Influence of Enzyme Quantity and Distribution on the Self-Propulsion

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Influence of enzyme quantity and distribution on the selfpropulsion of non-Janus urease powered micromotors Tania Patiño, Natalia Feiner-Gracia, Xavier Arque, Albert Miguel-López, Anita Jannasch, Tom Stumpp, Erik Schäffer, Lorenzo Albertazzi, and Samuel Sanchez J. Am. Chem. Soc., Just Accepted Manuscript • DOI: 10.1021/jacs.8b03460 • Publication Date (Web): 22 May 2018 Downloaded from http://pubs.acs.org on May 22, 2018

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Journal of the American Chemical Society

Influence of enzyme quantity and distribution on the selfself-propulsion of nonnon-Janus urease powered micromotors Tania Patiño§‡, Natalia Feiner-Gracia§‡, Xavier Arqué§, Albert Miguel-López§, Anita Jannasch#, Tom Stumpp#, Erik Schäffer#, Lorenzo Albertazzi§†*, Samuel Sánchez§ǁ* §Institute for Bioengineering of Catalonia (IBEC), The Barcelona Institute of Science and Technology (BIST), Baldiri i Reixac 10-12, 08028 Barcelona, Spain # Center for Plant Molecular Biology (ZMBP), University of Tübingen, Auf der Morgenstelle 32, 72076 Tübingen, Germany ǁInstitució Catalana de Recerca i Estudis Avançats (ICREA), Pg. Lluís Companys 23, 08010 Barcelona, Spain †Department of Biomedical Engineering, Institute for Complex Molecular Systems (ICMS), Eindhoven University of Technology, 5612AZ Eindhoven, The Netherlands Bio-catalytic micromotors, self-propulsion, STORM, nanoscopy, nanomotors, optical tweezers ABSTRACT: The use of enzyme catalysis to power micro and nanomachines offers unique features such as biocompatibility, versatility and fuel bioavailability. Yet, the key parameters underlying the motion behavior of enzyme-powered motors are not completely understood. Here, we investigate the role of enzyme distribution and quantity on the generation of active motion. Two different micromotor architectures based on either polystyrene (PS) or polystyrene coated with a rough silicon dioxide shell (PS@SiO2) were explored. A directional propulsion with higher speed was observed for PS@SiO2 motors when compared to their PS counterparts. We made use of stochastically optical reconstruction microscopy (STORM) to precisely detect single urease molecules conjugated to the micromotors surface, with a high spatial resolution. An asymmetric distribution of enzymes around the micromotor surface was observed for both PS and PS@SiO2 architectures, indicating that the enzyme distribution was not the only parameter affecting the motion behavior. We quantified the number of enzymes present on the micromotor surface and observed a 10-fold increase in the number of urease molecules for PS@SiO2 motors compared to PS-based micromotors. To further investigate the number of enzymes required to generate a selfpropulsion, PS@SiO2 particles were functionalized with varying amounts of urease molecules and the resulting speed and propulsive force were measured by optical tracking and optical tweezers, respectively. Surprisingly, both speed and force depended in a non-linear fashion on the enzyme coverage. To break symmetry for active propulsion, we found that a certain threshold number of enzymes molecules per micromotor was necessary, indicating that activity may be due to a critical phenomenon. Taken together, these results provide new insights into the design features of micro-/nanomotors to ensure an efficient development.

INTRODUCTION Catalytic microswimmers are artificial systems able to selfpropel thanks to the conversion of chemical energy into a mechanical force which ultimately translates into active motion.1 While chemically powered micro and nanomotors have shown promising applicability in many fields such as environmental remediation,2–7 cargo transport and delivery,8–13 tissue and cell penetration,14,15 and active drug delivery to the stomach in-vivo16 , their implementation in biomedicine is often restricted by either the inherent toxicity of the fuel or its limited availability within the organism.17 Recently, the use of enzyme catalysis has emerged as an attractive alternative to replace commonly used toxic fuels since it offers unique features including biocompatibility, versatility and fuel bioavailability.18 In this regard, the use of urease, catalase, and glucose oxidase has shown to increase the diffusion of nanosized particles at physio-

