Tracking bacterial lineages in complex and dynamic environments with applications for growth control and persistence

Antimicrobial peptides in nematode secretions - Unveiling biotechnological opportunities for therapeutics and beyond

Gastrointestinal (GI) parasitic nematodes threaten food security and affect human health and animal welfare globally. Current anthelmintics for use in humans and livestock are challenged by continuous re-infections and the emergence and spread of multidrug resistance, underscoring an urgent need to identify novel control targets for therapeutic exploitation. Recent evidence has highlighted the occurrence of complex interplay between GI parasitic nematodes of humans and livestock and the resident host gut microbiota. Antimicrobial peptides (AMPs) found within nematode biofluids have emerged as potential effectors of these interactions. This review delves into the occurrence, structure, and function of nematode AMPs, highlighting their potential as targets for drug discovery and development. We argue that an integrated approach combining advanced analytical techniques, scalable production methods, and innovative experimental models is needed to unlock the full potential of nematode AMPs and pave the way for the discovery and development of sustainable parasite control strategies.

Observation of persister cell histories reveals diverse modes of survival in antibiotic persistence

Bacterial persistence is a phenomenon in which a small fraction of isogenic bacterial cells survives a lethal dose of antibiotics. Although the refractoriness of persistent cell populations has classically been attributed to growth-inactive cells generated before drug exposure, evidence is accumulating that actively growing cell fractions can also generate persister cells. However, single-cell characterization of persister cell history remains limited due to the extremely low frequencies of persisters. Here, we visualize the responses of over one million individual cells of wildtype Escherichia coli to lethal doses of antibiotics, sampling cells from different growth phases and culture media into a microfluidic device. We show that when cells sampled from exponentially growing populations were treated with ampicillin or ciprofloxacin, most persisters were growing before antibiotic treatment. Growing persisters exhibited heterogeneous survival dynamics, including continuous growth and fission with L-form-like morphologies, responsive growth arrest, or post-exposure filamentation. Incubating cells under stationary phase conditions increased both the frequency and the probability of survival of non-growing cells to ampicillin. Under ciprofloxacin, however, all persisters identified were growing before the antibiotic treatment, including samples from post-stationary phase culture. These results reveal diverse persister cell dynamics that depend on antibiotic types and pre-exposure history.

Mechanistic basis for non-exponential bacterial growth

Bacterial populations typically exhibit exponential growth under resource-rich conditions, yet individual cells often deviate from this pattern. Recent work has shown that the elongation rates of Escherichia coli and Caulobacter crescentus increase throughout the cell cycle (super-exponential growth), while Bacillus subtilis displays a mid-cycle minimum (convex growth), and Mycobacterium tuberculosis grows linearly. Here, we develop a single-cell model linking gene expression, proteome allocation, and mass growth to explain these diverse growth trajectories. By calibrating model parameters with experimental data, we show that DNA-proportional mRNA transcription produces near-exponential growth, whereas deviations from this proportionality yield the observed non-exponential growth patterns. Analysis of gene expression perturbations reveals that ribosome expression primarily controls dry mass growth rate, whereas envelope expression more strongly affects cell elongation rate. Fitting our model to single-cell experimental data reproduces convex, super-exponential, and linear modes of growth, demonstrating how envelope and ribosome expression schedules drive cell-cycle-specific behaviors. These findings provide a mechanistic basis for non-exponential single-cell growth and offer insights into how bacterial cells dynamically regulate elongation rates within each generation.

A link between aging and persistence

Despite the various strategies that microorganisms have evolved to resist antibiotics, survival to drug treatments can be driven by subpopulations of susceptible bacteria in a transient state of dormancy. This phenotype, known as bacterial persistence, arises due to a natural and ubiquitous heterogeneity of growth states in bacterial populations. Nonetheless, the unifying mechanism of persistence remains unknown, with several pathways being able to trigger the phenotype. Here, we show that asymmetric damage partitioning, a form of cellular aging, produces the underlying phenotypic heterogeneity upon which persistence is triggered. Using single-cell microscopy and microfluidic devices, we demonstrate that deterministic asymmetry in exponential phase populations leads to a state of growth stability, which prevents the spontaneous formation of persisters. However, as populations approach stationary phase, aging bacteria-those inheriting more damage upon division-exhibit a sharper growth rate decline, increased probability of growth arrest, and higher persistence rates. These results indicate that persistence triggers are biased by bacterial asymmetry, thus acting upon the deterministic heterogeneity produced by cellular aging. This work suggests unifying mechanisms for persistence and offers new perspectives on the treatment of recalcitrant infections.

Single-cell analysis reveals antibiotic affects conjugative transfer by modulating bacterial growth rather than conjugation efficiency

Antibiotic resistance genes (ARGs) pose a significant threat to human health and the environment. Quantifying the efficiency of horizontal gene transfer (HGT) is challenging due to diverse biological and environmental influences. Single-cell level approaches are well-suited for investigating conjugative transfer, given its reliance on cell-to-cell contact nature and its capacity to offer insights into population-level responses. This study introduces a self-developed system for automated time-lapse image acquisition and analysis. Using a custom dual-chamber microfluidic chip and Python-based image analysis pipeline, we dynamically quantify the ARGs conjugation efficiency at single-cell level. By combining experiments with individual-based modelling, we isolate the effects of subinhibitory antibiotic concentrations on conjugation efficiency from those related to bacterial growth dynamics. No significant variation in Escherichia coli conjugation efficiency was observed across kanamycin concentrations (0 to 50 mg l-1). Moreover, recipient cells with higher growth rates show a greater propensity for plasmid acquisition, suggesting the physiological state of cells pre-conjugation influences their susceptibility to gene transfer. Our methodology eliminates population growth bias, revealing the intrinsic nature of conjugation efficiency. This approach advances our understanding of the factors influencing HGT efficiency and holds promise for studying other microbial interactions. SYNOPSIS: This study employs single-cell analysis to reveal that subinhibitory concentrations of antibiotics affect the conjugative transfer of antibiotic resistance genes by modulating bacterial growth rate rather than conjugation efficiency.

