Beyond One-Size-Fits-All: Cancer Biology Shapes Nanoparticle Behavior

Beyond One-Size-Fits-All: Cancer Biology Shapes Nanoparticle Behavior | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Beyond One-Size-Fits-All: Cancer Biology Shapes Nanoparticle Behavior Maria Bravo, Guillermo Solís-Fernandez, Sandra Krzyzowska, Steven Huysecom, and 7 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7865764/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 21 Apr, 2026 Read the published version in Journal of Nanobiotechnology → Version 1 posted 21 You are reading this latest preprint version Abstract Nanoparticles (NPs) are a promising tool for cancer therapy, yet few have successfully reached clinical application. Current nanomedicine development pipelines are focused on optimizing the physical properties of NPs, overlooking the impact of cancer biology on their behavior. Here, we show that the same NPs exhibit distinct accumulation and penetration patterns in 3D spheroids derived from four spheroid models (representative of lung, colon, breast, and cervical cancer). We uncover an inverse relationship between NP uptake and penetration: spheroids with slower internalization show deeper NP diffusion. Proteomic analysis revealed that tumor-specific expression of endocytic and extracellular matrix proteins underlies this variability. Our findings challenge the prevailing ‘one-size-fits-all’ approach and highlight the need to integrate cancer biology into NP design. Tailoring NPs to the unique cellular and extracellular features of each tumor type will be critical for developing more effective and clinically relevant nanotherapies. Nanoparticles 3D Cell Models Cancer Spheroids Nanoparticle Cellular Uptake Endosomal Trafficking ECM Composition Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Introduction Nanoparticles (NPs) have emerged as a promising alternative to conventional cancer therapies, offering unique advantages such as improved targeting, localized drug delivery, and reduced systemic toxicity 1 , 2 . Yet, clinical translation remains limited, partly due to inconsistencies in preclinical testing that undermine the reliability of current screening approaches 2 . Traditionally, NP development and screening rely on two-dimensional (2D) tumor models that fail to replicate key aspects of cancer physiology, masking critical biological variability between tumors 1 , 2 . The introduction of three-dimensional (3D) tumor models, particularly multicellular tumor spheroids, has provided a more physiologically relevant platform to assess NP accumulation, penetration, and therapeutic efficacy, while maintaining compatibility with high-throughput screening 3 – 5 . However, even with these improved models, a “one-size-fits-all” approach persists, with most NP development pipelines focused on tuning physical properties – such as size, shape, and surface charge – while overlooking the influence of cancer biology on NP behavior. As a result, most studies evaluate NPs in a single tumor model and assume that findings are applicable across different cancers. However, cancer-specific features, including cell morphology, proliferation rates, vesicular trafficking, and extracellular matrix (ECM) composition, can influence NP uptake and diffusion 6 – 8 . This oversight has led to apparently conflicting results in the field. For instance, there is an ongoing debate concerning the effect of NP charge on spheroid penetration: Wang et al. reported that cationic PEGylated NPs exhibited higher penetration depth, and Sujai et al. found that anionic PEGylated gold NPs achieved enhanced penetration 9 , 10 . These findings, while at first glance contradictory, were obtained in different tumor models – breast and cervical cancer, respectively – and highlight how cancer biology critically affects NP-tumor interactions, underscoring the limitations of generalizing results across tumor types. This calls into question the common practice of generalizing results across cancer types and highlights the need for more cancer-contextualized nanomedicine research. In this report, we addressed this gap by systematically comparing NP accumulation, internalization, and penetration in four non-metastatic spheroid models using a single, well-defined NP formulation. Quantitative proteomic analysis was used to explore how cancer-specific differences in endocytic and ECM-related proteins contribute to the observed patterns. Our findings underscore the need to shift from platform-centric to tumor-centric strategies in NP design. Methods Materials Dulbecco’s modified eagle medium (DMEM), gentamicin, phosphate buffered saline (PBS, no calcium, no magnesium), paraformaldehyde (v/v 16%, methanol-free), Trypsin-ethylenediaminetetraacetic acid (Trypsin-EDTA, 0.5% solution, no phenol red), Hank’s balanced salt solution (HBSS, no phenol red), Triton X-100 (0.1%), UltraPure™ agarose (2%), sodium chloride (NaCl), GlutaMax TM , Trypan blue solution (0.4%, TC grade), CellMask TM DeepRed, precolumn PepMap Trap Cartridge (5 µm, 300 µm x 5 mm), Vanquish Neo UHPLC System, Easy-Spray PepMap RSLC C18 (3 µm, 75 µm x 15 cm), ECL Pico Plus chemiluminescent reagent, and iBright imager were purchased from ThermoFisher Scientific. Tetrachloroauric(III) acid trihydrate (HAuCl4 · 3H2O, 99%), sodium hydroxide (NaOH, 98%), hexadecyltrimethylammonium bromide (CTAB, ≥99%), tetraethyl orthosilicate (TEOS, 98%), hydrochloric acid (HCl, 1 N), methanol (MeOH, 99,8%), polyethyleneimine solution (PEI, 50% w/v in H 2 O), 3D Petri Dish® micro-mold, fetal bovine serum (FBS), CoverWell TM perfusion chambers (9 mm × 1.7 mm thickness), and RadioImmunoPrecipitation (RIPA) buffer were purchased from Sigma Aldrich. Phalloidin CruzFluor TM 647 was purchased from Santa Cruz Biotechnology. 1X Protease and phosphatase inhibitors were purchased from MedChemExpress. A549 (lung cancer), MCF7 (breast cancer) cell lines were a gift from Prof. Uji-i. All chemicals were used without further purification. Methods Nanoparticle Synthesis and Functionalization: Mesoporous silica-coated gold nanoparticles (Au@mSi) were synthesized using a one-pot method as described by Chen et al 11 . Initially, 50 mg of CTAB was dissolved in a solution containing 0.6 mL of 0.5 M NaOH and 24 mL of Milli-Q water. The mixture was stirred at 800 rpm and 80°C for 15 minutes. Subsequently, 1 mL of a 2% (wt) aqueous formaldehyde solution was added, followed by 0.8 mL of a 0.05 M HAuCl 4 aqueous solution. After 10 min, a solution of TEOS and ethanol (1 mL, containing 0.25 g TEOS and 0.50 g ethanol) was added, and the mixture was stirred for an additional hour. The resulting product was collected by centrifugation, dispersed in a 1.1 M HCl solution in water/ethanol (v/v = 1.25:10), and sonicated. The dispersion was then stirred vigorously at 60°C for 4 h to remove CTAB from the pores. The product was washed twice with Milli-Q water to neutralize the pH, followed by redispersion in 3.2 mL of Milli-Q water. The mixture was stirred magnetically at 500 rpm for 3 h, and the supernatant was removed by centrifugation and replaced with Milli-Q water. For PEI functionalization, a 0.75% (w/v) PEI solution (adjusted to pH 7) was added dropwise to the Au@mSi NPs in a 1:1 (v/v) ratio. The mixture was stirred for 3 h, washed by centrifugation, and redispersed in Milli-Q water. The concentration of particles in the colloidal solution was estimated to be 1.3 mg/mL. Nanoparticle Characterization: The physicochemical properties of the NPs were characterized using UV-Vis spectroscopy, scanning and transmission electron microscopy (SEM, TEM), Zeta Potential, and Dynamic Light Scattering (DLS). UV-Vis spectra were acquired using a Cary 60 Spectrophotometer (Agilent). High-resolution SEM images were captured with a field-emission SEM (FEI Quanta FEG250) microscope operated at 20.0 kV. For TEM measurements, Au@mSi-PEI NPs were diluted in a 1:100 ratio, and 6 μL of solution was cast onto TEM grids (300 mesh copper grid) and dried at room temperature. TEM images were collected on an FEI TecnaiF20 microscope (200 kV) (Ian Holmes Imaging Center, Bio21). Particle size, size distribution, polydispersity index, and Zeta Potential measurements were performed using a Zetasizer Nano ZS (Malvern Panalytical). All measurements were carried out in Milli-Q water at room temperature (25°C). Cell Culture: A549, HeLa, KM12C, and MCF7 cells were cultured in 25 cm 2 culture flasks at 37 °C under a 5% CO 2 atmosphere. Cells were maintained in culture medium (DMEM supplemented with 10% FBS, 1% L-glutamax, and 0.1% gentamicin) and passaged using trypsin-EDTA at 80-90% confluency. NP Screening in Spheroids (3D): At 90% confluency, cells were harvested for spheroid preparation. Spheroids were cultured in agarose microtissues (16 x 16 array of 256 wells), cast from ultra-pure agarose (20 mg/mL in Milli-Q water with 9 mg/mL NaCl) using a 3D micro-mold (3D Petri Dish®, Sigma Aldrich). Once solidified, the agarose microtissue was removed from the mold, placed in a sample dish, and equilibrated with DMEM for 15 min. After removing the DMEM, 190 μL of cell suspension was added to the mold. Cell concentrations were optimized to achieve spheroids of comparable sizes: A549 and MCF7 were seeded at 1.3×10 6 cells/mL, HeLa cells at 1.1×10 6 cells/mL, and KM12C at 2.5×10 5 cells/mL. Cells were allowed to sink into the agarose wells for 30 min, followed by the addition of DMEM around the microtissue. Samples were incubated for 4 days to obtain fully formed spheroids before incubation with NPs. Once spheroids were fully formed, the DMEM inside and around the microtissues was removed. A 200 μL suspension of Au@mSi-PEI NPs or Au@mSi (26 µg/mL) in DMEM was added to the microtissues and incubated for 30 min. DMEM was then added around the microtissues to prevent dilution of the NP suspension. To assess NP accumulation and penetration, spheroids were incubated with Au@mSi-PEI for 3, 6, 24, and 48h. Au@mSi assays were only performed for the 48h time point. Following incubation, the medium was removed and 1 mL of PBS was vigorously pipetted onto the microtissue to dislodge the spheroids, which were transferred to 1.5 mL microcentrifuge tubes. Harvested spheroids were stained with Phalloidin CruzFluor TM 647 12 . Spheroids were washed three times with PBS and collected using 10-second centrifugation (2000 G). They were then fixed with 500 μL 4% PFA for 20 min, followed by three PBS washes. Permeabilization was performed with 1 mL 0.1% Triton X-100 for 30 min. Spheroids were incubated overnight at room temperature with 500 μL of Phalloidin CruzFluor TM 647 (1:800 dilution in 3% BSA) for cytoskeleton (actin) staining. The next day, the spheroids were washed 3 times with PBS and resuspended in 200 μL of PBS before imaging. For spheroid imaging, 60 μL of spheroid suspension was transferred to a CoverWell TM perfusion chamber glued on a #1 glass coverslip. Brightfield Live Spheroid Imaging: Spheroid growth was monitored via live imaging using brightfield (4x air objective, N.A 0.13). Spheroids were prepared as previously described, and images were acquired every 6 hours for 6 days, to enable spheroid monitoring throughout the duration of the experiments (4 days of spheroid maturation and a maximum of 48 hours of NP incubation). Cell segmentation was performed using Ilastik 13 and manually training a model to identify the regions corresponding to cell aggregates. For the iLastik analysis, four training sets were made, one for each cell line. Each model was then used to analyze the corresponding videos. The masks obtained with this segmentation model were then analyzed with an in-house written Python script to obtain their