Luro, S. ; Potvin-Trottier, L. ; Okumus, B. ; Paulsson, J. Isolating live cells after high-throughput, long-term, time-lapse microscopy. . Nature Methods 2020, 17, 93-100.
Lord, N. D. ; Norman, T. M. ; Yuan, R. ; Bakshi, S. ; Losick, R. ; Paulsson, J. Stochastic antagonism between two proteins governs a bacterial cell fate switch. Science 2019, 366, 116–120. Publisher's VersionAbstract
Cell survival can require switching mechanisms that are flexible enough to accommodate environmental changes but also stable for the required duration. Lord et al. created a switching system in bacteria based on stochastic competition between two proteins: one is a transcriptional repressor, and the other is an antagonist that binds the repressor and locks it in an inactive state. They show that this system controls switching of the bacterium Bacillus subtilis from a motile, unicellular state to an immobile, multicellular state, and that the control system is transferable to another distantly related bacterium. Similar mechanisms could be more widely operable in biological systems than previously recognized.Science, this issue p. 116Cell fate decision circuits must be variable enough for genetically identical cells to adopt a multitude of fates, yet ensure that these states are distinct, stably maintained, and coordinated with neighboring cells. A long-standing view is that this is achieved by regulatory networks involving self-stabilizing feedback loops that convert small differences into long-lived cell types. We combined regulatory mutants and in vivo reconstitution with theory for stochastic processes to show that the marquee features of a cell fate switch in Bacillus subtilis—discrete states, multigenerational inheritance, and timing of commitments—can instead be explained by simple stochastic competition between two constitutively produced proteins that form an inactive complex. Such antagonistic interactions are commonplace in cells and could provide powerful mechanisms for cell fate determination more broadly.
Yan, J. ; Hilfinger, A. ; Vinnicombe, G. ; Paulsson, J. Kinetic Uncertainty Relations for the Control of Stochastic Reaction Networks. Phys. Rev. Lett. 2019, 123, 108101. Publisher's Version
Potvin-Trottier, L. ; Luro, S. ; Paulsson, J. Microfluidics and single-cell microscopy to study stochastic processes in bacteria. Current Opinion in Microbiology 2018, 43, 186–192.
Okumus, B. ; Baker, C. J. ; Arias-Castro, J. C. ; Lai, G. C. ; Leoncini, E. ; Bakshi, S. ; Luro, S. ; Landgraf, D. ; Paulsson, J. Single-cell microscopy of suspension cultures using a microfluidics-assisted cell screening platform. Nature Protocols 2018, 13, 170.
Paulsson, J. ; El Karoui, M. ; Lindell, M. ; Hughes, D. The processive kinetics of gene conversion in bacteria. Molecular Microbiology 2017, 104, 752–760.
Reuveni, S. ; Ehrenberg, M. ; Paulsson, J. Ribosomes are optimized for autocatalytic production. Nature 2017, 547, 293. Publisher's Version
Jajoo, R. ; Jung, Y. ; Huh, D. ; Viana, M. P. ; Rafelski, S. M. ; Springer, M. ; Paulsson, J. Accurate concentration control of mitochondria and nucleoids. Science 2016, 351, 169–172.
Hilfinger, A. ; Norman, T. M. ; Vinnicombe, G. ; Paulsson, J. Constraints on fluctuations in sparsely characterized biological systems. Physical Review Letters 2016, 116, 058101.
Okumus, B. ; Landgraf, D. ; Lai, G. C. ; Bakshi, S. ; Arias-Castro, J. C. ; Yildiz, S. ; Huh, D. ; Fernandez-Lopez, R. ; Peterson, C. N. ; Toprak, E. ; et al. Mechanical slowing-down of cytoplasmic diffusion allows in vivo counting of proteins in individual cells. Nature Communications 2016, 7 11641.
Potvin-Trottier, L. ; Lord, N. D. ; Vinnicombe, G. ; Paulsson, J. Synchronous long-term oscillations in a synthetic gene circuit. Nature 2016, 538, 514.
Hilfinger, A. ; Norman, T. M. ; Paulsson, J. Exploiting natural fluctuations to identify kinetic mechanisms in sparsely characterized systems. Cell Systems 2016, 2 251–259.
Uphoff, S. ; Lord, N. D. ; Potvin-Trottier, L. ; Okumus, B. ; Sherratt, D. J. ; Paulsson, J. Stochastic activation of a DNA damage response causes cell-to-cell mutation rate variation. Science 2016, 351, 1094-1097. Publisher's Version
Taheri-Araghi, S. ; Bradde, S. ; Sauls, J. T. ; Hill, N. S. ; Levin, P. A. ; Paulsson, J. ; Vergassola, M. ; Jun, S. Cell-size control and homeostasis in bacteria. Current Biology 2015, 25, 385–391.
Norman, T. M. ; Lord, N. D. ; Paulsson, J. ; Losick, R. Stochastic switching of cell fate in microbes. Annual Review of Microbiology 2015, 69, 381–403.
Hilfinger, A. ; Paulsson, J. Systems biology: Defiant daughters and coordinated cousins. Nature 2015, 519, 422.