logically relevant concentrations of the enzyme substrate.19–21 In addition, a directional propulsion can be achieved when using urease to power hollow silica Janus micromotors. Their motion can be switched on and off by the addition of salts and the trajectories can be modified on-demand by the application of a magnetic field, allowing a high degree of controllability.22 Furthermore, as a proofof-concept, enzyme powered nanomotors have recently demonstrated to be more efficient in the release of anticancer drug release and delivery to cells in vitro when compared to passive carriers.23–25 Despite these exciting outcomes, the field of enzymepowered micro- and nanomotors is still in its infancy and a deeper knowledge on the fundamental aspects underlying their motion behavior is required for a safe and efficient development.

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Figure 1. Fabrication and characterization of urease-powered micromotors. A) Schematic representation of the fabrication and functionalization strategy. B) SEM micrographs of PS and PS@SiO2 based micromotors, showing the differences in surface roughness. C) Comparative image showing a conventional fluorescence analysis of PS@SiO2 based micromotors functionalized with Cy5-labeled urease versus STORM imaging of the same field.

Despite the fact that an asymmetric structure and distribution of the catalyst has traditionally been claimed to be essential for the generation of active motion,26 Sen and coworkers reported the enhanced diffusion of non-Janus spherical motors powered by enzyme catalysis.20 Understanding the exact role of the micromotor architecture and whether structural asymmetry is needed to generate active motion is of special relevance in the field, since the requirements for fabricating complex structures such as tubular microjets, polymeric stomatocytes or spherical Janus particles involve expensive and time-consuming techniques that may compromise their scalability and, therefore, their applicability. In view of this background, the present study aimed at understanding whether the stochastic enzyme distribution is enough to generate self-propulsion of micromotors, as well as elucidating the relationship between the number of enzymes and micromotor’s propulsive speed and force. Up to now, the most commonly used techniques to visualize the presence of biomolecules on the surface of micro- and nanoparticles are based in optical imaging such as confocal microscopy. Unfortunately, the resolution of confocal microscopy is limited to about 250 nm, restricting the precise analysis of the exact distribution and quantification of such molecules on the particle surface. As an alternative, superresolution imaging techniques that allow imaging with sub-50-nm resolution are emerging as powerful tools to overcome the limitations of conventional optical microscopy. For example, STORM, which is based on the detection of individual stochastically blinking fluorophores, allows to

reach a resolution of around 20 nm.27 Apart from an excellent resolution, the possibility of molecule quantification makes STORM a very powerful and unique tool in optical microscopy. While its use has been widely exploited in cell biology,28,29 its potential for nanotechnology has remained rather unexplored. Recent studies demonstrated the use of STORM for the analysis and quantification of biomolecules on micro and nanoparticles surface paving the way towards new and promising nanotechnology-related applications.30–32 In the present study, we present the three-dimensional mapping of urease enzymes bound to the surface of micromotors with single-molecule resolution, resolved by STORM imaging. Enzyme mapping was performed in micromotors with varying surface roughness and a nonhomogeneous distribution was observed regardless of the micromotor surface type. However, the quantification of enzymes showed that rough surfaces allowed a higher enzyme binding, leading to active motion. To further understand the role of enzyme number in self-propulsion of micromotors, we correlated the number of enzymes, quantified by STORM, with the speed and propulsive force, which was measured using high-resolution optical tweezers.33,34 Taken together, these results provide new insights into the design features of micro- and nanomotors powered by enzyme catalysis, which may simplify future fabrication steps, finding crucial parameters such as enzyme amount, distribution and forces, which are essential to ensure an efficient and optimal fabrication.


