Landing Dynamics of Swimming Bacteria on a Polymeric Surface

Mar 16, 2017 - We monitored the bacteria landing dynamics, which shows that the density distribution, the probability, and the orientation for collisi...
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Landing Dynamics of Swimming Bacteria on a Polymeric Surface: Effect of Surface Properties Meng Qi, Xiangjun Gong,* Bo Wu, and Guangzhao Zhang* Faculty of Materials Science and Engineering, South China University of Technology, Guangzhou 510640, P. R. China S Supporting Information *

ABSTRACT: Landing of bacteria for adhesion on a surface is a common phenomenon in our life. However, how surface properties are involved in this process remains largely unclear. Using digital holographic microscopy, we investigated the three-dimensional motions of flagellate Escherichia coli swimming near polymeric surfaces with different properties in aqueous solution before adhesion. We monitored the bacteria landing dynamics, which shows that the density distribution, the probability, and the orientation for collisions of the bacteria are determined by their motility but are slightly affected by the surface properties. However, surface hydrophobicity reduces the near-wall velocity of the bacteria through collisions and slightly increases the collision duration. This promotes the landing and adhesion of bacteria. By contrast, most bacteria collide with the surface using their flagella, which resist adhesion.

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INTRODUCTION Most bacteria function on surfaces. Their landing for adhesion onto a surface is a preliminary step for their colonization. It is an important issue in marine antifouling,1 medical disinfection,2 and food industry.3 In principle, the landing dynamics is determined by not only the motion of the bacteria before adhesion but also the surface properties.4 Flagellate bacteria such as Escherichia coli generally have motion features quite different from those of Brownian colloids.5 In bulk solution, an E. coli cell rapidly swims in an arbitrary direction and randomly changes direction through unbundling of flagella, which is referred to as “tumbling”.6,7 Upon a surface, it exhibits some unique motions such as near-wall accumulation,8 circular motion parallel to the surface,9 and subtumbling.10 The behaviors cannot be explained by Derjaguin−Landau− Verwey−Overbeek (DLVO) theory, in which the doublelayer repulsion and van der Waals attraction are considered to be the driving forces.11 It is proposed that the hydrodynamic interaction with a nonslipping condition of flow on the surface is responsible for them.8,9,12,13 Recently, some studies have revealed that the near-wall behaviors are closely related to the collision between bacteria and the surface. A theoretical study shows that both collision and Brownian motion can affect the near-wall density distribution of bacteria.14 An experimental study further demonstrates that the ciliary contact between Chlamydomonas and a surface dominates its collision and escape.15 However, the interplay between the motion of bacteria and bacteria−surface interactions or whether surface properties alter the near-wall motion of bacteria and their adhesion remains unknown. Tracking the motile bacteria swimming or even colliding with surfaces of different properties can provide new insights into the adhesion mechanism. Here, we report the three-dimen© XXXX American Chemical Society

sional (3D) observation of E. coli swimming near polymeric surfaces with different surface properties, that is, potential and hydrophobicity. The real-time 3D tracking of motile E. coli cells near the surfaces is realized with home setup digital holographic microscopy (DHM), which gives time and spatial dependence of 3D motions of E. coli cells before adhesion.16−18 This enables us to analyze and compare the motion properties of bacteria near or even upon these surfaces, that is, the 3D velocities, density distributions, as well as the probability, duration, and orientation during collisions. As a result, key factors affecting the landing of motile bacteria on a surface are discussed.

