Myxococcus xanthus cellular structures during early development

Aggregation underlies collective dynamics of a diversity of organisms, enabling the formation of complex structures and emergent behaviours in interaction with the environment. Cellular aggregation constitutes one of the routes to collective motility and multicellular development. Myxococcus xanthus, a social bacterium, is a valuable model for studying the aggregative way to multicellularity, a major transition in the evolutionary history of life. While the collective developmental behaviour of M. xanthus has been largely studied in  high cellular densities, there is a lack of understanding at low density conditions that can be ecologically relevant. In this work, we study the early stages of emergent collective behaviour of M. xanthus under nutrient-poor and low density conditions, uncovering the formation of diverse cellular structures with different shapes and sizes, ranging from individual cells to networks comprising thousands of cells. We study their motility patterns and their prevalence along development, and discuss their cross-scale role on the population’s exploratory dynamics. This work contributes to understanding key, yet largely understudied, aspects in the early stages of multicellular development in myxobacteria, shedding light on the dynamics underlying aggregative processes in this and other taxa and study systems.

Methods
The samples were observed through TPM agar medium using an inverted optical microscope with a 10X objective lens (Olympus UPlanSApo series, 0.4 NA), illuminated with amber light and recorded with a Basler acA 1600-20uc with overall resolution of 0.52 μm/pixel and 1000 μm x 624 μm field of vision. The microscope was set inside an isolated chamber with a controlled temperature of 28 C. Micrographs were taken for two of the three replicates, while 15 min videos were taken at t=0, 4 and 8 h from the onset of starvation (after the spots dried) for the third replica at an acquisition rate of 1 frame per second. Micrographs and video frames were binarized using segmentation through edge-detection and intensity-threshold criteria implemented with the Image Processing Toolbox of MATLAB.  Analysis from the resulting database was conducted using R v.4.2.0.