$$\rightleftharpoonup{xx}$$
$$\longleftharp{xx}$$,
$$\longrightharp{xx}$$,
Bacterial adhesion is a process whereby bacteria attach to other cells or surfaces. Successful establishment of infection by bacterial pathogens requires adhesion to host cells, colonization of tissues, and in some cases, invasion of host cells1,2,3. Emerging infectious diseases constitute major public health threats, as evidenced by the recent COVID-19 pandemic4,5,6. Importantly, new or emerging pathogens may not be readily discerned using genomic-based approaches, especially in cases where the pathogen has been engineered to evade detection or does not contain genomic signatures that identify it as pathogenic. Therefore, the identification of potential pathogens using methods that directly assess hallmarks of pathogenicity, like bacterial adherence to host cells, can play a critical role in pathogen identification.
Bacterial adherence to host cells has been used to evaluate mechanisms of bacterial pathogenesis for decades1,7. Microscopic imaging8,9 and the enumeration of bacterial colony-forming unit (CFU)10,11,12,13 by post-infection plating are two well-developed laboratory methods for testing microbial adherence and/or infection of host cells14. Considering the micrometer scale size of bacterial cells, the enumeration of the adherent bacterial cells generally requires the use of advanced high-magnification microscopy techniques, as well as high-resolution imaging approaches, including electron microscopy, expansion microscopy (ExM)15,16, and three-dimensional imaging17. Alternatively, the enumeration of bacteria bound to or internalized within host cells can be performed by plating the dilution series of harvested bacteria on solid agar and counting the resultant CFUs10,12,13. This method is laborious and includes many manual steps, which introduces difficulties in establishing a standardized or automated procedure required for high-throughput analyses18,19. Therefore, the development of new methods for evaluating host cell attachment would address current limitations in the field.
One such method is described here that uses automated high throughput microscopy, combined with high throughput image processing and statistical analysis. To demonstrate the approach, experiments with several bacterial pathogens were performed, including Pseudomonas aeruginosa, an opportunistic Gram-negative bacterial pathogen of humans, animals, and plants14,20, which is frequently found to colonize the respiratory tract of patients with impaired host defense functions. This approach optimized the microscopic imaging process described in previous studies14,20. The imaging detection was simplified by fluorescence-labeled host cells and bacteria to rapidly track the proximity of them, which dramatically reduced the microscopy workload to get high-resolution images for distinguishing bacteria. In addition, the automated statistical analysis of images in counting host cells and bacteria replaced the hand-on experiment of bacterial CFU plating to estimate the ratio of adherent bacterial counts per host cell. To confirm the compatibility of this method, multiple bacterial strains and host cell types have also been tested, like Listeria monocytogenes, Staphylococcus aureus, Bacillus cereus, and Klebsiella pneumoniae, as well as human umbilical vein endothelial cells (HUVECs), and the results support the diversity and effectiveness of the method.