area. Finally, the data on these variables were plotted and tested for statistical significance using Graphpad Prism (Kruskal-Wallis test comparing the mean differences of all samples to the control condition). NP Screening in Cell Monolayers (2D): Cells (2×10 5 cells/dish) were seeded in 29-mm glass-bottom dishes (Cellvis, Mountain View, CA, USA) and incubated for 24 hours to reach 60-80% confluency. NP uptake was assessed at 3, 6, 24, and 48h. Cells were incubated with Au@mSi-PEI NPs (26 µg/mL) for 3 hours, then carefully washed three times with PBS. For the 3-hour time point, cells were immediately stained and imaged. For subsequent time points, fresh DMEM was added, and samples were incubated at 37°C for the remaining time. Before imaging, the cell membrane was stained with CellMask™ Deep Red (1 μM in HBSS, 3 min), washed three times with PBS, and imaged in HBSS. Fluorescence Microscopy: Confocal fluorescence imaging of 2D and 3D samples was performed on a Leica TCS SP8 dive inverted microscope. This system is equipped with a multi-photon (MP) Insight X3 laser (range 680-1300 nm), two HyD detectors (non‑descanned), a 25× water immersion objective (NA 0.95, FLUOTAR VISIR) for 3D models, and a 63× oil immersion objective (NA 1.4) for 2D models. Sequential scanning (between stacks) was performed, starting with the Phalloidin CruzFluor 647 TM channel (1200 nm, 3.8 mW at the objective, 610-661 nm detection), followed by the Au@mSi/Au@mSi-PEI NP channel (800 nm, 3.8 mW at the objective, 503-553 nm detection). For 2D imaging, live imaging was performed in HBSS at 37°C and 5% CO 2 . In this case, the first sequence targeted the CellMask TM Deep Red channel using a 638 nm diode laser (detection 641-691 nm). Image stacks of 1024 × 1024 pixels were acquired at a scanning speed of 400 Hz, 0.57 or 0.30 µm z-step, for 3D and 2D assays, respectively. NP Behavior Analysis: In-house algorithms were used to determine NP accumulation/penetration in 3D spheroids and uptake in 2D cell monolayers. NP distribution profiles in 3D were obtained from the analysis of at least 20 spheroids. For 3D spheroids, the algorithm first preprocessed cytoskeleton-staining images of the spheroid to identify the spheroid edge by applying smoothing, dilation, and edge detection. The spheroid edge was then used to obtain the spheroid center and a spherical coordinate system. This coordinate system enabled the calculation of edge-to-center distance at each angle of elevation (θ) and azimuth (ψ). For the NP channel, the same spherical coordinates were used to calculate the NP penetration depth by measuring the distance of the NPs from the spheroid edge at the same angles θ and ψ. Integration of pixel intensities across penetration depths provided individual penetration profiles, which were then averaged to obtain the mean distribution profile (Fig. S12). NP behavior in 2D was analyzed from at least 20 images of different fields of view. The algorithm was used to quantify the total intensity of the NPs in the cytoplasm. First, the cytoplasm and cellular membrane were segmented based on the membrane staining intensity by using the Cellpose algorithm in MatLab 14 . NPs were quantified by integrating the fluorescence intensity signal of the NP signal within each cell area (Fig. S13). LC-MS/MS and proteomic analysis: Cell pellets of spheroids from all the cell lines in the absence of NPs (day 5 of incubation) were collected via centrifugation. The cell pellets were then lysed with RIPA buffer containing 1X protease and phosphatase inhibitors. Total protein concentration was obtained by the tryptophan quantification method, as described by Wiśniewski et al. 15 , and confirmed by Coomassie blue staining after reducing 10% PAGE-SDS. Once protein extracts from spheroids in the presence or absence of NPs were obtained, 10 µg of each extract was reduced, alkylated, and trypsin digested on SP3 magnetic beads as previously described 16 . For LC-MS/MS, peptides were analyzed in an Orbitrap Astral mass spectrometer coupled to a Vanquish Neo UHPLC System. Peptide samples were loaded into the precolumn PepMap Trap Cartridge (5 µm, 300 µm x 5 mm) and eluted in an Easy-Spray PepMap RSLC C18 (3 µm, 75 µm x 15 cm) heated at 50°C. The mobile phase flow rate was 300 nL/min, and 0.1% formic acid (FA) in MilliQ water and 0.1% FA in 80% acetonitrile (ACN) were used as elution buffers A and B, respectively. The 15 min elution gradient was: 4%-10% buffer B for 2 min, 10%-40% buffer B for 11 min, 40%-99% buffer B for 0.5 min, and 99% buffer B for 1.5 min. Prior to injection, samples were re-suspended in 20 µL of buffer A, and 2 µL of each sample were injected and analyzed in data-independent acquisition (DIA) mode. For ionization, 1900 V of liquid junction voltage and 280°C capillary temperature were used. The full scan method employed a m/z 380-980 mass selection, an Orbitrap resolution of 240000 (at m/z 200), an automatic gain control (AGC) value of 500%, and a maximum injection time (IT) of 5 ms. The MS/MS was performed with the Astral mass analyzer, using an AGC of 500%, an IT of 3 ms, and a normalized collision energy (NCE) of 25 for fragmentation of precursors. The scan range was set from 380 to 980 m/z, with an isolation window of 2 m/z, and window placement optimization was enabled. Thus, a total of 299 windows were analyzed in each cycle. Raw data were analyzed with Spectronaut (version 19.1.240724.62635) using standardized workflows. DIA raw data and Uniprot UP000005640_9606.fasta Homo sapiens (march, 2024) database (20,418 protein entries) were used for the construction of the spectral library by directDIA. Trypsin/P and Lys/P were selected as the digestion enzymes, and a maximum of 2 missed cleavages were allowed. Carbamidomethylation of cysteines was set as a fixed modification, and methionine oxidation and N-terminal acetylation were set as variable modifications. For DIA analysis, a standard workflow was used. The maximum false discovery rate (FDR) for peptide spectral match (PSM), peptide, and protein identifications was set at 0.01, automatic cross-run normalization was enabled, and all identified peptides were used for protein quantification. Protein inference was performed using the IDPicker algorithm. Non-imputation was performed during peptide and protein identification and quantification with Spectronaut. Gene Ontology Analysis: The list of proteins identified through proteomic analysis was filtered to obtain which components of the gene ontology terms “Extracellular Matrix” (0031012, 1826 annotations), and “Endocytic Vesicle” (0030139, 958 annotations) in homo sapiens were present in the cells under study. Each list of filtered proteins was then used to perform Principal Component Analysis (PCA) based on the expression levels of the proteins. PCA plots were obtained using Python and sklearn.decomposition and sklearn.preprocessing packages. The list of filtered proteins after removing repeated annotations can be found in the supplementary Tables S4 and S5. Western Blot: For WB analysis, 12.5 µg of each protein extract from the 4 cell lines were separated on 12.5% SDS-PAGE under reducing conditions. Transference to nitrocellulose membranes was performed at 25V for 90 min using the Invitrogen XCell II™ Blot Module. Membranes blocked with 0.1% Tween PBS 1× supplemented with 3% skimmed milk were incubated with primary antibodies at optimized dilutions (Table S1) overnight (O/N) at 4 °C in the same solution. After three washes with 0.1% Tween PBS 1×, membranes were incubated with the appropriate HRP-conjugated or Alexa488-conjugated secondary antibody (Table S1) for 1 h at RT. Membranes were then washed as above. Finally, the signal developed with the ECL Pico Plus chemiluminescent reagent was detected on an iBright imager. For the membranes that were incubated with Alexa488-conjugated secondary antibodies, the fluorescence was directly detected on the same iBright imager. Protein levels were measured using Fiji’s gel tool, and the data from each protein were normalized to its corresponding level of GAPDH. Results and Discussion Nanoparticle Synthesis and Characterization Au@mSi NPs were prepared as previously described by Chen et al 11 using a one-pot method. Transmission electron microscopy (TEM) and scanning electron microscopy (SEM) revealed spherical NPs with homogeneous size distribution and minimal aggregation. AuNP cores exhibited an average diameter of 43 nm, while the mSi shell was approximately 15 nm thick, resulting in an overall diameter of 77 ± 8 nm (Figure 1a, b). The localized surface plasmon resonance (LSPR) was characterized using UV-VIS spectroscopy (Figure 1c), showing a redshift of the plasmonic band after mSi coating, due to an increase in the local refractive index 18 . Zeta potential measurements showed that AuNP cores initially displayed a positive charge of 36 ± 1 mV due to the presence of CTAB (Cetylmethylammonium bromide) on the NP surface. To enhance aqueous stability, the mSi layer was first added, followed by the removal of CTAB, which reduced toxicity 19 . During this process, CTAB acted as a pore-generating agent, forming micelles that serve as templates for silica condensation 20 . The resulting Au@mSi displayed a negative charge (-30 ± 1 mV), as a result of the deprotonated hydroxyl groups on the silica surface 17 . To enhance cellular uptake, Au@mSi NPs were coated with polyethyleneimine (PEI, Mw = 1.3 kDa) via electrostatic interactions 17 (Au@mSi-PEI NPs). PEI functionalization was confirmed by a marked shift in the zeta potential from –30 ± 1 mV for Au@mSi NPs to +43 ± 1 mV after coating. The resulting positive surface charge arises from the protonated amine groups of PEI on the nanoparticle surface, which promotes interaction with the negatively charged cell membrane and facilitates internalization via endocytic pathways 21 . NP distribution in 3D spheroids depends on cancer cell type To investigate how cancer biology influences NP behavior, we analyzed the behavior of Au@mSi-PEI NPs in four non-metastatic spheroid models representative of high-incident cancers: lung (A549), colon (KM12C), breast (MCF7), and cervical (HeLa) 2,22–24 . Lung and colorectal cancers rank amongst the most commonly diagnosed worldwide, while breast and cervical cancer are highly prevalent among women 22 . Spheroids were incubated for 3, 6, 24, and 48 h to capture the temporal dynamics of NP transport within spheroids. Clear differences in NP accumulation were observed across cell types (Figure 2). At early time points (3 and 6 h), overall accumulation was low in all spheroids, although KM12C and MCF7 spheroids exhibited slightly higher NP levels compared to A549 and HeLa (Figure 2b). By 24 h, NP accumulation more than doubled in all models, with KM12C showing the highest accumulation, and A549 the lowest. After 48 h, KM12C spheroids maintained the highest NP levels, while HeLa exhibited the lowest accumulation. Notably, HeLa spheroids, which had relatively high NP levels at 24 h, exhibited a marked drop by 48 h. This coincided with a decrease in spheroid size as observed in the fluorescence images (Figure S4). In contrast, size analysis from time-lapse imaging (Figures S5 and S6) showed that HeLa spheroids continued to grow over time. This discrepancy suggests that the proliferative outer layer, which contained most of the NPs, detached and was lost during sample handling. As a result, NP accumulation and penetration data in HeLa spheroids became less reliable at later time points. NP penetration profiles followed a similar time-dependent trend (Figure 2c). At 3 and 6 h, NP distribution was restricted to the outer cell layers, with 95% of the NPs (P95) found within the first 25 µm of the spheroids. By 24 h, MCF7 and KM12C spheroids exhibited deeper penetration, with P95 values of 36 µm and 27 µm, respectively, while NPs in A549 and HeLa spheroids remained confined to a smaller region (P95 between 22 and 19 µm). After 48 h, MCF7 spheroids showed the greatest penetration depth, with 5% of the NPs beyond 47 µm, followed by KM12C spheroids (P95 of 35 µm). In contrast, NP penetration in A549 and HeLa spheroids remained low (P95 of 21 and 28 µm, respectively). These findings reveal tumor-dependent differences in NP accumulation and penetration within 3D spheroids, suggesting that cancer-specific factors govern NP distribution. To investigate underlying mechanisms driving this variability, we evaluated NP-cell interactions at the single-cell level. Cellular uptake kinetics provide clues to NP penetration in spheroids Understanding how efficiently cancer cells internalize NPs is essential for optimizing delivery strategies and maximizing therapeutic efficacy. While 3D spheroids better mimic tumor physiology, their size and complexity hinder the quantification of NP uptake within individual cells. To address this limitation, we used 2D monolayers as a complementary model to precisely monitor subcellular NP localization. 