Fundamental Aspects of Enzyme-Powered Micro- and Nanoswimmers

Article Cite This: Acc. Chem. Res. 2018, 51, 2662−2671

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Fundamental Aspects of Enzyme-Powered Micro- and Nanoswimmers Published as part of the Accounts of Chemical Research special issue “Fundamental Aspects of Self-Powered Nano- and Micromotors”. Tania Patiño,† Xavier Arqué,† Rafael Mestre,† Lucas Palacios,† and Samuel Sánchez*,†,‡ Acc. Chem. Res. 2018.51:2662-2671. Downloaded from pubs.acs.org by KAOHSIUNG MEDICAL UNIV on 11/20/18. For personal use only.

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Institute for Bioengineering of Catalonia (IBEC), The Barcelona Institute of Science and Technology (BIST), Baldiri i Reixac 10-12, 08028 Barcelona, Spain ‡ Institució Catalana de Recerca i Estudis Avançats (ICREA), Pg. Lluís Companys 23, 08010 Barcelona, Spain CONSPECTUS: Self-propulsion at the nanoscale constitutes a challenge due to the need for overcoming viscous forces and Brownian motion. Inspired by nature, artificial micro- and nanomachines powered by catalytic reactions have been developed. Due to the toxicity of the most commonly used fuels, enzyme catalysis has emerged as a versatile and biocompatible alternative to generate selfpropulsion. Different swimmer sizes, ranging from the nanoscale to the microscale, and geometries, including tubular and spherical shapes, have been explored. However, there is still a lack of understanding of the mechanisms underlying enzyme-mediated propulsion. Size, shape, enzyme quantity and distribution, as well as the intrinsic enzymatic properties, may play crucial roles in motion dynamics. In this Account, we present the efforts carried out by our group and others by the community on the use of enzymes to power micro- and nanoswimmers. We examine the different structures, materials, and enzymes reported so far to fabricate biocatalytic micro- and nanoswimmers with special emphasis on their effect in motion dynamics. We discuss the development of tubular micro- and nanojets, focusing on the different fabrication methods and the effect of length and enzyme localization on their motion behavior. In the case of spherical swimmers, we highlight the role of asymmetry in enzyme coverage and how it can affect their motion dynamics. Different approaches have been described to generate asymmetric distribution of enzymes, namely, Janus particles, polymeric vesicles, and non-Janus particles with patchlike enzyme distribution that we recently reported. We also examine the correlation between enzyme kinetics and active motion. Enzyme activity, and consequently speed, can be modulated by modifying substrate concentration or adding specific inhibitors. Finally, we review the theory of active Brownian motion and how the size of the particles can influence the analysis of the results. Fundamentally, nanoscaled swimmers are more affected by Brownian fluctuations than microsized swimmers, and therefore, their motion is presented as an enhanced diffusion with respect to the passive case. Microswimmers, however, can overcome these fluctuations and show propulsive or ballistic trajectories. We provide some considerations on how to analyze the motion of these swimmers from an experimental point of view. Despite the rapid progress in enzyme-based micro- and nanoswimmers, deeper understanding of the mechanisms of motion is needed, and further efforts should be aimed to study their lifetime, long-term stability, and ability to navigate in complex media.

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INTRODUCTION

such as fuel biocompatibility, bioavailability, and versatility. So far, a handful of enzymes have proven to generate active motion, either tethered to a particle or free in solution,7−11 with encouraging potential applications. For instance, enhanced drug delivery12 and higher blood−brain barrier penetration13 by enzyme-powered nanoswimmers has been recently reported. These exciting results could open new avenues toward the reinvention of current therapeutic approaches in biomedicine.

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Inspired by biological motors, artificial micro- and nanoswimmers, able to self-propel by harnessing free chemical energy from in situ chemical reactions and its conversion into mechanical work, have been fabricated. A wide variety of shapes and materials, from rigid2,3 to soft,4 have been explored. Most of the reported systems have relied on the use of hydrogen peroxide as fuel, which have demonstrated potential applications including environmental remediation, tissue drilling, cargo transport, and diagnostics.2,3,5,6 An alternative power source relies on the incorporation of enzymes as catalytic engines, which offers unique advantages © 2018 American Chemical Society

