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Gold Nanoparticle Quantitation by Whole Cell Tomography Aric W. Sanders, Kavita M. Jeerage, Cindi L. Schwartz, Alexandra E. Curtin, and Ann N. Chiaramonti ACS Nano, Just Accepted Manuscript • DOI: 10.1021/acsnano.5b03815 • Publication Date (Web): 13 Nov 2015 Downloaded from http://pubs.acs.org on November 15, 2015

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Manuscript and Figures

Gold Nanoparticle Quantitation by Whole Cell Tomography Aric W. Sandersǂ,1,*, Kavita M. Jeerageǂ,2, Cindi L. Schwartz ƚ,3, Alexandra E. Curtin1,2, Ann N. Chiaramonti2 1

Quantum Electronics and Photonics Division, National Institute of Standards and Technology (NIST), Boulder Colorado, USA 2

Applied Chemicals and Materials Division, National Institute of Standards and Technology (NIST), Boulder, Colorado, USA

3

Department of Molecular, Cell, and Developmental Biology, University of Colorado, Boulder, Colorado, USA *

Address correspondence to [email protected] ǂ

ƚ

Authors contributed equally to this work

Author now at Electron Microscopy Unit, Rocky Mountain Laboratories, National Institute of Allergy and Infectious Diseases, National Institutes of Health, Hamilton, Montana, USA

Contribution of NIST, not subject to copyright in the United States

ACS Paragon Plus Environment

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Table of Contents Graphic


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Abstract

Many proposed biomedical applications for engineered gold nanoparticles require their incorporation by mammalian cells in specific numbers and locations. Here, the number of gold nanoparticles inside of individual mammalian stem cells was characterized using fast focused ion beam – scanning electron microscopy based tomography. Enhanced optical microscopy was used to provide a multiscale map of the in vitro sample, which allows cells of interest to be identified within their local environment. Cells were then serially sectioned using a gallium ion beam and imaged using a scanning electron beam. To confirm the accuracy of single cross sections, nanoparticles in similar cross sections were imaged using transmission electron microscopy and scanning helium ion microscopy. Complete tomographic series were then used to count the nanoparticles inside of each cell and measure their spatial distribution. We investigated the influence of slice thickness on counting single particles and clusters as well as nanoparticle packing within clusters. For 60 nm citrate stabilized particles, the nanoparticle cluster packing volume is 2.15 ± 0.20 times the volume of the bare gold nanoparticles.

Keywords gold nanoparticles; focused ion beam tomography; helium ion microscopy; multiscale correlative microscopy; nanoparticle internalization; scanning electron microscopy


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Gold nanoparticles (GNPs) are gaining importance as cell labeling and medical diagnostic tools,1-2 as gene or chemical delivery vehicles,3-4 and as phototherapeutic agents.5 GNPs are uniquely suited for these biological uses because of their chemical stability, novel optical properties, and broad potential for functionalization.6 Additionally, each of these beneficial properties is further enhanced by the ability to manufacture GNPs in an almost endless combination of sizes and shapes. This versatility has allowed researchers to access and modify biological processes inside a large variety of cells. However, for many applications, success depends on the effectiveness and efficiency of the targeting strategy. For example, in thermal cancer therapy it has been estimated that 5000 nanoparticles must be delivered to each cell.7 For nanoparticles carrying drugs or genes, the efficiency of cellular uptake, which depends on size, must be balanced with the therapeutic cargo carried per particle.3 Similarly, for labeling applications such as tracking implanted stem cells,1, 8 cellular uptake efficiency must be balanced with the absorption cross section.9 To evaluate and tune the effect of functionalized GNPs on cells, characterization of affected organisms and tissues is required from the macroscopic to nanoscopic level; specifically, techniques that correlate the cellular environment with nanoparticle uptake are crucial. Ideally, nanoparticle uptake could be quantified at the single-cell level to understand the distribution of cellular outcomes for a given administered dose. Although previous measurements of GNPs have been made that indicate the relative quantity of particles associated with cells or resolve single particles inside of cellular structures, it has been a challenge to resolve all the particles inside the volume of individual cells. GNPs associated with cells can be visualized by dark field microscopy10 and by fluorescence microscopy,9 but these techniques do not provide quantitative information. While transmission electron microscopy (TEM) can


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provide detailed localization information,11-12 the information rate of this method is small. Typically, only a fraction of the volume of one cell can be examined, after embedding and thinsectioning the cells of interest. Even high throughput scanning transmission electron microscopy (STEM) methods, recently employed to quantify GNP accumulation in large volumes of liver tissue,13 require lengthy sample preparation and thin-sectioning. In contrast, GNP uptake by large numbers of mammalian cells has typically been quantified by inductively coupled plasma atomic emission spectrometry (ICP-AES) or mass spectrometry (ICP-MS), which detect elemental gold. These techniques can provide useful information, such as trends related to particle geometry, cell type, and exposure time, all of which are averaged over thousands of cells.3, 8, 12, 14 ICP-based techniques, however, do not provide information on the location of GNPs within individual cells or cell-to-cell variability. As a result, most quantitative studies of GNP uptake have utilized homogeneous cell models.14-15 These models do not quantify nanoparticle uptake by specific cell types within complex primary cell cultures.16 To truly measure the number of nanoparticles inside of individual cells and correlate that with cell type and cell environment requires a technique that can resolve the external structure of the cell and each nanoparticle within the cell. Here we introduce a correlative approach that bridges existing techniques, creating a balance in information and resolution space that provides cell-level information at a rate that enables entire cell volumes to be examined. In particular, we present a reflection mode optical mosaic that catalogs the external cellular environment of neural stem cells from the millimeter scale to the single cell level. The neural stem cells were exposed to GNPs stabilized by the adsorption of serum proteins, which are expected to be internalized by receptor-mediated endocytosis and confined to endosomes.11 Using this mosaic, the exterior of single cells and nanoparticles


