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. 2020 Feb 25;14(2):2224-2237.
doi: 10.1021/acsnano.9b09213. Epub 2020 Feb 10.

Tuning Nanoparticle Interactions with Ovarian Cancer through Layer-by-Layer Modification of Surface Chemistry

Santiago Correa  1 , Natalie Boehnke  2 , Antonio E Barberio  3 , Elad Deiss-Yehiely  4 , Aria Shi  1 , Benjamin Oberlton  1 , Sean G Smith  2   3 , Ioannis Zervantonakis  5 , Erik C Dreaden  2 , Paula T Hammond  2   3   6
Affiliations

Tuning Nanoparticle Interactions with Ovarian Cancer through Layer-by-Layer Modification of Surface Chemistry

Santiago Correa et al. ACS Nano. .

Abstract

Nanoparticle surface chemistry is a fundamental engineering parameter that governs tumor-targeting activity. Electrostatic assembly generates controlled polyelectrolyte complexes through the process of adsorption and charge overcompensation utilizing synthetic polyions and natural biomacromolecules; it can yield films with distinctive hydration, charge, and presentation of functional groups. Here, we used electrostatic layer-by-layer (LbL) assembly to screen 10 different surface chemistries for their ability to preferentially target human ovarian cancer in vitro. Our screen identified that poly-l-aspartate, poly-l-glutamate, and hyaluronate-coated LbL nanoparticles have striking specificity for ovarian cancer, while sulfated poly(β-cyclodextrin) nanoparticles target noncancerous stromal cells. We validated top candidates for tumor-homing ability with a murine model of metastatic disease and with patient-derived ovarian cancer spheroids. Nanoparticle surface chemistry also influenced subcellular trafficking, indicating strategies to target the cell membrane, caveolae, and perinuclear vesicles. Our results confirm LbL is a powerful tool to systematically engineer nanoparticles and achieve specific targeting.

Keywords: layer-by-layer; nanomedicine; nanoparticles; ovarian cancer; subcellular targeting; surface chemistry; tumor-targeting.

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Conflict of interest statement

The authors declare no competing financial interest.

