Biology subjects

Julou, T.

Publications and source records attributed to Julou, T..

10 recordsLinked to original sources

bioRxiv2026

Single-cell growth rate fluctuations contribute independently from protein production to gene expression noise and decrease with average growth rate

Phenotypic heterogeneity is a universal feature in microbial life and has profound consequences for the behavior of single cells. However, dissecting and quantifying the sources of phenotypic noise is challenging as it requires precise quantification of the dynamics of gene expression fluctuations in vivo. Here, we analyze time-lapse microscopy data of single E. coli cells carrying fluorescent reporters for constitutive and ribosomal promoters to quantify how fluctuations in protein production, growth rate, and random sampling at division contribute to noise in protein concentrations. We find that growth rate fluctuations are largely uncorrelated with protein production and contribute substantially to noise in protein concentrations. In addition, as nutrients are varied, growth rate fluctuations decrease both in amplitude and duration with the mean growth rate. In contrast, fluctuations in production do not show a systematic dependence on mean growth rate, but exhibit surprisingly variable behaviors, even across different constitutive promoters. Moreover, although production and growth rate fluctuations are only weakly correlated, they exhibit complex cross-correlation patterns with growth rate that are both condition-and promoter-dependent. These observations suggest that, while growth rate fluctuations might be dominated by a single underlying mechanism, there are multiple sources of protein production fluctuations that vary in strength across conditions and affect different promoters to different extents. Finally, the sampling noise of proteins at cell division only contributes substantially to protein concentration noise for low-expressed genes with ten or fewer molecules per cell.

systems biology↗

bioRxiv2025

RealTrace: Uncovering biological dynamics hidden under measurement noise in time-lapse microscopy data

One of the most powerful approaches for identifying the mechanisms underlying complex biological phenomena is not just to measure bulk populations, or even take single-cell snapshots, but to directly track the behaviors of single cells in time. Indeed, in recent years there has been a steep rise in the use of time-lapse microscopy with fluorescent reporters to quantify single-cell time dynamics in many different biological systems. However, there is a major challenge with the analysis of such data. Since biological changes are inherently small on short time scales, much of the true biological dynamics is hidden under unavoidable measurement noise, but we do not know what functional form the time dynamics may take, and smoothing over neighboring time points simply replaces the true dynamics with a time average. Here, we present RealTrace, a Bayesian method that rigorously solves this challenge using only the assumption that, while true biological fluctuations are correlated on short time scales, measurement errors are uncorrelated. We show that this assumption can be formalized in a Bayesian model using maximum entropy process priors and that this model can be accurately solved by recursively approximating the non-linear dynamics over short time intervals. Given raw input cell size and fluorescence measurements, RealTrace calculates the estimated true sizes, total GFP contents, instantaneous growth rates, and volumic GFP production rates across all cells and time, together with rigorous error bars on all these quantities. We demonstrate the broad applicability of RealTrace by analyzing time-lapse microscopy datasets from E. coli bacteria, mouse embryonic stem cells, and entire C. elegans embryos. To exemplify the subtle dynamical features RealTrace can uncover, we perform an in-depth study of E. coli cells carrying fluorescent reporters for constitutive and ribosomal promoters, across multiple growth conditions. We find growth rates of single cells substantially fluctuate in time and, as the average growth rate decreases, these fluctuations systematically increase in amplitude and duration. Both growth rate and gene expression vary systematically across the cell cycle, but while the pattern of growth rate variation is condition-independent, the systematic variation in gene expression across the cell cycle is both condition and promoter-dependent. Finally, under a sudden change in nutrients, single cells exhibit highly consistent transient changes in growth rate and gene expression. While gene expression rates settle into a new steady-state surprisingly rapidly, the growth rate shows a dramatic drop followed by an overshoot that takes several cell cycles to settle down. All these observations give fundamental new insights into the single-cell physiology of bacteria. RealTrace is a highly versatile method that accurately removes measurement noise from any fluorescence time-lapse microscopy data, enabling the identification and quantification of subtle features in the biological dynamics that would otherwise be hidden under measurement noise.

