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## *Article* **Silica Nanoparticles Provoke Cell Death Independent of p53 and BAX in Human Colon Cancer Cells**

**Susanne Fritsch-Decker, Zhen An, Jin Yan, Iris Hansjosten, Marco Al-Rawi, Ravindra Peravali, Silvia Diabaté \* and Carsten Weiss \***

Institute of Toxicology and Genetics, Karlsruhe Institute of Technology, Hermann-von-Helmholtz-Platz 1, 76344 Eggenstein-Leopoldshafen, Germany

**\*** Correspondence<EMAIL_ADDRESS>(S.D<EMAIL_ADDRESS>(C.W.); Tel.: +49-72160822692 (S.D.); +49-72160824906 (C.W.)

Received: 10 July 2019; Accepted: 12 August 2019; Published: 16 August 2019

**Abstract:** Several in vitro studies have suggested that silica nanoparticles (NPs) might induce adverse effects in gut cells. Here, we used the human colon cancer epithelial cell line HCT116 to study the potential cytotoxic effects of ingested silica NPs in the presence or absence of serum. Furthermore, we evaluated different physico-chemical parameters important for the assessment of nanoparticle safety, including primary particle size (12, 70, 200, and 500 nm) and surface modification (–NH2 and –COOH). Silica NPs triggered cytotoxicity, as evidenced by reduced metabolism and enhanced membrane leakage. Automated microscopy revealed that the silica NPs promoted apoptosis and necrosis proportional to the administered specific surface area dose. Cytotoxicity of silica NPs was suppressed by increasing amount of serum and surface modification. Furthermore, inhibition of caspases partially prevented silica NP-induced cytotoxicity. In order to investigate the role of specific cell death pathways in more detail, we used isogenic derivatives of HCT116 cells which lack the pro-apoptotic proteins p53 or BAX. In contrast to the anticancer drug cisplatin, silica NPs induced cell death independent of the p53–BAX axis. In conclusion, silica NPs initiated cell death in colon cancer cells dependent on the specific surface area and presence of serum. Further studies in vivo are warranted to address potential cytotoxic actions in the gut epithelium. The unintended toxicity of silica NPs as observed here could also be beneficial. As loss of p53 in colon cancer cells contributes to resistance against anticancer drugs, and thus to reoccurrence of colon cancer, targeted delivery of silica NPs could be envisioned to also deplete p53 deficient tumor cells.

**Keywords:** synthetic amorphous silica; nanoparticles; colon cells; in vitro toxicity; cell death

#### **1. Introduction**

Synthetic amorphous silica (SAS) nanoparticles (NPs) are produced in large amounts for applications in industry and medicine. Many consumer products including food contain SAS, which occurs in different forms depending on the process of its manufacture. Food-grade SAS (E551) includes fumed (pyrogenic) silica and hydrated silica (precipitated silica, silica gel, and hydrous silica). Colloidal silica (silica sol) is not authorized as a food additive [1], but is an emerging material for various biomedical applications such as drug delivery [2]. Addition of SAS to food products has, for example, stabilizing, anti-caking, anti-settling, and emulsifying effects [3].

SAS food additive has been used for decades and is considered to be safe for consumers. Yang et al. [4] reported the occurrence of SAS at 1.3–16.3 mg Si/g dry food product, mainly in processed food such as coffee creamer, pudding powder, cake mix, or in probiotic tablets. In all exposure scenarios studied by EFSA (European Food Safety Authority), the lowest exposure was reported in the elderly,

while the highest was in infants and in children, ranging from 3.9 to 74.2 mg/kg body weight per day and from 8.4 to 162.7 mg/kg body weight per day, respectively [1].

The effects of oral uptake of food-grade SAS nanoparticles have been reviewed recently [1,3,5–8]. In vivo studies with rats found no acute toxicity due to SAS ingestion, but fibrosis in the liver at high dosages [9]. In a study with mice, upon oral exposure to SAS NPs, increased pro-inflammatory cytokine levels (IL-1β, IL-6, and TNF-α) were detected in the colons. Additionally, ingested SAS NPs increased the richness and diversity of microbial species within the intestinal tract [10]. Recently, Siemer et al. found that nanomaterials including SAS impact the (patho)biology of bacteria occurring in the gut [11].

In addition, in vitro studies suggest that food-grade SAS is potentially hazardous. In particular, fumed SAS is more toxic than previously assumed. The study of Zhang et al. [12] showed that fumed silica was more active in producing ROS (Reactive Oxygen Species) and causing red blood cell hemolysis. In Caco-2 cells, fumed SiO2 NPs induced cytotoxicity, DNA damage, and glutathione (GSH) depletion in serum-free medium [13,14]. Using human HT29 colon cells, it has been demonstrated that silica NPs are taken up into cells and stimulate cell proliferation under high-serum conditions. Under low-serum-conditions, cytotoxic effects were observed [15]. The study also found that SiO2-NPs enhanced the biosynthesis of GSH via the mitogen activated protein kinase (MAPK) pathway and caused oxidative DNA damage. Activation of the MAPK pathway and cytotoxicity in response to silica NPs were also observed in the human stomach cell line GXF252L [16].

Winter et al. found that SiO2-NPs affected the viability of murine dendritic cells and activated the inflammasome, suggesting that oral administration of these NPs could promote intestinal inflammatory responses [17]. More recently, the Toll-like receptor signaling pathway was demonstrated to be upstream of the inflammasome, and is essential in murine dendritic cells for the induction and release of IL-1β in response to food-grade nanosilica [18].

Before the particles reach the cells of the intestinal mucosa, they must pass the gastrointestinal tract, encompassing the oral cavity, esophagus, stomach, and intestine, and are exposed to different chemical conditions. The fluids in the gastrointestinal lumen contain complex mixtures of biomolecules such as digestive enzymes and food in different stages of digestion. Furthermore, osmotic concentration, pH, and the gut microbiome change during the passage through the gastrointestinal tract. All these parameters affect the ingested NPs and can alter the physico-chemical properties of the particles, which may influence the toxicological outcome [19]. Therefore, it is very difficult or nearly impossible to mimic all the parameters and physiological conditions encountered in vivo in an in vitro experiment. Furthermore, it has been demonstrated that biomolecules, in particular, proteins, bind to the particle surface and form a so-called biomolecular corona [20,21], which has consequences for the biological activity of nanomaterials [22,23]

The epithelial barrier of the gut represents a target for potentially cytotoxic effects of ingested silica NPs. As in the absence of a protective protein corona silica NPs induce cell death, for example, in lung epithelial and phagocytic cells, reminiscent of apoptosis [24,25], we wanted to investigate the role of specific cell death pathways in intestinal epithelial cells in more detail. Therefore, we used the human colon epithelial cancer cell line HCT116 as a model to evaluate the cytotoxic effects of SiO2-NPs as a function of different physico-chemical parameters important for the assessment of nanoparticle safety [26,27], i.e., concentration (10, 25, 50, and 100 µg/mL), duration of exposure (5, 24, and 48 h), particle size (12, 70, 200, and 500 nm), surface modification (–NH2 and –COOH), and protein coating with fetal bovine serum (FBS). Besides the wild-type cells, we used their isogenic derivatives, which lack the p53 or bax gene [28], to clarify the role of these pro-apoptotic factors for SiO2-NP-induced cell death.

#### **2. Materials and Methods**

#### *2.1.  All particles are listed in Table 1.

Enhanced chemiluminescence (ECL) reagents were obtained from GE Healthcare Amersham (cat no 2232). Antibodies for detection of cleaved caspase 3 (Asp 175, cat no 9661), caspase 8 (1C12, cat no 9746), and cleaved caspase 9 (Asp 315, cat no 9505) were purchased from Cell Signaling Technology (Frankfurt am Main, Germany). Antibodies against p53 (DO-1, cat no sc-126) and PCNA (PC-10, cat no sc-56) were from Santa Cruz (Heidelberg, Germany). Secondary horseradish-peroxidase (HRP)-conjugated antibodies: DAKO (Hamburg, Germany), pan-caspase inhibitor Q-VD-OPh: 5-(2,6-Difluorophenoxy)-3-[[3-methyl-1-oxo-2-[(2-quinolinylcarbonyl)amino]butyl]amino]-4 oxo-pentanoic acid hydrate: MP Biomedicals, Heidelberg, Germany, cat no 03OPH10901.

#### *2.2. Particle Suspensions and Characterization*

For treatment of cells, the particle stock suspensions were generated just before preparing the diluted particle suspensions in cell culture medium with or without serum, which were added to the cells. Aerosil® 200 NPs delivered as powder were suspended in sterile deionized water at 1 mg/mL, shortly vortexed and probe sonified with 15 strokes, 15% cycle duty, output control 5 (Branson Sonifier 250, Schwäbisch Gmünd, Germany). The other colloidal silica particles, which were delivered as suspension, were diluted to 1 mg/mL in deionized water and vortexed. These stock solutions were further diluted in medium to the desired concentrations.

For analysis by dynamic light scattering (DLS), NPs were further diluted to 50 µg/mL in deionized water or DMEM with or without serum. The samples were then analyzed directly after vortexing using the Zetasizer Nano ZS (Malvern Instruments Ldt., Herrenberg, Germany) at 25 ◦C.

For analysis by transmission electron microscopy (TEM), the particle suspensions in water were transferred onto TEM grids (Plano, SF162-6), dried, and analyzed by a Zeiss electron microscope (Zeiss 109T, Oberkochen, Germany) [29].

