements in this field. Here we present the role of computer vision-based image segmentation techniques in capturing topological properties of bacteria in biofilms which provide valuable insights about pathogen surface interactions. To this end we used You Only Look Once (YOLO v8) image segmentation pretrained models to perform segmentation of AFM bacterial images of dried films of Pantoea sp. YR343. This is a gram-negative bacterium known for promoting plant growth. Previous research has shown that Pantoea sp. YR343 forms biofilms with a honeycomb morphology on hydrophobic surfaces, but it does not readily attach to hydrophilic surfaces. We analyzed high resolution Panteoa bacterial AFM images to quantify morphological characteristics of the bacteria that can provide insights of the attachment patterns of the bacteria on abiotic surfaces. Using the image segmentation models we captures several morphological properties such as total cell count, cell orientation, cell eccentricity, cell perimeter and cell area for every bacterium in the image. The images collected from AFM are in the Gwydion (.gwy) format which are initially converted into regular portable network graph (png) format. These files were then flattened using a combination of thresholding methods to even out the tilt in the images which is a pre-requisite for analyzing AFM images. The flattened images are then normalized and fed into the image analysis pipeline performed using the Roboflow platform. The pipeline includes steps like annotation, data split, image resizing and data augmentation. The obtained dataset is fed into the pre-trained Yolov8 image segmentation model which is widely applied to perform image segmentation tasks. The model gave a validation Precision and Recall and of 0.64 and 0.78 respectively and a Mean Average Precision (mAP50) of 0.73. The obtained modes were then deployed to obtain the dimensions of the masks for every bacterium in the image. The masks are then used to calculate cell properties like cell area, count, perimeter, orientation, centroids and eccentricity using Scikit-Image’s Regionprops method. With varying degrees of mask thresholding, every bacterium in the image was identified. The methodology is integrated into the automated analysis pipelines of AFM image analysis we developed in-house. The study demonstrates a methodology to integrate highly versatile image segmentation models to specific AFM image analysis tasks to enable rapid analysis of large- area biofilms at nanoscale resolution. Although promising, we still believe that for YOLO to gain traction in AFM contexts, further customization may be required to fine-tune the model outputs for detecting nanoscale objects and differentiating subtle textures within AFM data.