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associated with the cell membrane are further investigated using scanning electron microscopy. The interior of single cells, and every nanoparticle inside, are then revealed using focused ion beam-scanning electron tomography.

Results and Discussion Correlation of Optical and Electron Microscopies. Developing neural stem cells exposed to 60 nm diameter GNPs (NIST reference materials) were used as a model system. The full culture of approximately 60 000 cells was first imaged in reflected brightfield mode using a 5x objective. Single fields of view were 1.9 mm x 1.4 mm and every field of view was a composite of 10 images obtained at different objective-sample distances. Each pixel was assigned to the one that was best focused by use of a specially designed focus grid. Individual fields of view were stitched together to create a 10 mm x 10 mm, real-color, extended-depth-of-focus mosaic (Figure 1A). Regions of interest might be selected from this large mosaic based on position within a gradient or proximity to an injection site (for tissue slices). Here, a region of interest was selected based solely on cell density. This region of interest was then imaged using a 100x objective. Single fields of view were 95 µm x 71 µm and every field of view was a composite of 200 different objective-sample distances (Figure 1B). A smaller region was chosen in which single cells were clearly resolved (Figure 1C), and three individual cells are shown in greater detail (Figure 1D). The ability to zoom in from a mosaic that contains thousands of cells to the single cell level, permits cells to be selected for further analysis based on morphology or other features.


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Figure 1. Multiscale Optical Characterization. A combination of two-dimensional mosaics and extended-depth-of-focus imaging allows the characterization of the in vitro sample from approximately 60 000 cells to the single-cell level. (A) is a 8 x 10 mosaic of the full sample, imaged with a 5x objective and 10 different sample-to-objective heights. The red square in (A) indicates a region of interest. (B) is a 5 x 5 mosaic of this region of interest, imaged with a 100x objective and 200 different sample-to-objective heights. The dashed box in (B) indicates a group of cells. In (A) and (B), each pixel was chosen at maximum focus using a specially designed focus grid. (C) indicates three cells of interest, with highly magnified images of these cells in (D). Cell 3 is at the border of two mosaic images, evidenced by a line through the middle.

Cell 1 appeared to have a gold cluster on the upper portion of its cell membrane, but the particles might simply have settled onto the growth substrate. To enhance the contrast of this specific cell and its local environment, reflected differential interference contrast (DIC) was


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chosen (Figure 2A). This imaging mode converts small height differences into contrast and reveals cell bodies and projections along the surface of the growth substrate in more detail than brightfield. However, the GNPs and their relationship to the cell membrane were still not resolvable by optical microscopy (Figure 2B). To better resolve this relationship, the same cell was located and imaged by scanning electron microscopy (SEM) (Figure 2C). GNPs were easily resolved by SEM, and were observed to be a cluster of 109 nanoparticles on the membrane of the cell (Figure 2D). The average area occupied by a single nanoparticle was (3600 ± 1200) nm2, as measured optically, and (3800 ± 270) nm2, as measured by SEM. These values were calculated by repeatedly measuring the area occupied by all of the nanoparticles and dividing by the number of nanoparticles clearly resolvable in the SEM image. For a single nanoparticle with a diameter of 55.4 nm (Supporting Information), the expected area occupied is 2410 nm2.


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Figure 2. Exterior of Cell 1. Positional correlation of GNPs on the cell membrane by optical microscopy and scanning electron microscopy. (A) shows an extendeddepth-of-focus DIC optical image and (B) shows a single cell with a gold cluster on the cell membrane. The same cell is imaged in a SEM image (C) which can be used to resolve single nanoparticles within the gold cluster (D).

The ability to correlate SEM images with optical microscopy provides a powerful tool for imaging nanoparticles associated with the cell membrane. Because SEM images show appreciable contrast for elements that have a high secondary electron yield compared to carbon, nanoparticles composed of transition metals and their oxides can be detected. Particles relevant to biomedical applications include titanium and titanium oxides, iron and iron oxides, silver, and platinum. For particles located on the cell exterior, such as those in Figure 2D, the resolution of the SEM instrument (which depends on the electron beam diameter) determines the minimum detectable particle size. For a high quality instrument, this could be as small as 1 nm. Figure 2 reveals one of the limitations of most previous studies, that is, the reliance on ICPAES or ICP-MS to quantify GNP concentration via measurement of Au concentration. Although cells are carefully rinsed to remove GNPs not bound to the cell membrane, all measurements based on these techniques include GNPs associated with but not incorporated by cells, such as those in Figure 2D. Distinguishing these two nanoparticle populations has been recognized as a measurement challenge for many years.17 Also notable in Figure 2A are the larger clusters of nanoparticles settled on the growth substrate. While these clusters are easily observed, they are much larger than the single particles and small agglomerates the cells were exposed to. GNPs dispersed in low-serum culture medium were dominated by single GNPs and small agglomerates of two or three GNPs (Supporting Information); however, larger agglomerates likely formed and


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sedimented onto the substrate during the three-day incubation time. Because GNP uptake is expected to be complete within as little as 6 h,3, 12 these agglomerates might be eliminated by exchanging the culture medium. This was not done to avoid disrupting the neural stem cells.

