The ability to seamlessly merge electronic devices with biological systems at the cellular length scale is an exciting prospect for exploring new fundamental cell biology and in designing next-generation therapeutic devices. Semiconductor nanowires are well suited for achieving this goal because of their intrinsic size and wide range of possible configurations. However, current studies have focused primarily on delivering substrate-bound nanowire devices through mechanical abrasion or electroporation, with these bulkier substrates negating many of the inherent benefits of using nanoscale materials. To improve on this, an important next step is learning how to distribute these devices in a drug-like fashion, where cells can naturally uptake and incorporate these electronic components, allowing for truly noninvasive device integration. We show that silicon nanowires (SiNWs) can potentially be used as such a system, demonstrating that label-free SiNWs can be internalized in multiple cell lines (96% uptake rate), undergoing an active “burst-like” transport process. Our results show that, rather than through exogenous manipulation, SiNWs are internalized primarily through an endogenous phagocytosis pathway, allowing cellular integration of these materials. To study this behavior, we have developed a robust set of methodologies for quantitatively examining high–aspect ratio nanowire-cell interactions in a time-dependent manner on both single-cell and ensemble levels. This approach represents one of the first dynamic studies of semiconductor nanowire internalization and offers valuable insight into designing devices for biomolecule delivery, intracellular sensing, and photoresponsive therapies.

Keywords

( A ) Schematic illustration of SiNW internalization. ( B ) Confocal fluorescence micrograph of HUVECs (actin, red; tubulin, green) demonstrating SiNW internalization (blue scattering). Maximum projection in the xy plane (left; scale bar, 10 μm), interpolated projection in the yz plane (middle; height, 3.5 μm), and a thin confocal section taken along the dashed line segment n (right; height, 3.5 μm; length, 48.3 μm). ( C ) SEM micrograph of a HUVEC containing a SiNW [scale bar (top), 10 μm]. The magnified highlighted region indicates that the SiNW is embedded under the cell’s membrane [scale bar (bottom), 300 nm]. ( D ) TEM micrograph of a HUVEC thin section (~250 nm thick), with higher magnification insets, illustrating the distribution of internalized wires, both in vesicles and in the cytosol [scale bars, 1 μm (top) and 200 nm (insets)].

Previous work has shown that both label-free ( 27 , 28 ) and surface-modified ( 29 , 30 ) SiNWs can be internalized at the single-cell level in a substrate-free manner, making them a promising candidate for fulfilling these criteria. However, little is known about how these devices enter cells, especially in a time-dependent manner. In addition, without the use of labeling reagents to help elucidate the nano-bio interface, studying nanoparticle-cell interactions has been a challenge. However, the use of labeling reagents can also change the device’s surface functionalization, leading to altered surface recognition, making them potentially disruptive to any native nanowire-cell interactions. Yet, this label-free biointerface knowledge is critical in informing future device design and in implementing cell-specific targeting. To expand on this outlook, we demonstrate here that label-free SiNWs can be spontaneously internalized in cellular systems, showing that these materials are primarily endocytosed via a phagocytosis pathway and, once internalized, undergo active intracellular transport, eventually clustering in the perinuclear region. An overview of this can be seen in Fig. 1A , where a cell is depicted as initiating nanowire internalization. To study this process, we have developed a series of label-free tracking protocols for both individual- and ensemble-level dynamics. Finally, we will discuss how using these methods helps fit nanowire internalization into a more familiar biological picture, while raising many exciting opportunities in using nanowires as intracellular nanotherapeutics and diagnostic devices.