bioinformatics↗

bioRxiv2024

Transient transcription factor depletions explain diverse single-cell responses of LexA target promoters to mild DNA damage

In bacteria, the effects of transcription factors (TFs) on the expression of their target genes are highly stochastic at the single-cell level. Not only do TF concentrations fluctuate in time and from cell to cell, in each cell the actual binding and unbinding of TFs to promoters is also stochastic. However, how this noise propagation from TFs to their targets determines the expression fluctuations in target genes of the same TF has so far not been quantitatively characterized. Here we use fluorescence time-lapse microscopy in combination with microfluidics and automated image analysis to quantitatively track the single-cell growth and gene expression dynamics of different target promoters of the LexA SOS response regulator in E. coli under mild DNA damage induced by the antibiotic ciprofloxacin (CIP). We find that, at the single-cell level, LexA target promoters exhibit stochastic bursts in protein production with specific quantitative characteristics. First, the durations of protein production bursts are short, identical for all promoters, and independent of CIP levels. Second, the heights of the production peaks are exponentially distributed for each promoter, and independent of CIP level, but with different average heights for different promoters. Third, the frequency of the production bursts increases with CIP level for all promoters, but differs across promoters. Importantly, we show that these observations are inconsistent with popular equilibrium models of bacterial gene regulation that assume that the dynamics of binding and unbinding of TFs is fast relative to the time-scale of TF concentration fluctuations. Instead, all observations can be quantitatively fit by a relatively simple model in which DNA damage events lead to short transient dips in LexA concentration, and an expression burst occurs in a cell whenever LexA unbinds from the target promoter during such a short dip in LexA concentration. Moreover, because these transient dips in LexA concentration occur on the same time-scale as the stochastic unbinding of LexA from promoters, the response of a target promoter to an individual DNA damage event is stochastic and depends in a complex manner on the strengths of its LexA binding sites and the duration of the event. Consequently, the expression levels of different LexA target genes in a given cell reflect distinct statistics of the frequency and durations of DNA damage events in the recent history of that cell. Our results show that, even in the simple scenario where a single TF is induced, different target promoters can exhibit distinct stochastic responses that depend on the precise kinetics of the induction events.

systems biology↗

bioRxiv2024

E. coli leverages growth arrest to remodel its proteome upon entry into starvation

It is widely believed that due to nutrient limitations in natural environments, bacteria spend most of their life in non-growing states. However, very little is known about how bacteria change their phenotype during starvation and what controls the concentration of different gene products inside the cells. Here we used microfluidics with quantitative fluorescence microscopy to quantitatively monitor growth and gene expression in many independent single-cell E. coli lineages as cells were switched from exponential growth to carbon starvation. In contrast to the hypothesis that stationary phase at the population level may reflect a balance between continuing growth and death in different sub-populations, we found that all cells immediately enter growth arrest, that cells further in their cell cycle subsequently undergo reductive division, and no cell death occurs for more than two days. Second, we observed dramatic time-dependent changes in protein production that are highly homogeneous across single cells. Some promoters shut off protein production immediately, some show a slow exponential decay of production on a 10 h time scale, while others exhibit a transient burst of increased production before decaying exponentially at different rates. Notably, the reduction in protein production 30-60 h into starvation relative to production in exponential phase varies by more than two orders of magnitude across promoters and is highly correlated with production in the first 10 h of starvation. Control experiments show that protein degradation itself also decays expo-nentially and using mathematical modeling we show how the fold-change in a genes protein concentration between exponential phase and late starvation depends on the size of the expression burst at the onset of starvation, the rate of subsequent production decay, and the rate of degradation decay. For many genes, the expression in late starvation is driven by production during the first 10 h. Finally, we establish that this expression program at the onset of starvation is critical for cell viability. In particular, by inhibiting gene expression during different periods of starvation, we show that tolerance to stress later in starvation is determined by gene expression occurring during the first 10 h. Our study provides a foundation for quantitative studies of bacterial starvation by uncovering a gene expression program that fundamentally remodels the proteome during the first 10 h of starvation, is highly homogeneous across single cells, sets the proteome later in starvation, and is crucial for stress tolerance.