Live Imaging of Fission Yeast Single-Cell Lineages Using a Microfluidic Device

Mother machine (MM) is a microfluidic device originally developed for long-term live imaging of Escherichia coli bacterial cells under a microscope. The simple yet sophisticated design has enabled microbiologists to track multiple single-cell lineages cultured under highly controlled external environments. Here, I describe how to fabricate a fission yeast version of MM with photolithography and soft lithography. Procedures for setting up the microfluidic device for long-term live microscopy are also explained.

Diverse phage communities are maintained stably on a clonal bacterial host

Bacteriophages are the most abundant and phylogenetically diverse biological entities on Earth, yet the ecological mechanisms that sustain this extraordinary diversity remain unclear. In this study, we discovered that phage diversity consistently outstripped the diversity of their bacterial hosts under simple experimental conditions. We assembled and passaged dozens of diverse phage communities on a single, nonevolving strain of Escherichia coli until the phage communities reached equilibrium. In all cases, we found that two or more phage species coexisted stably, despite competition for a single, clonal host population. Phage coexistence was supported through host phenotypic heterogeneity, whereby bacterial cells adopting different growth phenotypes served as niches for different phage species. Our experiments reveal that a rich community ecology of bacteriophages can emerge on a single bacterial host.

Contribution of the infection ecosystem and biogeography to antibiotic failure in vivo

The acquisition of antibiotic resistance in bacteria, though a deeply concerning international issue, is reasonably well-understood at a mechanistic level. Less well-understood is why bacteria that are sensitive in vitro to well-established and widely-used antibiotics sometimes fail to respond to these agents in vivo. This is a particularly common problem in chronic, polymicrobial infection scenarios. Here, we discuss this in vitro-in vivo disconnect from the perspective of the bacterium, focusing in particular on how infection micro/macro-environment, biogeography, and the presence of co-habiting species affect the response to antibiotics. Using selected exemplars, we also consider interventions that might improve treatment outcomes, as well as ecologically 'eubiotic' approaches that have less of an impact on the patient's commensal microflora. In our view, the accrued data strongly suggest that we need a more comprehensive understanding of the in situ microbiology at infection sites.

Structural and functional analyses of nematode-derived antimicrobial peptides support the occurrence of direct mechanisms of worm-microbiota interactions

The complex relationships between gastrointestinal (GI) nematodes and the host gut microbiota have been implicated in key aspects of helminth disease and infection outcomes. Nevertheless, the direct and indirect mechanisms governing these interactions are, thus far, largely unknown. In this proof-of-concept study, we demonstrate that the excretory-secretory products (ESPs) and extracellular vesicles (EVs) of key GI nematodes contain peptides that, when recombinantly expressed, exert antimicrobial activity in vitro against Bacillus subtilis. In particular, using time-lapse microfluidics microscopy, we demonstrate that exposure of B. subtilis to a recombinant saposin-domain containing peptide from the 'brown stomach worm', Teladorsagia circumcincta, and a metridin-like ShK toxin from the 'barber's pole worm', Haemonchus contortus, results in cell lysis and significantly reduced growth rates. Data from this study support the hypothesis that GI nematodes may modulate the composition of the vertebrate gut microbiota directly via the secretion of antimicrobial peptides, and pave the way for future investigations aimed at deciphering the impact of such changes on the pathophysiology of GI helminth infection and disease.

Identification and genetic dissection of convergent persister cell states

Persister cells, rare phenotypic variants that survive normally lethal levels of antibiotics, present a major barrier to clearing bacterial infections1. However, understanding the precise physiological state and genetic basis of persister formation has been a longstanding challenge. Here we generated a high-resolution single-cell2 RNA atlas of Escherichia coli growth transitions, which revealed that persisters from diverse genetic and physiological models converge to transcriptional states that are distinct from standard growth phases and instead exhibit a dominant signature of translational deficiency. We then used ultra-dense CRISPR interference3 to determine how every E. coli gene contributes to persister formation across genetic models. Among critical genes with large effects, we found lon, which encodes a highly conserved protease4, and yqgE, a poorly characterized gene whose product strongly modulates the duration of post-starvation dormancy and persistence. Our work reveals key physiologic and genetic factors that underlie starvation-triggered persistence, a critical step towards targeting persisters in recalcitrant bacterial infections.

Phenotypic Heterogeneity in Pathogens

Pathogen diversity within an infected organism has traditionally been explored through the lens of genetic heterogeneity. Hallmark studies have characterized how genetic diversity within pathogen subpopulations contributes to treatment escape and infectious disease progression. However, recent studies have begun to reveal the mechanisms by which phenotypic heterogeneity is established within genetically identical populations of invading pathogens. Furthermore, exciting new work highlights how these phenotypically heterogeneous subpopulations contribute to a pathogen population better equipped to handle the complex and fluctuating environment of a host organism. In this review, we focus on how bacterial pathogens, including Staphylococcus aureus, Salmonella typhimurium, Pseudomonas aeruginosa, and Mycobacterium tuberculosis, establish and maintain phenotypic heterogeneity, and we explore recent work demonstrating causative links between this heterogeneity and infection outcome.

