In bacteria, chromosomal architecture shows strong spatial and temporal organization, and regulates key cellular functions, such as transcription. Tracking the motion of chromosomal loci at short timescales provides information related to both the physical state of the nucleo-protein complex and its local environment, independent of large-scale motions related to genome segregation. Here we investigate the short-time (0.1-10 s) dynamics of fluorescently labelled chromosomal loci in Escherichia coli at different growth rates. At these timescales, we observe for the first time a dependence of the loci's apparent diffusion on both their subcellular localization and chromosomal coordinate, and we provide evidence that the properties of the chromosome are similar in the tested growth conditions. Our results indicate that either non-equilibrium fluctuations due to enzyme activity or the organization of the genome as a polymer-protein complex vary as a function of the distance from the origin of replication.

The mean size of exponentially dividing Escherichia coli cells in different nutrient conditions is known to depend on the mean growth rate only. However, the joint fluctuations relating cell size, doubling time, and individual growth rate are only starting to be characterized. Recent studies in bacteria reported a universal trend where the spread in both size and doubling times is a linear function of the population means of these variables. Here we combine experiments and theory and use scaling concepts to elucidate the constraints posed by the second observation on the division control mechanism and on the joint fluctuations of sizes and doubling times. We found that scaling relations based on the means collapse both size and doubling-time distributions across different conditions and explain how the shape of their joint fluctuations deviates from the means. Our data on these joint fluctuations highlight the importance of cell individuality: Single cells do not follow the dependence observed for the means between size and either growth rate or inverse doubling time. Our calculations show that these results emerge from a broad class of division control mechanisms requiring a certain scaling form of the "division hazard rate function," which defines the probability rate of dividing as a function of measurable parameters. This "model free" approach gives a rationale for the universal body-size distributions observed in microbial ecosystems across many microbial species, presumably dividing with multiple mechanisms. Additionally, our experiments show a crossover between fast and slow growth in the relation between individual-cell growth rate and division time, which can be understood in terms of different regimes of genome replication control.

We designed a microfluidic chemostat consisting of 600 sub-micron trapping/growth channels connected to two feeding channels. The microchemostat traps E. coli cells and forces them to grow in lines for over 50 generations. Excess cells, including the mother cells captured at the start of the process, are removed from both ends of the growth channels by the media flow. With the aid of time-lapse microscopy, we have monitored dynamic properties such as growth rate and GFP expression at the single-cell level for many generations while maintaining a population of bacteria of similar age. We also use the microchemostat to show how the population responds to dynamic changes in the environment. Since more than 100 individual bacterial cells are aligned and immobilized in a single field of view, the microchemostat is an ideal platform for high-throughput intracellular measurements. We demonstrate this capability by tracking with sub-diffraction resolution the movements of fluorescently tagged loci in more than one thousand cells on a single device. The device yields results comparable to conventional agar microscopy experiments with substantial increases in throughput and ease of analysis.

With the rise in antibiotic resistance amongst pathogenic bacteria, the study of antibiotic activity and transport across cell membranes is gaining widespread importance. We present a novel, label-free microfluidic assay that quantifies the permeability coefficient of a broad spectrum fluoroquinolone antibiotic, norfloxacin, across lipid membranes using the UV autofluorescence of the drug. We use giant lipid vesicles as highly controlled model systems to study the diffusion through lipid membranes. Our technique directly determines the permeability coefficient without requiring the measurement of the partition coefficient of the antibiotic.

The physical nature of the bacterial chromosome has important implications for its function. Using high-resolution dynamic tracking, we observe the existence of rare but ubiquitous 'rapid movements' of chromosomal loci exhibiting near-ballistic dynamics. This suggests that these movements are either driven by an active machinery or part of stress-relaxation mechanisms. Comparison with a null physical model for subdiffusive chromosomal dynamics shows that rapid movements are excursions from a basal subdiffusive dynamics, likely due to driven and/or stress-relaxation motion. Additionally, rapid movements are in some cases coupled with known transitions of chromosomal segregation. They do not co-occur strictly with replication, their frequency varies with growth condition and chromosomal coordinate, and they show a preference for longitudinal motion. These findings support an emerging picture of the bacterial chromosome as off-equilibrium active matter and help developing a correct physical model of its in vivo dynamic structure.

Behaviour is a sensitive and integrative readout of nervous system function and therefore an attractive measure for assessing the effects of mutation or drug treatment on animals. Video data provide a rich but high-dimensional representation of behaviour, and so the first step of analysis is often some form of tracking and feature extraction to reduce dimensionality while maintaining relevant information. Modern machine-learning methods are powerful but notoriously difficult to interpret, while handcrafted features are interpretable but do not always perform as well. Here, we report a new set of handcrafted features to compactly quantify Caenorhabditis elegans behaviour. The features are designed to be interpretable but to capture as much of the phenotypic differences between worms as possible. We show that the full feature set is more powerful than a previously defined feature set in classifying mutant strains. We then use a combination of automated and manual feature selection to define a core set of interpretable features that still provides sufficient power to detect behavioural differences between mutant strains and the wild-type. Finally, we apply the new features to detect time-resolved behavioural differences in a series of optogenetic experiments targeting different neural subsets.This article is part of a discussion meeting issue ‘Connectome to behaviour: modelling C. elegans at cellular resolution’.

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Animal behavior is increasingly being recorded in systematic imaging studies that generate large data sets. To maximize the usefulness of these data there is a need for improved resources for analyzing and sharing behavior data that will encourage re-analysis and method development by computational scientists 1 . However, unlike genomic or protein structural data, there are no widely used standards for behavior data. It is therefore desirable to make the data available in a relatively raw form so that different investigators can use their own representations and derive their own features. For computational ethology to approach the level of maturity of other areas of bioinformatics, we need to address at least three challenges: storing and accessing video files, defining flexible data formats to facilitate data sharing, and making software to read, write, browse, and analyze the data. We have developed an open resource to begin addressing these challenges using worm tracking as a model.To store video files and the associated feature and metadata, we use a Zenodo.org community (an open-access repository for data) that provides durable storage, citability, and supports contributions from other groups. We have also developed a web interface that enables filtering based on feature histograms that can return, for example, fast and curved worms in addition to more standard searches for particular strains or genotypes ( Fig. 1 and http://movement.openworm.org/). The database consists of 14,874 single-worm tracking experiments representing 386 genotypes (building on 9,203 experiments and 305 genotypes in a previous publication 2 ) and includes data from several larval stages as well as ageing data consisting of over 2,700 videos of animals tracked daily from the L4 stage to death. Full resolution videos are available in HDF5 containers that include gzip-compressed video frames, timestamps, worm outline and midline, feature data, and experiment metadata. HDF5 files are compatible with multiple languages including MATLAB, R, Python, and C. We have also developed an HDF5 video reader that allows video playback with adjustable speed and zoom (important when reviewing high-resolution, multi-worm tracking data), as well as toggling of worm segmentation over the original video to verify segmentation accuracy during playback.Secondly, we have defined an interchange format named Worm tracker Commons Object Notation (WCON), to facilitate data sharing and software reuse among groups working on worm behavior. WCON uses the widely supported JSON format to store tracking data as text that is both human and machine readable. It is compatible with single and multi-worm 3 data, at any resolution: from a single point representing worm position over time 4 , to many points representing the high-resolution skeleton of a moving worm 2 . Importantly, it also supports custom feature additions so that individual labs can store their own specific data sets alongside the universal set of basic worm data. WCON readers are available for Python, MATL...