SuperSegger: robust image segmentation, analysis and lineage tracking of bacterial cells

Resolving spatiotemporal dynamics in bacterial multicellular populations: approaches and challenges

SUMMARYThe development of multicellularity represents a key evolutionary transition that is crucial for the emergence of complex life forms. Although multicellularity has traditionally been studied in eukaryotes, it originates in prokaryotes. Coordinated aggregation of individual cells within the confines of a colony results in emerging, higher-level functions that benefit the population as a whole. During colony differentiation, an almost infinite number of ecological and physiological population-forming forces are at work, creating complex, intricate colony structures with divergent functions. Understanding the assembly and dynamics of such populations requires resolving individual cells or cell groups within such macroscopic structures. Addressing how each cell contributes to the collective action requires pushing the resolution boundaries of key technologies that will be presented in this review. In particular, single-cell techniques provide powerful tools for studying bacterial multicellularity with unprecedented spatial and temporal resolution. These advancements include novel microscopic techniques, mass spectrometry imaging, flow cytometry, spatial transcriptomics, single-bacteria RNA sequencing, and the integration of spatiotemporal transcriptomics with microscopy, alongside advanced microfluidic cultivation systems. This review encourages exploring the synergistic potential of the new technologies in the study of bacterial multicellularity, with a particular focus on individuals in differentiated bacterial biofilms (colonies). It highlights how resolving population structures at the single-cell level and understanding their respective functions can elucidate the overarching functions of bacterial multicellular populations.

Growing bacterial colonies harness emergent genealogical demixing to regulate organizational entropy

Spatiotemporal organization of individuals within growing bacterial colonies is a key determinant of intraspecific interactions and colony-scale heterogeneities. The evolving cellular distribution, in relation to the genealogical lineage, is thus central to our understanding of bacterial fate across scales. Yet, how bacteria self-organize genealogically as a colony expands has remained unknown. Here, by developing a custom-built label-free algorithm, we track and study the genesis and evolution of emergent self-similar genealogical enclaves, whose dynamics are governed by biological activity. Topological defects at enclave boundaries tune finger-like morphologies of the active interfaces. The Shannon entropy of cell arrangements reduce over time; with faster-dividing cells possessing higher spatial affinity to genealogical relatives, at the cost of a well-mixed, entropically favorable state. Our coarse-grained lattice model demonstrates that genealogical enclaves emerge due to an interplay of division-mediated dispersal, stochasticity of division events, and cell-cell interactions. The study reports so-far hidden emergent self-organizing features arising due to entropic suppression, ultimately modulating intraspecific genealogical distances within bacterial colonies.

OmniSegger: A time-lapse image analysis pipeline for bacterial cells

Time-lapse microscopy is a powerful tool for studying the cell biology of bacterial cells. The development of pipelines that facilitate the automated analysis of these datasets is a long-standing goal of the field. In this paper, we describe OmniSegger , an updated version of our SuperSegger pipeline, developed as an open-source, modular, and holistic suite of algorithms whose input is raw microscopy images and whose output is a wide range of quantitative cellular analyses, including dynamical cell cytometry data and cellular visualizations. The updated version described in this paper introduces two principal refinements: (i) robustness to cell morphologies and (ii) support for a range of common imaging modalities. To demonstrate robustness to cell morphology, we present an analysis of the proliferation dynamics of Escherichia coli treated with a drug that induces filamentation. To demonstrate extended support for new image modalities, we analyze cells imaged by five distinct modalities: phase-contrast, two brightfield modalities, and cytoplasmic and membrane fluorescence. Together, this pipeline should greatly increase the scope of tractable analyses for bacterial microscopists.

Coupling of cell growth modulation to asymmetric division and cell cycle regulation in Caulobacter crescentus

In proliferating bacteria, growth rate is often assumed to be similar between daughter cells. However, most of our knowledge of cell growth derives from studies on symmetrically dividing bacteria. In many α-proteobacteria, asymmetric division is a normal part of the life cycle, with each division producing daughter cells with different sizes and fates. Here, we demonstrate that the functionally distinct swarmer and stalked daughter cells produced by the model α-proteobacterium Caulobacter crescentus can have different average growth rates under nutrient-replete conditions despite sharing an identical genome and environment. The discrepancy in growth rate is due to a growth slowdown associated with the cell cycle stage preceding DNA replication (the G1 phase), which initiates in the late predivisional mother cell before daughter cell separation. Both progenies experience a G1-associated growth slowdown, but the effect is more severe in swarmer cells because they have a longer G1 phase. Activity of SpoT, which produces the (p)ppGpp alarmone and extends the G1 phase, accentuates the cell cycle-dependent growth slowdown. Collectively, our data identify a coupling between cell growth, the G1 phase, and asymmetric division that C. crescentus may exploit for environmental adaptation through SpoT activity. This coupling differentially modulates the growth rate of functionally distinct daughter cells, thereby altering the relative abundance of ecologically important G1-specific traits within the population.

Quantitative microbiology with widefield microscopy: navigating optical artefacts for accurate interpretations

Time-resolved live-cell imaging using widefield microscopy is instrumental in quantitative microbiology research. It allows researchers to track and measure the size, shape, and content of individual microbial cells over time. However, the small size of microbial cells poses a significant challenge in interpreting image data, as their dimensions approache that of the microscope's depth of field, and they begin to experience significant diffraction effects. As a result, 2D widefield images of microbial cells contain projected 3D information, blurred by the 3D point spread function. In this study, we employed simulations and targeted experiments to investigate the impact of diffraction and projection on our ability to quantify the size and content of microbial cells from 2D microscopic images. This study points to some new and often unconsidered artefacts resulting from the interplay of projection and diffraction effects, within the context of quantitative microbiology. These artefacts introduce substantial errors and biases in size, fluorescence quantification, and even single-molecule counting, making the elimination of these errors a complex task. Awareness of these artefacts is crucial for designing strategies to accurately interpret micrographs of microbes. To address this, we present new experimental designs and machine learning-based analysis methods that account for these effects, resulting in accurate quantification of microbiological processes.

A benchmarked comparison of software packages for time-lapse image processing of monolayer bacterial population dynamics

Time-lapse microscopy offers a powerful approach for analyzing cellular activity. In particular, this technique is valuable for assessing the behavior of bacterial populations, which can exhibit growth and intercellular interactions in a monolayer. Such time-lapse imaging typically generates large quantities of data, limiting the options for manual investigation. Several image-processing software packages have been developed to facilitate analysis. It can thus be a challenge to identify the software package best suited to a particular research goal. Here, we compare four software packages that support the analysis of 2D time-lapse images of cellular populations: CellProfiler, SuperSegger-Omnipose, DeLTA, and FAST. We compare their performance against benchmarked results on time-lapse observations of Escherichia coli populations. Performance varies across the packages, with each of the four outperforming the others in at least one aspect of the analysis. Not surprisingly, the packages that have been in development for longer showed the strongest performance. We found that deep learning-based approaches to object segmentation outperformed traditional approaches, but the opposite was true for frame-to-frame object tracking. We offer these comparisons, together with insight into usability, computational efficiency, and feature availability, as a guide to researchers seeking image-processing solutions.

IMPORTANCE

Time-lapse microscopy provides a detailed window into the world of bacterial behavior. However, the vast amount of data produced by these techniques is difficult to analyze manually. We have analyzed four software tools designed to process such data and compared their performance, using populations of commonly studied bacterial species as our test subjects. Our findings offer a roadmap to scientists, helping them choose the right tool for their research. This comparison bridges a gap between microbiology and computational analysis, streamlining research efforts.

Multiple resistance factors collectively promote inoculum-dependent dynamic survival during antimicrobial peptide exposure in Enterobacter cloacae

Antimicrobial peptides (AMPs) are a promising tool with which to fight rising antibiotic resistance. However, pathogenic bacteria are equipped with several AMP defense mechanisms, whose contributions to AMP resistance are often poorly defined. Here, we evaluate the genetic determinants of resistance to an insect AMP, cecropin B, in the opportunistic pathogen Enterobacter cloacae. Single-cell analysis of E. cloacae's response to cecropin revealed marked heterogeneity in cell survival, phenotypically reminiscent of heteroresistance (the ability of a subpopulation to grow in the presence of supra-MIC concentration of antimicrobial). The magnitude of this response was highly dependent on initial E. cloacae inoculum. We identified 3 genetic factors which collectively contribute to E. cloacae resistance in response to the AMP cecropin: The PhoPQ-two-component system, OmpT-mediated proteolytic cleavage of cecropin, and Rcs-mediated membrane stress response. Altogether, our data suggest that multiple, independent mechanisms contribute to AMP resistance in E. cloacae.

