Gear-like MOF microrobots for single cell mechanotransduction of microvilli

Domain: chemistry
Published: 2026-02-26
Editorial outcome: minor_revision

Abstract
Cellular mechanotransduction, mediated by specialized structures such as microvilli, regulates processes ranging from tissue homeostasis to disease progression. Existing tools for microvilli-specific biomechanical intervention suffer from limited spatiotemporal precision and non-physiological constraints, restricting mechanistic studies and targeted therapies. Here, we develop a magnetically driven gear-like metal-organic framework microrobot (MOFbot) for programmable mechanical manipulation of single-cell microvilli. MOFbots are fabricated through epitaxial growth of heterogeneous MOF structures followed by deposition of Ni/Au nanofilms. Under a rotating magnetic field, they perform rolling and obstacle negotiation. Their rotating gear structure entangles microvilli, exerting quantified pulling forces via Förster resonance energy transfer and traction force microscopy. This mechanical stimulation triggers intracellular calcium influx and enhanced focal adhesion kinase phosphorylation, indicating mechanotransduction pathway activation. Consequently, rotating MOFbots increase membrane permeability, enabling on-demand transmembrane delivery of therapeutics into targeted single cells. This work establishes a targeted cellular mechanomodulation strategy and informs future micro/nanorobotic biomedical designs.

Introduction
Cells exist within dynamic mechanical microenvironments where external forces regulate essential processes through mechanotransduction pathways1, 2. These mechanical stimuli are converted into biochemical signals via mechanosensitive ion channels (e.g., Piezo1, TRPV4)3, 4, cytoskeletal reorganization5, and nuclear mechanotransduction mechanisms (e.g., YAP/TAZ signaling)6, 7, ultimately shaping tissue homeostasis and disease progression. Among cellular surface microstructures, microvilli emerge as force-transducing scaffold8. For instance, brush-border microvilli in renal proximal tubules convert hydrodynamic torque into cytoskeletal rearrangements to regulate ion transport9, while immune cell microvilli probe antigen-presenting surfaces to initiate T-cell activation10. Quantitative studies reveal that fluid-induced torque at microvillar tips generates a ~40-fold mechanical amplification at cortical actin anchoring points11, underscoring their role as biological force multipliers. Despite this significance, current tools still lack spatiotemporal precision to dissect their mechanotransduction dynamics. Developing methodologies for localized mechanical perturbation of microvilli is thus essential to unravel their mechanobiological contributions to tissue homeostasis and disease12, 13.

Bulk fluid shear stress has been used to study the effect of microvilli mechanics on cell mechanoresponses14, 15, while nanoscale geometric confinement (e.g., 200-nm nanopores) is capable of imposing non-specific mechanical constraints and altering mechanical signaling outcomes through artificial geometric restrictions, contributing to the enhancement of T-cell receptor clustering and activation16. Nevertheless, these methods cannot selectively target microvilli subpopulations or replicate physiological force dynamics. Recent advances in micro/nanorobotics offer transformative solutions through energy-converting actuators capable of drug delivery17, 18, microsurgery19-21 and cell manipulation22-24. Among them, magnetic micro/nanorobots stand out due to their contactless manipulation capability, real-time controllability and inherent miniaturization25, 26, all of which are aligning with the demands of precision mechanobiology. Unlike substrate-based methods, untethered magnetic micro/nanorobots could deliver programmable mechanical stimuli to specific microvilli without physical confinement artifacts. However, no existing platform integrates targeted mechanical actuation, physiological compatibility, and multifunctional operation for microvilli mechanical modulation. Bridging this gap requires a miniature device capable of applying spatially resolved forces while maintaining native cellular microenvironments, a challenge central to advancing mechanotherapeutic innovation.

Herein, we present a magnetic gear-like MOFbot capable of targeted regulation of cellular mechanics through precise manipulation of microvilli substructures. The MOFbots featured with gear-like shape are constructed by epitaxial growth of heterogeneous MOF, and subsequently coated with Ni and Au layers. By applying a rotating magnetic field within different directions, two distinct motion modes of rolling and climbing can be achieved. Taking advantage of the gear-like structure, the MOFbots can effectively engage and entangle with microvilli on the cell membrane, enhancing its adhesion stability and mechanical interaction with living cells under magnetic field. Thus, the gear-like MOFbot can directly apply mechanical stimulation onto HeLa cells, leading to increased level of tractive force and intracellular calcium signaling. By remotely navigating the gear-like MOFbots to target single cancer cell and stimulate the microvilli mechanically, the membrane’s permeability can be effectively altered, facilitating targeted intracellular delivery of anti-cancer drugs preloaded within the MOFbots’ porous framework. Therefore, the multifunctional MOFbots demonstrate tremendous potential for programmable mechanical manipulation of cellular membrane mechanics and as well as targeted mechano/chemo-synergic therapy.

Results
Fabrication and characterization of magnetic gear-like MOFbot

We constructed a hybrid nanostructure by growing zinc-based MOF materials (ZIF-8) at the specific corner regions of a titanium-based MOF crystals (MIL-125), using a controlled method known as site-selective growth. This enabled the controlled formation of a heterogeneous MOF composite, MIL‐125@ZIF‐8, whose gear-like structural features are illustrated schematically in Fig. 1a. Afterwards, we deposited Ni and Au films onto the hybrid MOF composite using electron beam evaporation, resulting in a gear-like magnetic microrobot, termed a MOFbot. More specifically, the MIL-125 MOF with a tetragonal shape was first prepared using a solvothermal approach. Field-emission scanning electron microscopy (SEM) images (Fig. 1b) reveal the well-dispersed MIL-125 nanoparticles with a side length of approximately 1 μm. ZIF-8 MOF was then grown directly onto the four corners of MIL-125, resulting in the formation of MOF-on-MOF (MIL-125@ZIF-8) heterostructures. SEM and fluorescence images (Fig. 1c and Supplementary Fig. 1) of the synthesized MIL-125@ZIF-8 display a distinct and uniform gear-like morphology. The line scanning profile and elemental mapping (Supplementary Fig. 2) reveal that Zn element from ZIF-8 is concentrated at these corners, while Ti element from MIL-125 is located within the central core.

The Fourier transform infrared (FTIR) spectra (Fig. 1d) of MIL-125@ZIF-8 shows bands at 2903 cm-1, 1262 cm-1, and 1526 cm-1, corresponding to the CH group of 2-Methylimidazole in ZIF-8 and the carboxyl group of aminoterephthalate in MIL-125, respectively. The nitrogen (N2) adsorption/desorption isotherms (Fig. 1e, Supplementary Fig. 3 and Supplementary Table 1) confirm the microporous nature of MIL-125@ZIF-8 and the specific surface area was identified to be 856.2 m2/g, indicating its potential for drug loading applications. As shown in Fig. 1f, powder X-ray diffraction (XRD) analysis verified the high crystallinity of the prepared ZIF-8, MIL-125 and MIL125@ZIF-8. Furthermore, enlarged XRD patterns (Fig. 1g) demonstrate that the lattice parameters of the MIL-125 remain unchanged during the growth of ZIF-8. However, in comparison to the diffraction peaks of individual ZIF-8 and MIL-125, the peaks corresponding to the {110} and {002} crystal planes of ZIF-8 in MIL-125@ZIF-8 show slight right shifts, suggesting lattice modifications in ZIF-8 as it integrates with MIL-12527.

To impart magnetism to the as-prepared MIL-125@ZIF-8, a 50 nm Ni layer was first deposited on its surface by electron beam evaporation, followed by a 10 nm Au layer to enhance biocompatibility. As shown in Fig. 1h and 1i, the resulting MIL-125@ZIF-8@Ni@Au structure retains the original gear-like morphology of MIL-125@ZIF-8. Elemental mapping confirms that the Ni and Au layers uniformly were coated on the surface, covering the entire structure and validating the successful fabrication of the magnetic gear-like MOFbot.

Controlled motion performance of magnetic gear-like MOFbot

The locomotion of the gear-like MOFbot was actuated by rotating magnetic fields generated by a custom-built magnetic field generator. This setup allowed for real-time and dynamic control over the magnetic field’s strength, frequency and direction through modulation of the input current parameters, thereby enabling precise regulation of the MOFbot’s motion, including its speed, direction, and trajectory. (Fig. 2a).

Under a horizontally applied rotating magnetic field, the MOFbot exhibited a distinct self-rotating behavior, as captured in the time-lapse images from Supplementary Movie 1 (Fig. 2b, upper row). The rotational dynamics were critically dependent on the frequency of the magnetic field at a fixed strength of 6 mT (Fig. 2c). At frequencies below a critical threshold of 7 Hz, the rotational rate of the MOFbot increased proportionally with the field frequency. In this regime, the magnetic torque applied to the MOFbot was sufficient to overcome the viscous resistance of the fluid medium. However, when the frequency exceeded this critical point (7 Hz), the rotational speed began to decline with further increases in field frequency. This phenomenon occurred because the available magnetic torque could no longer keep pace with the rapidly increasing fluid resistance, leading to asynchronous motion between the MOFbot and the driving field. This critical threshold, known as the step-out frequency, is a key performance parameter that is highly sensitive to the viscosity of the surrounding liquid.

In contrast, when subjected to a vertically rotating magnetic field, the MOFbot demonstrated efficient rolling and translational propulsion, as shown in Supplementary Movie 2 and the corresponding time-lapse images (Fig. 2b, lower row). Similar to the rotational behavior, the translational speed was a function of the external magnetic field frequency. It increased from 1.01 μm/s at 1 Hz to 6.45 μm/s at 6 Hz (Fig. 2d), showcasing controllable directional movement.

To gain insight into the underlying hydrodynamic interactions, COMSOL Multiphysics 6.1 simulations were employed to analyze the fluid velocity profiles and stress distributions on the surface of the rotating gear-like MOFbot. The simulations revealed that the flow velocity peaked at the four corners of the MOFbot, corresponding to the points of maximum linear velocity during rotation (Fig. 2e). Both the induced flow velocity and the associated stress on the MOFbot surface were influenced by the magnetic field parameters, reaching maximum values of approximately 46 μm/s and 0.55 Pa, respectively, at the critical frequency of 7 Hz (Fig. 2f, Supplementary Fig. 4). The concentration of stress at the corners and the optimal hydrodynamic output at this step-out frequency establish a functional threshold for biomedical applications. The maximized shear forces and pressure generated at 7 Hz are particularly relevant for potential applications such as regulating cell membrane permeability or triggering mechanosensitive signaling pathways in drug delivery and cellular mechanotransduction.

Furthermore, to evaluate the locomotion performance of the gear-like MOFbot on complex surfaces, microtopographical patterns with a constant cross-sectional profile were fabricated using a laser direct writing (LDW) system (Fig. 2g-i). A platform with a height of 1.5 μm and a 90° inclination angle served as a barrier to test climbing capability. As demonstrated in Fig. 2j and Supplementary Movie 3, under the actuation of a vertically rotating magnetic field (6 mT, 6 Hz), the gear-like MOFbot successfully navigated the three-dimensional structured surface and overcame the vertical obstacle via a “climbing-stairs” effect. The instantaneous speed recorded during this maneuver reached a maximum of approximately 9 μm/s (Fig. 2k). In a control experiment, spherical microrobot (i.e., PS@Ni@Au fabricated by depositing Ni and Au layers on polystyrene microspheres) failed to traverse the same obstacles (Fig. 2l, Supplementary Fig. 5-6, Supplementary Movie 4). This comparative result underscores that the unique gear-like morphology significantly enhances the motion adaptability and terrain negotiation capabilities of the MOFbot, which is crucial for practical applications in complex biological environments.

Interaction between the magnetic gear-like MOFbot and cell microvilli

The cell membrane features intricate structures, among which microvilli stand out as actin-based membrane protrusions with versatile functions, including nutrient absorption, secretion, and mechanotransduction. Confocal laser scanning microscope (CLSM) and SEM images have revealed an abundance of microvilli on the surface of HeLa cells (Supplementary Fig. 7). We labeled the gear-like MOFbots with the green fluorescent dye (FITC) and then observed the interaction between the gear-like MOFbots and cellular microvilli using CLSM. As shown in Fig. 3a and Supplementary Movie 5, the green fluorescent gear-like MOFbots were embedded within the red fluorescent cellular microvilli. Additionally, in the 3D view of CLSM, the green fluorescent MOFbots appear yellow due to their colocalization with the red fluorescent microvilli on the cell membrane. SEM image also clearly shows that the gear-like MOFbots were intertwined with the microvilli, residing within their internal structure (Fig. 3a). These results confirm that, under a magnetic field, the gear-like MOFbots exerted significant mechanical interactions with the microvilli on the cell membrane, including pulling and intertwining.

In contrast, under static conditions with no applied magnetic field, the MOFbots remained passively adhered to the cell surface and showed no observable entanglement with microvilli (Fig. 3b). To directly test the role of morphology in microvilli entanglement, we performed shape-control experiments using spherical microrobots fabricated by coating polystyrene microspheres with sequential layers of Ni and Au. Although these spherical microrobots exhibited continuous rotation when subjected to the same magnetic actuation, both CLSM and SEM images confirmed their inability to entangle with microvilli (Fig. 3c). In both experimental scenarios including absence of motion or non-geared morphology, the microrobots predominantly reside on the surface of the microvilli, with minimal evidence of intertwining or deep interaction. The differences from the dynamically rotating gear-like MOFbots demonstrates that both magnetic field-driven motion and gear morphology are essential for achieving biologically significant mechanical coupling with cellular structures.

To quantitatively analyze the distribution of gear-like MOFbots within cellular microvilli, the Unet++ network model (Fig. 3d) was employed for semantic segmentation of 2D slices (z-stacks) from 3D confocal image volumes. This enables precise extraction of target cell regions and voxel-level identification, distinguishing cell structures from background. Following segmentation, feature parameters, namely saturation, color gamut, and brightness, were quantified across the entire 3D volume at the voxel level. This enabled voxel-wise classification, including discrimination between red and green staining signals, as well as normalized statistical evaluation of their spatial distribution. The details about the Unet++ network model are provided in Supplementary Information. This results further demonstrate that the gear-like MOFbots, owing to their distinctive structure and autonomous properties, engage in mechanical interactions with the microvilli, facilitating deep penetration and interfacial coupling within the cellular microvilli network (Fig. 3e-g and Supplementary Fig. 8). This dynamic interaction enables precise spatiotemporal control over mechanical stimuli directly applied onto the cell.

Analysis of mechanical stimulation on microvilli via magnetic gear-like MOFbot

Cells utilize traction forces to sense mechanical cues in their environment. As shown in Fig. 4a, the physical characteristics of the traction forces exerted by the magnetic gear-like MOFbot on the microvilli of HeLa cells were quantified using traction force microscopy (TFM). In this approach, HeLa cells were cultured on polyacrylamide (PA) gels embedded with fluorescent beads as tracking markers. The lateral deformation changes of the PA gels can be accurately tracked and measured as a reflection of the traction forces applied by the magnetic gear-like MOFbot onto the HeLa cells. We performed the imaging of cells before and after treatment with the magnetic gear-like MOFbot. The displacement of fluorescent beads and the traction stress exerted by cells in response to the magnetic gear-like MOFbot, both in the absence and presence of a magnetic field, were calculated using TFM, as described in previous studies28. As shown in Fig. 4b and 4c, when the gear-like MOFbots remain stationary, the fluorescent beads embedded within the PA gels exhibit minimal displacement, measuring approximately 0.12 μm. However, when the gear-like MOFbots rotate on the cell’s surface and engages with the microvilli, the displacement of the fluorescent beads dramatically increases to approximately 0.88 μm, correlating with mechanical engagement of cellular microvilli. Corresponding traction stress maps (Fig. 4b, d) further demonstrated that rotating MOFbots induce localized mechanical stresses averaging 1.5 kPa via microvilli entanglement, a magnitude 15-fold greater than stationary controls (~0.1 kPa). This mechanical disparity directly confirms that the rotational motion of the gear-like MOFbots generates significant traction forces at the interface between the cell and the MOFbots.