microbiology↗

bioRxiv2022

Growth rate controls the sensitivity of gene regulatory circuits

Unicellular organisms adapt to their changing environments by gene regulatory switches that sense chemical cues and induce specific target genes when the inducing signal is over a critical threshold. Using mathematical modeling we here show that, because growth rate sets the dilution rate of intracellular molecules, the sensitivity of gene regulatory switches automatically couples to growth rate, in a way that can be exploited by natural selection. We confirm the modeling predictions by experimentally demonstrating that, as nutrient quality is varied, the concentration of inducer required for activating the lac operon in E. coli increases quadratically with the populations growth rate. Our theory further predicts that, when growth rate is instead modulated by translation inhibition, critical inducer levels are invariant, and we experimentally validate this prediction as well. Moreover, we establish that this growth-coupled sensitivity allows bacteria to implement concentration-dependent sugar preferences, in which a new carbon source is used only if its concentration is high enough to improve upon the current growth rate of the cells. Using microfluidics in combination with time-lapse microscopy, we validate experimentally that this strategy governs how mixtures of glucose and lactose are used in E. coli at single-cell level. Overall, growth-coupled sensitivity provides a general mechanism through which cells can mute external signals in beneficial conditions when growth is fast, and become highly sensitive to alternative nutrients or stresses when growth is slow or arrested.

systems biology↗

bioRxiv2020

Response to comment on 'Initiation of chromosome replication controls both division and replication cycles in E. coli through a double-adder mechanism'

Last year we published an article (Witz et al., 2019) in which we used time-lapse microscopy in combination with microfluidics to measure growth, division and replication in single E. coli cells on the one hand, and developed a new statistical analysis method to calculate the ability of different cell cycle models to capture the correlation structure observed in the data on the other hand. This led us to propose a new model of cell cycle control in E. coli which we called the double-adder model. Recently Le Treut et al. published a comment (Le Treut et al., 2020) on our article which made a number of highly critical claims, including allegations that our own data support a different model than the one we proposed, and that our model cannot reproduce the adder phenotype observed in the data. We here show that all these allegations are false and based on basic analysis errors. Although our focus is on explaining the errors in the analysis of Le Treut et al, we have attempted to make the presentation of interest to a broader scientific audience by discussing the issues in the context of what our current understanding is of the bacterial cell cycle, and to what extent recent data either support or reject various proposed models.

systems biology↗

bioRxiv2020

Subpopulations of sensorless bacteria drive fitness in fluctuating environments

Populations of bacteria often undergo a lag in growth when switching conditions. Because growth lags can be large compared to typical doubling times, variations in growth lag are an important but often overlooked component of bacterial fitness in fluctuating environments. We here explore how growth lag variation is determined for the archetypical switch from glucose to lactose as a carbon source in E. coli. First, we show that single-cell lags are bimodally distributed and controlled by a single-molecule trigger. That is, gene expression noise causes the population before the switch to divide cells with zero pre-existing into subpopulations with zero and nonzero lac operon expression. While ’sensorless’ lac expression at the switch have long lags because they are unable to sense the lactose signal, any nonzero lac operon expression suffices to ensure a short lag. Second, we show that the growth lag at the population level depends crucially on the fraction of sensorless cells, and that this fraction in turn depends sensitively on the growth condition before the switch. Consequently, even small changes in basal expression affecting the fraction of sensorless cells can significantly affect population lags and fitness under switching conditions, and may thus be subject to significant natural selection. Indeed, we show that condition-dependent population lags vary across wild E. coli isolates. Since many sensory genes are naturally low expressed in conditions where their inducer is not present, bimodal responses due to subpopulations of sensorless cells may be a general mechanism inducing phenotypic heterogeneity and controlling population lags in switching environments. This mechanism also illustrates how gene expression noise can turn even simple sensory gene circuits into a bet-hedging module, and underlines the profound role of gene expression noise in regulatory responses.Competing Interest StatementThe authors have declared no competing interest.View Full Text

systems biology↗

bioRxiv2019

Noise propagation shapes condition-dependent gene expression noise in Escherichia coli