Interrogation of RNA-protein interaction dynamics in bacterial growth

Characterising RNA-protein interaction dynamics is fundamental to understand how bacteria respond to their environment. In this study, we have analysed the dynamics of 91% of the Escherichia coli expressed proteome and the RNA-interaction properties of 271 RNA-binding proteins (RBPs) at different growth phases. We find that 68% of RBPs differentially bind RNA across growth phases and characterise 17 previously unannotated proteins as bacterial RBPs including YfiF, a ncRNA-binding protein. While these new RBPs are mostly present in Proteobacteria, two of them are orthologs of human mitochondrial proteins associated with rare metabolic disorders. Moreover, we reveal novel RBP functions for proteins such as the chaperone HtpG, a new stationary phase tRNA-binding protein. For the first time, the dynamics of the bacterial RBPome have been interrogated, showcasing how this approach can reveal the function of uncharacterised proteins and identify critical RNA-protein interactions for cell growth which could inform new antimicrobial therapies.

Glycogen phase separation drives macromolecular rearrangement and asymmetric division in E. coli

Bacteria often experience nutrient limitation in nature and the laboratory. While exponential and stationary growth phases are well characterized in the model bacterium Escherichia coli, little is known about what transpires inside individual cells during the transition between these two phases. Through quantitative cell imaging, we found that the position of nucleoids and cell division sites becomes increasingly asymmetric during transition phase. These asymmetries were coupled with spatial reorganization of proteins, ribosomes, and RNAs to nucleoid-centric localizations. Results from live-cell imaging experiments, complemented with genetic and 13C whole-cell nuclear magnetic resonance spectroscopy studies, show that preferential accumulation of the storage polymer glycogen at the old cell pole leads to the observed rearrangements and asymmetric divisions. In vitro experiments suggest that these phenotypes are likely due to the propensity of glycogen to phase separate in crowded environments, as glycogen condensates exclude fluorescent proteins under physiological crowding conditions. Glycogen-associated differences in cell sizes between strains and future daughter cells suggest that glycogen phase separation allows cells to store large glucose reserves without counting them as cytoplasmic space.

Tools and methods for high-throughput single-cell imaging with the mother machine

Despite much progress, image processing remains a significant bottleneck for high-throughput analysis of microscopy data. One popular platform for single-cell time-lapse imaging is the mother machine, which enables long-term tracking of microbial cells under precisely controlled growth conditions. While several mother machine image analysis pipelines have been developed in the past several years, adoption by a non-expert audience remains a challenge. To fill this gap, we implemented our own software, MM3, as a plugin for the multidimensional image viewer napari. napari-MM3 is a complete and modular image analysis pipeline for mother machine data, which takes advantage of the high-level interactivity of napari. Here, we give an overview of napari-MM3 and test it against several well-designed and widely used image analysis pipelines, including BACMMAN and DeLTA. Researchers often analyze mother machine data with custom scripts using varied image analysis methods, but a quantitative comparison of the output of different pipelines has been lacking. To this end, we show that key single-cell physiological parameter correlations and distributions are robust to the choice of analysis method. However, we also find that small changes in thresholding parameters can systematically alter parameters extracted from single-cell imaging experiments. Moreover, we explicitly show that in deep learning-based segmentation, 'what you put is what you get' (WYPIWYG) - that is, pixel-level variation in training data for cell segmentation can propagate to the model output and bias spatial and temporal measurements. Finally, while the primary purpose of this work is to introduce the image analysis software that we have developed over the last decade in our lab, we also provide information for those who want to implement mother machine-based high-throughput imaging and analysis methods in their research.

Protocol to train a support vector machine for the automatic curation of bacterial cell detections in microscopy images

Manual curation of bacterial cell detections in microscopy images remains a time-consuming and laborious task. This work offers a comprehensive, step-by-step tutorial on training a support vector machine to autonomously distinguish between good and bad cell detections. Jupyter notebooks are included to perform feature extraction, labeling, and training of the machine learning model. This method can readily be incorporated into profiling pipelines aimed at extracting a multitude of features across large collections of individual cells, strains, and species. For complete details on the use and execution of this protocol, please refer to Govers et al.1.

Multiple resistance factors collectively promote inoculum-dependent dynamic survival during antimicrobial peptide exposure in Enterobacter cloacae

Antimicrobial peptides (AMPs) are a promising tool with which to fight rising antibiotic resistance. However, pathogenic bacteria are equipped with several AMP defense mechanisms, whose contributions to AMP resistance are often poorly defined. Here, we evaluate the genetic determinants of resistance to an insect AMP, cecropin B, in the opportunistic pathogen Enterobacter cloacae. Single-cell analysis of E. cloacae's response to cecropin revealed marked heterogeneity in cell survival, phenotypically reminiscent of heteroresistance (the ability of a subpopulation to grow in the presence of supra-MIC concentration of antimicrobial). The magnitude of this response was highly dependent on initial E. cloacae inoculum. We identified 3 genetic factors which collectively contribute to E. cloacae resistance in response to the AMP cecropin: The PhoPQ-two-component system, OmpT-mediated proteolytic cleavage of cecropin, and Rcs-mediated membrane stress response. Altogether, this evidence suggests that multiple, independent mechanisms contribute to AMP resistance in E. cloacae.

Polar accumulation of pyoverdin and exit from stationary phase

Pyoverdin is a water-soluble metal-chelator synthesized by members of the genus Pseudomonas and used for the acquisition of insoluble ferric iron. Although freely diffusible in aqueous environments, preferential dissemination of pyoverdin among adjacent cells, fine-tuning of intracellular siderophore concentrations, and fitness advantages to pyoverdin-producing versus nonproducing cells, indicate control of location and release. Here, using time-lapse fluorescence microscopy to track single cells in growing microcolonies of Pseudomonas fluorescens SBW25, we show accumulation of pyoverdin at cell poles. Accumulation occurs on cessation of cell growth, is achieved by cross-feeding in pyoverdin-nonproducing mutants and is reversible. Moreover, accumulation coincides with localization of a fluorescent periplasmic reporter, suggesting that pyoverdin accumulation at cell poles is part of the general cellular response to starvation. Compatible with this conclusion is absence of non-accumulating phenotypes in a range of pyoverdin mutants. Analysis of the performance of pyoverdin-producing and nonproducing cells under conditions promoting polar accumulation shows an advantage to accumulation on resumption of growth after stress. Examination of pyoverdin polar accumulation in a multispecies community and in a range of laboratory and natural species of Pseudomonas, including P. aeruginosa PAO1 and P. putida KT2440, confirms that the phenotype is characteristic of Pseudomonas.

Tools and methods for high-throughput single-cell imaging with the mother machine

Despite much progress, image processing remains a significant bottleneck for high-throughput analysis of microscopy data. One popular platform for single-cell time-lapse imaging is the mother machine, which enables long-term tracking of microbial cells under precisely controlled growth conditions. While several mother machine image analysis pipelines have been developed in the past several years, adoption by a non-expert audience remains a challenge. To fill this gap, we implemented our own software, MM3, as a plugin for the multidimensional image viewer napari. napari-MM3 is a complete and modular image analysis pipeline for mother machine data, which takes advantage of the high-level interactivity of napari. Here, we give an overview of napari-MM3 and test it against several well-designed and widely-used image analysis pipelines, including BACMMAN and DeLTA. Researchers often analyze mother machine data with custom scripts using varied image analysis methods, but a quantitative comparison of the output of different pipelines has been lacking. To this end, we show that key single-cell physiological parameter correlations and distributions are robust to the choice of analysis method. However, we also find that small changes in thresholding parameters can systematically alter parameters extracted from single-cell imaging experiments. Moreover, we explicitly show that in deep learning based segmentation, "what you put is what you get" (WYPIWYG) - i.e., pixel-level variation in training data for cell segmentation can propagate to the model output and bias spatial and temporal measurements. Finally, while the primary purpose of this work is to introduce the image analysis software that we have developed over the last decade in our lab, we also provide information for those who want to implement mother-machine-based high-throughput imaging and analysis methods in their research.