To date, SiNW-cell interactions have been primarily studied from the perspective of substrate-bound wires (those wires that are still joined to an underlying bulk material) ( 3 , 20 – 23 ) using gravity, electroporation, and/or adhesive forces to access the cell interior ( 18 , 24 ) or form invaginations ( 23 ). Although these configurations allow nanoscale devices to be connected directly to external macroscopic electronics, they lead to bulkier designs, which can be less desirable for certain applications. First, larger devices can exacerbate the mismatch in material properties (such as Young’s modulus, curvature, etc.) between typical inorganic semiconductors and biological samples, leading to poor device integration and increased irritation ( 25 ). Second, the incorporation of macroscopic substrates negates many of the benefits of using nanoscale materials in the first place, such as surgery-free device distribution and point-like localized probing. Finally, many of these devices require extensive wiring that extends outside the body, which can be disruptive to existing biological architecture and hinders the use of these devices at the single-cell level. Therefore, a more desirable approach would be to use substrate-independent devices capable of being dispersed in a drug-like manner and of being wholly internalized within a single cell. These materials could act as precursors for future “artificial bionanomachines” ( 26 ) or as therapeutic devices and probes that can operate independently at the subcellular level.

Inorganic nanomaterials have emerged as a promising class of materials for interfacing with biological systems ( 1 , 2 ), with nanoscale devices being used for both fundamental biophysical studies ( 3 – 5 ) and next-generation therapeutics ( 6 , 7 ). Among these materials, silicon nanowires (SiNWs) are of particular interest because of their excellent electronic properties, distinct one-dimensional (1D) structure, and potential biocompatibility ( 8 ). In addition, SiNW synthesis can be controllably tuned to incorporate a diverse set of functionalities, including modulation in structural morphology ( 9 , 10 ), surface functionalization ( 11 ), and composition ( 12 , 13 ). This allows for the realization of a large library of SiNW-based tools. As a result, SiNWs have been used as a successful nanoscale platform for a variety of applications, including single-cell electrophysiology experiments ( 14 ), biomarker detection ( 15 , 16 ), DNA and drug delivery ( 17 , 18 ), and protein kinetics studies ( 19 ). Despite these successes, there is still much to be understood about the interface between SiNWs and cellular systems, particularly for substrate-independent devices.

RESULTS

Serving as the inner lining of blood vessel walls, endothelial cells act as a filtration system between the bloodstream and the rest of the body, helping to regulate the uptake of drugs and clearing apoptotic blood cells and other extracellular materials. To study substrate-free SiNW endocytosis, human umbilical vascular endothelial cells (HUVECs) were selected as a model cell line. Because SiNWs can potentially be distributed in a drug-like fashion, endothelial cells would play a key role in mediating biointegration, with HUVECs, in particular, having been shown to recapitulate many of the features found in native vascular endothelial cells (31).

To show that label-free SiNWs could be internalized by HUVECs, we used both optical and electron microscopy techniques. First, confocal fluorescent microscopy was used to reconstruct 3D volumes containing HUVECs with internalized SiNWs (Fig. 1B). For these experiments, cells were incubated with SiNWs for 24 hours, and the cytoskeleton was labeled using the fluorescent markers tetramethylrhodamine-phalloidin (actin, red) and anti–α-tubulin Alexa Fluor 488 (microtubules, green), with label-free SiNWs visualized using optical scattering (28). The resulting 3D volumes and the line-scan cross section (Fig. 1B, right) were seen to contain SiNWs, demonstrating that SiNWs could be spontaneously internalized by HUVECs.

We next examined these interfaces in greater detail using electron microscopy (EM) techniques. First, HUVECs cultured with SiNWs were chemically fixed, critical point–dried, and then imaged using scanning EM (SEM). The resulting samples were observed to have SiNWs running beneath the cell membrane (Fig. 1C). To show that wires were actually contained within the cell body, cryopreserved thin cell sections were imaged using transmission EM (TEM). To prepare these samples, trypsinized cells were rapidly fixed using high-pressure freezing and processed using freeze substitution techniques (27). The resulting samples were then segmented using an ultramicrotome, yielding thin cell sections (~200 to 300 nm). TEM measurements revealed that internalized SiNWs showed a mixed distribution, with some wires floating free in the cytosol, whereas others were contained in small vesicles (Fig. 1D). This confirmed that SiNWs could be spontaneously internalized by HUVECs without additional surface modification, independent of external mechanical forces or electroporation. In addition, the formation of encapsulation vesicles suggested that the SiNWs entered the cells via an endogenous endocytosis process, rather than through mechanical abrasion (that is, puncturing the cell membrane). It was also observed that multiple SiNWs could be contained within a single larger vesicle, reminiscent of a lysosome (fig. S1), causing SiNWs to become clustered in the perinuclear region. This suggested a dynamic process where SiNWs could be shuttled to a specific cellular regions.