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EXPERIMENTAL SECTION

Preparation and Characterization of the Surfaces. Potassium 3-sulfopropyl methacrylate (SPMA) (98%, Sigma-Aldrich) and [2(methacryloyloxy)ethyl]dimethyl-(3-sulfopropyl)ammonium hydroxide (SBMA) (97%, Sigma-Aldrich) were used as received. 2hydroxyethyl methacrylate (HEMA) (97%, Sigma-Aldrich) was purified by passing through a alumina column. n-butyl methacrylate (BMA) (99%, Aladdin) was purified by treatment with 5% aqueous NaOH and distillation. Coverslips (Fisher Scientific) were washed by immersion in hot Piranha solution (H2O2/H2SO4, 3:7 v/v; 90 °C) for 120 min and sonication in deionized water and ethanol and were dried with nitrogen before use. The cleaned coverslips were immersed in 10.0 mM toluene solution of 3-(2-bromoisobutyryl)undecyl triethoxysilane prepared following a reported procedure,19 at room temperature for 16 h to form a monolayer of initiators. Afterwards, SPMA, SBMA, HEMA, or BMA monomers were polymerized on the coverslips by using surface-initiated atom transfer radical polymerization (SI-ATRP).20 The resultant coverslips bearing poly(potassium 3-sulfopropyl methacrylate) (PSPMA), poly(sulfobetaine methacryReceived: February 8, 2017 Revised: March 15, 2017 Published: March 16, 2017 A

DOI: 10.1021/acs.langmuir.7b00439 Langmuir XXXX, XXX, XXX−XXX

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where Es(r, 0) is the scattered amplitude in the focal plane, r = (x, y) is the position in the horizontal plane, and h(r, −z) is the Rayleigh− Sommerfeld propagator, which is related to r, z, and the wavenumber of the incident light.22 The reconstructed intensity volume consisted of a stack of images, ranging from 0 to 40 μm away from the bottom surface with a spacing of 0.16 μm. Local intensity maxima within the reconstructed volume were identified as candidate positions for bacteria in this volume as far as no brighter pixel existed within a cubic of a specific length (an integer comparable with the size of an E. coli cell). These candidate 3D positions were thus refined to discriminate “false” ones by linking them into continuous trajectories between timeevolving reconstructed volumes, using a home-made code written with Python. Finally, the localization accuracy in the vertical direction using DHM was determined to be around 0.4 μm as shown in Figure S4 and the Supplementary Methods.25 Analysis of Bacterial Motions. From the 3D locations of each cell, the instantaneous 3D velocity (V3D), density distribution [n(z)], mean square displacement (MSD), and bacterial collisions with the surface could be monitored. As a result, the corresponding motion features of the bacteria at the beginning of the observation (0 min), 2 h later (120 min), and 4 h later (240 min) with increased adhesion rate of E. coli onto the surfaces were compared in the later discussions. In each subgroup of holograms, we set the position of the bacterium with the smallest reconstructed distance to the focal plane to be at the bottom surface (z = 0); the heights of other bacteria were then correlated with this position to get their absolute distances from the surface (z). First, bacterial trajectories were classified according to different motility clues. MSD, representing the motility of a cell, was computed for each trajectory as