To verify the role of cellular microvilli in force transduction, we performed siRNA-mediated knockdown of ezrin, which is a key scaffolding protein essential for microvilli integrity. Transient transfection of HeLa cells with ezrin-specific siRNA led to efficient suppression of ezrin expression, which in turn induced pronounced destabilization of microvillar protrusions. As a consequence, ezrin-depleted cells exhibited a substantial reduction in microvilli density. This morphological impairment was corroborated by both SEM and CLSM analyses, which consistently demonstrated a marked loss of surface microvilli relative to control cells (Supplementary Fig. 9). Under identical MOFbots stimulation, these microvilli-deficient HeLa cells exhibited significantly attenuated bead displacement and traction stress (Fig. 4b-d). These results demonstrate that mechanical forces are transmitted via microvilli to the underlying substrate, confirming the essential role of microvilli in mediating the physical interactions between MOFbots and cellular structures.

In addition, an actin tension probe based on Förster resonance energy transfer (FRET) technology29 was employed to detect intracellular forces exerted by both stationary and rotating gear-like MOFbots. As illustrated in Fig. 4e, the probe consists of a dipole orientation-based FRET sensor positioned between β-actin monomers. This design enables real-time visualization of mechanical perturbations transmitted through the actin cytoskeleton. Under mechanical loading, the relative orientation of the donor-acceptor dipole pair is rotated from an approximately parallel alignment toward a more diagonal configuration, resulting in a characteristic decrease in FRET efficiency. This geometric transition provides a sensitive readout of forces acting on the actin filaments. To quantitatively evaluate the mechanical influence of gear-like MOFbots on HeLa cells, we monitored temporal changes in FRET efficiency under controlled magnetic actuation. Progressive extension of the magnetic field switching duration produced a monotonic decline in FRET efficiency, indicating a significant mechanical interaction between the gear-like MOFbots and the cells under magnetic influence. Upon cessation of the magnetic field, FRET signals gradually returned to baseline, demonstrating the reversibility of the mechanical perturbation and the dissipation of MOFbot-induced forces (Fig. 4f, 4h and Supplementary Movie 6). Statistical analysis further confirmed the mechanical contribution of MOFbot rotation. Cells exposed to rotating gear-like MOFbots exhibited significantly lower FRET efficiencies, which correspond to higher intracellular traction forces, compared with cells incubated with stationary counterparts (Fig. 4i). These results collectively demonstrate that the rotational dynamics and gear-like architecture of MOFbots enable effective transmission of mechanical cues into the actin cytoskeleton, thereby eliciting measurable intracellular force responses. Critically, in control experiments performed under identical conditions without magnetic fields, the FRET efficiency in HeLa cells remained stable over time (Supplementary Fig. 10), effectively excluding contributions from spontaneous cellular deformation or photobleaching.

In addition, FRET efficiency measurements in microvilli-deficient HeLa cells revealed markedly reduced molecular tension and a lack of response to MOFbot-induced mechanical stimulation, further corroborating that the disruption of microvilli leads to a significant decrease in cellular traction forces and abolishes the cells’ ability to transduce mechanical cues (Fig. 4g-i). These findings are consistent with the cell traction force measurements, reinforcing the critical role of microvilli in mediating cellular mechanotransduction. Moreover, treatment of HeLa cells with cytochalasin D, which is a known actin-disrupting agent, also induced microvilli collapse and cytoskeletal disorganization (Supplementary Fig. 11). Under these conditions, both TFM and FRET measurements showed nearly complete abrogation of MOFbot-induced traction forces and intracellular force responses (Supplementary Fig. 12, 13). These results demonstrate that an intact actin cytoskeleton including microvilli is indispensable for the force transmission elicited by MOFbot stimulation. These complementary lines of evidence confirm the essential role of microvilli in mediating the physical interactions between MOFbots and cellular structures.

Overall, the rotational movement of the gear-like MOFbots interacting with the cell microvilli generated a significant mechanical effect on the cells. This dynamic interaction not only induced considerable traction forces but also influenced the mechanical properties of the cell membrane, indicating that the MOFbots can actively modulate cellular behavior. These findings underscore the potential of gear-like MOFbots as miniaturized artificial tool for manipulating cellular mechanics.

To further investigate the mechanical impact of rotating MOFbots on cellular microvilli, we developed a three-dimensional solid mechanics model in COMSOL Multiphysics 6.1. In the model, each microvillus was idealized as a 2 µm-long, 100 nm-diameter cylinder fixed to the surface of a hemispherical cell and assigned hyperelastic material properties, while the MOFbot was treated as a rigid body due to its higher structural stiffness (Supplementary Fig. 14). The MOFbot was set to rotate at 6.8 r/s, producing transient contact along its rotational trajectory. Contact interactions were simulated using a penalty-based contact mechanics approach, capturing microvillus bending, lateral deflection, and strain distribution. Geometric nonlinearity was included to ensure numerical stability under large deformations. Simulation results reveal pronounced lateral bending of the microvillus upon MOFbot contact, with traction concentrated from the proximal to middling regions, consistent with forced-bending morphology (Supplementary Fig. 15a). Surface integration of traction magnitudes estimated a maximum contact force of ~46 pN (Supplementary Fig. 15b), which emerges naturally from the MOFbot’s rotational kinematics (tangential velocity ~4.3 × 10-5 m/s). These results corroborate experimental observations, indicating that MOFbot rotation can locally perturb microvilli, inducing deformation and intracellular force transmission.

Influence of the mechanical stimulation on calcium influx and phosphorylation levels of focal adhesion kinase (FAK)

Since mechanotransduction is closely linked to changes in calcium ion influx, the effects of magnetically induced mechanical forces on cells can be effectively monitored through fluorescence changes in the highly sensitive Ca2+ indicator (pCMV-jGCaMP7s) (Fig. 5a). To investigate the contribution of specific mechanosensitive channels to MOFbot-triggered Ca2+ influx, we pharmacologically inhibited PIEZO1 and TRPV4, which are two mechanoreceptors implicated in distinct force-sensing regimes. PIEZO1 generates rapid mechano-activated cation currents, while TRPV4 confers sensitivity to piconewton-scale forces. HeLa cells were pretreated with GsMTx4 (PIEZO1 inhibitor) and HC-067047 (TRPV4-specific antagonist). Time-lapse images of representative untreated control cell (Supplementary Movie 7, Fig. 5b) under MOFbot stimulation revealed a progressive increase in jGCaMP7s fluorescence intensity over time, corresponding directly to elevated cytosolic Ca2+ levels and thus demonstrating active calcium influx in response to mechanical force. Quantitative analysis confirmed that, in untreated control cells, the mean Ca2+ fluorescence intensity at t = 480 s was approximately twice that at baseline (t = 0 s) (Fig. 5c). In contrast, cells pre-incubated with either inhibitor exhibited markedly attenuated Ca2+ responses, with only negligible changes in fluorescence intensity over the same period. To assess population‑level behavior, Ca2+ influx heat maps were generated for 50 cells under each of three conditions: untreated control, GsMTx4‑treated, and HC‑067047‑treated (Fig. 5d-f). Each horizontal line represents the time‑dependent intracellular Ca2+ change in an individual cell. In control cells, despite the cell-to-cell variation of calcium response, the majority of cells exhibited a significant increase in cytosolic Ca2+ concentration following mechanical stimulation (Fig. 5d). Conversely, more than 90% of cells pretreated with either inhibitor displayed suppressed Ca2+ signals, with minimal fluorescence elevation over time (Fig. 5e, f). Collectively, these results demonstrate that gear-like MOFbots elicit a robust intracellular calcium response, and that both PIEZO1 and TRPV4 channels are essential for efficient Ca2+ entry upon MOFbot-mediated mechanical stimulation.

To further elucidate the spatial redistribution of the mechanosensitive channels in response to force, we performed immunofluorescence staining for PIEZO1 and TRPV4 following MOFbot stimulation. HeLa cells incubated with MOFbots were exposed to a rotating magnetic field to apply localized mechanical force, with parallel control samples maintained without magnetic actuation to isolate the effect of the force itself. After fixation, cells were immunostained for PIEZO1 and TRPV4, with microvilli stained using phalloidin and nuclei labeled with DAPI. CLSM imaging revealed a force-dependent redistribution of both channels. In the absence of magnetic actuation, PIEZO1 and TRPV4 signals were diffuse along the plasma membrane (Supplementary Fig. 16a and Supplementary Fig. 17a). In contrast, upon MOFbot actuation, both channels exhibited pronounced translocation and concentrated localization to microvilli structures (Supplementary Fig. 16b, 17b). This enhanced co-localization with microvilli under mechanical stimulation indicates an active recruitment process, aligning with their functional role in microvilli-mediated mechanotransduction.

FAK undergoes autophosphorylation at tyrosine 397 in response to cytoskeletal contraction, thereby transmitting mechanical stimuli to a range of downstream signaling cascades30. We analyzed the phosphorylation levels of FAK at tyrosine 397 (pFAK) in HeLa cells treated with rotating gear-like MOFbots using immunofluorescence techniques. As shown in Fig. 5g, h, pFAK levels in HeLa cells were significantly elevated due to the mechanical forces exerted by the gear-like MOFbots. These findings suggest that the motion of the MOFbots indeed induced significant intracellular tension, which could further potentially deform the membrane structure of the cell.

Single cell targeted intracellular drug delivery by gear-like MOFbots

Having established that MOFbot-mediated mechanical forces on microvilli activate mechanotransduction pathways (e.g., Ca²⁺ signaling and FAK phosphorylation), we next investigated whether such mechanical perturbations could directly regulate the physical properties of the plasma membrane. Previous studies suggest that localized mechanical stress can induce lipid bilayer fluidization and transient pore formation, thereby enhancing membrane permeability31, 32. As a proof-of-concept therapeutic application, we maneuvered the magnetic gear-like MOFbot to approach a targeted cell, climbing onto the cell surface to interact with the cellular microvilli, then utilized the mechanical forces exerted by the MOFbots to enhance the transmembrane delivery of loaded substances into the targeted cell (Fig. 6a). Firstly, the biomechanical transformations were theoretically validated through coarse-grained molecular dynamics (CGMD) simulations (Fig. 6b and Supplementary Fig. 18). As time prolonged, magnetic field-driven rotation of MOFbots induced progressive disordering of phospholipid packing at microvilli interfaces. The phenomenon not only confirms programmable mechanotransduction at cellular interfaces but also introduces a magnetically controllable strategy for efficient transmembrane cargo delivery. Subsequently, a typical experiment demonstrating the locomotion of a gear-like MOFbot as it moves toward, climbs onto and rotates on a cell is shown in Supplementary Movie 8. The corresponding optical images and the plot of instantaneous velocity versus time are shown in Fig. 6c, d. The whole process is divided into three stages as follows. ⅰ) Targeting the cell: in the initial stage, under the influence of the vertical rotating magnetic field, the gear-like MOFbot approaches the target cell at an average speed of 3.5 μm/s. ⅱ) Contacting and climbing onto the cell surface: at t=12 s, the MOFbot makes contact with the cell. Driven by the magnetic field, it then climbs onto the cell surface. ⅲ) Linear movement on the cell surface: at t=14 s, the MOFbot moves across the cell surface at an average speed of 9 μm/s. Though velocity fluctuations were observed, a phenomenon similar to that recently reported with microparticles propelled on surfaces with microtopographies33, an enhanced translational velocity was noted when moving on the cell surface. In our experiment, the adherent cells generated a surface topography that increased friction, thereby facilitating the rolling motion of the MOFbot and resulting in similar observations. At t=24 s, the magnetic field direction is switched to the x-y axis, prompting the MOFbot to rotate on the cell surface and engage with the cell’s microvilli.

To investigate the effect of mechanical stimulation exerted by the gear-like MOFbot on cell membrane permeability, propidium iodide (PI) is selected as a model drug molecule. PI is a red fluorescent dye that cannot cross the intact membrane of live cells, making it an ideal probe for studying transmembrane transport. Thus, by using PI, we can directly observe how the mechanical action of the MOFbots influences the permeability of the cell membrane. As shown in Fig. 6e, f and Supplementary Movie 9, upon rotation of the gear-like MOFbot on the cell surface, PI fluorescence first appears in the cell nucleus. Over time, the intensity of the red fluorescence in the nucleus progressively increases. By 60 s, the fluorescence inside the cell stabilizes. This result confirms that the MOFbot’s rotational movement likely alters the cell membrane’s permeability, allowing PI to enter the intracellular environment to bind with DNA in the nucleus. Both CLSM and flow cytometry results consistently show that the PI fluorescence intensity in cells with magnetic field activation is more than 8 times higher than the static control group (Supplementary Fig. 19, 20). To exclude the possibility that increased cell membrane permeability is due to cell death, cell viability is assessed using the CCK-8 assay after treatment with the gear-like MOFbots. As shown in Supplementary Fig. 21, under both magnetic and non-magnetic field conditions, the cell viability remained above 90%, even at a MOFbot concentration of 320 μg/mL. This indicates that the motion of the MOFbots has negligible impact on cell viability within the time period under study. These findings confirm that the gear-like MOFbot effectively enhances the permeability of the cell membrane.

Due to its porous structure, the gear-like MOFbot is highly efficient in adsorbing and carrying a wide range of small molecules. As a result, we successfully loaded the anticancer drug doxorubicin (DOX) into the MOFbot’s nanopores, achieving a drug loading capacity of approximately 321 μg/mg (Supplementary Fig. 22, 23). We investigated the impact of the autonomous movement of gear-like MOFbots on the release behavior of DOX in vitro. The results revealed a significant increase in DOX release when the MOFbots were rotated by a magnetic field, as compared to the group without magnetic field, which indicates that the autonomous motion of the gear-like MOFbots could accelerate DOX release (Supplementary Fig. 24), providing feasibility of remotely controlled drug release at targeted site. Subsequently, we then assessed the effect of the MOFbot’s motion on DOX uptake by the cells. As shown in Fig. 6g and Supplementary Fig. 25, the DOX fluorescence intensity inside cells treated with magnetic-field-driven gear-like MOFbots is approximately 2.8 times higher than in cells treated with stationary MOFbots. Moreover, at the same concentration of MOFbots, cell viability in the rotating MOFbot group under the magnetic field is consistently lower than that in the control group, which is treated with stationary MOFbots (Fig. 6h). Take together, these results provide clear evidence that the motion of the gear-like MOFbots significantly enhances transmembrane drug delivery. Meanwhile, the successful internalization of cargo molecules serves as a functional readout of microrobot-generated mechanostimulation. The observed changes in cell membrane permeability can be attributed to two main factors: First, the gear-like MOFbot, driven by magnetic forces under the influence of the magnetic field, interacts with the microvilli on the cell surface. This interaction based on mechanical forces may stretch and deform the microvilli, leading to structural alterations in the cell membrane. These changes in membrane structure likely affect its flexibility and elasticity, thereby influencing its permeability5, 34. Furthermore, the plasma membrane plays a key role in cellular mechanical responses via mechanosensitive ion channels. These channels, typically proteins embedded in the cell membrane, undergo conformational changes in response to local forces. Such forces can arise from increased membrane tension, alterations in membrane curvature, or mechanical forces exerted by the cytoskeletal network and/or the extracellular matrix on the channels35, 36. Therefore, the mechanical movement of the gear-like MOFbot may activate these mechanosensitive ion channels, ultimately regulating the permeability of the cell membrane and facilitating transmembrane drug delivery.

Methods
Materials and characterization. Titanium isopropoxide (TPOT, 97.0%), aminoterephthalate (99%), zinc nitrate hexahydrate (Zn(NO3)2.6H2O, 99.0%), 2-methy-limidazole (HmIm, 98%), methanol (MeOH, 99.5%), N,N-dimethylformamide (DMF, 99.9%), acrylamide, bis-acrylamide, ammonium persulfate (APS), tetramethylethylenediamine (TEMED), phalloidin-TRITC, lipo8000 transfection reagent, plasmid, propidium iodide (PI), rabbit monoclonal anti-phospho-FAK (Tyr397), goat anti-rabbit Alexa Fluor 488 were purchased commercially and used as-received. HeLa cells (Lot: BNCC342189) were purchased from BeNa Culture Collection, China. Scan electron microscope (SEM) images and energy dispersive X-ray (EDX) spectroscopy were captured by Carl Zeiss Microscopy GmbH Gemini SEM 300 and Oxford instruments X-Max, respectively. Powder X-ray diffraction (XRD) patterns were characterized by Rigaku D/max-2500PC diffractometer (Tokyo) at 40 kV, 200 mA. Nitrogen adsorption studies were performed with Micromeritics ASAP 2020 HD88 at 77 K up to 1 bar. Prior to sorption measurements, samples were activated under vacuum at 100℃ for 12 h. FT-IR spectra was analyzed by a Fourier-transformed infrared spectrometer (Nicolet380, Thermo). An electron beam (e-beam) evaporation system (Beijing Technol Science Co.,Ltd. ;TEMD500) was used to coat a layer of material (Au, Ni) on the surface of the MIL-125@ZIF-8. Optical videos were taken by a Leica inverted optical microscope (Leica DMi8) with a 40× air objective. Fluorescence images of HeLa cells were taken by confocal laser scanning microscope (CLSM, NiKon A1).