#### *2.3. Calculation of the E*ff*ective Density and the Deposited Dose*

To compare the effects of differently sized SiO2 particles, it was necessary to calculate the real dose deposited onto the cells over 24 h, as already described previously [30]. Briefly, the effective density of the particles in the respective medium was determined by the volumetric centrifugation method (VCM) according to Deloid et al. [31] at the highest applied concentration of 100 µg/mL. 1 mL of the particle suspension was filled into a packed cell volume (PCV) tube (TPP Techno Plastic Products, Trasadingen, Switzerland) and centrifuged in a swinging bucket rotor at 3.000 *g* for 1 h to collect the agglomerates in the capillary section of the tube. The volume of the pellet was determined using a measuring device from TPP Techno Plastic Products (Trasadingen, Swizerland). The effective density was then calculated according to the formula given in Deloid et al. [32]. The relative in vitro dose (RID) was determined by calculating the particle mass deposited onto the cell surface after 24 h using the distorted grid (DG) nanotransport simulator [33], based on hydrodynamic size (Table S1), effective density (Table S2), and other parameters in the respective media.

#### *2.4. Cells*

HCT116 wt, p53−/−, and BAX−/− cells (kindly provided by B. Vogelstein, John Hopkins University, Baltimore, MD, USA) were cultured as described before [34]. Briefly, the cells were maintained in Dulbecco's modified Eagle's medium (DMEM) supplemented with 10% fetal bovine serum (FBS), 100 U/mL penicillin and 100 mg/mL streptomycin at 37 ◦C in a humidified atmosphere containing 5% CO2. Medium was changed every 2 days. Control cells were treated with medium alone or with 0.1% DMSO, the solvent used for the caspase inhibitor.

#### *2.5. Cell Death Analysis by Fluorescence Microscopy*

For detection of cell number and stages of cell death by automated microscopy, 8000 cells were seeded per well of a 96 well plate. On the next day, the cell culture medium was discarded and the cells were treated according to the experimental design. After the incubation period, analysis was performed as previously described [30]. Briefly, Hoechst 33342 and propidium iodide (PI) were added to a final concentration of 0.3 µg/mL and 0.5 µg/mL, respectively. After 30 min incubation in the dark, bright field (BF) and fluorescence images were acquired from four positions in the well using an automated Olympus IX81 fluorescence microscope and a 10× objective (Olympus, Hamburg, Germany). The Hoechst dye was detected at excitation and emission wavelengths of 350 and 450 nm, respectively. PI dye was detected at 488 nm and 590 nm, respectively. The images were analyzed by the scanˆR analysis software (version 2.7.3, Olympus, Hamburg, Germany) to obtain the total number of cells (Hoechst channel) and the number of early apoptotic, late apoptotic, and necrotic cells (combination of Hoechst and PI channel), as described previously [30].

#### *2.6. Real-Time Imaging at the Single Cell Level*

For real-time imaging at single cell level, HCT116 cells were first seeded in 96 well plates, as described above, and incubated overnight. Real-time imaging was performed as published previously [25]. Briefly, before treatment with particles, cells were stained with 0.1 µg/mL Hoechst and 0.083 µg/mL PI for 1 h at 37 ◦C and 5% CO2, followed by an incubation with SiO2 NPs over 24 h in a microscope incubator box (EMBLEM, Heidelberg, Germany) under control of CO2, humidity, and temperature (37 ◦C, 5% CO2). Two images per well and channel (bright-field, Hoechst and PI) were acquired using the automated fluorescence microscope IX81 (Olympus, Hamburg, Germany) with a 20-fold objective. The NIH ImageJ Software (version 1.50b, Bethesda, MD, USA) was used to convert images to videos.

#### *2.7. LDH Cytotoxicity Assay*

The LDH assay was performed as described previously [35]. Briefly, after treatment with particles, control cells were treated with 1% (v/v) of Triton X-100 for 30 min to detect the maximum LDH release (positive control). The whole plates were then centrifuged at 1500 rpm for 5 min and 50 µL of the supernatant was transferred into a 96 well plate. 50 µL PBS and 100 µL of the LDH working solution, prepared according to the manufacturer's instructions, were added per well and incubated at room temperature for 10 min. By the addition of 50 µL of 1 N HCl, the reaction was stopped. Absorbance was measured at 490 nm using a multi-well plate reader and analyzed by the software package SoftMaxPro (version 3.0, Molecular Devices, Ismaning, Germany). Cytotoxicity data are depicted as percentage relative to the positive control set to 100%.

#### *2.8. AlamarBlue Viability Assay*

The AlamarBlue® assay was performed as described previously [35], using the same plate with exposed cells as for the LDH assay. The remaining medium was removed and 100 µL of diluted AlamarBlue® reagent (1:20 in HBSS) was added. After 60 min in the incubator, the samples were analyzed using a multi-well plate fluorescent reader (Bio-Tek FL600, software package KC4 version 2.7, MWG-Biotech AG, Ebersberg, Germany) at 560 nm excitation and 620 nm emission wavelengths. Viability data are depicted as percentage relative to the negative control (untreated cells, 100%).

#### *2.9. Protein Detection by Western Blot*

Western blot was performed as described before [36]. The cells, which were seeded into 6 well plates, were treated with the particles according to the experimental design. After treatment, the cells were lysed in 2× Laemmli buffer (125 mM Tris–HCl, 4% SDS, 20% glycerol, 8% beta-mercaptoethanol, pH 6.8), boiled at 95 ◦C for 5 min, and sonicated in an ultrasonic water bath (Bandelin Sonorex, Berlin, Germany) for 10 min. Lysates were loaded on 12 or 15% gels depending on the molecular size of the protein to be detected for SDS-PAGE. After electrophoresis, the proteins were transferred onto Immobilon membranes (Millipore, Darmstadt, Germany). Membrane blocking was performed in 5% (w/v) non-fat dry milk powder in Tris-buffered saline containing 1% (v/v) Tween20 (TBS-T) for 1 h. Appropriate primary antibodies were applied in 5% (w/v) non-fat dry milk in TBS-T overnight at 4 ◦C. After incubation with appropriate HRP-conjugated secondary antibodies for 1 h, enhanced chemiluminescence (ECL) detection was performed according to the manufacturers' instructions.

#### *2.10. Statistical Analyses*

Data are expressed as mean ± standard deviation (SD). The significance of difference between two mean values was assessed by the Student's *t*-test or the Mann–Whitney Rank Sum test using the SigmaPlot software (version 11.0, Systat Software GmbH, Erkrath, Germany). A *p*-value < 0.05 was considered to be statistically significant.

#### **3. Results and Discussion**

#### *3.1. Characterization of Silica Particles*

In this study, we used commercial amorphous silica nanoparticles synthesized by different methods and of different sizes and surface modifications, as listed in Table 1. SiO2—12 nm, also known as Aerosil® 200, is a hydrophilic pyrogenic silica NP produced by flame synthesis and was delivered as a powder. SiO2—70 nm NPs were produced by a wet process known as the Stöber method [37] and were delivered in an aqueous suspension. Amine- and carboxyl-modified silica NPs of the same size were used to study the effects of surface modification on the toxicity of colloidal silica NPs. Furthermore, the effect of size was studied by using 200 and 500 nm Stöber-synthesized silica particles. The primary particle diameter analyzed by TEM was used to calculate the specific surface area of the particles in m2 /g. The particles were further characterized in medium without and with 10% FBS by dynamic light scattering (Table S1). In DMEM without FBS, all Stöber-synthesized particles showed a hydrodynamic diameter very close to their primary diameter, indicating a monodisperse suspension. The hydrodynamic diameter was slightly increased in the medium with 10% FBS, most likely due to the formation of a protein corona. SiO2—12 nm NPs were already aggregated to 232 nm in medium without FBS (presumably due to sintering [22]), and further agglomerated in medium with 10% FBS. A more detailed characterization of Aerosil® 200 according to OECD test guidelines is provided in Mülhopt et al. [38]. In these studies, trace amounts of Ni (0.1 µg/g) and Cu (0.2 µg/g) were detected by inductively coupled plasma-mass spectrometry (ICP-MS), which, however, were below the level of toxicological relevance.

**Table 1.** Characteristics of silica nano- and microparticles.

a Data provided by the supplier, b analyzed by transmission electron microscopy (TEM), c calculated from the primary particle size, and the density 2.0 g/cm3 for SiO2 nanoparticles (NPs). d Already published in Reference [38], e already published in Reference [25].

#### *3.2. Silica NPs Induced Apoptotic and Necrotic Cell Death Which Was Suppressed by the Presence of Serum*

The effects of SiO2—12 nm NPs on cell viability after 24 h were tested using the AlamarBlue and the LDH release assays (Figure 1a,b). Viable cells reduce the AlamarBlue dye by mitochondrial dehydrogenases and thus change its color. Reduction of AlamarBlue did not change when cells were exposed to particles dispersed in medium with 10% FBS. However, it decreased dose-dependently after treatment in the absence of FBS, even at 10 µg/mL of SiO2—12 nm NPs. The results of the LDH assay correlate well with the AlamarBlue assay. Release of LDH is an indicator of plasma membrane damage. For cells treated with 10 µg/mL of SiO2—12 nm NPs in FBS-free medium, there was already a strong release of LDH, which further increased with higher doses. There was no LDH release detected when cells were exposed to particles suspended in medium with 10% FBS. The cytotoxic effects of SiO2—12 nm NPs observed after 48 h were similar to those after exposure for 24 h (Figure S1a,b), indicating a rapid onset of cell damage. Indeed, silica NPs triggered membrane rupture as early as 5 h after exposure (Figure S2a). In order to mimic the exposure of colon cells from the luminal side, we further reduced the amount of serum in the exposure medium. Interestingly, silica NPs also provoked membrane damage at 24 h in the presence of 1% FBS, albeit reduced compared to the levels induced in the total absence of serum (Figure S2b). The protective effects of just 1% FBS in the exposure medium were even more obvious at an earlier time point, i.e., 5 h after treatment (Figure S2a). In conclusion, silica NPs promoted cytotoxicity in HCT116 cells dependent on time, concentration, and the presence of serum, as previously reported for other cell types [15,16,20,22,23,39].