2D cellular systems also enable pulse-chase experiments, where NPs are incubated for a defined period and then removed to track intracellular NP dynamics over time – an approach less feasible in spheroids due to the difficulty of removing extracellular NPs without disturbing the 3D structure. Here, cells were incubated with Au@mSi-PEI NPs for 3 h, after which NPs were removed. Imaging was performed immediately (defined as 3 h) and at 6, 24, and 48 h after NPs addition, to monitor NP uptake and retention over time (Figure 3a). NP uptake kinetics varied across tumor models, displaying distinct trends over time (Figure 3b). While A549 and HeLa internalized substantial amounts of NPs within the first 3 h, KM12C and MCF7 displayed minimal uptake. By 6 h, NP uptake in HeLa and KM12C cells increased, reaching levels comparable to A549. MCF7 consistently exhibited the lowest internalization, with NPs largely remaining at the cell membrane rather than within the cytoplasm. At later time points (24 and 48 h), NP accumulation became more uniform across cell lines, showing no significant differences. These findings show that NP uptake dynamics vary significantly across tumor cell lines and inversely correlate with NP penetration in 3D spheroids. For example, rapid NP uptake by A549 cells correlated with NP retention in the spheroid’s outer layers. In contrast, MCF7 displayed the slowest NP uptake in monolayers, which was associated with the deepest NP penetration in spheroids (Figure 2c, 3b). This suggests that rapid NP uptake by peripheral cells may act as a trap, limiting further diffusion into deeper layers. Conversely, slower uptake in KM12C and MCF7 cells likely enables NPs to travel further before being internalized, potentially via paracellular transport. Reduced cellular uptake enhances NP penetration into tumor spheroids To test whether NP uptake and spheroid penetration are indeed inversely related, we evaluated the behavior of NPs with expected lower cellular uptake. Specifically, we used bare silica-shell gold NPs lacking PEI coating (Au@mSi), which carry a negative surface charge and are therefore less likely to interact with the cell membrane and be internalized (Figure 1) 17,21 . In agreement with previous reports, removal of the PEI coating reduced NP internalization and resulted in a more uniform uptake across all tumor models in 2D monolayers (Figures 4a-b) 17,21 . This decrease in cellular uptake translated into enhanced NP penetration in 3D spheroids (Figures 4c-d). At 48 h, penetration depths increased across most spheroid models: A549 spheroids, which previously showed highly restricted diffusion, exhibited the largest improvement (P95 increasing from 21 µm to 41 µm). KM12C and MCF7 spheroids also showed moderate increases in penetration (P95 from 35 µm to 46 µm, and from 47 µm to 49 µm, respectively). In contrast, HeLa spheroids showed no increase in the P95 value, likely due to the loss of the outer proliferative rim, which compromised spheroid integrity and affected penetration analysis. These results support an inverse relationship between cellular uptake and NP penetration, showing that reduced cellular internalization can facilitate deeper NP diffusion into tumor spheroids. Proteomic differences in endocytic and ECM proteins correlate with cell line-specific NP behavior Given the key role of endocytosis in NP internalization, we hypothesized that the inverse relationship between NP uptake and penetration might be driven by cancer-specific differences in the expression of endocytic proteins. To test this, we performed a quantitative proteomic analysis across the 4 tumor models, identifying 5976 different proteins (Table S2). To focus on uptake-related processes, we analyzed proteins classified under the gene ontology term “Endocytic Transport” (0030139). Principal component analysis (PCA) showed high reproducibility across biological replicates and revealed that KM12C and MCF7 spheroids shared similar endocytic protein profiles, distinct from those of A549 and HeLa (Figure 5a). This grouping mirrored the NP uptake trends, with breast and colorectal cancer models displaying slower internalization compared to lung and cervical models. To pinpoint candidate regulators of NP uptake, we analyzed proteomic data for proteins with significant expression changes compared to MCF7 cells, which showed the highest NP penetration and lowest uptake (fold-change ≥ 2 or ≤ 0.5; Table S3, Table S4, Figure S11a). From this list, we selected candidates for further validation by western blot (WB) analysis (Figure 5b, Figure S11b). Among the top hits, the synaptosome-associated protein-23 (SNAP23), essential for membrane fusion and early endosomal function, was upregulated in A549, HeLa, and KM12C cells, suggesting enhanced cellular trafficking in these tumors 25 . Caveolin-1 (CAV1), a key component of caveolae-mediated endocytosis 26 , showed strong overexpression in A549, HeLa, and KM12C, mirroring their higher cellular uptake. Similarly, cavin-1 (PTRF), crucial for caveolae formation and function 27 , was also upregulated in A549, HeLa, and KM12C. These results suggest that caveolae-dependent mechanisms are a major contributor to Au@mSi-PEI NP internalization in these tumor models. Given that NP transport is influenced not only by cellular uptake but also by the surrounding tumor microenvironment, we investigated whether tumor ECM composition also contributes to the distinct NP behavior observed across tumor models. Previous studies have shown that ECM properties can impact NP diffusion in solid tumor models 28,29 . Using the gene ontology classification “Extracellular Matrix” (0031012), we identified tumor-specific differences in the expression of ECM-related proteins (Table S3, Table S5, Figure 5c, Figure S11a). As observed for endocytic proteins, PCA analysis of ECM proteins showed high reproducibility across replicates and revealed clustering of MCF7 and KM12C samples. Relevant candidates were selected for further validation by WB analysis. Compared to MCF7 spheroids, A549, HeLa, and KM12C spheroids, models with lower NP penetration, showed increased levels of key structural ECM components associated with increased matrix density and stiffness, including fibronectin (FN1), vitronectin (VTN), and laminin β1 (LAMB1) 2,30 . These findings suggest that a denser ECM may act as an additional physical barrier to NP diffusion, complementing the role of cellular uptake in modulating NP transport. Conclusions and Future Prospects Our results show that tumor-intrinsic characteristics affect NP behavior, challenging the current ‘one-size-fits-all’ paradigm in NP design. Distinct tumor types displayed unique NP accumulation and penetration profiles in 3D spheroids, driven by differences in endocytosis-related protein expression and extracellular matrix (ECM) composition. Using 2D monolayers, we uncovered an inverse relationship between NP internalization and diffusion depth, where tumors with slower NP uptake allowed for deeper NP penetration, likely via paracellular routes. This hypothesis was supported by the enhanced penetration of NPs without PEI. Proteomic analysis confirmed that higher expression of endocytosis-related proteins aligned with increased NP internalization, while ECM-stiffening proteins were upregulated in tumor models showing reduced NP diffusion. These findings highlight the need to move beyond solely optimizing NP physicochemical properties and instead tailor designs to tumor-specific biological features. Moreover, therapeutic goals should guide optimization strategies: drug delivery applications may benefit from high NP uptake, whereas therapies like photothermal treatments may require deeper NP diffusion into the tumor mass. By integrating tumor biology into NP development, therapeutic efficacy can be improved, ultimately accelerating the clinical translation of nanomedicines. Declarations Author Contribution MB - Data acquisition and curation, Formal Analysis, Investigation, Methodology, Validation, Visualization, Writing–original draft, Writing–review and editing. GS - Data acquisition and curation, Formal Analysis, Investigation, Methodology, Validation, Visualization, Writing–original draft, Writing–review and editing, SK - Investigation, Methodology, Writing–review and editing. SH - Investigation, Methodology, Data analysis, Writing–review and editing. AMC - Data Acquisition, Investigation, Methodology, Writing–review and editing, IV - Supervision, Writing–review and editing. BL - Supervision, Writing–review and editing. JH - Supervision, Writing–review and editing. RB - Supervision, Writing–review and editing. BF - Conceptualization, Data curation, Supervision, Writing. SR - Conceptualization, Funding acquisition, Project administration, Software, Supervision, Writing. All authors contributed to manuscript preparation and revision Acknowledgement The author(s) declare that financial support was received for the research, authorship, and/or publication of this article. MB acknowledges the Global PhD partnership program between KU Leuven and Melbourne University (GPUM/21/025). We acknowledge additional financial support from Research Foundation of Flanders (FWO) research grants (G0D4519N, G081916N, VS08523N, G0C1821N, and G022724N), postdoctoral fellowships (for BF: 12X1419N and 12X1423N, for IV: 12A6N25N, for GS: 12AML24N, for BL: 12AGZ24N), PhD fellowships (for SH: 11A0S25N) and from the KU Leuven (IDN/20/021, C14/15/053, C14/19/079, and C14/22/085). This work also received financial support from PI20CIII/00019 and PI23CIII/00027 grants from the AES-ISCIII program cofounded by FEDER funds to R.B. Data Availability The data underlying this study are available in the main manuscript and its Supporting Information. Supporting Information Fluorescence microscopy images of additional spheroid samples showing Au@mSi-PEI NP penetration; Average total Au@mSi-PEI NP accumulation per spheroid, shown for each cell line over time; Au@mSi-PEI NP penetration profiles per cell line over time; Spheroid size over time, calculated from fluorescence microscopy images; Spheroid size over time when samples are not manipulated; Fluorescence microscopy images of additional samples of Au@mSi-PEI NP internalization in 2D monolayers; Average Au@mSi-PEI NP intensity inside the cells per cell line over time; Fluorescence microscopy images of additional 2D and 3D samples showing internalization and penetration of Au@mSi NPs; Au@mSi NP penetration profiles per cell line; Protein expression levels of relevant endocytic and ECM-related proteins in each cell line; WB analysis of the proteomics data; List of primary and secondary antibodies used for WB; Overview of the software for NP behavior analysis in 3D spheroids; Overview of the software for NP behavior analysis in 2D cell monolayers (PDF). 