Preparation of experimental device. As shown in Fig. 2a, the reservoir was placed at the center of a home-made triaxial Helmholtz coil, which can create a rotating magnetic field. The sine-wave current was generated using an adjustable function generator and signal amplifier, then input into orthogonally arranged Helmholtz coils to produce a uniform rotating magnetic field in any desired direction. The iron core surrounding by coil enhanced the strength of the generated magnetic field. In addition, the direction of rotation axis, rotational frequency and field strength can be regulated. The coil system was installed on Leica optical inverted microscope equipped with a 40× objective lens.

Synthesis of NH2-MIL-125. TPOT (0.6 mL) and aminoterephthalate were dissolved in a mixture of DMF (36 mL) and CH3OH (4 mL). After stirring for 5 min, the mixture was transferred to 100 mL Teflon-line autoclave and heated at 150℃ for 24 h. The obtained yellow solid was isolated by centrifugation, washed for three times with a mixture DMF and CH3OH at the volume ratio of 9:1 and finally dispersed in methanol (30 mL) to make a stock solution for further use.

Synthesis of gear-like NH2-MIL-125@ZIF-8 particles. 0.4 mL of stock solution containing NH2-MIL-125 was added into 6 mL 2-Methylimidazole (25 mM) and 10 mL Zn(NO3)2.6H2O (25 mM). The mixture was allowed to react for 4 h at room temperature. The product was collected by centrifugation and washed for three times with methanol.

Synthesis of magnetic gear-like NH2-MIL-125@ZIF-8 MOFbots. The previously prepared NH2-MIL-125@ZIF-8 particles were suspended in methanol. Then, 80 µL of methanol suspension containing NH2-MIL-125@ZIF-8 particles were dropped onto the glass slide (Φ = 40 mm) which was cleaned by sonication and treated with oxygen plasma for 5 min. A layer of 10 nm Ni and 10 nm Au were then sequentially sputtered onto the NH2-MIL-125@ZIF-8 microparticles using an electron beam (e-beam) evaporation system. The formed NH2-MIL-125@ZIF-8@Ni@Au MOFbots were collected by sonication in deionized water and centrifugation.

Magnetic actuation and motion analysis. The magnetic actuation system used for driving the gear-like MOFbots comprises five electromagnetic coils, each with 600 turns of 0.4 mm diameter wire and an 8 mm diameter ferrite core to enhance magnetic field strength. A programmable power supply controlled by a myRIO embedded system was employed to generate the coil input signals, allowing flexible and reproducible application of magnetic fields during experiments. The solution containing the magnetic gear-like MOFbots was dropped into the glass slide which was placed in the center of electromagnetic coil pairs (magnetic strength: 6 mT). Horizontal and vertical rotating magnetic fields with different frequency (1 Hz-9 Hz) were employed to power the magnetic gear-like MOFbots. The movements of the gear-like MOFbots were recorded by a charge coupled device (CCD) camera at the frame rate of 20 fps and analysed via python script.

Interaction of magnetic gear-like MOFbots with HeLa cells. The HeLa cells were cultured with DMEM containing 10% FBS and 1% antibiotics. Then, HeLa cells (~5×105 cells mL-1) were seed on a glass substrate which can be mounted in the center of magnetic field. 10 μL of magnetic gear-like MOFbots were added to the glass substrate and manipulated on the surface of HeLa cell under rotating magnetic field. The movement of the magnetic gear-like MOFbots on the surface of HeLa cells and the permeability change of the cell membrane were observed using Leica optical inverted microscope equipped with a 40× objective lens.

Cells staining. After 20 min of exposure to the magnetic field, HeLa cells were fixed in 4% paraformaldehyde for 15 min and washed three times with PBS. Thereafter, phalloidin-TRITC (5×10-6 M) solution were used to stain the microvilli of HeLa cells for 45 min. Finally, the cells were washed three times and observed through confocal laser scanning microscopy (CLSM).

SEM observation of the cells. HeLa cells were grown on a glass substrate and interacted with the magnetic gear-like MOFbots in a rotating magnetic field for 20 min. Then, HeLa cells were fixed in 4% paraformaldehyde for 15 min and washed three times with PBS. The cells were sequentially dehydrated in ethanol solutions at concentrations of 30%, 50%, 70%, 80%, and 90% for 15 min each, followed by a 30 min dehydration in 100% ethanol. Afterward, the cells were dried using Leica EM CPD300 automated critical point dryer, coated with Au for SEM observation.

Preparation of hydrogels conjugated with fluorescent beads. To measure weak traction forces more accurately, traction force microscopy method based on elastic polyacrylamide-bis-acrylamide (PA) hydrogels containing fluorescent beads were employed. 5 μL red fluorescent carboxylate-modified 200 nm beads (10 mg/mL) were suspended in the 500 μL polyacrylamide mixture containing 7.85% (w/v) acrylamide and 0.27% (w/v) bis-acrylamide, followed by the addition of APS and TEMED. Immediately. 10 µL of the hydrogel solution was placed onto the surface-treated confocal dish and cover it with a coverslip. After 30 min, polymerised gels were stuck at the bottom of confocal dish. The gel surface was activated with 0.5 mg/mL sulfo-SANPAH and then coated with 50 µg/mL collagen I.

Measurement of traction force. HeLa cells were seed onto the hydrogels at a density ~5×103 cells/mL and incubated for 24 h. The images of cells and fluorescent beads underneath cell colonies were obtained. Subsequently, the magnetic gear-like MOFbot s were added into the cells to interact with them under a magnetic field. The image of fluorescent beads underneath cell colonies treated by rotating magnetic field was obtained. 50 μL of pre-warmed (37℃) 10% SDS (in PBS solution) was added to lyse the cells completely and the beads were photographed as initial position for result analysis after cell detachment. For the traction force experiments, all of the data were acquired at 40× magnification. Sample drift was corrected by tracking the displacement of fluorescent beads located at the edges away from cells, and the displacement field exerted by cells on the gel was measured by analyzing the positions of the fluorescent beads before and after cell detachment. Based on the pre-measured substrate displacement field and the Young’s modulus (18.5 kPa) of the substrate, we reconstructed the cellular traction stress field in ImageJ v1.53.

Plasmid transfection. HeLa cells were spread on the confocal dish at a density of ~5×105 cells/mL and were allowed to grow for 12 hours. To express β-actin-CFP/YFP-β-actin fusion proteins, the Lipo8000 Transfection Reagent (Beyotime Biotechnology, China) was employed to introduce the plasmid of Actin-cpstFRET-Actin (Addgene plasmid # 80643) into the cells. After a successful transfection, the magnetic gear-like MOFbots were added into the cells. Under a horizontal rotating magnetic field (7 Hz), the dynamic changes in FRET signals were observed. To assess potential baseline fluctuations in intracellular FRET signals, HeLa cells expressing actin-based force probes were subjected to the same experimental conditions as the MOFbots stimulation assays, except that no magnetic field was applied. To explore the changes in Ca2+ concentration in HeLa cell, the plasmid of pCMV-jGCaMP7s (Beyotime Biotechnology, China) were transfected using the same protocol described above.

Ezrin-siRNA transfection. HeLa cells were cultured in DMEM medium containing 20% FBS and then transfected with 50 nM ezrin small interfering RNA (siRNA) using Lipo8000 Transfection Reagent (Beyotime Biotechnology, China) for 48 h. The designed ezrin-targeting siRNA sequences are TCAGGAACATCTCTTTCAA, GGACTATGAGGAGAAGACA, CTGCGGAGCTTGCAGAATA.

Inhibition of mechanosensitive ion channels. HeLa cells transfected with plasmid of pCMV-jGCaMP7s were pretreated with specific pharmacological inhibitors. For PIEZO1 inhibition, cells were incubated with 1 μM GsMTx4 (MedChemExpress, USA), a peptide toxin derived from the Chilean rose tarantula (Grammostola spatulata) that selectively blocks PIEZO channels. For TRPV4 inhibition, cells were treated with 1 μM HC-067047 (MedChemExpress, USA), a selective TRPV4 antagonist. Inhibitors were added to the culture medium 30 min prior to MOFbots stimulation and maintained throughout the imaging process. Following inhibitor treatment, calcium dynamics were monitored and fluorescence changes were recorded under magnetic field.

Immunofluorescence staining. HeLa cells were fixed in 4% paraformaldehyde for 15 min and washed three times with PBS. Subsequently, the cells were permeabilized using 0.25% v/v PBST (Triton-X 100 in PBS) for 10 min. To avoid nonspecific protein adsorption, the samples were incubated for 1 h in a blocking solution containing 1% (w/v) bovine serum albumin (BSA) in PBST (0.1% v/v). Next, samples were incubated overnight at 4℃ with primary antibodies against phospho-FAK (Tyr397) (1:300, 44-624G, Thermo Scientific), Piezo1 (1:200, 28511-1-AP, Proteintech), TRPV4 (1:200, ACC-124-25UL, Thermo Scientific). After incubating with the primary antibody, the samples were washed twice with PBST and three times with PBS. Subsequently, the samples were then incubated at room temperature with goat anti-rabbit Alexa Fluor 488 (Thermo Scientific, 1:500 dilution) and DAPI for 60 min, after which they were washed twice with PBST and three times with PBS. Immunofluorescence images were examined by confocal laser scanning microscope (CLSM, NiKon A1).

Transmembrane transportation of PI. HeLa cells were cultured on a glass substrate. The glass slide was positioned at the center of a magnetic field (7 Hz, 6 mT) and placed on the platform of an inverted optical microscope. 10 μL of magnetic gear-like MOFbots adsorbed with PI were added to the cells and guided toward the target cell and altered their cell membrane permeability. The motion of the magnetic gear-like MOFbots and the entry of PI into the cell were observed using fluorescence microscopy. In addition, the fluorescence intensity of PI within the cells was measured by flow cytometry using FlowJo v10 software.

CCK-8 assay. HeLa cells were seeded at a density of 5×103 cells per well in 96-well plates and cultured in a 5% CO2 atmosphere at 37°C for 24 h. Various concentrations of magnetic gear-like MOFbots adsorbed with PI and DOX were separately introduced into the culture medium. After a duration of 10 min in the rotating magnetic field (7 Hz, 6 mT), the medium was removed and the cells were washed with PBS. Following this, CCK-8-containing medium was added to each well, and the cells were incubated for another 1 h. The absorbance at 450 nm was then measured using a microplate reader.