Received: June 18, 2018 Published: October 10, 2018 2662

DOI: 10.1021/acs.accounts.8b00288 Acc. Chem. Res. 2018, 51, 2662−2671

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Accounts of Chemical Research

higher efficiency than the catalytic platinum (Pt)-based microjets. Different techniques such as electrodeposition (Figure 1E,F),17−19 electrospinning (Figure 1A,B),20 layerby-layer assembly,21 and reactive inkjet printing22 have been used to fabricate microjets with tunable size, shape, and enzyme localization. Recently, our group reported bubble-free propulsion of silica nanojets powered by urease, which were fabricated using a template based synthesis approach that allowed the control of enzyme localization, as indicated in Figure 1G.23 Depending on the enzyme position and nanojet length, different motion dynamics were observed. Despite the exciting outcomes on the use of tubular shapes for biocatalytic self-propulsion of microswimmers, the limitations encountered by the fabrication procedures in terms of size tunability and material versatility have led to a more extensive development of spherical micro- and nanoswimmers. For spherical swimmers, the diameter of the particle strongly determines the motion dynamics, as shown in Figure 2A. While particles with sizes ranging from sub-100 to 800 nm in diameter have demonstrated enhanced diffusion, ballistic motion has been achieved when using 2 μm microspheres. Additionally, regardless of their size, an asymmetry is required to avoid null net forces. The most common approach to induce asymmetric distribution of enzymes has been the fabrication of spherical Janus particles, where only one half of the particle is coated with enzymes. This strategy has been successfully achieved by our group when using mesoporous silica particles of 90 nm (Figure 2B),24 390 nm (Figure 2D),25 and 2 μm (Figure 2J,K)26 diameter by either electron beam evaporation or sputter coating techniques. The fabrication of Janus mesoporous silica clusters of 100 nm diameter was also efficient for active motion driven by catalase.27 Other asymmetric complex structures have been reported, such as the chemotactic synthetic vesicles described by Battaglia and co-workers,13 where the asymmetry is achieved by a heterogeneous copolymer composition (Figure 2C), and the stomatocytes described by Wilson and co-workers, where the asymmetry is achieved by the generation of a cavity from which the enzymatic products are released (Figure 2F,G).28 Nonetheless, enzyme-mediated self-propulsion has also been observed for non-Janus, fully coated spherical particles. Sen and co-workers reported polystyrene particles homogeneously coated with enzymes displaying enhanced diffusion and chemotactic behavior toward the fuel-rich areas (Figure 2I).29 Likewise, our group has reported the enhanced diffusion of 350 nm diameter non-Janus mesoporous silica nanoparticles (Figure 2E) in both water and PBS.12 To study the effect of enzyme distribution and number on the motion dynamics of micromotors, our group and our collaborators recently used stochastic optical reconstruction microscopy (STORM, Figure 3A).30 Two different types of particles, based on either polystyrene (PS) or polystyrene coated with a silicon dioxide shell (PS@SiO2), were used (Figure 3B,C). An asymmetric distribution of the enzymes in the form of patches was found for both types of particles (Figure 3D,E), where a higher binding efficiency for PS@SiO2 particles when compared to PS counterparts was observed (Figure 3F,G). Moreover, a threshold number of enzyme molecules was necessary to generate self-propulsion (Figure 3H). Independent experiments using optical tweezers (Figure 3I) showed the same threshold effect, and maximum forces of 0.17 ± 0.03 pN (mean ± standard error of the mean) were obtained (Figure 3J). These results highlight the relevance of

Nonetheless, it is not completely understood how fundamental aspects such as size, shape, enzyme number and distribution, or other enzymatic properties affect the motion behavior of such micromotors. At smaller lengths, it is difficult to differentiate between the diffusive motion arising from random fluctuations and the motion dynamics arising from enzyme activity. In these cases, the analysis of particle trajectories needs to be characterized by robust statistical approaches. In this Account, we discuss the aforementioned aspects toward a better understanding of enzymatic swimmer design to ensure efficient navigation.

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DESIGN AND FABRICATION STRATEGIES TO TUNE MOTION DYNAMICS The first prototypes of biocatalytic microswimmers were based on fibers and tubular structures.14,15 Mano and Heller fabricated a carbon fiber (0.5−1 cm length and 7 μm diameter) able to self-propel at the water−air interface.14 Later, Feringa and collaborators showed that the combination of glucose oxidase (GOx) and catalase enzymes resulted in bubble generation by aggregates of multiwalled carbon nanotubes.15 Afterward, tubes have emerged as successful structures for micro-nanojets (Figure 1). Sánchez et al. made use of rolling-up technology to fabricate, in a controllable manner, microtubes containing gold inner layers functionalized with thiol groups to bind catalase enzymes (Figure 1C,D).16 Catalase-powered microjets were able to self-propel by bubble generation inside the tubular cavity, showing about 10 times

Figure 1. Tubular micro- and nanojets powered by enzymes: (A, B) tubular microjets fabricated by electrospinning;20 (C, D) Ti/Au microjet fabricated by rolling-up technology;16 (E, F) tubular microjets fabricated by electrodepositionl;18 (G, H) silica nanojets fabricated by template synthesis, where different enzyme configurations were explored.23 Panel B reproduced with permission from ref 20. Copyright 2016 Wiley. Panel D reproduced with permission from ref 16. Copyright 2010 American Chemical Society. Panel F reproduced with permission from ref 18. Copyright 2016 Royal Society of Chemistry. Panel H reproduced with permission from ref 23. Copyright 2016 American Chemical Society. 2663


