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Confinement discerns swarmers from planktonic bacteria

Powered by flagella, many bacterial species exhibit collective motion on a solid surface commonly known as swarming. As a natural example of active matter, swarming is also an essential biological phenotype associated with virulence, chemotaxis, and host pathogenesis. Physical changes like cell elongation and hyper-flagellation have been shown to accompany the swarming phenotype. Less studied, however, are the contrasts of collective motion between the swarming cells and their counterpart planktonic cells of comparable cell density. Here, we show that confining bacterial movement in circular microwells allows distinguishing bacterial swarming from collective swimming. On a soft agar plate, a novel bacterial strain Enterobacter sp. SM3 in swarming and planktonic states exhibited different motion patterns when confined to circular microwells of a specific range of sizes. When the confinement diameter was between 40 μm and 90 μm, swarming SM3 formed a single-swirl motion pattern in the microwells whereas planktonic SM3 formed multiple swirls. Similar differential behavior is observed across several other species of gram-negative bacteria. We also observed ‘rafting behavior’ of swarming bacteria upon dilution. We hypothesize that the rafting behavior might account for the motion pattern difference. We were able to predict these experimental features via numerical simulations where swarming cells are modeled with stronger cell–cell alignment interaction. Our experimental design using PDMS microchip disk arrays enabled us to observe bacterial swarming on murine intestinal surface, suggesting a new method for characterizing bacterial swarming under complex environments, such as in polymicrobial niches, and for in vivo swarming exploration.


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Motility is an essential characteristic for bacteria. Although energy-consuming, it provides high 35 returns, enabling cells to uptake nutrients efficiently and escape from noxious environments 1 . In a 36 host environment, bacterial motility is also an essential phenotype that intimately relates to 37 virulence through complex regulatory networks 2 . Swimming and swarming are two common microbiologists believe that swarming and swimming are fundamentally different motility types. 46 For instance, studies found that compared with swimming cells, the requirement for flagella torque 47 is higher for swarming B. subtilis 7 ; swarming E. coli remodel their chemotaxis pathway 8 ; and in 48 swarming P. aeruginosa, the production of virulence factors and antibiotic resistance increases 9 .

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A recent study has demonstrated a medically relevant distinction between swarming and 50 swimming: a particular strain of swarming endobacteria protect against mice intestinal 51 inflammation while their swimming counterparts could not 10 . The evidence to date that shows 52 swarming is different from swimming comes mostly from biological data. However, precise 53 biophysical visualization and quantitation of these differences are lacking. In this report, using 54 Enterobacter sp. SM3, a novel bacteria strain that possesses both swimming and swarming 55 motilities, we show distinct biophysical characteristics between these two types of motility under 56 confined, circular geometry of a particular range of sizes. 57 Studies have shown that geometric constraints have profound influence on patterns of 58 microswimmers' collective motion. For example, these constraints may create mesoscopic or 59 macroscopic coherent structures such as swirls and jets [11][12][13] . Circular confinement, in particular,  This striking difference persists in several well depths, except that the concentrated cells yields 77 small but non-zero vortex order parameter (VOP, defined as illustrated in Fig. 1E) in deeper wells, 78 as opposed to nearly zero VOP in shallow wells (Fig. 1F).

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The confinement well diameter has strong influence on the motion pattern in the wells. In smaller 80 wells like 31 μm, even concentrated planktonic SM3 forms a single vortex ( Fig. 2A) whereas in 81 larger wells, such as ones of 112 μm diameter, swarming SM3 also breaks into mesoscale vortices 82 (Fig. 2B). The phase diagram shows a single swirl in small confinement for both types. The 83 patterns diverge as confinement size increases, but they converge towards multiple swirls as 84 confinement size reaches 144 μm and larger (Fig. 2C). To further compare the dynamics of the 85 confined swarming and swimming SM3, spatial correlation of the velocity field was calculated for 86 d = 90 μm where the motion patterns differ for swarming and swimming SM3 and for d = 500 μm 87 where both motilities show mesoscale vortices (see method). We computed the correlation 88 function for the inscribed square within a well, which shows the extent to which the velocity at an 89 arbitrary location correlated with the velocity at a distance of Δr away from that location. In 90 90 μm wells, swarming SM3 velocity correlates positively or negatively throughout the whole well 91 (negative values have resulted from the opposite sides of a single swirl). In contrast, swimming velocity of planktonic cells of comparable concentration does not correlate anymore as Δr went up 93 beyond 25 μm (Fig. 2D). However, in a large open space where both swarming and swimming 94 SM3 break into small vortices, the correlation functions look similar. The characteristic length as 95 the curve first crosses Cr(Δr) = 0, which also represents the size of the mesoscale vortices, of 96 planktonic SM3 is 27 μm and 33 μm for swarming SM3 (Fig. 2E). 97 We also tested other bacteria such as Enterobacter Sp. SM1, Serratia marcescens (including one 98 lab strain Db10 and another strain H3 isolated from a human patient), Citrobacter koseri (H6), and 99 Bacillus subtilis 3610. All the tested strains showed similar motion pattern divergence between 100 confined planktonic cells and swarming cells like SM3 with the exception of B. subtilis (Fig. S1A,   101 see discussion).