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EXPERIMENTAL SECTION Fabrication and characterization of amine-PS@SiO2 microparticles. A silicon dioxide shell was grown onto 2 μm particles based on polystyrene (PS) (Sigma-Aldrich cat. no. 78452), using previously reported co-condensation method. Briefly, a mixture consisting of 250 uL of polystyrene particles (stock solution, 10% solids), 0.5 ml ethanol 99% (Panreac Applichem cat. no. 131086-1214), 0.4 ml Mili-Q water, and 25 μl ammonium hydroxide solution (Sigma-Aldrich cat. no. 221228) was stirred for 5 min at room temperature (RT). Next, 2.5 μl of 3aminopropyltriethoxysilane (APTES) 99% (Sigma-Aldrich cat. no. 440140) were added to the mixture. At 6h 7.5 μl of tetraethylorthosilicate (TEOS) ≥99% (Sigma-Aldrich cat. no. 86578) were added to the solution, which was let reacting overnight at RT and under magnetic stirring. The resulting polystyrene beads coated with a silicon shell were washed out thrice in ethanol and stored at RT until their use. To characterize their surface properties, PS and PS@SiO2 microparticles were imaged under a Scanning Electron Microscopy (SEM) (FEI NOVA NanoSEM 230). The presence of amine groups on the surface provided by APTES was confirmed by measuring the electrophoretic mobility of the particles using a Wyatt Möbius detector coupled with an Atlas cell pressurization system. Urease labelling. Urease was dispersed in 0.1 M Sodium Bicarbonate buffer (pH=8.5, Sigma Aldrich) at 1.5 mg/mL following by the addition of 1.5 eq. of Cy5-NHS (Lumiprobe). The reaction was shaken at 300 r.p.m. for 4 hours at room temperature. The proteins were then dialyzed with 1kDa pore size (Spectrum Lab) against Sodium Bicarbonate buffer for 24 hours. The concentration of dye per protein was then quantified using a Nanodrop ND-1000 Spectrophotometer, by measuring the adsorption at 280 nm (protein) and 650 nm (dye), to be 1.03 molecules Cy5/molecule urease. Urease functionalization. In parallel, amine-modified polystyrene beads and PS@SiO2 microparticles were washed thrice with Mili-Q water, followed by three more washes in in 1X Phosphate-buffered saline (PBS) (pH = 7.4) (Thermo Fischer Scientific cat. no. 70011-036). Then, microparticles were suspended in a PBS solution containing glutaraldehyde linker (GA) (2.5 wt %) (Sigma-Aldrich cat. no. G6257) and kept under end-to-end mixing for 3 h. After this, the GA-functionalized particles were washed 3 times in PBS 1x and suspended in a urease-PBS solution at different concentrations. For the motion comparison between PS and PS@SiO2 micromotors, a concentration of 400 µg/ml urease was used. For the correlation of enzyme number with speed and force, 400, 200, 100 and 20 µg/ml urease concentrations were used. To allow STORM visualization, 1% of the urease used for the functionalization was labelled with Cy5 as mentioned in the previous section. The mixture was incubated for 2h at room temperature, followed by three washes in PBS. Urease-functionalized particles were kept at 4 ºC until their use. Motion analysis by optical video recording and MSD analysis. For the sample preparation, 5 uL of micromotors solution were mixed with 5 uL of 200 mM Urea in a glass slide. A coverslip was placed on top of the mixture to avoid any drifting effect leading to artifacts. The micromotors

were recorded during 20 s at a rate of 25 frames per second, using an inverted optical microscope (Leica Dmi8) equipped with a water immersion 63x objective and a Hamamatsu camera. In all cases, at least 15 particles were recorded per condition. The obtained videos were analyzed using a custom-made Python code which allowed to extract the trajectories and calculate the MSD according to the following equation, where, i=2 for 2D analysis:

