Biology subjects

Batt, G.

Publications and source records attributed to Batt, G..

11 recordsLinked to original sources

bioRxiv2026

Coupling cell growth to protein secretion enables pooled screening at single-cell resolution in bioreactors

Preserving the genotype-phenotype linkage is critical in high-throughput screens exploring large parameter spaces. Screening pooled libraries for recombinant protein secretion is therefore particularly difficult, since protein diffusion into the medium disrupts this linkage. Existing methods impose significant restrictions on the environment of the screened cells. Here, we present AutoGrowth, a growth-based screening strategy linking growth rate to secretion via a single-cell biosensor. We demonstrated the proportional modulation of growth rate by secretion and validated the approach on five model proteins. As proof of concept, we screened a signal peptide library in bioreactors for improved secretion of a single-chain variable fragment by coupling AutoGrowth with deep sequencing. Additionally, through spike-in experiments, we showed that AutoGrowth can identify favorable variants diluted at ratios as low as one in hundreds of millions in three days. Our growth-based strategy provides unprecedented throughput potential and ensures selection of genetic variants presenting advantageous secretion and fitness capabilities.

synthetic biology↗

bioRxiv2025

InSillyClo, a user-friendly web application to assist large-scale Golden Gate Cloning and MoClo workflows

Systems and synthetic biology developments often require the construction of many variants of a genetic circuit of interest, resulting in large-scale cloning campaigns. Golden Gate and Modular Cloning (MoClo), two powerful technologies enabling the scale-up of cloning workflows, play a central role for efficient circuit construction. These workflows include a number of dry-lab tasks, which are time-consuming and error-prone at scale. Currently, no software tool is available to handle these tasks in a dedicated, time-saving, and user-friendly manner. We present InSillyClo, an open-source web application to assist large-scale Golden Gate cloning and MoClo workflows. It supports an easy specification of genetic designs at any scale, followed by the automated generation of comprehensive workflow-related data. Moreover, InSillyClo leverages Modular Cloning with a versatile typing system of parts to generate user-defined workflows. InSillyClo is open source, accessible with or without user registration, and can also be used locally.

synthetic biology↗

bioRxiv2024

Harnessing CRISPR interference to re-sensitize laboratory strains and clinical isolates to last resort antibiotics

The global race against antimicrobial resistance requires novel antimicrobials that are not only effective in killing specific bacteria, but also minimize the emergence of new resistances. Recently, CRISPR/Cas-based antimicrobials were proposed to address killing specificity with encouraging results. However, the emergence of target sequence mutations triggered by Cas-cleavage was identified as an escape strategy, posing the risk of generating new antibiotic-resistance gene (ARG) variants. Here, we evaluated an antibiotic re-sensitization strategy based on CRISPR interference (CRISPRi), which inhibits gene expression without damaging target DNA. The resistance to four antibiotics, including last resort drugs, was significantly reduced by individual and multi-gene targeting of ARGs in low- to high-copy numbers in recombinant E. coli. Escaper analysis confirmed the absence of mutations in target sequence, corroborating the harmless role of CRISPRi in the selection of new resistances. E. coli clinical isolates carrying ARGs of severe clinical concern were then used to test the robustness of CRISPRi under different growth conditions. Meropenem, colistin and cefotaxime susceptibility was successfully increased in terms of MIC (up to >4-fold) and growth delay (up to 11-hours) in a medium-dependent fashion. To our knowledge, this is the first demonstration of CRISPRi-mediated re-sensitization to last-resort drugs in clinical isolates. This study laid the foundations for further leveraging CRISPRi as antimicrobial agent or research tool to selectively repress ARGs and investigate resistance mechanisms.

synthetic biology↗

bioRxiv2022

Maximizing protein production by keeping cells at optimal secretory stress levels using real-time control approaches

The production of recombinant proteins is a problem of major industrial and pharmaceutical importance. Secretion of the protein by the host cell considerably simplifies downstream purification processes. However, it is also the limiting production step for many hard-to-secrete proteins. Current solutions involve extensive chassis engineering to favor trafficking and limit protein degradation triggered by excessive secretion-associated stress. Here, we propose instead a regulation-based strategy in which induction is dynamically adjusted based on the current stress level of the cells. Using a small collection of hard-to-secrete proteins and a bioreactor-based platform with automated cytometry measurements, we demonstrate that the regulation sweet spot is indicated by the appearance of a bimodal distribution of internal protein and of secretory stress levels, when a fraction of the cell population accumulates high amounts of proteins, decreases growth, and faces significant stress, that is, experiences a secretion burn-out. In these cells, adaptations capabilities are overwhelmed by a too strong production. With these notions, we define an optimal stress level based on physiological readouts. Then, using real-time control, we demonstrate that a strategy that keeps the stress at optimal levels increases production of a single-chain antibody by 70%.

