@article{10939, abstract = {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 = {Davidović, Anđela and Chait, Remy P and Batt, Gregory and Ruess, Jakob}, issn = {1553-7358}, journal = {PLoS Computational Biology}, number = {3}, publisher = {Public Library of Science}, title = {{Parameter inference for stochastic biochemical models from perturbation experiments parallelised at the single cell level}}, doi = {10.1371/journal.pcbi.1009950}, volume = {18}, year = {2022}, } @article{9822, abstract = {Attachment of adhesive molecules on cell culture surfaces to restrict cell adhesion to defined areas and shapes has been vital for the progress of in vitro research. In currently existing patterning methods, a combination of pattern properties such as stability, precision, specificity, high-throughput outcome, and spatiotemporal control is highly desirable but challenging to achieve. Here, we introduce a versatile and high-throughput covalent photoimmobilization technique, comprising a light-dose-dependent patterning step and a subsequent functionalization of the pattern via click chemistry. This two-step process is feasible on arbitrary surfaces and allows for generation of sustainable patterns and gradients. The method is validated in different biological systems by patterning adhesive ligands on cell-repellent surfaces, thereby constraining the growth and migration of cells to the designated areas. We then implement a sequential photopatterning approach by adding a second switchable patterning step, allowing for spatiotemporal control over two distinct surface patterns. As a proof of concept, we reconstruct the dynamics of the tip/stalk cell switch during angiogenesis. Our results show that the spatiotemporal control provided by our “sequential photopatterning” system is essential for mimicking dynamic biological processes and that our innovative approach has great potential for further applications in cell science.}, author = {Zisis, Themistoklis and Schwarz, Jan and Balles, Miriam and Kretschmer, Maibritt and Nemethova, Maria and Chait, Remy P and Hauschild, Robert and Lange, Janina and Guet, Calin C and Sixt, Michael K and Zahler, Stefan}, issn = {19448252}, journal = {ACS Applied Materials and Interfaces}, number = {30}, pages = {35545–35560}, publisher = {American Chemical Society}, title = {{Sequential and switchable patterning for studying cellular processes under spatiotemporal control}}, doi = {10.1021/acsami.1c09850}, volume = {13}, year = {2021}, } @article{19, abstract = {Bacteria regulate genes to survive antibiotic stress, but regulation can be far from perfect. When regulation is not optimal, mutations that change gene expression can contribute to antibiotic resistance. It is not systematically understood to what extent natural gene regulation is or is not optimal for distinct antibiotics, and how changes in expression of specific genes quantitatively affect antibiotic resistance. Here we discover a simple quantitative relation between fitness, gene expression, and antibiotic potency, which rationalizes our observation that a multitude of genes and even innate antibiotic defense mechanisms have expression that is critically nonoptimal under antibiotic treatment. First, we developed a pooled-strain drug-diffusion assay and screened Escherichia coli overexpression and knockout libraries, finding that resistance to a range of 31 antibiotics could result from changing expression of a large and functionally diverse set of genes, in a primarily but not exclusively drug-specific manner. Second, by synthetically controlling the expression of single-drug and multidrug resistance genes, we observed that their fitness-expression functions changed dramatically under antibiotic treatment in accordance with a log-sensitivity relation. Thus, because many genes are nonoptimally expressed under antibiotic treatment, many regulatory mutations can contribute to resistance by altering expression and by activating latent defenses.}, author = {Palmer, Adam and Chait, Remy P and Kishony, Roy}, issn = {0737-4038}, journal = {Molecular Biology and Evolution}, number = {11}, pages = {2669 -- 2684}, publisher = {Oxford University Press}, title = {{Nonoptimal gene expression creates latent potential for antibiotic resistance}}, doi = {10.1093/molbev/msy163}, volume = {35}, year = {2018}, } @article{613, abstract = {Bacteria in groups vary individually, and interact with other bacteria and the environment to produce population-level patterns of gene expression. Investigating such behavior in detail requires measuring and controlling populations at the single-cell level alongside precisely specified interactions and environmental characteristics. Here we present an automated, programmable platform that combines image-based gene expression and growth measurements with on-line optogenetic expression control for hundreds of individual Escherichia coli cells over days, in a dynamically adjustable environment. This integrated platform broadly enables experiments that bridge individual and population behaviors. We demonstrate: (i) population structuring by independent closed-loop control of gene expression in many individual cells, (ii) cell-cell variation control during antibiotic perturbation, (iii) hybrid bio-digital circuits in single cells, and freely specifiable digital communication between individual bacteria. These examples showcase the potential for real-time integration of theoretical models with measurement and control of many individual cells to investigate and engineer microbial population behavior.}, author = {Chait, Remy P and Ruess, Jakob and Bergmiller, Tobias and Tkacik, Gasper and Guet, Calin C}, issn = {20411723}, journal = {Nature Communications}, number = {1}, publisher = {Nature Publishing Group}, title = {{Shaping bacterial population behavior through computer interfaced control of individual cells}}, doi = {10.1038/s41467-017-01683-1}, volume = {8}, year = {2017}, } @article{1290, abstract = {We developed a competition-based screening strategy to identify compounds that invert the selective advantage of antibiotic resistance. Using our assay, we screened over 19,000 compounds for the ability to select against the TetA tetracycline-resistance efflux pump in Escherichia coli and identified two hits, β-thujaplicin and disulfiram. Treating a tetracycline-resistant population with β-thujaplicin selects for loss of the resistance gene, enabling an effective second-phase treatment with doxycycline.