Peer review rounds
[{"round": 1, "reviewer_comments": {"reviewer_1": "The authors describe micrometer-size gear-like particles that can be moved with a magnetic field. They show that these particles can be brought into contact with single cells and that ensuing interaction may trigger signaling events such as intracellular calcium rise or kinase activation and enhance intracellular drug entry. They conclude that these so-called micro-robots might be used to study cell response to forces and design innovative therapeutic strategies.\nThis reviewer feels that this robotic device is quite original, and its construction is a remarkable achievement. The study of the microrobot behavior in a magnetic field is carefully conducted and there is no doubt that the actuation of particles bound to cells generates signaling events. However, he is not convinced by data interpretation and the suggested therapeutic potential of presented system. Also, he does not think that the microrobot control and analysis of interaction with cell microvilli is sufficiently accurate to help improve our understanding of cell response to forces. Also, he feels that the manuscript is not easily understandable by a biologically trained reader.\nThe following specific comments are meant to support this opinion.\nIt is not obvious that microvilli act as \"critical mechanical antennae\" (lines (55-56) rather than mainly exploratory structures generating multiple contacts between cell surface receptors and their environment as previously hypothesized (see e.g. Nature 323:712, 1986 or Current opinion in Cell Biology 18:18-25, 2006). Thus, if we consider the example of T cells (line 61), T cell receptors that are concentrated at the tip of microvilli (Ghosh et al., 2020, Cell Reports 30, 3434–3447) are presumably responsible for mechanosensation (J. Immunol.2010, 184: 5959–5963), rather than microvilli \"per se\". This point is important : while the authors suggest that micro-robots stimulate cells by exerting forces on microvilli wich which they are suggested to be \"entangled\", other possibilities should be considered : cell-microrobot interaction might be mediated by molecular interactions between some cell surface receptors and particle molecular patterns ; also, microrobot might be entangled with glycocalyx elements rather than microvilli. These points are not discussed, leading the reviewer to feel that data interpretation is questionable.\nLines 97-98 \"Taking advantage of the gear-like structure, the MOFbots can effectively engage and entangle with microvilli on the cell membrane\" this is fairly difficult to understand, and an crude figure would be useful to show \"on scale\" what the \"gear like\" structure looks like and how it can be entangled with microvilli.\nLine 110: \"the heterogeneous MIL-125@ZIF-8 hybrid MOFs were synthesized through site-selective growth of ZIF-8 on MIL-125\" This is indeed very difficult to understand for a biologically-trained reader who never heard about ZIF-8 or MIL-125.\nWhile Figure 3a strongly suggests that micro-robots are in contact with microvilli, it is not obvious that their are entangled. Also, it is not obvious that reconstruced images faithfully represent molecular contacts. Further, there is not detail on the Unet++ network model (line 245)\nWhile the authors performed extensive study to demonstrate their ability to control micro-robot motion (lines 258 to 321) it is not obvous to know precisely which forces are exerted on a microvillus : how is the shear stress applied ? which force (in piconewton) is experienced and what is the loading rate ? Indeed, much more exquisite determination of applied forces were presented in previous reports, e.g. relevant to T cell stimulation (Cell 157, 357–368, 2014)\nWhile there is no doubt that micro-robots triggered cell signaling events, this finding is not new :it was reported more than three decades ago that mechanical forces could trigger calcium rise and cytoskeletal reorganization (Cell Biophys 17:243, 1990, DOI: 10.1007/BF02990720).\nAnother point is that therapeutic methods should be aimed at targeting well defined cell populations rather than single cells. Thus, it is likely that targeting should be more efficiently obtained with conventional receptor-ligand interactions rather than specific control with magnetic fields.\nPr. Pierre Bongrand", "reviewer_2": "In this manuscript, Prof. Ma and his colleagues presented a magnetically actuated, gear-structured microrobot based on a heterostructured metal-organic framework (MOFbot), designed for targeted mechanical stimulation of cellular microvilli, enabling mechanotransduction modulation and enhanced drug delivery. They addressed critical limitations in precision and physiological relevance of existing microvilli-targeted tools. This work establishes a groundbreaking platform for single-cell mechanotherapy. The MOFbot’s ability to spatially target microvilli and enhance drug delivery via mechanical force opens avenues for fundamental studies and therapeutic applications. The work is highly innovative and technically rigorous, with strong potential for impact in mechanobiology and nanorobotics. The manuscript is well-structured and clearly presented.\nThe study’s innovation and interdisciplinary impact align with Nature Communications’ scope. I recommend to accept this manuscript after the authors make minor revision, including fully validating the mechanism and translational relevance.\nSpecific suggestions are outlined below:\n1. While SEM (Fig. S6) shows MOFbot-microvilli interaction, no experiments directly prove that force transduction requires intact microvilli. May the author treat cells with cytochalasin D (disrupts actin in microvilli) and repeat TFM/FRET assays? If force generation is abolished, this confirms microvilli-dependence. Please compare mechanoresponses in microvilli-deficient cell lines (e.g., CRISPR-edited HeLa lacking ezrin).\n2. Calcium/FAK data (Fig. 5) imply mechanosensing but do not identify specific ion channels (e.g., Piezo1/TRPV4) responsible. Use pharmacological inhibitors (e.g., GsMTx4 for Piezo1) to block calcium influx upon MOFbot stimulation. Include immunofluorescence/co-IP for Piezo1/TRPV4 recruitment to microvilli during force application.\n3. Fig. S16: DOX release curves lack statistical testing (perform ANOVA), weakening claims of magnetic-field-enhanced release. In addition, Label axes clearly. \"% DOX released\" vs. \"Time\" is suggested.\n4. CCK-8 data (Fig. S13) only assess 20-min exposure. Chronic toxicity (e.g., 24-h exposure) and immune clearance risks are unexplored. It is suggested to test viability/apoptosis after prolonged MOFbot exposure (6–24 h).\n5. Discuss challenges for in vivo translation (e.g., shear stress in blood, opsonization) in the Discussion.\n6. The gear-like MOFbot is fabricated from a porous material. Could the authors clarify the pore size distribution parameters of the MOF structure?\n7. In this study, the authors employed confocal microscopy to demonstrate that microrobots engineered into a spherical morphology (shape-control group) primarily localize on the microvilli surface with minimal entanglement or deep penetration under a magnetic field. To further strengthen this conclusion, SEM can be used to directly visualize the interaction between spherical microrobots and cellular microvilli.\n8. How does the instantaneous velocity of PS@Ni@Au composite microspheres vary in response to the surface topography when moving across a patterned substrate?\n9. Figures revisions:\n1) Please provide the comparative fluorescence images of DOX in HeLa cells treated with drug-loaded, gear-like MOFbots under both magnetic field-free (control) and magnetically activated (experimental) conditions.\n2) Please added scale bar to Figure 3d, and replace pixel-based Unet++ plots with 3D reconstructions of MOFbot-microvilli entanglement (video supplement suggested) in Fig. 3e-g.\n3) The color bar in Figure 4b and 4d is unclear.\n4) The scale bar in Figure S5a seems incorrect.\n5) Figure S6 confirms MOFbot-microvilli entanglement, but no disassembly control (e.g., cytochalasin).\n10. Text optimization:\n1) Condense broad mechanotransduction background (Lines 48–79) to focus on microvilli knowledge gaps in the introduction.\n2) Elaborate on disease relevance (e.g., microvillus inclusion disease, metastasis) beyond HeLa cells in the Discussion.\n3) Specify magnetic field parameters (coil geometry, gradient strength) omitted in Lines 532–538, and clarify if FRET controls included baseline cell deformation (Line 590) in the Methods."}, "author_response": "1 Response to Reviewer #1\n3 magnetic field. They show that these particles can be brought into contact with single\n4 cells and that ensuing interaction may trigger signaling events such as intracellular\n5 calcium rise or kinase activation and enhance intracellular drug entry. They conclude\n6 that these so-called micro-robots might be used to study cell response to forces and\n7 design innovative therapeutic strategies.\n9 remarkable achievement. The study of the microrobot behavior in a magnetic field is\n10 carefully conducted and there is no doubt that the actuation of particles bound to cells\n11 generates signaling events.\n13 microrobot design. In light of the reviewer’s constructive comments, we have\n14 thoroughly revised our manuscript and implemented revisions to enhance its clarity and\n15 scientific rigor. We are confident that these revisions have substantially strengthened\n16 the work, and we provide the following point-by-point responses to address the points\n17 raised.\n19 potential of presented system.\n21 regarding data interpretation and the therapeutic relevance of our system. The\n22 comments have prompted us to further substantiate our findings and more clearly\n23 articulate the potential applications of our platform. To address the question of data\n24 reliability and biological relevance, we have conducted a series of additional control\n25 experiments. These include comparative studies using cells with intact versus disrupted\n26 microvilli, as well as entanglement tests under different magnetic actuation modes and\n27 with microrobots of varying geometries. The consistent results observed across these\n28 experimental conditions strongly support the conclusion that the mechanotransduction\n29 effects we report are specifically attributable to interactions between the magnetically-\n30 actuated MOFbots and cellular microvilli.\n32 section to clarify that our aim is not to replace established receptor-mediated targeting\n33 strategies, but rather to introduce microrobotic actuation as a complementary platform.\n35 intervention, and receptor-independent operation—that may be advantageous in\n36 specific biomedical contexts. We also emphasize that future integration of magnetic\n37 guidance with molecular recognition elements could synergize the spatiotemporal\n38 control of robotics with the selectivity of biochemical targeting, thereby enhancing in\nP\nA\nG\nE\n\\\n39 vivo applicability and therapeutic efficacy.\n41 4, and 8. We hope such revisions can address the reviewer’s concerns at a satisfactory\n42 level.\n44 microvilli is sufficiently accurate to help improve our understanding of cell response to\n45 forces.\n47 of microrobot control and the analysis of interactions with microvilli. In response, we\n48 would like to clarify that our experimental approach employs Förster Resonance Energy\n50 (TFM), which together offer robust quantitative assessment of cellular-scale\n51 mechanical forces. Although these methods do not achieve piconewton-level resolution\n52 at individual microvilli, they enable us to quantitatively correlate controlled\n53 microrobotic actuation with downstream mechanotransduction events. To further\n54 address concerns about control precision and interaction analysis, we have conducted\n55 additional simulations and quantitative evaluations of the contact forces generated by\n57 microrobots are both reproducible and regulable, thereby reinforcing the relevance of\n58 our platform for studying cellular force response mechanisms. Further methodological\n59 details and supporting data are provided in our response to Comment 6.\n61 reader.\n63 the accessibility of the manuscript, particularly for readers with a biological background.\n65 interdisciplinary readability of our work. A major focus has been placed on simplifying\n66 specialized terminology from materials preparation and magnetic control experiments,\n67 ensuring that concepts are conveyed in a manner more intuitive. Furthermore, we have\n68 introduced new schematic diagrams to visually illustrate the design, actuation principle,\n69 and mechanism of cellular interaction of the MOFbots. These additions provide an\n70 immediate conceptual overview and guide the reader through the experimental\n71 workflow and key findings.\n73 design and fabrication of the magnetically driven gear-like MOFbot, to its controlled\n74 locomotion under a rotating magnetic field, its active entanglement with cellular\n75 microvilli, and the subsequent quantitative analysis of force-induced signaling and\nP\nA\nG\nE\n\\\n76 enhanced drug delivery. By refining the text and strengthening visual support, we\n77 believe the manuscript is now more transparent and accessible, allowing a broad\n78 interdisciplinary audience to fully appreciate both the methodological advances and\n79 biological implications of our microrobotic platform.\n81 1. It is not obvious that microvilli act as “critical mechanical antennae” (lines (55-56))\n82 rather than mainly exploratory structures generating multiple contacts between cell\n83 surface receptors and their environment as previously hypothesized (see e.g. Nature\n84 323:712, 1986 or Current opinion in Cell Biology 18:18-25,2006). Thus, if we consider\n85 the example of T cells (line 61), T cell receptors that are concentrated at the tip of\n86 microvilli (Ghosh et al.,2020, Cell Reports 30, 3434-3447) are presumably responsible\n87 for mechanosensation (J. Immunol.2010,184: 5959-5963), rather than microvilli “per\n88 se”. This point is important: while the authors suggest that micro-robots stimulate cells\n89 by exerting forces on microvilli which they are suggested to be “entangled”, other\n90 possibilities should be considered: cell-microrobot interaction might be mediated by\n91 molecular interactions between some cell surface receptors and particle molecular\n92 patterns; also, microrobot might be entangled with glycocalyx elements rather than\n93 microvilli. These points are not discussed, leading the reviewer to feel that data\n94 interpretation is questionable.\n96 specific role of microvilli in our mechanostimulation experiments, and for highlighting\n97 alternative potential mechanisms—such as interactions mediated by cell surface\n98 receptors or entanglement with the glycocalyx—that could explain the observed cell-\n99 microrobot interactions. We agree that a clear distinction between the mechanical role\n100 of microvilli and other possible mechanisms is essential for accurate interpretation of\n101 our findings.\n103 mechanoresponses observed in our study, we performed additional experiments using\n104 microvilli-deficient HeLa cells. These cells were generated via knockdown of ezrin, a\n105 key actin-cytoskeleton linker protein known to be essential for microvilli formation and\n106 maintenance [ACS Nano 2021, 15, 15803; Sci. Adv. 2022, 8, eabo2779]. As detailed in\n107 the Experimental Section of revised manuscript, HeLa cells were transiently transfected\n108 with ezrin-targeting siRNA, leading to efficient degradation of ezrin mRNA and a\n109 marked reduction in ezrin protein levels. This led to marked disruption of microvilli\n110 stability and a significant loss of their surface density. Scanning electron microscopy\n111 (SEM) and confocal laser scanning microscopy (CLSM) imaging (Figure R1.1)\n112 confirmed the effective reduction of microvilli structures in these cells compared to\n113 normal HeLa controls.\nP\nA\nG\nE\n\\\n114\n116 cell treated with siRNA targeting ezrin (c, d).\n118 microvilli-deficient HeLa cells. TFM-based analysis in Figure R1.2 revealed a\n119 significant decrease (p < 0.0001) in bead displacement and traction stress in microvilli-\n120 deficient cells, regardless of whether MOFbots were actuated by external magnetic field\n121 (MF). This indicates a clear decay in localized mechanical stress transmission at the\n122 cell-microrobot interface upon microvilli disruption. Furthermore, FRET-based\n123 analysis of intracellular forces showed that magnetic actuation of MOFbots induced a\n124 sharp reduction in FRET efficiency in normal HeLa cells, which gradually recovered\n125 after the magnetic field was turned off. In contrast, microvilli-deficient cells exhibited\n126 no significant changes in FRET efficiency under the same conditions (Figure R1.3),\n127 indicating a loss of mechanical responsiveness to microrobot stimulation.\nP\nA\nG\nE\n\\\n128\n130 on normal HeLa cells and microvilli-deficient HeLa cells. (a, b) Representative displacement and\n131 traction stress maps of MOFbot-induced substrate deformation in normal and microvilli-deficient\n134\n136 cells and microvilli-deficient HeLa cells. (a, b) Representative time-lapse images displaying the\n137 change of FRET signals in normal and microvilli-deficient HeLa cells under the influence of\n139 over time. (d) Average FRET signal in normal and microvilli-deficient HeLa cells at the initial time\n140 point without MF (0 min) and after 15 min with MF application (15 min) (N =22 cells).\n142 mechanotransduction elicited by MOFbots. The pronounced attenuation of both\nP\nA\nG\nE\n\\\n143 extracellular traction forces and intracellular mechanosensitive responses upon\n144 microvilli disruption strongly supports our model that microvilli serve as a primary\n145 force-transducing scaffold, concentrating and delivering mechanical energy from the\n146 microrobots to the cell. That said, we acknowledge that the cell-microrobot interface is\n147 complex, and it is plausible that other factors—such as receptor-mediated interactions\n148 or glycocalyx entanglement—may also contribute to the overall response. Our model\n149 positions microvilli as the critical physical scaffold responsible for collecting and\n150 focusing the microrobot-applied force onto the cell, without excluding the potential\n151 involvement of auxiliary mechanisms. We have incorporated a discussion of these\n152 possibilities and related limitations in the revised manuscript, and we think future\n153 studies using functionalized microrobots or glycocalyx-modulated models would help\n154 further dissect these contributions.\n156 “force-transducing scaffold” in the manuscript to more precisely reflect this\n157 demonstrated mechanical role. Figure 4b-4d, 4g-4i, Figure S9 and the following\n158 paragraph have been added to the revised manuscript.