late) (PSBMA), poly(2-hydroxyethyl methacrylate) (PHEMA), or poly(n-butyl methacrylate) (PBMA) chains were washed and dried with nitrogen before use. X-ray photoelectron spectroscopy (XPS) survey scan spectra confirmed the existence of these chains on the surfaces. Surface potentials of the polymeric surfaces in motility buffer (MB) were measured using a DelsaNano C particle and zeta potential analyzer (Beckman Coulter).21 Static water contact angle (WCA) on the surface was determined using a theta optical tensiometer (T200Auto1B, Biolin Scientific). WCA measurements were repeated three times at different places on the surface to obtain an average value. Morphology, roughness, and elastic modulus of the surface (Figure S1) in the MB were measured (see the Supplementary Methods) using an atomic force microscope (XE-100, Park Systems). All experiments were performed at 25 °C. Bacterial Strain and Culture. Wild-type E. coli (strain HCB1) was used in this study. E. coli cells were streaked on LB agar plates (1% tryptone, 0.5% NaCl, 0.5% yeast extract, and 1.5% agar in Millipore-Q water; Tryptone, yeast extract, and agar were purchased from BD Difco) and grown at 37 °C for 24 h. A monoclonal colony was inoculated into a tube containing 3.0 mL of fresh tryptone medium (1% tryptone and 0.5% NaCl in Millipore-Q water) and grown in a shaker (ZWY-200D, Zhicheng Instruments) at 33 °C overnight. Thirty microliters of the culture were then reinoculated into 3 mL of fresh tryptone medium and further cultured for 4 h to mid-log phase (OD600 = 0.4). The E. coli suspension (1:50 v/v) was then diluted into the MB (10 mM potassium phosphate buffer containing 0.1 mM EDTA−2Na and 10 mM glucose; pH 7.2), yielding a suspension with an E. coli concentration of ∼106 cells/mL. Centrifugation was avoided to preserve the motility of the bacterial appendages. The suspension was used immediately for observation. Characterization of the E. coli cells can be referred to in Figures S2 and S3 and the Supplementary Methods. 3D Tracking of E. coli Using DHM. Real-time 3D tracking of E. coli was performed using a home-made in-line digital holographic microscope. A digital holographic microscope is capable of 3D position detection based on optical interferences between an incident light and its scattered light when passing through an object.22 It was equipped on an inverted microscope (IX83, Olympus) with a 40× objective (NA = 0.6, Olympus). A collimated light-emitting diode (LED, λ = 455 nm, Thorlabs) provided uniform illumination through the sample. The MB suspension of E. coli was injected into a chamber (∼150 μL) formed with a 500 μm deep silicone isolator (20 mm diameter, Molecular Probes) sandwiched between two coverslips. The polymeric surface was used as the bottom one. After equilibrium for approximately 10 min, the focal plane was located 10 μm below the polymeric surface to record defocus images (holograms) of E. coli in the region of interest. Holograms with the dimensions of 1024 × 1024 pixels were recorded at a frame rate of 20 Hz for 2 min (2400 images) using an sCMOS camera (Zyla-5.5-CL3, Andor Tech.) at the beginning of the observation (0 min). Such 2 min video clips were recorded every 10 min for the whole time span of 6 h. After that, a background image was generated by averaging the holograms in each subgroup at different times. The number of bacteria adhered onto the surfaces (Nb) was obtained from the background image at different times because only the stationary signals of the adhered bacteria could be reserved after averaging. The 3D location of each E. coli cell captured in the hologram was extracted using the following steps. First, removal of the background was performed with each subgroup of holograms by subtracting the corresponding background image. In this way, disturbance from the attached bacteria and imperfections in the optical path could be avoided. After that, the volumetric intensity distribution of the scattered light of each hologram was reconstructed with a MATLAB routine based on Rayleigh−Sommerfeld back propagation, which is described elsewhere.23,24 Briefly, the scattered optical field Es at any height z above the hologram plane can be reconstructed by solving a convolution

Es(r, z) = Es(r, 0) ⊗ h(r, − z)

MSD(Δt ) = ⟨|r(t0 + Δt ) − r(t0)|2 ⟩

(2)

where r(t0) and r(t0 + Δt) are the 3D positions of an E. coli cell at times t0 and (t0 + Δt), respectively. The resultant MSD curve was fitted using MSD(Δt) = D(Δt)υ for each bacterium. The power index υ was acquired for the first 10% points of each trajectory with at least 30 consecutive points. The histogram of υ (see Figure S5) could be obtained for bacteria swimming upon different surfaces. υ < 1 corresponds to a subdiffusive motion, and υ > 1 is an active motion. These bacteria cells actively swimming near each surface were selected for further discussions. Three-dimensional velocity, density distribution, tumbling, and collision analysis were all applied to active bacteria trajectories (see the Supplementary Methods). For velocity analysis, the instantaneous 3D velocity of bacteria was first calculated to be the division of the displacement between adjacent trajectory points and the time interval between frames. Distribution of 3D velocity along the z direction could be obtained (Figure S6a). The average 3D velocity (V3D) at specific z was estimated using a Gaussian fit of the ensemble of velocities distributed at z (Figure S6b). It should be noted that for z = 25 and 35 μm, V3D values were obtained from the merged region of 20−30 and 30−40 μm, respectively, to reach a sufficient sampling quantity because fewer bacteria could be tracked that far from the surface. On the other hand, the density distributions [n(z)] of bacteria near different surfaces were obtained by normalizing the counts of these bacteria located around a given z. For these E. coli cells undergoing active motion, n(z) was mainly determined by the hydrodynamic attractions between the bacteria and the surface, which could be described using a force dipole model8

⎡ ⎛1 n(z) 1 ⎟⎞⎤ = exp⎢L⊥⎜ + ⎥, ⎝ ⎣ n0 z H − z ⎠⎦

L⊥ =

3p 64πηD⊥

(3)

where n0 is the population density concentration of E. coli in the bulk solution and H is the thickness of the cell chamber. L⊥ represents the operating range of hydrodynamic interactions on the bacteria cells, which is determined by the force dipole (p), the diffusion coefficient (D⊥) of E. coli, and the viscosity of the medium (η).