Diversification of division mechanisms in endospore-forming bacteria revealed by analyses of peptidoglycan synthesis in Clostridioides difficile

The bacterial enzymes FtsW and FtsI, encoded in the highly conserved dcw gene cluster, are considered to be universally essential for the synthesis of septal peptidoglycan (PG) during cell division. Here, we show that the pathogen Clostridioides difficile lacks a canonical FtsW/FtsI pair, and its dcw-encoded PG synthases have undergone a specialization to fulfill sporulation-specific roles, including synthesizing septal PG during the sporulation-specific mode of cell division. Although these enzymes are directly regulated by canonical divisome components during this process, dcw-encoded PG synthases and their divisome regulators are dispensable for cell division during normal growth. Instead, C. difficile uses a bifunctional class A penicillin-binding protein as the core divisome PG synthase, revealing a previously unreported role for this class of enzymes. Our findings support that the emergence of endosporulation in the Firmicutes phylum facilitated the functional repurposing of cell division factors. Moreover, they indicate that C. difficile, and likely other clostridia, assemble a distinct divisome that therefore may represent a unique target for therapeutic interventions.

Interspecies interactions determine growth dynamics of biopolymer-degrading populations in microbial communities

Microbial communities perform essential ecosystem functions such as the remineralization of organic carbon that exists as biopolymers. The first step in mineralization is performed by biopolymer degraders, which harbor enzymes that can break down polymers into constituent oligo- or monomeric forms. The released nutrients not only allow degraders to grow, but also promote growth of cells that either consume the degradation products, i.e., exploiters, or consume metabolites released by the degraders or exploiters, i.e., scavengers. It is currently not clear how such remineralizing communities assemble at the microscale-how interactions between the different guilds influence their growth and spatial distribution, and hence the development and dynamics of the community. Here, we address this knowledge gap by studying marine microbial communities that grow on the abundant marine biopolymer alginate. We used batch growth assays and microfluidics coupled to time-lapse microscopy to quantitatively investigate growth and spatial distribution of single cells. We found that the presence of exploiters or scavengers alters the spatial distribution of degrader cells. In general, exploiters and scavengers-which we collectively refer to as cross-feeder cells-slowed down the growth of degrader cells. In addition, coexistence with cross-feeders altered the production of the extracellular enzymes that break down polymers by degrader cells. Our findings reveal that ecological interactions by nondegrading community members have a profound impact on the functions of microbial communities that remineralize carbon biopolymers in nature.

Tracking bacteria at high density with FAST, the Feature-Assisted Segmenter/Tracker

Most bacteria live attached to surfaces in densely-packed communities. While new experimental and imaging techniques are beginning to provide a window on the complex processes that play out in these communities, resolving the behaviour of individual cells through time and space remains a major challenge. Although a number of different software solutions have been developed to track microorganisms, these typically require users either to tune a large number of parameters or to groundtruth a large volume of imaging data to train a deep learning model-both manual processes which can be very time consuming for novel experiments. To overcome these limitations, we have developed FAST, the Feature-Assisted Segmenter/Tracker, which uses unsupervised machine learning to optimise tracking while maintaining ease of use. Our approach, rooted in information theory, largely eliminates the need for users to iteratively adjust parameters manually and make qualitative assessments of the resulting cell trajectories. Instead, FAST measures multiple distinguishing 'features' for each cell and then autonomously quantifies the amount of unique information each feature provides. We then use these measurements to determine how data from different features should be combined to minimize tracking errors. Comparing our algorithm with a naïve approach that uses cell position alone revealed that FAST produced 4 to 10 fold fewer tracking errors. The modular design of FAST combines our novel tracking method with tools for segmentation, extensive data visualisation, lineage assignment, and manual track correction. It is also highly extensible, allowing users to extract custom information from images and seamlessly integrate it into downstream analyses. FAST therefore enables high-throughput, data-rich analyses with minimal user input. It has been released for use either in Matlab or as a compiled stand-alone application, and is available at https://bit.ly/3vovDHn, along with extensive tutorials and detailed documentation.

Analyses of cell wall synthesis in Clostridioides difficile reveal a diversification in cell division mechanisms in endospore-forming bacteria

Current models of bacterial cell division assume that the core synthases of the multiprotein divisome complex, FtsW-FtsI, are the primary drivers of septal peptidoglycan (PG) synthesis. These enzymes are typically encoded in the highly conserved division and cell wall (dcw) cluster and are considered to be universally essential for cell division. Here, we combine bioinformatics analyses with functional characterization in the pathogen Clostridioides difficile to show that dcw-encoded PG synthases have undergone a surprising specialization in the sole endospore-forming phylum, Firmicutes, to fulfill sporulation-specific roles. We describe a novel role for these enzymes in synthesizing septal PG during the sporulation-specific mode of cell division in C. difficile. Although these enzymes are directly regulated by canonical divisome components during this process, dcw-encoded PG synthases and their divisome regulators are unexpectedly dispensable for cell division during normal growth. Instead, C. difficile uses its sole bifunctional class A penicillin-binding protein (aPBP) to drive cell division, revealing a previously unreported role for this class of PG synthases as the core divisome enzyme. Collectively, our findings reveal how the emergence of endosporulation in the Firmicutes phylum was a key driver for the functional repurposing of an otherwise universally conserved cellular process such as cell division. Moreover, they indicate that C. difficile, and likely other clostridia, assemble a divisome that differs markedly from previously studied bacteria, thus representing an attractive, unique target for therapeutic purposes.

Nanoscale clustering by O-antigen-Secretory Immunoglobulin-A binding limits outer membrane diffusion by encaging individual Salmonella cells

Secreted immunoglobulins, predominantly SIgA, influence the colonization and pathogenicity of mucosal bacteria. While part of this effect can be explained by SIgA-mediated bacterial aggregation, we have an incomplete picture of how SIgA binding influences cells independently of aggregation. Here we show that akin to microscale crosslinking of cells, SIgA targeting the Salmonella Typhimurium O-antigen extensively crosslinks the O-antigens on the surface of individual bacterial cells at the nanoscale. This crosslinking results in an essentially immobilized bacterial outer membrane. Membrane immobilization, combined with Bam-complex mediated outer membrane protein insertion results in biased inheritance of IgA-bound O-antigen, concentrating SIgA-bound O-antigen at the oldest poles during cell growth. By combining empirical measurements and simulations, we show that this SIgA-driven biased inheritance increases the rate at which phase-varied daughter cells become IgA-free: a process that can accelerate IgA escape via phase-variation of O-antigen structure. Our results show that O-antigen-crosslinking by SIgA impacts workings of the bacterial outer membrane, helping to mechanistically explain how SIgA may exert aggregation-independent effects on individual microbes colonizing the mucosae.

★Track: Inferred counting and tracking of replicating DNA loci

Fluorescent microscopy is the primary method to study DNA organization within cells. However, the variability and low signal/noise commonly associated with live-cell time-lapse imaging challenges quantitative measurements. In particular, obtaining quantitative or mechanistic insight often depends on the accurate tracking of fluorescent particles. Here, we present ★Track, an inference method that determines the most likely temporal tracking of replicating intracellular particles such DNA loci while accounting for missing, merged, and spurious detections. It allows the accurate prediction of particle copy numbers as well as the timing of replication events. We demonstrate ★Track's abilities and gain new insight into plasmid copy number control and the volume dependence of bacterial chromosome replication initiation. By enabling the accurate tracking of DNA loci, ★Track can help to uncover the mechanistic principles of chromosome organization and dynamics across a range of systems.

