Downloaded by guest on September 27, 2021 www.pnas.org/cgi/doi/10.1073/pnas.1811722116 a Eckhardt Bruno Jeckel Hannah bacterial expansion of diagram phase space-time the Learning eciigtednmc nalphases. for all sufficient in are dynamics interactions the cell–cell describing physical that simula- infer particle-based to with tions together then identification phase We this evolution. of use spatiotemporal their phases and dynamical nonequilibrium bacterial to the modeling identify computational quantitatively and learning with technique machine microscopy unsupervised high-speed dynamics adaptive an macroscale combining gap and by the microscale, bridge expression, we gene Here, between migration. bacterial that collective mechanisms underlie physical and links physiological, the molecular, of between unsolved understanding an the remained limiting com- has fundamentally scales challenge, a time and Therefore, dynamics length multiple swarming possible. technical across spatiotemporal the been to of not characterization Due macroscale plete has established. the scales be and microscopic and macroscopic at to acquisition data level simultaneous yet fast single-cell limitations, micro- has the the dynamics between at swarm connection processes gradi- causal scopic the swarming osmolarity films during However, swim of liquid cells the (36–43). thin role which maintaining through into surface, the in the differentiation above production and surfactant the (28–35) of and as ents aspects types such particular cell behavior, control distinct swarming that physiolog- local factors important the biophysical revealed and have ical studies multicel- matter for Previous active system and (10–27). model motility, a development, self-organization, as lular biophysics and investigated microbiology widely been in has ecolog- and swarming nutrient transmission, biomedical bacterial its importance, disease to of ical Due for (1–9). exploration evolution and implications flow, the gene profound and with expansions patches, range facilitates C microbiology behavior collective in unified complexity a behavioral by provide multiscale dominated swarms. results bacterial are These of phases understanding interactions. motility cell–cell swarming physical whereas swarm microscopic kinetics, growth macroscopic cellular the by that driven modeling, primarily reveal is continuum expansion simulations experiments, Our particle-based space. and in magnitude phases of behavior as collective to determine develop macroscopic which that learning, and mechanisms microscopic machine physical distinct and with biological approach key high-through- suit- a identify microscopy techniques combine adaptive we analysis Here, put and dynamics. acquisition multiscale due for data challenge able major in com- a limitations a remained Establishing to has scales. behaviors all macroscopic intercellular, on and intracellular, between life connection of causal 2018) aspect prehensive, 7, matter essential July active review an in for (received components are 2018 individual 11, of December approved dynamics and Coordinated MA, Cambridge, University, Harvard Weitz, A. David by Edited Universit Technische 02139; Philipps-Universit SYNMIKRO, a lnkIsiuefrTretilMcoilg,303Mrug Germany; Marburg, 35043 Microbiology, Terrestrial for Institute Planck Max letv irto fflglae el cossurfaces, across that behavior bacterial cells fundamental a flagellated is swarming, of termed migration ollective d eateto ehnclEgneig ascuet nttt fTcnlg,Cmrde A019 and 02139; MA Cambridge, Technology, of Institute Massachusetts Engineering, Mechanical of Department | biofilm a,b,c | b tBri,163Bri,Germany Berlin, 10623 Berlin, at ¨ ailssubtilis Bacillus swarming J , rcJelli Eric , r Dunkel orn ¨ tMrug 53 abr,Germany; Marburg, 35032 Marburg, at ¨ | elcl interactions cell–cell a,b am Hartmann Raimo , c,1 wrsepn vrfieorders five over expand swarms n ntDrescher Knut and , | a rve .Singh K. Praveen , a,b,1 c eateto ahmtc,MsahstsIsiueo ehooy abig,MA Cambridge, Technology, of Institute Massachusetts Mathematics, of Department b aheec hskadLEEZnrmf Zentrum LOEWE and Physik Fachbereich 1073/pnas.1811722116/-/DCSupplemental. y at online information supporting contains article This 1 S4 oeiaie ies . C BY-NC-ND) (CC 4.0 NoDerivatives License distributed under is article access open This Submission.y Direct PNAS a is paper. article the This interest.y wrote of K.D. conflict and no J.D., declare H.J., H.J., authors and tools; The data; reagents/analytic analyzed new K.D. per- and contributed L.V. J.D., R.M. and B.E., J.F.T., J.F.T., and R.H., P.S., E.J., E.J., R.H., H.J., H.J., research; research; designed formed K.D. and J.D. contributions: Author coordinate space-time each at in 1 extracted described (Fig. observables is rafts 23 of moving non- list include and of well formation which clusters as the motility, observables, characterize motile and that statistical ratio parameters emergent aspect of as as list such represent a parameters we this single-cell by data, visualize time-resolved movie and microscopic analyze, each 1B of compress, (Fig. amount To cells S1). large individual Fig. positions, all Appendix, time-dependent of SI the velocities extracted ( and we diameter orientations, movie, swarm each the adap- From determined on line the based a of length of tively the duration with through 10-h 1A), line the (Fig. one swarm over along the Hz recorded 200 were of Movies experiment. rate acquir- single frame 1), a (Fig. at time and movies expanding space ing radially in a resolution area image single-cell scanning to at us swarm the allows of technique This feature movement 1A). image and (Fig. automated between an times feedback and at live recognition movies a by high-speed determined acquires locations that adap- an developed microscope we level, tive single-cell the at space of in behavior magnitude swarming the track To Discussion and Results owo orsodnemyb drse.Eal [email protected] Email: addressed. [email protected]. y or be may correspondence whom To eilsamn n htcl–elitrcin ihneach within interactions. interactions mechanical by cell–cell dominated bac- are that phase of and swarming and diagram learning, phase swarming the machine terial reveal genetics, is to with modeling work mathematical combined This system. we model technique, which microscopy time, adaptive a high-throughput in a magnitude as by enabled of swarming orders bacterial six orders and using length five in spanning magnitude development, of multicellular behav- during collective macroscale iors to con- physiology study intracellular Our nects determine function. components biological microscopic multicellular that under-macroscopic, of units to properties is individual how challenge the and fundamental stand of A tissues those organization. this than to drive larger scales much length cells are on individual that self-organization collective from display systems, swarms, living Most Significance h ptoeprleouino hs bevbe during observables these of evolution spatiotemporal The . a ahlMok Rachel , aeil n Methods and Materials c,d a rdrkTotz Frederik Jan , . y e raieCmosAttribution-NonCommercial- Commons Creative nttt o hoeia Physics, Theoretical for Institute rSnhtsh Mikrobiologie Synthetische ur ¨ .subtilis B. www.pnas.org/lookup/suppl/doi:10. aeil n Methods). and Materials and NSLts Articles Latest PNAS e ui Vidakovic Lucia , C Table Appendix, SI vrfieodr of orders five over and .Tefull The D). | f6 of 1 and a y ,