Bacterial cell size modulation along the growth curve across nutrient conditions

Under stable growth conditions, bacteria maintain cell size homeostasis through coordinated elongation and division. However, fluctuations in nutrient availability result in dynamic regulation of the target cell size. Using microscopy imaging and mathematical modelling, we examine how bacterial cell volume changes over the growth curve in response to nutrient conditions. We find that two rod-shaped bacteria, Escherichia coli and Salmonella enterica, exhibit similar cell volume distributions in stationary phase cultures irrespective of growth media. Cell resuspension in rich media results in a transient peak with a five-fold increase in cell volume ≈ 2h after resuspension. This maximum cell volume, which depends on nutrient composition, subsequently decreases to the stationary phase cell size. Continuous nutrient supply sustains the maximum volume. In poor nutrient conditions, cell volume shows minimal changes over the growth curve, but a markedly decreased cell width compared to other conditions. The observed cell volume dynamics translate into non-monotonic dynamics in the ratio between biomass (optical density) and cell number (colony-forming units), highlighting their non-linear relationship. Our findings support a heuristic model comparing modulation of cell division relative to growth across nutrient conditions and providing novel insight into the mechanisms of cell size control under dynamic environmental conditions.

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.

Phenotypic resistant single-cell characteristics under recurring ampicillin antibiotic exposure in Escherichia coli

Non-heritable, phenotypic drug resistance toward antibiotics challenges antibiotic therapies. Characteristics of such phenotypic resistance have implications for the evolution of heritable resistance. Diverse forms of phenotypic resistance have been described, but phenotypic resistance characteristics remain less explored than genetic resistance. Here, we add novel combinations of single-cell characteristics of phenotypic resistant E. coli cells and compare those to characteristics of susceptible cells of the parental population by exposure to different levels of recurrent ampicillin antibiotic. Contrasting expectations, we did not find commonly described characteristics of phenotypic resistant cells that arrest growth or near growth. We find that under ampicillin exposure, phenotypic resistant cells reduced their growth rate by about 50% compared to growth rates prior to antibiotic exposure. The growth reduction is a delayed alteration to antibiotic exposure, suggesting an induced response and not a stochastic switch or caused by a predetermined state as frequently described. Phenotypic resistant cells exhibiting constant slowed growth survived best under ampicillin exposure and, contrary to expectations, not only fast-growing cells suffered high mortality triggered by ampicillin but also growth-arrested cells. Our findings support diverse modes of phenotypic resistance, and we revealed resistant cell characteristics that have been associated with enhanced genetically fixed resistance evolution, which supports claims of an underappreciated role of phenotypic resistant cells toward genetic resistance evolution. A better understanding of phenotypic resistance will benefit combatting genetic resistance by developing and engulfing effective anti-phenotypic resistance strategies.

IMPORTANCE

Antibiotic resistance is a major challenge for modern medicine. Aside from genetic resistance to antibiotics, phenotypic resistance that is not heritable might play a crucial role for the evolution of antibiotic resistance. Using a highly controlled microfluidic system, we characterize single cells under recurrent exposure to antibiotics. Fluctuating antibiotic exposure is likely experienced under common antibiotic therapies. These phenotypic resistant cell characteristics differ from previously described phenotypic resistance, highlighting the diversity of modes of resistance. The phenotypic characteristics of resistant cells we identify also imply that such cells might provide a stepping stone toward genetic resistance, thereby causing treatment failure.

Microbial life in slow and stopped lanes

Microbes in nature often lack nutrients and face extreme or widely fluctuating temperatures, unlike microbes in growth-optimized settings in laboratories that much of the literature examines. Slowed or suspended lives are the norm for microbes. Studying them is important for understanding the consequences of climate change and for addressing fundamental questions about life: are there limits to how slowly a cell's life can progress, and how long cells can remain viable without self-replicating? Recent studies began addressing these questions with single-cell-level measurements and mathematical models. Emerging principles that govern slowed or suspended lives of cells - including lives of dormant spores and microbes at extreme temperatures - are re-defining discrete cellular states as continuums and revealing intracellular dynamics at new timescales. Nearly inactive, lifeless-appearing microbes are transforming our understanding of life.

Pseudomonas aeruginosa breaches respiratory epithelia through goblet cell invasion in a microtissue model

Pseudomonas aeruginosa, a leading cause of severe hospital-acquired pneumonia, causes infections with up to 50% mortality rates in mechanically ventilated patients. Despite some knowledge of virulence factors involved, it remains unclear how P. aeruginosa disseminates on mucosal surfaces and invades the tissue barrier. Using infection of human respiratory epithelium organoids, here we observed that P. aeruginosa colonization of apical surfaces is promoted by cyclic di-GMP-dependent asymmetric division. Infection with mutant strains revealed that Type 6 Secretion System activities promote preferential invasion of goblet cells. Type 3 Secretion System activity by intracellular bacteria induced goblet cell death and expulsion, leading to epithelial rupture which increased bacterial translocation and dissemination to the basolateral epithelium. These findings show that under physiological conditions, P. aeruginosa uses coordinated activity of a specific combination of virulence factors and behaviours to invade goblet cells and breach the epithelial barrier from within, revealing mechanistic insight into lung infection dynamics.

Scaling of stochastic growth and division dynamics: A comparative study of individual rod-shaped cells in the Mother Machine and SChemostat platforms

Microfluidic platforms enable long-term quantification of stochastic behaviors of individual bacterial cells under precisely controlled growth conditions. Yet, quantitative comparisons of physiological parameters and cell behaviors of different microorganisms in different experimental and device modalities is not available due to experiment-specific details affecting cell physiology. To rigorously assess the effects of mechanical confinement, we designed, engineered, and performed side-by-side experiments under otherwise identical conditions in the Mother Machine (with confinement) and the SChemostat (without confinement), using the latter as the ideal comparator. We established a protocol to cultivate a suitably engineered rod-shaped mutant of Caulobacter crescentus in the Mother Machine and benchmarked the differences in stochastic growth and division dynamics with respect to the SChemostat. While the single-cell growth rate distributions are remarkably similar, the mechanically confined cells in the Mother Machine experience a substantial increase in interdivision times. However, we find that the division ratio distribution precisely compensates for this increase, which in turn reflects identical emergent simplicities governing stochastic intergenerational homeostasis of cell sizes across device and experimental configurations, provided the cell sizes are appropriately mean-rescaled in each condition. Our results provide insights into the nature of the robustness of the bacterial growth and division machinery.