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Figure 2. Spherical enzyme-powered micro- and nanoswimmers classification with respect to their size, structure, and enzyme distribution. (A) Schematic representation of micro- and nanoswimmers, where blue dots represent enzyme molecules. (B) TEM micrograph of JMSNPs.24 (C) Negative staining of a polymersome for the differentiation of membrane compositions using TEM imaging.13 (D) SEM imaging of a hollow mesoporous Janus silica nanoswimmer.25 (E) TEM imaging of non-Janus core−shell mesoporous silica nanoswimmers.12 (F, G) TEM imaging of polymeric stomatocytes (F, scale bar = 200 μm) and EDX sulfur mapping (G, yellow) for the specific detection of cysteines and methionines present in catalase and GOx enzymes.28 (H) CSLM imaging of polymeric Janus microswimmers consisting of PEG inactivated and trypsin (fluorescently labeled) decorated hemispheres. Scale bar = 10 and 5 μm (inset).34 (I) Fluorescence image of non-Janus polystyrene based microswimmers, where catalase is stained in red.29 (J, K) Hollow silica Janus microcapsules analyzed by SEM (J) and fluorescence microscopy (K, 2 μm) where urease is stained in red.26 (L, M) Non-Janus silica based microswimmers, where (L) shows a SEM micrograph and (M) the nonhomogeneus distribution of urease enzyme (red) by STORM imaging.30 Panel B reproduced with permission from ref 24. Copyright 2017 Elsevier. Panel C reproduced with permission from ref 13. Copyright 2017 AAAS. Panel D reproduced with permission from ref 25. Copyright 2015 American Chemical Society. Panel E reproduced with permission from ref 12. Copyright 2018 Wiley. Panels F and G reproduced with permission from ref 28. Copyright 2016 American Chemical Society. Panel H reproduced with permission from ref 34. Copyright 2017 American Chemical Society. Panel I reproduced with permission from ref 29. Copyright 2015 American Chemical Society. Panels J and K reproduced with permission from ref 26. Copyright 2016 American Chemical Society. Panels L and M reproduced with permission from ref 30. Copyright 2018 American Chemical Society.

combining different techniques such as super-resolution molecular imaging and optical tweezers to understand the role of enzyme distribution and number in micro- and nanoswimmer motion behavior.

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Urease has also been extensively utilized to power microand nanoswimmers by the hydrolyzation of urea (CO(NH2)2) into carbon dioxide (CO2) and ammonia (NH3) (CO(NH2)2+H2O → CO2+NH3). Below the micrometer scale, urease has been reported by our group to enhance the diffusion of JMSNP25 and mesoporous silica nanobots12 and by Sen and co-workers to power polystyrene microparticles.29 In addition, at the micrometer scale, it can induce self-propulsion of 2 μmsized structures, such as PS@SiO2 microparticles,30 Janus hollow mesoporous silica microparticles,26 and silica nanojets.23 These examples show the versatility of catalase and urease, but other enzymes have been used on occasion, for instance, (i) GOx to propel JMSNPs by the degradation of β-D-glucose (β-D-glucose + O2 + H2O → gluconic acid + H2O2),25 (ii) acetylcholinesterase (AChE), which consumes acetylcholine (acetylcholine + H2O → acetic acid + choline) to power hollow silica microcapsules (unpublished data), and (iii) trypsin to propel PLL-coated silica Janus microparticles cleaving a peptidic bond of bis(benzyloxycarbonyl-L-arginine amide) derivative of rhodamine 110 (BA-Rho-110) into the resulting monoamide (MA-Rho-110) (BA-Rho-110 → MARho-110 + R).34

EFFECT OF ENZYMATIC PROPERTIES ON MOTION BEHAVIOR

Over the last years, the library of enzymes used as power sources for micro- and nanoswimmers has kept expanding (Figure 4).7 The use of individual enzymes is the predominant strategy in the field, catalase being one of the most widely used by the community since it was first reported by Sánchez and co-workers to propel microjets,16 decomposing hydrogen peroxide (H2O2) into water (H2O) and oxygen (O2) (2H2O2 → 2H2O + O2). Other structures using catalase as power engine have been reported by our group, such as Janus Mesoporous Silica Nanoparticles (JMSNPs) of 90 and 389 nm in diameter,24,25 as well as silica nanoclusters with sub-100 nm diameters.27 Several other groups incorporated this approach to propel a varied range of spherical micro- and nanoswimmers,29,31−33 and bubble propelled tubular microjets of various sizes.17−21 2664