(1) B

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In the collision analysis for the active bacteria, their orientation relative to the horizontal plane (θ ranging from −π/2 to π/2; −π/2 corresponds to the incident surface normal direction, and π/2 corresponds to the outward surface normal direction) was determined to be the angle between the horizontal direction and their smoothed velocity vectors calculated as the fourth-order central difference25 instead of instantaneous velocity vectors. The smoothed velocity vectors were also used in the determination of tumbling events (see the Supplementary Methods). The average length of bacterial body (2a) and flagella bundle (Lf) were measured with tens of E. coli cells attached on coverslips, using DHM, scanning electron microscopy (SEM), and atomic force microscopy (AFM), respectively (Figure S3). As a result, for a head collision, the following criterion should be satisfied

⎧ π ⎪− < θ < 0 ⎨ 2 ⎪ ⎩|a sin θ| ≥ z

Article

RESULTS AND DISCUSSION Characterization of Polymeric Surfaces. Four polymeric surfaces, PSPMA, PSBMA, PHEMA, and PBMA, were selected for this study because their monomers are quite similar, except for differing in the end groups. The surface potential (ξ) and the WCA of these surfaces were measured in an MB with a salt concentration of ∼10 mM. In terms of ξ (Figure 1a), the

(4)

A tail collision happens if

π ⎧ ⎪0 ≤ θ ≤ 2 ⎨ ⎪|(L + a)sin θ| ≥ z ⎩ f

(5)

For the same kind of collision (head or tail), consecutive recognized collisions were merged as one collision event. Collision events were identified and analyzed through all trajectories as follows: The probability for head or tail collisions for an E. coli cell was obtained by normalizing the number of head/tail collision events within one trajectory by its total points and then averaging over all tracked bacteria. The z distribution of the collision probability was obtained by dividing the number of head/tail collision events by the number of trajectory points around a specific z and further averaging over all tracked bacteria. The duration of a head/tail collision was obtained by averaging the time length of all events of one kind. Moreover, the scattered angle (θout) was defined as the orientation of the bacteria relative to the surface at the end point of each collision event. The mean values of θout were obtained for collided bacteria near the surfaces by averaging the distributions of θout through all collision events of the same kind. Angular velocity (Ωout) was determined as the angle between the cell orientation of the last point and its former during a collision event divided by the time interval. Surface Force Measurements between E. coli and Surfaces Using AFM. We used AFM to measure the forces between E. coli cells covered on an atomic force microscope colloidal probe and a surface. Two E. coli strains, the wild-type and a flagella-deficient mutant (RP437Δf liC), were used. SiO2 microspheres (12.7 μm, Nanomicro) were treated with a plasma cleaner and incubated in 1 mg/mL poly(ethyleneimine) (PEI, Mw = 70 000, Aladdin) solution overnight. The deposited PEI layer on the microsphere surface acts as an adhesive to enhance bacterial attachment. The bead was attached to the end of a silicon cantilever (CSG01, NT-MDT) using EPIKOTE 1004 resin (Shell) to make a colloidal probe. Bacterial cells were prepared by harvesting a mid-exponential phase bacterial culture in tryptone medium and washed with 10 mM NaCl solution. The colloidal probe was brought in contact with the suspension of bacterial cells for 2 h and further treated with 2.5% glutaraldehyde solution at 4 °C for 2 h.26 Then, the cantilever was rinsed with the MB and used immediately for force measurements. Interactions between the bacteria-attached colloidal probe and the polymeric surfaces in MB were measured in the form of force-apparent separation curves.27 Surface attraction force, which denotes the force value corresponding to the maximum tip deflection upon cantilever retraction, was averaged over 32 force curves for each surface. The average attraction force normalized by the colloidal radius (γ) was obtained from the force statistics. The same probe was used throughout all experiments and well-controlled to retract from the surface with a contact duration approximating that of a bacterial collision (tens of milliseconds). All force measurements were performed at 25 °C.