\n159 (Page 12) To verify the role of cellular microvilli in force transduction, we performed\n160 siRNA-mediated knockdown of ezrin, which is a key scaffolding protein essential for\n161 microvilli integrity. Transient transfection of HeLa cells with ezrin-specific siRNA led\n162 to efficient suppression of ezrin expression, which in turn induced pronounced\n163 destabilization of microvillar protrusions. As a consequence, ezrin-depleted cells\n164 exhibited a substantial reduction in microvilli density. This morphological impairment\n165 was corroborated by both SEM and CLSM analyses, which consistently demonstrated\n166 a marked loss of surface microvilli relative to control cells (Figure S9). Under identical\n168 attenuated bead displacement and traction stress (Figure 4b-4d). These results\n169 demonstrate that mechanical forces are transmitted via microvilli to the underlying\n170 substrate, confirming the essential role of microvilli in mediating the physical\n171 interactions between MOFbots and cellular structures.\n172 (Page 13) In addition, FRET efficiency measurements in microvilli-deficient HeLa\n173 cells revealed markedly reduced molecular tension and a lack of response to MOFbot-\n174 induced mechanical stimulation, further corroborating that the disruption of microvilli\n175 leads to a significant decrease in cellular traction forces and abolishes the cells’ ability\n176 to transduce mechanical cues (Figure 4g-4i). These findings are consistent with the cell\n177 traction force measurements, reinforcing the critical role of microvilli in mediating\n178 cellular mechanotransduction.\n179 (Page 27) Ezrin-siRNA transfection. HeLa cells were cultured in DMEM medium\n180 containing 20% FBS and then transfected with 50 nM ezrin small interfering RNA\n181 (siRNA) using Lipo8000 Transfection Reagent (Beyotime Biotechnology, China) for\nP\nA\nG\nE\n\\\n182 48 h. The designed ezrin-targeting siRNA sequences are\n185 (Page 22-23) While our findings establish microvilli as a critical force-transducing\n186 scaffold essential for the mechanoresponse elicited by MOFbots, we acknowledge the\n187 inherent complexity of cell-surface interactions. Our model positions microvilli as the\n188 primary physical structure for collecting and focusing mechanical force onto the cell.\n190 transduction, or initial contact mediated by the glycocalyx, warrant further investigation.\n192 directly probe the interplay between mechanical force application and receptor-specific\n193 signaling pathways. Similarly, detailed investigations into glycocalyx mechanics and\n194 its potential entanglement with microrobots would provide a more comprehensive\n195 understanding of the initial adhesion dynamics.\n196 2. Lines 97-98 “Taking advantage of the gear-like structure, the MOFbots can\n197 effectively engage and entangle with microvilli on the cell membrane” this is fairly\n198 difficult to understand, and a crude figure would be useful to show “on scale” what the\n199 “gear like” structure looks like and how it can be entangled with microvilli.\n201 interaction mechanism, we have now included a schematic diagram in the revised\n202 manuscript, which visually depicts the gear-like morphology of the MOFbots and their\n203 scale-comparable entanglement with cellular microvilli. As schematically illustrated in\n205 alternating protrusions and recesses along their periphery. When actuated by an external\n206 magnetic field, these microrobots engage with the cell surface such that microvilli\n207 physically penetrate into the recesses and wrap around the protrusions, resulting in\n208 effective mechanical entanglement and force transmission at the interface. This\n209 illustration has been incorporated as part of Figure 3a in the revised manuscript to aid\n210 reader comprehension.\n211\nP\nA\nG\nE\n\\\n213 3. Line 110: “the heterogeneous MIL-125@ZIF-8 hybrid MOFs were synthesized\n214 through site-selective growth of ZIF-8 on MIL-125” This is indeed very difficult to\n215 understand for a biologically-trained reader who never heard about ZIF-8 or MIL-125.\n217 acknowledge that the original description of the MOF synthesis—using technical terms\n218 such as “MIL-125@ZIF-8 hybrid MOFs” and “site-selective growth”—may present a\n219 barrier to readers primarily trained in biology. To improve accessibility, we have revised\n220 the relevant section in the manuscript to provide a clearer, more intuitive explanation.\n222 by connecting metal ions with organic molecules, much like building a tailored scaffold\n223 at the molecular scale. Different combinations of metal nodes and ligands give rise to\n224 diverse MOF families, such as ZIFs (Zeolitic Imidazolate Frameworks) and MILs\n225 (Materials of the Institute Lavoisier), each denoted by specific names and numerical\n226 codes (e.g., ZIF-8, MIL-125) reflecting their distinct composition and structure.\n228 synthesized titanium‐based MIL‐125 crystals with exposed {110} facets at their corners.\n230 favorable lattice matching between the two crystalline frameworks at the interface. This\n231 process resembles fitting pieces of a puzzle: the surface structure of MIL-125 at corners\n232 complements the crystal arrangement of ZIF-8, allowing ZIF-8 to form preferentially\n233 at those corners. As a result, a well-defined MOF-on-MOF composite is obtained, as\n234 illustrated schematically in the Figure R1.5.\n235\n238 the core idea of the synthesis strategy without oversimplifying the scientific approach.\n241 (Page 4) We constructed a hybrid nanostructure by growing zinc-based MOF materials\n242 (ZIF-8) at the specific corner regions of a titanium-based MOF crystals (MIL-125),\n243 using a controlled method known as site-selective growth. This enabled the controlled\n244 formation of a heterogeneous MOF composite, MIL‐125@ZIF‐8, whose gear-like\n245 structural features are illustrated schematically in Figure 1a. Afterwards, we deposited\nP\nA\nG\nE\n\\\n247 resulting in a gear-like magnetic microrobot, termed a MOFbot.\n248 4. While Figure 3a strongly suggests that micro-robots are in contact with microvilli, it\n249 is not obvious that they are entangled. Also, it is not obvious that reconstructed images\n250 faithfully represent molecular contacts.\n252 evidence for microrobot–microvilli entanglement. To address these points directly, we\n253 performed additional CLSM and SEM experiments.\n255 stained with TRITC-phalloidin (red). After magnetic actuation of the MOFbots on\n257 within the dense network of microvilli. Corresponding SEM image further\n258 demonstrated that microvilli physically wrap around the protrusions of the gear-like\n260 transmission at the interface. In control experiments where the magnetic field was\n261 absent (Figure R1.6b), static MOFbots merely rested on the cell surface without visible\n262 microvilli entanglement.\n263\n265 views and SEM images illustrating the distribution of MOFbots on HeLa cells. (a) gear-like\n267 field. (c) Spherical microrobots under external magnetic field actuation. In pseudo-colored SEM\n268 images, microrobots and microvilli were marked with yellow and red colors, respectively.\nP\nA\nG\nE\n\\\n270 magnetic actuation, we fabricated spherical microrobots by depositing Ni and Au layers\n271 on polystyrene (PS) microspheres. Under magnetic actuation, these spherical robots\n272 rotated on the cell surface but failed to entangle with microvilli, as confirmed by both\n274 morphology of the MOFbots combined with magnetic actuation is essential for\n275 achieving mechanical entanglement with microvilli.\n278 of this interaction. We believe these additional data convincingly demonstrate the\n279 entanglement between MOFbots and microvilli.\n280 5. Further, there is not detail on the Unet++ network model (line245)\n282 model to perform semantic segmentation on each 2D slice (z-stack) of our cellular 3D\n283 confocal image volumes (Figure 3 in the revised manuscript). The subsequent analysis\n284 of feature parameters (saturation, color gamut, brightness) were conducted across the\n285 entire 3D volume at the voxel level. The quantitative plots in Figure 3e-g of revised\n286 manuscript are, in fact, the direct result of 3D analysis, enabling precise extraction of\n287 target cell regions and effectively distinguishing cell voxels from background. This\n288 allows us to investigate the interaction between microrobots and microvilli using\n292 (Page S2-S4) UNET++\n294 adopted for cell image segmentation. UNET++, an advanced medical image\n295 segmentation architecture, introduces nested dense skip connections, deep supervision\n296 mechanisms based on the classical UNET framework, and an encoder-decoder\n297 structure1. By incorporating intermediate convolutional layers, this architecture\n298 progressively bridges the semantic gap between encoder and decoder feature maps.\n300 segmentation accuracy for edge details and target regions. The design ensures robust\n301 capture of multi-contextual information while improving generalization capability. The\n𝐶𝑜𝑛𝑣(𝑋𝑖−1,𝑗), 𝑗 = 0\n303 𝑋𝑖,𝑗 = {\n𝐶𝑜𝑛𝑣([𝑋𝑖,𝑗−1,𝑋̃𝑖−1,𝑗]), 𝑗 > 0\n304 (1)\nP\nA\nG\nE\n\\\n307 channel dimension. 𝑋̃𝑖−1,𝑗 corresponds to the upsampled feature map.\n310 (UNet++), where the encoder utilizes VGG11 as the backbone network with ImageNet\n311 pre-trained weights to accelerate model convergence and enhance generalization\n312 capability2. Input images are resized to 224×224 pixels with 3 channels (RGB). In the\n313 encoder pathway, the model progressively extracts multi-scale features through four\n314 downsampling stages. Each stage consists of two 3×3 convolutional layers (stride = 1,\n315 padding = 1) with ReLU activation, followed by 2×2 max pooling (stride = 2) that\n316 halves spatial dimensions while doubling feature channels to enrich representational\n317 capacity. After four downsampling operations, the spatial resolution of feature maps\n318 reduces sequentially from 224×224 to 14×14, with channel depth expanding from 64\n319 to 512.\n321 spatial resolution. Each stage performs 2×2 transposed convolution (stride = 2),\n322 followed by dense skip connections that fuse features from corresponding encoder\n323 stages. The concatenated features are then refined through two 3×3 convolutions with\n325 feature interaction, enhancing the capture of boundary details and local structures.\n327 matching the input dimensions. A final 1×1 convolution projects channels to the number\n328 of classes, generating precise segmentation masks.\n330 improving sensitivity to local details, making it particularly suitable for boundary-\n331 sensitive tasks such as cell segmentation. For optimization, multi-class cross-entropy\n332 loss is adopted to minimize discrepancies between predictions p and ground truth labels\n333 y. The loss function for a given input image is defined as:\n334 ℒ = − 1 ∑𝑁 ∑1 𝑦 log (𝑝 ) (2)\n𝐶𝐸 𝑖=1 𝑐=0 𝑖,𝑐 𝑖,𝑐\n𝑁\n335 where N denotes the total number of pixels (224 × 224 = 50,176). y represents the\ni,c\n336 ground truth label (0 or 1) for pixel i belonging to class c. p indicates the predicted\ni, c\n337 probability of pixel i being classified as class c.\n339 defined as:\n340 𝐴𝑐𝑐𝑢𝑟𝑎𝑐𝑦 = 1 ∑𝑁 𝕀(𝑦̂ = 𝑦 ) (3)\n𝑖=1 𝑖 𝑖\n𝑁\n𝑖\nP\nA\nG\nE\n\\\n344 224×224 pixels and normalized to the [0,1] range to mitigate adverse effects of varying\n345 resolutions and intensity distributions on model training. Subsequently, images were\n346 converted to three-channel RGB format to ensure consistent input feature dimensions.\n348 regions) marked as 1 and background as 0. Training data annotations were meticulously\n349 segmented by domain experts based on nuclear staining patterns and morphological\n350 characteristics observed under microscopy, ensuring high-quality supervision signals.\n352 subsets. Hyperparameter optimization was implemented through a grid search strategy,\n353 evaluating multiple combinations of learning rates, optimizer types, and training epochs.\n356 0.01 using the Adam optimizer over 80 epochs. Further analysis revealed that UNet++’s\n357 nested skip connections effectively mitigated gradient vanishing and information\n358 bottlenecks in deep supervision, resulting in quantifiable accuracy improvements for\n359 cell-stained image segmentation tasks.\n360 1. Zhou, Z., Rahman Siddiquee, M. M., Tajbakhsh, N., & Liang, J. Unet++: A nested u-net\n361 architecture for medical image segmentation. In International workshop on deep learning in medical\n362 image analysis. Springer International Publishing: Cham 2018, 3-11.\n363 2. Simonyan, K., & Zisserman, A.. Very deep convolutional networks for large-scale image\n364 recognition. arXiv preprint arXiv 2014, 1409,1556.\n365 6. While the authors performed extensive study to demonstrate their ability to control\n366 micro-robot motion (lines 258 to 321) it is not obvious to know precisely which forces\n367 are exerted on a microvillus: how is the shear stress applied? which force (in\n368 piconewton) is experienced and what is the loading rate? Indeed, much more exquisite\n369 determination of applied forces were presented in previous reports, e.g., relevant to T\n370 cell stimulation (Cell 157, 357-368,2014)\n372 characterization of mechanical forces applied by the MOFbots on microvilli. We agree\n373 that a detailed quantification of the force magnitude, mode of application, and loading\n374 rate is essential for a thorough understanding of the mechanotransduction process\n375 elicited by our approach. The reference cited by the reviewer [Cell, 2014, 157, 357]\n376 indeed represents a landmark study in quantitative mechanobiology, employing\n377 micropipette aspiration to apply well-defined piconewton-scale forces to isolated T\n378 cells with high spatiotemporal resolution. We acknowledge that such direct, single-\n379 molecule level force measurements set a rigorous standard for the field.\n381 mechanical stimulation to cells via contact with cellular microvilli, and that this\n382 stimulation triggers detectable downstream biological responses. To assess the resulting\nP\nA\nG\nE\n\\\n383 mechanical transduction at the cellular level, we utilized FRET-based and TFM\n384 techniques. These methods provide reliable, quantitative readouts of force transmission\n385 to the cell body, including changes in local traction stresses and actin cytoskeletal\n386 tension. However, as the reviewer correctly notes, these techniques do not directly\n387 resolve the piconewton-scale forces exerted on individual microvilli or explicitly\n388 quantify the shear stress and loading rate at the microvillus tip during MOFbot\n389 interaction.\n391 single microvillus during dynamic MOFbot actuation, we turned to computational\n392 modeling to obtain a quantitative estimate. We developed a three-dimensional solid\n393 mechanics model using COMSOL Multiphysics 6.1 (Figure R1.7a). In this model, the\n394 microvillus was idealized as a cylindrical structure with a length of 2 µm and a diameter\n395 of 100 nm, based on established literature [Front. Immunol., 2020, 11, 2187] and\n396 corroborated by our own SEM and CLSM images. One end of the microvillus was fixed\n397 to a hemispherical cell body. To accurately capture its mechanical behavior, the\n398 microvillus was assigned hyperelastic material properties, reflecting the flexible, fiber-\n399 like response of biological structures. In contrast, the gear-shaped MOFbot was treated\n400 as a rigid body due to its significantly higher structural stiffness, with its diameter set\n401 to 2 µm as measured experimentally. The simulation placed a single MOFbot amidst\n402 four microvilli arranged in a square pattern, such that the four corner protrusions of the\n404 interaction was simulated using a penalty-based contact mechanics approach, capturing\n405 the ensuing bending, lateral deflection, and strain evolution in the microvilli. Geometric\n406 nonlinearity was included to ensure solution stability under large deformations. All\n407 relevant simulation parameters—including boundary conditions, geometry, and\n408 material properties—are summarized in Figure R1.7b.\n409\n412 parameters, boundary constraints, and material assignments used in the analysis. Ref [1]. Adv. Funct.\n415 (6 mT, 7 Hz), resulting in a measured angular velocity of approximately 6.8 r/s. Given\nP\nA\nG\nE\n\\\n416 the 2 µm diameter of the MOFbot, this corresponds to a tangential shearing rate of about\n417 4.3 × 10-5 m/s at its periphery. Under these dynamic conditions, our simulation analyzed\n418 the mechanical response of a microvillus upon contact with a rotating MOFbot\n419 protrusion. As shown in Figure R1.8, transient contact induces pronounced lateral\n420 bending of the microvillus, accompanied by a marked increase in traction along its\n421 proximal-to-middle region. By integrating the traction magnitude over the microvillus\n422 surface, we estimated the maximum force exerted on a single microvillus by the\n425 actuation conditions, the rotating MOFbot applies a shear force to a microvillus via its\n426 corner protrusions with a loading rate on the order of 4.3 × 10-5 m/s and a peak force\n427 magnitude around 46 pN. These numerically derived values provide a quantitative\n428 approximation of the mechanical stimulus delivered at the microvillus level. We hope\n429 this analysis can address your question at a satisfactory level.\n430\n432 was actuated by the external magnetic field (6 mT, 7Hz). (a) Distribution of shear stress along each\n433 microvillus at different time. (b) Force induced by the shearing of MOFbot on microvilli as time\n434 went on.\n436 the following:\n437 (Page 15) To further investigate the mechanical impact of rotating MOFbots on cellular\n438 microvilli, we developed a three-dimensional solid mechanics model in COMSOL\n440 diameter cylinder fixed to the surface of a hemispherical cell and assigned hyperelastic\n441 material properties, while the MOFbot was treated as a rigid body due to its higher\n442 structural stiffness (Figure S14). The MOFbot was set to rotate at 6.8 r/s, producing\n443 transient contact along its rotational trajectory. Contact interactions were simulated\n444 using a penalty-based contact mechanics approach, capturing microvillus bending,\n445 lateral deflection, and strain distribution. Geometric nonlinearity was included to ensure\n446 numerical stability under large deformations. Simulation results reveal pronounced\n447 lateral bending of the microvillus upon MOFbot contact, with traction concentrated\nP\nA\nG\nE\n\\\n448 from the proximal to middle regions, consistent with forced-bending morphology\n449 (Figure S15a). Surface integration of traction magnitudes estimated a maximum contact\n450 force of ~46 pN (Figure S15b), which emerges naturally from the MOFbot’s rotational\n451 kinematics (tangential velocity ~4.3 × 10-5 m/s). These results corroborate experimental\n452 observations, indicating that MOFbot rotation can locally perturb microvilli, inducing\n453 deformation and intracellular force transmission.