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16:19:40","extension":"png","order_by":33,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":408503,"visible":true,"origin":"","legend":"","description":"","filename":"Onlinefloatimage3.png","url":"https://assets-eu.researchsquare.com/files/rs-7865764/v1/8946245bebbdada352caf478.png"},{"id":95312098,"identity":"e374f429-9abb-4978-b469-a986db50c73b","added_by":"auto","created_at":"2025-11-06 15:46:59","extension":"png","order_by":34,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":188487,"visible":true,"origin":"","legend":"","description":"","filename":"Onlinefloatimage4.png","url":"https://assets-eu.researchsquare.com/files/rs-7865764/v1/4a2008e62f31e859d5923b87.png"},{"id":95033490,"identity":"c52b28da-8011-4e34-9523-dfa2483ebadf","added_by":"auto","created_at":"2025-11-03 14:52:49","extension":"png","order_by":35,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":41779,"visible":true,"origin":"","legend":"","description":"","filename":"Onlinefloatimage5.png","url":"https://assets-eu.researchsquare.com/files/rs-7865764/v1/d851b66fdba9b4c47f784de9.png"},{"id":95033491,"identity":"40d5d90a-a47b-4332-8c2b-89131b6c83eb","added_by":"auto","created_at":"2025-11-03 14:52:49","extension":"xml","order_by":36,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":95839,"visible":true,"origin":"","legend":"","description":"","filename":"d56abd96651540c9b23ba0b30a28763e1structuring.xml","url":"https://assets-eu.researchsquare.com/files/rs-7865764/v1/ea3bfcf8f677cb402dd3be3e.xml"},{"id":95033498,"identity":"9fa019b5-ca0c-4de0-b312-6ba4ed4dd61c","added_by":"auto","created_at":"2025-11-03 14:52:49","extension":"html","order_by":37,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":106118,"visible":true,"origin":"","legend":"","description":"","filename":"earlyproof.html","url":"https://assets-eu.researchsquare.com/files/rs-7865764/v1/7747593d64c0174313b7ce53.html"},{"id":95033450,"identity":"b6c8d0e6-e2e3-43bd-a333-e9441df0312d","added_by":"auto","created_at":"2025-11-03 14:52:48","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":500392,"visible":true,"origin":"","legend":"\u003cp\u003e(a) Transmission electron Microscopy (TEM) image of Au@mSi NPs (Scale bar: 20 nm). (b) Size distribution of Au@mSi NPs fitted with gaussian curve. (c) Extinction spectra of AuNPs before and after mSi coating (in water). (d) Zeta potential measurements of AuNP, Au@mSi, and Au@mSi-PEI, given as mean ± SD (n = 3).\u003c/p\u003e","description":"","filename":"Figure1.png","url":"https://assets-eu.researchsquare.com/files/rs-7865764/v1/9e73d9b6c999af087bdf485d.png"},{"id":95221783,"identity":"067a7504-8d0e-404d-9dd6-3ed3deb65a7e","added_by":"auto","created_at":"2025-11-05 16:19:42","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":6648947,"visible":true,"origin":"","legend":"\u003cp\u003e\u0026nbsp;(a) Confocal fluorescence microscopy images of NP distribution in 3D tumor spheroids from A549 (lung), HeLa (cervical), KM12C (colorectal), and MCF7 (breast) cancer cell lines, at the mid-plane of the spheroids (with a diameter of approximately 250 µm). Au@mSi-PEI distribution was assessed at 3, 6, 24, and 48 h, with columns showing the different time points and rows corresponding to the cell lines. Nanoparticles (green) were detected via photoluminescence following multiphoton laser excitation, and actin filaments (magenta) were stained with Phalloidin CruzFluor\u003csup\u003eTM\u003c/sup\u003e 647. Scale bar = 50 µm. Representative images of additional samples are provided in Supplementary Figure S1. (b) Total NP accumulation per spheroid, based on total NP signal intensity within the spheroid volume. Average total NP accumulation per cell line over time is provided in Supplementary Figure S2. (c) NP penetration profiles displayed as cumulative NP distribution (intensity%) relative to the distance from the spheroid outer rim at each time point. Comparison of NP penetration profiles for each cell line over time is provided in Supplementary Figure S3. A549, HeLa, KM12C, and MCF7 are represented in green, orange, blue, and pink, respectively. Error bars indicate ± SD, with ns meaning not significant; * (p \u0026lt; 0.05), ** (p \u0026lt; 0.01), *** (p \u0026lt; 0.001), and **** (p \u0026lt; 0.0001) (n ≥ 20).\u003c/p\u003e","description":"","filename":"Figure2.png","url":"https://assets-eu.researchsquare.com/files/rs-7865764/v1/2b24b03f53dc3d616e33211e.png"},{"id":95033462,"identity":"0e3add2c-a300-4ae7-bd82-4e3eb862eaf3","added_by":"auto","created_at":"2025-11-03 14:52:48","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":12857680,"visible":true,"origin":"","legend":"\u003cp\u003e(a) Confocal fluorescence microscopy images of NP internalization in 2D cell monolayers from A549 (lung), HeLa (cervical), KM12C (colorectal), and MCF7 (breast) cancer cell lines. AuNP@mSi@PEI uptake was assessed at 3, 6, 24, and 48 h, with columns showing the different time points and rows representing cell lines. Nanoparticles (green) were detected via photoluminescence following multi-photon laser excitation, and the cell membrane (magenta) was stained with CellMaskTM DeepRed. Scale bar = 20 µm. Representative images of additional samples are provided in Supplementary Figure S7. The central square represents a single xy plane, while the bottom and left panels are the xz and yz cross-sections. (b) Average NP intensity inside the cells, normalized to the cell area. A549, HeLa, KM12C, and MCF7 are represented in green, orange, blue, and pink, respectively. Average NP intensity per cell line over time is provided in Supplementary Figure S8. Error bars indicate ± SD, with ns meaning not significant; * (p \u0026lt; 0.05), ** (p \u0026lt; 0.01), *** (p \u0026lt; 0.001), and **** (p \u0026lt; 0.00010 (n ≥ 20).\u003c/p\u003e","description":"","filename":"Figure3.png","url":"https://assets-eu.researchsquare.com/files/rs-7865764/v1/f413464946f015e4d3d0bde5.png"},{"id":95033457,"identity":"d9f9e575-11c6-4538-9ecd-735c643250d4","added_by":"auto","created_at":"2025-11-03 14:52:48","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":5072058,"visible":true,"origin":"","legend":"\u003cp\u003e(a) Confocal fluorescence microscopy images of Au@mSi internalization in 2D cell monolayers of A549 (lung), HeLa (cervical), KM12C (colorectal), and MCF7 (breast) cancer cell lines. AuNP@mSi uptake was assessed at 3 h. Nanoparticles (green) were detected via photoluminescence following multi-photon laser excitation, and the cell membrane (magenta) was stained with CellMaskTM DeepRed. Representative images of additional samples are provided in Supplementary Figure S9. Scale bar = 20 µm. (b) Average NP intensity inside the cells, normalized to the cell area. For each cell line, NP uptake for Au@mSi-PEI and Au@mSi is compared. (c) Confocal fluorescence microscopy images of NP distribution in 3D tumor spheroids from A549, HeLa, KM12C, and MCF7 cancer cell lines, at the mid-plane of the spheroids (with a diameter of approximately 250 µm). Au@mSi distribution was assessed at 48 h. Nanoparticles (green) were detected via photoluminescence following multiphoton laser excitation, and actin filaments (magenta) were stained with Phalloidin CruzFluorTM 647. Scale bar = 50 µm. Representative images of additional samples are provided in Supplementary Figure S9. (d) NP penetration profiles are displayed as cumulative NP distribution (Intensity%) relative to the distance from the spheroid outer rim, comparing NP distribution of Au@mSi-PEI and Au@mSi NPs for each cell line at 48 h. A549, HeLa, KM12C, and MCF7 are represented in green, orange, blue, and pink, respectively. Supplementary Figure S10 shows a direct comparison between the different cell lines. Error bars indicate ± SD, with ns meaning not significant; * (p \u0026lt; 0.05), ** (p \u0026lt; 0.01), *** (p \u0026lt; 0.001), and **** (p \u0026lt; 0.00010 (n ≥ 20).\u003c/p\u003e","description":"","filename":"Figure4.png","url":"https://assets-eu.researchsquare.com/files/rs-7865764/v1/8d156fc203ae009f6de38bf3.png"},{"id":95033456,"identity":"e36d8796-17ba-4123-b795-cf3d5482f5a2","added_by":"auto","created_at":"2025-11-03 14:52:48","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":496369,"visible":true,"origin":"","legend":"\u003cp\u003e(a) Principal component analysis (PCA) of endocytosis-related proteins across the four tumor spheroid models. PCA was performed on quantitative proteomic data from four biological replicates per condition, and the four points inside each cluster represent the four biological replicates used for proteomics analysis. \u0026nbsp;(b) Western blot analysis of protein expression levels of some of the most relevant endocytic proteins (SNAP23, CAV1, and PTRF). (c) Principal component analysis (PCA) of ECM-related proteins across the four tumor spheroid models. PCA was performed on quantitative proteomic data from four biological replicates. (d) Western blot analysis of protein expression levels of some of the most relevant ECM proteins (FN1, VTN, and LAMB1). A549, HeLa, KM12C, and MCF7 are represented in green, orange, blue, and pink, respectively. Error bars indicate ± SD, with ns meaning not significant; * (p \u0026lt; 0.05), ** (p \u0026lt; 0.01), *** (p \u0026lt; 0.001), and **** (p \u0026lt; 0.00010 (n ≥ 4).\u003c/p\u003e","description":"","filename":"Figure5.png","url":"https://assets-eu.researchsquare.com/files/rs-7865764/v1/2337584ee30a09efaf715ab0.png"},{"id":107928157,"identity":"353a985a-e400-4068-9023-e5fc2834277a","added_by":"auto","created_at":"2026-04-27 16:08:32","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":25709898,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7865764/v1/ff3d8cb5-8e9a-42b8-9f5f-471b57e61ba6.pdf"},{"id":95033453,"identity":"89993030-03d4-4086-94dc-afb56752a120","added_by":"auto","created_at":"2025-11-03 14:52:48","extension":"xlsx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":62306,"visible":true,"origin":"","legend":"","description":"","filename":"S4filteredgenesimputedendocyticnew.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-7865764/v1/f87762d7aa2a416999a487f4.xlsx"},{"id":95222344,"identity":"1a4d9dbc-45b0-4561-b618-68c99b72b448","added_by":"auto","created_at":"2025-11-05 16:20:30","extension":"xlsx","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":28731,"visible":true,"origin":"","legend":"","description":"","filename":"S3GOendocyticvesicleextracellularmatrixtest.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-7865764/v1/2bfd95b940b07ed86a0bbba9.xlsx"},{"id":95033455,"identity":"d323069d-e9b5-438c-85c9-85779f561332","added_by":"auto","created_at":"2025-11-03 14:52:48","extension":"xlsx","order_by":3,"title":"","display":"","copyAsset":false,"role":"supplement","size":43132,"visible":true,"origin":"","legend":"","description":"","filename":"S5filteredgenesimputedECMnew.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-7865764/v1/031335340ed128fb20d77df6.xlsx"},{"id":95222342,"identity":"161e4fd3-613f-466b-8e32-971f55b04e2b","added_by":"auto","created_at":"2025-11-05 16:20:30","extension":"xlsx","order_by":4,"title":"","display":"","copyAsset":false,"role":"supplement","size":1310660,"visible":true,"origin":"","legend":"","description":"","filename":"S2ImputallCellsNew.