bioengineering↗

bioRxiv2021

Parameter inference for stochastic biochemical models from perturbation experiments parallelised at the single cell level

Understanding and characterising biochemical processes inside single cells requires experimental platforms that allow one to perturb and observe the dynamics of such processes as well as computational methods to build and parameterise models from the collected data. Recent progress with experimental platforms and optogenetics has made it possible to expose each cell in an experiment to an individualised input and automatically record cellular responses over days with fine time resolution. However, methods to infer parameters of stochastic kinetic models from single-cell longitudinal data have generally been developed under the assumption that experimental data is sparse and that responses of cells to at most a few different input perturbations can be observed. Here, we investigate and compare different approaches for calculating parameter likelihoods of single-cell longitudinal data based on approximations of the chemical master equation (CME) with a particular focus on coupling the linear noise approximation (LNA) or moment closure methods to a Kalman filter. We show that, as long as cells are measured sufficiently frequently, coupling the LNA to a Kalman filter allows one to accurately approximate likelihoods and to infer model parameters from data even in cases where the LNA provides poor approximations of the CME. Furthermore, the computational cost of filtering-based iterative likelihood evaluation scales advantageously in the number of measurement times and different input perturbations and is thus ideally suited for data obtained from modern experimental platforms. To demonstrate the practical usefulness of these results, we perform an experiment in which single cells, equipped with an optogenetic gene expression system, are exposed to various different light-input sequences and measured at several hundred time points and use parameter inference based on iterative likelihood evaluation to parameterise a stochastic model of the system. Author summaryA common result for the modelling of cellular processes is that available data is not sufficiently rich to uniquely determine the biological mechanism or even just to ensure identifiability of parameters of a given model. Perturbing cellular processes with informative input stimuli and measuring dynamical responses may alleviate this problem. With the development of novel experimental platforms, we are now in a position to parallelise such perturbation experiments at the single cell level. This raises a plethora of new questions. Is it more informative to diversify input perturbations but to observe only few cells for each input or should we rather ensure that many cells are observed for only few inputs? How can we calculate likelihoods and infer parameters of stochastic kinetic models from data sets in which each cell receives a different input perturbation? How does the computational efficiency of parameter inference methods scale with the number of inputs and the number of measurement times? Are there approaches that are particularly well-suited for such data sets? In this paper, we investigate these questions using the CcaS/CcaR optogenetic system driving the expression of a fluorescent reporter protein as primary case study.

systems biology↗

bioRxiv2021

Using single-cell models to predict the functionality of synthetic circuits at the population scale

Mathematical modeling has become a major tool to guide the characterization and synthetic construction of cellular processes. However, models typically lose their capacity to explain or predict experimental outcomes as soon as any, even minor, modification of the studied system or its operating conditions is implemented. This limits our capacity to fully comprehend the functioning of natural biological processes and is a major roadblock for the de novo design of complex synthetic circuits. Here, using a specifically constructed yeast optogenetic differentiation system as an example, we show that a simple deterministic model can explain system dynamics in given conditions but loses validity when modifications to the system are made. On the other hand, deploying theory from stochastic chemical kinetics and developing models of the systems components that simultaneously track single-cell and population processes allows us to quantitatively predict emerging dynamics of the system without any adjustment of model parameters. We conclude that carefully characterizing the dynamics of cell-to-cell variability using appropriate modeling theory may allow one to unravel the complex interplay of stochastic single-cell and population processes and to predict the functionality of composed synthetic circuits in growing populations before the circuit is constructed.

systems biology↗

bioRxiv2021

A model-based approach to characterize enzyme-mediated response to antibiotic treatments: going beyond the SIR classification

Commensal and pathogenic E. coli strains are increasingly found to be resistant to {beta}-lactams, one of the most widely prescribed classes of antibiotics. Understanding escape to such treatments is complex since {beta}-lactams have several cellular targets and since several mechanisms might be involved in treatment escape in a combined manner. Surprisingly, the accumulated knowledge has not yet proven effective enough to predict the bacterial response to antibiotic treatments at both cellular and population levels with quantitative accuracy for -producing bacteria. Here, we propose a mathematical model that captures in a comprehensive way key phenomena happening at the molecular, cellular, and population levels, as well as their interactions. Our growth-fragmentation model gives a central role to cellular heterogeneity and filamentation as a way for cells to gain time until the degradation of the antibiotic by the {beta}-lactamases released by the dead cell population. Importantly, the model can account for the observed temporal evolution of the total (live and dead) biomass and of the live cell numbers for various antibiotic concentrations. To our knowledge, this is the first model able to quantitatively reconciliate these two classical views on cell death (OD and CFUs) for clinical isolates expressing extended-spectrum beta-lactamases (ESBL). Moreover, our model has a strong predictive power. When calibrated using a slight extension of OD-based data that we propose here, it can predict the CFU profiles in initial and delayed treatments despite inoculum effects, and suggest non-trivial optimal treatments. Generating quality data in quantity has been essential for model development and validation on non-model E. coli strains. We developed protocols to increase the reproducibility of growth kinetics assays and to increase the throughput of CFU assays.