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Figure 3. Enzyme distribution and quantification analysis by STORM imaging. (A) Comparison between the conventional fluorescence and STORM images. SEM micrographs of (B) PS and (C) PS@SiO2 microswimmers. Three-dimensional enzyme density maps of (D) PS and (E) PS@SiO2. Two-dimensional enzyme detection in (F) PS and (G) PS@SiO2 for quantitative analysis. (H) Speed of microswimmers related to the number of enzyme molecules bound to their surface. (I) Schematic representation of the optical trapping of microswimmers for force measurements. (J) Forces exerted by microswimmers with increasing number of detected urease molecules. All panels are reproduced with permission from ref 30. Copyright 2018 American Chemical Society.

Figure 4. Schematic of enzymes reported to power micro- and nanoswimmers arranged in ascending value of turnover number (kcat): trypsin,34 glucose oxidase (GOx),25 GOx coupled with catalase,35,28 acetylcholinesterase (AChE), urease,12,23,25,26,29 and catalase.16,24,25,29 Adapted with permission from refs 16, 23, 25, 26, 28, 29, 34, and 35. Copyrights 2010, 2015, 2016, and 2017 American Chemical Society. Adapted with permission from ref 12. Copyright 2018 Wiley. Adapted with permission from ref 24. Copyright 2017 Elsevier. 2665


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Figure 5. Motion dynamics of micro- and nanoswimmers depending on the enzymatic kinetics. (A) Average diffusion coefficients of enzymatic JMSNP, powered by glucose oxidase (GOx), urease (UR), and catalase (CAT), exposed to optimal substrate concentrations to obtain the maximum self-propulsion.25 (B) Average speeds of the different enzymatic hollow silica microcapsules, powered by aldolase (ALS), GOx, acetylcholinesterase (AChE), and UR, exposed to optimal substrate concentrations (unpublished data). (C) Average diffusion coefficients of JMSNP powered by CAT and bare mesoporous silica nanoparticles measured by DLS.24 (D) Average speed of Janus hollow mesoporous silica microparticles versus fuel concentration.26 (E) Average diffusion coefficients of JMSNP powered by GOx vs substrate concentration.25 (F) Real time “on−off” motion control of Janus hollow silica microcapsule powered by UR, by addition of noncompetitive inhibitor (Ag+ or Hg2+) and DTT.26 Panel A adapted with permission from ref 25. Copyright 2015 American Chemical Society. Panel C reproduced with permission from ref 24. Copyright 2017 Elsevier. Panels D and F reproduced with permission from ref 26. Copyright 2016 American Chemical Society. Panel E reproduced with permission from ref 25. Copyright 2015 American Chemical Society.

Since a specific compound can be at the same time product of a reaction and substrate of a different one, some groups have increased the complexity of the system, combining two or more enzymes to chain their reactions. The most prominent example is the conjugation of GOx with catalase (GOx−Cat) in order to convert first glucose and oxygen into gluconic acid and H2O2, where the product H2O2 can be then decomposed by catalase (β-D-glucose + O2 + H2O → gluconic acid + H2O2; 2H2O2 → 2H2O + O2).13,15,28,35 The GOx−Cat tandem motor powered the first biocatalytic nanoswimmers,15 and since then, the community has greatly harnessed this power source, partially because of the unsuitability of catalase alone for biomedical applications, due to the use of a toxic fuel, and the low motion generation capabilities of GOx, mainly due to its low turnover number (kcat). Gáspar et al. showed other examples of coupled enzymes used to power polypyrrole−gold nanorods that achieve active motion by attaching horseradish peroxidase (HRP) on the polypyrrole side of the nanorod and

either HRP (HRP−HRP), cytochrome C (HRP−CytC), or catalase (HRP−Cat) on the gold side.14,36,37 The conjugation of multiple catalytic elements is not limited to enzyme−enzyme coupling, as there are some examples of nanoswimmers powered by an enzymatic pathway38 or by enzymes combined with inorganic catalysts like Pt,39 and even by adding an independent enzyme to obtain a double-fueled nanoswimmer.34 All this progress is the result of a decade of research and, despite the diversity of enzymes and combinations explored, the mechanism ruling self-propulsion of enzyme-powered micro- and nanoswimmers still remains unclear. Different mechanisms have been experimentally observed, namely, bubble propulsion16−18,20,21 and self-electrophoresis.14,36,37,39 From a theoretical point of view, self-diffusiophoresis has been also suggested.40−42 Other mechanisms have been proposed for single enzymes, such as thermal effect,9 collective heat,43 and the conformational changes arising from catalysis8,44 or 2666