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-7865764/v1/597f67df3c4bd33909d289fc.xlsx"},{"id":95222071,"identity":"f8d36824-ade1-47bd-a439-c15c1870f5dc","added_by":"auto","created_at":"2025-11-05 16:20:05","extension":"docx","order_by":5,"title":"","display":"","copyAsset":false,"role":"supplement","size":8044961,"visible":true,"origin":"","legend":"","description":"","filename":"SI.docx","url":"https://assets-eu.researchsquare.com/files/rs-7865764/v1/d009cdd12fcbefe142a7242d.docx"},{"id":95033458,"identity":"3e8d9343-55a8-45ad-842f-d0ce2cc9e329","added_by":"auto","created_at":"2025-11-03 14:52:48","extension":"png","order_by":6,"title":"","display":"","copyAsset":false,"role":"supplement","size":1110374,"visible":true,"origin":"","legend":"","description":"","filename":"GA.png","url":"https://assets-eu.researchsquare.com/files/rs-7865764/v1/9f743ae115c9eeb454959fbf.png"}],"financialInterests":"No competing interests reported.","formattedTitle":"Beyond One-Size-Fits-All: Cancer Biology Shapes Nanoparticle Behavior","fulltext":[{"header":"Introduction","content":"\u003cp\u003eNanoparticles (NPs) have emerged as a promising alternative to conventional cancer therapies, offering unique advantages such as improved targeting, localized drug delivery, and reduced systemic toxicity\u003csup\u003e\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e,\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u003c/sup\u003e. Yet, clinical translation remains limited, partly due to inconsistencies in preclinical testing that undermine the reliability of current screening approaches\u003csup\u003e\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003eTraditionally, NP development and screening rely on two-dimensional (2D) tumor models that fail to replicate key aspects of cancer physiology, masking critical biological variability between tumors\u003csup\u003e\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e,\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u003c/sup\u003e. The introduction of three-dimensional (3D) tumor models, particularly multicellular tumor spheroids, has provided a more physiologically relevant platform to assess NP accumulation, penetration, and therapeutic efficacy, while maintaining compatibility with high-throughput screening\u003csup\u003e\u003cspan additionalcitationids=\"CR4\" citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e\u003c/sup\u003e. However, even with these improved models, a \u0026ldquo;one-size-fits-all\u0026rdquo; approach persists, with most NP development pipelines focused on tuning physical properties \u0026ndash; such as size, shape, and surface charge \u0026ndash; while overlooking the influence of cancer biology on NP behavior. As a result, most studies evaluate NPs in a single tumor model and assume that findings are applicable across different cancers. However, cancer-specific features, including cell morphology, proliferation rates, vesicular trafficking, and extracellular matrix (ECM) composition, can influence NP uptake and diffusion\u003csup\u003e\u003cspan additionalcitationids=\"CR7\" citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003eThis oversight has led to apparently conflicting results in the field. For instance, there is an ongoing debate concerning the effect of NP charge on spheroid penetration: Wang \u003cem\u003eet al.\u003c/em\u003e reported that cationic PEGylated NPs exhibited higher penetration depth, and Sujai \u003cem\u003eet al.\u003c/em\u003e found that anionic PEGylated gold NPs achieved enhanced penetration\u003csup\u003e\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e,\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e\u003c/sup\u003e. These findings, while at first glance contradictory, were obtained in different tumor models \u0026ndash; breast and cervical cancer, respectively \u0026ndash; and highlight how cancer biology critically affects NP-tumor interactions, underscoring the limitations of generalizing results across tumor types. This calls into question the common practice of generalizing results across cancer types and highlights the need for more cancer-contextualized nanomedicine research.\u003c/p\u003e\u003cp\u003eIn this report, we addressed this gap by systematically comparing NP accumulation, internalization, and penetration in four non-metastatic spheroid models using a single, well-defined NP formulation. Quantitative proteomic analysis was used to explore how cancer-specific differences in endocytic and ECM-related proteins contribute to the observed patterns. Our findings underscore the need to shift from platform-centric to tumor-centric strategies in NP design.\u003c/p\u003e"},{"header":"Methods","content":"\u003ch2\u003eMaterials\u003c/h2\u003e\n\u003cp\u003eDulbecco\u0026rsquo;s modified eagle medium (DMEM), gentamicin, phosphate buffered saline (PBS, no calcium, no magnesium), paraformaldehyde (v/v 16%, methanol-free), Trypsin-ethylenediaminetetraacetic acid (Trypsin-EDTA, 0.5% solution, no phenol red), Hank\u0026rsquo;s balanced salt solution (HBSS, no phenol red), Triton X-100 (0.1%), \u0026nbsp;UltraPure\u0026trade; agarose (2%), sodium chloride (NaCl), GlutaMax\u003csup\u003eTM\u003c/sup\u003e, Trypan blue solution (0.4%, TC grade), CellMask\u003csup\u003eTM\u003c/sup\u003e DeepRed, precolumn PepMap Trap Cartridge (5 \u0026micro;m, 300 \u0026micro;m x 5 mm), Vanquish Neo UHPLC System, Easy-Spray PepMap RSLC C18 (3 \u0026micro;m, 75 \u0026micro;m x 15 cm), ECL Pico Plus chemiluminescent reagent, and iBright imager were purchased from ThermoFisher Scientific. Tetrachloroauric(III) acid trihydrate (HAuCl4 \u0026middot; 3H2O, 99%), sodium hydroxide (NaOH, 98%), hexadecyltrimethylammonium bromide (CTAB, \u0026ge;99%), tetraethyl orthosilicate (TEOS, 98%), hydrochloric acid (HCl, 1 N), methanol (MeOH, 99,8%), polyethyleneimine solution (PEI, 50% w/v in H\u003csub\u003e2\u003c/sub\u003eO), 3D Petri Dish\u0026reg; micro-mold, fetal bovine serum (FBS), CoverWell\u003csup\u003eTM\u003c/sup\u003e perfusion chambers (9 mm \u0026times; 1.7 mm thickness), and RadioImmunoPrecipitation (RIPA) buffer were purchased from Sigma Aldrich. Phalloidin CruzFluor\u003csup\u003eTM\u003c/sup\u003e 647 was purchased from Santa Cruz Biotechnology. 1X Protease and phosphatase inhibitors were purchased from MedChemExpress. \u0026nbsp;A549 (lung cancer), MCF7 (breast cancer) cell lines were a gift from Prof. Uji-i. \u0026nbsp;All chemicals were used without further purification.\u0026nbsp;\u003c/p\u003e\n\u003ch2\u003eMethods\u003c/h2\u003e\n\u003cp\u003e\u003cstrong\u003eNanoparticle Synthesis and Functionalization:\u003c/strong\u003e Mesoporous silica-coated gold nanoparticles (Au@mSi) were synthesized using a one-pot method as described by Chen \u003cem\u003eet al\u003c/em\u003e\u003csup\u003e11\u003c/sup\u003e. Initially, 50 mg of CTAB was dissolved in a solution containing 0.6 mL of 0.5 M NaOH and 24 mL of Milli-Q water. The mixture was stirred at 800 rpm and 80\u0026deg;C for 15 minutes. Subsequently, 1 mL of a 2% (wt) aqueous formaldehyde solution was added, followed by 0.8 mL of a 0.05 M HAuCl\u003csub\u003e4\u003c/sub\u003e aqueous solution. After 10 min, a solution of TEOS and ethanol (1 mL, containing 0.25 g TEOS and 0.50 g ethanol) was added, and the mixture was stirred for an additional hour. The resulting product was collected by centrifugation, dispersed in a 1.1 M HCl solution in water/ethanol (v/v = 1.25:10), and sonicated. The dispersion was then stirred vigorously at 60\u0026deg;C for 4 h to remove CTAB from the pores. The product was washed twice with Milli-Q water to neutralize the pH, followed by redispersion in 3.2 mL of Milli-Q water. The mixture was stirred magnetically at 500 rpm for 3 h, and the supernatant was removed by centrifugation and replaced with Milli-Q water. For PEI functionalization, a 0.75% (w/v) PEI solution (adjusted to pH 7) was added dropwise to the Au@mSi NPs in a 1:1 (v/v) ratio. The mixture was stirred for 3 h, washed by centrifugation, and redispersed in Milli-Q water. The concentration of particles in the colloidal solution was estimated to be 1.3 mg/mL.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eNanoparticle Characterization:\u0026nbsp;\u003c/strong\u003eThe physicochemical properties of the NPs were characterized using UV-Vis spectroscopy, scanning and transmission electron microscopy (SEM, TEM), Zeta Potential, and Dynamic Light Scattering (DLS). UV-Vis spectra were acquired using a Cary 60 Spectrophotometer (Agilent). High-resolution SEM images were captured with a field-emission SEM (FEI Quanta FEG250) microscope operated at 20.0 kV.\u0026nbsp;For TEM measurements, Au@mSi-PEI NPs were diluted in a 1:100 ratio, and 6 \u0026mu;L of solution was cast onto TEM grids (300 mesh copper grid) and dried at room temperature. TEM images were collected on an FEI TecnaiF20 microscope (200 kV) (Ian Holmes Imaging Center, Bio21). Particle size, size distribution, polydispersity index, and Zeta Potential measurements were performed using a Zetasizer Nano ZS (Malvern Panalytical). All measurements were carried out in Milli-Q water at room temperature (25\u0026deg;C).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCell Culture:\u0026nbsp;\u003c/strong\u003eA549, HeLa, KM12C, and MCF7 cells were cultured in 25 cm\u003csup\u003e2\u003c/sup\u003e culture flasks at 37\u0026nbsp;\u0026deg;C under a 5% CO\u003csub\u003e2\u003c/sub\u003e atmosphere. Cells were maintained in culture medium (DMEM supplemented with 10% FBS, 1% L-glutamax, and 0.1% gentamicin) and passaged using trypsin-EDTA at 80-90% confluency. \u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eNP Screening in Spheroids (3D):\u0026nbsp;\u003c/strong\u003eAt 90% confluency, cells were harvested for spheroid preparation. Spheroids were cultured in agarose microtissues (16 x 16 array of 256 wells), cast from ultra-pure agarose (20 mg/mL in Milli-Q water with 9 mg/mL NaCl) using a 3D micro-mold (3D Petri Dish\u0026reg;, Sigma Aldrich). Once solidified, the agarose microtissue was removed from the mold, placed in a sample dish, and equilibrated with DMEM for 15 min. After removing the DMEM, 190\u0026nbsp;\u0026mu;L of cell suspension was added to the mold. Cell concentrations were optimized to achieve spheroids of comparable sizes: A549 and MCF7 were seeded at 1.3\u0026times;10\u003csup\u003e6\u003c/sup\u003e cells/mL, HeLa cells at 1.1\u0026times;10\u003csup\u003e6\u003c/sup\u003e cells/mL, and KM12C at 2.5\u0026times;10\u003csup\u003e5\u003c/sup\u003e cells/mL. Cells were allowed to sink into the agarose wells for 30 min, followed by the addition of DMEM around the microtissue. Samples were incubated for 4 days to obtain fully formed spheroids before incubation with NPs.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eOnce spheroids were fully formed, the DMEM inside and around the microtissues was removed. A 200\u0026nbsp;\u0026mu;L suspension of Au@mSi-PEI NPs or Au@mSi (26\u0026nbsp;\u0026micro;g/mL) in DMEM was added to the microtissues and incubated for 30 min. DMEM was then added around the microtissues to prevent dilution of the NP suspension. To assess NP accumulation and penetration, spheroids were incubated with\u0026nbsp;Au@mSi-PEI\u0026nbsp;for 3, 6, 24, and 48h. Au@mSi assays were only performed for the 48h time point. Following incubation, the medium was removed and 1 mL of PBS was vigorously pipetted onto the microtissue to dislodge the spheroids, which were transferred to 1.5 mL microcentrifuge tubes.