MSD∆ =< + ∆ − > 1 By fitting the MSD to equation 1, the speed and diffusion were obtained.35 STORM sample preparation and imaging. To perform direct STORM (dSTORM) imaging micromotors with 1%5% of Cy5-labelled urease were immobilized onto the surface of a Nunc™ Lab-Tek™ chamber (Thermofisher) pre-treated with poly-L-lysine to increase the adsorption. After being incubated for 10-15 minutes unbound micromotors and PBS were removed and replaced by STORM buffer. STORM buffer contains PBS, an oxygen scavenging system (0.5 mg mL-1 glucose oxidase, 40 µg mL-1 catalase), glucose (5% w/v) and cysteamine (100 mM). STORM images were acquired using a Nikon N-STORM system configured for total internal reflection fluorescence (TIRF) imaging. Cy5 labelled urease was imaged by means of a 647nm laser (160 mW). No activation UV light was employed for the 2D acquisition and for 3D acquisition a 405 nm laser (80 mW) was used. Fluorescence was collected by means of a Nikon 100x, 1.49 NA oil immersion objective and passed through a quad-band pass dichroic filter (97335 Nikon). For the 2D acquisition images were acquired onto a 256x256 pixel region (pixel size 0.16µm) of a Hamamatsu ORCA- Flash 4.0 camera at 10 ms integration time. 30,000 frames per image were acquired for the 647 channel. STORM images were analyzed with the STORM module of the NIS element Nikon software. For the 3D acquisition an astigmatism lens was introduced into the light path. Images were acquired onto a 128x128 pixel region (pixel size 0.16 µm) at 5 ms integration time. A total of 12-15 stacks were collected for each image with a 250 nm step, and 7000 frames were acquired per stack. STORM Data analysis. During STORM-imaging, the NIS elements Nikon software generates a list of localizations by Gaussian fitting of blinking dyes in the acquired movie of conventional microscopic images. This analysis takes around 2 minutes per image and can be run in a batch mode. To avoid overcounting blinkings detected in consecutive frames are counted as single by the software. The total number of nanoparticles analyzed was at least 50 for each condition. In the single molecule calibration images, the list of localizations was imported and analyzed by a Matlab script we previously developed to quantify the number of localizations detected for molecule.32. The calibration is extensively described in Supplementary Information Force measurements using optical tweezers. To ensure thermal stability in the sample during optical tweezers measurements, both the trapping and condenser objective temperature were set to 29.2 °C with an absolute accuracy of about 0.2 °C but a precision of 0.001 °C controlled by a temperature feedback system.31 Laser intensity fluctua-



tions were less than 0.01 %.31 The position was measured for 1 min with a sampling rate of 4000 s-1. The optical trap was calibrated using a power spectral density analysis combined with a drag force method using a sinusoidal stage movement. This method allowed us to measure for each individual micromotor with high precision the diameter, the drag coefficient, the trap stiffness, and the displacement sensitivity for all spatial directions.36,37 The trap stiffness averaged over all micromotors was 4.1 ± 0.2 fN/nm (mean ± standard error of the mean, N = 24) with no significant difference between the trap stiffness measured with or without fuel. The precision in the trap stiffness calibration for an individual micromotor was about 3.5 %. The data was analyzed using custom-written Python scripts. The fuel concentration was 100 mM urea. The measurements with fuel started about 4 min after mixing the micromotors with the fuel.

RESULTS AND DISCUSSION Urease-micromotors fabrication and characterization. Two different approaches were used for the fabrication of non-Janus urease-powered micromotors, as depicted in Figure 1A. First, polystyrene (PS) based micromotors were fabricated by conjugating 2 µm amino-functionalized polystyrene microspheres with urease through glutaraldehyde (GA) as linker. Second, SiO2 based micromotors were synthesized by coating the same type of PS microspheres with a SiO2 shell through a previously reported co-condensation method using APTES and TEOS as silica precursors22. The amino groups in the SiO2 surface provided by APTES were used for the conjugation of urease. The catalytic conversion of urea into carbon dioxide and ammonia ((NH2)2CO + H2O CO2 + 2NH3) was used as the power source for active motion generation.19,22 The morphology of micromotors was characterized by SEM imaging (Figure 1B), where an increase in the surface roughness was observed for PS@SiO2 when compared to their PS counterparts. The modifications on the surface charge along with the functionalization process were monitored by measuring the electrophoretic mobility of the particles (Figure S3). Initially, the particles showed a positively charged surface (41.4 ± 1.4 mV, mean ± standard error of the mean, N=9) due to the presence of amino groups on their surface. After the incubation with GA, a shift from positive to negative surface charges (-14.8 ± 2.2 mV) was observed. Upon the addition of urease, the negative charges were slightly reduced, but they remained negative (-9.4 ± 4.4 mV). Additionally, to allow visualization under STORM, 1% of the urease molecules were labelled with the Cy5 fluorescent dye. Figure 1D shows the difference between a conventional fluorescence microscope image, where a diffraction limited fluorescence signal around the microparticle was detected, and a STORM image in which the urease signal was only detected on the surface of the motors with a high spatial resolution. Thus, STORM imaging further confirmed the presence of the enzyme on the micromotors surface. Motion behavior. The dynamics of both types of micromotors in the presence or absence of fuel was analyzed by optical tracking (videos S1 and S2). Figure 2A shows representative trajectories of micromotors in the presence or absence of 100 mM urea, recorded over 20 s at 25 fps. A