Mechanisms of cell size regulation in slow-growing Escherichia coli cells: discriminating models beyond the adder

Under ideal conditions, Escherichia coli cells divide after adding a fixed cell size, a strategy known as the adder. This concept applies to various microbes and is often explained as the division that occurs after a certain number of stages, associated with the accumulation of precursor proteins at a rate proportional to cell size. However, under poor media conditions, E. coli cells exhibit a different size regulation. They are smaller and follow a sizer-like division strategy where the added size is inversely proportional to the size at birth. We explore three potential causes for this deviation: degradation of the precursor protein and two models where the propensity for accumulation depends on the cell size: a nonlinear accumulation rate, and accumulation starting at a threshold size termed the commitment size. These models fit the mean trends but predict different distributions given the birth size. To quantify the precision of the models to explain the data, we used the Akaike information criterion and compared them to open datasets of slow-growing E. coli cells in different media. We found that none of the models alone can consistently explain the data. However, the degradation model better explains the division strategy when cells are larger, whereas size-related models (power-law and commitment size) account for smaller cells. Our methodology proposes a data-based method in which different mechanisms can be tested systematically.

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.

Deep model predictive control of gene expression in thousands of single cells

Gene expression is inherently dynamic, due to complex regulation and stochastic biochemical events. However, the effects of these dynamics on cell phenotypes can be difficult to determine. Researchers have historically been limited to passive observations of natural dynamics, which can preclude studies of elusive and noisy cellular events where large amounts of data are required to reveal statistically significant effects. Here, using recent advances in the fields of machine learning and control theory, we train a deep neural network to accurately predict the response of an optogenetic system in Escherichia coli cells. We then use the network in a deep model predictive control framework to impose arbitrary and cell-specific gene expression dynamics on thousands of single cells in real time, applying the framework to generate complex time-varying patterns. We also showcase the framework's ability to link expression patterns to dynamic functional outcomes by controlling expression of the tetA antibiotic resistance gene. This study highlights how deep learning-enabled feedback control can be used to tailor distributions of gene expression dynamics with high accuracy and throughput without expert knowledge of the biological system.

Staphylococcus aureus AbcA transporter enhances persister formation under β-lactam exposure

We evaluated the role of Staphylococcus aureus AbcA transporter in bacterial persistence and survival following exposure to the bactericidal agents nafcillin and oxacillin at both the population and single-cell levels. We show that AbcA overexpression resulted in resistance to nafcillin but not oxacillin. Using distinct fluorescent reporters of cell viability and AbcA expression, we found that over 6-14 hours of persistence formation, the proportion of AbcA reporter-expressing cells assessed by confocal microscopy increased sixfold as cell viability reporters decreased. Similarly, single-cell analysis in a high-throughput microfluidic system found a strong correspondence between antibiotic exposure and AbcA reporter expression. Persister cells grown in the absence of antibiotics showed neither an increase in nafcillin MIC nor in abcA transcript levels, indicating that survival was not associated with stable mutational resistance or abcA overexpression. Furthermore, persister cell levels on exposure to 1×MIC and 25×MIC of nafcillin decreased in an abcA knockout mutant. Survivors of nafcillin and oxacillin treatment overexpressed transporter AbcA, contributing to an enrichment of the number of persisters during treatment with pump-substrate nafcillin but not with pump-non-substrate oxacillin, indicating that efflux pump expression can contribute selectively to the survival of a persister population.

Analyzing bacterial persistence and dormancy: A bibliometric exploration of 21st century scientific literature

In response to growing concerns about the efficacy of antibiotic treatment, there has been a significant increase in research on bacteria that are resistant to antibiotics over the past two centuries. Such investigations might bring a spotlight on the field's evolution and future prospects. The study was aimed at conducting a measurable bibliometric review of the scientific literature on bacterial persistence and dormancy in the 21st Century. A scientific literature published during 21st Century was analyzed to gain insights into and identify research trends and outputs in persistent bacteria. Bibliometrix (R language package) and the VOS viewer were used to conduct a bibliometric investigation to determine the globally indexed persistent bacteria research output. WoS Core Collection databases were searched for persistent bacteria selected as the subject. A total of 1,160 published documents from 495 sources from the preceding two decades were reviewed. Maximum publications of 112 were observed in 2021 with 860 citations; however, 82 publications appeared in 2015 and were able to get the highest number of citations (4,214), only 43 (3.7%) were single-authored, whereas 1,117 (96.3%) publications are the result of collaborative works. Out of the top 10 countries ranked for publications, the USA took the top spot for the most highly productive country with 435 articles. Dormancy' appeared 2,351 times, followed by 'Escherichia coli" (1,744, and 'Growth' 1,184 times) in research publications on bacterial persistence research. The findings from this study will aid in the creation of strategies and guidelines for regulating and avoiding bacterial persistence status.

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.

Emerging tools for uncovering genetic and transcriptomic heterogeneities in bacteria

Bacterial communities display an astonishing degree of heterogeneities among their constituent cells across both the genomic and transcriptomic levels, giving rise to diverse social interactions and stress-adaptation strategies indispensable for proliferating in the natural environment (Ackermann in Nat Rev Microbiol 13:497-508, 2015). Our knowledge about bacterial heterogeneities and their physiological ramifications critically depends on our ability to unambiguously resolve the genetic and phenotypic states of the individual cells that make up the population. In this short review, I highlight several recently developed methods for studying bacterial heterogeneities, primarily focusing on single-cell techniques based on advanced sequencing and microscopy technologies. I will discuss the working principle of each technique as well as the types of problems each technique is best positioned to address. With significant improvements in resolution and throughput, these emerging tools together offer unprecedented and complementary views of various types of heterogeneities found within bacterial populations, paving the way for mechanistic dissections and systematic interventions in laboratory and clinical settings.