Characterization of an atypical but widespread type IV secretion system for transfer of the integrative and conjugative element (ICEclc) in Pseudomonas putida

Conjugation of DNA relies on multicomponent protein complexes bridging two bacterial cytoplasmic compartments. Whereas plasmid conjugation systems have been well documented, those of integrative and conjugative elements (ICEs) have remained poorly studied. We characterize here the conjugation system of the ICEclc element in Pseudomonas putida UWC1 that is a model for a widely distributed family of ICEs. By in frame deletion and complementation, we show the importance on ICE transfer of 22 genes in a 20-kb conserved ICE region. Protein comparisons recognized seven homologs to plasmid type IV secretion system components, another six homologs to frequent accessory proteins, and the rest without detectable counterparts. Stationary phase imaging of P. putida ICEclc with in-frame fluorescent protein fusions to predicted type IV components showed transfer-competent cell subpopulations with multiple fluorescent foci, largely overlapping in dual-labeled subcomponents, which is suggestive for multiple conjugation complexes per cell. Cross-dependencies between subcomponents in ICE-type IV secretion system assembly were revealed by quantitative foci image analysis in a variety of ICEclc mutant backgrounds. In conclusion, the ICEclc family presents an evolutionary distinct type IV conjugative system with transfer competent cells specialized in efficient transfer.

Cell aggregation is associated with enzyme secretion strategies in marine polysaccharide-degrading bacteria

Polysaccharide breakdown by bacteria requires the activity of enzymes that degrade polymers either intra- or extra-cellularly. The latter mechanism generates a localized pool of breakdown products that are accessible to the enzyme producers themselves as well as to other organisms. Marine bacterial taxa often show marked differences in the production and secretion of degradative enzymes that break down polysaccharides. These differences can have profound effects on the pool of diffusible breakdown products and hence on the ecological dynamics. However, the consequences of differences in enzymatic secretions on cellular growth dynamics and interactions are unclear. Here we study growth dynamics of single cells within populations of marine Vibrionaceae strains that grow on the abundant marine polymer alginate, using microfluidics coupled to quantitative single-cell analysis and mathematical modelling. We find that strains that have low extracellular secretions of alginate lyases aggregate more strongly than strains that secrete high levels of enzymes. One plausible reason for this observation is that low secretors require a higher cellular density to achieve maximal growth rates in comparison with high secretors. Our findings indicate that increased aggregation increases intercellular synergy amongst cells of low-secreting strains. By mathematically modelling the impact of the level of degradative enzyme secretion on the rate of diffusive oligomer loss, we find that enzymatic secretion capability modulates the propensity of cells within clonal populations to cooperate or compete with each other. Our experiments and models demonstrate that enzymatic secretion capabilities can be linked with the propensity of cell aggregation in marine bacteria that extracellularly catabolize polysaccharides.

Chlorate Contamination in Commercial Growth Media as a Source of Phenotypic Heterogeneity within Bacterial Populations

Under anaerobic conditions, chlorate is reduced to chlorite, a cytotoxic compound that triggers oxidative stress within bacterial cultures. We previously found that BD Bacto Casamino Acids were contaminated with chlorate. In this study, we investigated whether chlorate contamination is detectable in other commercial culture media. We provide evidence that in addition to different batches of BD Bacto Casamino Acids, several commercial agar powders are contaminated with chlorate. A direct consequence of this contamination is that, during anaerobic growth, Escherichia coli cells activate the expression of msrP, a gene encoding periplasmic methionine sulfoxide reductase, which repairs oxidized protein-bound methionine. We further demonstrate that during aerobic growth, progressive oxygen depletion triggers msrP expression in a subpopulation of cells due to the presence of chlorate. Hence, we propose that chlorate contamination in commercial growth media is a source of phenotypic heterogeneity within bacterial populations. IMPORTANCE Agar is arguably the most utilized solidifying agent for microbiological media. In this study, we show that agar powders from different suppliers, as well as certain batches of BD Bacto Casamino Acids, contain significant levels of chlorate. We demonstrate that this contamination induces the expression of a methionine sulfoxide reductase, suggesting the presence of intracellular oxidative damage. Our results should alert the microbiology community to a pitfall in the cultivation of microorganisms under laboratory conditions.

Single-Molecule Tracking of RNA Polymerase In and Out of Condensates in Live Bacterial Cells

Biomolecular condensates, first discovered in eukaryotic cells, were recently found in bacteria. The small size of these organisms presents unique challenges for identifying and characterizing condensates. Here, we describe a single-molecule approach for studying biomolecular condensates in live bacterial cells. Specifically, we outline a protocol to quantify the mobility of RNA polymerase in E. coli using HILO (highly inclined and laminated optical sheet) illumination with the photoconvertible fluorophore mMaple3. Our analysis classifies the trajectories of individual molecules by their local density, enabling a comparison of molecular mobilities between different subcellular compartments.

Adaptation delay causes a burst of mutations in bacteria responding to oxidative stress

Understanding the interplay between phenotypic and genetic adaptation is a focus of evolutionary biology. In bacteria, the oxidative stress response prevents mutagenesis by reactive oxygen species (ROS). We hypothesise that the stress response dynamics can therefore affect the timing of the mutation supply that fuels genetic adaptation to oxidative stress. We uncover that sudden hydrogen peroxide stress causes a burst of mutations. By developing single-molecule and single-cell microscopy methods, we determine how these mutation dynamics arise from phenotypic adaptation mechanisms. H₂ O₂ signalling by the transcription factor OxyR rapidly induces ROS-scavenging enzymes. However, an adaptation delay leaves cells vulnerable to the mutagenic and toxic effects of hydroxyl radicals generated by the Fenton reaction. Resulting DNA damage is counteracted by a spike in DNA repair activities during the adaptation delay. Absence of a mutation burst in cells with prior stress exposure or constitutive OxyR activation shows that the timing of phenotypic adaptation directly controls stress-induced mutagenesis. Similar observations for alkylation stress show that mutation bursts are a general phenomenon associated with adaptation delays.

High-throughput imaging and quantitative analysis uncovers the nature of plasmid positioning by ParABS

The faithful segregation and inheritance of bacterial chromosomes and low-copy number plasmids requires dedicated partitioning systems. The most common of these, ParABS, consists of ParA, a DNA-binding ATPase and ParB, a protein that binds to centromeric-like parS sequences on the DNA cargo. The resulting nucleoprotein complexes are believed to move up a self-generated gradient of nucleoid-associated ParA. However, it remains unclear how this leads to the observed cargo positioning and dynamics. In particular, the evaluation of models of plasmid positioning has been hindered by the lack of quantitative measurements of plasmid dynamics. Here, we use high-throughput imaging, analysis and modelling to determine the dynamical nature of these systems. We find that F plasmid is actively brought to specific subcellular home positions within the cell with dynamics akin to an over-damped spring. We develop a unified stochastic model that quantitatively explains this behaviour and predicts that cells with the lowest plasmid concentration transition to oscillatory dynamics. We confirm this prediction for F plasmid as well as a distantly-related ParABS system. Our results indicate that ParABS regularly positions plasmids across the nucleoid but operates just below the threshold of an oscillatory instability, which according to our model, minimises ATP consumption. Our work also clarifies how various plasmid dynamics are achievable in a single unified stochastic model. Overall, this work uncovers the dynamical nature of plasmid positioning by ParABS and provides insights relevant for chromosome-based systems.

Omnipose: a high-precision morphology-independent solution for bacterial cell segmentation

Advances in microscopy hold great promise for allowing quantitative and precise measurement of morphological and molecular phenomena at the single-cell level in bacteria; however, the potential of this approach is ultimately limited by the availability of methods to faithfully segment cells independent of their morphological or optical characteristics. Here, we present Omnipose, a deep neural network image-segmentation algorithm. Unique network outputs such as the gradient of the distance field allow Omnipose to accurately segment cells on which current algorithms, including its predecessor, Cellpose, produce errors. We show that Omnipose achieves unprecedented segmentation performance on mixed bacterial cultures, antibiotic-treated cells and cells of elongated or branched morphology. Furthermore, the benefits of Omnipose extend to non-bacterial subjects, varied imaging modalities and three-dimensional objects. Finally, we demonstrate the utility of Omnipose in the characterization of extreme morphological phenotypes that arise during interbacterial antagonism. Our results distinguish Omnipose as a powerful tool for characterizing diverse and arbitrarily shaped cell types from imaging data.