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Figure 6. Dynamics of micro- and nanoswimmers. (top) MSD simulations of active Brownian particles of (A) ⌀ = 350 nm,12 (B) ⌀ = 800 nm,29 and (C) ⌀ = 2 μm,26 moving at three different speeds. (bottom) Real MSD data from self-propelled particles powered by the decomposition of urea by urease. Panel A reproduced with permission from ref 12. Copyright 2018 Wiley. Panel B reproduced with permission from ref 29. Copyright 2015 American Chemical Society. Panel C reproduced with permission from ref 26. Copyright 2016 American Chemical Society.

binding−unbinding interactions,10,11,45 which may need to be studied for self-propulsion of micro- and nanoswimmers. Enzyme kinetics can also influence the motion dynamics of swimmers. In this sense, our group conjugated catalase, urease, and GOx to power JMSNPs (⌀ = 389 nm). When comparing the active motion of the JMSNPs exposed to their respective optimal substrate concentration, it can be observed how the diffusion increases in the same order as the turnover number at which each reaction is catalyzed (Figure 5A).25 We found the same trend when analyzing the speeds of hollow silica microcapsules propelled by urease, AChE, GOx, and aldolase (Figure 5B) (unpublished data). This key role of catalysis and the correlation of the conversion rate with active motion is further supported by the results obtained when modifying the substrate concentration. By altering the substrate availability, kcat is affected, and the active motion generated also changes. In several reports, a substrate concentration-dependent enhanced diffusion12,24,25,29,34,35 (Figure 5C) or speed23,26 (Figure 5D) has been proven, depending on the regime of motion. In fact, the growth of active motion follows the Michaelis−Menten kinetics fashion,23,24,26 reaching a plateau when the time needed for two consecutive collisions of enzyme−substrate to occur is less than the time that it takes to catalyze the substrate into products. High substrate concentrations can also produce substrate inhibition due to the presence of a high number of substrate molecules that compete for active site binding, leading to a decreased motion (Figure 5E). However, the decline in motion at high substrate concentrations can also occur due to an increased viscosity of the media.25 The use of inhibitors has been reported to modulate the motion of enzymatic microswimmers, all of them acting noncompetitively (Figure 5F).26 This phenomenon is based on the interaction of inhibitors with allosteric sites of the enzyme,

without competing for the active site. This is the case of a previous study in our lab, where urease Janus hollow silica microcapsules were inhibited by heavy metals (Ag+ or Hg2+) at 50 mM urea substrate.26

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CONSIDERATIONS OF MOTION DYNAMICS ANALYSIS The dynamics of self-propelled particles are usually analyzed by calculating the mean squared displacement (MSD) of their positions over time.46,47 By assuming a constant speed over time and randomization of the particle’s position and orientation due to Brownian fluctuations, one can obtain the following MSD: 2 MSD(Δt ) = ⟨( r (⃗ Δt ) − r (0)) ⟩ ⃗ Ä ÉÑ 2 2Å ÑÑ v τr ÅÅÅ 2Δt − 2 Δ t / τ r ÅÅ = 4Dt Δt + +e − 1ÑÑÑÑ Å ÑÑÖ 2 ÅÅÇ τr

where r⃗(0) is the position of the particle at the initial time, r⃗(Δt) is the position of the particle after a time Δt, Dt is the translational diffusion coefficient, τr is the rotational diffusion time, and ν is the speed of the particle. This well-known equation only applies when particle dynamics are characterized by constant speed and the particle experiences no torques. However, we can distinguish two different regimes that simplify this equation. At longer time scales (Δt ≫ τr), it can be written as MSD(Δt ) = (4Dt + v 2τr)Δt = 4DeΔt

which is analogous to the case of a passive Brownian particle and is referred to as enhanced diffusion. At shorter time scales (Δt ≪ τr), it takes the form: 2667


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Figure 7. Analyses of motion of different enzymatically propelled micro- and nanoswimmers. (A) Translational diffusion coefficient of a nanoswimmer obtained by optical tracking and DLS measurements for different concentrations of urea.12 (B) MSD from optical tracking of 2 μm microswimmers. A fitting of the form tα can help differentiate diffusivity (α = 1) from propulsion (α = 2).30 (C) Effective rotational diffusion coefficient with (100 mM, red) and without (0 mM, black) fuel (dashed line is theoretical value of Brownian, Dr). (D) Velocity autocorrelation function of enzyme-powered nanojets (200 nm diameter) of different lengths.23 Panel A reproduced with permission from ref 12. Copyright 2018 Wiley. Panel B reproduced with permission from ref 30. Copyright 2018 American Chemical Society. Panels C and D reproduced with permission from ref 23. Copyright 2016 American Chemical Society.

