SUMMARY The physical nature of the bacterial cytoplasm is poorly understood even though it determines cytoplasmic dynamics and hence cellular physiology and behavior. Through single-particle tracking of protein filaments, plasmids, storage granules and foreign particles of different sizes, we find that the bacterial cytoplasm displays properties characteristic of glass-forming liquids and changes from liquid-like to solid-like in a component size-dependent fashion. As a result, the motion of cytoplasmic components becomes disproportionally constrained with increasing size. Remarkably, cellular metabolism fluidizes the cytoplasm, allowing larger components to escape their local environment and explore larger regions of the cytoplasm. Consequently, cytoplasmic fluidity and dynamics dramatically change as cells shift between metabolically active and dormant states in response to fluctuating environments. Our findings provide insight into bacterial dormancy and have broad implications to our understanding of bacterial physiology as the glassy behavior of the cytoplasm impacts all intracellular processes involving large components.

Summary With the realization that bacteria display phenotypic variability among cells and exhibit complex subcellular organization critical for cellular function and behavior, microscopy has re-emerged as a primary tool in bacterial research during the last decade. However, the bottleneck in today’s single-cell studies is quantitative image analysis of cells and fluorescent signals. Here, we address current limitations through the development of Oufti, a stand-alone, open-source software package for automated measurements of microbial cells and fluorescence signals from microscopy images. Oufti provides computational solutions for tracking touching cells in confluent samples, handles various cell morphologies, offers algorithms for quantitative analysis of both diffraction and non-diffraction-limited fluorescence signals, and is scalable for high-throughput analysis of massive datasets, all with subpixel precision. All functionalities are integrated in a single package. The graphical user interface, which includes interactive modules for segmentation, image analysis, and post-processing analysis, makes the software broadly accessible to users irrespective of their computational skills.

We describe a general computational approach to designing self-assembling helical filaments from monomeric proteins and use this approach to design proteins that assemble into micrometer-scale filaments with a wide range of geometries in vivo and in vitro. Cryo–electron microscopy structures of six designs are close to the computational design models. The filament building blocks are idealized repeat proteins, and thus the diameter of the filaments can be systematically tuned by varying the number of repeat units. The assembly and disassembly of the filaments can be controlled by engineered anchor and capping units built from monomers lacking one of the interaction surfaces. The ability to generate dynamic, highly ordered structures that span micrometers from protein monomers opens up possibilities for the fabrication of new multiscale metamaterials.

Supplemental figure legends9 Figure S1. Related to Figure 1. Cell and nucleoid morphology of E. coli cells in different growth 10 media. 11 A. Representative phase contrast and DAPI images of E. coli cells (CJW6324) grown in liquid cultures 12of M9 medium supplemented with the indicated carbon source and other chemicals (CAAT: 0.1% 13 casamino acids and 1 µg/ml thiamine) at 37 °C. For a full description of the growth media, see Table 14 S1. Cell contours (green) were generated using Oufti. 15 B. Bar graph showing the average doubling times of cultures when growing in exponential phase in 16 the indicated growth media. Errors bars indicate the standard deviation between three independent 17 biological replicates. Colors correspond to those used in Figure 1B. 18 C. Scatter plot of growth medium osmolality versus average NC ratio for E. coli cells (CJW6324) grown 19 in the media indicated in B. The color scheme corresponds to the one shown in B. Error bars indicate 20 95% confidence intervals. 21 22