Cross Sectioning and Imaging the Cell Interior. To access the cell interior for SEM imaging, cells of interest were chosen and cross-sectioned using a focused ion beam (FIB). During cross-sectioning, a focused beam of Ga ions was used to polish off a thin slice of material normal to the surface of the growth substrate. After a prescribed thickness was polished away by the Ga beam, polishing was temporally stopped and a single SEM image of the exposed surface was acquired, after which polishing was reinitiated and the process was repeated until the full region of interest was consumed. For the data presented in Figures 3, 4, and 5, the slice thickness was chosen to be equal to or smaller than the nanoparticle diameter. The lateral resolution of each slice was always smaller than the nanoparticle diameter (12 nm for Cell 2, 15 nm for Cell 3), resulting in voxels that completely sampled a nanoparticle’s volume. Multiple whole cells were cross-sectioned; however, for clarity, we restrict our discussion to two totally consumed cells. In Figure 3, the results of repeated FIB cross-sectioning and SEM imaging of Cell 2 (Figure 3A) are presented. This cell was sectioned a total of 813 times in sections of 11 nm thickness (Figure 3B). Each section was then imaged at a lateral resolution of 12 nm. Several important features about the cross-sectioning process are apparent from Cell 2. First, at a section thickness of 11 nm and image resolution of 12 nm, single nanoparticles are clearly resolved. In Figure 3C, an area of 500 nm square is shown for every other slice of two distinct collections of nanoparticles. These images show a portion of the cell membrane (cell exterior, light gray) and


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the cell interior (black). Due to the small field of view, the substrate is not necessarily visible. The first (solid red box) illustrates that a single isolated nanoparticle appears for more than ten slices, with the degree of contrast increasing gradually over the first seven slices, remaining relatively constant for the next four slices, then decreasing rapidly in the final slice. This particle has a maximum measured area of 2800 nm2 on the 568th slice (not shown), corresponding to a radius of 30 nm. The isolated particle appears for approximately 110 nm of sliced thickness, although its diameter is only 60 nm, because the electrons used for imaging sample into the volume of the cell. In particular, we estimate that for 40 nm before the Ga beam begins to etch away the nanoparticle, the nanoparticle is evident inside of the cell. This is a strong function of the parameters of the electron beam used to view the sample, in our case a primary beam energy of 5 kV, and detection of the secondary electrons by a standard Everhart-Thornley detector. In addition to this “shine-through” effect, particle tracking (Supporting Information) reveals that nanoparticle clusters etch slightly faster than isolated nanoparticles. This can be seen in Figure 3C for the nine particle cluster that is shown in the blue dashed box. Four distinct nanoparticles are observed on slices 715-717. In contrast to the single particle case, the two particles in slice 715 are not visible by slice 721 and none of these four nanoparticles are visible by slice 723.


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Figure 3. FIB-based Slicing of Cell 2. The cell is shown before FIB milling in (A) and after 100, 300, 500, and 700 slices in (B). In total, 813 slices were imaged. Boxes in (A) represent the location of a single GNP (red, solid border) and a GNP cluster (blue, dashed border) that were discovered within the cell. Images from these regions are shown in (C); scale bar is 200 nm. A single nanoparticle is detectable in slices 559-570, a subset is shown with the slice number on each image (red, solid border). The maximum nanoparticle area of 2836 nm2 was measured in slice 568 (not shown). A cluster of 9 nanoparticles is visible in slices 715-727, again a subset is shown (blue, dashed border).

By inspecting each imaged slice, we can count all GNPs located inside the cell. For Cell 2 we find that there are (85 ± 4) nanoparticles arranged in 11 distinct clusters ranging in size from a single isolated nanoparticle to a cluster with 35 nanoparticles. The particle-number-dependent