Toprak, E. ; Veres, A. ; Yildiz, S. ; Pedraza, J. M. ; Chait, R. ; Paulsson, J. ; Kishony, R. Building a morbidostat: an automated continuous-culture device for studying bacterial drug resistance under dynamically sustained drug inhibition. Nature Protocols 2013, 8 555-67.Abstract
We present a protocol for building and operating an automated fluidic system for continuous culture that we call the 'morbidostat'. The morbidostat is used to follow the evolution of microbial drug resistance in real time. Instead of exposing bacteria to predetermined drug environments, the morbidostat constantly measures the growth rates of evolving microbial populations and dynamically adjusts drug concentrations inside culture vials in order to maintain a constant drug-induced inhibition. The growth rate measurements are done using an optical detection system that is based on measuring the intensity of back-scattered light from bacterial cells suspended in the liquid culture. The morbidostat can additionally be used as a chemostat or a turbidostat. The whole system can be built from readily available components within 2-3 weeks by biologists with some electronics experience or engineers familiar with basic microbiology.
Norman, T. M. ; Lord, N. D. ; Paulsson, J. ; Losick, R. Memory and modularity in cell-fate decision making. Nature 2013, 503, 481-6.Abstract
Genetically identical cells sharing an environment can display markedly different phenotypes. It is often unclear how much of this variation derives from chance, external signals, or attempts by individual cells to exert autonomous phenotypic programs. By observing thousands of cells for hundreds of consecutive generations under constant conditions, we dissect the stochastic decision between a solitary, motile state and a chained, sessile state in Bacillus subtilis. We show that the motile state is 'memoryless', exhibiting no autonomous control over the time spent in the state. In contrast, the time spent as connected chains of cells is tightly controlled, enforcing coordination among related cells in the multicellular state. We show that the three-protein regulatory circuit governing the decision is modular, as initiation and maintenance of chaining are genetically separable functions. As stimulation of the same initiating pathway triggers biofilm formation, we argue that autonomous timing allows a trial commitment to multicellularity that external signals could extend.
Lau, B. T. C. ; Malkus, P. ; Paulsson, J. New quantitative methods for measuring plasmid loss rates reveal unexpected stability. Plasmid 2013, 70, 353-61.Abstract
Plasmid loss rate measurements are standard in microbiology and key to understanding plasmid stabilization mechanisms. The conventional assays eliminate selection for plasmids at the beginning of the experiment and screen for the appearance of plasmid-free cells over long-term population growth. However, it has been long appreciated in plasmid biology that the growth rate differential between plasmid-free and plasmid-containing cells at some point overshadows the effect of primary loss events, such that the assays can greatly over-estimate inherent loss rates. The standard solutions to this problem are to either consider the very early phase of loss where the fraction of plasmid-free cells increases linearly, or to measure the growth rate difference either by following the population for longer time or by measuring growth rates separately. Here we mathematically show that in all these cases, seemingly small experimental errors in the growth rate estimates can overshadow the estimates of the loss rates. For many plasmids, loss rates may thus be much lower than previously thought, and for some plasmids, the estimated loss rate may have nothing to do with actual loss rates. We further modify two independent experimental methods to separate inherent losses from growth differences and apply them to the same plasmids. First we use a high-throughput microscopy-based approach to screen for plasmid-free cells at extremely short time scales--tens of minutes rather than tens of generations--and apply it to a par⁻ version of mini-R1. Second we modify a counterselection-based plasmid loss assay inspired by the Luria-Delbrück fluctuation test that completely separates losses from growth, and apply it to various R1 and pSC101 derivatives. Concordant results from the two assays suggest that plasmids are lost at a lower frequency than previously believed. In fact, for par⁻ mini-R1 the observed loss rate of about 10⁻³ per cell and generation seems to be so low as to be inconsistent with what we know about the R1 stabilization mechanisms, suggesting these well characterized plasmids may have some additional and so far unknown stabilization mechanisms, for example improving copy number control or partitioning at cell division.
Tal, S. ; Paulsson, J. Evaluating quantitative methods for measuring plasmid copy numbers in single cells. Plasmid 2012, 67, 167-73.Abstract
The life of plasmids is a constant battle against fluctuations: failing to correct copy number fluctuations can increase the plasmid loss rate by many orders of magnitude, as can a failure to more evenly divide the copies between daughters at cell division. Plasmids are therefore long-standing model systems for stochastic processes in cells, much thanks to the efforts of Kurt Nordström to whose memory this issue is dedicated. Here we analyze a range of experimental methods for measuring plasmid copy numbers in single cells, focusing on challenges, trade-offs, and necessary experimental controls. In particular we analyze published and unpublished strategies to infer copy numbers from expression of plasmid-encoded reporters, direct labeling of plasmids with fluorescent probes or DNA binding proteins fused to fluorescent reporters, PCR based methods applied to single cell lysates, and plasmid-specific replication arrest. We conclude that no method currently exists to measure plasmid copy numbers in single cells, and that most methods are overwhelmed by various types of experimental noise. We also discuss how accurate methods can be developed.