Single-cell patterning and characterisation of antibiotic persistent bacteria using bio-sCAPA

In microbiology, accessing single-cell information within large populations is pivotal. Here we introduce bio-sCAPA, a technique for patterning bacterial cells in defined geometric arrangements and monitoring their growth in various nutrient environments. We demonstrate bio-sCAPA with a study of subpopulations of antibiotic-tolerant bacteria, known as persister cells, which can survive exposure to high doses of antibiotics despite lacking any genetic resistance to the drug. Persister cells are associated with chronic and relapsing infections, yet are difficult to study due in part to a lack of scalable, single-cell characterisation methods. As >105 cells can be patterned on each template, and multiple templates can be patterned in parallel, bio-sCAPA allows for very rare population phenotypes to be monitored with single-cell precision across various environmental conditions. Using bio-sCAPA, we analysed the phenotypic characteristics of single Staphylococcus aureus cells tolerant to flucloxacillin and rifampicin killing. We find that antibiotic-tolerant S. aureus cells do not display significant heterogeneity in growth rate and are instead characterised by prolonged lag-time phenotypes alone.

Bet-hedging: Bacterial ribosome dynamics during growth transitions

It is known that bacteria reduce their ribosome numbers during nutrient starvation. New research shows that this regulation leads to the formation of two subpopulations with distinct ribosomal RNA levels. The distinct levels affect the growth recovery when nutrients become available, suggesting a possible bet-hedging strategy.

Coupled environmental and demographic fluctuations shape the evolution of cooperative antimicrobial resistance

There is a pressing need to better understand how microbial populations respond to antimicrobial drugs, and to find mechanisms to possibly eradicate antimicrobial-resistant cells. The inactivation of antimicrobials by resistant microbes can often be viewed as a cooperative behaviour leading to the coexistence of resistant and sensitive cells in large populations and static environments. This picture is, however, greatly altered by the fluctuations arising in volatile environments, in which microbial communities commonly evolve. Here, we study the eco-evolutionary dynamics of a population consisting of an antimicrobial-resistant strain and microbes sensitive to antimicrobial drugs in a time-fluctuating environment, modelled by a carrying capacity randomly switching between states of abundance and scarcity. We assume that antimicrobial resistance (AMR) is a shared public good when the number of resistant cells exceeds a certain threshold. Eco-evolutionary dynamics is thus characterised by demographic noise (birth and death events) coupled to environmental fluctuations which can cause population bottlenecks. By combining analytical and computational means, we determine the environmental conditions for the long-lived coexistence and fixation of both strains, and characterise a fluctuation-driven AMR eradication mechanism, where resistant microbes experience bottlenecks leading to extinction. We also discuss the possible applications of our findings to laboratory-controlled experiments.

Single-cell mass distributions reveal simple rules for achieving steady-state growth

IMPORTANCE

Microbiologists have watched clear liquid turn cloudy for over 100 years. While the cloudiness of a culture is proportional to its total biomass, growth rates from optical density measurements are challenging to interpret when cells change size. Many bacteria adjust their size at different steady-state growth rates, but also when shifting between starvation and growth. Optical density cannot disentangle how mass is distributed among cells. Here, we use single-cell mass measurements to demonstrate that a population of cells in batch culture achieves a stable mass distribution for only a short period of time. Achieving steady-state growth in rich medium requires low initial biomass concentrations and enough time for individual cell mass accumulation and cell number increase via cell division to balance out. Steady-state growth is important for reliable cell mass distributions and experimental reproducibility. We discuss how mass variation outside of steady-state can impact physiology, ecology, and evolution experiments.

High-throughput functional trait testing for bacterial pathogens

Functional traits are characteristics that affect the fitness and metabolic function of a microorganism. There is growing interest in using high-throughput methods to characterize bacterial pathogens based on functional virulence traits. Traditional methods that phenotype a single organism for a single virulence trait can be time consuming and labor intensive. Alternatively, machine learning of whole-genome sequences (WGS) has shown some success in predicting virulence. However, relying solely on WGS can miss functional traits, particularly for organisms lacking classical virulence factors. We propose that high-throughput assays for functional virulence trait identification should become a prominent method of characterizing bacterial pathogens on a population scale. This work is critical as we move from compiling lists of bacterial species associated with disease to pathogen-agnostic approaches capable of detecting novel microbes. We discuss six key areas of functional trait testing and how advancing high-throughput methods could provide a greater understanding of pathogens.

Bacterial cell-size changes resulting from altering the relative expression of Min proteins

The timing of cell division, and thus cell size in bacteria, is determined in part by the accumulation dynamics of the protein FtsZ, which forms the septal ring. FtsZ localization depends on membrane-associated Min proteins, which inhibit FtsZ binding to the cell pole membrane. Changes in the relative concentrations of Min proteins can disrupt FtsZ binding to the membrane, which in turn can delay cell division until a certain cell size is reached, in which the dynamics of Min proteins frees the cell membrane long enough to allow FtsZ ring formation. Here, we study the effect of Min proteins relative expression on the dynamics of FtsZ ring formation and cell size in individual Escherichia coli bacteria. Upon inducing overexpression of minE, cell size increases gradually to a new steady-state value. Concurrently, the time required to initiate FtsZ ring formation grows as the size approaches the new steady-state, at which point the ring formation initiates as early as before induction. These results highlight the contribution of Min proteins to cell size control, which may be partially responsible for the size fluctuations observed in bacterial populations, and may clarify how the size difference acquired during asymmetric cell division is offset.

