Abstract
The incessant activity of swimming microorganisms has a direct physical effect on surrounding microscopic objects, leading to enhanced diffusion far beyond the level of Brownian motion with possible influences on the spatial distribution of nonmotile planktonic species and particulate drifters. Here we study in detail the effect of eukaryotic flagellates, represented by the green microalga Chlamydomonas reinhardtii, on microparticles. Macro and microscopic experiments reveal that microorganismcolloid interactions are dominated by rare close encounters leading to large displacements through direct entrainment. Simulations and theoretical modelling show that the ensuing particle dynamics can be understood in terms of a simple jumpdiffusion process, combining standard diffusion with Poissondistributed jumps. This heterogeneous dynamics is likely to depend on generic features of the nearfield of swimming microorganisms with frontmounted flagella.
Introduction
Swimming microorganisms navigate commonly through fluids characterized by a variety of suspended microparticles, with which they inevitably interact. From malarial parasites meandering around densely packed red blood cells and infecting them^{1}, to protists which regulate primary production in oceans and lakes by grazing on sparsely distributed microalgae and bacteria^{2,3}, these interactions can have important biological and ecological implications. Some, like biogenic mixing, are still intensely debated^{4,5,6}. Understanding the basic mechanisms of such interactions is timely, as a vast number of plastic micro and nanoparticles of anthropogenic origin scattered throughout the world’s oceans (up to ∼10^{5} m^{−3}) appear to be easily ingested by microorganisms, which then recycle plastics within the marine food web with currently unknown consequences^{7}. From a physics perspective, these systems are particularly appealing. Abstracted as binary suspensions^{8,9}, where an active, selfpropelled species interacts with a passive, thermalized one, they represent a naturally occurring category of outofequilibrium stochastic systems driven by energy produced directly within the bulk, rather than transmitted through the system’s boundaries. Easily accessible experimentally, and amenable to detailed quantitative modelling^{10}, they are uniquely placed to provide benchmark tests for theories of outofequilibrium statistical mechanics^{11,12,13}. Currently best characterized are the socalled bacterial baths. Wu and Libchaber^{14}, and more recently^{15,16,17,18}, showed that colloidal particles within bacterial suspensions perform a persistent random walk leading in bulk to a diffusivity up to ∼10 × the thermal value^{18}. This increase is proportional, at least in the dilute limit, to the product of bacterial speed and concentration, a consequence of random successive singleparticle interactions^{5,19,20,21,22,23}. The intrinsic nonequilibrium nature of bacterial baths is also known to lead to other peculiar effects, including motilityinduced colloidal pair interactions^{10} and coupling between enhanced translational and rotational diffusion^{24}.
Microparticles’ interactions with the other major class of microorganisms, eukaryotic flagellates, is distinctly less explored. Almost exclusively larger than bacteria (∼10–100 μm), these species probe a new and biologically relevant physical regime, where the microparticles’ size is significantly smaller than that of the microorganisms (but see also ref. 25 for the effect of bacterial motion on molecular diffusion). Working with the microalga Chlamydomonas reinhardtii (CR), often studied as model eukaryotic microswimmer, the pioneering study of Leptos et al.^{26}—followed by ref. 27—reported an increase in particle diffusivity of magnitude similar to the bacterial case, as a consequence of looplike particle trajectories induced by the farfield flow of the algae^{21,22,28,29}. Particle displacements followed a diffusively scaling fattailed distribution which, if valid for arbitrary large observation times, would imply a violation of the Central Limit Theorem^{30}. These fattailed distributions have indeed been observed in short simulations^{21,31}, but should converge to normal distributions at long times^{29,31}. This prediction, however, has not been tested experimentally.
Here we revisit the behaviour of colloids within a suspension of CR, taken as representative of eukaryotic flagellates, and reveal that their overall dynamics is in fact dominated by rare but dramatic entrainment events. These jumps underpin the surprisingly large diffusivity we observe directly in both sedimentation and collective spreading experiments, ≳40 × greater than values reported previously for the same geometry. The colloids’ behaviour, alternating entrainments and periods of standard enhanced diffusion, is fundamentally different from the persistent random walk common with bacterial suspensions. Through microscopic experiments, simulations and analytical modelling we show instead that this dynamics is well captured by a simple jumpdiffusion model.