Although EM studies can provide detailed structural information, they only offer a static view of the internalization process. To further characterize this in a time-dependent fashion, we turned to dynamics studies, examining SiNW endocytosis on both the ensemble and single-cell level, with ensemble dynamics encompassing the large-scale interactions between many cells and many nanowires and single nanowire dynamics encompassing the interactions between individual nanowires and single cells.

Single nanowire dynamics Individual SiNW internalization dynamics were studied using scatter-enhanced phase-contrast (SEPC) microscopy, which allowed for a clear visualization of both SiNWs and adherent cells (Fig. 2A) (27). For this study, SiNWs were sonicated into growth medium and allowed to settle before they were introduced to HUVECs. Internalization dynamics were then monitored using a custom tracking algorithm, returning the position of each SiNW tip as a function of time (fig. S2). Nanowire dynamics were approximated as being 2D, a reasonable assumption, given the large aspect ratio of the nanowires and the thin volume of the lamellipodium, where transport is initiated. Tracking the nanowire’s tip positions allowed for a precise determination of both the nanowire’s velocity and mode of transport. To correct for stage drift, stationary particles were simultaneously tracked and used to adjust the relative path of the SiNW (fig. S3). Tracking individual SiNWs revealed an active transport process, where nanowires are shuttled to the perinuclear region within ~5 to 30 min of coming into contact with the internalizing cell (see movie S1). Fig. 2 Active SiNW transport. (A) SEPC micrograph of a SiNW before (top) and during (bottom; t = ~7 min per frame) internalization (scale bar, 15 μm), with tips 1 and 2 indicated by red and blue markers, respectively. (B) Path of travel for both tips of the SiNW as a function of time. (C) Instantaneous velocity of the SiNW before (15-frame interval) (I), during (II), and after (III) active transport, with the corresponding rolling MSD diffusivity exponent α, indicating an active transport process. The diffusivity exponent α was obtained over a rolling 30-frame period. All values given are for tip 1 (red). Briefly summarizing this process, we initially presettled SiNWs onto the underlying substrate, allowing for improved transport quantification by avoiding the large temporal variance associated with the initial SiNW seeding process. Settled nanowires were initially stationary (Fig. 2, B and C, region I), but upon contacting the cell, SiNWs were seen to be “grabbed” (region II), getting shuttled from the lamellipodium to the perinuclear region (Fig. 2, B and C, region II). Here, SiNWs displayed “burst-like” velocities, where the nanowire would be transported in sudden large spurts of speed, punctuated by brief pauses (up to 5 min), during which the SiNWs would display Brownian or restricted diffusion before continuing active transport. Here, SiNW transport begins almost immediately (region II), displaying a relatively high mobility, with a maximum instantaneous velocity of 99.4 nm/s (velocities averaged over a 15-frame interval). In most of the studies, transport trajectories were linear (fig. S4), proceeding along approximately straight tracks; however, in some cases, SiNWs could also undergo rotational rearrangements (fig. S5). After transport, SiNWs would eventually settle around the nucleus (although excluded from the nuclear envelope), displaying only small diffusive movements (Fig. 2B, region III). Similar dynamics were also noted for SiNWs in the absence of protein serum (fig. S6A) and for wires with different configurations (fig. S6B), such as a kinked “L-shaped” wire, with this type of wire having previously been used for biophysical force studies (27). This indicated that a diversity of device configurations are tolerated and that protein opsonization is not critical for nanowire uptake. To distinguish between different modes of SiNW transport, we used a rolling frame mean-squared displacement (MSD) metric, where the MSD is the average distance that a particle travels as a function of lag time, given by MSD = 〈 Δ r 2 ( τ ) 〉 = q τ α (1)where Δr, τ, q, and α are the nanowire displacement, lag