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clear increase in the motion of PS@SiO2 based micromotors was observed when compared to their PS counterparts. Using a custom-made Python-based code, the mean squared displacement (MSD) as a function of the time interval (Δt) was approximated by extracting the x-y coordinates from the optical tracking (Figure 2B). In the presence of fuel, the MSD curve of PS@SiO2 showed a parabolic shape, which corresponds to the propulsive regime of an active particle.35 In this case, the MSD is expected to follow equation 2: = 4 + , #2 where D is the diffusion coefficient and v the speed. In the case of passive particles only the linear term remains. The inset of figure 2B shows the result of fitting to = at , with the objective of discerning the type of motion in each condition. An α close to 1 would correspond to a linear MSD (Brownian motion), while an α close to 2 agrees with equation (2) and therefore implies active motion. In all cases values close to 1 were observed, except PS@SiO2 exposed to urea, where a value close to 2 was found. In this case, by fitting the MSD to equation (2) we estimated the speed to be 5.1±0.4 µm/s (mean ± standard error of the mean). These results could be explained by the differences in micromotors surface properties.

Figure 2. Motion behavior of PS and PS@SiO2 based micromotors. A) Representative trajectories of the different types of micromotors in the presence or absence of fuel (100 mM urea) over 20 s. Scale bar=10 µm. B) Averaged MSDs of the different types of motors either in the presence or absence of fuel, obtained from the optical tracking. Inset: Comparison of PS and PS@SiO2 micromotor speed. Results are shown as the mean ± standard error of the mean (N=15).


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Figure 3. Analysis of 3D enzyme distribution on the micromotors surface. A, D) 3D reconstruction of single urease molecules detected by STORM. B, E) 3D density maps obtained by computational analysis of STORM imaging. C, F) Frequency of enzyme density detections per µm2.

For example, surface roughness can increase the motion capabilities of catalytic microswimmers—Janus Pt-Sibased micromotors.38 For those microswimmers, the speed increase was attributed to a higher catalytic surface area of rough micromotors. However, this phenomenon has not been reported so far for enzyme-powered microswimmers. In addition, besides surface topography, a clear asymmetric distribution of the catalyst has been traditionally claimed to be essential for active motion generation. While an enhanced diffusion of nanomotors powered by urease has been reported,20,23 a directional motion of fully coated particles has not been described so far. In the present study, a full coating of the particles with enzymes was expected, since there were no induced structural asymmetries. Surprisingly, upon substrate addition, we observed a clear directional-motion behavior of PS@SiO2 micromotors with a 4-fold increase in speed when compared to control conditions (i.e. without substrate). These results are of special relevance, since a directional propulsion of a micromotor powered by enzyme catalysis has only been reported for Janus spherical architectures. Since the fabrication of spherical Janus micromotors requires expensive and specialized equipment, the present strategy could help to achieve a more efficient development with higher scalability and higher applicability. Due to this finding, it is of utmost interest to better understand the amount and distribution of enzymes on the surface of micromotors and its effect on the motion behavior.

Three-dimensional enzyme distribution analysis. To understand the differences observed with regard to the motion behavior, astigmatism-based STORM imaging39 was used to resolve the three-dimensional distribution of Cy5 labelled urease molecules on the surface of the micromotors. Figure 3A and 3D show representative 3D reconstructions of the z-stacks of the micromotors. Different percentages of labelled enzyme were used for the different types of particles to optimize the visualization of 3D enzyme distribution. The color code indicates the enzyme position along the z-axis. From the 3D enzyme positions obtained by STORM, a computer-aided code was used to present the density distribution map of the enzymes around the particles (Figure 3, B, E). A full and detailed explanation on the code used for the elaboration of density maps can be found in the Supporting Information. Briefly, clustering methods were used to detect the particles and the resulting detections were fitted to a spherical shape. Then, density maps were computed by counting the number of detections within a radius of 144 nm ± 3 nm from each detection point. From the three-dimensional reconstruction, we observed high-density enzyme patches asymmetrically distributed in a random fashion on both types of particles (blue spots). Moreover, the frequency for each enzyme density was quantified (Figure 3C and 3F) and different peaks were observed, indicating a non-homogeneous distribution of enzymes on the micromotor surface. Therefore, an asymmetry is generated from the chemical functionalization without the need of a Janus geometry. This



asymmetry in the enzyme distribution could be sufficient for generating a propulsive force in a stochastic way and

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thus lead to the motion of micromotors.