Next, we used automated fluorescence microscopy to further identify the mechanisms of cell death. After 24 h in the absence of FBS, the total cell number decreased significantly at increasing levels of SiO2—12 nm NPs, while, concomitantly, the proportion of dead cells increased (Figure 2c,d). Similar effects were found at 48 h of exposure (Figure S3). Dead cells showed typical features of cell death, such as DNA condensation and loss of membrane integrity, and were mainly categorized as late apoptotic and necrotic cells (Figure 2a,b,d, Video S1). As also documented above using the AlamarBlue and LDH assays (Figure 1), 10% serum in the exposure medium largely prevented cytotoxicity. In accordance with the rapid damage of the cell membrane detected by the LDH assay at 5 h in the absence of serum (Figure S2a), an early influx of propidium iodide indicative of membrane rupture was also observed at the single cell level (Figure S4a,b). Furthermore, in accordance with the LDH assay (Figure S2b), the presence of 1% FBS reduced cell death prompted by silica NPs (Figure S5a,b). These findings, together with previous results obtained with human colon carcinoma [15] and lung epithelial cells [24], indicate that the ratio of the specific particle surface area and concentration of proteins in the exposure medium has a critical impact on the occurrence of cell death upon treatment with silica NPs.

μ

μ

**Figure 1.** Cell viability decreased after incubation with silica NPs in the absence of fetal bovine serum (FBS). HCT116 wt cells were incubated with SiO2—12 nm NPs at the indicated concentration in the presence or absence of 10% FBS. After 24 h, cell viability was detected by the AlamarBlue assay (**a**). The values were normalized to the negative control (no particles were added, 100%). Lactate dehydrogenase (LDH) release is shown in (**b**). The values were normalized to the positive control (1% Triton X-100, 100%). The data represent the means of seven independent experiments ± SD performed with six replicates. \*\*\* *p* < 0.001 indicates significant differences in the response of cells treated with the same amount of NPs in the absence (0% FBS) or the presence of serum (10% FBS).

#### *3.3. Silica NPs Triggered Cell Death Dependent on Size, Specific Surface Area, and Surface Modification*

To address the impact of size on silica NP induced cell death under serum-free conditions, we studied colloidal silica spheres with nominal diameters of 70, 200, and 500 nm (Table 1). Administration of particles with increasing diameter but at the same mass concentration (10 and 50 µg/mL) revealed an inverse relationship of particle size and cytotoxicity, i.e., larger particles were less toxic compared to smaller particles (Figure 3a). Similarly to the effects observed for SiO2—12 nm NPs derived from flame synthesis (Figure 2), colloidal silica NPs also provoked apoptotic and necrotic cell death dependent on dose and size (Figure 3b, Figure S6). Silica NPs are surface active materials and interact with biological surfaces, i.e., cell membranes. The specific particle surface area (SSA, cm2 at a defined mass or volume) increases linearly with a reciprocal decrease in particle size. The dose–response related to the nominal concentration in the exposure media indicated increased toxicity of smaller versus larger silica NPs (Figure 3a,b). However, normalization of the dose response curves to the administered SSA showed a clear correlation of SSA and cytotoxicity (Figure 3c). As the deposited, i.e., effective, dose of particles is controlled by sedimentation and diffusion, which depend on particle size and effective density, we also calculated the delivered cellular dose [33,40]. While for the larger particles of 200 and 500 nm there was no major difference in the effective SSA dose, for the smaller nanoparticles (12 and 70 nm), the effective SSA (Figure 3e,f) was drastically reduced compared to the nominal dose (Figure 3c,d). As deposition of nanoparticles is mainly driven by diffusion, the relative fraction of administered NPs interacting with cells was lower compared to the larger particles, highlighting the importance of computational modeling of the relative in vitro dose for hazard ranking of differently

sized materials. Indeed, several in vitro and in vivo studies have shown that cytotoxicity of insoluble NPs better correlates with the SSA dose than with the particle mass dose [24,41–43].

**Figure 2.** Silica NPs induce apoptotic and necrotic cell death in the absence of serum. HCT116 wt cells were incubated with SiO2—12 nm NPs at the indicated concentration in the presence (10% FBS) or absence (0% FBS) of serum. After 24 h, the cells were stained with Hoechst and propidium iodide (PI), and images were acquired by automated microscopy and analyzed by the scanˆR software to deduce cell numbers and the different modes of cell death. (**a**,**b**) Representative images of cells treated as indicated in the brightfield, the Hoechst, and the PI channels. (**c**) The total cell number divided into living and dead cells after treatment, as indicated. (**d**) The percentage of dead cells relative to the total cell number divided into the different classes of cell death, as indicated. Data are represented as mean values of three independent experiments carried out with four replicates (*n* = 12). The error bars are SD values related to the total cell number (**c**) or the percentage of dead cells (**d**). \*\* *p* < 0.01, \*\*\* *p* < 0.001 indicate significant differences in the response of cells treated with particles at corresponding concentrations in the absence (0% FBS) or the presence of serum (10% FBS).

**Figure 3.** Silica NPs induce cell death dependent on size and specific surface area. HCT116 wt cells were incubated with particles as indicated in the absence of FBS. After 24 h, the cells were processed and analyzed as described in Figure 2. (**a**) The total cell number divided into living and dead cells. (**b**) The percentage of dead cells relative to the total cell number divided into the different classes of cell death as indicated. Data show mean values of two independent experiments carried out with four replicates (*n* = 8). The error bars are SD values related to the total cell number (**a**) or the percentage of total dead cells (**b**), respectively. \* *p* < 0.05, \*\*\* *p* < 0.001 indicate significant differences in the response of cells treated with particles in comparison to untreated control cells (-). The percentage of viable (**c**) and dead cells (**d**) are plotted against the nominal specific particle surface area dose per cell area (cm2 /cm2 , logarithmic scale). For comparison (**e**,**f**), the calculated deposited dose has been used as a metric. Note that the nominal (**c**,**d**) and calculated (**e**,**f**) dose in case of larger particles (200 and 500 nm) were rather similar, whereas for nanoparticles (12 and 70 nm), only a small fraction was deposited. Further details are described under Methods.

Chemical surface functionalization of silica NPs modulates the cellular response [44,45]. Therefore, we also investigated in HCT116 cells the effects of colloidal silica NPs of the same size (70 nm) with a plain, NH2—, or COOH—modified surface. As observed in other cell models, chemical modification of the silica surface suppressed cytotoxicity (Figure 4a,b; Figure S7). Replacement of

reactive silanol groups by functional groups is supposed to prevent their detrimental interaction with cellular membranes [46,47].

**Figure 4.** Surface modifications reduces cytotoxicity of silica NPs. HCT116 wt cells were exposed to SiO2—NPs (70 nm) with a plain, NH2—, or COOH—modified surface in the absence of FBS. After 24 h, the cells were processed and analyzed as described in Figure 2. (**a**) The total cell number divided into living and dead cells. (**b**) The percentage of dead cells relative to the total cell number divided into the different classes of cell death as indicated. Data show mean values of two independent experiments carried out with four replicates (*n* = 8). The error bars are SD values related to the total cell number (**a**) or the percentage of total dead cells (**b**), respectively. \*\*\* *p* < 0.001 indicates significant differences in the number of viable cells (**a**) and total dead cells (**b**) after administration of modified NPs compared to corresponding concentrations of plain particles.

#### *3.4. Silica NPs Promoted Cell Death Dependent on Caspases and Independent of p53 or BAX*

Silica NPs induced cell death in HCT116 cells via apoptosis and necrosis, as evidenced by the image analysis outlined above. Execution of cell death is controlled by intricate signaling networks and is of relevance for the toxicity of several nanomaterials [48]. The most prominent determinants of cell death are the tumor suppressor protein p53 and its target BAX [49]. p53 is a transcription factor which is activated due to cellular stress, and regulates cell growth, cell cycle progression, and cell death via apoptosis, necroptosis, or ferroptosis. BAX acts on mitochondria to promote apoptosis and is regulated by p53, which upregulates transcription of the gene encoding BAX. In order to further investigate the mechanism of cell death triggered by silica NPs, we explored wild-type HCT116 cells and their isogenic derivatives in which the p53 gene was deleted by homologous recombination [28]. Compared to wild-type cells, silica-NP-induced cell death monitored by automated microscopy at 24 h was not reduced in p53 knock-out cells (Figure 5a,b). The similar sensitivity of p53 deficient cells was also confirmed by the LDH and AlamarBlue assays (compare Figure S8a,b and Figure 1). Additionally, at an earlier time point (5 h), no reduced cell death but rather, enhanced cytotoxicity could be observed in p53 knock-out cells (Figure S9a,b; Figure S10; Figure S2a). Similarly, at 48 h after exposure to nanosilica, no major difference in the percentage of cell death was obvious when wildtype and p53 knock-out cells were compared (Figure S11). Next, we analyzed the involvement of the p53 target BAX in the execution of nanosilica-induced cell death. Again, as found in the case of p53 deficient cells, BAX knock-out cells were not protected against the detrimental action of nanosilica (Figure 5a,b). In summary and in contrast to the action of some genotoxins [28,34], the p53–BAX axis seems not to be involved in the execution of cell death prompted by exposure to nanosilica in HCT116 cells. Previous reports have suggested a pro-apoptotic role of p53 and BAX in nanosilica-induced cell death [50,51]. After treatment with silica NPs, an upregulation of the p53 and BAX proteins in human fetal hepatocytes and hepatoma cells could be demonstrated, which correlates with the onset of cell death. However, further studies in these and other cell types are warranted to really confirm a

functional role of p53 and BAX (e.g., by genetic or pharmacological interference) in nanosilica-induced cell death.