\n454 (Page 23) In parallel, combining MOFbot-mediated stimulation with high-resolution\n455 force measurement tools such as molecular tension sensors, optical tweezers, or DNA-\n456 based force probes could bridge the gap between biologically relevant actuation and\n457 precise pico- to nanonewton-scale force quantification. Together, these directions\n458 would deepen our mechanistic understanding of how targeted mechanical perturbations\n459 are transduced into biochemical signals at the single-cell level, while expanding the\n460 biomedical potential of micro/nano-robotic systems.\n461 (Page S4-S6) Simulation of mechanical interactions between MOFbot and cellular\n462 microvilli\n464 simulated using the Solid Mechanics module in COMSOL Multiphysics 6.1. The\n466 a compressible hyperelastic material using the Neo-Hookean model. The governing\n467 equation for momentum conservation in the Lagrangian framework is:\n468 𝜌\n∂2𝑢\n= ∇⋅(𝐅𝐒)T +𝐹 (7)\n𝑐 ∂𝑡2 𝑣\n469 where 𝑢 is the displacement vector, 𝜌 is the cell density, 𝐅 = I+∇u is the\n𝑐\n470 deformation gradient, 𝐒 is the second Piola–Kirchhoff stress tensor, and 𝐹 denotes\n𝑣\n471 external body forces. The Cauchy stress tensor 𝝈 is obtained from:\n472 𝜎 = 1 𝐅𝐒𝐅T (8)\n𝐽\n473 𝐽 = 𝑑𝑒𝑡(𝐅) (9)\n475 𝑊 = 1 𝜇(𝐼 −3)−𝜇ln (𝐽 )+ 1 𝜆[ln (𝐽 )]2\n𝑠 1 el el\n2 2\n476 (10)\n477 where 𝐼 is the first invariant of the right Cauchy-Green deformation tensor, 𝐽 is\n1 el\n478 the elastic volume ratio, 𝜇 and 𝜆 are the Lamé parameters.\n480 modeled using a penalty-based contact formulation in COMSOL Multiphysics.\n482 boundaries as interacting surfaces, while other pairs were disabled to isolate the\n483 collision scenario. The contact condition was enforced via the normal penalty method,\n484 wherein the normal contact pressure 𝑇 is written\n𝑛\nP\nA\nG\nE\n\\\n−𝑝 𝑔 , 𝑔 ≤ 0\n485 𝑇 = { 𝑛 𝑛 𝑛\n𝑛 0, 𝑔 > 0\n𝑛\n486 (11)\n𝐸\n487 𝑝 = 𝑓 char (12)\n𝑛 𝑝\nℎmin\n𝑛 𝑛\n489 𝑓 is the penalty factor (set to 1), 𝐸 is the characteristic Young’s modulus of the\n𝑝 char\n490 softer body (i.e., the cell), and ℎ represents the minimum mesh size at the contact\nmin\n491 interface. This formulation allows for slight penetration to compute restorative forces,\n492 ensuring stable contact enforcement without compromising the physical realism of\n493 stress transmission. Contact was modeled as frictionless, and the pairwise contact\n494 algorithm operated under a quasi-static assumption to capture instantaneous mechanical\n495 responses during MOFbot-cell collision events.\n497 per unit area. At a point on a surface with outward unit normal vector 𝐧, traction is\n498 defined as\n499 𝐭=𝛔⋅𝐧 (13)\n501 component:\n502 𝐭 = 𝐭 +𝐭 (14)\nn τ\n504 perpendicular to the surface：\n505 𝐭 = (𝐭⋅𝐧)𝐧=p (15)\n𝑛\n507 𝐭 = 𝐭−(𝐭 )𝐧 (16)\n𝜏 𝑛\n509 its Euclidean norm\n510 𝜏 =∥ 𝐭 ∥ (17)\n𝜏\n512 vector can be integrated over the area 𝐴:\n513 𝐅 = ∫ 𝐭𝑑𝐴 = ∫ 𝝈⋅𝐧𝑑𝐴 (18)\n𝑟 𝑆 𝑆\n515 processed. The fluid pressure and viscous stresses from the fluid dynamics module were\n516 applied as boundary conditions to the solid mechanics module.\n517 7. While there is no doubt that micro-robots triggered cell signaling events, this finding\n518 is not new: it was reported more than three decades ago that mechanical forces could\n519 trigger calcium rise and cytoskeletal reorganization (Cell Biophys 17:243, 1990,\nP\nA\nG\nE\n\\\n522 of our findings. We fully acknowledge the pioneering work in mechanobiology,\n523 including the seminal study cited [Cell. Biophys. 17:243, 1990], which established the\n524 fundamental principle that mechanical forces can trigger calcium signaling and\n525 cytoskeletal reorganization. Our study does not seek to challenge or rediscover this\n526 well-established phenomenon.\n528 driven, programmable microrobotic platform that enables highly localized and dynamic\n529 mechanical stimulation specifically targeting microvilli on individual living cells. In\n530 contrast to conventional methods such as substrate stretching or fluid shear, which\n531 typically apply forces over large cell populations or lack spatial precision, our approach\n532 allows spatiotemporally controlled force delivery at the subcellular level. This\n533 capability is coupled with real-time quantitative readouts, including FRET-based\n534 molecular tension probes and TFM, which together provide direct evidence of force\n535 transmission and downstream signaling activation.\n537 canonical mechanotransduction events—such as calcium influx and FAK\n538 phosphorylation—but also facilitates controlled enhancement of membrane\n539 permeability, enabling on-demand therapeutic delivery. These functionalities represent\n540 a significant step beyond the general observation that “mechanical forces trigger\n541 signaling,” and open new avenues for precise mechanomodulation at the single-cell\n542 level.\n544 with strong potential for impact in mechanobiology and nanorobotics.” We believe that\n545 the novelty of this study resides not in proposing a new biological mechanism, but in\n546 introducing a versatile microrobotic tool that bridges fundamental mechanobiology\n547 with emerging biomedical applications—offering a new means to interrogate and\n548 manipulate cellular mechanical signaling with high precision.\n549 8. Another point is that therapeutic methods should be aimed at targeting well defined\n550 cell populations rather than single cells. Thus, it is likely that targeting should be more\n551 efficiently obtained with conventional receptor-ligand interactions rather than specific\n552 control with magnetic fields.\n554 targeting strategies. We fully acknowledge that conventional receptor-ligand\n555 interactions represent a mature and highly effective approach for targeting defined cell\n556 populations, particularly in systemic therapeutic contexts. Our work does not seek to\n557 replace these established methods, but rather to introduce microrobotic systems as a\n558 complementary platform that offers unique capabilities beyond pure biochemical\nP\nA\nG\nE\n\\\n559 recognition—especially in scenarios requiring spatial precision, mechanical\n560 intervention, or receptor-independent operation.\n562 (e.g., chemical, magnetic, optical, acoustic) into controlled propulsion and motion at\n563 microscopic scales, represent an emerging frontier in precision biomedicine. Their core\n564 promise lies in enabling active, precise, and programmable operations in hard-to-reach\n565 biological environments, potentially shifting medical paradigms from passive delivery\n566 to actively guided therapy [Science, 2023, 382, 1120; Nat. Rev. Mater., 2025, 10, 889;\n568 offer several distinctive advantages: (i) they function independently of specific surface\n569 receptor expression, which is particularly valuable in heterogeneous or receptor-\n570 downregulated environments; (ii) they permit real-time reconfiguration of target\n571 location and trajectory via external fields; (iii) and they enable localized mechanical\n572 stimulation and triggered payload release at subcellular resolutions.\n574 mechanomodulation at the single-cell and even subcellular (microvilli) level. Although\n575 this serves as a proof-of-concept at the single-cell scale, the same principles can be\n576 extended to multi-cellular or tissue-level targeting through swarm navigation and\n577 collective actuation, as evidenced by recent advances in microrobotic collectives [Nat.\n579 are particularly compelling in complex biomedical scenarios. For example, in solid\n580 tumors characterized by heterogeneous receptor expression, microrobots could bypass\n581 variations in antigen density to deliver agents directly to hypoxic or poorly accessible\n582 regions. In another envisioned application, microrobots could be deployed to perform\n583 localized mechanical stimulation and therapeutic release at specific vascular or neural\n584 interfaces where conventional targeting lacks the necessary spatial resolution.\n586 study illustrates how emerging microrobotic technologies can expand the therapeutic\n587 toolbox and open new directions in biomedical intervention. Looking forward,\n588 microrobots need not function in isolation from biochemical strategies; they can be\n589 functionalized with targeting ligands to combine magnetic guidance with molecular\n590 recognition, thereby merging spatial programmability with biochemical specificity.\n592 efficacy across varying biological contexts.\n594 of the revised manuscript to explicitly discuss how future integration of magnetic\n595 actuation with molecular recognition elements—such as antibodies or aptamers—may\nP\nA\nG\nE\n\\\n596 synergize the spatiotemporal precision of robotic control with the selectivity of\n597 biochemical targeting, further advancing in vivo applicability and therapeutic efficacy.\n599 (Page 23) Notably, microrobotic platforms need not operate independently of\n600 molecular targeting paradigms. By conjugating magnetic MOFbots with specific\n601 targeting ligands (e.g., antibodies, peptides), it becomes possible to synergize externally\n602 controlled navigation with intrinsic biochemical recognition, thereby merging spatial\n603 programmability with biochemical specificity. Such a hybrid strategy, rather than\n604 replacing conventional targeting, extends the therapeutic toolkit and may substantially\n605 improve localization efficiency under physiological flow conditions.\n606\nA\nG\nE\n\\\n610 structured microrobot based on a heterostructured metal-organic framework (MOFbot),\n611 designed for targeted mechanical stimulation of cellular microvilli, enabling\n612 mechanotransduction modulation and enhanced drug delivery. They addressed critical\n613 limitations in precision and physiological relevance of existing microvilli-targeted tools.\n616 mechanical force opens avenues for fundamental studies and therapeutic applications.\n618 in mechanobiology and nanorobotics. The manuscript is well-structured and clearly\n619 presented. The study’s innovation and interdisciplinary impact align with Nature\n621 minor revision, including fully validating the mechanism and translational relevance.\n624 the reviewer’s comments, which greatly helped us to improve this manuscript. We\n625 provide the response point-by-point as following.\n627\n628 1. While SEM (Fig. S6) shows MOFbot-microvilli interaction, no experiments\n629 directly prove that force transduction requires intact microvilli. May the author treat\n630 cells with cytochalasin D (disrupts actin in microvilli) and repeat TFM/FRET assays?\n632 mechanoresponses in microvilli-deficient cell lines (e.g., CRISPR-edited HeLa lacking\n633 ezrin).\n635 directly test the role of intact microvilli in MOFbot-mediated force transduction. In\n636 response, we have performed two complementary experimental approaches—ezrin\n637 knockdown and cytochalasin D treatment—to systematically evaluate the structural and\n638 functional contribution of microvilli to the observed mechanoresponses.\n640 microvilli-deficient HeLa cells. These cells were generated via knockdown of ezrin, a\n641 key actin-cytoskeleton linker protein known to be essential for microvilli formation and\n642 maintenance [ACS Nano 2021, 15, 15803-15814; Sci. Adv. 2022, 8, eabo2779]. As\n643 detailed in the Experimental Section of revised manuscript, HeLa cells were transiently\n644 transfected with ezrin-targeting siRNA, leading to efficient degradation of ezrin mRNA\nP\nA\nG\nE\n\\\n645 and a marked reduction in ezrin protein levels. This led to marked disruption of\n646 microvilli stability and a significant loss of their surface density. SEM and CLSM\n647 imaging (Figure R2.1) confirmed the effective reduction of microvilli structures in\n648 these cells compared to normal HeLa controls.\n649\n651 cell treated with siRNA targeting ezrin (c, d).\n653 microvilli-deficient HeLa cells. TFM analysis revealed a significant decrease (p <\n654 0.0001) in bead displacement and traction stress in microvilli-deficient cells, regardless\n655 of whether MOFbots were magnetically actuated (Figure R2.2). This indicates a clear\n656 decay in localized mechanical stress transmission at the cell-microrobot interface upon\n657 microvilli disruption. Furthermore, FRET-based analysis of intracellular forces showed\n658 that magnetic actuation of MOFbots induced a sharp reduction in FRET efficiency in\n659 normal HeLa cells, which gradually recovered after the magnetic field was turned off.\n661 efficiency under identical conditions (Figure R2.3), indicating a loss of mechanical\n662 responsiveness to microrobot stimulation. These results confirm that microvilli\n663 integrity is essential for efficient mechanotransduction.\nP\nA\nG\nE\n\\\n664\n666 on normal HeLa cells and microvilli-deficient HeLa cells. (a, b) Representative displacement and\n667 traction stress maps of MOFbot-induced substrate deformation in normal and microvilli-deficient\n670\n672 cells and microvilli-deficient HeLa cells. (a, b) Representative time-lapse images displaying the\n673 change of FRET signals in normal and microvilli-deficient HeLa cells under the influence of\n675 over time. (d) Average FRET signal in normal and microvilli-deficient HeLa cells at the initial time\n676 point without MF (0 min) and after 15 min with MF application (15 min) (N =22 cells).\n678 cytochalasin D, a known actin-disrupting agent, and repeated TFM and FRET assays.\nP\nA\nG\nE\n\\\n680 targeting microvilli, its treatment indeed induced microvilli collapse and cytoskeletal\n681 disorganization (Figure R2.4). Under these conditions, both TFM and FRET\n682 measurements showed nearly complete abrogation of MOFbot-induced traction forces\n683 and intracellular force responses (Figures R2.5 and R2.6). These observations reinforce\n684 the conclusion that an intact actin cytoskeleton—including microvilli—is indispensable\n685 for the force transmission elicited by MOFbot stimulation.\n686\n688\n690 cytochalasin D. (a) Representative displacement and traction stress maps of MOFbot-induced\n691 substrate deformation in HeLa cell treated with cytochalasin D, derived from PA-induced lateral\n692 deformation. (b, c) Quantitative analysis of displacement and traction stress (N = 22 cells).\nA\nG\nE\n\\\n694\n696 treated with cytochalasin D. (a) Representative time-lapse images displaying the change of FRET\n697 signals in HeLa cells treated with cytochalasin D under the influence of MOFbots. (b) Quantitative\n698 analysis of the FRET signal intensity changes in HeLa cells treated with cytochalasin D over time.\n699 (c) Average FRET signal intensities in HeLa cells treated with cytochalasin D, assessed at the initial\n700 time point without MF (0 min) and after 15 min with MF application (15 min) (N = 22 cells).\n702 microvilli act as a primary mechanical scaffold, enabling localized force transmission\n703 from the MOFbots to the cell interior. The consistent loss of mechanoresponse upon\n704 either specific ezrin knockdown or general actin disruption underscores the functional\n705 importance of microvilli in this process. We have incorporated these new experimental\n706 results and corresponding discussions into the revised manuscript.\n708 added to the revised manuscript:\n709 (Page 12) To verify the role of cellular microvilli in force transduction, we performed\n710 siRNA-mediated knockdown of ezrin, which is a key scaffolding protein essential for\n711 microvilli integrity. Transient transfection of HeLa cells with ezrin-specific siRNA led\n712 to efficient suppression of ezrin expression, which in turn induced pronounced\n713 destabilization of microvillar protrusions. As a consequence, ezrin-depleted cells\n714 exhibited a substantial reduction in microvilli density. This morphological impairment\n715 was corroborated by both SEM and CLSM analyses, which consistently demonstrated\n716 a marked loss of surface microvilli relative to control cells (Figure S9). Under identical\n718 attenuated bead displacement and traction stress (Figure 4b-4d). These results\n719 demonstrate that mechanical forces are transmitted via microvilli to the underlying\n720 substrate, confirming the essential role of microvilli in mediating the physical\nP\nA\nG\nE\n\\\n721 interactions between MOFbots and cellular structures.\n722 (Page 13) In addition, FRET efficiency measurements in microvilli-deficient HeLa\n723 cells revealed markedly reduced molecular tension and a lack of response to MOFbot-\n724 induced mechanical stimulation, further corroborating that the disruption of microvilli\n725 leads to a significant decrease in cellular traction forces and abolishes the cells’ ability\n726 to transduce mechanical cues (Figure 4g-4i). These findings are consistent with the cell\n727 traction force measurements, reinforcing the critical role of microvilli in mediating\n728 cellular mechanotransduction. Moreover, treatment of HeLa cells with cytochalasin D,\n729 which is a known actin-disrupting agent, also induced microvilli collapse and\n730 cytoskeletal disorganization (Figure S11). Under these conditions, both TFM and FRET\n731 measurements showed nearly complete abrogation of MOFbot-induced traction forces\n732 and intracellular force responses (Figures S12 and S13). These results demonstrate that\n733 an intact actin cytoskeleton including microvilli is indispensable for the force\n734 transmission elicited by MOFbot stimulation. These complementary lines of evidence\n735 confirm the essential role of microvilli in mediating the physical interactions between\n737 (Page 27) Ezrin-siRNA transfection. HeLa cells were cultured in DMEM medium\n738 containing 20% FBS and then transfected with 50 nM ezrin small interfering RNA\n739 (siRNA) using Lipo8000 Transfection Reagent (Beyotime Biotechnology, China) for\n740 48 h. The designed ezrin-targeting siRNA sequences are\n743 2. Calcium/FAK data (Fig. 5) imply mechanosensing but do not identify specific ion\n744 channels (e.g., Piezo1/TRPV4) responsible. Use pharmacological inhibitors (e.g.,\n746 immunofluorescence/co-IP for Piezo1/TRPV4 recruitment to microvilli during force\n747 application.\n749 of the specific mechanosensitive ion channels involved in the calcium response\n750 triggered by MOFbot stimulation. In response to this comment, we performed a series\n751 of pharmacological and imaging experiments to examine the potential roles of two\n752 major mechanosensitive channels—PIEZO1 and TRPV4—in this process.\n754 used selective pharmacological inhibitors. HeLa cells were pre-treated with 1 μM\n757 of intracellular Ca2+ levels was then performed during magnetic actuation of gear-like\nP\nA\nG\nE\n\\\n760 baseline (Figure R2.7a, b). By contrast, in cells pre-incubated with either GsMTx4 or\n762 in fluorescence intensity over the same period. This indicates that the blockade of either\n763 channel substantially impairs the magneto-mechanically evoked Ca2+ influx.