Figure 1. Characterization of PSPMA, PSBMA, PHEMA, and PBMA surfaces: (a) surface potential (ξ) and (b) WCA.

surfaces are in the order PSPMA (−34.8 mV) < PBMA (−16.6 mV) < PSBMA (−7.4 mV) < PHEMA (0.9 mV), where PHEMA is almost neutral and the rest are negatively charged in the buffer. On the other hand, WCA measurements show that the surface hydrophobicity increases in the order PSPMA (10.2°) < PSBMA (12.1°) < PHEMA (51.7°) < PBMA (73.9°) (Figure 1b). Furthermore, Figure 2 shows the normalized

Figure 2. Normalized surface attraction force (γ) between the surfaces and E. coli obtained using AFM measurements.

surface attraction force (γ) to bacteria during contact using AFM measurements. It increases sharply from 0.001 to 0.08 nN/μm as the surface hydrophobicity increases. Figure 3 shows the time dependence of the number of adhered E. coli (Nb) on four surfaces. Clearly, the surfaces are in the order PSPMA < PSBMA < PHEMA < PBMA corresponding to the adhesion rate of E. coli cells on the surface within 5 h. Generally, the negatively charged E. coli cell (Figure S2b) does not favor adhesion to a negatively charged surface. However, the above order cannot be explained in terms of electrostatic repulsion because PBMA is more negatively C

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Figure 3. Time dependence of the number of bacteria adhered onto the surfaces (Nb). Three typical time points 0, 120, and 240 min are marked by the dashed lines. The error bars indicate the standard deviations of three independent measurements.

charged than PHEMA. Instead, it is consistent with the order regarding surface hydrophobicity, namely, the surface hydrophobic attraction plays a critical role in the adhesion. Note that E. coli cells become more negatively charged as time increases probably due to volume shrinking as the viability decreases, that is, ξ of an E. coli cell varies from −40 to −48 mV in 4 h (Figure S2c). Meanwhile, its motility decreases because of the exhaustion of nutrients in a sealed chamber. By contrast, Nb increases with time. This is because E. coli with a declined motility has a low velocity, which is favorable for its landing. We will be back to this point later. Velocity and Density Distribution of the E. coli Cells Near Different Surfaces. E. coli cells swimming actively (υ > 1) at three typical time pointsat the starting time for the measurement (0 min), 120 min later, and 240 min laterare analyzed. A typical set of holograms contains 100−300 individual E. coli trajectories. Figure 4 shows the 3D velocity (V3D) distribution of these E. coli cells along the z direction in the range of 0 < z < 40 μm at 0, 120, and 240 min, where z is the distance between bacteria and the surface. We find that at a specific z, as time increases from 0 to 240 min, V3D decreases from 35 to 25 μm/s. This is probably because E. coli becomes less energetic because of food consumption. Meanwhile, V3D does not have z dependence until the bacteria are quite close to the surface (z = 1 μm). Particularly, the most hydrophobic PBMA surface results in an obvious difference in the distribution of V3D compared with the other three surfaces. This is clearly shown in Figure 5, where we compare V3D at z = 1 (V1μm) and 10 μm (V10μm) for all surfaces at 0, 120, and 240 min. Deviations between V1μm and V10μm are obtained as (V1μm − V10μm)/V10μm. For PSPMA, PSBMA, and PHEMA surfaces, increases in V3D (≤10%) are found as z decreases from 10 to 1 μm. This is reasonable mainly for two reasons. First, as z decreases, bacteria swim in a direction parallel to the surface instead of random orientations.9 It is known that the motion perpendicular to a surface will be hindered by the surface more significantly than that in the parallel plane28 in the near-wall region. As a result, an E. coli cell swimming parallel to the surface at a smaller z may have a higher velocity than the one swimming in other directions at a higher z. On the other hand, the tumbling motion, which largely reduces the velocity, is prevented as bacteria approach the surface.10 Especially, as time increases, the positive deviation between V1μm and V10μm for the PHEMA surface decreases. By contrast, for the PBMA surface, a decrease in V3D of bacteria is found as z decreases from 10 to 1 μm, and the decrease is enhanced as time increases. To further confirm the decrease in V3D as z decreases upon a

Figure 4. Three-dimensional velocity (V3D) of E. coli cells near the surface as a function of distance from the surface (z) at 0, 120, and 240 min, where V3D is obtained using Gaussian fit of all velocities distributed at z = 1, 5, 10 μm and in the region of 20−30 and 30−40 μm.