biophysics↗

bioRxiv2021

A light tunable differentiation system for the creation and control of consortia in yeast

Artificial microbial consortia seek to leverage division-of-labour to optimize function and possess immense potential for bioproduction. Co-culturing approaches, the preferred mode of generating a consortium, remain limited in their ability to give rise to stable consortia having finely tuned compositions. Here, we present an artificial differentiation system in budding yeast capable of generating stable microbial consortia with custom functionalities from a single strain at user-defined composition in space and in time based on optogenetically-driven genetic rewiring. Owing to fast, reproducible, and light-tunable dynamics, our system enables dynamic control of consortia composition in continuous cultures for extended periods. We further demonstrate that our system can be extended in a straightforward manner to give rise to consortia with multiple subpopulations. Our artificial differentiation strategy establishes a novel paradigm for the creation of complex microbial consortia that are simple to implement, precisely controllable, and versatile to use.

synthetic biology↗

bioRxiv2021

MicroMator: Open and Flexible Software for Reactive Microscopy

Microscopy image analysis has recently made enormous progress both in terms of accuracy and speed thanks to machine learning methods. This greatly facilitates the online adaptation of microscopy experimental plans using real-time information of the observed systems and their environments. Here we report MicroMator, an open and flexible software for defining and driving reactive microscopy experiments, and present applications to single-cell control and single-cell recombination.

systems biology↗

bioRxiv2020

Enhancing multi-bioreactor platforms for automated measurements and reactive experiment control

New small-scale, low-cost bioreactors provide researchers with exquisite control of environmental parameters of microbial cultures over long durations, allowing them to perform sophisticated, high-quality quantitative experiments that are particularly useful in systems biology, synthetic biology and bioengineering. However, existing setups are limited in their automated measurement capabilities, primarily because sensitive and specific measurements require bulky, expensive, stand-alone instruments. Here, we present ReacSight, a generic and flexible strategy to enhance bioreactor arrays for automated measurements and reactive experiment control. On the hardware side, ReacSight leverages a pipetting robot for sample collection, handling and loading. On the software side, ReacSight provides a versatile instrument control architecture and a generic event system for reactive experiment control. ReacSight is ideally suited to integrate open-source, open-hardware components but can also accommodate closed-source, GUI-only components (e.g. cytometers). We use ReacSight to assemble a platform for cytometry-based characterization and reactive optogenetic control of parallel yeast continuous cultures. Using a dedicated bioreactor array, we showcase its capabilities on three applications. First, we achieve parallel real-time control of gene expression with light in different bioreactors. Second, we explore the impact of nutrient scarcity on fitness and cellular stress using well-controlled, high-information content competition assays. Third, we exploit nutrient scarcity to achieve dynamic control over the composition of a two-strain consortium. To illustrate the genericity of ReacSight, we also assemble an equivalent platform using the optogenetic-ready, open-hardware and commercially available Chi.Bio bioreactors.

synthetic biology↗

bioRxiv2018

Dissecting bacterial resistance and resilience in antibiotic responses

An essential property of microbial communities is the ability to survive a disturbance. Survival can be achieved through resistance, the ability to absorb effects of a disturbance without a significant change, or resilience, the ability to recover after being perturbed by a disturbance. These concepts have long been applied to the analysis of ecological systems, though their interpretations are often subject to debate. Here we show that this framework readily lends itself to the dissection of the bacterial response to antibiotic treatment, where both terms can be unambiguously defined. The ability to tolerate the antibiotic treatment in the short term corresponds to resistance, which primarily depends on traits associated with individual cells. In contrast, the ability to recover after being perturbed by an antibiotic corresponds to resilience, which primarily depends on traits associated with the population. This framework effectively reveals the phenotypic signatures of bacterial pathogens expressing extended spectrum {beta}-lactamases (ESBLs), when treated by a {beta}-lactam antibiotic. Our analysis has implications for optimizing treatment of these pathogens using a combination of a {beta}-lactam and a {beta}-lactamase (Bla) inhibitor. In particular, our results underscore the need to dynamically optimize combination treatments based on the quantitative features of the bacterial response to the antibiotic or the Bla inhibitor.

cell biology↗