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etch rate and shine-through effects lead to some ambiguity, especially when counting nanoparticles in clusters. Because each nanoparticle appears in multiple sections, particles must be tracked through the tomographic series to identify unique nanoparticles and avoid counting them more than once. However, as demonstrated in Figure 3C, finely slicing the cell does not simplify the identification of unique nanoparticles. For this purpose, it would be ideal if each nanoparticle appeared in only one slice, which can be achieved with coarser slicing. Cell 3 was sectioned a total of 130 times in sections of 59 nm thickness. Each section was then imaged at a lateral resolution of 15 nm. By choosing the slice thickness to be approximately equal to the GNP diameter, we reduced the acquisition time and reduced the number of slices that had the same particles twice. For Cell 3 we find that there are (431 ± 13) nanoparticles arranged in 9 distinct clusters ranging in size from a single isolated nanoparticle to a large cluster with 248 nanoparticles. Note that despite a significantly larger section thickness of 59 nm and with a similar image resolution of 15 nm, single nanoparticles are still clearly resolved. Nanoparticles were counted manually here (Supporting Information), but could be counted with software to simplify analysis of large image sets. Although ion beams have been previously employed by us and others to cross section cells and expose internalized GNPs,18-19 to our knowledge, this is the first demonstration of nanoparticle quantitation by whole cell tomography. For particles within the cell interior, such as those in Figure 3C, determining the minimum detectable particle size is complex, because the resolution of the SEM instrument is not the limiting factor. During the sectioning process, when the cell is sliced by the Ga beam, a variety of processes occur when the Ga ions interact with the sample: sputtered ions and atoms from the sample (the desired process), secondary electron generation, Ga ion implantation into the sample, and material transformation. The depth of the damage zone depends on many factors but is


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approximately 30 nm.20 To explore the minimum detectable particle size, we sectioned and imaged cells from replicate in vitro samples exposed to 30 nm GNPs and 10 nm GNPs (NIST reference materials). Although individual 30 nm particles were resolved and clusters of 10 nm particles were detected, individual 10 nm particles could not be resolved. Optimizing methods for the detection of particles smaller than ca. 30 nm requires systematic evaluation and minimization of sources of Ga beam induced damage.21

Comparison of Sections Imaged by SEM with TEM and HIM Imaging. To further understand the cross-sectioning process, a replicate in vitro sample was prepared for transmission electron microscopy (TEM), the standard tool for resolving single GNPs inside of cells (Figure 4A). Several cross sections with exposed clusters of GNPs were also preserved for imaging via helium ion microscopy (HIM). These cross-sections were first imaged using SEM and then the Ga beam was used to remove a thin layer, after which the cross-sections were imaged by HIM (Figures 4B and 4C). One final layer (ca. 100 nm thick) was removed because of the surface sensitivity of helium ions; imaging the same cross-section by SEM and then HIM can lead to artifacts.22 Figure 4 presents a comparison of each of these methods with imaged slices from Cells 2 and 3. Note that the TEM image provides information on cellular structures, whereas the SEM and HIM images did not, because their sample preparation was designed only to maximize contrast between cellular material and metal nanoparticles. It is possible to visualize internal cellular structures with minor modifications (Supporting Information). We see good agreement between the average area occupied per nanoparticle in the thin-sectioned TEM image (Figure 4A), SEM image (Figure 4B), and HIM image (Figure 4C). By determining the average area using electrons at vastly different energies and ions we conclude that any beam-dependent


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effects are smaller than the error in the measured area. This error is associated with the ability to find and distinguish the particle boundaries, which we determined by making repeated measurements of the area for Figures 4A, 4B, and 4C. Figure 4D compares the number of GNPs distinctly observed with the mean area of each GNP cluster for each slice of the FIB tomographic series. Cell 2 and Cell 3 were both evaluated. For each cell, we have summarized the average area per nanoparticle, (3100 ± 130) nm2 for Cell 2 and (3400 ± 100) nm2 for Cell 3. We have reported the weighted mean as the value and the variation of each slice from the weighted mean of all of the slices used in the area measurement as the error. In the case of nanoparticles appearing in more than one slice, a single slice with unique nanoparticles has been chosen and reported. The lines shown in Figure 4D are not best-fit lines, because the reliability of each data point depends on the number of nanoparticles in the slice. Instead these lines were constructed by multiplying area by the inverse of the average area per nanoparticle (i.e., the number of nanoparticles per area). Note the close correspondence of these lines despite the fact that Cell 2 didn’t provide data points for areas larger than 70000 nm2 (i.e., there were no large clusters in this cell).


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Figure 4. Average Area per Nanoparticle. Two FIB tomographic series are compared with other microscopy techniques. GNP clusters were imaged by TEM in a resin-embedded thin section (A), 15 nm gold particles added after preparation are also visible, by SEM at 54° tilt (B), and by HIM at 45° tilt (C). The particle cluster imaged in (C) is the same cluster, after a final slice, imaged in (B). In (D), areal measurements are correlated with the number of GNPs for all unique GNPs imaged in Cells 2 and 3.

Reconstruction of the Cell Interior. Although each slice in Figure 4D is a two-dimensional projection of a three-dimensional cluster, the average area per nanoparticle of the GNPs creates a


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statistical representation that can be used to estimate the number of GNPs in a cluster. For cells containing many hundreds or thousands of GNPs, particle boundaries can be determined by thresholding the cell interior of each slice. The measured area can then be converted to particle number. This procedure includes all nanoparticles in each imaged slice, unique or not. Therefore, the sectioning must be designed to minimize GNPs appearing in multiple slices. In two-dimensional measurements, the average area per nanoparticle is larger than the maximum cross-sectional area of a single nanoparticle, due to nanoparticle packing. For threedimensional measurements, we multiplied the area of each slice by the thickness of the slice to determine the volume of each cluster, as shown in Figure 5 for Cell 3 only. Nanoparticle clusters had volumes that were greater than that of nanoparticles in a close-packed arrangement (Figure 5A), which would be 1.35 times the bare nanoparticle volume. TEM images have previously indicated that surface functionalization impacts the packing of GNPs in endosomes,11 but packing volume has not previously been determined. 3D reconstructions can aid our understanding of the spatial distribution of GNPs in the volume of the cell. To reconstruct the volume, a median filter using a 20 pixel characteristic size was first used to define the edges of the cell while blurring nanoparticles inside of the cell. The set of median filtered images then had the same brightness threshold applied to each. The resulting series of images was then used as a mask that defined the interior of the cell. Each slice was reviewed, and any extra information or information lost inside of the cell was hand corrected. Finally, a cropping window was applied to each slice, with the relative motion of each slice taken into account. The resulting image series now contained only the cell of interest and internalized nanoparticles. It was then viewed using a plugin in for ImageJ.23 A volume representing the front section of the cell was removed in the software; this volume is represented by the dashed red line