Results
Experiments
A full description of the experimental procedures can be found in the Methods and Supplementary Methods sections. Briefly, wildtype CR strain CC125 was grown axenically in Trisacetatephosphate medium^{32} at 21 °C under continuous fluorescent illumination. Cells were harvested in the exponential phase (∼5 × 10^{6}cell ml^{−1}), concentrated by gentle spinning and then resuspended to reach the desired concentration. Polystyrene microparticles (1 μm diameter) were then added to the suspension, and their diffusivity at different CR concentrations (N_{c}) was measured from three different sets of microfluidic experiments (Supplementary Methods 1): mapping the particles’ sedimentation profile at steady state; measuring the relaxation dynamics of an inhomogeneous distribution of particles; and by long timescale direct tracking of individual colloids’ dynamics in a thin Hele–Shaw cell. Schematic representations of the experimental setups are shown Supplementary Figs 1 and 2. Except for the sedimentation experiments, the medium was densitymatched with the colloids using Percoll^{33}. This increased its viscosity (η_{percoll}=(1.5±0.1)η_{water}) as reflected in the slower average swimming speed, , of the algae. We measured =81.8±3.5 μm s^{−1}, =40.9±3.5 μm s^{−1} and =49.1±2.5 μm s^{−1} for the sedimentation, spreading and tracking experiments respectively. A colloidal Péclet number for tracer/swimmer interactions can be defined as Pe=vL/D_{0}, where v and L(∼10 μm) are CR’s characteristic speed and body length, and D_{0} the Brownian diffusivity of the colloids. We obtain Pe_{S}≃2,000, Pe_{CS}≃1,500 and Pe_{HS}≃1,750, respectively for the three experiments.
Macroscopic diffusion
Macroscopic diffusion experiments coarsegrain over the colloids’ microscopic dynamics and ensure the direct measurement of their effective transport properties, in the spirit of Jean Perrin’s seminal work on sedimentation equilibrium^{34}. We begin by characterizing the colloids’ steadystate sedimentation profile at increasing CR concentrations, always within the dilute regime (volume fractions ≲0.15%). The average concentrations probed will always be well below the threshold required to induce bioconvective instabilities within our ∼200 μmthick sample cells. At the same time the concentration profile is not exactly homogeneous, but slightly decreasing with height, see Supplementary Fig. 3 and Supplementary Note 1. However, as it will be evident from the results, this inhomogeneity did not appear to have appreciable consequences on our measurements. Figure 1a shows that even when the algae are present, the microparticles’ distributions are still in excellent agreement with simple exponential profiles, but crucially with different effective gravitational lengths l_{g,eff} (Fig. 1b). The exponential profiles are a standard consequence of the balance between the particles’ sedimentation speed v_{sed}, here due to a δρ=50 g l^{−1} density mismatch, and their effective diffusivity, combining passive and active processes. Boltzmannlike distributions are indeed what should be expected even in these outofequilibrium systems, at least for small enough v_{sed} (ref. 35), akin to what has been observed for active colloids alone^{36}. The characteristic length l_{g,eff}, experimentally observed to be proportional to the concentration of algae, allows us to measure the concentrationdependent effective diffusivity as D_{eff}=v_{sed} l_{g,eff} (Fig. 1c, orange squares, Supplementary Fig. 4 for a closeup at low concentrations). We obtain D_{eff}=D_{0}+α_{S}N_{c}, where D_{0}=0.40±0.01 μm^{2} s^{−1} is the thermal diffusivity, the algal concentration N_{c} is in units of 10^{6}cells ml^{−1}, and α_{S}=1.71±0.14(μm^{2} s^{−1})/(10^{6}cells ml^{−1}) (slopes α will be expressed in these units throughout the paper). Within the same experiments, however, the diffusivity can also be inferred microscopically from direct shortduration tracking of microparticles’ trajectories in bulk (mean tracks duration δt_{tracks}=2.6 s), as previously done in ref. 26. These measurements return a different estimate, D_{eff}=D_{0}+α_{T}N_{c} (Fig. 1c, orange circles), with a slope α_{T}=0.074±0.014 in reasonable agreement with previous results (α_{L}=0.041 in ref. 26) but more than 40times smaller than the sedimentation value α_{S}. The surprisingly large value of α_{S}, larger than any previously reported bulk value, calls for an independent verification. It was tested here at the macroscopic level by following the diffusive spreading of a uniform band of densitymatched colloids within a microfluidic device filled with a uniform concentration of cells (for experimental details see the Methods section, Supplementary Fig. 5 and Supplementary Methods 2). The band’s profile, initially tight around the middle third of a 2mmwide, 60μmthick channel and running along its full length (∼10 mm), spreads with a characteristically diffusive dynamics which enables to measure directly the effective diffusivity D_{eff} at different N_{c} values (Supplementary Fig. 5 and Supplementary Methods 2). The results are shown in Fig. 1c (green triangles) after being multiplied by the ratio of the cells’ swimming speeds to account for their slower motion in the densitymatched medium. As before, D_{eff} depends linearly on cell concentration, with a slope α_{CS}=1.62±0.14 in remarkable agreement with the sedimentation value.