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The large single swirl behavior is indicative of cohesive cell-cell interaction 103 We performed several experiments to explore the cause for the divergence of motion patterns in 104 confinement. First, we rule out cell density difference as the reason for the difference in the 105 confined motion patterns by concentrating planktonic cells to comparable density to that of a 106 naturally expanding swarm on agar (see methods) before mounting the PDMS chip. Second, we 107 noticed that SM3 tends to get elongated when they swarm. We hypothesize that elongated bacteria 108 may enhance the local alignment of the rod-shaped cells and increase the size of vortices in 109 mesoscale turbulence 18 . Thus, we treated SM3 planktonic cells with Cephalexin (CEP) which has 110 been shown to elongate E. coli 19 . This treatment indeed caused the cell length to reach that of 111 swarming cells on average (Fig. 3A). However, we found no significant change following 112 centrifugation and CEP treatment of the planktonic SM3 (Fig. 3B). Although CEP treated 113 planktonic SM3 has similar cell length, cell density, and cell speed as that of swarming SM3, we 114 could not restore the single swirl pattern in 74 μm confinement wells (Fig. 3C). Third, noticing a 115 surfactant rim on the swarming SM3 colony edge, we conjectured that surfactants secreted by 116 swarming SM3 might help align the swarmers in confinement. Surfactin was added in several 117 concentrations to planktonic SM3 in order to test whether it could promote a single swirl pattern.

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However, it did not establish a stable single swirl. Last but not least, we found that adding 119 lyophilized swarming supernatant to swimming SM3 also did not help increase the VOP (Fig. 3C).

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Unable to make the concentrated planktonic SM3 form a single swirl in the 74 μm well, we tackled 121 the problem from another angle, by altering the conditions of swarming SM3 in order to break the 122 single swirls. Initially, we tried to physically "damage" the swarming colony by rubbing the 123 swarming colony gently with a piece of PDMS offcut. This operation did not break the single swirl 124 pattern in the wells (Fig. 3D). Then, 0.2% D-mannose was added to the swarming colony to de-125 cluster bacteria bundles due to cells' sticking to each other. However, this treatment could not alter 126 the single swirl pattern, either (Fig. 3D). Finally, we diluted the swarming cells in LB by 20-fold.

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After re-concentrating the cells by centrifugation and removing extra LB to recover the initial cell 128 density, these "swarming" SM3 cells were pipetted back on the agar plate. After this treatment, 129 the previous single swirl turned to multiple swirls under the confinement (Fig. 3D), suggesting that 130 these cells now behave much like planktonic cells. We conclude that the single swirl pattern 131 depends on cohesive cell-cell interaction mediated by biochemical factor/s removable by matrix 132 dilution.

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Diluted swarming SM3 show unique dynamic clustering patterns 134 We suspected that specific interactions between the neighboring swarming cells were weakened   Numerical simulation reveals cell-cell interaction to be the key player 150 To further verify that rafting in swarming is a crucially relevant factor to the motion pattern 151 discrepancy, we performed computer simulations using the Zonal Model where the interactions 152 among the moving particles (short-range repulsion, velocity alignment, and anti-alignment) are 153 considered, all as functions of the particle-particle distance 21,22 . The speed of the particles is fixed 154 for simplicity, but the initial particle positions and initial moving directions are randomized. In the 155 simulations, we interpret the rafting as a lower repulsion force and stronger alignment among the   lost the single swirl pattern, we hypothesize that other than cell length or cell speed, the strong 197 cell-cell interaction may be a key factor responsible for the persistence of single swirls in the wells.

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The mechanism of the rafting phenomenon of swarming cells has not been fully deciphered yet 3 .  Reproducing the experimental results via computer simulation, we confirm that the lower repulsion 206 and higher alignment are the key factors, which differentiate swarmers and planktonic cells. Future 207 work is called upon to explore further the swarmer rafting phenomenon and to investigate on the 208 molecular level which category of substances are mainly responsible for the cell-cell cohesive 209 interaction among the swarming cells.