− − − − − − − − − − − − **Figure 5.** Silica NPs induce cell death independent of p53 and BAX. HCT116 wild-type (wt), p53 knock-out (−/−) and BAX knock-out (−/−) cells were incubated with SiO2—12 nm NPs as indicated in the absence of FBS. After 24 h, the cells were processed and analyzed as described in Figure 2. (**a**) The total cell number divided into living and dead cells. (**b**) The percentage of dead cells relative to the total cell number divided into the different classes of cell death as indicated. Data show mean values of five–six independent experiments with wt and p53−/− cells. For BAX−/− cells, one representative experiment out of two is shown. All experiments were carried out with three–four replicates. The error bars are SD values related to the total cell number (**a**) or the percentage of total dead cells (**b**), respectively. Note that there was no major difference in the response of p53−/− or BAX−/− compared to wild-type cells. \* *p* < 0.05.

Finally, we addressed the involvement of caspases in silica-NP-induced cell death. Indeed, inhibition of caspases partially protected cells from silica-NP-induced cell death, evidenced by an increase of viable cells (Figure 6a) and a decrease in apoptotic and necrotic cells (Figure 6b). As also shown for the positive controls, which were treated with the genotoxin cisplatin, caspases are required for the promotion of cell death initiated by silica NPs. Next, we monitored cleavage of caspase 8 and caspase 9, which act as initiator caspases in the extrinsic (as part of the death-inducing signaling complex) and intrinsic (as part of the apoptosome) apoptotic pathways, respectively [52]. Exposure of cells to increased amounts of silica NPs triggered cleavage of caspase 8 and 9 (Figure 6c), in line with a critical role of caspase activity in the execution of cell death (Figure 6a,b). Furthermore, in contrast to the positive control cisplatin, silica NPs did not elevate the protein levels of p53 (Figure 6c). This also supports the loss of function experiments, i.e., similar cytotoxicity in wt and p53 knock-out cells after exposure to silica NPs (Figure 5a,b).

μ μ μ μ μ **Figure 6.** Silica NPs induce cell death dependent on caspases. HCT116 wt cells were incubated with 10 µg/mL SiO2—12 nm NPs or with 50 µM of the positive control compound cisplatin (CDDP) in medium without FBS. Samples were co-treated as indicated either with the pan-caspase inhibitor Q-VD-OPh (10 µM) dissolved in DMSO (0.5%) or simply the solvent (DMSO, 0.5%). After 24 h, the cells were processed and analyzed as described in Figure 2. (**a**) The total cell number divided into living and dead cells; (**b**) the percentage of dead cells relative to the total cell number divided into the different classes of cell death as indicated. Data show mean values of two independent experiments carried out with four replicates (*n* = 8). The error bars are SD values related to the total cell number (**a**) or the percentage of total dead cells (**b**), respectively. \*\*\* *p* < 0.001 indicates significant differences in the response of cells treated with or without the pan-caspase inhibitor. (**c**) Activation, i.e., cleavage of caspase 8 and 9 was analyzed by western blotting after treatment with 0 (co.), 5 and 10 µg/mL SiO2—12 nm NPs or with 50 µM CDDP in the absence of serum for 22 h. PCNA was used as a loading control. The results are representative of two independent experiments.

#### **4. Conclusions and Perspectives**

Engineered silica nanoparticles are ingested as food additives and belong, together with titania NPs, to the most frequently used nanomaterials in food products [8]. The present study revealed cytotoxic actions of silica NPs in the colon cancer cell line HCT116, specifically in the absence or presence of low amount of serum, corroborating previous studies in the two different human colon cancer cell lines Caco-2 and HT29 [13,14]. In Caco-2 cells, a pro-inflammatory action of silica NPs was also observed indicated by the release of IL-8 [53]. Interestingly, differentiated Caco-2 cells were less sensitive to the noxious effects of silica NPs. The physiological relevance of in vitro studies needs to be considered, and specific test systems and guidelines still need to be optimized in case of hazard and risk assessment of NPs upon ingestion [8]. With respect to dosimetry, we chose a realistic dose range for all different silica nanoparticles below the calculated relevant in vitro dose of about 2 µg/cm2 (NP mass/cellular surface area), approximated from the daily average intake of food grade silica (1.8 mg/body weight/day) in humans [8]. Unfortunately, there is a lack of in vivo studies investigating toxicity of silica NPs in the gut. Recently, production of pro-inflammatory cytokines upon oral administration of nanosilica (2.5 mg/kg bw/day) for 7 days was observed in mice, which coincided with slight histological changes, i.e., crypt damage and inflammatory cell infiltration [10]. Thus, it is tempting to speculate that microlesions in the gut epithelium due to cell death could contribute to the inflammatory symptoms. In the colonic crypts, there is a constant renewal of epithelial cells, which originate from stem cells and progressively differentiate to become mature enterocytes. Clearly, more detailed studies in vivo are warranted to address the impact of nanosilica on the viability of epithelial cells, and also in relation to their differentiation status. It is of note that the in vitro experiments of Wiemann et al. with alveolar macrophages cultivated under serum-free conditions have been demonstrated to predict the short-term inhalation toxicity in vivo for 18 different nanomaterials, including nanosilica [54]. Upon inhalation, NPs are covered by the lung lining fluid, mostly comprised of surfactant (primarily phospholipids) and a small amount of proteins, and are not covered by serum proteins. Therefore, in vitro exposure of lung cells in the presence of high levels of serum proteins (i.e., 10% FBS) does not closely mimic the physiological conditions in the lung. Concerning a realistic exposure scenario for the gastrointestinal tract, NPs are embedded in a complex food matrix, which is further altered upon ingestion and digestion. Therefore, the corona, i.e., the adsorbed molecules on the NP surface, is ill-defined and comprised of a multitude of different molecules including carbohydrates, protein, and lipids. However, compared to localization of NPs in the blood stream after direct injection (in the case of medical applications) or after translocation from the gut into the portal vein, the amount of serum proteins in the gut lumen is much lower. Hence, the presence of high levels of serum proteins as used in most in vitro experiments might passivate the silica surface and suppress the interaction of silica NPs with critical targets such as the cellular membrane or certain receptors. Although in vitro experiments employing monocultures can be used to address specific processes and pathways, co-cultures of colon epithelial cells together with mucus producing goblet cells or lymphoid cells might even be better suited to predicting the more complex situation in vivo. Indeed, exposure of differentiated Caco-2 cells co-cultured with mucus producing HT29-MTX cells to titania NPs triggered the production of reactive oxygen species [55–57] and an inflammatory response, which was absent in Caco-2 cells grown as monocultures.

Finally, in vitro studies in toxicology often rely on the use of transformed cancer cells, as primary cells of various organs are difficult to cultivate and are often not available. Therefore, adverse effects observed in vitro might indicate toxicity in vivo, but need to be confirmed in follow-up investigations. However, the unintended toxicity of NPs determined in vitro might also be beneficial and exploited to selectively eliminate cancer cells. Specifically, silica NPs also kill p53-deficient colon cancer cells, as shown here, which are resistant to chemotherapy by conventional drugs such as cisplatin. Hence, oral delivery of silica NPs could be envisioned for the treatment of colon cancer. Selective accumulation of silica NPs via tumor-targeting ligands could be combined with the tunable unmasking of the silica surface, e.g., by dePEGylation initiated by near infrared radiation, as shown recently [58].

**Supplementary Materials:** The following are available online at http://www.mdpi.com/2079-4991/9/8/1172/s1, Table S1: Characterization of SiO2 particle suspensions. Table S2: Nominal and effective i.e., calculated dose of differently sized SiO2 particles. Figure S1: Cell viability decreases after incubation with SiO2—12 nm NPs for 48 h in the absence of FBS. Figure S2: Low amount of serum (1% FBS) delays and reduces but does not totally prevent membrane damage provoked by SiO2—12 nm NPs. Figure S3: Cell death upon treatment with SiO2—12 nm NPs for 48 h in the absence of FBS. Figure S4: SiO2—12 nm NPs disturb the integrity of the cell membrane in the absence of FBS as indicated by the influx of propidium iodide already after 5 h. Figure S5: The toxic effects of SiO2—12 nm NPs after 24 h are diminished in the presence of 1% FBS. Figure S6: Silica NPs induce apoptotic and necrotic cell death dependent on size. Figure S7: Cytotoxicity of silica NPs is suppressed by surface

modification. Figure S8: Cell viability decreases after incubation of HCT p53−/− cells with SiO2—12 nm NPs for 24 h in the absence of FBS. Figure S9: HCT116 p53−/− cells are more sensitive to SiO2—12 nm NPs than HCT116 wt cells. Figure S10: Low amount of serum (1% FBS) reduces membrane damage provoked by SiO2—12 nm NPs. Figure S11: The response of HCT116 wt and p53−/− cells to SiO2—12 nm NP exposure after 48 h is similar. Figure S12: Summary of the results from the different toxicity assays to demonstrate the similar sensitivity of HCT wt and p53−/− cells to SiO2—12 nm NPs dependent on the concentration of FBS. Video S1: Time course of cell death after incubation to SiO2—12 nm NPs in the absence of serum.