Microscopic entrainment and diffusion
The quantitative agreement between steadystate and timedependent macroscopic measurements suggests that our understanding of the microscopic interaction between particles and microorganisms, based on the effect of the swimmers’ farfield flows^{29,37,38,39} and leading to α_{T}(<<α_{S}), is missing a key element. The crucial microscopic insight is provided by longtime tracking of the particles at a range of N_{c} values. This is achieved here by confining the system within a 26 μmthick Hele–Shaw cell, which allows us to follow individual colloids for ∼200 s. Figure 2a and Supplementary Movie 1 show a typical colloidal trajectory in this confined geometry. The dynamics, which leads to a dramatically larger spreading than simple Brownian motion (Fig. 2a inset), results from the combination of three different effects of wellseparated magnitudes. The weakest component is standard Brownian motion, which dominates the dynamics when the algae are more than ∼25 μm (ref. 26) away from the colloid. At closer separation, but before close contact, the farfield flows of the microorganisms induce looplike trajectories^{21,22,31,39,40}. Originally reported in ref. 26, these loops provide the concentrationdependent contribution to the particles’ diffusivity previously estimated as α_{T}N_{c} (ref. 26). Finally, particles within the near field can be occasionally entrained by the algae over distances up to tens of microns (see Fig. 2b), giving rise to the sudden jumps in the trajectory highlighted as red solid lines in Fig. 2a. These jumps, a fundamental feature of the dynamics never observed previously, are the microscopic origin of the term α_{S}N_{c}, the unexpectedly large contribution to the particle diffusivity observed in the macroscopic experiments. This is confirmed directly on Supplementary Movie 2, where we show such an entrainment event taking place during a sedimentation experiment. For our 1 μmdiameter tracers, the jump length L is (mostly) exponentially distributed with a characteristic length L_{J}=7.5±0.5 μm, above a threshold value L_{T}=7.5±0.5 μm (Fig. 3a) (see Supplementary Methods 3 and Supplementary Fig. 6 for details on the extraction of the jumps from the trajectories). The average displacement is =12.6±0.5 μm, although we did record values of L up to ∼70 μm. The effect of jumps is also clear in the full probability distribution functions of tracer displacements, Fig. 4a,b, which display exponentiallike tails much larger than those characteristic of looplike perturbations only^{26}. Both distributions eventually converge to Gaussians with different variances as the observation time increases, a consequence of the Central Limit Theorem already predicted in refs 29, 31, see Fig. 4 and Supplementary Note 2.
Jumps are fast (Fig. 3a, inset), lasting on average =1.5 s (=1.7 s if considering only jumps with L≥L_{T}) but they are rare: as shown in Fig. 2b and Supplementary Movie 3, the algae need to meet a microparticle almost head on in order to entrain it (∼65% of all jumps have initial impact parameter ≤2 μm). As a consequence, even at the highest cell concentration probed, N_{C}≃5 × 10^{6} cells ml^{−1}, the average time between two consecutive jumps is rather long, ≳16 s. Because is dictated by the cell–microparticle encounter rate, for a purely random process one would expect : Fig. 3b shows that this hypothesis is clearly supported experimentally. Further support comes from the analysis of individual interjump intervals, which appear to be exponentially distributed as predicted for a simple Poisson process (Fig. 3b inset, see Supplementary Methods 4 for details on the computation of ).
Computing the meansquare displacement from these long colloidal trajectories offers a microscopic estimate of the effective particle diffusivity D_{eff}, Supplementary Fig. 7 and Supplementary Note 3. The results are shown in Fig. 1c (blue circles) after multiplication by the swimming speeds ratio to account for the trivial dependence of the encounter frequency on microalgal speed: D_{eff} is linear with cell concentration, with a slope α_{HS}=1.67±0.13 in excellent quantitative agreement with both macroscopic values. This agreement confirms that the properties of the colloids’ microscopic dynamics, which includes important but rare jumps, have indeed been probed appropriately.