Mechanisms of Cell Size Regulation in Slow-Growing Escherichia coli Cells: Discriminating Models Beyond the Adder

Under ideal conditions, Escherichia coli cells divide after adding a fixed cell size, a strategy known as the adder. This concept applies to various microbes and is often explained as the division that occurs after a certain number of stages, associated with the accumulation of precursor proteins at a rate proportional to cell size. However, under poor media conditions, E. coli cells exhibit a different size regulation. They are smaller and follow a sizer-like division strategy where the added size is inversely proportional to the size at birth. We explore three potential causes for this deviation: precursor protein degradation, nonlinear accumulation rate, and a threshold size termed the commitment size. These models fit mean trends but predict different distributions given the birth size. To validate these models, we used the Akaike information criterion and compared them to open datasets of slow-growing E. coli cells in different media. the degradation model could explain the division strategy for media where cells are larger, while the commitment size model could account for smaller cells. The power-law model, finally, better fits the data at intermediate regimes.

Unbalanced response to growth variations reshapes the cell fate decision landscape

The global regulation of cell growth rate on gene expression perturbs the performance of gene networks, which would impose complex variations on the cell-fate decision landscape. Here we use a simple synthetic circuit of mutual repression that allows a bistable landscape to examine how such global regulation would affect the stability of phenotypic landscape and the accompanying dynamics of cell-fate determination. We show that the landscape experiences a growth-rate-induced bifurcation between monostability and bistability. Theoretical and experimental analyses reveal that this bifurcating deformation of landscape arises from the unbalanced response of gene expression to growth variations. The path of growth transition across the bifurcation would reshape cell-fate decisions. These results demonstrate the importance of growth regulation on cell-fate determination processes, regardless of specific molecular signaling or regulation.

A genetic switch controls Pseudomonas aeruginosa surface colonization

Efficient colonization of mucosal surfaces is essential for opportunistic pathogens like Pseudomonas aeruginosa, but how bacteria collectively and individually adapt to optimize adherence, virulence and dispersal is largely unclear. Here we identified a stochastic genetic switch, hecR-hecE, which is expressed bimodally and generates functionally distinct bacterial subpopulations to balance P. aeruginosa growth and dispersal on surfaces. HecE inhibits the phosphodiesterase BifA and stimulates the diguanylate cyclase WspR to increase c-di-GMP second messenger levels and promote surface colonization in a subpopulation of cells; low-level HecE-expressing cells disperse. The fraction of HecE+ cells is tuned by different stress factors and determines the balance between biofilm formation and long-range cell dispersal of surface-grown communities. We also demonstrate that the HecE pathway represents a druggable target to effectively counter P. aeruginosa surface colonization. Exposing such binary states opens up new ways to control mucosal infections by a major human pathogen.

Reconsidering Dogmas about the Growth of Bacterial Populations

The growth of bacterial populations has been described as a dynamic process of continuous reproduction and cell death. However, this is far from the reality. In a well fed, growing bacterial population, the stationary phase inevitably occurs, and it is not due to accumulated toxins or cell death. A population spends the most time in the stationary phase, where the phenotype of the cells alters from the proliferating ones, and only the colony forming unit (CFU) decreases after a while, not the total cell concentration. A bacterial population can be considered as a virtual tissue as a result of a specific differentiation process, in which the exponential-phase cells develop to stationary-phase cells and eventually reach the unculturable form. The richness of the nutrient had no effect on growth rate or on stationary cell density. The generation time seems not to be a constant value, but it depended on the concentration of the starter cultures. Inoculations with serial dilutions of stationary populations reveal a so-called minimal stationary cell concentration (MSCC) point, up to which the cell concentrations remain constant upon dilutions; that seems to be universal among unicellular organisms.

Dynamic proteome trade-offs regulate bacterial cell size and growth in fluctuating nutrient environments

Bacteria dynamically regulate cell size and growth to thrive in changing environments. While previous studies have characterized bacterial growth physiology at steady-state, a quantitative understanding of bacterial physiology in time-varying environments is lacking. Here we develop a quantitative theory connecting bacterial growth and division rates to proteome allocation in time-varying nutrient environments. In such environments, cell size and growth are regulated by trade-offs between prioritization of biomass accumulation or division, resulting in decoupling of single-cell growth rate from population growth rate. Specifically, bacteria transiently prioritize biomass accumulation over production of division machinery during nutrient upshifts, while prioritizing division over growth during downshifts. When subjected to pulsatile nutrient concentration, we find that bacteria exhibit a transient memory of previous metabolic states due to the slow dynamics of proteome reallocation. This allows for faster adaptation to previously seen environments and results in division control which is dependent on the time-profile of fluctuations.

Super-exponential growth and stochastic size dynamics in rod-like bacteria

Proliferating bacterial cells exhibit stochastic growth and size dynamics, but the regulation of noise in bacterial growth and morphogenesis remains poorly understood. A quantitative understanding of morphogenetic noise control, and how it changes under different growth conditions, would provide better insights into cell-to-cell variability and intergenerational fluctuations in cell physiology. Using multigenerational growth and width data of single Escherichia coli and Caulobacter crescentus cells, we deduce the equations governing growth and size dynamics of rod-like bacterial cells. Interestingly, we find that both E. coli and C. crescentus cells deviate from exponential growth within the cell cycle. In particular, the exponential growth rate increases during the cell cycle irrespective of nutrient or temperature conditions. We propose a mechanistic model that explains the emergence of super-exponential growth from autocatalytic production of ribosomes coupled to the rate of cell elongation and surface area synthesis. Using this new model and statistical inference on large datasets, we construct the Langevin equations governing cell growth and size dynamics of E. coli cells in different nutrient conditions. The single-cell level model predicts how noise in intragenerational and intergenerational processes regulate variability in cell morphology and generation times, revealing quantitative strategies for cellular resource allocation and morphogenetic noise control in different growth conditions.