GEMA-An Automatic Segmentation Method for Real-Time Analysis of Mammalian Cell Growth in Microfluidic Devices

Nowadays, image analysis has a relevant role in most scientific and research areas. This process is used to extract and understand information from images to obtain a model, knowledge, and rules in the decision process. In the case of biological areas, images are acquired to describe the behavior of a biological agent in time such as cells using a mathematical and computational approach to generate a system with automatic control. In this paper, MCF7 cells are used to model their growth and death when they have been injected with a drug. These mammalian cells allow understanding of behavior, gene expression, and drug resistance to breast cancer. For this, an automatic segmentation method called GEMA is presented to analyze the apoptosis and confluence stages of culture by measuring the increase or decrease of the image area occupied by cells in microfluidic devices. In vitro, the biological experiments can be analyzed through a sequence of images taken at specific intervals of time. To automate the image segmentation, the proposed algorithm is based on a Gabor filter, a coefficient of variation (CV), and linear regression. This allows the processing of images in real time during the evolution of biological experiments. Moreover, GEMA has been compared with another three representative methods such as gold standard (manual segmentation), morphological gradient, and a semi-automatic algorithm using FIJI. The experiments show promising results, due to the proposed algorithm achieving an accuracy above 90% and a lower computation time because it requires on average 1 s to process each image. This makes it suitable for image-based real-time automatization of biological lab-on-a-chip experiments.

Cell region fingerprints enable highly precise single-cell tracking and lineage reconstruction

Experimental studies of cell growth, inheritance and their associated processes by microscopy require accurate single-cell observations of sufficient duration to reconstruct the genealogy. However, cell tracking-assigning identical cells on consecutive images to a track-is often challenging, resulting in laborious manual verification. Here, we propose fingerprints to identify problematic assignments rapidly. A fingerprint distance compares the structural information contained in the low frequencies of a Fourier transform to measure the similarity between cells in two consecutive images. We show that fingerprints are broadly applicable across cell types and image modalities, provided the image has sufficient structural information. Our tracker (Trac^X) uses fingerprints to reject unlikely assignments, thereby increasing tracking performance on published and newly generated long-term data sets. For Saccharomyces cerevisiae, we propose a comprehensive model for cell size control at the single-cell and population level centered on the Whi5 regulator, demonstrating how precise tracking can help uncover previously undescribed single-cell biology.

DeepBacs for multi-task bacterial image analysis using open-source deep learning approaches

This work demonstrates and guides how to use a range of state-of-the-art artificial neural-networks to analyse bacterial microscopy images using the recently developed ZeroCostDL4Mic platform. We generated a database of image datasets used to train networks for various image analysis tasks and present strategies for data acquisition and curation, as well as model training. We showcase different deep learning (DL) approaches for segmenting bright field and fluorescence images of different bacterial species, use object detection to classify different growth stages in time-lapse imaging data, and carry out DL-assisted phenotypic profiling of antibiotic-treated cells. To also demonstrate the ability of DL to enhance low-phototoxicity live-cell microscopy, we showcase how image denoising can allow researchers to attain high-fidelity data in faster and longer imaging. Finally, artificial labelling of cell membranes and predictions of super-resolution images allow for accurate mapping of cell shape and intracellular targets. Our purposefully-built database of training and testing data aids in novice users' training, enabling them to quickly explore how to analyse their data through DL. We hope this lays a fertile ground for the efficient application of DL in microbiology and fosters the creation of tools for bacterial cell biology and antibiotic research.

A bistable prokaryotic differentiation system underlying development of conjugative transfer competence

The mechanisms and impact of horizontal gene transfer processes to distribute gene functions with potential adaptive benefit among prokaryotes have been well documented. In contrast, little is known about the life-style of mobile elements mediating horizontal gene transfer, whereas this is the ultimate determinant for their transfer fitness. Here, we investigate the life-style of an integrative and conjugative element (ICE) within the genus Pseudomonas that is a model for a widespread family transmitting genes for xenobiotic compound metabolism and antibiotic resistances. Previous work showed bimodal ICE activation, but by using single cell time-lapse microscopy coupled to combinations of chromosomally integrated single copy ICE promoter-driven fluorescence reporters, RNA sequencing and mutant analysis, we now describe the complete regulon leading to the arisal of differentiated dedicated transfer competent cells. The regulon encompasses at least three regulatory nodes and five (possibly six) further conserved gene clusters on the ICE that all become expressed under stationary phase conditions. Time-lapse microscopy indicated expression of two regulatory nodes (i.e., bisR and alpA-bisDC) to precede that of the other clusters. Notably, expression of all clusters except of bisR was confined to the same cell subpopulation, and was dependent on the same key ICE regulatory factors. The ICE thus only transfers from a small fraction of cells in a population, with an estimated proportion of between 1.7–4%, which express various components of a dedicated transfer competence program imposed by the ICE, and form the centerpiece of ICE conjugation. The components mediating transfer competence are widely conserved, underscoring their selected fitness for efficient transfer of this class of mobile elements.

Horizontal gene transfer processes among prokaryotes have raised wide interest, which is attested by broad public health concern of rapid spread of antibiotic resistances. However, we typically take for granted that horizontal transfer is the result of some underlying spontaneous low frequency event, but this is not necessarily the case. As we show here, mobile genetic elements from the class of integrative and conjugative elements (ICEs) impose a coordinated program on the host cell in order to transfer, leading to an exclusive differentiated set of transfer competent cells. We base our conclusions on single cell microscopy studies to compare the rare activation of ICE promoters in individual cells in bacterial populations, and on mutant and RNA-seq analysis to show their dependency on ICE factors. This is an important finding because it implies that conjugation itself is subject to natural selection, which would lead to selection of fitter elements that transfer better or become more widespread.

Live cell microscopy: From image to insight

Live-cell microscopy is a powerful tool that can reveal cellular behavior as well as the underlying molecular processes. A key advantage of microscopy is that by visualizing biological processes, it can provide direct insights. Nevertheless, live-cell imaging can be technically challenging and prone to artifacts. For a successful experiment, many careful decisions are required at all steps from hardware selection to downstream image analysis. Facing these questions can be particularly intimidating due to the requirement for expertise in multiple disciplines, ranging from optics, biophysics, and programming to cell biology. In this review, we aim to summarize the key points that need to be considered when setting up and analyzing a live-cell imaging experiment. While we put a particular focus on yeast, many of the concepts discussed are applicable also to other organisms. In addition, we discuss reporting and data sharing strategies that we think are critical to improve reproducibility in the field.

Development of a Dual-Fluorescent-Reporter System in Clostridioides difficile Reveals a Division of Labor between Virulence and Transmission Gene Expression

The bacterial pathogen Clostridioides difficile causes gastroenteritis by producing toxins and transmits disease by making resistant spores. Toxin and spore production are energy-expensive processes that are regulated by multiple transcription factors in response to many environmental inputs. While toxin and sporulation genes are both induced in only a subset of C. difficile cells, the relationship between these two subpopulations remains unclear. To address whether C. difficile coordinates the generation of these subpopulations, we developed a dual-transcriptional-reporter system that allows toxin and sporulation gene expression to be simultaneously visualized at the single-cell level using chromosomally encoded mScarlet and mNeonGreen fluorescent transcriptional reporters. We then adapted an automated image analysis pipeline to quantify toxin and sporulation gene expression in thousands of individual cells under different medium conditions and in different genetic backgrounds. These analyses revealed that toxin and sporulation gene expression rarely overlap during growth on agar plates, whereas broth culture increases this overlap. Our results suggest that certain growth conditions promote a “division of labor” between transmission and virulence gene expression, highlighting how environmental inputs influence these subpopulations. Our data further suggest that the RstA transcriptional regulator skews the population to activate sporulation genes rather than toxin genes. Given that recent work has revealed population-wide heterogeneity for numerous cellular processes in C. difficile, we anticipate that our dual-reporter system will be broadly useful for determining the overlap between these subpopulations.