Figure 4. Quantification of the number of enzymes present on the micromotors surface by STORM imaging and its effect on the motion behavior. A) Quantification of urease molecules present on the surface of PS@SiO2 (top) and PS (bottom) micromotors. B) Number of molecules on PS@SiO2 micromotors functionalized with decreasing amounts of urease (400, 200, 100 and 2 µg/ml of enzyme, from top to bottom, respectively). C) Correlation between the initial amount of enzyme incubated with the particles and the number of molecules per micromotor, detected by STORM. D) Representative trajectories of PS@SiO2 micromotors functionalized with different concentrations of enzymes (scale bar=10 µm) E) MSD (right) of PS@SiO2 micromotors functionalized with different amount of enzymes. F) Speed of micromotors with increasing urease molecules, represented as the mean +/- the standard error of the mean (N=15).


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However, the enzyme distribution alone cannot be the only factor for directed motion, since no significant differences were found for PS and PS@SiO2 based micromotors, whereas they displayed a clear difference in their motion capabilities. Enzyme quantification by STORM. Since the enzyme distribution was an important but not the determining factor in the generation of directional motion, we hypothesized that the differences in surface roughness of our motors could be affecting the enzyme conjugation efficiency. In this sense, knowing, first, the number of enzymes required for motion generation and, second, how the micromotors surface roughness affect enzyme binding moieties might provide useful insights for the fabrication of efficient bio-catalytic micromotors. To quantify the number of enzymes functionalized on the particles surface, we took advantage of the high accuracy and sensitivity in molecule detection by STORM (Figure 3A). We used 2D STORM to study differences in the number of enzymes focusing on the equatorial plane of the motor. Since STORM imaging is reconstructed from the multiple blinks of each dye, a single-molecule calibration was performed to estimate the total number of enzymes in each micromotor, as previously described.32,40 The procedure followed for the calibration is extensively described in Supporting Information. Figure 4A shows a representative image of the two types of micromotors clearly revealing a significantly lower number of localizations in the PS micromotors compared to the PS@SiO2 micromotors. Next to these images, the histograms reporting the number of urease molecules estimated for both populations of micromotors (N=150) is shown. For PS@SiO2, a broad distribution of the number of enzymes per micromotor was observed with a mean number of 942 molecules per micromotor. Surprisingly, the number of enzymes quantified for the PS micromotors was at least 80 times lower, with a mean number of 11 detected enzymes per micromotor. This enormous difference in the number of enzymes per motor may explain the difference in the speed of the micromotors. To further investigate the relevance of the enzyme number for the motion behavior, we fabricated PS@SiO2 motors with a varying concentration of enzymes during the functionalization (400 µg/mL, 200 µg/mL, 100 µg/mL and 20 µg/mL). Next, STORM microscopy was used to image the different micromotors. Figure 4B shows a representative image of each condition. As expected, we observed a decrease in the density of enzymes with decreasing concentrations of enzymes used during the fabrication. The quantification of the number of enzymes per micromotor is represented as a frequency histogram in Figure 4B. The histograms also show that with increasing concentrations, micromotors have more enzymes coupled to their surface with distributions shifting towards larger numbers. Moreover, the mean number of molecules increased linearly with the amount of enzyme used in the functionalization (Fig. 4C). For the tested concentrations, there was no obvious sign of saturation. We next investigated the motion and speed of these different micromotors and correlated them with the number of urease molecules bound to their surface. Figure 4D, 4E and 4F show representative trajectories, MSDs and speeds, respectively, of PS@SiO2 micromotors functionalized with different urease concentrations in the presence of 100 mM