Figure 5. Comparison of V3D at z = 1 (V1μm) and 10 μm (V10μm) for the surfaces at 0, 120, and 240 min. The percentage in the figure is calculated to be (V1μm − V10μm)/V10μm × 100. The decrease in V3D of bacteria near the PBMA surface is found as z decreases from 10 to 1 μm, whereas increases in V3D are found for other surfaces (below 10%).

hydrophobic surface, E. coli swimming near another surface, a polystyrene (PS)-coated surface, which is highly hydrophobic, was examined. V10μm and V1μm of the motile bacteria swimming near the PS surface are obtained as 32.1 and 27.0 μm/s in Figure S7, that is, V3D decreases by over 15%. We speculate that the hydrophobicity of the surface affects V3D through the exertion of a surface attraction against the motility of the bacteria during collisions. This is later confirmed in the collision analysis. Before landing, E. coli cells tend to swim and accumulate in the vicinity of the surface because of hydrodynamic attractions, D

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Figure 6. (a) Density distribution of E. coli cells swimming near the PBMA surface at 0 min. The result by fitted eq 2 yields a characteristic distance (L⊥) of the profile as 4.92 μm. (b) L⊥ of E. coli cells near the surfaces.

Figure 7. Three-dimensional trajectories of motile E. coli cells that collide with the PSPMA and PBMA surfaces by tail and head. (a) E. coli cell approaches and collides with the PSPMA surface. (b) Cell collides and escapes from the PSPMA surface. (c) Cell approaches and collides with the PBMA surface. (d) Cell collides and escapes from the PBMA surface.

so their population increases as z decreases.29,30 Figure 6a shows a typical density distribution [n(z)] of 132 E. coli cells with active motion along the z direction near the PBMA surface at 0 min. Active swimming near a surface leads to a surfaceinduced hydrodynamic attraction, dragging a bacteria cell toward the surface. Previously, a force dipole model as shown in eq 3 was successfully applied8 to explain the density distribution induced by the hydrodynamic attraction. As a result, L⊥, which describes the range of this hydrodynamic attraction, can be obtained as 4.92 μm by fitting this profile using eq 3. Figure 6b gives L⊥ values at 0, 120, and 240 min for each surface. Generally, L⊥ is approximately 4 to 5 μm in the observation time regardless of the surfaces, close to the half-length of an E. coli cell with flagella (Figure S3). This indicates that most of the swimming E. coli cells adopt a direction parallel to the surface, and the charging or hydrophobicity of the surface slightly affects their population density distribution near the surface. Figure 6b also shows that L⊥ decreases with time, further indicating that more E. coli cells accumulate close to the surface as motility decreases. We also examined the tumbling of E. coli (Figure S8). An E. coli cell changes its swimming direction by tumbling. We found that the tumbling frequency (FT) almost does not depend on

the surface charging or hydrophobicity. This is understandable because the near-wall tumbling is mainly determined by longrange hydrodynamic interactions on flagella, which work in the range of several times the flagellum length.10 Collisions between the E. coli Cells and the Surfaces. In this section, we explore the motion of a motile E. coli cell during collisions. Once an E. coli cell swims near a surface, it tends to be in a direction parallel to the surface driven by hydrodynamic interactions.9 However, because of rotation of its flagella and Brownian rotation, an E. coli cell may deviate from the direction, with the orientation wobbling around the parallel plane,31 leading to the collision with the surface. Collision is an essential step for the adhesion of bacteria on a surface. An E. coli cell can collide with the surface with either head (body) or tail (flagella), which can be described by its orientation angle relative to the surface (θ). The bacterium undergoes a tail collision at θ > 0° but a head collision at θ < 0°. Figure 7 shows four typical trajectories of E. coli colliding with the PSPMA and PBMA surfaces where tail and head collisions are highlighted. It is clearly shown that collisions happen only when the bacteria swim in the vicinity of a surface. Moreover, because the bacterium has a flagellum (∼9 μm) much longer than its body E

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Figure 8. z dependence of collision probability and the average duration of a collision event for motile E. coli cells swimming near the surfaces at 0, 120, and 240 min. (a) z distribution of the tail collision probability (Ptail); (b) z distribution of the head collision probability (Phead); (c) duration of a tail collision (ttail); and (d) duration of a head collision (thead).