IMPORTANCEClostridioides difficile is an important nosocomial pathogen that causes severe diarrhea by producing toxins and transmits disease by producing spores. While both processes are crucial for C. difficile disease, only a subset of cells express toxins and/or undergo sporulation. Whether C. difficile coordinates the subset of cells inducing these energy-expensive processes remains unknown. To address this question, we developed a dual-fluorescent-reporter system coupled with an automated image analysis pipeline to rapidly compare the expression of two genes of interest across thousands of cells. Using this system, we discovered that certain growth conditions, particularly growth on agar plates, induce a “division of labor” between toxin and sporulation gene expression. Since C. difficile exhibits phenotypic heterogeneity for numerous vital cellular processes, this novel dual-reporter system will enable future studies aimed at understanding how C. difficile coordinates various subpopulations throughout its infectious disease cycle.

Positive Charges Are Important for the SOS Constitutive Phenotype in recA730 and recA1202 Mutants of Escherichia coli K-12

In Escherichia coli K-12, RecA binds to single-strand DNA (ssDNA) created by DNA damage to form a protein-DNA helical filament that serves to catalyze LexA autoproteolysis, which induces the SOS response. The SOS constitutive (SOS^C) mutations recA730(E38K) and recA1202(Q184K) are both on the outside of the RecA filament, opposite to the face that binds DNA. recA730(E38K) is also able to suppress the UV sensitivity caused by recF mutations. Both SOS^C expression and recF suppression are thought to be due to RecA730's ability to compete better for ssDNA coated with ssDNA-binding protein than the wild type. We tested whether other positively charged residues at these two positions would lead to SOS^C expression and recF suppression. We found that 5/6 positively charged residues were SOS^C and 4/5 of these were also recF suppressors. While other mutations at these two positions (and others) were recF suppressors, none were SOS^C. Three recF suppressors could be made moderately SOS^C by adding a recA operator mutation. We hypothesize two mechanisms for SOS^C expression: the first suggests that the positive charge at positions 38 and 184 attract negatively charged molecules that block interactions that would destabilize the RecA-DNA filament, and the second involves more stable filaments caused by increases in mutant RecA concentration. IMPORTANCE In Escherichia coli K-12, SOS constitutive (SOS^C) mutants of recA turn on the SOS response in the absence of DNA damage. Some SOS^C mutants are also able to indirectly suppress the UV sensitivity of recF mutations. Two SOS^C mutations, recA730(E38K) and recA1202(Q184K), define a surface on the RecA-DNA filament opposite the surface that binds DNA. Both introduce positive charges, and recA730 is a recF suppressor. We tested whether the positive charge at these two positions was required for SOS^C expression and recF suppression. We found a high correlation between the positive charge, SOS^C expression and recF suppression. We also found several other mutations (different types) that provide recF suppression but no SOS^C expression.

Dynamic gene expression and growth underlie cell-to-cell heterogeneity in Escherichia coli stress response

Individual bacteria that share identical genomes and growth environments can display substantial cell-to-cell differences in expression of stress-response genes and single-cell growth rates. This phenotypic heterogeneity can impact the survival of single cells facing sudden stress. However, the windows of time that cells spend in vulnerable or tolerant states are often unknown. We quantify the temporal expression of a suite of stress-response reporters, while simultaneously monitoring growth. We observe pulsatile expression across genes with a range of stress-response functions, finding that single-cell growth rates are often anticorrelated with reporter levels. These dynamic phenotypic differences have a concrete link to function, in which individual cells undergoing a pulse of elevated expression and slow growth are predisposed to survive antibiotic exposure.

Cell-to-cell heterogeneity in gene expression and growth can have critical functional consequences, such as determining whether individual bacteria survive or die following stress. Although phenotypic variability is well documented, the dynamics that underlie it are often unknown. This information is important because dramatically different outcomes can arise from gradual versus rapid changes in expression and growth. Using single-cell time-lapse microscopy, we measured the temporal expression of a suite of stress-response reporters in Escherichia coli, while simultaneously monitoring growth rate. In conditions without stress, we found several examples of pulsatile expression. Single-cell growth rates were often anticorrelated with reporter levels, with changes in growth preceding changes in expression. These dynamics have functional consequences, which we demonstrate by measuring survival after challenging cells with the antibiotic ciprofloxacin. Our results suggest that fluctuations in both gene expression and growth dynamics in stress-response networks have direct consequences on survival.

Types and functions of heterogeneity in mycobacteria

The remarkable ability of Mycobacterium tuberculosis to survive attacks from the host immune response and drug treatment is due to the resilience of a few bacilli rather than a result of survival of the entire population. Maintenance of mycobacterial subpopulations with distinct phenotypic characteristics is key for survival in the face of dynamic and variable stressors encountered during infection. Mycobacterial populations develop a wide range of phenotypes through an innate asymmetric growth pattern and adaptation to fluctuating microenvironments during infection that point to heterogeneity being a vital survival strategy. In this Review, we describe different types of mycobacterial heterogeneity and discuss how heterogeneity is generated and regulated in response to environmental cues. We discuss how this heterogeneity may have a key role in recording memory of their environment at both the single-cell level and the population level to give mycobacterial populations plasticity to withstand complex stressors.

No Cell Left behind: Automated, Stochastic, Physics-Based Tracking of Every Cell in a Dense, Growing Colony

Motivation: Precise tracking of individual cells—especially tracking the family lineage, for example in a developing embryo—has widespread applications in biology and medicine. Due to significant noise in microscope images, existing methods have difficulty precisely tracking cell activities. These difficulties often require human intervention to resolve. Humans are helpful because our brain naturally and automatically builds a simulation “model” of any scene that we observe. Because we understand simple truths about the world—for example cells can move and divide, but they cannot instantaneously move vast distances—this model “in our heads” helps us to severely constrain the possible interpretations of what we see, allowing us to easily distinguish signal from noise, and track the motion of cells even in the presence of extreme levels of noise that would completely confound existing automated methods. Results: Here, we mimic the ability of the human brain by building an explicit computer simulation model of the scene. Our simulated cells are programmed to allow movement and cell division consistent with reality. At each video frame, we stochastically generate millions of nearby “Universes” and evolve them stochastically to the next frame. We then find and fit the best universes to reality by minimizing the residual between the real image frame and a synthetic image of the simulation. The rule-based simulation puts extremely stringent constraints on possible interpretations of the data, allowing our system to perform far better than existing methods even in the presense of extreme levels of image noise. We demonstrate the viability of this method by accurately tracking every cell in a colony that grows from 4 to over 300 individuals, doing about as well as a human can in the difficult task of tracking cell lineages.

Subcellular Localization Defects Characterize Ribose-Binding Mutant Proteins with New Ligand Properties in Escherichia coli

Periplasmic binding proteins have been previously proclaimed as a general scaffold to design sensor proteins with new recognition specificities for nonnatural compounds. Such proteins can be integrated in bacterial bioreporter chassis with hybrid chemoreceptors to produce a concentration-dependent signal after ligand binding to the sensor cell. However, computationally designed new ligand-binding properties ignore the more general properties of periplasmic binding proteins, such as their periplasmic translocation, dynamic transition of open and closed forms, and interactions with membrane receptors. In order to better understand the roles of such general properties in periplasmic signaling behavior, we studied the subcellular localization of ribose-binding protein (RbsB) in Escherichia coli in comparison to a recently evolved set of mutants designed to bind 1,3-cyclohexanediol. As proxies for localization, we calibrated and deployed C-terminal end mCherry fluorescent protein fusions. Whereas RbsB-mCherry coherently localized to the periplasmic space and accumulated in (periplasmic) polar regions depending on chemoreceptor availability, mutant RbsB-mCherry expression resulted in high fluorescence cell-to-cell variability. This resulted in higher proportions of cells devoid of clear polar foci and of cells with multiple fluorescent foci elsewhere, suggesting poorer translocation, periplasmic autoaggregation, and mislocalization. Analysis of RbsB mutants and mutant libraries at different stages of directed evolution suggested overall improvement to more RbsB-wild-type-like characteristics, which was corroborated by structure predictions. Our results show that defects in periplasmic localization of mutant RbsB proteins partly explain their poor sensing performance. Future efforts should be directed to predicting or selecting secondary mutations outside computationally designed binding pockets, taking folding, translocation, and receptor interactions into account. IMPORTANCE Biosensor engineering relies on transcription factors or signaling proteins to provide the actual sensory functions for the target chemicals. Since for many compounds there are no natural sensory proteins, there is a general interest in methods that could unlock routes to obtaining new ligand-binding properties. Bacterial periplasmic binding proteins (PBPs) form an interesting family of proteins to explore for this purpose, because there is a large natural variety suggesting evolutionary trajectories to bind new ligands. PBPs are conserved and amenable to accurate computational binding pocket predictions. However, studying ribose-binding protein in Escherichia coli, we discovered that designed variants have defects in their proper localization in the cell, which can impair appropriate sensor signaling. This indicates that functional sensing capacity of PBPs cannot be obtained solely through computational design of the ligand-binding pocket but must take other properties of the protein into account, which are currently very difficult to predict.