**Author Contributions:** Conceptualization, C.W.; methodology, I.H., M.A.-R., R.P.; investigation, Z.A., J.Y., S.F.-D.; writing—original draft preparation, S.D., C.W., S.F.-D.; writing—review and editing, S.D., C.W., S.F.-D.

**Funding:** Z.A. and J.Y. were funded by the China Scholarship Council (CSC).

**Acknowledgments:** We acknowledge support by the KIT-Publication Fund of the Karlsruhe Institute of Technology.

**Conflicts of Interest:** The authors declare no conflict of interest.

#### **References**

© 2019 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).

## *Article* **Improving Quality in Nanoparticle-Induced Cytotoxicity Testing by a Tiered Inter-Laboratory Comparison Study**

**Inge Nelissen 1,\* , Andrea Haase 2 , Sergio Anguissola 3,4, Louise Rocks 3,5, An Jacobs 1 , Hanny Willems 1 , Christian Riebeling 2 , Andreas Luch 2 , Jean-Pascal Piret 6 , Olivier Toussaint 6,**† **, Bénédicte Trouiller 7 , Ghislaine Lacroix 7 , Arno C. Gutleb 8 , Servane Contal 8 , Silvia Diabaté 9 , Carsten Weiss 9 , Tamara Lozano-Fernández 10,11 , África González-Fernández 10,12 , Maria Dusinska 13, Anna Huk 13,14, Vicki Stone 15 , Nilesh Kanase 15, Marek Nocu ´n 16,17, Maciej St ˛epnik 16 , Stefania Meschini 18 , Maria Grazia Ammendolia 18 , Nastassja Lewinski 19,20, Michael Riediker 19,21,22 , Marco Venturini 23, Federico Benetti 23, Jan Topinka 24, Tana Brzicova 24,25, Silvia Milani 26 , Joachim Rädler 26, Anna Salvati 3,27 and Kenneth A. Dawson 3**

Received: 25 June 2020; Accepted: 17 July 2020; Published: 22 July 2020

**Abstract:** The quality and relevance of nanosafety studies constitute major challenges to ensure their key role as a supporting tool in sustainable innovation, and subsequent competitive economic advantage. However, the number of apparently contradictory and inconclusive research results has increased in the past few years, indicating the need to introduce harmonized protocols and good practices in the nanosafety research community. Therefore, we aimed to evaluate if best-practice training and inter-laboratory comparison (ILC) of performance of the 3-(4,5-dimethylthiazol-2-yl)- 5-(3-carboxymethoxyphenyl)-2-(4-sulfophenyl)-2H-tetrazolium (MTS) assay for the cytotoxicity assessment of nanomaterials among 15 European laboratories can improve quality in nanosafety testing. We used two well-described model nanoparticles, 40-nm carboxylated polystyrene (PS-COOH) and 50-nm amino-modified polystyrene (PS-NH2). We followed a tiered approach using well-developed standard operating procedures (SOPs) and sharing the same cells, serum and nanoparticles. We started with determination of the cell growth rate (tier 1), followed by a method transfer phase, in which all laboratories performed the first ILC on the MTS assay (tier 2). Based on the outcome of tier 2 and a survey of laboratory practices, specific training was organized, and the MTS assay SOP was refined. This led to largely improved intra- and inter-laboratory reproducibility in tier 3. In addition, we confirmed that PS-COOH and PS-NH2 are suitable negative and positive control nanoparticles, respectively, to evaluate impact of nanomaterials on cell viability using the MTS assay. Overall, we have demonstrated that the tiered process followed here, with the use of SOPs and representative control nanomaterials, is necessary and makes it possible to achieve good inter-laboratory reproducibility, and therefore high-quality nanotoxicological data.

**Keywords:** nanosafety; cytotoxicity; inter-laboratory comparison; best practice; training

#### **1. Introduction**

Within the last 20 years, there has been a tremendous increase in numbers of publications on nanomaterial (NM) toxicity, many of which report inconclusive and controversial results, often apparently conflicting. This has generated a wide debate on the quality and relevance of published papers in nanosafety, including—for instance—a discussion opened up by the *Nature Nanotechnology* journal [1,2], which was followed by several commentaries and other examples [3,4]. A similar discussion about the reliability and reproducibility of experimental data has also been raised for science in general [5–7], as it was demonstrated for pre-clinical studies on cancer [8]: using in-house data trying to validate the published results, the authors found that at most only 25% were in line with published data. Efforts to adhere to the biological models as used in the original publications did not improve these results. Conversely, reproducible results were also transferable between models [8]. Largely, discrepancies arise from honest mistakes and flawed statistics, but a recent focus on bad practices and fraud in science has also uncovered instances of the latter [9,10]. Most of the published research has been performed in research laboratories, e.g., at universities, which usually do not adhere to good laboratory practice (GLP) or similar standards. GLP was developed specifically out of an experience of data manipulation and fraud in toxicological contract research [11–13]. In regulatory toxicology, adherence to GLP, Organization for Economic Cooperation and Development (OECD) guidelines and International Organization for Standardization (ISO) standards is therefore paramount. Applying similar standards to the existing literature on nanotoxicology by checking publications against a defined set of criteria, such as physico-chemical material characterization or detailed descriptions of the assays applied, including solid statistical data evaluation, resulted in approximately 68% [14] to 90% [15] of the studies being rejected.

Even if it sounds trivial, it has to be stressed that in nanotoxicology all steps from synthesis route to nanoparticle (NP) sample preparation for testing and every step in-between will have an effect on the outcome of the experiments. NMs may become altered throughout all these processes. For biological testing, the dispersion of NMs into media has two crucial aspects, the type of dispersion [16] and the composition of the media [17]. The energy used for the dispersion can, for instance, passivate the surface, influence the agglomeration state and cause dissolution of molecules into ions [18]. Liquid media and biological fluids can influence the same parameters and, importantly, the presence of proteins and other biomolecules leads to the formation of a biocorona on the surface of the NMs [19]. The impact of the corona on NP-cell interactions is demonstrated by the differential cytotoxicity of NPs in the absence or presence of serum, as a general paradigm for all NMs, such as for instance silica [20,21], positively charged NPs [22], carbon nanotubes [23] and graphene oxides [24]. While under realistic exposure conditions in biological fluids the NMs are passivated by the presence of this layer of biomolecules from the surrounding environment, in artificially simplified laboratory conditions, such as serum-free medium, the bare surfaces of NMs can adhere so strongly to the cell surface that they generate damage and other biological processes. Importantly, even the amount and identity of proteins present affects NP outcome on cells [25–27], opening up new challenges for determining realistic exposure scenarios. Other unique features of NMs affecting the outcomes of toxicity testing in comparison to standard chemicals include the interference of the NM itself with the testing method: NMs can adsorb and scatter light, interfering with tests based on absorbance, luminescence and fluorescence detection. Furthermore, NMs can also adsorb the reagents used for the tests on their surface, thus causing artefacts that could be misinterpreted for signals [28]. These are just some examples of the many unique features of materials at the nanoscale, which have caused the need for the development of new methods and procedures, as well as specific laboratory practices, in order to be able to generate robust and reproducible data in nanosafety.

These issues are the main reasons why most studies are incomparable [4]. To achieve improved comparability and reproducibility, protocols have to be harmonized and standardized. Moreover, there is a need for the development of alternative testing strategies, as the vast possibilities of engineering NMs would result in a high number of animal studies if current regulatory protocols were to be followed. Animal studies are not only costly, time consuming and ethically fraught; the legislation of cosmetics in the European Union already prohibits this type of testing. Alternative in vitro testing might be a useful tool to prioritize animal testing to NMs of concern. In addition, these methods might be used during R&D to eliminate substances with hazardous properties. Standardization of in vitro procedures requires examining all involved materials and specifying every single step in a protocol. For instance, cell lines have to be identical [29] and free of mycoplasma [30], and even the way how cells are seeded into a multi-well plate has consequences on the test result [31].

In this study, an inter-laboratory comparison (ILC) study among 15 European research laboratories was organized to test how easy it was to generate reproducible data on NP-induced toxicity using standard operating procedures (SOPs), and define processes to enhance the proficiency of nanosafety research laboratories in achieving reliable NM toxicity data. We have employed the in vitro 3-(4,5-dimethylthiazol-2-yl)-5-(3-carboxymethoxyphenyl)-2-(4-sulfophenyl)-2H-tetrazolium (MTS) cytotoxicity assay of the tetrazolium salt reduction-type [32] as a benchmark, which has been addressed before by other ILC consortia consisting of 8 [33], 5 [34] or 6 [35] independent laboratories. Briefly, SOPs were generated by laboratories with previous experience on this assay, as well as in working according to the principles of GLP. The SOP for the MTS assay, including the 96-well plate lay-out and performance criteria, was in agreement with Rösslein et al. [36] and the recently published ISO 19007:2018 standard [37]. Cells, serum and NPs, all from the same batches, were shared among the participating laboratories across Europe, which were enrolled based on the outcome of a first tier on cell culturing proficiency. The second tier was a first MTS inter-laboratory comparison study. Its results, followed by a questionnaire sent to all participants to collect more information on how the procedure was followed, clearly highlighted the need for further optimization of the developed SOP, but also the need of a more precise training in executing this kind of standardized testing. After training of the participants, the third tier consisted of a new round of the MTS assay using the revised SOP. The final results showed a strong reduction in the variability within and across laboratories. Furthermore, our data endorsed the potential of amine-modified polystyrene nanoparticles (PS-NH2) and carboxyl-modified polystyrene nanoparticles (PS-COOH) as positive and negative control nanomaterials, respectively, for cytotoxicity assessment using the MTS assay.