\n765 from 50 individual cells treated with rotating gear-like MOFbots under each condition\n766 (Figure R2.7c-e). Each color-scaled horizontal line represents the time-dependent\n767 changes in intracellular Ca2+ levels for an individual cell. While the majority of\n768 untreated control cells exhibited a pronounced increase in cytosolic Ca2+ following\n769 mechanical stimulation, more than 90% of cells treated with either GsMTx4 or HC-\n770 067047 showed suppressed Ca2+ signals. These results strongly suggest that both\n772 mechanical stimulation.\n773\n775 the gear-like MOFbots. (a, b) Fluorescence images and normalized fluorescence intensity of\n776 intracellular Ca2+ in response to gear-like MOFbot treatment under three conditions: untreated\n777 control cells, cells pre-incubated with 1 μM GsMTx4 (Piezo1 inhibitor), and cells pre-incubated\n778 with HC-067047 (TRPV4 antagonist, 1 μM). (c, d, e) Color-scaled heat map depicting the Ca2+\n779 response in HeLa cells under three conditions: untreated control, cells incubated with Piezo1\n780 inhibitor GsMTx4, and cells incubated with TRPV4 antagonist HC-067047, upon treatment with\n781 gear-like MOFbots (N = 50 cells).\n783 spatial redistribution of the mechanosensitive ion channels PIEZO1 and TRPV4 in\n784 response to force stimulation generated by the magnetically actuated MOFbots. Briefly,\n786 field to induce localized mechanical stimulation on the cell membrane. As a control\nP\nA\nG\nE\n\\\n787 group, parallel samples were maintained under identical conditions but without\n788 magnetic actuation, ensuring that any observed redistribution was specifically\n789 attributable to magnetically induced mechanical force rather than MOFbots attachment\n790 alone. After treatment, cells were fixed and subjected to immunostaining using primary\n791 antibodies against PIEZO1 and TRPV4, followed by Alexa Fluor 488 conjugated\n792 secondary antibodies. Microvilli were counterstained with TRITC-phalloidin (red) to\n793 delineate the microvilli-rich membrane regions, and nuclei were labeled with DAPI.\n795 localization of both channels between the two groups. As illustrated in Figures R2.8\n796 and R2.9, under no-field conditions, the green fluorescence signals of PIEZO1 and\n798 indicating a basal inactive state. In contrast, upon MOFbot actuation via the magnetic\n799 field, a marked redistribution occurred: both PIEZO1 and TRPV4 signals exhibited a\n800 clear and concentrated localization to the microvilli structures. The enhanced co-\n801 localization of PIEZO1 and TRPV4 with microvilli under mechanical stimulation\n802 strongly suggests that these channels are actively recruited to the microvilli region in\n803 response to force. This recruitment is consistent with their functional involvement in\n804 microvilli-mediated mechanotransduction.\n806 and TRPV4 act as key mediators of MOFbot-induced Ca2+ signaling, likely functioning\n807 in a complementary or synergistic manner. This is in line with previous reports (e.g.,\n809 mechanotransduction, with PIEZO1 often mediating rapid force-gated cation entry and\n811 into the revised manuscript.\nP\nA\nG\nE\n\\\n812\n814 with gear-like MOFbots under conditions without (a) or with (b) magnetic field application.\n815\n817 gear-like MOFbots under conditions without (a) or with (b) magnetic field application.\n819 the following.\n820 (Page 15-16) To investigate the contribution of specific mechanosensitive channels to\n822 which are two mechanoreceptors implicated in distinct force-sensing regimes. PIEZO1\nP\nA\nG\nE\n\\\n823 generates rapid mechano-activated cation currents, while TRPV4 confers sensitivity to\n824 piconewton-scale forces. HeLa cells were pretreated with GsMTx4 (PIEZO1 inhibitor)\n825 and HC-067047 (TRPV4-specific antagonist). Time-lapse images of representative\n826 untreated control cell (Video S7, Figure 5b) under MOFbot stimulation revealed a\n827 progressive increase in jGCaMP7s fluorescence intensity over time, corresponding\n828 directly to elevated cytosolic Ca2+ levels and thus demonstrating active calcium influx\n829 in response to mechanical force. Quantitative analysis confirmed that, in untreated\n830 control cells, the mean Ca2+ fluorescence intensity at t = 480 s was approximately twice\n831 that at baseline (t = 0 s) (Figure 5c). In contrast, cells pre-incubated with either inhibitor\n832 exhibited markedly attenuated Ca2+ responses, with only negligible changes in\n833 fluorescence intensity over the same period. To assess population‑level behavior, Ca2+\n834 influx heat maps were generated for 50 cells under each of three conditions: untreated\n835 control, GsMTx4‑treated, and HC‑067047‑treated (Figure 5d-5f). Each horizontal line\n836 represents the time‑dependent intracellular Ca2+ change in an individual cell. In control\n837 cells, despite the cell-to-cell variation of calcium response, the majority of cells\n838 exhibited a significant increase in cytosolic Ca2+ concentration following mechanical\n839 stimulation (Figure 5d). Conversely, more than 90% of cells pretreated with either\n840 inhibitor displayed suppressed Ca2+ signals, with minimal fluorescence elevation over\n841 time (Figure 5e and 5f). Collectively, these results demonstrate that gear-like MOFbots\n842 elicit a robust intracellular calcium response, and that both PIEZO1 and TRPV4\n843 channels are essential for efficient Ca2+ entry upon MOFbot-mediated mechanical\n844 stimulation.\n845 (Page 16) To further elucidate the spatial redistribution of the mechanosensitive\n846 channels in response to force, we performed immunofluorescence staining for PIEZO1\n847 and TRPV4 following MOFbot stimulation. HeLa cells incubated with MOFbots were\n848 exposed to a rotating magnetic field to apply localized mechanical force, with parallel\n849 control samples maintained without magnetic actuation to isolate the effect of the force\n850 itself. After fixation, cells were immunostained for PIEZO1 and TRPV4, with\n851 microvilli stained using phalloidin and nuclei labeled with DAPI. CLSM imaging\n852 revealed a force-dependent redistribution of both channels. In the absence of magnetic\n853 actuation, PIEZO1 and TRPV4 signals were diffuse along the plasma membrane\n854 (Figure S16a and S17a). In contrast, upon MOFbot actuation, both channels exhibited\n855 pronounced translocation and concentrated localization to microvilli structures (Figure\n857 stimulation indicates an active recruitment process, aligning with their functional role\n858 in microvilli-mediated mechanotransduction.\n859 (Page 27) Inhibition of mechanosensitive ion channels. HeLa cells transfected with\n860 plasmid of pCMV-jGCaMP7s were pretreated with specific pharmacological inhibitors.\nP\nA\nG\nE\n\\\n863 that selectively blocks PIEZO channels. For TRPV4 inhibition, cells were treated with\n864 1 μM HC-067047 (MedChemExpress, USA), a selective TRPV4 antagonist. Inhibitors\n865 were added to the culture medium 30 min prior to MOFbots stimulation and maintained\n866 throughout the imaging process. Following inhibitor treatment, calcium dynamics were\n867 monitored and fluorescence changes were recorded under magnetic field.\n868 (Page 27) Immunofluorescence staining. HeLa cells were fixed in 4%\n869 paraformaldehyde for 15 min and washed three times with PBS. Subsequently, the cells\n870 were permeabilized using 0.25% v/v PBST (Triton-X 100 in PBS) for 10 min. To avoid\n871 nonspecific protein adsorption, the samples were incubated for 1 h in a blocking\n872 solution containing 1% (w/v) bovine serum albumin (BSA) in PBST (0.1% v/v). Next,\n873 samples were incubated overnight at 4℃ with primary antibodies against phospho-FAK\n874 (Tyr397) (1:300, Thermo Scientific), Piezo1 (1:200, Proteintech), TRPV4 (1:500,\n876 3. Fig. S16: DOX release curves lack statistical testing (perform ANOVA),\n877 weakening claims of magnetic-field- enhanced release. In addition, Label axes clearly.\n878 “% DOX released” vs. “Time” is suggested.\n880 the DOX release profiles and for the suggestion regarding axis labeling. In response,\n881 we have now performed one-way ANOVA with Tukey’s post hoc test to quantitatively\n882 evaluate the differences in DOX release under magnetic field versus no-field conditions.\n884 enhancement in DOX release across multiple time points (Figure R2.10). In addition,\n885 following the reviewer’s suggestion, we have relabeled the axes as “% Cumulative\n888 the release of DOX from gear-like MOFbots.\n889\n891 of a magnetic field. Error bars indicate standard deviation (N = 3). Significance displays were\n892 measured using one-way ANOVA with Tukey’s post hoc test. *P < 0.05, **P < 0.01, ***P < 0.001,\n893 and ****P < 0.0001.\nP\nA\nG\nE\n\\\n894 4. CCK-8 data (Fig. S13) only assess 20-min exposure. Chronic toxicity (e.g., 24-h\n895 exposure) and immune clearance risks are unexplored. It is suggested to test\n896 viability/apoptosis after prolonged MOFbot exposure (6-24 h).\n898 potential chronic cytotoxicity and biocompatibility of MOFbots under prolonged\n899 exposure. To address this concern, we conducted additional CCK-8 assays to\n900 systematically evaluate cell viability after extended incubation periods (6, 12, and 24 h)\n901 across a range of MOFbot concentrations (Figure R2.11). The results indicate that at\n902 the highest concentration tested (160 μg/mL), cell viability remained above 90% after\n903 12 h of exposure, demonstrating minimal acute cytotoxicity within this timeframe.\n905 reduction in viability to approximately 70% was observed, suggesting that prolonged\n906 exposure at high loading levels may induce partial cytotoxic effects.\n908 this study, the MOFbot concentration was maintained well below these thresholds\n909 (≤160 μg/mL). Therefore, the mechanoresponses and associated biological effects\n910 reported here are unlikely to be confounded by cytotoxicity, and the safety profile of\n912\n915 5. Discuss challenges for in vivo translation (e.g., shear stress in blood, opsonization)\n916 in the Discussion.\n918 incorporated a dedicated discussion on the key challenges for in vivo translation of\n919 gear-like MOFbots into the revised manuscript (Pages 23). The added paragraph reads\n920 as follows:\n921 (Page 23) Our results demonstrate the potential of MOFbots for precise\n922 mechanostimulation in controlled settings, yet several key challenges must be\n923 addressed before their translation to in vivo applications. The dynamic and complex\nP\nA\nG\nE\n\\\n924 hydrodynamic environment of the circulatory system, for instance, imposes substantial\n925 shear stresses that may compromise stable contact between MOFbots and target cells.\n927 opsonization and rapid clearance by the mononuclear phagocyte system, which would\n928 significantly limit their bioavailability and accumulation at desired sites. To overcome\n929 these barriers, future designs could incorporate shape optimization to improve flow\n930 characteristics and surface modifications—such as PEGylation—to reduce protein\n931 adsorption and immune recognition. In parallel, advanced real-time imaging and\n932 navigation technologies will be essential for guiding MOFbots in vivo with high\n933 spatiotemporal precision. Notably, microrobotic platforms need not operate\n934 independently of molecular targeting paradigms. By conjugating magnetic MOFbots\n935 with specific targeting ligands (e.g., antibodies, peptides), it becomes possible to\n936 synergize externally controlled navigation with intrinsic biochemical recognition,\n937 thereby merging spatial programmability with biochemical specificity. Such a hybrid\n938 strategy, rather than replacing conventional targeting, extends the therapeutic toolkit\n939 and may substantially improve localization efficiency under physiological flow\n940 conditions. Finally, thorough evaluation of safety, biocompatibility, and therapeutic\n941 efficacy in physiologically relevant animal models will be crucial. Despite these\n942 challenges, the unique capacity of MOFbots to deliver localized mechanical cues in situ\n943 offers a promising avenue for mechanobiology research and targeted mechanotherapy.\n944 6. The gear-like MOFbot is fabricated from a porous material. Could the authors clarify\n945 the pore size distribution parameters of the MOF structure?\n947 characteristics of the gear-like MOFbot. The MOFbot is fabricated from a hybrid\n948 material system incorporating both MIL-125 and ZIF-8 frameworks. Nitrogen\n949 adsorption-desorption measurements conducted at 77 K confirm the microporous\n950 nature of the material, with pore size distribution analysis revealing a predominant pore\n951 width in the range of 0.7-1.6 nm (Figure R2.12). These results indicate that the gear-\n952 like microrobot preserves the intrinsic microporous framework of the constituent MOFs,\n953 which is essential for molecular encapsulation and mass transport. The complete pore\n954 size distribution profiles have been included as Figure S3 in the revised Supporting\nP\nA\nG\nE\n\\\n956\n958 7. In this study, the authors employed confocal microscopy to demonstrate that\n959 microrobots engineered into a spherical morphology (shape-control group) primarily\n960 localize on the microvilli surface with minimal entanglement or deep penetration under\n961 a magnetic field. To further strengthen this conclusion, SEM can be used to directly\n962 visualize the interaction between spherical microrobots and cellular microvilli.\n964 our earlier confocal microscopy findings, we have performed additional SEM\n965 experiments to directly visualize the interface between microrobots and cellular\n966 microvilli. The resulting SEM images (Figure R2.13) clearly show that spherical\n967 microrobots under magnetic fields merely rest on the microvilli layer without\n968 significant entanglement or penetration. In contrast, gear-like MOFbots under the same\n969 magnetic conditions become physically embedded within the dense filamentous surface\n970 structures. These SEM observations provide direct morphological evidence that\n971 strongly supports our conclusion of morphology-dependent microrobot-microvilli\n972 interactions.\n973\n975 cell (red: microvilli; yellow: microrobots): (a) gear-like MOFbots in the presence of a magnetic\n976 field, (b) gear-like MOFbots in the absence of a magnetic field, and (c) PS@Ni@Au spherical\n977 microrobots in the presence of a magnetic field.\n978 8. How does the instantaneous velocity of PS@Ni@Au composite microspheres vary\n979 in response to the surface topography when moving across a patterned substrate?\nP\nA\nG\nE\n\\\n981 regarding the dynamic response of spherical microrobot to surface topography. In\n982 response, we conducted a quantitative analysis of the instantaneous velocity of\n984 magnetic actuation. Our results demonstrate that these microspheres maintain a\n985 relatively stable forward velocity of approximately 2 μm/s prior to encountering\n986 topographic obstacles, which is lower than that of gear-like MOFbots under identical\n987 magnetic actuation conditions. Besides, upon reaching the obstacle pattern, they fail to\n988 traverse the barrier and instead exhibit a characteristic reciprocating motion along the\n989 obstacle edges. In this regime, the instantaneous velocity oscillates around 0 μm/s,\n990 reflecting the absence of net forward displacement (Figure R2.14). This pronounced\n991 contrast with gear-like MOFbots—which effectively navigate same topographic\n992 patterns under identical magnetic actuation conditions—highlights the critical role of\n993 morphology in determining locomotive performance on structured surfaces. The\n994 constrained mobility observed for spherical microrobots further underscores the\n995 functional advantage of gear-like architectures in complex microenvironments.\n996\n998 substrate (1.5 μm height) under a perpendicularly rotating magnetic field (6 Hz, 6 mT).\n999 9. Figures revisions:\n1000 1) Please provide the comparative fluorescence images of DOX in HeLa cells treated\n1001 with drug-loaded, gear-like MOFbots under both magnetic field-free (control) and\n1002 magnetically activated (experimental) conditions.\n1004 included comparative fluorescence images depicting DOX distribution in HeLa cells\n1005 following treatment with drug-loaded, gear-like MOFbots under both magnetic field-\n1006 free (control) and magnetically activated (experimental) conditions. As shown in Figure\n1008 magnetic actuation compared to the static control, indicating that the rotational motion\n1009 of MOFbots under a magnetic field enhances the cellular uptake of DOX. This visual\nP\nA\nG\nE\n\\\n1010 evidence supports our conclusion that mechanical stimulation via rotating MOFbots\n1011 promotes transmembrane drug delivery. Additional representative fluorescence images\n1012 are provided as Figure S25 in the revised Supporting Information.\n1013\n1015 absence (a) and presence (b) of magnetic field.\n1016 2) Please added scale bar to Figure 3d, and replace pixel-based Unet++ plots with 3D\n1017 reconstructions of MOFbot-microvilli entanglement (video supplement suggested) in\n1020 been added to Figure 3d as requested. In addition, we thank the reviewer for this\n1021 insightful suggestion and fully agree that Unet++ plots with 3D reconstructions are\n1022 crucial for demonstrating the entanglement. We appreciate the opportunity to clarify\n1023 our methodology. The quantitative plots in Figure 3e-g are, in fact, the direct result of\n1024 3D analysis. Specifically, our Unet++ model was applied to perform semantic\n1025 segmentation on each 2D slice (z-stack) of our cellular 3D confocal image volumes.\n1027 were conducted across the entire 3D volume at the voxel level. As shown in Figure\n1029 comprehensive 3D distribution, allowing for rigorous quantitative comparison of the\n1030 interactions between microrobots and microvilli using CLSM images.\n1031\n1033 voxel type (red and green) in stained cells treated under different conditions: (a) gear-like MOFbots\n1034 with a magnetic field, (b) gear-like MOFbots without a magnetic field, and (c) PS@Ni@Au\n1035 microrobots with a magnetic field.\nP\nA\nG\nE\n\\\n1036 3) The color bar in Figure 4b and 4d is unclear.\n1038 revised the color bars in Figure 4b and 4d (Figure 4b in the revised manuscript) to\n1039 enhance clarity. The updated figure is provided below (Figure R2.17).\n1040\n1042 substrate deformation in normal and microvilli-deficient HeLa cells, derived from PA-induced\n1043 lateral deformation.\n1044 4) The scale bar in Figure S5a seems incorrect.\n1046 (Figure S7a in the revised manuscript) have been revised. The updated figure is\n1047 provided below for your convenience (Figure R2.18). We have verified the accuracy of\n1048 all scale bars in the manuscript to prevent similar issues.