Figure 9. Average scattered angle (θout) of motile E. coli cells at 0 min when they collide with the surface with tail (a) or head (b). The corresponding θout distributions at 0 min for tail (c) and head (d) collisions upon the surfaces.

9 μm colliding with the surfaces by tail. As z decreases from 9 to 0.4 μm, Ptail increases from ∼1 to 40% for all surfaces. However, head collisions fail to happen as far as z > 2 μm. Moreover, Phead sharply increases from ∼1 to 60% as z decreases from 2 to 0.1 μm. Because tumbling and rotational Brownian motion, which may lead to collisions, are independent of the surface properties, no obvious differences in Ptail and Phead are found for motile bacteria near different surfaces. Figure 8c,d shows that the duration of one tail collision (ttail) or head collision (thead) event is around 70 and 55 ms, respectively. Therefore, a tail collision is of longer duration. This is understandable because the flagella bundle has an

(2−4 μm) (Figure S3), tail collision events happen more frequently than head collision. From our observation, because most of the E. coli cells accumulate near the surface, around 70% of the tracked cells can collide with the surfaces. However, collision events for a specific cell happen at a probability of around 10% for tail and 1% for head during swimming, which slightly increases with time (Figure S9). Statistics for the probability and duration of a collision by tail and head are shown in Figure 8. Figure 8a,b shows the collision probability for tail (Ptail) and head (Phead) for the surfaces plotted against z at 0 min, which indicates the probability for tail or head collision for an active E. coli cell swimming around z. It is possible to find an E. coli cell with z ≲ F

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Figure 10. Three-dimensional velocity (V3D) of E. coli cells near the surface as a function of the distance from the surface (z) during (a) tail collisions, (c) head collisions, and (b,d) E. coli swimming without collisions. V3D during tail collisions for the PBMA surface are lower than that of the other three surfaces at z < 2 μm, where more than 60% of total tail collisions are located.

collisions, head collisions, and those swimming without collisions. For an arbitrary z, tail and head collisions decrease V3D by around 10 and 30%, respectively, compared with those points without collisions. During tail collisions, an increase in V3D as z decreases is found for PSPMA, PSBMA, and PHEMA surfaces (Figure 10a). This is because the bacteria that collide with the surface at a higher z must align at a larger θ with the surface according to eq 5. As a result, their motions are strongly hindered by the surface compared with those colliding the surface with a smaller θ. Besides, during tail collisions, V3D for the PBMA surface is notably lower than that for the other three surfaces at z < 2 μm, where more than 60% of total tail collisions are located (shown in Figure 8). During head collisions, no obvious differences in V3D are found with regard to all surfaces. Considering that the head collision probability is comparably small (∼1% at z = 1 μm, Figure 8b), we attribute the decrease in V3D as bacteria approach the PBMA surface to attractions raised by the surface hydrophobicity against the motility of bacteria during tail collisions. A decrease in V3D means the existence of a net force opposite to the swimming direction, namely, toward the surface. The above results show that the landing of E. coli cells on a surface depends on their collision with the surface. The landing probability should be correlated with the force acting on the bacteria during collision and the collision probability. Thus, we focus on the forces acting on an E. coli cell when it collides with the surface. Besides the elasticity of the collided surfaces, the motion of flagellate bacteria during collision is driven by the combination of the forces in the system, including their intrinsic propulsion, frictional force from the medium containing hydrodynamic interactions, and surface forces.37 Considering that the polymer chains form hard surfaces for the collided E. coli cells, the surfaces make little difference regarding the effect of elasticity on the collision. Adhesion takes place only when the motile bacteria cannot escape from the surface on collision, where the net force (Fnet) toward the surface is attractive (Fnet < 0,