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in Figure 5B. Two examples of the resulting 3D model for Cell 3 are shown in Figures 5C and 5D.

Figure 5. FIB-based Reconstruction of Cell 3. In (A) a plot of the threedimensional volume of the nanoparticle clusters normalized to the volume of the nanoparticles inside of the cluster. The 3D packing ratio of nanoparticles is 2.15 times the volume of the individual nanoparticles in the intercellular cluster. In (B) the red dashed line indicates the area of the tomogram shown. In (C) a three dimensional reconstruction of the cell shows three GNP clusters in false color. The front of the cell has been computationally removed to show the GNPs more clearly. The large GNP cluster at the edge of the cell is magnified in (D), again in false color. This cluster contains 248 nanoparticles, a significant fraction of the 431 total nanoparticles, contained in 9 clusters, observed in the cell.


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Expanding Single Cell Resolution to Cell Populations. Previous studies of GNP incorporation by cells have consistently indicated receptor-mediated endocytosis3, 11-12 of citratestabilized particles surrounded by bovine serum albumin and other serum proteins, though sizedependent trends have not been consistent. At least two studies with similar gold core dimensions have been reported. For 50 nm GNPs, Chithrani et al. reported incorporation of approximately 6000 particles per HeLa cell based on ICP-AES measurements.12 For functionalized GNPs with 55 nm cores, Au equivalent to approximately 1000 particles per HeLa cell was measured by Elbakry et al., again by ICP-AES measurements.3 The cross-sectioned cells in Figures 3-5 (as well as additional cross-sectioned cells) had smaller total GNP counts. This is likely due to differences between neural stem cells and the cancer-derived HeLa cell line. Other studies have demonstrated GNP uptake by neural stem cells19, 24 and mature cells of the central nervous system,10 but did not include quantitative measures. Interestingly, Cell 3 had approximately five times more GNPs than Cell 2. Significant cell-to-cell variability in GNPs associated with dividing cells was recently measured by ion beam analysis.25 Though we specifically chose non-dividing cells to avoid cell cycle effects, to date we have cross-sectioned ten cells from this culture, including the two presented here. Therefore, we can only comment on the variability of GNP incorporation by a small number of differentiating neural stem cells. One drawback of the current methodology is that it is time consuming compared to populationlevel measurements. To examine statistically relevant populations by whole cell tomography requires minimization of the time required for sectioning, imaging, and analysis. Once an individual cell is selected and positioned normal to the ion column, sectioning and imaging are fully controlled by the instrument software based on user-defined parameters. For the specific


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cells sectioned here, the “fine sectioning” of Cell 2 required 3 h, 38 min, whereas the “coarse sectioning” of Cell 3 required 58 min. The majority of this time was used for image acquisition, not sectioning, as each SEM image (1024 pixels x 768 pixels) required 10.2 s to acquire. For example, Cell 2 required 2 h, 18 min for image acquisition. Although image acquisition dominated the overall time, Figure 3B shows that the acquired images had extraneous information (cell exterior and surrounding region) that didn’t change significantly after each slice. The cell body occupies approximately one-third of the image during slicing, and the crosssectioned interior is an even smaller fraction of the overall image. By restricting the images to the cell interior with software changes, we could reduce the acquisition time dramatically. Restricting images to the cell interior would also facilitate image analysis and the quantitation of smaller particles. By reducing the size of each pixel and increasing the dwell time (thus reversing some of the gains achieved by restricting the overall image region), particles would have greater contrast from the cell background. Automatic thresholding could then be used to create binary images and particle regions within the cell interior could be measured without laborious manual tracing. The number of nanoparticles within each region could be determined from a calibration plot for the specific particles under investigation or by software-based identification of individual particles. With the improvements described above, the analysis of sub-populations of cells within complex in vitro cultures, which cannot be analyzed by other methods, will become feasible. We have previously demonstrated that neurons, astrocytes, and progenitor cells can be identified in neural stem cell cultures from seven to ten days of development,26 indicating that there were three cell types available during GNP exposure. Cells 2 and 3 (which had very different morphologies, see Figure 4D insets) may represent different cell types. Complex in vitro cultures


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containing multiple cell types mimic cell – cell interactions and other aspects of in vivo tissue that cell lines cannot. For example, neuron – astrocyte interactions can provide resiliency against toxic chemicals;27-29 or allow chemicals to be metabolized into their toxic form.30 Many applications proposed for gold nanoparticles are likely to involve specific sub-populations of cells, such as circulating cancer cells2 or sensory neurons in the retina.31 Whole cell tomography will provide cell-level information on exterior and interior particle localization for the subpopulations of interest.