Numerical simulations
The agreement between the macroscopic and microscopic diffusivity measurements highlights the importance of entrainment events, and suggests a combination of jumps and (farfieldenhanced) diffusion as a plausible minimal model of microparticles’ dynamics, at least over the mediumtolong timescales we are interested in. Consequently, we model the twodimensional projection of the stochastic trajectory of a colloid in the Hele–Shaw experiments, (X(t), Y(t)), as
This combines standard Wiener processes dW_{X,Y}(t) leading to diffusion with diffusivity D_{WJ}, with a Poisson process for the jumps, dP(t), characterized by the average time interval 〈ΔT_{J}(N_{c})〉 between jumps (Fig. 3b, solid red line). Farfield effects are included at a coarsegrained level by choosing D_{WJ}=D_{0}+α_{WJ}N_{c}, where α_{WJ}=0.33±0.05. This is the effective diffusivity, rescaled by , which is measured from the microscopic experimental tracks when the entrainment events have been removed. Notice that α_{WJ}>α_{T} due to our conservative choice for what constitutes a jump, which in the simulation we draw from an exponential fit to the part of the experimental distribution strictly above L_{T} (Fig. 3a, solid red line). As a result, the compounded effect of shorter jumps is included within the coefficient α_{WJ}. The jumps’ orientation θ is uniformly distributed to give an isotropic process, and their duration =1.7 s is constant.
This dynamics, simulated with a simple acceptancerejection method (see Methods section), produces trajectories very similar to the experimental, Supplementary Fig. 8 and Supplementary Note 4. The motion is characterized by an effective diffusivity in excellent agreement with the values from the microscopic experiments (see Fig. 3c, red and blue circles respectively), and ≳5 × larger than D_{WJ} at the corresponding cell concentration. The simulations, then, highlight the striking influence that jumps have on particle dynamics, despite their rarity. At the same time, they allow us to explore easily parameter values that have not yet been probed experimentally. For example, Fig. 3c (red diamonds and dashed red line) shows that within the experimental range of cell concentrations, even a drastic reduction of the entrainment duration to 0.1 s has only a minimal effect on particle diffusivity. The exact value of , however, will have a major influence as soon as , leading to a plateau in the effective diffusivity as the cell concentration grows above a threshold (Fig. 3c inset), at least as long as collective effects do not modify our singleparticle picture.
Analytical theory
The experimental and simulation results can be tied together further through a simple continuum theory for the dynamics of microparticles, which rationalizes the dependence of the effective diffusivity D_{eff}(N_{c}) on the concentration of algae. Here we extend to two dimensions the onedimensional approach developed in ref. 41. Briefly, we consider two populations whose densities at position (x, y) and time t are ρ_{d}(x, y, t) and ρ_{b}(x, y, t, φ), corresponding respectively to particles diffusing with diffusivity D_{WJ}, and to particles moving ballistically with a constant velocity u in the direction φ. Particles switch from diffusion to directed motion and vice versa with constant transition rates λ_{d}(=1/〈ΔT_{J}〉) and λ_{b}(=1/) respectively. The system then obeys the following set of equations:
which can be easily solved by Fourier–Laplace transform, Supplementary Note 5, yielding the time evolution of the particles’ meansquare displacement and hence their diffusivity. At long timescales this is given by
where we wrote D_{WJ}=D_{0}+α_{WJ}N_{c}, λ_{b}=u/〈L〉 and λ_{d}=γvN_{c} as the frequency of entrainment events is proportional to the product of the speed v and concentration N_{c} of microorganisms, also called ‘active flux’^{16}. The quantity γ can be interpreted as a crosssection for entrainment: as an alga swims past, the tracer will be entrained with a given probability if it is contained within a region of area γ around the line of motion of the alga^{19,21}. For our Hele–Shaw experiments, using and the experimental values of 〈ΔT_{J}〉 (Fig. 3b) we obtain γ=299±35 mm^{2} (respectively γ=299±35 μm^{2} if N_{c} is expressed in units of cells ml^{−1} rather than 10^{6}cells ml^{−1}). At low concentrations, where λ_{d}<<λ_{b} (or equally ), D_{eff} becomes
which is independent of the jumps’ duration , here equivalent to independence on u, as suggested by the simulations. Equation (4) recovers the clear division between thermal, farfield and entrainment contributions to the diffusivity that we previously discussed in the context of microscopic experiments. Notice that the contribution from the jumps, by far the most important in our experiments, is simply what should be expected if we interpreted the colloidal trajectory as a freely jointed chain where bonds with exponentially distributed length of mean 〈L〉 are added at a rate λ_{d}=γvN_{c}.