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eHarvested spheroids were stained with Phalloidin CruzFluor\u003csup\u003eTM\u003c/sup\u003e 647\u003csup\u003e12\u003c/sup\u003e. Spheroids were washed three times with PBS and collected using 10-second centrifugation (2000 G). They were then fixed with 500\u0026nbsp;\u0026mu;L 4% PFA for 20 min, followed by three PBS washes. Permeabilization was performed with 1 mL 0.1% Triton X-100 for 30 min. Spheroids were incubated overnight at room temperature with 500\u0026nbsp;\u0026mu;L of Phalloidin CruzFluor\u003csup\u003eTM\u003c/sup\u003e 647 (1:800 dilution in 3% BSA) for cytoskeleton (actin) staining. The next day, the spheroids were washed 3 times with PBS and resuspended in 200\u0026nbsp;\u0026mu;L of PBS before imaging. For spheroid imaging, 60 \u0026mu;L of spheroid suspension was transferred to a CoverWell\u003csup\u003eTM\u003c/sup\u003e perfusion chamber glued on a #1 glass coverslip.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eBrightfield Live Spheroid Imaging:\u0026nbsp;\u003c/strong\u003eSpheroid growth was monitored via live imaging using brightfield (4x air objective, N.A 0.13). Spheroids were prepared as previously described, and images were acquired every 6 hours for 6 days, to enable spheroid monitoring throughout the duration of the experiments (4 days of spheroid maturation and a maximum of 48 hours of NP incubation). Cell segmentation was performed using Ilastik\u003csup\u003e13\u003c/sup\u003e and manually training a model to identify the regions corresponding to cell aggregates. For the iLastik analysis, four training sets were made, one for each cell line. Each model was then used to analyze the corresponding videos. The masks obtained with this segmentation model were then analyzed with an in-house written Python script to obtain their area. Finally, the data on these variables were plotted and tested for statistical significance using Graphpad Prism (Kruskal-Wallis test comparing the mean differences of all samples to the control condition).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eNP Screening in Cell Monolayers (2D):\u0026nbsp;\u003c/strong\u003eCells (2\u0026times;10\u003csup\u003e5\u003c/sup\u003e cells/dish) were seeded in 29-mm glass-bottom dishes (Cellvis, Mountain View, CA, USA) and incubated for 24 hours to reach 60-80% confluency. NP uptake was assessed at 3, 6, 24, and 48h. Cells were incubated with Au@mSi-PEI\u0026nbsp;NPs (26\u0026nbsp;\u0026micro;g/mL) for 3 hours, then carefully washed three times with PBS. For the 3-hour time point, cells were immediately stained and imaged. For subsequent time points, fresh DMEM was added, and samples were incubated at 37\u0026deg;C for the remaining time. Before imaging, the cell membrane was stained with CellMask\u0026trade; Deep Red (1 \u0026mu;M in HBSS, 3 min), washed three times with PBS, and imaged in HBSS.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFluorescence Microscopy:\u0026nbsp;\u003c/strong\u003eConfocal fluorescence imaging of 2D and 3D samples was performed on a Leica TCS SP8 dive inverted microscope. This system is equipped with a multi-photon (MP) Insight X3 laser (range 680-1300 nm), two HyD detectors (non‑descanned), a 25\u0026times; water immersion objective (NA 0.95, FLUOTAR VISIR) for 3D models, and a 63\u0026times; oil immersion objective (NA 1.4) for 2D models. Sequential scanning (between stacks) was performed, starting with the Phalloidin CruzFluor 647\u003csup\u003eTM\u003c/sup\u003e channel (1200 nm, 3.8 mW at the objective, 610-661 nm detection), followed by the Au@mSi/Au@mSi-PEI NP channel (800 nm, 3.8 mW at the objective, 503-553 nm detection). For 2D imaging, live imaging was performed in HBSS at 37\u0026deg;C and 5% CO\u003csub\u003e2\u003c/sub\u003e. In this case, the first sequence targeted the CellMask\u003csup\u003eTM\u003c/sup\u003e Deep Red channel using a 638 nm diode laser (detection 641-691 nm). Image stacks of 1024 \u0026times; 1024 pixels were acquired at a scanning speed of 400 Hz, 0.57 or 0.30 \u0026micro;m z-step, for 3D and 2D assays, respectively.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eNP Behavior Analysis:\u0026nbsp;\u003c/strong\u003eIn-house algorithms were used to determine NP accumulation/penetration in 3D spheroids and uptake in 2D cell monolayers. NP distribution profiles in 3D were obtained from the analysis of at least 20 spheroids. For 3D spheroids, the algorithm first preprocessed cytoskeleton-staining images of the spheroid to identify the spheroid edge by applying smoothing, dilation, and edge detection. The spheroid edge was then used to obtain the spheroid center and a spherical coordinate system. This coordinate system enabled the calculation of edge-to-center distance at each angle of elevation (\u0026theta;) and azimuth (\u0026psi;). For the NP channel, the same spherical coordinates were used to calculate the NP penetration depth by measuring the distance of the NPs from the spheroid edge at the same angles \u0026theta; and \u0026psi;. Integration of pixel intensities across penetration depths provided individual penetration profiles, which were then averaged to obtain the mean distribution profile (Fig. S12).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eNP behavior in 2D was analyzed from at least 20 images of different fields of view. The algorithm was used to quantify the total intensity of the NPs in the cytoplasm. First, the cytoplasm and cellular membrane were segmented based on the membrane staining intensity by using the Cellpose algorithm in MatLab\u003csup\u003e14\u003c/sup\u003e. NPs were quantified by integrating the fluorescence intensity signal of the NP signal within each cell area (Fig.\u0026nbsp;S13).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eLC-MS/MS and proteomic analysis:\u0026nbsp;\u003c/strong\u003eCell pellets of spheroids from all the cell lines in the absence of NPs (day 5 of incubation) were collected via centrifugation. The cell pellets were then lysed with RIPA buffer containing 1X protease and phosphatase inhibitors. Total protein concentration was obtained by the tryptophan quantification method, as described by Wiśniewski \u003cem\u003eet al.\u003c/em\u003e\u003csup\u003e15\u003c/sup\u003e, and confirmed by Coomassie blue staining after reducing 10% PAGE-SDS. Once protein extracts from spheroids in the presence or absence of NPs were obtained, 10\u0026nbsp;\u0026micro;g of each extract was reduced, alkylated, and trypsin digested on SP3 magnetic beads as previously described\u003csup\u003e16\u003c/sup\u003e.\u003c/p\u003e\n\u003cp\u003eFor LC-MS/MS, peptides were analyzed in an Orbitrap Astral mass spectrometer coupled to a Vanquish Neo UHPLC System. Peptide samples were loaded into the precolumn PepMap Trap Cartridge (5 \u0026micro;m, 300 \u0026micro;m x 5 mm) and eluted in an Easy-Spray PepMap RSLC C18 (3 \u0026micro;m, 75 \u0026micro;m x 15 cm) heated at 50\u0026deg;C. The mobile phase flow rate was 300 nL/min, and 0.1% formic acid (FA) in MilliQ water and 0.1% FA in 80% acetonitrile (ACN) were used as elution buffers A and B, respectively. The 15 min elution gradient was: 4%-10% buffer B for 2 min, 10%-40% buffer B for 11 min, 40%-99% buffer B for 0.5 min, and 99% buffer B for 1.5 min. Prior to injection, samples were re-suspended in 20 \u0026micro;L of buffer A, and 2 \u0026micro;L of each sample were injected and analyzed in data-independent acquisition (DIA) mode. For ionization, 1900 V of liquid junction voltage and 280\u0026deg;C capillary temperature were used. The full scan method employed a m/z 380-980 mass selection, an Orbitrap resolution of 240000 (at m/z 200), an automatic gain control (AGC) value of 500%, and a maximum injection time (IT) of 5 ms. The MS/MS was performed with the Astral mass analyzer, using an AGC of 500%, an IT of 3 ms, and a normalized collision energy (NCE) of 25 for fragmentation of precursors. The scan range was set from 380 to 980 m/z, with an isolation window of 2 m/z, and window placement optimization was enabled. Thus, a total of 299 windows were analyzed in each cycle.\u003c/p\u003e\n\u003cp\u003eRaw data were analyzed with Spectronaut (version 19.1.240724.62635) using standardized workflows. DIA raw data and Uniprot UP000005640_9606.fasta Homo sapiens (march, 2024) database (20,418 protein entries) were used for the construction of the spectral library by directDIA. Trypsin/P and Lys/P were selected as the digestion enzymes, and a maximum of 2 missed cleavages were allowed. Carbamidomethylation of cysteines was set as a fixed modification, and methionine oxidation and N-terminal acetylation were set as variable modifications. For DIA analysis, a standard workflow was used. The maximum false discovery rate (FDR) for peptide spectral match (PSM), peptide, and protein identifications was set at 0.01, automatic cross-run normalization was enabled, and all identified peptides were used for protein quantification. Protein inference was performed using the IDPicker algorithm. Non-imputation was performed during peptide and protein identification and quantification with Spectronaut.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eGene Ontology Analysis:\u0026nbsp;\u003c/strong\u003eThe list of proteins identified through proteomic analysis was filtered to obtain which components of the gene ontology terms \u0026ldquo;Extracellular Matrix\u0026rdquo; (0031012, 1826 annotations), and \u0026ldquo;Endocytic Vesicle\u0026rdquo; (0030139, 958 annotations) in homo sapiens were present in the cells under study. Each list of filtered proteins was then used to perform Principal Component Analysis (PCA) based on the expression levels of the proteins. PCA plots were obtained using Python and sklearn.decomposition and sklearn.preprocessing packages. The list of filtered proteins after removing repeated annotations can be found in the supplementary Tables S4 and S5. \u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eWestern Blot:\u0026nbsp;\u003c/strong\u003eFor WB analysis, 12.5 \u0026micro;g of each protein extract from the 4 cell lines were separated on 12.5% SDS-PAGE under reducing conditions. Transference to nitrocellulose membranes was performed at 25V for 90 min using the Invitrogen XCell II\u0026trade; Blot Module. Membranes blocked with 0.1% Tween PBS 1\u0026times; supplemented with 3% skimmed milk were incubated with primary antibodies at optimized dilutions (Table S1) overnight (O/N) at 4 \u0026deg;C in the same solution. After three washes with 0.1% Tween PBS 1\u0026times;, membranes were incubated with the appropriate HRP-conjugated or Alexa488-conjugated secondary antibody (Table S1) for 1 h at RT. Membranes were then washed as above. Finally, the signal developed with the ECL Pico Plus chemiluminescent reagent was detected on an iBright imager. For the membranes that were incubated with Alexa488-conjugated secondary antibodies, the fluorescence was directly detected on the same iBright imager. Protein levels were measured using Fiji\u0026rsquo;s gel tool, and the data from each protein were normalized to its corresponding level of GAPDH.