Conclusions We demonstrated correlative microscopy methods for determining the relevant extracellular environment of an in vitro culture optically and the intracellular landscape using ion and electron microscopy. For the first time, we directly measured the number and three-dimensional distribution of gold nanoparticles inside of entire cell volumes. We confirmed that the apparent size of gold nanoparticles measured in 2D images is consistent between FIB sectioned slices and TEM, SEM, HIM, and optical microscopy images. By multiplying by the slice thickness, we converted areal measurements into volume measurements for the nanoparticles in a single cell. We used this information to determine the 3D packing volume. Specifically, 60 nm citratestabilized nanoparticles with adsorbed serum proteins occupy approximately 2.15 times the volume of the individual nanoparticles contained in the clusters. The methods developed here can be applied to other metallic nanoparticles, such as particles made from titanium, iron, silver, or platinum, and to sub-populations of cells within in vitro cultures, opening new avenues for tailoring the design of engineered nanoparticles for biological applications.


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Experimental Methods* Neural Cell Culture and Nanoparticle Exposure. Rat cortex neural stem cells from Stem Cell Technologies were seeded (7.5 x 104 cells/cm2) onto the glass portion of 35 mm dishes (MatTek) treated with 15 µg/mL poly-L-ornithine. Low-serum differentiation medium (0.2 mg/mL bovine serum albumin) was added to each dish 15 min after seeding and this medium was not exchanged during the ten day culture period. To rapidly surround the particles with serum proteins, GNPs were dispersed into low-serum medium while vortexing.32 Serum increases the initial core size of GNPs by about 10 nm.33 Dynamic light scattering indicated that GNPs dispersed in low-serum medium were a collection of individual particles and small agglomerates of 2-3 particles; there was no evidence for large agglomerates (Supporting Information). After seven days of culture, GNPs were added to each culture dish to give a final exposure concentration of 5.2 µg/mL Au or 3.0 x 109 particles/mL or approximately 1.0 x 105 GNPs / cell. After ten days of culture, one set of samples was fixed with 4% formaldehyde in phosphate buffered saline for 30 min and dehydrated in a series of ethanol/water solutions, ending with 100% ethanol. At this point, coverslips were detached from the dishes and employed for optical microscopy, FIB milling, SEM imaging, and HIM imaging. Another set of samples was prepared for TEM imaging (see below). Optical Microscopy. After fixation and dehydration, the in vitro sample was imaged using a 5x microscope objective in reflection mode. Each optical field of view was 1.9 mm x 1.4 mm and 80 images with 10% overlap were used as tiles in a mosaic that captured the full sample. Each field consisted of 10 frames on the z-axis, and every pixel was selected for the best focus and corrected for vinetting and field curvature, using a reference image of a highly polished Si surface. The resolution of the mosaic was 1.84 µm / pixel, and from this overview mosaic, a


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ACS Nano

region of interest was chosen. This region of interest was mapped at successively higher magnifications, ending with a 100x microscope objective with a field of view 95 µm x 71 µm and a resolution of 93 nm / pixel. To demonstrate contrast differences, a 200 frame image stack was combined for the best focus in each pixel for both standard bright field and differential interference contrast modes. Focused Ion Beam Tomography. After the in vitro sample was characterized optically, it was mounted to an aluminum SEM specimen mount with conductive paint and sputter coated with a thin (ca. 30 nm) layer of Pt to insure electrical conductivity. The region of interest mapped at high magnification by optical microscopy was located by its distance from a known feature obvious from the large scale optical mosaic at a known orientation of the sample. Individual cells were then imaged by SEM followed by FIB tomography. Regions were scanned at 0° tilt using an inlens detector for high contrast to locate the region of interest. Once the region of interest was located, the sample was tilted to 54° (normal to the FIB column) and a standard EverhartThornley detector was selected to minimize the effects of substrate charging during image collection. The acceleration voltage of the Ga ions was 30 kV, and the nominal beam current was 120 pA. “Fine sectioning” and “coarse sectioning” were achieved by controlling the number of passes the Ga beam made over the sample between each image. Secondary electron images were collected at an acceleration voltage of 5 kV, with a point dwell period of 1.2 µs, and all measurements of area were corrected for 54° of tilt. Helium Ion Microscopy. Cellular cross sections were imaged at 45° tilt. The acceleration voltage of the helium ions was 35 kV, and the nominal beam current was 0.3 pA. Secondary electrons produced from the sample were collected with an Everhart-Thornley detector, with a point dwell period of 10 µs.