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Fig. 1. Adaptive microscopy reveals complete multiscale dynamics of bacterial swarm expansion. (A) Movies at single-cell resolution are acquired at different locations in the swarm, starting from 1 cell to 100 cells, and follow swarm expansion until the agar plate is completely colonized. The number of movies and locations where movies are acquired (indicated by colored squares) are determined adaptively, depending on the detected swarm size. B–D, Top show qualitatively different bacterial behavioral dynamics observed at distinct space-time points, which are marked in E–H by corresponding magenta symbols. B–D, Bottom demonstrate automated extraction of single-cell positions, orientations, and cell velocities (B) as well as collective behaviors, such as formation of nonmotile clusters (C) and motile rafts (D), corresponding to groups of aligned cells that move in the same direction. Cells assigned to the same nonmotile cluster or motile raft by the classification algorithms share the same color; cells labeled in white have not been identified as belonging to any motile raft or nonmotile cluster. Magenta arrows in D indicate the average velocity of a raft. (Scale bars, 10 µm.) (E) Heatmap of the cell density, obtained by averaging single-cell data as in B–D for each movie at each space-time coordinate. The lag phase, a period following inoculation during which the swarm does not expand, as well as the expansion phase, is indicated. (F–H) Additional heatmaps for the cell speed, fraction of cells that are in nonmotile clusters in a given field of view, and fraction of cells that are in motile rafts. A total of 23 statistical observables analogous to E–H were determined at each space-time position (Materials and Methods and SI Appendix, Table S4).