time, diffusion coefficient, and “diffusivity exponent,” respectively. The diffusivity exponent α can be used as a metric of transport properties, distinguishing between Brownian diffusion (α = 1), restricted diffusion (α < 1), and active transport (α > 1) processes. Values for α were obtained by fitting the Ln-Ln plot of the MSD with a linear regression over a rolling interval, with the slope yielding the relative diffusivity (fig. S7). Using the MSD of the SiNW, different modes of cellular transport can be assessed, providing some insight into the underlying mechanism. The present case strongly suggests that SiNWs are being treated as cargo by the cell and are being actively shuttled by cellular machinery. First, this is suggested by the relatively high velocities and by the observed active transport (α = 1.94) (Fig. 2C, region II). Although cell motility can also display temporary directional motion (α = 1.6), wire transport coupled to cell motility displayed relatively low velocities (~20 nm/s) (fig. S5B, region II), distinguishing it from motor protein–assisted transport. Second, the burst-like transport displayed here is reminiscent of other motor protein–powered intracellular transport (32). Exploring this behavior in more detail, we used nocodazole as a potent microtubule inhibitor to disrupt retrograde dynein-dependent transport (33). In nocodazole-treated cells, SiNWs displayed considerably lower transport velocities (fig. S8), showing a maximum instantaneous velocity of ~10 nm/s in the present case. In addition, although active transport was observed during the initial internalization step (which is likely an actin-dependent process), no further retrograde transport to the perinuclear region was noted. Instead, the SiNW came to rest ~6.5 μm away from the nuclear envelope after its initial internalization, a sizable distance compared to nontreated SiNWs, which on average ended active transport much closer to the nucleus (0.86 ± 0.63 μm, n = 8; maximum observed distance, ~1.4 μm). This indicates that microtubules and, likely, dynein are critical during SiNW transport. Together with the linear trajectories (fig. S4), retrograde movement (Fig. 2), and previously observed vesicle formation (Fig. 1D), this strongly suggests that cells treat the SiNWs as intracellular cargo. In addition, we noted that, in SiNWs samples that have been co-incubated for longer time scales (~3 days), a “tug-of-war” style of bidirectional motion was also observed (movie S2 and fig. S9). The bidirectional motion indicates a competing retrograde and anterograde transport process and suggests the participation of both kinesin and dynein motor proteins in at least the later stages of the SiNW intracellular interface. To determine the specific route of endocytosis, we have adopted several strategies. Initially, lysosome tracking was pursued (fig. S10). Because of the relatively large length of SiNWs, they were able to colocalize with multiple lysosomes simultaneously. To measure these dynamics, we first examined the ensemble overlap of SiNWs with lysosomes as a function of incubation time (fig. S10, A and B). Here, we saw that, within 3 hours of co-incubation, ~75% of SiNWs that were overlapping cells also showed colocalization with one or more lysosomes, suggesting a preferential interaction. Next, we examined the dynamics of single wires, observing that individual internalized wires could remain colocalized with individual lysosomes on the hour time scale (fig. S10, C and D), showing highly correlated movement with the lysosome as compared to external particles (fig. S10E). However, because of the relatively large size of the SiNWs, they were often overlapping with multiple lysosomes, making it difficult to distinguish a primary internalization vesicle. In addition, any correlation in movement was difficult to distinguish from the overall cell motility. Therefore, to study the specific route of endocytosis in more detail, we turned to an ensemble model based on the SiNW-cell overlap (fig. S11). Using this method allowed for both a simple-to-implement single–time point measurement to assess cell type–dependent internalization and a dynamic ensemble study based on a 2D random walker model, which lead to identifying a specific mechanism of internalization.