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Transmission Electron Microscopy. After ten days of culture, these samples were fixed on ice with 2% paraformaldehyde in 0.15 mol/L sodium cacodylate buffer for 30 min and with 1% osmium tetroxide plus 1.5% potassium ferrocyanide in buffer for 30 min. Cells were then exposed to 1% tannic acid in buffer for 15 min. Cells were then fixed with 2% osmium tetroxide in distilled water, rinsed with distilled water, and then stained en bloc with 2% aqueous uranyl acetate. At this point, coverslips were detached from the dishes, dehydrated into anhydrous acetone, and embedded in a hard Epon/Araldite formulation. The glass was then dissolved using 50% hydrofluoric acid in water. Individual cells of interest were excised and remounted on blank resin stubs and 300 nm sections were obtained on a 0.7% Formvar coated slot grid. Sections were stained with 2% aqueous uranyl acetate and Reynold's lead citrate. 15 nm gold fiducials were added to the surface of the sections for a size comparison. Sections were imaged in a Tecnai TF30 instrument operating at 300 kV with a Gatan Ultrascan CCD camera (1 nm pixel size). Acknowledgements. We would like to thank the staff of the Boulder Laboratory for 3D Electron Microscopy of Cells (supported by grant P41GM103431-42 from NIGMS to A. Hoenger) for valuable guidance in electron microscopy. Supporting Information Available. Information is available on nanoparticle dispersion and characterization of suspensions, particle tracking and quantitation of nanoparticle clusters, and imaging nanoparticle clusters with cellular contrast. This material is available free of charge via the Internet at http://pubs.acs.org. * Certain commercial equipment, instruments, or materials are identified in this document. Such identification does not imply recommendation or endorsement by the National Institute of


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Standards and Technology, nor does it imply that the products identified are necessarily the best available for the purpose.

References and Notes 1. Ricles, L. M.; Nam, S. Y.; Trevino, E. A.; Emelianov, S. Y.; Suggs, L. J. A Dual Gold Nanoparticle System for Mesenchymal Stem Cell Tracking. J. Mater. Chem. B 2014, 2, 82208230. 2. Maltez-da Costa, M.; de la Escosura-Muniz, A.; Nogues, C.; Barrios, L.; EIbanez, E.; Merkoci, A. Detection of Circulating Cancer Cells using Electrocatalytic Gold Nanoparticles. Small 2012, 8, 3605-3612. 3. Elbakry, A.; Wurster, E. C.; Zaky, A.; Liebl, R.; Schindler, E.; Bauer-Kreisel, P.; Blunk, T.; Rachel, R.; Goepferich, A.; Breunig, M. Layer-by-Layer Coated Gold Nanoparticles: Size Dependent Delivery of DNA into Cells. Small 2012, 8, 3847-3856. 4. Prades, R.; Guerrero, S.; Araya, E.; Moina, C.; Salas, E.; Zurita, E.; Selva, J.; Egea, G.; Lopez-Iglesias, C.; Teixido, M. et al. Delivery of Gold Nanoparticles to the Brain by Conjugation with a Peptide that Recognizes the Transferrin Receptor. Biomaterials 2012, 33, 7194-7205. 5. El-Sayed, I. H.; Huang, X.; El-Sayed, M. A. Selective Laser Photo-Thermal Therapy of Epithelial Carcinoma using Anti-EGFR Antibody Conjugated Gold Nanoparticles. Cancer Lett. 2006, 239, 129-136. 6. Sperling, R. A.; Gil, P. R.; Zhang, F.; Zanella, M.; Parak, W. J. Biological Applications of Gold Nanoparticles. Chem. Soc. Rev. 2008, 37, 1896-1908. 7. Jain, S.; Hirst, D. G.; O'Sullivan, J. M. Gold Nanoparticles as Novel Agents for Cancer Therapy. Brit. J. Radiol. 2012, 85, 101-113. 8. Ricles, L. M.; Nam, S. Y.; Sokolov, K.; Emelianov, S. Y.; Suggs, L. J. Function of Mesenchymal Stem Cells Following Loading of Gold Nanotracers. Int. J. Nanomed. 2011, 6, 407-416. 9. He, H.; Xie, C.; Ren, J. Nonbleaching Fluorescence of Gold Nanoparticles and its Applications in Cancer Cell Imaging. Anal. Chem. 2008, 80, 5951-5957. 10. Hutter, E.; Boridy, S.; Labreque, S.; Lalancette-Hebert, M.; Kriz, J.; Winnik, F.; Maysinger, D. Microglial Response to Gold Nanoparticles. ACS Nano 2010, 4, 2595-2606. 11. Nativo, P.; Prior, I. A.; Brust, M. Uptake and Intracellular Fate of Surface-Modified Gold Nanoparticles. ACS Nano 2008, 2, 1639-1644. 12. Chithrani, B. D.; Ghazani, A. A.; Chan, W. C. W. Determining the Size and Shape Dependence of Gold Nanoparticle Uptake into Mammalian Cells. Nano Lett. 2006, 6, 662-668. 13. Kempen, P. J.; Thakor, A. S.; Zavaleta, C.; Gambhir, S. S.; Sinclair, R. A Scanning Transmission Electron Microscopy Approach to Analyzing Large Volumes of Tissue to Detect Nanoparticles. Microsc. Microanal. 2013, 19, 1290-1297. 14. Soenen, S. J.; Manshian, B.; Montenegro, J. M.; Amin, F.; Meermann, B.; Thiron, T.; Cornelissen, M.; Vanhaecke, F.; Doak, S.; Parak, W. J. et al. Cytotoxic Effects of Gold Nanoparticles: A Multiparametric Study. ACS Nano 2012, 6, 5767-5783.