urea. In this case, we observed a non-linear relationship between the number of urease molecules and the speed of micromotors. A threshold number of urease molecules/micromotor was needed to generate active propulsion. Beyond the threshold, micromotors functionalized with a higher enzyme concentration showed no further increase in their speed. Force measurements with optical tweezers. To measure the propulsion force of urease-powered PS@SiO2 micromotors, we used stable, high-resolution optical tweezers.33,34 Optical tweezers act like a 3D Hookean spring. Interestingly, we only observed motor activity in the vertical direction suggesting that micromotors were oriented in the optical trap. For the vertical, laser propagation direction, the force Ftrap is equal to the product of the trap stiffness κtrap and the displacement Δz of the microsphere from the trap center (Ftrap=κ trapΔz) (Figure 5A). Note that the trap stiffness in the vertical direction is smaller compared to the lateral directions. Due to Brownian motion, the trapped microsphere also fluctuates around the trap center in the absence of fuel. After a calibration,36,37 these positional fluctuations can be converted into force fluctuations with a nearly normal distribution (Figure 5B top). The small asymmetry in the distribution is due to an expected asymmetry in trap potential in the z-axis41 not accounted for in the linear calibration. Nevertheless, the standard deviation (SD) of the distribution is hardly affected by the asymmetry. For high-coverage, urease-powered micromotors in the presence of fuel, the fluctuations of holding forces were significantly larger compared to pure Brownian motion resulting in a wider histogram with a larger SD (Figure 5B, bottom). The increased fluctuations are a convolution of active propulsion and Brownian motion, which we cannot separate. However, forces larger than three SDs of the Brownian motion in the absence of fuel are mostly (99.7%) due to active motion. Therefore, a lower estimate for the maximum force the active micromotor can generate is given by the maximum trap force—roughly equal to three SDs of the active force distribution—minus three SDs from the distribution without fuel. We measured this force estimate for micromotors with different enzyme surface coverage and plotted the propulsion force as a function of the urease concentration used during fabrication (Figure 5C). In analogy to the speed measurement (Figure 4E), we observed a threshold behavior with constant forces above the threshold. Below the threshold, forces were not significantly different from zero. Above the threshold, the largest force was 0.17 ± 0.03 pN (mean±standard error of the mean, N=7). Based on Stokes drag, this holding force corresponds to a lower estimate for a maximum swimming speed of about 8 µm/s about twice the speed determined by the MSD analysis (Figure 4F). Since the MSD value is an average including all speeds and the speed estimate based on the holding force is an estimate for the maximum speed, we expected the latter to be larger and of the same order of magnitude compared to the former, which is what we observed. Thus, our tweezers-based measurements are consistent with our MSD measurements.



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PS@SiO2 micromotors when compared to their PS counterparts, which could explain the differences observed with regard to the motion behavior. Lastly, we studied the effect of the enzyme number on the speed and force generation of PS@SiO2 micromotors, which was found to be nonlinear, whereby a threshold of a certain number of urease molecules/micromotor was necessary to break symmetry and generate active motion. Such a threshold may indicate a critical phenomenon in analogy to a phase transition. These results provide a deeper knowledge on the fundamental aspects underlying enzyme-powered motors and might help to establish an efficient design to ensure their applicability in the near future.

ASSOCIATED CONTENT Video S1, PS@SiO2 micromotors Video S2, PS micromotors Details on micromotors surface properties and data processing including Figures S1-S10 (PDF) This material is available free of charge via the Internet at http://pubs.acs.org.

AUTHOR INFORMATION Corresponding Authors Authors *[email protected] *[email protected] ‡These authors contributed equally.

ACKNOWLEDGMENTS ACKNOWLEDGMENTS

Figure 5. Force measurement using optical tweezers. A) Schematic illustration of the force balance between the optical trap (Ftrap) and the micromotor (Fself-propulsion). See text for details. B) Histogram of the trapping force of one ureasepowered micromotor (400 µg/ml) without fuel (top) and with fuel (100 mM urea, bottom). The gray and red shaded region indicate the mean ± 3 SDs without and with fuel, respectively. C) Force with increasing urease molecules, represented as the mean +/- the standard error of the mean.