Ensemble nanowire dynamics We assayed the ensemble SiNW uptake using the rate of SiNW-cell overlap. To achieve this, first, SiNWs were allowed to settle on a substrate before seeding cells. During incubation, cells could then migrate over the surface, picking up SiNWs as they moved. During this process, both dark field (DF) and phase contrast (PC) micrographs were collected to determine the percentage of SiNWs overlapping with cells (Fig. 3). Although not a direct confirmation of internalization, SiNW-cell overlap acts as a reporter of nanowire-cell interactions. In a random noninteracting system, we would expect minimal clustering of the wires and an “overlap reporter” value (β) at unity (β = 1), where β is equal to the percentage of nanowires overlapping with cells at time t (Y t ), divided by the cell confluence (C t ), that is, the percentage of area covered by cells, such that β = Y t /C t . However, in the case of positive SiNW uptake, we would expect to see wires clustered into small regions corresponding to the position of each cell (Fig. 3A), resulting in a larger SiNW-cell overlap reporter value (β > 1). In this regard, the use of overlap values β enables an easy-to-perform optical assay to study a variety of SiNW-cell interactions, including both the cell line and nanowire length dependence on internalization. Fig. 3 Ensemble SiNW internalization dynamics using SiNW-cell overlap. (A) Corresponding PC (top) and DF (bottom) micrographs taken at 2 hours (top set) and 20 hours (bottom set) after HUVEC incubation with SiNWs (scale bars, 25 μm), indicating increased SiNW-cell overlap and clustering in the perinuclear region (artificial cell outline highlighted in teal). (B) Uptake statistics of SiNWs with varying growth lengths (HUVECs) (top) and in multiple cell lines (bottom) after 24 hours, showing that longer wires are more likely to be internalized on the basis of geometric considerations and that cardiomyocytes (Cardio) and DRG neurons (Neuron) do not internalize unmodified SiNWs, whereas J774A.1 macrophages (Macro) show larger uptake rates. Average SiNW length by growth time: 10 min: 9.8 μm; 20 min: 14.5 μm; 30 min: 23.1 μm; 40 min: 31.7 μm. (C) Example ensemble SiNW-cell overlap (black dots) and cell confluence (red dots) trace as a function of time for unmodified SiNWs in HUVECs. Larger-than-confluence overlap indicates SiNW internalization. Expected overlap trend (black line) fit using the 2D random walker model (D t = 410 μm2/hour, R2 = 0.93). Cell confluence modeled as an exponential fit (red line). Using this overlap reporter model, we examined the importance of nanowire length on SiNW uptake (Fig. 3B). SiNWs of different lengths were prepared by varying growth time during synthesis, with increased durations resulting in longer wires on average after sonication. For each sample, β was then determined after 24 hours of co-incubation with HUVECs. In all cases, label-free SiNW internalization was observed (β > 1), with longer wires showing a higher rate of overlap with cells (~45% increase in β). Although, at first, this suggests that HUVECs prefer longer SiNWs, this result is consistent with the fact that longer wires are more likely to come into contact with cells. When normalized by the length of the wire, no significant difference was noted between the longest and shortest growth times (Student’s t test, P > 0.9), indicating that SiNW length is not a critical factor in determining which nanowires can be internalized once they are already in contact with a cell. During this time, we also noted that the cells tolerated a range of SiNWs concentrations (fig. S12). Using an MTT colorimetric assay, we examined the effects of SiNW concentration on the cell’s metabolism, with an approximately fourfold increase in concentration, leading to only a mild reduction in the cell’s activity (28 ± 4% reduction after 3 days). Surprisingly, at smaller SiNW concentrations, an increase in the cell’s metabolic activity was observed, as compared to the control samples. Because it has previously been reported that nanowires can temporarily increase reactive oxygen species in cells (34), this metabolic spike is likely the result of some initial oxidative stress; however, the same study showed that, over slightly longer time scales, the nanowires did not elicit any cytotoxic effects, suggesting that a similar behavior may be occuring here. The overlap reporter model also provided insight into cell line–specific internalization. Along with HUVECs, human aortic smooth muscle cells (HASMCs) were selected as a model muscle system because they can