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15. Mironava, T.; Hadjiargyrou, M.; Simon, M.; Rafailovich, M. H. Gold Nanoparticles Cellular Toxicity and Recovery: Adipose Derived Stromal Cells. Nanotoxicology 2014, 8, 189-201. 16. Pinkernelle, J.; Calatayud, P.; Goya, G. F.; Fansa, H.; Keilhoff, G. Magnetic Nanoparticles in Primary Neural Cell Cultures are Mainly Taken Up by Microglia. BMC Neuroscience 2012, 13, 32. 17. Gottstein, C.; Wu, G.; Wong, B. J.; Zasadzinski, J. A. Precise Quantification of Nanoparticle Internalization. ACS Nano 2013, 7, 4933-4945. 18. García, C. P.; Sumbayev, V.; Gilliland, D.; Yasinska, I. M.; Gibbs, B. F.; Mehn, D.; Calzolai, L.; Rossi, F. Microscopic Analysis of the Interaction of Gold Nanoparticles with Cells of the Innate Immune System. Sci. Rep. 2013, 3, 1326. 19. Jeerage, K. M.; Oreskovic, T. L.; Curtin, A. E.; Sanders, A. W.; Schwindt, R. K.; Chiaramonti, A. N. Citrate-Stabilized Gold Nanoparticles as Negative Controls for Measurements of Neurite Outgrowth. Toxicol. In Vitro 2015, 29, 187-194. 20. Prenitzer, B. I.; Urbanik-Shannon, C. A.; Giannuzzi, L. A.; Brown, S. R.; Irwin, R. B.; Shofner, T. L.; Stevie, F. A. The Correlation between Ion Beam / Material Interactions and Practical FIB Specimen Preparation. Microsc. Microanal. 2003, 9, 216-236. 21. Drobne, D.; Milani, M.; Leser, V.; Tatti, F. Surface Damage Induced by FIB Milling and Imaging of Biological Samples is Controllable. Microsc. Res. Techniq. 2007, 70, 895-903. 22. Hlawacek, G.; Veligura, V.; van Gastel, R.; Poelsema, B. Helium Ion Microscopy. J. Vac. Sci. Technol. B 2014, 32. 23. Schmid, B.; Schindelin, J.; Cardona, A.; Longair, M.; Heisenberg, M. A High-Level 3D Visualization API for Java and ImageJ. BMC Bioinformatics 2010, 11, 274. 24. Soderstjerna, E.; Johansson, F.; Klefbohm, B.; Johansson, U. E. Gold and Silver Nanoparticles Affect the Growth Characteristics of Human Embryonic Neural Precursor Cells. PLOS One 2013, 8. 25. Jeynes, J. C. G.; Jeynes, C.; Merchant, M. J.; Kirkby, K. J. Measuring and Modelling Cellto-Cell Variation in Uptake of Gold Nanoparticles. Analyst 2013, 138, 7070-7074. 26. Jeerage, K. M.; Oreskovic, T. L.; Hume, S. L. Neurite Outgrowth and Differentiation of Rat Cortex Progenitor Cells are Sensitive to Lithium Chloride at Non-Cytotoxic Exposures. Neurotoxicology 2012, 33, 1170-1179. 27. Giordano, G.; Kavanagh, J.; Costa, L. G. Mouse cerebellar astrocytes protect cerebellar granule neurons against toxicity of the polybrominated diphenyl ether (PBDE) mixture DE-71. Neurotoxicology 2009, 30, 326-329. 28. Morken, T. S.; Sonnewald, U.; Aschner, M.; Syversen, T. Effects of methylmercury on primary brain cells in mono- and co-culture. Toxicol. Sci. 2005, 87, 169-175. 29. Woehrling, E. K.; Hill, E. J.; Coleman, M. D. Evaluation of the importance of astrocytes when screening for acute toxicity in neuronal cell systems. Neurotox. Res. 2010, 17, 103-113. 30. Ransom, B. R.; Kunis, D. M.; Irwin, I.; Langston, J. W. Astrocytes convert the parkinsonian inducing neurotoxin, MPTP, to its active metabolite, MPP+. Neurosci. Lett. 1987, 75, 323-328. 31. Carvalho-de-Souza, J. L.; Treger, J. S.; Dang, B.; Kent, S. B. H.; Pepperberg, D. R.; Bezanilla, F. Photosensitivity of Neurons Enabled by Cell-Targeted Gold Nanoparticles. Neuron 2015, 86, 207-217. 32. Zook, J. M.; MacCuspie, R. I.; Locasio, L. E.; Halter, M. D.; Elliott, J. T. Stable nanoparticle aggregates/agglomerates of different sizes and the effect of their size on hemolytic cytotoxicity. Nanotoxicology 2011, 5, 517-530.


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33. Tsai, D.-H.; DelRio, F. W.; Keene, A. M.; Tyner, K. M.; MacCuspie, R. I.; Cho, T. J.; Zachariah, M. R.; Hackley, V. A. Adsorption and Conformation of Serum Albumin Protein on Gold Nanoparticles Investigated using Dimensional Measurements and In Situ Spectroscopic Methods. Langmuir 2011, 27, 2464-2477.


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Gold Nanoparticle Quantitation by Whole Cell ... - ACS Publications

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