MSD(Δt ) = 4Dt Δt + v 2Δt 2

unreliable the analysis of particles below 200 nm. Finally, nanoparticle-tracking analysis (NTA) is also suggested by some authors as a third way of analyzing the MSD.28 This method is similar to DLS, although it couples laser light scattering to a camera to track a single particle’s position, overcoming some disadvantages of DLS, like aggregation of particles, since their motion is not treated as an ensemble.48 In any of the three cases, it is important to consider the concentration of the particles for possible effects arising from interactions among them,35 which have already been reported for other types of self-propelled particles.49 Moreover, other effects, such as fuel depletion or the presence of salts in the medium12 should always be considered. Independent of the method employed to obtain the MSD, the analysis should follow the same specific guidelines for a correct evaluation of the results. For instance, we discourage reporting the instantaneous speed of a particle, as it will not be accurate and will heavily depend on the tracking algorithm used. Measuring the real speed of a Brownian particle requires very complex and dedicated experiments50 and optical tracking cannot have enough sensitivity. Therefore, the instantaneous speed of passive Brownian particles should not be reported, and if done, it should be only for comparative purposes. Thus, for nanoswimmers, only the enhanced diffusion coefficient should be used as a proper metric to study the motion, while for microswimmers, a quadratic fitting could be performed to obtain an average propulsive speed of the particle. It is a known characteristic of the MSD that when the elapsed time Δt increases, the variance of the data increases due to a lack of data points. Therefore, it is recommended to have large time sensitivity (high frames per second when tracking) and take only the initial part of the MSD (around 10% of the total time

which is called the propulsive or ballistic regime, since we should see an effective directional movement where the particle seems to continuously propel in a specific direction. These equations are commonly used for the motion analysis of catalytic and biocatalytic (enzymatic) micro- and nanoswimmers, since they can give statistically averaged results. The shape of the MSD curve can change depending on the size of the particle, as the rotational diffusion time increases with the cube of its size (Figure 6).46 For nanoparticles, the rotational diffusion time is very small compared to the time resolution of typical equipment, and therefore, only the enhanced diffusion regime can be observed (Figure 6A). In these cases, one can only obtain an enhanced or effective diffusion by fitting the experimental MSD to a linear function. For near micrometersized particles, a short propulsive (quadratic) regime should theoretically be visible, although it might appear hidden if the time resolution is low (Figure 6B). For microparticles, the rotational diffusion time lays within the observable time. In this case, the propulsive regime appears at times shorter than the rotational diffusion time and the diffusive regime at longer times (Figure 6C). The MSD is usually obtained by two methods, dynamic light scattering (DLS) and optical tracking. DLS can only be used for sub-micrometer active particles, since larger particles exceed the resolution limit of most equipment. This technique does not usually need postprocessing of results, it is less timeconsuming than optical tracking and both can show comparable results (Figure 7A).12 Optical tracking can be used for both micro- and nanoswimmers. However, the optical resolution limit (usually around 200 nm) might render 2668


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Accounts of Chemical Research or even less) so that the errors of the fitting can be minimized. Moreover, the total length of the videos should also depend on the size of the swimmers, since larger particles need a longer elapsed time to capture both diffusive and propulsive regimes (Figure 6C). It is important to underline again that the well-known MSD formula and its approximations only stand for spherical particles moving in Newtonian fluids at constant propulsive speeds.51 In this and other cases, one should consider deviations from this formula. These include particles rotating due to an applied torque (e.g., dimers of particles), particles that have nonconstant speed profiles,52 or particles that swim in non-Newtonian fluids such as blood or mucus.53 Here, it could be interesting to report a log−log plot of the MSD instead of a linear one, in order to find anomalous tendencies.13,30,54 A more general equation following the form MSD(Δt) ≈ tα could be considered, where α is a scaling factor (Figure 7B). If α = 1, we find a diffusive regime, and if α < 1, we find a subdiffusive regime. However, if 1 < α < 2 we are in a superdiffusive regime, with α = 2 being a propulsive regime. When analyzing the motion of nonspherical enzymepowered swimmers, the analysis based on the MSD might need some modifications. For instance, the value of the rotational diffusion coefficient for microtubes depends on both the length (Figure 7C) and the radius. In this case, the calculation of the velocity autocorrelation function could be a more efficient way of assessing the directionality of the movement (Figure 7D). This analysis shows that the slower the decay, the more directional the motion is. Although MSD can show clear linear and propulsive regimes, an apparent propulsive regime can be a consequence of drift in the solution. When plotting the MSD, this effect might not be noticeable and could lead to misrepresentative results, especially for sub-micrometer particles, which might show only short-timed propulsive regimes. Typically, drift is noticed when all particles move in the same direction with a very linear trajectory. One way of avoiding this problem is the use of microfluidic channels to control the flow of the particles into the system.10,29 Moreover, if a propulsive regime in the MSD lasts beyond the rotational diffusion time, it is recommended to record longer videos to confirm that both regimes appear.