}, author = {Stone, Laura and Baym, Michael and Lieberman, Tami and Chait, Remy P and Clardy, Jon and Kishony, Roy}, journal = {Nature Chemical Biology}, number = {11}, pages = {902 -- 904}, publisher = {Nature Publishing Group}, title = {{Compounds that select against the tetracycline-resistance efflux pump}}, doi = {10.1038/nchembio.2176}, volume = {12}, year = {2016}, } @article{1332, abstract = {Antibiotic-sensitive and -resistant bacteria coexist in natural environments with low, if detectable, antibiotic concentrations. Except possibly around localized antibiotic sources, where resistance can provide a strong advantage, bacterial fitness is dominated by stresses unaffected by resistance to the antibiotic. How do such mixed and heterogeneous conditions influence the selective advantage or disadvantage of antibiotic resistance? Here we find that sub-inhibitory levels of tetracyclines potentiate selection for or against tetracycline resistance around localized sources of almost any toxin or stress. Furthermore, certain stresses generate alternating rings of selection for and against resistance around a localized source of the antibiotic. In these conditions, localized antibiotic sources, even at high strengths, can actually produce a net selection against resistance to the antibiotic. Our results show that interactions between the effects of an antibiotic and other stresses in inhomogeneous environments can generate pervasive, complex patterns of selection both for and against antibiotic resistance.}, author = {Chait, Remy P and Palmer, Adam and Yelin, Idan and Kishony, Roy}, journal = {Nature Communications}, publisher = {Nature Publishing Group}, title = {{Pervasive selection for and against antibiotic resistance in inhomogeneous multistress environments}}, doi = {10.1038/ncomms10333}, volume = {7}, year = {2016}, } @article{1342, abstract = {A key aspect of bacterial survival is the ability to evolve while migrating across spatially varying environmental challenges. Laboratory experiments, however, often study evolution in well-mixed systems. Here, we introduce an experimental device, the microbial evolution and growth arena (MEGA)-plate, in which bacteria spread and evolved on a large antibiotic landscape (120 × 60 centimeters) that allowed visual observation of mutation and selection in a migrating bacterial front.While resistance increased consistently, multiple coexisting lineages diversified both phenotypically and genotypically. Analyzing mutants at and behind the propagating front,we found that evolution is not always led by the most resistant mutants; highly resistant mutants may be trapped behindmore sensitive lineages.TheMEGA-plate provides a versatile platformfor studying microbial adaption and directly visualizing evolutionary dynamics.}, author = {Baym, Michael and Lieberman, Tami and Kelsic, Eric and Chait, Remy P and Gross, Rotem and Yelin, Idan and Kishony, Roy}, journal = {Science}, number = {6304}, pages = {1147 -- 1151}, publisher = {American Association for the Advancement of Science}, title = {{Spatiotemporal microbial evolution on antibiotic landscapes}}, doi = {10.1126/science.aag0822}, volume = {353}, year = {2016}, } @article{499, abstract = {Exposure of an isogenic bacterial population to a cidal antibiotic typically fails to eliminate a small fraction of refractory cells. Historically, fractional killing has been attributed to infrequently dividing or nondividing "persisters." Using microfluidic cultures and time-lapse microscopy, we found that Mycobacterium smegmatis persists by dividing in the presence of the drug isoniazid (INH). Although persistence in these studies was characterized by stable numbers of cells, this apparent stability was actually a dynamic state of balanced division and death. Single cells expressed catalase-peroxidase (KatG), which activates INH, in stochastic pulses that were negatively correlated with cell survival. These behaviors may reflect epigenetic effects, because KatG pulsing and death were correlated between sibling cells. Selection of lineages characterized by infrequent KatG pulsing could allow nonresponsive adaptation during prolonged drug exposure.}, author = {Wakamoto, Yurichi and Dhar, Neraaj and Chait, Remy P and Schneider, Katrin and Signorino Gelo, François and Leibler, Stanislas and Mckinney, John}, journal = {Science}, number = {6115}, pages = {91 -- 95}, publisher = {American Association for the Advancement of Science}, title = {{Dynamic persistence of antibiotic-stressed mycobacteria}}, doi = {10.1126/science.1229858}, volume = {339}, year = {2013}, } @article{4228, abstract = {Suppressive drug interactions, in which one antibiotic can actually help bacterial cells to grow faster in the presence of another, occur between protein and DNA synthesis inhibitors. Here, we show that this suppression results from nonoptimal regulation of ribosomal genes in the presence of DNA stress. Using GFP-tagged transcription reporters in Escherichia coli, we find that ribosomal genes are not directly regulated by DNA stress, leading to an imbalance between cellular DNA and protein content. To test whether ribosomal gene expression under DNA stress is nonoptimal for growth rate, we sequentially deleted up to six of the seven ribosomal RNA operons. These synthetic manipulations of ribosomal gene expression correct the protein-DNA imbalance, lead to improved survival and growth, and completely remove the suppressive drug interaction. A simple mathematical model explains the nonoptimal regulation in different nutrient environments. These results reveal the genetic mechanism underlying an important class of suppressive drug interactions.}, author = {Bollenbach, Tobias and Quan, Selwyn and Remy Chait and Kishony, Roy}, journal = {Cell}, number = {4}, pages = {707 -- 718}, publisher = {Cell Press}, title = {{Nonoptimal Microbial Response to Antibiotics Underlies Suppressive Drug Interactions}}, doi = {10.1016/j.cell.2009.10.025}, volume = {139}, year = {2009}, }