The coefficient α_{WJ}, representing farfield effects, is proportional to the speed v of the microalgae^{21,26,29}, leading to an overall contribution (D_{eff}−D_{0}) which scales with the active flux vN_{c}, as originally predicted in ref. 19. Figure 1c shows indeed that the diffusivity curves from different experiments collapse when rescaled by the corresponding velocities. In turn, then, the distribution of jump lengths should be independent of the average velocity of the microorganisms, as expected at low Reynolds numbers. Within the model, however, this proportionality is limited to sufficiently low cell concentrations. As N_{c} increases and 〈ΔT_{J}〉 becomes closer to , nonlinearities become important, and eventually D_{eff} plateaus to a dependent value as seen in the simulations (Fig. 3c, inset) and well captured by the present model, Supplementary Fig. 9 and Supplementary Note 5.
Finally, the short timescale limit of the particles’ meansquare displacement returns D_{eff}=x_{d}D_{WJ}, where x_{d} is the fraction of particles that are in the diffusing state. Estimating , we can assume x_{d}≃1 within the whole range of cell concentrations probed experimentally. Short timescale tracking of microparticles, then, will inevitably return D_{eff}≃D_{WJ} rather than the full expression in equation (4). This is the reason for the small diffusivity reported in ref. 26, which we also observe from direct shortduration tracking of microparticles in the sedimentation experiments.
Discussion
Proposed theoretically either as a consequence of microparticle capture within ‘wake bubbles’^{21} or as a consequence of Darwin drift^{28}, particle entrainment by microorganisms was expected to provide at best a contribution similar to that observed for to farfield loops^{42} and most likely much smaller^{22}. At the same time, lack of experimental evidence for entrainment questioned not just its importance for particle–microswimmer interactions, but its existence as well.
Here we show experimentally not only that entrainment of microparticles by microorganisms exists but also that these rare but large events can dominate particle dynamics, leading in the present case to a diffusivity more than 40 × larger than previously reported. Simulation and analytical results support a jumpdiffusion process as a good minimal model for the mediumtolong timescales dynamics of the colloids. The simplified continuum model we discuss provides a theoretical support for the observed dependence of the experimental diffusivities on the active flux vN_{c}, already introduced in the bacterial context^{16,17,43}. At the same time, it clarifies that longduration particle tracking is necessary to sample correctly the microscopic dynamics and recover the real long timescale impact of microorganisms.
Although we cannot yet pinpoint the specific mechanism leading to entrainment, the structure of the nearfield flow is likely to play a crucial role. The entrainment we observe requires almost headon collisions, which is likely to be facilitated by the type of stagnation point found (on average) in front of the cell^{37,38}. After reaching the cell apex, the entrained particles slide down the sides of the cell body along a highshear region almost comoving with the microorganism, and are eventually left behind having spent slightly more than half of the jump in the front part of the cell (54±9%). The noslip boundary on the bodies of microorganisms, and the stagnation points in their flow fields, have in fact been argued to play a major role in large microparticle displacements^{5,21,29}, which are seen here to dominate the effective diffusivity. Our results support these conjectures.
Eukaryotic microswimmers with multiple frontmounted flagella will share much of the nearfield flow structure found in Chlamydomonas: entrainment is then likely to be a generic feature of this whole class of microorganisms. Several of these species are predators^{2,3}, and prey on cells of size similar to our plastic particles. Frontmounted flagella, then, would spontaneously lead to contact with the prey at a predictable location on the cell body within easy reach of the flagella, and therefore facilitate the ingestion of both natural preys and environmental microplastics.
Methods
Cell culture
Cultures of CR strain CC125 were grown axenically in a TrisAcetatePhosphate medium^{32} at 21 °C under continuous fluorescent illumination (100 μE m^{−2} s^{−1}, OSRAM Fluora). Cells were harvested at ∼5 × 10^{6}cells ml^{−1} in the exponentially growing phase, then centrifuged at 800 r.p.m. for 10 min and the supernatants replaced by DIwater (sedimentation experiment) or by a Percoll solution (tracking and spreading experiments; Percoll Plus, Sigma) already containing the desired concentration of PS colloids (Polybead Microspheres, diameter d=1±0.02 μm).