#### **2. Materials and Methods**

#### *2.1. Recruitment of Laboratories*

Laboratories involved in nanosafety research across Europe that were (associated) partners of the QualityNano Research Infrastructure consortium were invited to the inter-laboratory comparison study. Before the start of the study, an online questionnaire was sent out to the candidate laboratories to inquire about and evaluate them against criteria mentioned in ISO/IEC 17043:2010 'Conformity assessment—general requirements for proficiency testing' [38], including: experience in biological assessments of NMs or in performing tests similar to the proposed method and/or cell model; the availability of the technical requirements for accommodation, environmental conditions and endpoint measurements; high quality standards for biological testing implemented, such as good cell culture practice or GLP; and trained personnel. In total, 15 laboratories from academia (33.3%), research organizations (60.0%) and industry (6.7%) joined the study. The majority of them were not familiar with the requested high-quality standards that have been developed primarily for regulatory testing or method validation. Moreover, the test performers had varying qualifications (lab technician, PhD student or post-doctoral scientist) and a varied numbers of years of experience in biological testing (from a few months to over 20 years). In most cases, they were not trained in the proposed SOPs.

#### *2.2. Choice of Cytotoxicity Test and Materials Used*

As mentioned above, we selected the in vitro MTS cytotoxicity assay as a benchmark assay to evaluate and improve laboratories' proficiency. The CellTiter 96® Aqueous One assay (Promega, Leiden, The Netherlands) used in this study has been identified as superior to other cell viability assays for NP assessment [39]. The assay has originally been developed by Tim Mosmann [32] for the measurement of cell viability using 3-(4,5-dimethylthiazol-2-yl)-2,5-diphenyltetrazolium bromide (MTT). Soluble tetrazolium salts, including 3-(4,5-dimethylthiazol-2-yl)-5-(3-carboxymethoxyphenyl)- 2-(4-sulfophenyl)-2H-tetrazolium (MTS), have later been developed, of which the tetrazolium ring is reduced by cellular nicotinamide adenine dinucleotide (phosphate)-dependent oxidoreductase enzymes in the presence of an intermediate electron acceptor (phenazine ethosulfate), to form a formazan derivative that is quantified in a spectrophotometer. The product therefore reflects the metabolic activity and by extension viability of cells, and, hence, can be used to determine a toxic dose of a substance. Information on the cytotoxicity of a substance in alternative methods using cell culture is a crucial first screening step to any more detailed investigation, or as part of a safe-by-design approach. Test methods to assess the in vitro cytotoxicity of medical devices have been described in ISO 10993-5:2009 [40]. A standard specifically dedicated to nanomaterials using the MTS assay as an in vitro cytotoxicity assay has more recently been published [37].

The human A549 alveolar epithelial cell line was chosen as a cell type representing the respiratory route of exposure and a major site of deposition of small nanoparticles [41,42]. Moreover, this cell model is widely used in nanosafety laboratories and easy to maintain, and therefore suitable for standardization among different laboratories.

Alternative methods for toxicity testing require substances with known potential, either positive or negative control substances, which are crucial as quality controls in SOPs. So far, several NM controls have been suggested for in vitro nanoparticle toxicity studies, including tungsten carbide-cobalt [43] as positive, and barium sulfate [44] and carboxylated nanodiamonds [45] as negative controls. As is true for chemicals, any NM that would be used as a control for a specific assay needs to be thoroughly characterized with respect to its physico-chemical properties, and its performance in this assay also needs to be well described. However, for NMs special care has to be taken, because of possible batch-to-batch variation, contamination and long-term stability [46]. In our study, we have selected 50-nm amine-modified polystyrene NPs (PS-NH2) and 40-nm carboxyl-modified polystyrene NPs (PS-COOH), which are known to form stable dispersions when diluted in cell culture medium [47–49]. Furthermore, their impact on cells has been characterized in detail [22,26,48,50–52]. Thus, they constituted ideal candidates as starting materials for this ILC study.

#### *2.3. Standardization Procedures and SOP Development*

Standard operating procedures for cell culturing and cell growth rate determination (tier 1), and cytotoxicity assessment using the MTS assay (tier 2 and 3) were adopted from existing protocols, and adapted to implement the spirit of Good Cell Culture Practice [53] and GLP [13], and to be in line with the ISO 19007:2018(E) standard [37]. Forms for detailed registration of performance of the protocol steps were prepared and filled in by the partner laboratories, to make it possible to formulate corrective actions in case of deviations in a laboratory's results. For data analysis and reporting, spreadsheet (Microsoft Excel) templates and web forms were prepared and distributed to the laboratories. These enabled automated calculations and immediate evaluation of compliance with the test acceptance criteria as formulated in the SOPs. After a first ILC on the MTS assay (tier 2) with the developed SOPs, further changes were made to the MTS assay SOP, and forms based on the feedback from the participating laboratories through an online questionnaire. The optimized SOPs and the spreadsheet templates are available in Supplementary Materials.

To enhance standardization in cell growth and test performance, all laboratories used the most critical materials, such as fetal bovine serum and test NPs from a centrally held stock prepared at one location. Fresh aliquots were shipped to the laboratories prior to the start of the studies. In addition, identical frozen cell stocks of the human A549 alveolar epithelial cell line were obtained by the participating laboratories from a central laboratory, which purchased a parent cell line (ATCC, CCL-185, passage number 82) and subcultured the cells up to a master cell bank (passage number

88). A harmonized SOP for thawing, freezing and subculturing of the A549 cell line, and testing for mycoplasma contamination according to an in-house protocol as an essential quality control before freezing the cells, was developed and used by the study partners to generate their own working cell bank. Other critical reagents, such as cell culturing reagents, MTS reagent and staurosporine were used from the same supplier by the laboratories.

#### *2.4. Nanoparticles and Chemical Control*

PS-NH2 (50 nm) and fluorescently labelled PS-COOH (40 nm) were purchased from Bangs Laboratories Inc. (Fishers, IN, USA; catalogue number PA02N) and Molecular Probes (ThermoFisher Scientific, Bio-Sciences, Dublin, Ireland; catalogue number F8795), respectively. Aliquots of diluted NPs (10 mg/mL) in milliQ water (resistivity of 18.2 mΩ.cm at 25 ◦C) were prepared centrally, distributed among all participants, and stored at 4 ◦C. Dispersions of the NPs of 100 µg/mL in complete cell culture medium (CCM) containing minimal essential medium with GlutaMAX™ (Gibco®, Life Technologies, Paisley, UK), supplemented with 10% (*v*/*v*) non-heat inactivated fetal bovine serum (FBS; Gibco®, Life Technologies, Paisley, UK), 100 U/mL penicillin and 100 µg/mL streptomycin (Gibco®, Invitrogen, Paisley, UK) were prepared by each laboratory. This was done by pipetting 20 µL of the NP stock suspensions in 1980 µL medium and mixing on a vortex for 30 s.

Staurosporine (Proteinkinase, Biaffin GmbH & Co., KG, Kassel, Germany; catalogue number PKI-STSP-001) was used as a positive chemical control to serve as an internal control of the biological cell response and MTS assay performance. As this compound was observed to become instable and lose its activity during transport and storage when prepared as solution, all laboratories were asked to purchase their own lot of lyophilized powder from the same company and with the same batch number. A stock solution of 1 mM staurosporine was prepared by dissolving the powder in 214 µL dimethyl sulfoxide (DMSO) and mixing on a vortex, followed by immediate further dilution to 2140 µL DMSO. Aliquots of 50 µL stock solution were stored at −80 ◦C without loss of activity for at least 6 months, and a fresh tube was thawed for each experiment. Solutions of 1000 nM staurosporine were prepared in complete CCM containing 1% milliQ water to ensure identical vehicle to the NPs. Details on the test item preparations are described in the SOP for the MTS cytotoxicity assay (Supplementary Materials).

#### *2.5. Cell Culture and Exposure*

Human A549 alveolar epithelial cells were maintained in complete CCM without antibiotics. The cells were subcultured every 3–4 days when the cell monolayer reached 70–80% confluence, with medium renewal after 2 days. Cells were used for testing up to passage number 20, to ensure equally low passage numbers among experiments and laboratories. Cells were used at >90% viability. Details are in the SOP for A549 cell culturing in Supplementary Materials.

Cell growth curves were obtained as detailed in the SOP for assessment of A549 cell growth rate and viability (Supplementary Materials) by determining the cell number and viability by trypan blue exclusion at 24 h, 48 h and 72 h after seeding.

Assessment of NP-induced cytotoxicity by the MTS assay was performed in 96-well plates, in which 200 µL of cell suspension were seeded per well, followed by 24 h incubation at 37 ◦C, 5% CO2 to allow for cell adhesion prior to exposure to the test items. The dosing plate layout, shown in Figure S1, contained three replicate series of 6 concentrations of either the PS-NH2 or PS-COOH NPs (0; 1; 10; 25; 50 and 100 µg/mL) and 6 concentrations of staurosporine (0; 62.5; 125; 250; 500; and 1000 nM) as a positive chemical control, in addition to six replicate wells for each of 4 assay controls, according to Rösslein et al. [36]. Triplicate dilution series of the NPs and staurosporine were each started from a separate preparation of the highest test concentration, in order to estimate within-laboratory variability in the preparation. Dose series of the test items were obtained by serial dilutions in complete CCM supplemented with antibiotics, and containing 1% milliQ water to ensure identical vehicle in each well. Assay controls consisted of untreated cells in complete CCM with 1% milliQ water, blank wells (no cells) containing complete CCM with 1% milliQ water, and blank wells (no cells) containing the highest

test concentrations of the NPs and staurosporine to control for potential interference with the assay read-out measurement. Cells were exposed by the removal of the medium from each well and transfer of 100 µL of the chemical and NP doses from the dosing plate, and, again, incubated for 24 h. All details and plate layouts can be found in the SOP for the MTS cytotoxicity assay (Supplementary Materials).