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A spectrum of swarming bacteria has the same characteristic as SM3 (Fig. S1A). These bacteria   Bacterial growth and confinement (Fig. 6A). Enterobacter sp. SM3 is a novel swarming 263 bacterial strain isolated from inflammatory mice 10 . SM3 was transferred from -80℃ glycerol stock 264 to fresh LB (Lysogeny Broth: water solution with 10 g/L tryptone, 5 g/L yeast, and 5 g/L NaCl) 265 and shaken overnight (~ 16 h) in a 37℃ incubator at 200 rpm. For swarming under confinement 266 assay (Fig. 6A, red arrows), 2 μL overnight bacterial culture was inoculated on the center of a LB 267 agar plate (10 g/L tryptone, 5 g/L yeast, 5 g/L NaCl, and 5 g/L Agar; volume = 20 mL/plate) and 268 kept in a 37℃ incubator. After 2.5 h of swarming, a PDMS chip (~ 1 cm 2 ) was mounted upon the 269 edge of the swarming colony and the Petri dish was transferred onto the microscope stage for  Bacterial cell density measurement (Fig. 6B). 2.5 h freshly grown SM3 was subjected to 285 different factors of dilution in LB, such as 10 2 , 10 3 , until 10 8 . 50 μL of each diluted culture was 286 inoculated and spread on 1.5 % LB agar plate (10 g/L tryptone, 5 g/L yeast, 5 g/L NaCl, and 15 287 g/L Agar; volume = 20 mL/plate) and was incubated at 37℃ for 16 h. Bacterial colonies appeared focus. The thickness of the swarming colony was calculated based on the calibration of the knob 300 turning tick readings. Then the cell density was estimated by CFU/mL. CFU was calculated for 301 both swarming and swimming SM3 to make sure the cell densities of these two cases were 302 comparable inside the wells. We consider colony forming unit counting a better way to control the 303 live cell number than simply using the volume fraction because: 1, Dead cells that count in the 304 volume fraction will not contribute to the motion in the well, but they will be excluded in CFU 305 calculation; 2, It is technically difficult to measure the volume of dense bacterial suspension using 306 pipetting method due to high viscosity.

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The velocity field was marked for the confined bacteria and the VOP was calculated using the 344 equation in Fig. 1E. Using the velocity field information, the spatial autocorrelation function was term . The direction of motion + of each particle is updated by the interaction terms , which 367 includes alignment, anti-alignment and repulsion effects with all neighboring particles and also the 368 rotational diffusion term with diffusivity of Dr and noise term : The details of the binary interaction terms , and , are provided in the Supplementary Material.

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The simulation starts with random initial position and orientations, followed by numerical   SM3 and swimming SM3. Swarming SM3 cell density is measured after SM3 swarming on an agar surface 429 for 2.5 h while swimming SM3 cell density is measured for overnight SM3 culture being regrown in fresh 430 Lysogeny Broth (LB) for 2.5 h. Since cell density of swarming SM3 was higher than that of planktonic 431 SM3, the latter was concentrated before being applied on the agar plate to acquire comparable cell density.    were coated with 0.5 μm fluorescent beads and mounted on SM3 inoculated colitic (A) or normal (B) mice 538 intestine tissue surfaces. Average velocity field was calculated by tracing the beads motion using PIV 539 toolkit. (A) On colitic tissue, wells with VOP > 0.7 were found and marked with yellow squares. We 540 conclude that, in these wells, the single swirl motion pattern of the beads was powered by the confined 541 swarming SM3. Since the tissue surface was not as smooth as on agar surface, the motion of the beads in 542 some wells did not form a complete vortex, yet jets indicating partial vortices can be seen. (B) On a normal 543 tissue lacking swarming bacteria, the average velocity of the beads in the wells due to random motion is 544 close to zero, thus the VOP is uniformly small. We could infer that the confined SM3 in these wells were 545 predominantly swimming rather than swarming. 546

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Detecting bacterial motility on mouse intestine tissue using PDMS chips. Six-week-old female 548 C57BL/6 mice (Jackson Laboratories, Bar Harbor, ME; #000664) were administered 3%(w/v) 549 DSS (Dextran Sulfate Sodium) (MPI; # 160110) in animal facility drinking water daily to induce 550 acute colitis. After 9-12 days, when the mice weight loss reached 20%, mice were euthanized using 551 isoflurane anesthesia and large intestines were harvested. For control, conventional six-week-old 552 female C57BL/6 mice exposed to drinking water not containing DSS treatment were also 553 sacrificed and the intestines were collected. This study was approved by the Institute of Animal dimensional space using the overdamped Langevin-based equations, assuming that inertia is 564 negligible in a low Reynolds number environment. The position r and orientation q of particle i 565 are calculated using the following stochastic differential equations: Based on our experimental observations, bacterial velocity in the suspension is largely independent 567 of the local cell density. Accordingly, the self-propulsion speed of particles is set to be a constant kr is the magnitude of the constant repulsion interaction that applies over distance of rr around the 583 particle (Fig. 1). The second term in Eq.
where m + and mare the strength of alignment and anti-alignment interactions, respectively.
(1) and (2) using the first order Euler scheme. Initially, the particles 588 are randomly distributed with random orientations. The integration time step ∆ is selected 589 sufficiently small to ensure both numerical stability and also independence of long-term statistics 590 from ∆ . The simulation time is set long enough to let the system reach a dynamic steady-state.

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The interaction of particles with the bounded circular domain is modeled via a reflective boundary In order to differentiate the Swarming and Planktonic cases, two different sets of interaction 599 parameters have been used, which are summarized in Table 1. The values are unitless. In both 600 systems, we set the exclusion parameters kex and dex to fixed values of 0.02 and 0.035, respectively.

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It is also assumed that particles only experience a rotational diffusion Dr of 0.75. The simulations 602 for both Swarming and Planktonic cases have been performed at two particle densities r = N/Adom, 603 where N is the number of particles and A is the area of the simulation domain. In the high density 604 case, r = 4300 and in the diluted case, we set r = 235. In the diluted case, in order to further 605 minimize the boundary effects, we replace the bounded domain with a periodic boundary.