\u0026nbsp;\u003c/p\u003e"},{"header":"Results and Discussion","content":"\u003ch2\u003eNanoparticle Synthesis and Characterization\u003c/h2\u003e\n\u003cp\u003eAu@mSi NPs were prepared as previously described by Chen \u003cem\u003eet al\u003c/em\u003e\u003csup\u003e11\u003c/sup\u003e using a one-pot method. Transmission electron microscopy (TEM) and scanning electron microscopy (SEM) revealed spherical NPs with homogeneous size distribution and minimal aggregation. AuNP cores exhibited an average diameter of 43 nm, while the mSi shell was approximately 15 nm thick, resulting in an overall diameter of 77 \u0026plusmn; 8 nm (Figure 1a, b). The localized surface plasmon resonance (LSPR) was characterized using UV-VIS spectroscopy (Figure 1c), showing a redshift of the plasmonic band after mSi coating, due to an increase in the local refractive index\u003csup\u003e18\u003c/sup\u003e. Zeta potential measurements showed that AuNP cores initially displayed a positive charge of 36 \u0026plusmn; 1 mV due to the presence of CTAB (Cetylmethylammonium bromide) on the NP surface. \u0026nbsp;To enhance aqueous stability, the mSi layer was first added, followed by the removal of CTAB, which reduced toxicity\u003csup\u003e19\u003c/sup\u003e. During this process, CTAB acted as a pore-generating agent, forming micelles that serve as templates for silica condensation\u003csup\u003e20\u003c/sup\u003e. The resulting Au@mSi displayed a negative charge (-30 \u0026plusmn; 1 mV), as a result of the deprotonated hydroxyl groups on the silica surface\u003csup\u003e17\u003c/sup\u003e.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eTo enhance cellular uptake, Au@mSi NPs were coated with polyethyleneimine (PEI, Mw = 1.3 kDa) via electrostatic interactions\u003csup\u003e17\u003c/sup\u003e(Au@mSi-PEI NPs). PEI functionalization was confirmed by a marked shift in the zeta potential from \u0026ndash;30 \u0026plusmn; 1 mV for Au@mSi NPs to +43 \u0026plusmn; 1 mV after coating. The resulting positive surface charge arises from the protonated amine groups of PEI on the nanoparticle surface, which promotes interaction with the negatively charged cell membrane and facilitates internalization via endocytic pathways\u003csup\u003e21\u003c/sup\u003e.\u003c/p\u003e\n\u003ch2\u003eNP distribution in 3D spheroids depends on cancer cell type\u003c/h2\u003e\n\u003cp\u003eTo investigate how cancer biology influences NP behavior, we analyzed the behavior of Au@mSi-PEI NPs in four non-metastatic spheroid models representative of high-incident cancers: lung (A549), colon (KM12C), breast (MCF7), and cervical (HeLa)\u003csup\u003e2,22\u0026ndash;24\u003c/sup\u003e. Lung and colorectal cancers rank amongst the most commonly diagnosed worldwide, while breast and cervical cancer are highly prevalent among women\u003csup\u003e22\u003c/sup\u003e. Spheroids were incubated for 3, 6, 24, and 48 h to capture the temporal dynamics of NP transport within spheroids.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eClear differences in NP accumulation were observed across cell types\u0026nbsp;(Figure 2). At early time points (3 and 6 h), overall accumulation was low in all spheroids, although KM12C and MCF7 spheroids exhibited slightly higher NP levels compared to A549 and HeLa (Figure 2b). By 24 h, NP accumulation more than doubled in all models, with KM12C showing the highest accumulation, and A549 the lowest. After 48 h, KM12C spheroids maintained the highest NP levels, while HeLa exhibited the lowest accumulation. Notably, HeLa spheroids, which had relatively high NP levels at 24 h, exhibited a marked drop by 48 h. This coincided with a decrease in spheroid size as observed in the fluorescence images (Figure S4). In contrast, size analysis from time-lapse imaging (Figures S5 and S6) showed that HeLa spheroids continued to grow over time. This discrepancy suggests that the proliferative outer layer, which contained most of the NPs, detached and was lost during sample handling. As a result, NP accumulation and penetration data in HeLa spheroids became less reliable at later time points.\u003c/p\u003e\n\u003cp\u003eNP penetration profiles followed a similar time-dependent trend (Figure 2c). At 3 and 6 h, NP distribution was restricted to the outer cell layers, with 95% of the NPs (P95) found within the first 25 \u0026micro;m of the spheroids. By 24 h, MCF7 and KM12C spheroids exhibited deeper penetration, with P95 values of 36 \u0026micro;m and 27 \u0026micro;m, respectively, while NPs in A549 and HeLa spheroids remained confined to a smaller region (P95 between 22 and 19 \u0026micro;m). After 48 h, MCF7 spheroids showed the greatest penetration depth, with 5% of the NPs beyond 47 \u0026micro;m, followed by KM12C spheroids (P95 of 35 \u0026micro;m). In contrast, NP penetration in A549 and HeLa spheroids remained low (P95 of 21 and 28 \u0026micro;m, respectively).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThese findings reveal tumor-dependent differences in NP accumulation and penetration within 3D spheroids, suggesting that cancer-specific factors govern NP distribution. To investigate underlying mechanisms driving this variability, we evaluated NP-cell interactions at the single-cell level.\u003c/p\u003e\n\u003ch2\u003eCellular uptake kinetics provide clues to NP penetration in spheroids\u003c/h2\u003e\n\u003cp\u003eUnderstanding how efficiently cancer cells internalize NPs is essential for optimizing delivery strategies and maximizing therapeutic efficacy. While 3D spheroids better mimic tumor physiology, their size and complexity hinder the quantification of NP uptake within individual cells. To address this limitation, we used 2D monolayers as a complementary model to precisely monitor subcellular NP localization. 2D cellular systems also enable pulse-chase experiments, where NPs are incubated for a defined period and then removed to track intracellular NP dynamics over time \u0026ndash; an approach less feasible in spheroids due to the difficulty of removing extracellular NPs without disturbing the 3D structure. Here, cells were incubated with Au@mSi-PEI NPs for 3 h, after which NPs were removed. Imaging was performed immediately (defined as 3 h) and at 6, 24, and 48 h after NPs addition, to monitor NP uptake and retention over time (Figure 3a).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eNP uptake kinetics varied across tumor models, displaying distinct trends over time (Figure 3b). While A549 and HeLa internalized substantial amounts of NPs within the first 3 h, KM12C and MCF7 displayed minimal uptake. By 6 h, NP uptake in HeLa and KM12C cells increased, reaching levels comparable to A549. MCF7 consistently exhibited the lowest internalization, with NPs largely remaining at the cell membrane rather than within the cytoplasm. At later time points (24 and 48 h), NP accumulation became more uniform across cell lines, showing no significant differences.\u003c/p\u003e\n\u003cp\u003eThese findings show that NP uptake dynamics vary significantly across tumor cell lines and inversely correlate with NP penetration in 3D spheroids. For example, rapid NP uptake by A549 cells correlated with NP retention in the spheroid\u0026rsquo;s outer layers. In contrast, MCF7 displayed the slowest NP uptake in monolayers, which was associated with the deepest NP penetration in spheroids (Figure 2c, 3b). This suggests that rapid NP uptake by peripheral cells may act as a trap, limiting further diffusion into deeper layers. Conversely, slower uptake in KM12C and MCF7 cells likely enables NPs to travel further before being internalized, potentially via paracellular transport.\u0026nbsp;\u003c/p\u003e\n\u003ch2\u003eReduced cellular uptake enhances NP penetration into tumor spheroids\u003c/h2\u003e\n\u003cp\u003eTo test whether NP uptake and spheroid penetration are indeed inversely related, we evaluated the behavior of NPs with expected lower cellular uptake. Specifically, we used bare silica-shell gold NPs lacking PEI coating (Au@mSi), which carry a negative surface charge and are therefore less likely to interact with the cell membrane and be internalized (Figure 1)\u003csup\u003e17,21\u003c/sup\u003e.\u003c/p\u003e\n\u003cp\u003eIn agreement with previous reports, removal of the PEI coating reduced NP internalization and resulted in a more uniform uptake across all tumor models in 2D monolayers (Figures 4a-b)\u003csup\u003e17,21\u003c/sup\u003e. This decrease in cellular uptake translated into enhanced NP penetration in 3D spheroids (Figures 4c-d). At 48 h, penetration depths increased across most spheroid models: A549 spheroids, which previously showed highly restricted diffusion, exhibited the largest improvement (P95 increasing from 21 \u0026micro;m to 41 \u0026micro;m). KM12C and MCF7 spheroids also showed moderate increases in penetration (P95 from 35 \u0026micro;m to 46 \u0026micro;m, and from 47 \u0026micro;m to 49 \u0026micro;m, respectively). In contrast, HeLa spheroids showed no increase in the P95 value, likely due to the loss of the outer proliferative rim, which compromised spheroid integrity and affected penetration analysis.\u003c/p\u003e\n\u003cp\u003eThese results support an inverse relationship between cellular uptake and NP penetration, showing that reduced cellular internalization can facilitate deeper NP diffusion into tumor spheroids.\u003c/p\u003e\n\u003ch2\u003eProteomic differences in endocytic and ECM proteins correlate with cell line-specific NP behavior\u003c/h2\u003e\n\u003cp\u003eGiven the key role of endocytosis in NP internalization, we hypothesized that the inverse relationship between NP uptake and penetration might be driven by cancer-specific differences in the expression of endocytic proteins.\u003c/p\u003e\n\u003cp\u003eTo test this, we performed a quantitative proteomic analysis across the 4 tumor models, identifying 5976 different proteins (Table S2). To focus on uptake-related processes, we analyzed proteins classified under the gene ontology term \u0026ldquo;Endocytic Transport\u0026rdquo; (0030139). Principal component analysis (PCA) showed high reproducibility across biological replicates and revealed that KM12C and MCF7 spheroids shared similar endocytic protein profiles, distinct from those of A549 and HeLa (Figure 5a). This grouping mirrored the NP uptake trends, with breast and colorectal cancer models displaying slower internalization compared to lung and cervical models.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eTo pinpoint candidate regulators of NP uptake, we analyzed proteomic data for proteins with significant expression changes compared to MCF7 cells, which showed the highest NP penetration and lowest uptake (fold-change \u0026ge; 2 or \u0026le; 0.5; Table S3, Table S4, Figure S11a). From this list, we selected candidates for further validation by western blot (WB) analysis (Figure 5b, Figure S11b). Among the top hits, the synaptosome-associated protein-23 (SNAP23), essential for membrane fusion and early endosomal function, was upregulated in A549, HeLa, and KM12C cells, suggesting enhanced cellular trafficking in these tumors\u003csup\u003e25\u003c/sup\u003e. Caveolin-1 (CAV1), a key component of caveolae-mediated endocytosis\u003csup\u003e26\u003c/sup\u003e, showed strong overexpression in A549, HeLa, and KM12C, mirroring their higher cellular uptake. Similarly, cavin-1 (PTRF), crucial for caveolae formation and function\u003csup\u003e27\u003c/sup\u003e, was also upregulated in A549, HeLa, and KM12C. These results suggest that caveolae-dependent mechanisms are a major contributor to Au@mSi-PEI NP internalization in these tumor models.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eGiven that NP transport is influenced not only by cellular uptake but also by the surrounding tumor microenvironment, we investigated whether tumor ECM composition also contributes to the distinct NP behavior observed across tumor models. Previous studies have shown that ECM properties can impact NP diffusion in solid tumor models\u003csup\u003e28,29\u003c/sup\u003e. Using the gene ontology classification \u0026ldquo;Extracellular Matrix\u0026rdquo; (0031012), we identified tumor-specific differences in the expression of ECM-related proteins (Table S3, Table S5, Figure 5c, Figure S11a). As observed for endocytic proteins, PCA analysis of ECM proteins showed high reproducibility across replicates and revealed clustering of MCF7 and KM12C samples. Relevant candidates were selected for further validation by WB analysis. Compared to MCF7 spheroids, A549, HeLa, and KM12C spheroids, models with lower NP penetration, showed increased levels of key structural ECM components associated with increased matrix density and stiffness, including fibronectin (FN1), vitronectin (VTN), and laminin\u0026nbsp;\u0026beta;1 (LAMB1)\u003csup\u003e2,30\u003c/sup\u003e. These findings suggest that a denser ECM may act as an additional physical barrier to NP diffusion, complementing the role of cellular uptake in modulating NP transport.