provide responsive contractility and have been previously used for intracellular nanowire force studies (27). In addition, mouse-derived J774A.1 monocyte macrophages were also selected as a model system for professional phagocytes because of their commercial availability, reproducibility as compared to primary macrophages, and facile use. Finally, because both neurons and cardiac cells are of particular interest for bioelectronic applications (35), primary cardiomyocytes and dorsal root ganglia (DRG) neurons from neonatal rat models were selected because of their widely studied properties (36) and common use in bioengineering (37). In the case of both HUVECs and HASMCs, clustering (Fig. 3A) and β values in excess of unity (Fig. 3C) were observed (β = 2.3 ± 0.3 and 2.15 ± 0.6, respectively), suggesting that both cell lines were capable of internalizing label-free SiNWs. J774A.1 cells were also seen to internalize nanowires at high rates (see movie S3), demonstrating clustering and high overlap values after 24 hours (β = 9.0 ± 1.9; Fig. 3C). This approximately fivefold increase in uptake is likely the result of macrophage cells being only partially adherent, allowing them greater cell motility, and is consistent with their role in keeping the bloodstream clear of foreign materials. However, for primary cardiomyocytes and DRG neurons, no clustering was observed and β values did not statistically exceed the baseline (β = 1.3 ± 0.4 and 1.6 ± 0.8, respectively), indicating that neither cell line was capable of internalizing label-free SiNWs (fig. S13). Although the DRG neurons (as determined by β-tubulin III staining) did show slightly elevated β values, this was ascribed primarily to their close association with other nonneuronal cells retained during primary culture, some of which appeared to be able to internalize SiNWs (clustering, β = 3.3 ± 1.5). Collectively, these results suggest heterogeneity in the cell-specific response to SiNWs, namely, that only certain cell types are capable of internalizing label-free wires. This is consistent with the fact that there are many different routes of entry for particles to be internalized (38, 39) but that not all of these endocytosis pathways are expressed across all cell types. To delineate between different modes of endocytosis, we turned to a dynamic ensemble model, looking at the SiNW-cell overlap as a function of time, a method reminiscent of previous phagokinetics studies (40). To understand the resulting dynamics, we parameterized the system using a 2D random walker model (eq. S1 to S10), which we derived from the equations of Dvoretzky and Erdös (41), with the ensemble percentage of SiNWs that were overlapping cells, Y t , being given by Y t = M w − B e − π D t ⋅ C t ⋅ t A cell (2)where t is time, D t is the effective cell migration constant, M w is the maximum percentage of SiNWs available for internalization, A cell is the average area covered by a single cell, C t is the percent cell confluence as a function of time, and B = M w − C 0 . This models differs significantly from other drug kinetics studies. In a typical drug delivery model, cells are considered stationary, while drugs are considered mobile, being able to freely diffuse throughout the solution. However, in the present model we consider the settled SiNWs to be stationary, instead assuming the cells are mobile, with cell motility bringing them into contact with new SiNWs as they travel across the culture surface. This model was observed to be in agreement with experimental values (Fig. 3D), and its use presented several advantages. First, as a relatively constrained system, this model offers distinct physical insights into the ensemble internalization process because all of the terms refer to directly measureable quantities. For instance, the maximum percentage of internalizable wires, M w , was found to be ~96%, with an average effective cell migration rate of 437 ± 36 μm2/hour (R2 = 0.89, n = 7) as determined for HUVECs using D t as the single free-fit parameter (linear least-squares regression). Second, this model can be used to create an expected rate of SiNW internalization in the absence of perturbations, potentially providing useful biophysical insights. For example, this model indicates that the presence of initially internalized SiNWs does not significantly affect later cell motility and uptake rates. Because a steady rate of cell migration is assumed, changes in cellular motility are readily observed using this technique. However, HUVECs did not show any significant deviation from the internally predicted uptake rates (fig. S14), suggesting that the presence of initial SiNWs did not greatly affect later internalization events, at least on the single-day time scale. This was corroborated by studying single nanowire dynamics, which showed that secondary internalization events preserved many of the salient features of initial nanowire internalization, such as active transport (maximum α = 1.9, maximum velocity = 87 nm/s) (fig. S15). In addition, the expected ensemble internalization rate provided by this model can be used to gauge the effect of different treatment regimes on SiNW internalization, using known endocytosis inhibitors to block specific routes of internalization. This strategy allows for a careful examination of the internalization process and can help point toward a specific method of endocytosis (Fig. 4A). Fig. 4 Mechanistic and morphological studies. (A) Positive control study of Cyto D (actin inhibitor) showing the SiNW-cell overlap (black dots), cell confluence (red dots), and the expected overlap trend (black line), modeled on the first 8 hours of internalization (internal control) before drug introduction (red arrow). Cell confluence modeled as an exponential fit (red line). (B) Endocytosis inhibitors: dynasore (Dynamin; left), chlorpromazine (Clathrin; middle), and nystatin (Lipid Rafts; right), indicating dynamin’s critical role in SiNW internalization. (C) SEM micrograph showing membrane extension along a SiNW (scale bar, 500 nm). (D) Time-lapse SEPC micrographs of a membrane extending along a SiNW before cellular uptake (left) (scale bar, 5 μm). Distance of the protrusion’s leading edge from the base of the SiNW over time (top right), with the corresponding instantaneous velocities (bottom right). Base membrane and nanowire tip distances given as solid and dotted lines, respectively. Velocities smoothed over an 11-frame interval. (E) TEM micrograph showing a long intercellular SiNW protruding from a vesicle into the cytosol (scale bar, 250 nm).

Mechanistic studies While using the dynamic SiNW-cell overlap model, an internal control was adopted to help correct for deviations in the rate of nanowire internalization, which was found to be sensitive to the initial seeded cell population (fig. S14A). To establish this internal control, HUVECs were cocultured with SiNWs for a minimum of 7.5 hours before administering endocytosis inhibitors. Then, using Eq. 2, the projected SiNW-cell overlap was determined in the absence of any perturbations. The resulting projection was found to be in agreement with experimentally observed values present in the negative control, where no inhibitors were administered (R2 = 0.91) (fig. S14B), suggesting the validity of this method. To quantitate the significance of a drug’s impact on internalization, a Pearson χ2 test was employed, using the projection from the internal control model as the expected result and the experimentally measured overlaps as the observed trend. In the case of the negative control, a nonsignificant deviation from the projected trend was observed χ2(7, n = 6) = 1.62, P > 0.95 (fig. 14B), with a ΔY 29 of only 0.6%, where ΔY t is the difference between the expected and experimentally observed SiNW-cell overlap values after t hours. As a positive control for drug efficacy, cell migration was halted using the potent actin polymerization inhibitor cytochalasin D (Cyto D), restricting the cells’ motility and their ability to access new SiNWs. This resulted in the abrupt termination of nanowire internalization (ΔY 23 = 28.4%), showing significant deviation from the expected SiNW-cell overlap, χ2(4, n = 6) = 35.26, P < 0.01. A similar trend was observed using dynasore (Fig. 4B, left), a cell-permeable dynamin inhibitor, which yields a statistically significant change from the internal control (ΔY 23 = 29.2%), χ2(4, n = 12) = 23.5, P < 0.01, indicating that dynamin, a protein that is responsible for regulating membrane curvature and vesicle scission (39), plays a critical role in nanowire internalization. This observation is important because it concretely links SiNW uptake to cell-regulated endocytosis, showing that the nanowires are being actively internalized through a protein-dependent process, rather than passively through mechanical abrasion. To examine this in more detail, both clathrin-dependent and clathrin-independent mechanisms were probed. Using chlorpromazine as a clathrin blocker (Fig. 4B, middle), no significant change was noted in nanowire internalization (ΔY 30 = 6.1%), χ2(6, n = 6) = 1.75, P = 0.94, suggesting a clathrin-independent pathway. This result is surprising, considering that