Stochastic gene expression causes phenotypic heterogeneity in a population of genetically identical bacterial cells. Such non-genetic heterogeneity can have important consequences for the population fitness, and therefore cells implement regulation strategies to either suppress or exploit such heterogeneity to adapt to their circumstances. By employing time-lapse microscopy of single cells, the fluctuation dynamics of gene expression may be analysed, and their regulatory mechanisms thus deciphered. However, a careful consideration of the experimental design and data-analysis is needed to produce useful data for deriving meaningful insights from them. In the present paper, the individual steps and challenges involved in a time-lapse experiment are discussed, and a rigorous framework for designing, performing, and extracting single-cell gene expression dynamics data from such experiments is outlined.

Background Deep-learning–based image segmentation models are required for accurate processing of high-throughput timelapse imaging data of bacterial cells. However, the performance of any such model strictly depends on the quality and quantity of training data, which is difficult to generate for bacterial cell images. Here, we present a novel method of bacterial image segmentation using machine learning models trained with Synthetic Micrographs of Bacteria (SyMBac). Results We have developed SyMBac, a tool that allows for rapid, automatic creation of arbitrary amounts of training data, combining detailed models of cell growth, physical interactions, and microscope optics to create synthetic images which closely resemble real micrographs, and is capable of training accurate image segmentation models. The major advantages of our approach are as follows: (1) synthetic training data can be generated virtually instantly and on demand; (2) these synthetic images are accompanied by perfect ground truth positions of cells, meaning no data curation is required; (3) different biological conditions, imaging platforms, and imaging modalities can be rapidly simulated, meaning any change in one’s experimental setup no longer requires the laborious process of manually generating new training data for each change. Deep-learning models trained with SyMBac data are capable of analysing data from various imaging platforms and are robust to drastic changes in cell size and morphology. Our benchmarking results demonstrate that models trained on SyMBac data generate more accurate cell identifications and precise cell masks than those trained on human-annotated data, because the model learns the true position of the cell irrespective of imaging artefacts. We illustrate the approach by analysing the growth and size regulation of bacterial cells during entry and exit from dormancy, which revealed novel insights about the physiological dynamics of cells under various growth conditions. Conclusions The SyMBac approach will help to adapt and improve the performance of deep-learning–based image segmentation models for accurate processing of high-throughput timelapse image data.

We present a novel method of bacterial image segmentation using machine learning based on Synthetic Micro-graphs of Bacteria (SyMBac). SyMBac allows for rapid, automatic creation of arbitrary amounts of training datathat combines detailed models of cell growth, physical interactions, and microscope optics to create synthetic images which closely resemble real micrographs, with access to the ground truth positions of cells. We also demonstrate that models trained on SyMBac data generate more accurate and precise cell masks than those trained on human annotated data, because the model learns the true position of the cell irrespective of imaging artefacts

Live cell imaging of microbial cells with microscopy has revolutionised quantitative microbiology. Micrographs are one of the most information-rich data types captured about a microbe, allowing quantification of the size and morphology of individual cells and their gene-expression over time. However, an optical microscope is a diffraction-limited system, and the comparable size of the point spread function of the microscope to the size of a microbial cell can lead to imaging artefacts which corrupt and bias the data. Additionally, the comparable thickness of a microbe to the depth of field of the microscope means that the 2D image contains compressed, projected 3D information. This makes it difficult to extract the underlying 3D distribution of photon emitters. For unknown distributions, the problem can be as ill-posed as a deconvolution problem, usually not having a unique solution. Together, the diffraction and projection effects affect our ability to accurately quantify the size and shape of microbial cells from their images and their contents from intensity measurements. In this paper, we use a mixture of simulations and experiments of microscopic image formation of microbial cells to illustrate the effects of diffraction and projection on cell segmentation and signal quantification. We use targeted experiments to validate the predictions where possible. Finally, we use the knowledge of these effects to design experiments which can help to reduce the errors and biases in our analysis. Awareness of these effects and the approaches towards alleviating them will help to accurately quantify microbiology from microscopy data.

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest Author contribution statementThis study was designed and coordinated by GH and JP. GH planned and undertook the practical aspects of the analyses with EK and NC. JP is the principal investigator with APB providing statistical and interpretation guidance for analysing results. The manuscript was written by GH and commented on by all authors. AbstractWord count: 317The performance of microbial communities exploited by industry are largely optimised by manipulating process parameters, such as flow rates, growth conditions, and reactor parameters. Conversely, the composition of microorganisms used are often viewed as a "black box". This is mostly due to the relatively high costs and technical expertise required to identify and quantify the microbial consortia, as well as limited tools to create functional assemblages. Unknown details about the interactions among species may impose a limit on how much microbial function can be optimised for industrial purposes. Here, a new workflow was developed for studying microbial consortia using high throughput, species and community specific measurements of growth rates and yields. Growth rate and yield among all single, pairwise, triple, quadruples, quintuple and sextuple combinations of six bacterial isolates on landfill leachate were evaluated. Additive, antagonistic (e.g. competitive) or synergistic (+/-) interactions can be inferred from the rate and yield data. We found that antagonistic interactions, which hinder growth and yield, were the dominant interaction type, with only a few synergistic interactions observed. Mixed effects models were used to investigate the relationship between interaction type and species richness (biodiversity). Community identity was found to be a more important factor in predicting yield determining interactions but not rate determining interactions. Species richness was a good predictor of rate determining interactions, with the most positive interactions happening at a low species richness. Regression tree analysis identified Lysinibacillus sp. as a keystone species, a genus previously associated with bioremediation. Its presence led to a drastic change in the function of the synthetic ecosystem, with both positive yield and rate determining interactions. We were able to infer interactions about specific pairs of species, and the competitive/synergistic tendencies of single species from only basic top-down growth measurements. In this way, we have demonstrated how factorial experiments using isolated microorganisms can be used to ultimately design synthetic consortia with desirable traits for industry. Abstract 18The performance of microbial communities exploited by industry are largely optimised by 19 manipulating process parameters, such as flow rates, growth conditions, and reactor parameters. 20Conversely, the composition of microorganisms used are often viewed as a "b...