Significance: Single-celled microorganisms are important in ecosystems, and their behaviors impact the Earth’s environments. To survive in harsh environments, these organisms frequently act as though exercising discretion. How do they achieve such intelligent behaviors? In this work, we focused on the accumulation of ciliates on solid/fluid interfaces, where they can obtain sufficient nutrients and a stable environment. This phenomenon is not described in the standard hydrodynamics of microswimmers. Our experiment and simulation revealed that simple principles, the anisotropic shape of the cell and the mechanosensing nature of cilia, induce the accumulation of ciliates on solid/fluid interfaces. The contribution of our work is that a simple response of the cellular apparatus and fluid dynamics explain the apparently clever behavior of ciliates.

In this Letter, a water-in-oil swimming droplet’s transition from straight to curvilinear motion is investigated experimentally and theoretically. An analysis of the experimental results and the model reveal that the motion transition depends on the susceptibility of the droplet’s direction of movement to external stimuli as a function of environmental parameters such as droplet size. The simplicity of the present experimental system and the model suggests implications for a general class of transitions in self-propelled swimmers.

Bacteria often grow into matrix‐encased three‐dimensional (3D) biofilm communities, which can be imaged at cellular resolution using confocal microscopy. From these 3D images, measurements of single‐cell properties with high spatiotemporal resolution are required to investigate cellular heterogeneity and dynamical processes inside biofilms. However, the required measurements rely on the automated segmentation of bacterial cells in 3D images, which is a technical challenge. To improve the accuracy of single‐cell segmentation in 3D biofilms, we first evaluated recent classical and deep learning segmentation algorithms. We then extended StarDist, a state‐of‐the‐art deep learning algorithm, by optimizing the post‐processing for bacteria, which resulted in the most accurate segmentation results for biofilms among all investigated algorithms. To generate the large 3D training dataset required for deep learning, we developed an iterative process of automated segmentation followed by semi‐manual correction, resulting in >18,000 annotated Vibrio cholerae cells in 3D images. We demonstrate that this large training dataset and the neural network with optimized post‐processing yield accurate segmentation results for biofilms of different species and on biofilm images from different microscopes. Finally, we used the accurate single‐cell segmentation results to track cell lineages in biofilms and to perform spatiotemporal measurements of single‐cell growth rates during biofilm development.

Kinesthetic sensing of cilia results in upstream motility for Tetrahymena pyriformis , a typical freshwater microorganism.

Some types of ciliates accumulate on solid/fluid interfaces. This behavior is advantageous to survival in nature due to the presence of sufficient nutrition and stable environments. Recently, the accumulating mechanisms of Tetrahymena pyriformis at the interface were investigated. The synergy of the ellipsoidal shape of the cell body and the mechanosensing feature of the cilia allow for cells to slide on interfaces, and the sliding behavior leads to cell accumulation on the interfaces. Here, to examine the generality of the sliding behavior of ciliates, we characterized the behavior of Paramecium caudatum, which is a commonly studied ciliate. Our experimental and numerical results confirmed that P. caudatum also slid on the solid/fluid interface by using the same mechanism as T. pyriformis. In addition, we evaluated the effects of cellular ellipticity on their behaviors near the wall with a phase diagram produced via numerical simulation.

Bacterial biofilms are highly abundant 3D living materials capable of performing complex biomechanical and biochemical functions, including programmable growth, self‐repair, filtration, and bioproduction. Methods to measure internal mechanical properties of biofilms in vivo with spatial resolution on the cellular scale have been lacking. Here, thousands of cells are tracked inside living 3D biofilms of the bacterium Vibrio cholerae during and after the application of shear stress, for a wide range of stress amplitudes, periods, and biofilm sizes, which revealed anisotropic elastic and plastic responses of both cell displacements and cell reorientations. Using cellular tracking to infer parameters of a general mechanical model, spatially‐resolved measurements of the elastic modulus inside the biofilm are obtained, which correlate with the spatial distribution of the polysaccharides within the biofilm matrix. The noninvasive microrheology and force‐inference approach introduced here provides a general framework for studying mechanical properties with high spatial resolution in living materials.