clathrin-coated pits are one of the dominant routes of entry for many spherical inorganic nanoparticles (NPs), including Au NPs (42) and SiO 2 NPs (~300 nm) (43). To confirm that clathrin pathways were effectively blocked at the present inhibitor concentrations, fluorescently labeled transferrin was used as a positive control, resulting in observable clathrin inhibition (~40%) (fig. S16). This means that, despite the nanoscale diameter of the SiNWs (20 to 250 nm), cells were able to distinguish the high aspect ratio of the material, suggesting a mechanism of topological sensing where a cell is able to gauge a material’s aspect ratio and thereby delineate its mode of cellular entry. One such pathway that shows shape-sensitive internalization at the nanoscale is caveolae-mediated endocytosis (44). Although the role of caveolae remains controversial (45), we examined this route by administering the drug nystatin (Fig. 4B, right), which disrupts lipid raft and caveolae formation by cholesterol binding (45). However, nystatin showed no significant change in nanowire internalization (ΔY 29 = 5.7%), χ2(7, n = 6) = 1.73, P > 0.95. Another pathway that is particle size–dependent is phagocytosis, because it requires particles to be completely encapsulated before internalization. Reviewing the cell line–dependent internalization, we noted that macrophages, primary phagocytes, were able to readily internalize SiNWs (Fig. 3C). In addition, reagents for blocking phagocytosis, including actin inhibitors such as Cyto D, showed a significant decrease in SiNW uptake (Fig. 4A). Together, this suggested that phagocytosis may play an important role in nanowire endocytosis. However, macrophages can present multiple endocytosis pathways, whereas actin polymerization can restrict cell migration, potentially leading to false positives. Therefore, to study this pathway in more detail, we first examined the cell’s morphology during internalization before using the competitive surface-binding protein annexin V (A5), which can inhibit phagocytosis without restricting cell migration (see table S1 for a summary of endocytosis inhibitor results).

Cell uptake morphology To determine whether particles were being engulfed during internalization, we examined the morphology of HUVECs using EM and SEPC studies. When examined under SEM, in some cases, cells co-incubated with SiNWs were seen to have membranes extending along the entire length of the wire (Fig. 4C), suggesting initiation of phagocytosis. However, SEM only offers a static view. To supplement this, SEPC was used to observe the real-time dynamics of membrane extension along a single SiNW (Fig. 4D). This was achieved using high–aspect ratio SiNWs (~33 μm in length), with longer wires requiring greater extension distances, thus enabling easier optical characterization. When using these wires, we observed that, starting at the base of the membrane, the cell would extend a protrusion along the entire length of the wire at a maximum rate of ~120 nm/s, eventually reaching past the wire’s tip before pausing briefly and then being retracted back to the basal level at similar speeds (~140 nm/s) (see movie S4 and Fig. 4D). Just after this retraction, the SiNW is then seen to enter the dynamic internalization process, as previously described. This membrane engulfment is phenotypical of phagocytosis, further suggesting this route of internalization. Surprisingly, during this process, we also observed that, in some cases, the membrane would not initially bring the entire SiNW into the cell but would instead retract as a punctured vesicle before continuing SiNW internalization. This behavior has been noted before in other high–aspect ratio particles, such as carbon nanotubes (46) and silver nanowires (47), and is often referred to as “frustrated phagocytosis.” This partial encapsulation was further confirmed using TEM (Fig. 4E), showing that, for longer SiNWs, a portion of the sample can extend past the internalization vesicle. Because of the nature of thin cell sectioning, only a portion of the SiNW segments was observable under TEM. This means that segments of the SiNWs can extend past the field of view, precluding precise quantification of the percentage of partially or wholly encapsulated SiNWs. The role of frustrated phagocytosis suggests that there is a maximum length at which cells can healthily internalize wires, a process that should be examined in further detail in future studies. Despite this partial encapsulation in some cases, overall, these observations reinforced phagocytosis as a mechanism for SiNW internalization.