Microfluidics and microscopy
The Percoll solution (38.5% vol/vol) was made to densitymatch the beads for the Hele–Shaw and spreading experiments, while preserving the Newtonian nature of the flows^{33}. The appropriate solution was injected into either 185 μm (sedimentation experiment) or 26 μm (tracking experiment) thick Polydimethylsiloxanebased microfluidic channels previously passivated with 0.15%w/w BSA solution in water. The microfluidic devices were then sealed using photocurable glue (Norland NOA68) to prevent evaporation. Regarding the spreading experiment, the band of colloids was initiated in a threearms forkshape channel (60 μm thick) by injecting at the same flowrate (using a PHD 2,000 Harvard Apparatus syringe pump and highprecision Hamilton 50 and 100 μl gastight syringes) the CR+beads Percoll solution in the central arm and the CR Percoll solution only in the two sided arms. Colloids were observed under either brightfield (sedimentation experiment) or phase contrast (tracking and spreading experiments) illumination on a Nikon TE2000U inverted microscope. A longpass filter (cutoff wavelength 765 nm) was added to the optical path to prevent phototactic response of the cells. Stacks of 200 images at 60 × magnification were acquired at 10 f.p.s. (camera Pike F100B, AVT) layer by layer by manually moving the plane of focus in order to reconstruct the density profiles for the sedimentation experiment. We used a × 40 oil immersion objective (Nikon CFI S Fluor × 40 oil) combined with an extra × 1.5 optovar magnification. The condenser iris was completely opened to minimize the depth of field. Regarding the Hele–Shaw experiment, several movies of 2 × 10^{4} images were recorded at 25 f.p.s. (camera Pike F100B, AVT) using a 20 × phase contrast objective (Nikon LWD ADL 20 × F). Particles trajectories were then digitized using a standard Matlab particle tracking algorithm (The code can be downloaded at http://people.umass.edu/kilfoil/downloads.html). Finally the spreading dynamics of the band of colloids was probed by recording the system for a few hours at × 10 magnification (Nikon ADL 10 × I) and low frame rate (0.5 f.p.s.) using a Nikon D5000 DSLR camera. The colloids were then featured using the same Matlab particle tracking algorithm in order to reconstruct the density profiles. In all experiments, the concentration of algae was determined in situ by imaging the system under darkfield illumination at low magnification (objective Nikon CFI Plan Achromat UW 2 ×).
Numerical Simulations
The parameters of the simulation correspond to the red lines in Fig. 3a,b. For each algae concentration, 1,000 trajectories of 2,000 s have been simulated using a timestep δt=0.004 s, which is 10 times smaller than the acquisition period in the Hele–Shaw experiment. At each time step a random walk with diffusivity D_{WJ} is performed. To simulate the Poisson process, we draw at each time step a random number in the open interval ]0;1[. If this number is within the centred closed interval [(1−δt/〈ΔT_{J}〉)/2;(1+δt/〈ΔT_{J}〉)/2], then we perform a jump that lasts =1.7 s (or 0.1 s) with a length taken out of the distribution PDF(L), along the direction corresponding to θ, which is uniformly distributed in [−π, π[. This technique allows to simulate accurately the Poisson process^{44}. We stress that within the simulation, once the particle has entered a jump, it will escape it after exactly =1.7 s (or 0.1 s). This is done in order to approximate the experiments, which show that the distribution function of jumps’ duration is tighter around the mean than that of jumps’ lengths. Notice that during the jumps the random walk component is switched off. After the jump has been performed, a new random number is picked in order to choose between a random step or a jump and continue the jumpdiffusion process.
Data availability
The authors declare that the data supporting the findings of this study are available on request.
Additional information
How to cite this article: Jeanneret, R. et al. Entrainment dominates the interaction of microalgae with micronsized objects. Nat. Commun. 7:12518 doi: 10.1038/ncomms12518 (2016).
References
 1
Heddergott, N. et al. Trypanosome motion represents an adaptation to the crowded environment of the verterbrate bloodstream. PLoS Pathog. 8, e1003023 (2012).
 2
Montagnes, D. J. S. et al. Selective feeding behaviour of key freeliving protists: avenues for continued study. Aquat. Microb. Ecol. 53, 83–98 (2008).
 3
Wetherbee, R. & Andersen, R. Flagella of a chrysophycean alga play an active role in prey capture and selection. Direct observations on Epiphysis pulchra using image enhanced video microscopy. Protoplasma 166, 1–7 (1992).
 4
Visser, A. W. Biomixing of the oceans? Science 316, 838–839 (2007).
 5
Thiffeault, J.L. & Childress, S. Stirring by swimming bodies. Phys. Lett. A 374, 3487–3490 (2010).
 6
Katija, K. Biogenic inputs to ocean mixing. J. Exp. Biol. 215, 1040–1049 (2012).
 7
Wright, S. L., Thompson, R. C. & Galloway, T. S. The physical impact of microplastics on marine organisms: a review. Env. Pol 178, 483–492 (2013).
 8
Mallory, S. A., Valeriani, C. & Cacciuto, A. Induced activation of a passive tracer in an active bath. Phys. Rev. E 90, 032309 (2014).