we provided insights into the analytic approaches to improve the robustness and reliability of the conclusions derived from such studies. However, most of the experimental approaches conducted so far have relied on the use of simple fluids such as water or PBS. A step forward into the development of enzymatic micro- and nanoswimmers as future tools in biomedicine will entail understanding how the different properties and components of physiologically relevant media can affect the motion capabilities of enzymatic swimmers and to what extent this will be dependent on their design and enzyme properties. Future directions will lead to studying multiple options of enzyme−fuel configurations for on-demand applications, the fundamental understanding of enzyme catalysis, the use of genetically modified enzymes for kcat tunability, and motion in complex fluids. In some cases, poor long-term stability has been indicated, although increased endurance has been recently reported after enzyme encapsulation. Therefore, a multidisciplinary approach where the biochemistry, nanotechnology, and theoretical analysis evolve together is needed to guarantee successful progress in the field.

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AUTHOR INFORMATION

Corresponding Author

*E-mail: [email protected]. ORCID

Samuel Sánchez: 0000-0002-5845-8941 Author Contributions

The manuscript was written through contributions of all authors. All authors have given approval to the final version of the manuscript. Funding

This work has been supported by the MINECO CTQ201568879-R (MICRODIA) and CTQ2015-72471-EXP (Enzwim) grants. T.P. thanks MINECO for the Juan de la Cierva fellowship (FJCI-2015-25578), and R.M. acknowledges the support of “la Caixa” Foundation through IBEC International PhD Programme “la Caixa” Severo Ochoa fellowships. X.A. and L.P. thank MINECO for the Severo Ochoa and FPI predoctoral fellowships, respectively. Notes

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The authors declare no competing financial interest.

CONCLUSIONS AND OUTLOOK Biocatalytic micro- and nanoswimmers are able to self-propel due to the conversion of substrates into products, mediated by enzymes. The use of enzymes as power source has emerged as a response to the need for biocompatible fuels. Due to the high diversity of enzyme types in nature, this system offers a unique versatility and the possibility to design specific swimmers that become active on demand when and where the substrate is present. Although the field is still in its infancy, a great spectrum of applications has been proposed with functions in sensing, imaging, environmental remediation, nanosurgery, and drug delivery, and several milestones toward them have been achieved, such as facilitated drug transport to cells and tissues. Nonetheless, to fully understand and predict the performance of enzymatic micro- and nanoswimmers, a deeper knowledge into the fundamental aspects underlying their motion behavior is required. Herein, we discussed the role of particle size, shape, enzyme number and localization, as well as the key enzymatic properties that affect motion dynamics. In addition,

Biographies Tania Patiñ o received her Ph.D. in Cell biology from the Autonomous University of Barcelona in 2015. Her research is focused on the development of self-propelled micro- and nanomotors for biomedical applications. Xavier Arqué received his M.Sc. in Molecular Biotechnology from the University of Barcelona, Spain, in 2017. His research interests are focused on studying the enzymatic properties affecting the active motion of micro- and nanoswimmers powered by biocatalysis. Rafael Mestre received a joint M.Sc. in Nanoscience and Nanotechnology by the Katholieke Universiteit Leuven and Grenoble Alpes Université in 2016. His research interests focus on the integration of biology, physics, and material science for the development of biohybrid systems. Lucas Palacios received his M.Sc. in Advanced Physics from the University of Barcelona in 2016. His research interests include active matter and soft interfaces. 2669