Super-Resolution Microscopy and Tracking of DNA-Binding Proteins in Bacterial Cells

The ability to detect individual fluorescent molecules inside living cells has enabled a range of powerful microscopy techniques that resolve biological processes on the molecular scale. These methods have also transformed the study of bacterial cell biology, which was previously obstructed by the limited spatial resolution of conventional microscopy. In the case of DNA-binding proteins, super-resolution microscopy can visualize the detailed spatial organization of DNA replication, transcription, and repair processes by reconstructing a map of single-molecule localizations. Furthermore, DNA-binding activities can be observed directly by tracking protein movement in real time. This allows identifying subpopulations of DNA-bound and diffusing proteins, and can be used to measure DNA-binding times in vivo. This chapter provides a detailed protocol for super-resolution microscopy and tracking of DNA-binding proteins in Escherichia coli cells. The protocol covers the genetic engineering and fluorescent labeling of strains and describes data acquisition and analysis procedures, such as super-resolution image reconstruction, mapping single-molecule tracks, computing diffusion coefficients to identify molecular subpopulations with different mobility, and analysis of DNA-binding kinetics. While the focus is on the study of bacterial chromosome biology, these approaches are generally applicable to other molecular processes and cell types.

DeLTA 2.0: A deep learning pipeline for quantifying single-cell spatial and temporal dynamics

Improvements in microscopy software and hardware have dramatically increased the pace of image acquisition, making analysis a major bottleneck in generating quantitative, single-cell data. Although tools for segmenting and tracking bacteria within time-lapse images exist, most require human input, are specialized to the experimental set up, or lack accuracy. Here, we introduce DeLTA 2.0, a purely Python workflow that can rapidly and accurately analyze images of single cells on two-dimensional surfaces to quantify gene expression and cell growth. The algorithm uses deep convolutional neural networks to extract single-cell information from time-lapse images, requiring no human input after training. DeLTA 2.0 retains all the functionality of the original version, which was optimized for bacteria growing in the mother machine microfluidic device, but extends results to two-dimensional growth environments. Two-dimensional environments represent an important class of data because they are more straightforward to implement experimentally, they offer the potential for studies using co-cultures of cells, and they can be used to quantify spatial effects and multi-generational phenomena. However, segmentation and tracking are significantly more challenging tasks in two-dimensions due to exponential increases in the number of cells. To showcase this new functionality, we analyze mixed populations of antibiotic resistant and susceptible cells, and also track pole age and growth rate across generations. In addition to the two-dimensional capabilities, we also introduce several major improvements to the code that increase accessibility, including the ability to accept many standard microscopy file formats as inputs and the introduction of a Google Colab notebook so users can try the software without installing the code on their local machine. DeLTA 2.0 is rapid, with run times of less than 10 minutes for complete movies with hundreds of cells, and is highly accurate, with error rates around 1%, making it a powerful tool for analyzing time-lapse microscopy data.

Spatiotemporal localization of proteins in mycobacteria

Although prokaryotic organisms lack traditional organelles, they must still organize cellular structures in space and time, challenges that different species solve differently. To systematically define the subcellular architecture of mycobacteria, we perform high-throughput imaging of a library of fluorescently tagged proteins expressed in Mycobacterium smegmatis and develop a customized computational pipeline, MOMIA and GEMATRIA, to analyze these data. Our results establish a spatial organization network of over 700 conserved mycobacterial proteins and reveal a coherent localization pattern for many proteins of known function, including those in translation, energy metabolism, cell growth and division, as well as proteins of unknown function. Furthermore, our pipeline exploits morphologic proxies to enable a pseudo-temporal approximation of protein localization and identifies previously uncharacterized cell-cycle-dependent dynamics of essential mycobacterial proteins. Collectively, these data provide a systems perspective on the subcellular organization of mycobacteria and provide tools for the analysis of bacteria with non-standard growth characteristics.

Zhu et al. develop a two-stage image analysis pipeline, MOMIA and GEMATRIA, that efficiently models the spatial and temporal dynamics of over 700 conserved proteins in M. smegmatis. Through the analysis they report spatial constraints of mycobacterial ribosomes and membrane complexes and reconstruct temporal dynamics from still image data.

Acyl carrier protein promotes MukBEF action in Escherichia coli chromosome organization-segregation

Structural Maintenance of Chromosomes (SMC) complexes act ubiquitously to compact DNA linearly, thereby facilitating chromosome organization-segregation. SMC proteins have a conserved architecture, with a dimerization hinge and an ATPase head domain separated by a long antiparallel intramolecular coiled-coil. Dimeric SMC proteins interact with essential accessory proteins, kleisins that bridge the two subunits of an SMC dimer, and HAWK/KITE proteins that interact with kleisins. The ATPase activity of the Escherichia coli SMC protein, MukB, which is essential for its in vivo function, requires its interaction with the dimeric kleisin, MukF that in turn interacts with the KITE protein, MukE. Here we demonstrate that, in addition, MukB interacts specifically with Acyl Carrier Protein (AcpP) that has essential functions in fatty acid synthesis. We characterize the AcpP interaction at the joint of the MukB coiled-coil and show that the interaction is necessary for MukB ATPase and for MukBEF function in vivo.

Analytics and visualization tools to characterize single-cell stochasticity using bacterial single-cell movie cytometry data

Background

Time-lapse microscopy live-cell imaging is essential for studying the evolution of bacterial communities at single-cell resolution. It allows capturing detailed information about the morphology, gene expression, and spatial characteristics of individual cells at every time instance of the imaging experiment. The image analysis of bacterial "single-cell movies" (videos) generates big data in the form of multidimensional time series of measured bacterial attributes. If properly analyzed, these datasets can help us decipher the bacterial communities' growth dynamics and identify the sources and potential functional role of intra- and inter-subpopulation heterogeneity. Recent research has highlighted the importance of investigating the role of biological "noise" in gene regulation, cell growth, cell division, etc. Single-cell analytics of complex single-cell movie datasets, capturing the interaction of multiple micro-colonies with thousands of cells, can shed light on essential phenomena for human health, such as the competition of pathogens and benign microbiome cells, the emergence of dormant cells (“persisters”), the formation of biofilms under different stress conditions, etc. However, highly accurate and automated bacterial bioimage analysis and single-cell analytics methods remain elusive, even though they are required before we can routinely exploit the plethora of data that single-cell movies generate.

Results

We present visualization and single-cell analytics using R (ViSCAR), a set of methods and corresponding functions, to visually explore and correlate single-cell attributes generated from the image processing of complex bacterial single-cell movies. They can be used to model and visualize the spatiotemporal evolution of attributes at different levels of the microbial community organization (i.e., cell population, colony, generation, etc.), to discover possible epigenetic information transfer across cell generations, infer mathematical and statistical models describing various stochastic phenomena (e.g., cell growth, cell division), and even identify and auto-correct errors introduced unavoidably during the bioimage analysis of a dense movie with thousands of overcrowded cells in the microscope's field of view.

Conclusions

ViSCAR empowers researchers to capture and characterize the stochasticity, uncover the mechanisms leading to cellular phenotypes of interest, and decipher a large heterogeneous microbial communities' dynamic behavior. ViSCAR source code is available from GitLab at https://gitlab.com/ManolakosLab/viscar.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12859-021-04409-9.