 9
Kümmel, F., Shabestari, P., Lozano, C., Volpe, G. & Bechinger, C. Formation, compression and surface melting of colloidal clusters by active particles. Soft Matter 11, 6187 (2015).
 10
Angelani, L., Maggi, C., Bernardini, M. L., Rizzo, A. & di Leonardo, R. Effective interactions between colloidal particles suspended in a bath of swimming cells. Phys. Rev. Lett. 107, 138302 (2011).
 11
Maggi, C. et al. Generalized energy equipartition in harmonic oscillators driven by active baths. Phys. Rev. Lett. 113, 238303 (2014).
 12
Koumakis, N., Maggi, C. & di Leonardo, R. Directed transport of active particles over asymmetric energy barriers. Soft Matter 10, 5695 (2014).
 13
Takatori, S. C. & Brady, J. F. A theory for the phase behavior of mixtures of active particles. Soft Matter 11, 7920–7931 (2015).
 14
Wu, X.L. & Libchaber, A. Particle diffusion in a quasitwodimensional bacterial bath. Phys. Rev. Lett. 84, 13 (2000).
 15
Valeriani, C., Li, M., Novosel, J., Arlt, J. & Marenduzzo, D. Colloids in a bacterial bath: simulations and experiments. Soft Matter 7, 5228 (2011).
 16
Miño, G. L. et al. Enhanced diffusion due to active swimmers at a solid surface. Phys. Rev. Lett. 106, 048102 (2011).
 17
Jepson, A., Martinez, V. A., SchwarzLinek, J., Morozov, A. & Poon, W. C. K. Enhanced diffusion of non swimmers in a threedimensional bath of motile bacteria. Phys. Rev. E 88, 041002 (R) (2013).
 18
Patteson, A. E., Gopinath, A., Purohit, P. K. & Arratia, P. E. Particle diffusion in active fluids is nonmonotonic in size. Soft Matter 12, 2365–2372 (2016).
 19
Underhill, P. T., HernandezOrtiz, J. P. & Graham, M. D. Diffusion and spatial correlations in suspensions of swimming particles. Phys. Rev. Lett. 100, 248101 (2008).
 20
Ishikawa, T., Locsei, J. T. & Pedley, T. J. Fluid particle diffusion in a semidilute suspension of model microorganisms. Phys. Rev. E 82, 021408 (2010).
 21
Lin, Z., Thiffeault, J.L. & Childress, S. Stirring by squirmers. J. Fluid Mech. 669, 167–177 (2011).
 22
Morozov, A. & Marenduzzo, D. Enhanced diffusion of tracer particles in dilute bacterial suspensions. Soft Matter 10, 2748–2758 (2014).
 23
Kasyap, T. V., Koch, D. L. & Wu, M. Hydrodynamic tracer diffusion in suspensions of swimming bacteria. Phys. Fluids 26, 081901 (2014).
 24
Peng, Y. et al. Diffusion of ellipsoids in bacterial suspensions. Phys. Rev. Lett. 116, 068303 (2016).
 25
Kim, M. & Breuer, K. Enhanced diffusion due to motile bacteria. Phys. Fluids 16, L78–L81 (2004).
 26
Leptos, K. C., Guasto, J. S., Gollub, J. P., Pesci, A. I. & Goldstein, R. E. Dynamics of enhanced tracer diffusion in suspensions of swimming eukaryotic microorganisms. Phys. Rev. Lett. 103, 198103 (2009).
 27
Kurtuldu, H., Guasto, J. S., Johnson, K. A. & Gollub, J. P. Enhancement of biomixing by swimming algal cells in twodimensional films. Proc. Natl Acad. Sci. USA 108, 10391–10395 (2011).
 28
Pushkin, D. O. & Yeomans, J. M. Fluid mixing by curved trajectories of microswimmers. Phys. Rev. Lett. 111, 188101 (2013).
 29
Thiffeault, J.L. Distribution of particle displacements due to swimming microorganisms. Phys. Rev. E 92, 023023 (2015).
 30
Zaid, I. M., Dunkel, J. & Yeomans, J. M. Lévy fluctuations and mixing in dilute suspensions of algae and bacteria. J. R. Soc. Interface 8, 1314–1331 (2011).
 31
Pushkin, D. O. & Yeomans, J. M. Stirring by swimmers in confined microenvironments. J. Stat. Mech. 4, P04030 (2014).
 32
Rochaix, J. D., Mayfield, S., GoldschmidtClermont, M. & Erickson, J. M. in Plant Molecular Biology: A Practical Approach ed. Schaw C. H. 253–275IRL Press (1988).