#### *2.6. MTS Assay*

At the end of the incubation of cells with the NPs and staurosporine doses, the medium was removed from each well and replaced with 150 µL of diluted CellTiter 96® AQueous One Solution Reagent containing MTS and the electron coupling reagent phenazine ethosulfate (Promega, Leiden, The Netherlands). Cell plates were incubated at 37 ◦C and 5% CO2 for 1 h, to allow for bioreduction of the tetrazolium compound to a colored, soluble formazan product. The absorbance of the product at 490 nm, which is directly proportional to the number of living cells in culture was recorded using a spectrophotometer. Measurements were performed in the presence of the cells, and—after SOP optimization—after transfer of 100 µL of the colored reagent from the cell plate into a new 96-well plate, to ensure absorbance read-out in the linear dynamic range relevant to the Lambert-Beer law.

#### *2.7. Statistical Data Analysis and Proficiency Testing*

At least 3 independent runs executed on different days using cell cultures of different passage numbers were performed for cell growth rate determination (tier 1) and the MTS assay (tier 2 and 3). An initial analysis of the raw data was done by the individual laboratories using a spreadsheet calculation template enabling immediate evaluation of compliance with the test acceptance criteria mentioned in the SOPs, as well as automated data analysis and plotting (Supplementary Materials). Only data sets from fully acceptable tests were considered valid to be included in the final statistical analysis.

In the determination of cell growth rate, the total cell counts and percentages of cell viability were calculated, based on the live and dead cell numbers. A curve presenting the live cell counts at different growth times (24, 48 and 72 h) was used for exponential fitting (Figure S2). The prefactor of the exponential power from the resulting equation indicated the relative growth rate (doublings per hour), and was used to calculate the cell doubling time (hours).

In a run of the MTS assay, the percentage cell survival was calculated as the fraction of cells that remained viable after treatment, by subtracting the average background absorbance of medium blank wells (Figure S1, column 7) from each raw absorbance value, and normalizing the resulting values to the average absorbance of untreated cells. The triplicate values from a single dose were used to calculate a mean value and standard deviation (SD).

The statistical analysis of the MTS assay data from all laboratories was automated using in-house programming in R software [54]. Fitted sigmoidal curves with the upper limit fixed to 100 and lower limit to 0 were generated per run based on the dose-response data, and used to calculate the effective concentrations causing 30% (EC30, for staurosporine) or 50% (EC50, for PS-NH2) inhibition of cell viability. For curve fitting the R package drc was used [55] and the four-parameter logistic model with parameters b, c, d, e:

$$\text{if } (\mathbf{x}, (\mathbf{b}, \mathbf{c}, \mathbf{d}, \mathbf{e})) = \mathbf{c} + (\mathbf{d} - \mathbf{c}) / (1 + \exp[\mathbf{b} \left( \log(\mathbf{x}) - \log(\mathbf{e}) \right)] \tag{1}$$

Parameter e corresponds to the EC30 or EC50, whereas parameter b denotes the relative slope around e. The logistic function is symmetric around e. Examples of resulting dose-response curves per run of two different laboratories are shown in Figure S3.

For proficiency testing by inter-laboratory comparisons, statistical methods according to ISO 13258:2005(E) were followed in this study. More specifically, a robust statistical approach described in algorithm A [56] was applied to calculate robust values of the mean and SD of cell doubling times, % cell survival per dose, and EC30 (or EC50) from at least 3 independent runs reported by the individual participants in a round of the proficiency testing scheme. Next, the robust overall mean and SD

from the data of all laboratories were calculated, starting from the robust mean data per lab using the algorithm A. Additionally, for the MTS data, based on the robust within-laboratory SD for each dose or EC30 (or EC50), a robust overall SD\* was calculated using algorithm S [56], which yields a robust pooled value of the SD values, to which it is applied. Intra- and inter-laboratory biases were interpreted on the basis of overall mean and SD values derived from all laboratories. We concluded that a laboratory was proficient if the robust within-laboratory mean value did not exceed the overall mean with more than 2-fold the overall SD. Similarly, if the SD values of one laboratory were within the range of twice the overall SD\*, then the laboratory bias at the individual dose or EC30 (or EC50) level was considered acceptable.

Finally, a coefficient of variation (CV) was calculated to evaluate the reproducibility of assay performance as: (robust SD/robust mean) × 100%. Variability of biological test results within or between laboratories was considered acceptable if CV < 30%.

#### **3. Results**

#### *3.1. Determination of Cell Growth Rate (Tier 1)*

In a first tier of the study, an SOP on A549 cell culturing and an SOP on determination of cell growth rate and viability (Supplementary Materials) were used by the 15 participating laboratories, to assess variability in the cell growth characteristics. Cell cultures for independent runs were started from independent cell vials of the working cell bank and tested for the absence of mycoplasma infection using in-house procedures. Relative cell viability was tested by trypan blue exclusion and was observed to fulfil the acceptance criterion (>90%) mentioned in the cell culturing SOP in the different laboratories. The mean cell doubling time derived from individual cell growth curves of all laboratories was 24.9 ± 2.4 h, and showed good agreement within and between the participating laboratories. Each laboratory produced at least two independent measurements, resulting in a mean doubling time within the boundaries of the overall mean ± 2SD, which indicated that they were all proficient in cell culturing (Figure 1). The largest variability was observed within laboratory 15, which obtained a mean cell doubling time of 28.4 ± 3.2 h.

**Figure 1.** Inter-laboratory evaluation of growth rate of the A549 cell line (tier 1). Three (some 2) independent runs (indicated with open circles) were performed in each laboratory to determine the cell doubling time. The mean of each lab (filled circles), and overall mean (N = 15; black solid line) with 1-, 2- or 3-fold of the overall standard deviation (SD) (black dotted lines) are indicated.

#### *3.2. Assessment of Laboratories' Inherent Proficiency in Performing the In Vitro MTS Assay (Tier 2)*

After benchmarking and confirming the proficiency of the laboratories in their cell culturing performance, they were enrolled in a second tier of the inter-laboratory comparison study involving the transfer of the SOP on the MTS assay. Here, we aimed to evaluate the inherent proficiency of each participating laboratory, and identify critical phases in the SOP that introduce bias in testing, to allow further optimization of the SOP. For this study tier, the laboratories performed independent runs using cells of different passage number from the same working cell bank vial. Results on cytotoxicity assessment of the positive chemical control staurosporine showed that eight out of 15 laboratories reported on at least three independent and valid runs, based on assessment against the test acceptance criteria mentioned in the SOP. For the tests with PS-COOH and PS-NH2, however, only five and four out of 15 participating laboratories, respectively, were able to generate at least three valid runs (Table 1). The most frequent reason for this low rate of valid runs was a deviation of more than 15% in the absorbance values from triplicate cultures treated with zero dose of the test item (staurosporine or NPs; Figure S1, row B), as compared to untreated cultures (Figure S1, column 6). This can be attributed to the differences in seeded cell numbers or cell densities in the respective wells of the multi-well plate, which may have multiple causes, such as the poor resuspension of cells while seeding, inaccurate pipetting volumes, wrong pipetting technique, etc. Furthermore, about half of the laboratories observed interference of the PS-NH2 with the absorbance read-out for more than 15% compared to blank wells containing no NPs, which was set as a limit for acceptance of the test. More detailed data investigation revealed that this was mainly due to high variability (CV >30%) between replicate wells of the NP blank within these laboratories.

**Table 1.** Compliance of laboratories with the acceptance criteria of the 3-(4,5-dimethylthiazol-2-yl)-5- (3-carboxymethoxyphenyl)-2-(4-sulfophenyl)-2H-tetrazolium (MTS) cytotoxicity assay. The number of compliant laboratories compared to the total number of laboratories is indicated per test item.

1 n/a, not applicable; 2 CV, coefficient of variation.

The dose-response data of staurosporine and NP-induced cytotoxicity in A549 cells were evaluated using robust statistical methods. Examples of dose-response data from single laboratories are shown in Figure S3. Dose-dependent decrease in percentage cell survival was observed for staurosporine and PS-NH2, whereas PS-COOH did not affect cell viability as expected. Intra- and inter-laboratory biases were calculated, while including either all data from both valid and non-valid runs of all laboratories (Figure S4), or only the data from the laboratories with at least three valid runs (Figure 2).

Overall variability in the dose-response data, represented by the SD of the mean % cell survival of all laboratories and the SD\*, which represents a pooled value of the within-laboratories' SD, was observed to decrease when non-valid runs were discarded, indicating improved inter-laboratory reproducibility. At the same time, the number of laboratories with an intra-laboratory bias exceeding the overall mean plus 2-fold of the overall SD decreased (Figure 2 vs. Figure S4). These findings highlight the need to apply acceptance criteria as quality measures to the biological test performance to enhance reproducibility of results and hence standardization.

