Cell size control is an intrinsic feature of the cell cycle. In bacteria, cell growth and division are thought to be coupled through a cell size threshold. Here, we provide direct experimental evidence disproving the critical size paradigm. Instead, we show through single-cell microscopy and modeling that the evolutionarily distant bacteria Escherichia coli and Caulobacter crescentus achieve cell size homeostasis by growing on average the same amount between divisions, irrespective of cell length at birth. This simple mechanism provides a remarkably robust cell size control without the need of being precise, abating size deviations exponentially within a few generations. This size homeostasis mechanism is broadly applicable for symmetric and asymmetric divisions as well as for different growth rates. Furthermore, our data suggest that constant size extension is implemented at or close to division. Altogether, our findings provide fundamentally distinct governing principles for cell size and cell cycle control in bacteria.

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.

Phenotyping with traditional behavioral assays constitutes a major bottleneck in the primary screening, characterization, and validation of genetic mouse models of disease, leading to downstream delays in drug discovery efforts. We present a novel and comprehensive one-stop approach to phenotyping, the PhenoCube™. This system simultaneously captures the cognitive performance, motor activity, and circadian patterns of group-housed mice by use of home-cage operant conditioning modules (IntelliCage) and custom-built computer vision software. We evaluated two different mouse models of Huntington’s Disease (HD), the R6/2 and the BACHD in the PhenoCube™ system. Our results demonstrated that this system can efficiently capture and track alterations in both cognitive performance and locomotor activity patterns associated with these disease models. This work extends our prior demonstration that PhenoCube™ can characterize circadian dysfunction in BACHD mice and shows that this system, with the experimental protocols used, is a sensitive and efficient tool for a first pass high-throughput screening of mouse disease models in general and mouse models of neurodegeneration in particular.