Computational Analysis of Morphological Changes in Lactiplantibacillus plantarum Under Acidic Stress

Shape and size often define the characteristics of individual microorganisms. Hence, characterizing cell morphology using computational image analysis can aid in the accurate, quick, unbiased, and reliable identification of bacterial morphology. Modifications in the cell morphology of Lactiplantibacillus plantarum were determined in response to acidic stress, during the growth stage of the cells at a pH 3.5 compared to a pH of 6.5. Consequently, we developed a computational method to sort, detect, analyze, and measure bacterial size in a single-species culture. We applied a deep learning methodology composed of object detection followed by image classification to measure bacterial cell dimensions. The results of our computational analysis showed a significant change in cell morphology in response to alterations of the environmental pH. Specifically, we found that the bacteria existed as a long unseparated cell, with a dramatic increase in length of 41% at a low pH compared to the control. Bacterial width was not altered in the low pH compared to the control. Those changes could be attributed to modifications in membrane properties, such as increased cell membrane fluidity in acidic pH. The integration of deep learning and object detection techniques, with microbial microscopic imaging, is an advanced methodology for studying cellular structures that can be projected for use in other bacterial species or cells. These trained models and scripts can be applied to other microbes and cells.