Breaking down microbial hierarchies

Microbial communities that degrade natural polysaccharides are thought to have a hierarchical organization and one-way positive interactions from higher to lower trophic levels. Daniels et al. have recently shown that reciprocal interactions between trophic levels can occur and that these interactions change over the duration of a batch culture.

Changes in interactions over ecological time scales influence single-cell growth dynamics in a metabolically coupled marine microbial community

Microbial communities thrive in almost all habitats on earth. Within these communities, cells interact through the release and uptake of metabolites. These interactions can have synergistic or antagonistic effects on individual community members. The collective metabolic activity of microbial communities leads to changes in their local environment. As the environment changes over time, the nature of the interactions between cells can change. We currently lack understanding of how such dynamic feedbacks affect the growth dynamics of individual microbes and of the community as a whole. Here we study how interactions mediated by the exchange of metabolites through the environment change over time within a simple marine microbial community. We used a microfluidic-based approach that allows us to disentangle the effect cells have on their environment from how they respond to their environment. We found that the interactions between two species-a degrader of chitin and a cross-feeder that consumes metabolic by-products-changes dynamically over time as cells modify their environment. Cells initially interact positively and then start to compete at later stages of growth. Our results demonstrate that interactions between microorganisms are not static and depend on the state of the environment, emphasizing the importance of disentangling how modifications of the environment affects species interactions. This experimental approach can shed new light on how interspecies interactions scale up to community level processes in natural environments.

Impact of lead (Pb2+) on the growth and biological activity of Serratia marcescens selected for wastewater treatment and identification of its zntR gene-a metal efflux regulator

Microorganisms isolated from contaminated areas play an important role in bioremediation processes. They promote heavy metal removal from the environment by adsorbing ions onto the cell wall surface, accumulating them inside the cells, or reducing, complexing, or precipitating these substances in the environment. Microorganism-based bioremediation processes can be highly efficient, low-cost and have low environmental impact. Thus, the present study aimed to select Pb²⁺-resistant bacteria and evaluate the growth rate, biological activity, and the presence of genes associated with metal resistance. Serratia marcescens CCMA 1010, that was previously isolated from coffee processing wastewater, was selected since was able to growth in Pb²⁺ concentrations of up to 4.0 mM. The growth rate and generation time did not differ from those of the control (without Pb²⁺), although biological activity decreased in the first hour of exposure to these ions and stabilized after this period. The presence of the zntR, zntA and pbrA genes was analysed, and only zntR was detected. The zntR gene encodes a protein responsible for regulating the production of ZntA, a transmembrane protein that facilitates Pb²⁺ extrusion out of the cell. S. marcescens CCMA 1010 demonstrated a potential for use as bioindicator that has potential to be used in bioremediation processes due to its resistance to high concentrations of Pb²⁺, ability to grow until 24 h of exposure, and possession of a gene that indicates the existence of mechanisms associated with resistance to lead (Pb²⁺).

SERS-Tags: Selective Immobilization and Detection of Bacteria by Strain-Specific Antibodies and Surface-Enhanced Raman Scattering

Efficient separation and sensitive identification of pathogenic bacterial strains is essential for a prosperous modern society, with direct applications in medical diagnostics, drug discovery, biodefense, and food safety. We developed a fast and reliable method for antibody-based selective immobilization of bacteria from suspension onto a gold-plated glass surface, followed by detection using strain-specific antibodies linked to gold nanoparticles decorated with a reporter molecule. The reporter molecules are subsequently detected by surface-enhanced Raman spectroscopy (SERS). Such a multi-functionalized nanoparticle is called a SERS-tag. The presented procedure uses widely accessible and cheap materials for manufacturing and functionalization of the nanoparticles and the immobilization surfaces. Here, we exemplify the use of the produced SERS-tags for sensitive single-cell detection of opportunistic pathogen Escherichia coli, and we demonstrate the selectivity of our method using two other bacterial strains, Staphylococcus aureus and Serratia marcescens, as negative controls. We believe that the described approach has a potential to inspire the development of novel medical diagnostic tools for rapid identification of bacterial pathogens.

Navigating Environmental Transitions: the Role of Phenotypic Variation in Bacterial Responses

The ability of bacteria to respond to changes in their environment is critical to their survival, allowing them to withstand stress, form complex communities, and induce virulence responses during host infection. A remarkable feature of many of these bacterial responses is that they are often variable across individual cells, despite occurring in an isogenic population exposed to a homogeneous environmental change, a phenomenon known as phenotypic heterogeneity. Phenotypic heterogeneity can enable bet-hedging or division of labor strategies that allow bacteria to survive fluctuating conditions. Investigating the significance of phenotypic heterogeneity in environmental transitions requires dynamic, single-cell data. Technical advances in quantitative single-cell measurements, imaging, and microfluidics have led to a surge of publications on this topic. Here, we review recent discoveries on single-cell bacterial responses to environmental transitions of various origins and complexities, from simple diauxic shifts to community behaviors in biofilm formation to virulence regulation during infection. We describe how these studies firmly establish that this form of heterogeneity is prevalent and a conserved mechanism by which bacteria cope with fluctuating conditions. We end with an outline of current challenges and future directions for the field. While it remains challenging to predict how an individual bacterium will respond to a given environmental input, we anticipate that capturing the dynamics of the process will begin to resolve this and facilitate rational perturbation of environmental responses for therapeutic and bioengineering purposes.