S.S. thanks the Spanish MINECO for grants CTQ2015-68879-R (MICRODIA) and CTQ2015-72471-EXP (Enzwim). T.P. thanks MINECO for the Juan de la Cierva fellowship. E.S. thanks the Eberhard Karls Universität Tübingen. The authors thank Steve Simmert and Tobias Jachowski for the development of Python scripts to analyse data obtained from optical tweezers. L.A. thanks the Spanish Ministry of Economy, Industry and Competitiveness through the Project SAF2016- 75241-R, by the Generalitat de Catalunya through the CERCA program. The authors also acknowledge the EuroNanoMed II plataform through the project NANOVAX and the foundation Obra Social La Caixa.

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CONCLUSIONS Here, we have demonstrated that directional selfpropulsion can be achieved using non-Janus spherical micromotors powered by enzyme catalysis. The micromotor’s surface properties played a key role in their motion behavior. The excellent single-molecule resolution provided by STORM imaging allowed us to understand the precise three-dimensional distribution of the urease enzymes onto the micromotors surface. We observed a nonhomogenous distribution, regardless of the surface properties of the motors (i.e. PS or PS@SiO2). While this asymmetric distribution could partially explain the selfpropulsion observed in PS@SiO2 micromotors, it was not sufficient, since PS micromotors also showed a nonhomogenous enzyme distribution but they did not display active propulsion. The quantification of enzyme molecules by STORM revealed a significantly higher binding onto

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Figure 1. Fabrication and characterization of urease-powered micromotors. A) Schematic representation of the fabrication and functionalization strategy. B) SEM micrographs of PS and PS@SiO2 based micromotors, showing the differences in surface roughness. C) Comparative image showing a conventional fluorescence analysis of PS@SiO2 based micromotors functionalized with Cy5-labeled urease versus STORM imaging of the same field. 271x169mm (300 x 300 DPI)


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Figure 2. Motion behavior of PS and PS@SiO2 based mi-cromotors. A) Representative trajectories of the different types of micromotors in the presence or absence of fuel (100 mM urea) over 20 s. Scale bar=10 µm. B) Averaged MSDs of the different types of motors either in the presence or ab-sence of fuel, obtained from the optical tracking. Inset: Com-parison of PS and PS@SiO2 micromotor speed. Results are shown as the mean ± standard error of the mean (N=15). 118x186mm (300 x 300 DPI)



Figure 3. Analysis of 3D enzyme distribution on the micromotors surface. A, D) 3D reconstruction of single urease mole-cules detected by STORM. B, E) 3D density maps obtained by computational analysis of STORM imaging. C, F) Frequency of en-zyme density detections per µm2. 303x186mm (300 x 300 DPI)


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Figure 4. Quantification of the number of enzymes present on the micromotors surface by STORM imaging and its effect on the motion behavior. A) Quantification of urease molecules present on the surface of PS@SiO2 (top) and PS (bottom) micromotors. B) Number of molecules on PS@SiO2 micromotors functionalized with decreasing amounts of urease (400, 200, 100 and 2 µg/ml of enzyme, from top to bottom, respectively). C) Correlation between the initial amount of enzyme incubated with the particles and the number of molecules per micromotor, detected by STORM. D) Representative trajectories of PS@SiO2 micromotors func-tionalized with different concentrations of enzymes (scale bar=10 µm) E) MSD (right) of PS@SiO2 micromotors functionalized with different amount of enzymes. F) Speed of micromotors with increasing urease molecules, represented as the mean +/- the standard error of the mean (N=15). 202x234mm (300 x 300 DPI)



Figure 5. Force measurement using optical tweezers. A) Schematic illustration of the force balance between the opti-cal trap (Ftrap) and the micromotor (Fself-propulsion). See text for details. B) Histogram of the trapping force of one urease-powered micromotor (400 g/ml) without fuel (top) and with fuel (100 mM urea, bottom). The gray and red shaded region indicate the mean ± 3 SDs without and with fuel, respectively. C) Force with increasing urease molecules, represented as the mean +/- the standard error of the mean. 138x258mm (600 x 600 DPI)


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Influence of Enzyme Quantity and Distribution on the Self-Propulsion

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