 33
Gachelin, J. et al. NonNewtonian viscosity of Escherichia coli suspensions. Phys. Rev. Lett. 110, 268103 (2013).
 34
Perrin, J. Molecular reality. Ann. Chim. Phys. 8, 1 (1909).
 35
Tailleur, J. & Cates, M. E. Sedimentation, trapping, and rectification of dilute bacteria. Eur. Phys. Lett. 86, 60002 (2009).
 36
Palacci, J., CottinBizonne, C., Ybert, C. & Bocquet, L. Sedimentation and effective temperature of active colloidal suspensions. Phys. Rev. Lett. 105, 088304 (2010).
 37
Drescher, K., Goldstein, R. E., Michel, N., Polin, M. & Tuval, I. Direct measurement of the flow field around swimming microorganisms. Phys. Rev. Lett. 105, 168101 (2010).
 38
Guasto, J. S., Johnson, K. A. & Gollub, J. P. Oscillatory flows induced by microorganisms swimming in two dimensions. Phys. Rev. Lett. 105, 168102 (2010).
 39
Dunkel, J., Putz, V. B., Zaid, I. M. & Yeomans, J. M. Swimmertracer scattering at low Reynolds number. Soft Matter 6, 4268–4276 (2010).
 40
Mathjissen, A. J. T. M., Pushkin, D. O. & Yeomans, J. M. Tracer trajectories and displacement due to a microswimmer near a surface. J. Fluid Mech. 773, 498–519 (2015).
 41
Méndez, V., Campos, D. & Bartumeus, F. Stochastic Foundations in Movement Ecology Ch. 3.3 Springer Complexity (2014).
 42
Pushkin, D. O., Shum, H. & Yeomans, J. M. Fluid transport by individual microswimmers. J. Fluid Mech. 726, 5–25 (2013).
 43
Miño, G. L., Dunstan, J., Rousselet, A., CleŽment, E. & Soto, R. Induced diffusion of tracers in a bacterial suspension: theory and experiments. J. Fluid Mech. 729, 423–444 (2013).
 44
Hanson, F. B. Applied Stochastic Processes and Control for JumpDiffusions: Modeling, Analysis, and Computation Ch. 1 SIAM (2007).
Author information
Affiliations
Contributions
R.J., D.O.P., V.K., M.P. designed the study; R.J. performed the experiments, analytical and numerical modelling; R.J. and M.P. analysed the results; R.J., V.K., M.P. wrote the manuscript.
Corresponding author
Ethics declarations
Competing interests
The authors declare no competing financial interests.
Supplementary information
Supplementary Information
Supplementary Figure 19, Supplementary Notes 15, Supplementary Methods and Supplementary References (PDF 1539 kb)
Supplementary Movie 1
Dynamics of a single microparticle in the HeleShaw experiment taken at 30x magnification, phase contrast, 25fps. The colour shows the instantaneous velocity of the bead (frametoframe displacement multiplied by frame rate). Colour code is the same used in Fig 2a of the main text. (MOV 14184 kb)
Supplementary Movie 2
Example of an entrainment event during the sedimentation experiment. 60x magnification, bright field, 10fps. (MOV 361 kb)
Supplementary Movie 3
High speed movie of an entrainment event in the HeleShaw experiment recorded at 500fps, 100x magnification, bright field. Slowed down 17x. (MOV 8088 kb)
Rights and permissions
This work is licensed under a Creative Commons Attribution 4.0 International License. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in the credit line; if the material is not included under the Creative Commons license, users will need to obtain permission from the license holder to reproduce the material. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/
About this article
Cite this article
Jeanneret, R., Pushkin, D., Kantsler, V. et al. Entrainment dominates the interaction of microalgae with micronsized objects. Nat Commun 7, 12518 (2016). https://doi.org/10.1038/ncomms12518
Received:
Accepted:
Published:
Further reading

Active carpets drive nonequilibrium diffusion and enhanced molecular fluxes
Nature Communications (2021)

Singletrajectory characterization of active swimmers in a flow
Physical Review E (2021)

Diffusive dynamics of elongated particles in active colloidal suspensions of motile algae
Colloid and Polymer Science (2021)

Bacterial activity hinders particle sedimentation
Soft Matter (2021)

Large Deviations for Continuous Time Random Walks
Entropy (2020)
Comments
By submitting a comment you agree to abide by our Terms and Community Guidelines. If you find something abusive or that does not comply with our terms or guidelines please flag it as inappropriate.