Competitive binding of MatP and topoisomerase IV to the MukB hinge domain

Structural Maintenance of Chromosomes (SMC) complexes have ubiquitous roles in compacting DNA linearly, thereby promoting chromosome organization-segregation. Interaction between the Escherichia coli SMC complex, MukBEF, and matS-bound MatP in the chromosome replication termination region, ter, results in depletion of MukBEF from ter, a process essential for efficient daughter chromosome individualization and for preferential association of MukBEF with the replication origin region. Chromosome-associated MukBEF complexes also interact with topoisomerase IV (ParC₂E₂), so that their chromosome distribution mirrors that of MukBEF. We demonstrate that MatP and ParC have an overlapping binding interface on the MukB hinge, leading to their mutually exclusive binding, which occurs with the same dimer to dimer stoichiometry. Furthermore, we show that matS DNA competes with the MukB hinge for MatP binding. Cells expressing MukBEF complexes that are mutated at the ParC/MatP binding interface are impaired in ParC binding and have a mild defect in MukBEF function. These data highlight competitive binding as a means of globally regulating MukBEF-topoisomerase IV activity in space and time.

Misic, a general deep learning-based method for the high-throughput cell segmentation of complex bacterial communities

Studies of bacterial communities, biofilms and microbiomes, are multiplying due to their impact on health and ecology. Live imaging of microbial communities requires new tools for the robust identification of bacterial cells in dense and often inter-species populations, sometimes over very large scales. Here, we developed MiSiC, a general deep-learning-based 2D segmentation method that automatically segments single bacteria in complex images of interacting bacterial communities with very little parameter adjustment, independent of the microscopy settings and imaging modality. Using a bacterial predator-prey interaction model, we demonstrate that MiSiC enables the analysis of interspecies interactions, resolving processes at subcellular scales and discriminating between species in millimeter size datasets. The simple implementation of MiSiC and the relatively low need in computing power make its use broadly accessible to fields interested in bacterial interactions and cell biology.

Spatial transcriptomics of planktonic and sessile bacterial populations at single-cell resolution

Capturing the heterogeneous phenotypes of microbial populations at relevant spatiotemporal scales is highly challenging. Here, we present par-seqFISH (parallel sequential fluorescence in situ hybridization), a transcriptome-imaging approach that records gene expression and spatial context within microscale assemblies at a single-cell and molecule resolution. We applied this approach to the opportunistic pathogen Pseudomonas aeruginosa, analyzing about 600,000 individuals across dozens of conditions in planktonic and biofilm cultures. We identified numerous metabolic- and virulence-related transcriptional states that emerged dynamically during planktonic growth, as well as highly spatially resolved metabolic heterogeneity in sessile populations. Our data reveal that distinct physiological states can coexist within the same biofilm just several micrometers away, underscoring the importance of the microenvironment. Our results illustrate the complex dynamics of microbial populations and present a new way of studying them at high resolution.

Advances and opportunities in image analysis of bacterial cells and communities

The cellular morphology and sub-cellular spatial structure critically influence the function of microbial cells. Similarly, the spatial arrangement of genotypes and phenotypes in microbial communities has important consequences for cooperation, competition, and community functions. Fluorescence microscopy techniques are widely used to measure spatial structure inside living cells and communities, which often results in large numbers of images that are difficult or impossible to analyze manually. The rapidly evolving progress in computational image analysis has recently enabled the quantification of a large number of properties of single cells and communities, based on traditional analysis techniques and convolutional neural networks. Here, we provide a brief introduction to core concepts of automated image processing, recent software tools and how to validate image analysis results. We also discuss recent advances in image analysis of microbial cells and communities, and how these advances open up opportunities for quantitative studies of spatiotemporal processes in microbiology, based on image cytometry and adaptive microscope control.

Nonrandom segregation of sister chromosomes by Escherichia coli MukBEF

Structural maintenance of chromosomes (SMC) complexes contribute to chromosome organization in all domains of life. In Escherichia coli, MukBEF, the functional SMC homolog, promotes spatiotemporal chromosome organization and faithful chromosome segregation. Here, we address the relative contributions of MukBEF and the replication terminus (ter) binding protein, MatP, to chromosome organization-segregation. We show that MukBEF, but not MatP, is required for the normal localization of the origin of replication to midcell and for the establishment of translational symmetry between newly replicated sister chromosomes. Overall, chromosome orientation is normally maintained through division from one generation to the next. Analysis of loci flanking the replication termination region (ter), which demark the ends of the linearly organized portion of the nucleoid, demonstrates that MatP is required for maintenance of chromosome orientation. We show that DNA-bound β2-processivity clamps, which mark the lagging strands at DNA replication forks, localize to the cell center, independent of replisome location but dependent on MukBEF action, and consistent with translational symmetry of sister chromosomes. Finally, we directly show that the older ("immortal") template DNA strand, propagated from previous generations, is preferentially inherited by the cell forming at the old pole, dependent on MukBEF and MatP. The work further implicates MukBEF and MatP as central players in chromosome organization, segregation, and nonrandom inheritance of genetic material and suggests a general framework for understanding how chromosome conformation and dynamics shape subcellular organization.

Microbiota-derived metabolites inhibit Salmonella virulent subpopulation development by acting on single-cell behaviors

Salmonella spp. express Salmonella pathogenicity island 1 Type III Secretion System 1 (T3SS-1) genes to mediate the initial phase of interaction with their host. Prior studies indicate short-chain fatty acids, microbial metabolites at high concentrations in the gastrointestinal tract, limit population-level T3SS-1 gene expression. However, only a subset of Salmonella cells in a population express these genes, suggesting short-chain fatty acids could decrease T3SS-1 population-level expression by acting on per-cell expression or the proportion of expressing cells. Here, we combine single-cell, theoretical, and molecular approaches to address the effect of short-chain fatty acids on T3SS-1 expression. Our in vitro results show short-chain fatty acids do not repress T3SS-1 expression by individual cells. Rather, these compounds act to selectively slow the growth of T3SS-1-expressing cells, ultimately decreasing their frequency in the population. Further experiments indicate slowed growth arises from short-chain fatty acid-mediated depletion of the proton motive force. By influencing the T3SS-1 cell-type proportions, our findings imply gut microbial metabolites act on cooperation between the two cell types and ultimately influence Salmonella's capacity to establish within a host.

Single-molecule imaging of LexA degradation in Escherichia coli elucidates regulatory mechanisms and heterogeneity of the SOS response

The bacterial SOS response stands as a paradigm of gene networks controlled by a master transcriptional regulator. Self-cleavage of the SOS repressor, LexA, induces a wide range of cell functions that are critical for survival and adaptation when bacteria experience stress conditions¹, including DNA repair², mutagenesis^3,4, horizontal gene transfer^5–7, filamentous growth, and the induction of bacterial toxins^8–12, toxin-antitoxin systems¹³, virulence factors^6,14, and prophages^15–17. SOS induction is also implicated in biofilm formation and antibiotic persistence^11,18–20. Considering the fitness burden of these functions, it is surprising that the expression of LexA-regulated genes is highly variable across cells^10,21–23 and that cell subpopulations induce the SOS response spontaneously even in the absence of stress exposure^{9,11,12,16,24,25}. Whether this reflects a population survival strategy or a regulatory inaccuracy is unclear, as are the mechanisms underlying SOS heterogeneity. Here, we developed a single-molecule imaging approach based on a HaloTag fusion to directly monitor LexA inside live Escherichia coli cells, demonstrating the existence of 3 main states of LexA: DNA-bound stationary molecules, free LexA and degraded LexA species. These analyses elucidate the mechanisms by which DNA-binding and degradation of LexA regulate the SOS response in vivo. We show that self-cleavage of LexA occurs frequently throughout the population during unperturbed growth, rather than being restricted to a subpopulation of cells, which causes substantial cell-to-cell variation in LexA abundances. LexA variability underlies SOS gene expression heterogeneity and triggers spontaneous SOS pulses, which enhance bacterial survival in anticipation of stress.