swarming is visualized in heatmaps (Fig. 1 E–H and SI Appendix, is necessary, but not sufficient, for rapid swarm expansion of Figs. S2–S6), where the color of each pixel is assigned according B. subtilis (29, 44, 45). To test whether srfA (surfactin synthase) to an averaged statistical observable of a movie. In our online expression coincides with the transition to the expansion phase, interactive data explorer (http://drescherlab.org/data/swarm/), we constructed a sfGFP-based srfA transcriptional reporter, cali- the space-time heatmap coordinates are linked to the associated brated by a constitutively expressed mKate2 signal (SI Appendix, microscopic movies within the swarm, to allow for a direct inspec- Fig. S7). By coupling our adaptive microscope control algorithms tion of the connection between microscopic and macroscopic to a confocal microscope, we were able to measure spatiotem- dynamics. poral dynamics of fluorescent reporters during swarm expan- sion. Since the simultaneous acquisition of high-speed movies Transition from Initial Lag Phase to Swarm Expansion. The swarm- was technically not possible, the mechanical observables were ing dynamics display striking macroscopic spatiotemporal pat- recorded in a separate set of experiments that exhibited the same terns (Fig. 1 E–H): A long initial lag phase in which the swarm highly reproducible expansion dynamics. Using the adaptive con- does not migrate outward for several hours (2) is followed by focal approach, we tracked gene transcriptional activity in space an abrupt transition to an exponential expansion phase, eventu- and time during all phases of swarm development (Fig. 2 A–C) ally resulting in the complete coverage of the 9-cm agar plate and found a strong increase in surfactin production just before within 5 h (Fig. 1E). Previous investigations have shown that the expansion phase (Fig. 2A). Noting the 14-min maturation the production of a peptide-based surfactant, termed surfactin, time of sfGFP (46), srfA expression likely starts earlier, yet a

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Fig. 3. Machine learning the swarming phases from microscopic dynamics. (A) Raw data of one swarm expansion experiment, consisting of ∼1,500 space- time points (columns) in a 23-dimensional observation space (rows). Additional replicates are shown in SI Appendix, Fig. S11. Color bar indicates relative magnitudes scaled to [0,1]. In the case of strongly correlated observables with high normalized mutual information (marked by red brackets), only one of them is included in the machine-learning analysis. (B) The values of the 14 remaining observables (rows) were binned into five categories as indicated by the color bar, providing the input data for machine learning. (C) The 2D representation of the data in B, obtained with t-SNE; k-means clustering robustly identifies five main dynamical phases during swarm expansion across independent experiments (n = 3; SI Appendix, Figs. S13–S16 and SI Text). Phases are labeled with different colors. t-SNE coordinates highlighted as large circles for each phase correspond to experimental snapshots shown in D. (D) Typical images for the phases (SI Appendix, Movie S1) identified in C: low-density single-cell phase (SC); high-density rafting phase (R) with a high percentage of comoving cells; biofilm phase (B) characterized by long, unseparated cells; and coexistence phases that contain single cells and rafts (SC + R) or rafts and biofilm precursors (R + BP). (E) For each phase, simulations were run with the cell shape, motility, and density extracted from the particular phase as input parameters (SI Appendix, Movie S2 and SI Text). (Scale bars, 10 µm.) (F) Detailed quantitative comparisons between experiments (small circles), the particular experimental states shown in D (large circles), and simulations (squares; error bars are SDs, n = 20) yield good quantitative agreement, except for the B phase, confirming that physical effects determine the four motility-based swarming phases. (G) The emergence of the different phases in time and space during swarm expansion. Colored circles correspond to space-time coordinates of images from D.

4 of 6 | www.pnas.org/cgi/doi/10.1073/pnas.1811722116 Jeckel et al. 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APPLIED PHYSICAL SCIENCES prepared as described earlier (64). B. subtilis strains used in this study were ACKNOWLEDGMENTS. This research was supported by grants from the Max derived from B. subtilis strain NCIB3610 and are listed in SI Appendix, Table Planck Society, the Human Frontier Science Program (CDA00084/2015-C), the S1. For genetic modifications in B. subtilis, we used a ∆comI derivative of Deutsche Forschungsgemeinschaft (SFB 987), and the European Research Council (StG-716734) (to K.D.); a James S. McDonnell Foundation Complex strain NCIB3610 (65). Systems Scholar Award and an Edmund F. Kelly Research Award (to J.D.); Additional details of experiments and data analysis methods are de- and a Massachusetts Institute of Technology (MIT) International Science and scribed in SI Appendix, SI Text. Technology Initiatives Germany seed grant (to K.D. and J.D.).

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