\u0026nbsp;\u003c/p\u003e"},{"header":"Conclusions and Future Prospects","content":"\u003cp\u003eOur results show that tumor-intrinsic characteristics affect NP behavior, challenging the current \u0026lsquo;one-size-fits-all\u0026rsquo; paradigm in NP design. Distinct tumor types displayed unique NP accumulation and penetration profiles in 3D spheroids, driven by differences in endocytosis-related protein expression and extracellular matrix (ECM) composition. Using 2D monolayers, we uncovered an inverse relationship between NP internalization and diffusion depth, where tumors with slower NP uptake allowed for deeper NP penetration, likely via paracellular routes. This hypothesis was supported by the enhanced penetration of NPs without PEI. Proteomic analysis confirmed that higher expression of endocytosis-related proteins aligned with increased NP internalization, while ECM-stiffening proteins were upregulated in tumor models showing reduced NP diffusion.\u003c/p\u003e\n\u003cp\u003eThese findings highlight the need to move beyond solely optimizing NP physicochemical properties and instead tailor designs to tumor-specific biological features. Moreover, therapeutic goals should guide optimization strategies: drug delivery applications may benefit from high NP uptake, whereas therapies like photothermal treatments may require deeper NP diffusion into the tumor mass. By integrating tumor biology into NP development, therapeutic efficacy can be improved, ultimately accelerating the clinical translation of nanomedicines.\u003c/p\u003e"},{"header":"Declarations","content":"\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\n\u003cp\u003eMB - Data acquisition and curation, Formal Analysis, Investigation, Methodology, Validation, Visualization, Writing\u0026ndash;original draft, Writing\u0026ndash;review and editing. GS - Data acquisition and curation, Formal Analysis, Investigation, Methodology, Validation, Visualization, Writing\u0026ndash;original draft, Writing\u0026ndash;review and editing, SK - Investigation, Methodology, Writing\u0026ndash;review and editing. SH - Investigation, Methodology, Data analysis, Writing\u0026ndash;review and editing. AMC - Data Acquisition, Investigation, Methodology, Writing\u0026ndash;review and editing, IV - Supervision, Writing\u0026ndash;review and editing. BL - Supervision, Writing\u0026ndash;review and editing. JH - Supervision, Writing\u0026ndash;review and editing. RB - Supervision, Writing\u0026ndash;review and editing. BF - Conceptualization, Data curation, Supervision, Writing. SR - Conceptualization, Funding acquisition, Project administration, Software, Supervision,\u0026nbsp;Writing. All authors contributed to manuscript preparation and revision\u003c/p\u003e\n\u003ch2\u003eAcknowledgement\u003c/h2\u003e\n\u003cp\u003eThe author(s) declare that financial support was received for the research, authorship, and/or publication of this article. MB acknowledges the Global PhD partnership program between KU Leuven and Melbourne University (GPUM/21/025). We acknowledge additional financial support from Research Foundation of Flanders (FWO) research grants (G0D4519N, G081916N, VS08523N, G0C1821N, and G022724N), postdoctoral fellowships (for BF: 12X1419N and 12X1423N, for IV: 12A6N25N, for GS: 12AML24N, for BL: 12AGZ24N), PhD fellowships (for SH: 11A0S25N) and from the KU Leuven (IDN/20/021, C14/15/053, C14/19/079, and C14/22/085). This work also received financial support from PI20CIII/00019 and PI23CIII/00027 grants from the AES-ISCIII program cofounded by FEDER funds to R.B.\u003c/p\u003e\n\u003ch2\u003eData Availability\u003c/h2\u003e\n\u003cp\u003eThe data underlying this study are available in the main manuscript and its Supporting Information.\u003c/p\u003e\u003cp\u003e\u003cstrong\u003eSupporting Information\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eFluorescence microscopy images of additional spheroid samples showing Au@mSi-PEI NP penetration; Average total Au@mSi-PEI NP accumulation per spheroid, shown for each cell line over time; Au@mSi-PEI NP penetration profiles per cell line over time; Spheroid size over time, calculated from fluorescence microscopy images; Spheroid size over time when samples are not manipulated; Fluorescence microscopy images of additional samples of Au@mSi-PEI NP internalization in 2D monolayers; Average Au@mSi-PEI NP intensity inside the cells per cell line over time; Fluorescence microscopy images of additional 2D and 3D samples showing internalization and penetration of Au@mSi NPs; Au@mSi NP penetration profiles per cell line; Protein expression levels of relevant endocytic and ECM-related proteins in each cell line; WB analysis of the proteomics data; List of primary and secondary antibodies used for WB; Overview of the software for NP behavior analysis in 3D spheroids; Overview of the software for NP behavior analysis in 2D cell monolayers (PDF).\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eVan Zundert, I., Fortuni, B. \u0026amp; Rocha, S. 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U.S.A.\u003c/em\u003e \u003cstrong\u003e120\u003c/strong\u003e, e2209260120 (2023).\u003c/li\u003e\n\u003cli\u003eStylianopoulos, T. \u003cem\u003eet al.\u003c/em\u003e Diffusion of Particles in the Extracellular Matrix: The Effect of Repulsive Electrostatic Interactions. \u003cem\u003eBiophysical Journal\u003c/em\u003e \u003cstrong\u003e99\u003c/strong\u003e, 1342\u0026ndash;1349 (2010).\u003c/li\u003e\n\u003cli\u003eBurgos-Panadero, R. \u003cem\u003eet al.\u003c/em\u003e Unraveling the extracellular matrix-tumor cell interactions to aid better targeted therapies for neuroblastoma. \u003cem\u003eInternational Journal of Pharmaceutics\u003c/em\u003e \u003cstrong\u003e608\u003c/strong\u003e, 121058 (2021).\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"journal-of-nanobiotechnology","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"jnan","sideBox":"Learn more about [Journal of Nanobiotechnology](http://jnanobiotechnology.biomedcentral.com)","snPcode":"12951","submissionUrl":"https://submission.nature.com/new-submission/12951/3","title":"Journal of Nanobiotechnology","twitterHandle":"@BioMedCentral","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"BMC/SO AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Nanoparticles, 3D Cell Models, Cancer Spheroids, Nanoparticle Cellular Uptake, Endosomal Trafficking, ECM Composition","lastPublishedDoi":"10.21203/rs.3.rs-7865764/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7865764/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eNanoparticles (NPs) are a promising tool for cancer therapy, yet few have successfully reached clinical application. Current nanomedicine development pipelines are focused on optimizing the physical properties of NPs, overlooking the impact of cancer biology on their behavior. Here, we show that the same NPs exhibit distinct accumulation and penetration patterns in 3D spheroids derived from four spheroid models (representative of lung, colon, breast, and cervical cancer). We uncover an inverse relationship between NP uptake and penetration: spheroids with slower internalization show deeper NP diffusion. Proteomic analysis revealed that tumor-specific expression of endocytic and extracellular matrix proteins underlies this variability. Our findings challenge the prevailing \u0026lsquo;one-size-fits-all\u0026rsquo; approach and highlight the need to integrate cancer biology into NP design. Tailoring NPs to the unique cellular and extracellular features of each tumor type will be critical for developing more effective and clinically relevant nanotherapies.\u003c/p\u003e","manuscriptTitle":"Beyond One-Size-Fits-All: Cancer Biology Shapes Nanoparticle Behavior","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-11-03 14:52:43","doi":"10.21203/rs.3.rs-7865764/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2025-11-10T08:18:30+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-11-05T14:11:57+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-11-04T07:34:16+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-11-03T01:34:33+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-11-01T07:54:34+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-10-31T17:48:10+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-10-31T01:52:25+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"109568549543721319752533241068645397378","date":"2025-10-29T13:15:30+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-10-28T08:17:00+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"314754178315361082199723918164090197971","date":"2025-10-28T04:31:42+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"15271894696235020088444169846662499169","date":"2025-10-28T04:00:30+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"311573341260782314238120924845901702019","date":"2025-10-25T16:18:21+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"129036053838560815463796659055880470546","date":"2025-10-24T08:49:34+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"15639218231220390616446897394739613990","date":"2025-10-24T01:46:59+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"132538326868344608159278580842738001196","date":"2025-10-23T21:19:21+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"46658543151821696154707884122579110736","date":"2025-10-23T15:46:12+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"308752989273420709467169386693576443607","date":"2025-10-23T02:13:00+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-10-23T01:18:11+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-10-15T09:37:06+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-10-15T09:36:12+00:00","index":"","fulltext":""},{"type":"submitted","content":"Journal of Nanobiotechnology","date":"2025-10-15T08:29:30+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"journal-of-nanobiotechnology","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"jnan","sideBox":"Learn more about [Journal of Nanobiotechnology](http://jnanobiotechnology.biomedcentral.com)","snPcode":"12951","submissionUrl":"https://submission.nature.com/new-submission/12951/3","title":"Journal of Nanobiotechnology","twitterHandle":"@BioMedCentral","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"BMC/SO AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"ee8a7dab-dab2-4c4b-9ed2-534aa8c67770","owner":[],"postedDate":"November 3rd, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2026-04-27T16:06:05+00:00","versionOfRecord":{"articleIdentity":"rs-7865764","link":"https://doi.org/10.1186/s12951-026-04395-1","journal":{"identity":"journal-of-nanobiotechnology","isVorOnly":false,"title":"Journal of Nanobiotechnology"},"publishedOn":"2026-04-21 16:00:05","publishedOnDateReadable":"April 21st, 2026"},"versionCreatedAt":"2025-11-03 14:52:43","video":"","vorDoi":"10.1186/s12951-026-04395-1","vorDoiUrl":"https://doi.org/10.1186/s12951-026-04395-1","workflowStages":[]},"version":"v1","identity":"rs-7865764","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7865764","identity":"rs-7865764","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}