Although it is well appreciated that gene expression is inherently noisy and that transcriptional noise is encoded in a promoters sequence, little is known about the variation in transcriptional noise across growth conditions. Using flow cytometry we here quantify transcriptional noise in E. coli genome-wide across 8 growth conditions, and find that noise and gene regulation are intimately coupled. Apart from a growth-rate dependent lower bound on noise, we find that individual promoters show highly condition-dependent noise and that condition-dependent expression noise is shaped by noise propagation from regulators to their targets. A simple model of noise propagation identifies TFs that most contribute to both condition-specific and condition-independent noise propagation. The overall correlation structure of sequence and expression properties of E. coli genes uncovers that genes are organized along two principal axes, with the first axis sorting genes by their mean expression and evolutionary rate of their coding regions, and the second axis sorting genes by their expression noise, the number of regulatory inputs in their promoter, and their expression plasticity.

systems biology↗

bioRxiv2019

Using fluorescence flow cytometry data for single-cell gene expression analysis in bacteria

Fluorescence flow cytometry is a highly attractive technology for quantifying single-cell expression distributions in bacteria in high-throughput. However, so far there has been no systematic investigation of the best practices for quantitative analysis of such data, what systematic biases exist, and what accuracy and sensitivity can be obtained. We here investigate these issues by systematically comparing flow cytometry measurements of fluorescent reporters in E. coli with measurements of the same strains in microscopic setups and develop a method for rigorous quantitative analysis of fluorescence flow cytometry data.\n\nWe find that forward and side scatter cannot be used to reliably estimate cell size in bacteria. Second, we show that cytometry measurements contain a large shot noise component that can be easily mistaken for intrinsic noise in gene expression, and show how calibration measurements can be used to correct for this measurement shot noise.\n\nTo aid other researchers with quantitative analysis of flow cytometry expression data in bacteria, we distribute E-Flow, an open-source R package that implements our methods for filtering cells based on forward and side scatter, and for estimating true biological expression means and variances from the fluorescence signal. The package is available at https://github.com/vanNimwegenLab/E-Flow.

bioinformatics↗

bioRxiv2019

Initiation of chromosome replication controls both division and replication cycles in E. coli through a double-adder mechanism

Living cells proliferate by completing and coordinating two essential cycles, a division cycle that controls cell size, and a DNA replication cycle that controls the number of chromosomal copies in the cell. Despite lacking dedicated cell cycle control regulators such as cyclins in eukaryotes, bacteria such as E. coli manage to tightly coordinate those two cycles across a wide range of growth conditions, including situations where multiple nested rounds of replication progress simultaneously. Various cell cycle models have been proposed to explain this feat, but it has been impossible to validate them so far due to a lack of experimental tools for systematically testing their different predictions. Recently new insights have been gained on the division cycle through the study of the structure of fluctuations in growth, size, and division in individual cells. In particular, it was found that cell size appears to be controlled by an adder mechanism, i.e. the added volume between divisions is held approximately constant and fluctuates independently of growth rate and cell size at birth. However, how replication initiation is regulated and coupled to cell size control remains unclear, mainly due to scarcity of experimental measurements on replication initiation at the single-cell level. Here, we used time-lapse microscopy in combination with microfluidics to directly measure growth, division and replication in thousands of single E. coli cells growing in both slow and fast growth conditions. In order to compare different phenomenological models of the cell cycle, we introduce a statistical framework which assess their ability to capture the correlation structure observed in the experimental data. Using this in combination with stochastic simulations, our data indicate that, instead of thinking of the cell cycle as running from birth to division, the cell cycle is controlled by two adder mechanisms starting at the initiation of replication: the added volume since the last initiation event controls the timing of both the next division event and the next replication initiation event. Interestingly the double-adder mechanism identified in this study has recently been found to explain the more complex cell cycle of mycobacteria, suggesting shared control strategies across species.

systems biology↗