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(22) Gregory, D. A.; Zhang, Y.; Smith, P. J.; Zhao, X.; Ebbens, S. J. Reactive Inkjet Printing of Biocompatible Enzyme Powered Silk Micro-Rockets. Small 2016, 12, 4048−4055. (23) Ma, X.; Hortelao, A. C.; Miguel-López, A.; Sánchez, S. BubbleFree Propulsion of Ultrasmall Tubular Nanojets Powered by Biocatalytic Reactions. J. Am. Chem. Soc. 2016, 138, 13782−13785. (24) Ma, X.; Sánchez, S. Bio-Catalytic Mesoporous Janus NanoMotors Powered by Catalase Enzyme. Tetrahedron 2017, 73, 4883− 4886. (25) Ma, X.; Jannasch, A.; Albrecht, U.-R.; Hahn, K.; Miguel-López, A.; Schäffer, E.; Sánchez, S. Enzyme-Powered Hollow Mesoporous Janus Nanomotors. Nano Lett. 2015, 15, 7043−7050. (26) Ma, X.; Wang, X.; Hahn, K.; Sánchez, S. Motion Control of Urea-Powered Biocompatible Hollow Microcapsules. ACS Nano 2016, 10, 3597−3605. (27) Ma, X.; Sanchez, S. A Bio-Catalytically Driven Janus Mesoporous Silica Cluster Motor with Magnetic Guidance. Chem. Commun. 2015, 51, 5467−5470. (28) Abdelmohsen, L. K. E. A.; Nijemeisland, M.; Pawar, G. M.; Janssen, G.-J. A.; Nolte, R. J. M.; van Hest, J. C. M.; Wilson, D. A. Dynamic Loading and Unloading of Proteins in Polymeric Stomatocytes: Formation of an Enzyme-Loaded Supramolecular Nanomotor. ACS Nano 2016, 10, 2652−2660. (29) Dey, K. K.; Zhao, X.; Tansi, B. M.; Méndez-Ortiz, W. J.; Córdova-Figueroa, U. M.; Golestanian, R.; Sen, A. Micromotors Powered by Enzyme Catalysis. Nano Lett. 2015, 15, 8311−8315. (30) Patiño, T.; Feiner-Gracia, N.; Arqué, X.; Miguel-López, A.; Jannasch, A.; Stumpp, T.; Schäffer, E.; Albertazzi, L.; Sánchez, S. Influence of Enzyme Quantity and Distribution on the SelfPropulsion of Non-Janus Urease-Powered Micromotors. J. Am. Chem. Soc. 2018, 140, 7896−7903. (31) Simmchen, J.; Baeza, A.; Ruiz, D.; Esplandiu, M. J.; Vallet-Regí, M. Asymmetric Hybrid Silica Nanomotors for Capture and Cargo Transport: Towards a Novel Motion-Based DNA Sensor. Small 2012, 8, 2053−2059. (32) Wu, Y.; Lin, X.; Wu, Z.; Möhwald, H.; He, Q. Self-Propelled Polymer Multilayer Janus Capsules for Effective Drug Delivery and Light-Triggered Release. ACS Appl. Mater. Interfaces 2014, 6, 10476− 10481. (33) Simmchen, J.; Baeza, A.; Ruiz-Molina, D.; Vallet-Regí, M. Improving Catalase-Based Propelled Motor Endurance by Enzyme Encapsulation. Nanoscale 2014, 6, 8907−8913. (34) Schattling, P. S.; Ramos-Docampo, M. A.; Salgueiriño, V.; Städler, B. Double-Fueled Janus Swimmers with Magnetotactic Behavior. ACS Nano 2017, 11, 3973−3983. (35) Schattling, P.; Thingholm, B.; Städler, B. Enhanced Diffusion of Glucose-Fueled Janus Particles. Chem. Mater. 2015, 27, 7412−7418. (36) Bunea, A.-I.; Pavel, I.-A.; David, S.; Gáspár, S. Modification with Hemeproteins Increases the Diffusive Movement of Nanorods in Dilute Hydrogen Peroxide Solutions. Chem. Commun. 2013, 49, 8803. (37) Pavel, I.-A.; Bunea, A.-I.; David, S.; Gáspár, S. Nanorods with Biocatalytically Induced Self-Electrophoresis. ChemCatChem 2014, 6, 866−872. (38) Nijemeisland, M.; Abdelmohsen, L. K. E. A.; Huck, W. T. S.; Wilson, D. A.; van Hest, J. C. M. A Compartmentalized Out-ofEquilibrium Enzymatic Reaction Network for Sustained Autonomous Movement. ACS Cent. Sci. 2016, 2, 843−849. (39) Bunea, A.-I.; Pavel, I.-A.; David, S.; Gáspár, S. Sensing Based on the Motion of Enzyme-Modified Nanorods. Biosens. Bioelectron. 2015, 67, 42−48. (40) Golestanian, R.; Liverpool, T. B.; Ajdari, A. Propulsion of a Molecular Machine by Asymmetric Distribution of Reaction Products. Phys. Rev. Lett. 2005, 94, 220801. (41) Golestanian, R.; Liverpool, T. B.; Ajdari, A. Designing Phoretic Micro- and Nano-Swimmers. New J. Phys. 2007, 9, 126−126. (42) Sabass, B.; Seifert, U. Dynamics and Efficiency of a SelfPropelled, Diffusiophoretic Swimmer. J. Chem. Phys. 2012, 136, 064508.

Samuel Sanchez is an ICREA Professor and Senior Group Leader at the Institute for Bioengineering of Catalonia, Barcelona. His research interest focuses on different types of self-propelled micro- and nanobots, biosensors, and nanobiotechnology, among others.

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Fundamental Aspects of Enzyme-Powered Micro- and Nanoswimmers

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