elasticity ( 0.008 nN/μm. This agrees with our AFM results (Figure 2), where PSPMA and PSBMA surfaces with γ = 0.004 and 0.001 nN/μm, respectively, show the least amount of adhered E. coli cells. The PHEMA surface shows intermediate adhesion with γ = 0.038 nN/μm. Additionally, γ between a flagella-deficient mutant of E. coli (RP437Δf liC) and the surfaces was measured using AFM (Figure S13). It shows a significant decrease in γ of the flagelladeficient mutant for the PHEMA and PBMA surfaces compared with the wild-type in Figure 2. This fact confirms that γ for the wild-type E. coli comes from the interaction between the flagella and the surface, which implies a higher affinity between the hydrophobic surfaces and the bacterial flagella rather than the body. For a head collision, assuming the contact area of the bacterium body with the surface is a circle of radius b, the loading required for detachment is 40 times larger than that for a tail collision at γ = 0.08 nN/μm (PBMA surface) or Fs ∼ 8 pN. On the other hand, Fe ≈ 1.5 nN for a deformation of 50 nm (Figure S11). In this case, Fs is always smaller than Fe, namely, it is harder for a surface to capture an E. coli cell by its head than tail. This agrees with Figure 10c that no obvious velocity drop is found for bacteria colliding with the surfaces by head. Besides, the motility declines with time, leading to Fp < 0.57 pN as time increases. In the extreme case where Fp = 0 pN, E. coli cells are driven by Fr and Fs, indicating an easier attachment on the substrate when E. coli lose their motility. This agrees with the fact that adhesion increases with time as discussed above.

ASSOCIATED CONTENT

S Supporting Information *

The Supporting Information is available free of charge on the ACS Publications website at DOI: 10.1021/acs.langmuir.7b00439. Morphology, RMS roughness, and the elastic modulus (E) of the surfaces; physical properties of E. coli; determination of the localization accuracy of DHM; motility of E. coli as a function of time; V3D distribution along the z direction and average V3D on PSPMA and PS surfaces; time dependence of the tumbling frequency (FT) of E. coli as a function of distance (z) from the surface; average collision probability of motile E. coli cells by tail (Ptail, a) and head (Phead, b) collisions with the surfaces; scattered angle (θout), scattered rotational rate (Ωout), and the percentage of E. coli with the dependence of the deformation (h) colliding with the PBMA surface by tail and head; average scattered rotational rate (Ωout) of motile E. coli cells colliding with the surfaces by tail; normalized surface attraction force (γ) between the surfaces and RP437Δf liC measured using AFM; dry and wet thicknesses, swollen ratio, number density (σ) of grafted chains, molecular weight (Mn), and relaxation time (τ) of the polymeric surfaces; surface characterization, physical features of E. coli, localization accuracy determination of DHM, and analysis of tumbling; estimation of the contribution of the forces on E. coli; and estimation of the elastic force by the bending of the flagella (PDF)

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AUTHOR INFORMATION

Corresponding Authors

*E-mail: [email protected] (X.G.). *E-mail: [email protected] (G.Z.). ORCID

Xiangjun Gong: 0000-0001-7049-944X Notes

The authors declare no competing financial interest.

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ACKNOWLEDGMENTS The financial support of the National Natural Science Foundation of China (21574046, 21234003), the Ministry of Science and Technology of China (2012CB933802), and the Fundamental Research Funds for the Central Universities is acknowledged.

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CONCLUSIONS In summary, the motion of E. coli in the vicinity of surfaces with varied surface natures is tracked in 3D. It is found that surface hydrophobicity reduces the 3D velocities of bacteria as they swim closely to the surface through tail collisions and slightly increases the collision duration. By contrast, the elastic forces exerted by the flagella or body prevent bacteria from adhesion to the surface. Meanwhile, as the propulsion force declines with time, the impacts of surface hydrophobicity are enhanced, that is, stronger reduction of near-wall velocities, increases in the scattered angle, and the probability and duration of collsions.

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DOI: 10.1021/acs.langmuir.7b00439 Langmuir XXXX, XXX, XXX−XXX

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DOI: 10.1021/acs.langmuir.7b00439 Langmuir XXXX, XXX, XXX−XXX