Cellular resource allocation strategies for cell size and shape control in bacteria

Bacteria are highly adaptive microorganisms that thrive in a wide range of growth conditions via changes in cell morphologies and macromolecular composition. How bacterial morphologies are regulated in diverse environmental conditions is a long-standing question. Regulation of cell size and shape implies control mechanisms that couple the growth and division of bacteria to their cellular environment and macromolecular composition. In the past decade, simple quantitative laws have emerged that connect cell growth to proteomic composition and the nutrient availability. However, the relationships between cell size, shape, and growth physiology remain challenging to disentangle and unifying models are lacking. In this review, we focus on regulatory models of cell size control that reveal the connections between bacterial cell morphology and growth physiology. In particular, we discuss how changes in nutrient conditions and translational perturbations regulate the cell size, growth rate, and proteome composition. Integrating quantitative models with experimental data, we identify the physiological principles of bacterial size regulation, and discuss the optimization strategies of cellular resource allocation for size control.

Synthetic Micrographs of Bacteria (SyMBac) allows accurate segmentation of bacterial cells using deep neural networks

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.

Microfluidic dose-response platform to track the dynamics of drug response in single mycobacterial cells

Preclinical analysis of drug efficacy is critical for drug development. However, conventional bulk-cell assays statically assess the mean population behavior, lacking resolution on drug-escaping cells. Inaccurate estimation of efficacy can lead to overestimation of compounds, whose efficacy will not be confirmed in the clinic, or lead to rejection of valuable candidates. Time-lapse microfluidic microscopy is a powerful approach to characterize drugs at high spatiotemporal resolution, but hard to apply on a large scale. Here we report the development of a microfluidic platform based on a pneumatic operating principle, which is scalable and compatible with long-term live-cell imaging and with simultaneous analysis of different drug concentrations. We tested the platform with mycobacterial cells, including the tubercular pathogen, providing the first proof of concept of a single-cell dose-response assay. This dynamic in-vitro model will prove useful to probe the fate of drug-stressed cells, providing improved predictions of drug efficacy in the clinic.

Microfluidics for long-term single-cell time-lapse microscopy: Advances and applications

Cells are inherently dynamic, whether they are responding to environmental conditions or simply at equilibrium, with biomolecules constantly being made and destroyed. Due to their small volumes, the chemical reactions inside cells are stochastic, such that genetically identical cells display heterogeneous behaviors and gene expression profiles. Studying these dynamic processes is challenging, but the development of microfluidic methods enabling the tracking of individual prokaryotic cells with microscopy over long time periods under controlled growth conditions has led to many discoveries. This review focuses on the recent developments of one such microfluidic device nicknamed the mother machine. We overview the original device design, experimental setup, and challenges associated with this platform. We then describe recent methods for analyzing experiments using automated image segmentation and tracking. We further discuss modifications to the experimental setup that allow for time-varying environmental control, replicating batch culture conditions, cell screening based on their dynamic behaviors, and to accommodate a variety of microbial species. Finally, this review highlights the discoveries enabled by this technology in diverse fields, such as cell-size control, genetic mutations, cellular aging, and synthetic biology.

The context matrix: Navigating biological complexity for advanced biodesign

Synthetic biology offers many solutions in healthcare, production, sensing and agriculture. However, the ability to rationally engineer synthetic biosystems with predictable and robust functionality remains a challenge. A major reason is the complex interplay between the synthetic genetic construct, its host, and the environment. Each of these contexts contains a number of input factors which together can create unpredictable behaviours in the engineered biosystem. It has become apparent that for the accurate assessment of these contextual effects a more holistic approach to design and characterisation is required. In this perspective article, we present the context matrix, a conceptual framework to categorise and explore these contexts and their net effect on the designed synthetic biosystem. We propose the use and community-development of the context matrix as an aid for experimental design that simplifies navigation through the complex design space in synthetic biology.

DetecDiv, a generalist deep-learning platform for automated cell division tracking and survival analysis

Automating the extraction of meaningful temporal information from sequences of microscopy images represents a major challenge to characterize dynamical biological processes. So far, strong limitations in the ability to quantitatively analyze single-cell trajectories have prevented large-scale investigations to assess the dynamics of entry into replicative senescence in yeast. Here, we have developed DetecDiv, a microfluidic-based image acquisition platform combined with deep learning-based software for high-throughput single-cell division tracking. We show that DetecDiv can automatically reconstruct cellular replicative lifespans with high accuracy and performs similarly with various imaging platforms and geometries of microfluidic traps. In addition, this methodology provides comprehensive temporal cellular metrics using time-series classification and image semantic segmentation. Last, we show that this method can be further applied to automatically quantify the dynamics of cellular adaptation and real-time cell survival upon exposure to environmental stress. Hence, this methodology provides an all-in-one toolbox for high-throughput phenotyping for cell cycle, stress response, and replicative lifespan assays.

Fast bacterial growth reduces antibiotic accumulation and efficacy

Phenotypic variations between individual microbial cells play a key role in the resistance of microbial pathogens to pharmacotherapies. Nevertheless, little is known about cell individuality in antibiotic accumulation. Here, we hypothesise that phenotypic diversification can be driven by fundamental cell-to-cell differences in drug transport rates. To test this hypothesis, we employed microfluidics-based single-cell microscopy, libraries of fluorescent antibiotic probes and mathematical modelling. This approach allowed us to rapidly identify phenotypic variants that avoid antibiotic accumulation within populations of Escherichia coli, Pseudomonas aeruginosa, Burkholderia cenocepacia, and Staphylococcus aureus. Crucially, we found that fast growing phenotypic variants avoid macrolide accumulation and survive treatment without genetic mutations. These findings are in contrast with the current consensus that cellular dormancy and slow metabolism underlie bacterial survival to antibiotics. Our results also show that fast growing variants display significantly higher expression of ribosomal promoters before drug treatment compared to slow growing variants. Drug-free active ribosomes facilitate essential cellular processes in these fast-growing variants, including efflux that can reduce macrolide accumulation. We used this new knowledge to eradicate variants that displayed low antibiotic accumulation through the chemical manipulation of their outer membrane inspiring new avenues to overcome current antibiotic treatment failures.