Figures

Figure 1.
Figure 1.
Layer-by-layer (LbL) modification was used to develop a panel of nanoparticles exploring diverse anionic surface chemistries. (a) Scheme of LbL functionalization; 100 nm cores were coated with poly-L-arginine followed by one of 10 different anionic polymers. (b) Size and polydispersity data were acquired by dynamic light scattering, and (c) zeta potential data were measured using laser Doppler electrophoresis. Error bars represent standard deviation of three technical replicates.
Figure 2.
Figure 2.
LbL-NPs with carboxylated terminal layers associate preferentially with ovarian cancer cells, in contrast to sulfated LbL-NPs and carboxylated non-LbL NPs. Fluorescent LbL-NPs were incubated with cells from a panel of 10 human ovarian cancer cell lines or seven noncancerous primary cells for 4 or 24 h and then analyzed by flow cytometry to determine nanoparticle-associated fluorescence. (a) Representative flow cytometry results showing nanoparticle association with Caov3 ovarian cancer cells. Pooled median nanoparticle fluorescence intensity for (b) 10 ovarian cancer cell lines and (c) seven primary noncancerous cell types at 4 and 24 h. (d) A binding saturation isotherm was conducted on OVCAR8 cells at 4 °C to calculate apparent Kd values for 1-PLD-NPs, 2-PLE-NPs, 5-HA-NPs, 6-DXS-NPs, and conventional CML-NPs and PEG-NPs. 1-PLD-NPs and 2-PLE-NPs had Kd values of 6.8 ± 1.6 and 6.2 ± 2.2 pM, respectively. 5-HA-NPs had a Kd value of 18.7 ± 18.4 pM. 6-DXS-NPs, PEG-NPs, and CML-NPs could not be fit to the model and appear to act via nonspecific binding interactions. (e) Hierarchical clustering of the flow cytometry data from the ovarian cancer cells groups NPs by surface chemistry. Data were column-normalized to facilitate comparison of 4 and 24 h data. In the right panel, results from noncancerous cells are displayed according to the cancer cell clustering. Error bars in (b), (c), and (d) represent SEM.
Figure 3.
Figure 3.
Different carboxylated LbL-NPs possess distinct subcellular fates despite surface chemistry similarities. OVCAR8 cells were incubated with LbL-NPs for 24 h and then fixed and analyzed by confocal microscopy to obtain representative Z-slices. Cell membranes are shown in red, nanoparticles are shown in green, and nuclei are shown in blue. Although less abundant, (a) 6-DXS-NPs bound to OVCAR8 cells were internalized. Relative to 6-DXS-NPs, many more (b) 5-HA-NPs were observed bound and internalized by OVCAR8 cells. (c) 1-PLD-NPs and (d) 2-PLE-NPs also accumulate on OVCAR8 cells, but a significant fraction of NPs remain associated with the membrane for 2-PLE-NPs, whereas the 3-PLD-NPs tended to accumulate at the membrane and then undergo slow caveolae-mediated uptake. Scale bar denotes 10 μm.
Figure 4.
Figure 4.
LbL-NP surface chemistry influences uptake pathways in cancer cells. Super-resolution microscopy was used to more precisely determine nanoparticle subcellular fates. (a) High-magnification Z-slices of super-resolution images indicating NP association with wheat-germ agglutinin stained cell membranes. 1-PLD-NPs and 2-PLE-NPs were abundant on the surface membrane. White arrows indicate association with cell membrane. (b) High-magnification Z-slices of NP association to CAV1+ caveolae. 5-HA-NPs were occasionally observed in caveolae. Membrane-bound 1-PLD-NPs were generally associated with caveolae, in contrast to 2-PLE-NPs. White arrows indicate association with caveolae. Purple arrow highlights membrane-bound NPs distant from caveolae. Scale bars in panels (a) and (b) denote 1 μm. (c) OVCAR8 cells were pretreated with endocytosis inhibitors prior to a 4 h incubation with NPs and then analyzed by flow cytometry to determine changes in NP-associated fluorescence. Results are normalized to DMSO controls. (d) To determine if membrane-bound NPs associate to cholesterol-rich lipid rafts, OVCAR8 cells were incubated with NPs for 24 h and then treated with cholesterol-depleting cyclodextrins for 4 h prior to analysis by flow cytometry. Error bars in panels (c) and (d) represent SEM. Multiple comparisons were performed using the FDR approach (Q = 5%) following one-way ANOVA or Student’s t test. Asterisks denote discoveries by FDR; ***q < 0.0002, * q < 0.002, *q < 0.047.
Figure 5.
Figure 5.
Tracking NP biodistribution in an orthotopic model of ovarian cancer reveals a durable accumulation of COOH LbL-NPs in neoplastic tissue relative to 6-DXS-NPs and PEG-NPs. NPs were administered IP or IV to murine models of metastatic ovarian cancer, and infrared fluorescence from nanoparticles was measured for each organ at the indicated time points. (a) Comparison of tumor accumulation following IP or IV administration of nanoparticles. (b) Quantification of the mean percent recovered NP fluorescence per gram of tumor following IP or IV administration. (c) Representative images of PLD-NPs accumulating in intestinal metastases by 24 h after either IP or IV administration. Refer to (a) for scale bars. (d) Tissue AUC for tumor, liver, and spleen over 72 h for each NP formulation following IP administration. (e) Representative images of NP biodistribution over 72 h following IP administration. (f) Quantification of percent recovered NP fluorescence per gram of tissue at 24, 48, and 72 h indicated that COOH LbL-NPs had significant and improved accumulation in neoplastic tissue relative to 6-DXS-NPs, PEG-NPs, or CML-NPs. Data were compiled from three independent experiments. CML-NPs were tested only at 24 h. Error bars represent SEM, n = 3, 4 for (b) and (f). Multiple comparisons were performed using the FDR approach (Q = 5%) following 2-way ANOVA. Asterisks denote discoveries by FDR; ****q < 0.0001, ***q < 0.001, **q < 0.01, *q < 0.045.
Figure 6.
Figure 6.
LbL-NPs penetrate into tumor tissue and patient-derived xenograft spheroids. NPs were administered IP to mice bearing orthotopic ovarian cancer xenografts, and after 24 h, tumors were collected and processed for histology or multiphoton whole-tissue imaging. (a) Fluorescence imaging of tumor sections 24 h after NP administration revealed NPs accumulate on the tumor surface, with some penetration into the tissue. Red and blue signals are pseudocolored autofluorescence from H&E staining; green signal indicates nanoparticle. (b) Representative slices and orthogonal views of z-stacks acquired from whole-tumor imaging using multiphoton microscopy. Blue signal corresponds to collagen autofluorescence, red corresponds to the mCherry signal from tumor cells, and green signal is from nanoparticles. (c) Z-stacks were quantified to measure NP penetration into tumor tissues. HA-coated NPs penetrated the deepest into tissue with an average depth of 200.0 ± 23.4 μm. Whiskers indicate minimum and maximum values, n = 6 for quantification of multiphoton imaging. (d) mCherry-expressing PDX spheroids were cultured with NPs in vitro for 24 h and then processed for flow cytometry. Single live cells were analyzed for association with NPs. (e) PDX spheroids were incubated with NPs for 24 h and then fixed and analyzed using confocal microscopy to obtain representative z-slices. Scale bars denote 100 μm in panels (a) and (b) and 50 μm in (e). Multiple comparisons were performed using the FDR approach (Q = 5%) following one-way ANOVA. Asterisks denote discoveries by FDR; ***q = 0.0002, **q = 0.0018, *q = 0.0043.
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