\n1049\n1051 5) Figure S6 confirms MOFbot-microvilli entanglement, but no disassembly control\n1052 (e.g., cytochalasin).\nP\nA\nG\nE\n\\\n1054 necessity of a disassembly control. In response, we performed additional experiments\n1055 in which HeLa cells were pre-treated with cytochalasin D to disrupt actin-dependent\n1056 microvilli integrity prior to MOFbot exposure under a magnetic field. As shown in\n1058 effectively abolished the MOFbot-microvilli entanglement observed in untreated cells.\n1060 required for the physical interaction between MOFbots and the cell surface.\n1061\n1063 after treated with cytochalasin D under a magnetic field (red: microvilli; yellow: microrobots).\n1064 10. Text optimization:\n1065 1) Condense broad mechanotransduction background (Lines 48-79) to focus on\n1066 microvilli knowledge gaps in the introduction.\n1068 manuscript, we have condensed the general mechanotransduction background and\n1069 shifted the focus toward microvilli-related knowledge gaps.\n1070 2) Elaborate on disease relevance (e.g., microvillus inclusion disease, metastasis)\n1071 beyond HeLa cells in the Discussion.\n1073 disease relevance beyond the HeLa model. In response to this valuable suggestion, we\n1074 have expanded the Discussion section of revised manuscript to include broader\n1075 pathological contexts in which microvilli play a critical role. Specifically, we now\n1076 highlight microvillus inclusion disease, where loss of functional microvilli leads to\n1077 severe nutrient absorption defects, and cancer metastasis, during which microvilli\n1078 remodeling promotes tumor cell adhesion and invasion. The MOFbot platform offers a\n1079 unique means to mechanically probe or therapeutically modulate such altered\n1080 mechanotransductive pathways in these diseases. Furthermore, we have extended the\n1081 discussion to immune cells, in which microvilli are known to facilitate antigen sensing\nP\nA\nG\nE\n\\\n1082 and immune synapse formation, thereby underscoring the versatility of our approach\n1083 for mechanically regulating microvilli-related functions across multiple disease models.\n1085 position the MOFbot as a promising tool for both investigating and potentially\n1086 intervening in microvilli-associated pathologies.\n1088 (Page 22) Beyond HeLa cells, the gear-like MOFbot platform is broadly relevant to\n1089 diseases where microvilli are central to pathophysiology. In microvillus inclusion\n1090 disease, defective or absent microvilli disrupt nutrient absorption, while in cancer\n1091 metastasis, microvilli remodeling influences tumor cell adhesion, migration, and\n1092 invasion. Our system could probe these altered mechanotransductive processes or serve\n1093 as a means to modulate them therapeutically. Extensions to immune cells, where\n1094 microvilli orchestrate antigen sensing and synapse formation, further highlight the\n1095 versatility of MOFbot-mediated mechanical regulation. These capabilities position our\n1096 approach as a powerful tool for studying and potentially intervening in microvilli-\n1097 associated diseases.\n1098 3) Specify magnetic field parameters (coil geometry, gradient strength) omitted in Lines\n1099 532-538, and clarify if FRET controls included baseline cell deformation (Line590) in\n1100 the Methods.\n1102 included detailed magnetic field system parameters in Methods section of the revised\n1103 manuscript.\n1104 (Page 25) The magnetic actuation system used for driving the gear-like MOFbots\n1105 comprises five electromagnetic coils, each with 600 turns of 0.4 mm diameter wire and\n1106 an 8 mm diameter ferrite core to enhance magnetic field strength. A programmable\n1107 power supply controlled by a myRIO embedded system was employed to generate the\n1108 coil input signals, allowing flexible and reproducible application of magnetic fields\n1109 during experiments.\n1111 baseline cell deformation in the absence of magnetic stimulation under otherwise\n1112 identical experimental conditions. These results confirm that intracellular FRET signals\n1113 remain stable over time (as shown in Figure R2.20), indicating no significant\n1114 spontaneous cellular deformation or photobleaching effects during the observation\n1115 window. This supports the conclusion that the FRET changes observed in our study are\n1116 specifically attributable to the mechanical stimulation by magnetically actuated\nP\nA\nG\nE\n\\\n1118\n1120 (b) in HeLa cells expressing actin force probes under control conditions, showing baseline\n1121 intracellular tension in the absence of external mechanical stimulation.\n1124 (Page 13) Critically, in control experiments performed under identical conditions\n1125 without magnetic fields, the FRET efficiency in HeLa cells remained stable over time\n1126 (Figure S10), effectively excluding contributions from spontaneous cellular\n1127 deformation or photobleaching.\n1128 (Page 27) To assess potential baseline fluctuations in intracellular FRET signals, HeLa\n1129 cells expressing actin-based force probes were subjected to the same experimental\n1130 conditions as the MOFbots stimulation assays, except that no magnetic field was\n1131 applied.\nP\nA\nG\nE\n\\\nResponse to Reviewer #1\nI thank the authors for their detailed answers to the reviewers’ queries and careful\ncorrection of the manuscript: This was significantly improved. However, I regret that\nmy general opinion on this paper was not changed and I shall try to explain this as\nclearly as possible.\nMy feeling is that three main messages are included:\ni) The manuscript provides a detailed description of a new platform involving so-called\nMOFbots (microscopic rigid objects controlled by magnetic fields). I think that this is\nwell described, it is novel and aesthetically appealing. This may be sufficient to warrant\npublication.\nResponse: We sincerely thank the reviewer for the thoughtful and constructive\nfeedback, as well for the encouraging recognition of our work. We are very glad to learn\nthat the MOFbot platform was considered novel, clearly described, and aesthetically\nappealing, and that the reviewer views these qualities as potentially sufficient for\npublication. We appreciate the reviewer’s effort in re-evaluating our manuscript and\nproviding clear explanations, which have helped us further reflect on the presentation\nand implications of our study. In this response, we have strived to address the reviewer’s\nconcerns, while also clarifying the scientific rationale and significance of our approach.\nWe hope to fully convey our respect for the reviewer’s perspective and our confidence\nin confidence\nin the validity and value of the reported findings.\nii) The authors used a number of powerful methods such as traction force microscopy,\nadvanced image analysis, SEM, CLSM, fluid mechanics simulation to support the\nhypothesis that MOFbots triggered cell signaling events (intracellular calcium rise,\nphosphorylation) by exerting mechanical forces on microvilli. This hypothesis is\nreasonable, but it is not very surprising. In addition to the comments included in my\nfirst review, I would add a personal recollection: when I began playing with fluorescent\ncalcium probes nearly four decades ago, it rapidly became apparent that a gentle blow\napplied on a cell with a flexible microneedle mounted on a micromanipulator generated\na calcium rise.\nResponse: We sincerely thank the reviewer for this insightful comment and for sharing\nthe valuable historical perspective from early mechanobiology research. We fully agree\nwith the reviewer that the fundamental principle—applied mechanical force triggering\nintracellular calcium elevation—is a well-established and seminal concept in the field,\nelegantly demonstrated decades ago by techniques such as microneedle manipulation.\nThe primary novelty we aim to convey is not in rediscovering this fundamental\nprinciple, but rather in establishing a new paradigm for how such mechanical\ninterrogation can be performed and what specific biological questions it can now\naddress. While a tethered microneedle delivers a point force, our wireless, gear-shaped\nMOFbot enables a distinct mode of interaction: programmable, spatially targeted, and\ngeometry-driven mechanical entanglement with specific subcellular structures—the\nmicrovilli. This allows us to move beyond asking if force causes signaling, to\ninvestigate how force delivered through a particular architectural module of the cell\ntranslates into downstream responses.\nConsequently, the key advance reported here is the use of this novel tool to demonstrate\nthat microvilli act as essential mechanotransduction conduits. Our data show that the\nMOFbot's force application is specifically coupled to microvilli, and critically, that\ndisrupting microvilli integrity via ezrin knockdown leads to a decrease in cellular\ntraction force responsiveness and abolishes the cells’ ability to transduce mechanical\ncues. This causal link—from robotic actuation, to microvilli-specific mechanical\nengagement, to defined signaling outputs—constitutes a new finding enabled by our\nplatform. The integration of TFM, FRET-based molecular tension sensors, and\nsimulations allows us to quantitatively correlate this structured mechanical input with\ncellular output in a way that was previously difficult with blunt or bulk stimulation\nmethods.\nTherefore, we posit that the significance of the work lies in introducing a versatile\nrobotic interface that opens the door to studying force transduction at the level of\nspecific cellular protrusions, providing both a new tool and a mechanistic insight into\nmicrovilli-mediated mechanosensing.\niii) The authors suggest that their platform may be useful for two main purposes:\ndissecting mechanotranduction cascades and drug targeting for medical purposes.\nMy feeling is that the mechanical forces generated by MOFbots are too ill-defined to\nimprove our understanding of mechanotransduction. As a single example, it is difficult\nto assess the meaning of the 46pN force mentioned in the revised paper. In addition to\nthe papers quoted in my first review, I would mention the report that a localized stress\nof order of one kPa could induce a kinase activation at a remote place within less that a\nsecond (Na et al. Pnas 105:6626, 2008) or that a force of 12pN exerted on an integrin\nreceptor could trigger signaling events (Wang et al., Science 340:991, 2013). More\nrecently, several authors described a number of \"platforms\" allow detailed monitoring\nof cel activation. My personal feeling is that detailed monitoring of well defined events\nwith high spatial and temporal resolution is.\nResponse: We thank the reviewer for citing these seminal works (Na et al., 2008; Wang\net al., 2013) and providing this critical perspective. We agree that traditional tethered\nmethods (such as AFM or optical tweezers) offer superior resolution for defining single-\nmolecule forces. However, we posit that the MOFbot platform fills a distinct and\ncomplementary niche by enabling wireless, non-invasive stimulation of subcellular\nstructures without the physical constraints of cantilevers or probes.\nRegarding the reported force magnitude (~46 pN), we emphasize that this value\nrepresents a quantitative approximation of the peak force exerted on a single\nmicrovillus via numerical simulation. As noted by the reviewer, forces as low as 12 pN\ncan trigger integrin signaling (Wang et al., 2013). Therefore, a localized force of 46 pN\nis well within the physiological range to elicit meaningful mechanotransduction events,\nas confirmed by our calcium and phosphorylation data.\nImportantly, the unique feature of the MOFbot platform lies in its ability to generate\ndynamic, collective, and repetitive mechanical interactions with cell-surface microvilli\nunder externally programmable magnetic control, which are difficult to replicate using\nstatic or single-point force application methods. Our data demonstrate that such\nmechanically driven interactions are sufficient to elicit reproducible downstream\nsignaling responses.\nThus, we view MOFbots not as a replacement for high-precision single-molecule\nspectroscopy, but as a complementary tool designed for scenarios where wireless,\nswarm-level, or long-duration mechanical stimulation is required.\nAlso, I feel that biochemical drug targeting (e.g. with antibodies) of drugs into particular\ncell populations is much more efficient and versatile than the actuation of MOFbots in\na living organism. I do not understand how magnetic fields could be used to facilitate\ncell-drug contacts.\nResponse: We sincerely thank the reviewer for raising this important point regarding\ntargeting efficiency and for providing an opportunity to further clarify the unique value\nproposition of our MOFbot platform. We fully acknowledge the exceptional specificity\nand established utility of biochemical targeting strategies, such as antibody-based\ndelivery, which represent a cornerstone of modern therapeutic approaches.\nHowever, our work explores a complementary strategy that addresses certain inherent\nlimitations of passive, diffusion-dependent delivery. The core distinction of our\nMOFbot platform lies in its capacity for active, magnetically guided transport. While\nsystemic circulation and passive diffusion govern the final distribution of most\nbiochemical agents, our system allows for the direct magnetic navigation of MOFbots\nto targeted sites. This capability can potentially overcome diffusional barriers and\nachieve higher localized therapeutic concentrations, particularly in tissues or\nmicroenvironments where passive diffusion is inherently limited—a scenario where\ntraditional targeting may see reduced efficacy.\nRegarding the reviewer’s specific question on how magnetic fields facilitate cell-drug\ncontacts, the key mechanism is dynamic mechanical actuation. The applied magnetic\nfield does not merely serve for global attraction; it enables us to precisely drive the\nMOFbots to perform controlled motions—such as rotation—in immediate proximity to\nthe target cells. This active mechanical motion physically enhances the local fluid\ndynamics and interaction frequency at the cell-fluid interface. Besides, we propose that\nthis direct, motion-facilitated contact not only increases the probability of payload\nexchange but also gently interact with the microvilli on cell membrane, thereby altering\nmembrane’s permeability and mechanically assisting the internalization processes.\nThis represents a fundamentally different paradigm from the static binding of a ligand\nto its receptor, which cannot actively promote this mechanical component of uptake.\nFurthermore, regarding versatility, while antibody efficacy is indeed highly specific,\nit is also inherently dependent on the consistent and sufficient expression of the target\nreceptor across the intended cell population. Our mechanically assisted approach offers\na potential alternative or complementary route for cells with low or variable receptor\nexpression, where sole reliance on biochemical targeting might be suboptimal. The\nplatform’s versatility stems from its decoupling of delivery efficiency from specific\nbiochemical recognition events.\nIn summary, the MOFbot platform introduces a layer of active mechanical facilitation\nand physical permeabilization enhancement to the drug delivery process. We propose\nthat this approach is fundamentally different from, and potentially complementary to,\nclassical biochemical ligand-receptor targeting, offering a new dimension for precise\nintervention at the cellular level. We thank the reviewer again for this insightful\ncomment, which has allowed us to elaborate on these distinguishing conceptual and\nmechanistic advantages.\nI agree that these feelings are personal, and that it may be difficult to predict the future\nuse of a novel technique. However, I was not convinced by the authors presentation of\nthese prospects.\nResponse: We thank the reviewer for this thoughtful comment. We agree that predicting\nlong-term applications is difficult at this stage. Our goal in this work has been more\nimmediate: to experimentally demonstrate that MOFbot-mediated mechanical\nstimulation of microvilli can actively trigger intracellular signaling and modulate\ncellular permeability for targeted drug delivery. We sincerely appreciate the reviewer's\nsuggestions throughout the review process, which have helped us improve the clarity\nand presentation of the manuscript. We believe the microrobotic platform established\nhere provides a solid and valuable contribution to the field, which may deserve\nconsideration for publication in Nature Communications.\nResponse to Reviewer #2\nThe manuscript now presents a robust and complete narrative that aligns well with the\nhigh standards of Nature Communications. While the interdisciplinary innovation\nremains a standout feature, the added mechanistic depth makes this a solid\nmechanobiological study. I recommend acceptance, subject to a few minor points\nintended to optimize the final presentation.\nResponse: We are grateful for the reviewer’s recommendation for acceptance and their\npositive assessment of the revised narrative. We have addressed the remaining minor\npoints point-by-point below to finalize the manuscript.\nSpecific Comments:\n1. The “ZIF-8 selective growth” area in Figure 1a is not clearly visible at the current\nresolution. A magnified view is suggested.\nResponse: We thank the reviewer for pointing this out. We have revised Figure 1a to\ninclude a magnified inset (or view) that clearly displays the “ZIF-8 selective growth”\narea. The figure has also been provided here as Figure R1 for your reference.\nFigure R1. Schematic illustration of the preparation process for the gear-like MOFbots.\n2. It is suggested to clearly label the microrobots in Figures 2j and 2l.\nResponse: We thank the reviewer for this helpful suggestion. We have updated Figures\n2j and 2l. The figures have also been provided here as Figure R2 for your reference.\nFigure R2. Time-lapse images of a moving gear-like MOFbot (a) and PS@Ni@Au\nspherical microrobot (b) on the patterned substrate.\n3. Please align the fluorescence labels (DAPI, FAK, etc.) in Figure 5g across the top of\nthe figure panel to show they apply to all groups, instead of restricting them to the\n“without MF” section.\nResponse: We appreciate this suggestion for improving figure readability. We have\nrealigned the fluorescence labels (DAPI, FAK, etc.) in Figure 5g across the top of the\npanel to clearly indicate that they apply to all experimental groups. The figure has also\nbeen provided here as Figure R3 for your reference.\nFigure R3. Representative fluorescence images of pFAK in HeLa cells exposed to the\ngear-like MOFbots under conditions with and without the application of a magnetic\nfield."}]

Decision letter