In Vivo Microrheology Reveals Local Elastic and Plastic Responses Inside 3D Bacterial Biofilms.

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.

Single-cell segmentation in bacterial biofilms with an optimized deep learning method enables tracking of cell lineages and measurements of growth rates.

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.

AIRE illuminates the feature of medullary thymic epithelial cells in thymic carcinoma.

Despite the clear distinction between cortical (cTECs) and medullary thymic epithelial cells (mTECs) in physiology, the cell of origin of thymic carcinomas (TCs) and other thymic epithelial tumors remained enigmatic. We addressed this issue by focusing on AIRE, an mTEC-specific transcriptional regulator that is required for immunological self-tolerance. We found that a large proportion of TCs expressed AIRE with typical nuclear dot morphology by immunohistochemistry. AIRE expression in TCs was supported by the RNA-seq data in the TCGA-THYM database. Furthermore, our bioinformatics approach to the recent single-cell RNA-seq data on human thymi has revealed that TCs hold molecular characteristics of multiple mTEC subpopulations. In contrast, TCs lacked the gene signatures for cTECs. We propose that TCs are tumors derived from mTECs.

Motion of a swimming droplet under external perturbations: A model-based approach.

Microdroplets driven by the Marangoni effect are known to continue to swim for hours despite their simple composition. This swimming microdroplet changes its motion from straight to curvilinear and further to chaotic as the Péclet number increases. In this study, we investigate the effect of external perturbations on the three-dimensional axis-asymmetric model of a droplet driven by the Marangoni effect. The aim here is to elucidate the contribution of external perturbation to the complex motion of the droplet and the change in its effect according to the droplet size. In this paper, first we provide a detailed explanation on the derivation of the model introduced in our previous work, which is next used to describe the motion of the droplet in the numerical study of the angular response to random perturbations. The numerical simulation of droplet motion with different types of noise indicates that the model does not converge them into a certain type of motion but rather helps to reflect the external perturbations. The obtained results suggest that the types and properties of external perturbation have a considerable effect on the droplet motion.

Simple dynamics underlying the survival behaviors of ciliates.

Ciliates are swimming microorganisms in aquatic environments. Habitats where ciliates accumulate include nutrient-rich solid-liquid interfaces such as pond bottom walls and waterweed surfaces. The ciliates stay near the walls to survive. We investigated the dynamics of the near-wall behavior of ciliates. In experiments, the ciliates were made to slide on a flat wall of glass substrate. When encountering the wall, the wall-side cilia of the cells stop their motion and lose their propelling activity, which indicates that the ciliates have a mechano-sensing system for cilia beating. Based on the experimental results, we hypothesized that the ciliary thrust force that propels the cell body becomes asymmetric, and the asymmetry of the thrust force generates a head-down torque to keep the cell sliding on the wall. To prove this hypothesis, we performed numerical simulations by using a developed hydrodynamic model for swimming ciliates. The model revealed that the loss of cilia activity on the wall side physically induces a sliding motion, and the aspect ratio of the cell body and effective cilium area are critical functions for the sliding behavior on a wall. In addition, we investigated the stability of the sliding motion against an external flow. We found that ciliates slide upstream on a wall. Interestingly, the dynamics of this upstream sliding, called rheotaxis, were also explained by the identical physical conditions for no-flow sliding. Only two simple physical conditions are required to explain the dynamics of ciliate survival behavior. This review article is an extended version of the Japanese article, Fluid Dynamic Model Reveals a Mechano-sensing System Underlying the Behavior of Ciliates, published in SEIBUTSU BUTSURI Vol. 61, p. 16-19 (2021).

Accumulation of Tetrahymena pyriformis on Interfaces.

The behavior of ciliates has been studied for many years through environmental biology and the ethology of microorganisms, and recent hydrodynamic studies of microswimmers have greatly advanced our understanding of the behavioral dynamics at the single-cell level. However, the association between single-cell dynamics captured by microscopic observation and pattern dynamics obtained by macroscopic observation is not always obvious. Hence, to bridge the gap between the two, there is a need for experimental results on swarming dynamics at the mesoscopic scale. In this study, we investigated the spatial population dynamics of the ciliate, Tetrahymena pyriformis, based on quantitative data analysis. We combined the image processing of 3D micrographs and machine learning to obtain the positional data of individual cells of T. pyriformis and examined their statistical properties based on spatio-temporal data. According to the 3D spatial distribution of cells and their temporal evolution, cells accumulated both on the solid wall at the bottom surface and underneath the air-liquid interface at the top. Furthermore, we quantitatively clarified the difference in accumulation levels between the bulk and the interface by creating a simple behavioral model that incorporated quantitative accumulation coefficients in its solution. The accumulation coefficients can be compared under different conditions and between different species.

Near-wall rheotaxis of the ciliate Tetrahymena induced by the kinesthetic sensing of cilia.

To survive in harsh environments, single-celled microorganisms autonomously respond to external stimuli, such as light, heat, and flow. Here, we elucidate the flow response of Tetrahymena, a well-known single-celled freshwater microorganism. Tetrahymena moves upstream against an external flow via a behavior called rheotaxis. While micrometer-sized particles are swept away downstream in a viscous flow, what dynamics underlie the rheotaxis of the ciliate? Our experiments reveal that Tetrahymena slides along walls during upstream movement, which indicates that the cells receive rotational torque from shear flow to control cell orientation. To evaluate the effects of the shear torque and propelling speed, we perform a numerical simulation with a hydrodynamic model swimmer adopting cilia dynamics in a shear flow. The swimmer orientations converge to an upstream alignment, and the swimmer slides upstream along a boundary wall. The results suggest that Tetrahymena automatically responds to shear flow by performing rheotaxis using cilia-stalling mechanics.

Straight-to-Curvilinear Motion Transition of a Swimming Droplet Caused by the Susceptibility to Fluctuations.

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.

Influence of cellular shape on sliding behavior of ciliates.

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.

Simple mechanosense and response of cilia motion reveal the intrinsic habits of ciliates.

An important habit of ciliates, namely, their behavioral preference for walls, is revealed through experiments and hydrodynamic simulations. A simple mechanical response of individual ciliary beating (i.e., the beating is stalled by the cilium contacting a wall) can solely determine the sliding motion of the ciliate along the wall and result in a wall-preferring behavior. Considering ciliate ethology, this mechanosensing system is likely an advantage in the single cell's ability to locate nutrition. In other words, ciliates can skillfully use both the sliding motion to feed on a surface and the traveling motion in bulk water to locate new surfaces according to the single "swimming" mission.