**Figure 2.** Intra- and inter-laboratory biases for percentage cell survival determined using the MTS assay based on valid runs (tier 2). Mean percentage cell survival per dose and per laboratory compared to the overall mean and SD (left panels), as well as SD of percentage cell survival per dose and per laboratory compared to the overall SD\* (right panels) are shown for (**A**,**B**) staurosporine (N = 8), (**C**,**D**) carboxyl-modified polystyrene nanoparticles (PS-COOH) (N = 5) and (**E**,**F**) amine-modified polystyrene nanoparticles (PS-NH2) (N = 4). Horizontal bars (left panel) indicate overall mean values of percentage cell survival, while grey shaded areas indicate the distances from the overall mean corresponding to 1-, 2- or 3-fold the overall SD (left panels) or SD\* (right panels).

To further assess the intra- and inter-laboratory bias of toxicity values obtained in the different laboratories, EC30 and EC50 values were derived from the fitted dose-response curves resulting from cell exposures to staurosporine and PS-NH2, respectively. Although an EC50 value can usually be derived more accurately than an EC30 value, approximately half of the participants were not able to observe 50% inhibition of cell viability at 1000 nM staurosporine. Therefore, the EC30 of staurosporine was reported and compared, since it corresponds to the threshold value for concluding on cytotoxicity according to ISO 10993-5:2009 [40]. When both non-valid and valid runs were taken into account, mean fitted EC30 of 329.7 ± 182.0 nM staurosporine and EC50 of 76.2 ± 20.4 µg/mL PS-NH2 were observed, with respective CVs of 55.2% for staurosporine and 26.8% for PS-NH2. These effective concentrations changed to an EC30 of 261.0 ± 121.1 nM staurosporine and EC50 of 76.2 ± 12.9 µg/mL PS-NH2 (Figure 3A,C) when only valid runs were considered, resulting in decreased CVs of 46.4% for staurosporine and 16.9% for PS-NH2, respectively. By excluding non-valid data the overall variability SD\* of the EC50 values determined for PS-NH2 exposed cells was again decreased (10.6 for non-valid and valid data, vs. 6.9 for valid data; Figure 3D), indicating that intra-laboratory performances were also improved. In contrast, this improvement was not observed for EC30 determinations in tests with staurosporine (SD\* of 78.7 vs. 84.6; Figure 3B), which, in general, showed lower reproducibility within and between laboratories. The latter is in agreement with the low stability of staurosporine in solution we observed during the study, and, therefore, other stable compounds, such as cadmium sulfate [34,37] are more suitable as a positive control.

#### **A. Staurosporine B.**

**Figure 3.** Intra- and inter-laboratory biases for effective concentration causing 30% inhibition of cell viability (EC30) and effective concentration causing 50% inhibition of cell viability (EC50) values determined using the MTS assay (tier 2). Mean values for EC30 and EC50 per laboratory compared to the overall mean and SD (left panels), as well as SD of EC30 and EC50 values per laboratory compared to the overall SD\* (right panels) are shown for (**A**,**B**) staurosporine (N = 15 for all runs, N = 8 for valid runs) and (**C**,**D**) PS-NH2 (N = 15 for all runs, N = 4 for valid runs). Horizontal bars (left panel) indicate overall mean values of percentage cell survival, while grey shaded areas indicate the distances from the overall mean corresponding to 1-, 2- or 3-fold of the overall SD (left panels) or SD\* (right panels).

Although variabilities were, in most cases, decreased when only valid results were included, these were still high (at the limit of what is acceptable) and warranted further investigation of possible causes and refinement of the benchmarking process. Thus, we examined in more detail if these first MTS experiments could highlight sensitive steps in the applied procedure, which introduced variability in the reported outcomes. This was done by collecting feedback on the interpretation of the SOP and performance of the MTS test by the participating laboratories using an online questionnaire. A list of critical steps that were reported in this survey is included in Table 2. These steps, including accurate pipetting volumes and seeding cell densities, were found to be quite similar to those already described in Rösslein et al. [36] and Elliott et al. [34].

**Table 2.** Critical steps in the standard operating procedure (SOP) for the MTS cytotoxicity assay. Following the analysis of the first data obtained for cells treated with staurosporine, PS-NH2 and PS-COOH, and the collection of responses of the participating laboratories to an online questionnaire on SOP interpretation and test performance, sensitive steps in the SOP that could generate and explain the observed variability of outcomes have been individuated.

Based on a thorough examination of the data and statistical analysis, and the collected feedback on critical phases in the test performance, we further optimized the SOP of the MTS cytotoxicity assay (available in the Supplementary Materials). One aspect concerned the possible contribution of cells and NPs present during assay read-out to variability in the absorbance values, frequently causing them to exceed a value of 2.0, which is outside the linear dynamic range that is relevant to applying the Lambert-Beer law, as previously suggested by Xia et al. [33]. Therefore, in the revised SOP, laboratories were asked to read out the assay plates in the presence and absence of cells. Other critical steps in the test performance which cannot simply be addressed in the SOP, such as the verification of instruments or pipetting techniques, constituted major challenges to be faced in this field of research, to ensure quality of results. This led us to organize a focused training to resolve these issues. Additionally, the majority of participating laboratories indicated that they were not familiar with the principles of an inter-laboratory comparison, as exemplified by the submission of a large number of non-valid data. Training was thus held by well documented instructions and a teleconference to introduce the relevant principles of GLP and proficiency testing, to transfer the optimized SOP and quality criteria, and to discuss in detail good practices for enhancing the test performance. Subsequently, the final tier of the ILC study was launched.

#### *3.3. Laboratories' Proficiency in Performing the In Vitro MTS Assay After Training (Tier 3)*

In the third and final tier of the study, six laboratories were enrolled, of which five also had participated in the first phase (tier 1 and 2) with involvement of the same operator, and four had not been able to provide a full set of valid data in the second tier. The other laboratories dropped out mainly because of a shift or lack of resources at the time of the last tier. The participating laboratories were provided with fresh FBS and NPs, and used the optimized MTS assay SOP. All laboratories sent in data on three independent, valid runs performed on cells of different passage number from the same working cell bank vial. The dose-response data were evaluated for intra- and inter-laboratory biases, similar to the statistical analyses in tier 2 (Figures S5–S7). In Table 3, the mean ± SD values of EC30 and EC50 obtained by the six laboratories involved in the study before and after training are presented, together with the overall mean ± SD values of all laboratories.

**Table 3.** Comparison of intra- and inter-laboratory biases for EC30 and EC50 values before (tier 2) and after (tier 3) training in quality aspects of MTS assay performance. Individual data from the six laboratories that were trained, as well as the overall data of all laboratories (N; in bold and between brackets) are included. Mean and SD (and SD\*, in bold and between brackets) values of calculated EC30 (nM staurosporine) and EC50 (µg/mL PS-NH2), coefficient of variation (CV) (%) and number of runs (n) are given. For tier 2, data from all (valid and non-valid) runs and valid runs only are indicated. Tier 3 data are presented in the presence and absence of cells. '-' indicates that no data were available, laboratory 6 participated only in tier 3.

When considering the entire data sets obtained in tier 2 (valid and non-valid runs) and tier 3 with read-out of the MTS assay in the presence of cells, the inter-laboratory variability assessed by means of CV (%) of all laboratories before (tier 2, N = 15) and after (tier 3, N = 6) training showed improvement due to training (respectively 55.2% compared to 49.6% for staurosporine, and 26.8% compared to 23.0% for PS-NH2). Furthermore, the reproducibility within the laboratories was found to be increased after training, which is obvious from the decrease in CV per laboratory (example of dose-response curves in Figure S8), as well as the decrease of overall SD\* in the case of staurosporine (Table 3). Finally, reading out the MTS assay in the absence of cells resulted in a mean fitted EC30 of 264.3 ± 140.0 nM staurosporine and EC50 of 80.7 ± 22.8 µg/mL PS-NH2. Although this additional step did not further decrease the inter-laboratory variability (CV of all laboratories of 53.0% for staurosporine, and 28.2% for PS-NH2), it did further improve intra-laboratory performances for most laboratories as compared to cells present in the wells, evident again by decreased CV of individual laboratories and SD\* (Figure S6 vs. Figure S5, Figure S7, and Table 3). Taking into account also the fact that in the second tier the majority of laboratories sent in non-valid run data (Table 1), because they were ignorant of good practice and ILC principles, these comparative analyses show that training improved the laboratories' proficiencies and ILC results.

#### **4. Discussion**

Nanotoxicological data reported in the literature have shown low reproducibility within and between different studies, and, consequently, conflicting conclusions. In the current ILC study, we aimed to tackle this problem by introducing SOPs for cytotoxicity testing via the MTS assay, using previously well characterized control NMs. In particular, we have evaluated the impact of training in these SOPs and in good laboratory practices on the proficiency of the participating laboratories.

The ILC study was composed of three tiers, including cell culturing and cell growth rate determination (tier 1), MTS assay for cytotoxicity measurement of NMs to evaluate the laboratories' inherent performance (tier 2) and, finally, a repetition of the MTS assay after training of the laboratories (tier 3). The process applied in this study has highlighted the degree of complexity that is related to good practice in nanosafety testing, where multiple consecutive steps of the workflow should all be tightly aligned, including material storage, NP dispersion, cell culture, cell seeding and exposure, and test performance. This has also been concluded in a round robin study performed by different partner laboratories of the German Priority Programme SPP1313, who observed that small variations in NP preparation, cell handling and the type of culture slide influenced NP stability and the outcomes of cell assays [57]. In another ILC study on the MTS cytotoxicity assay [34], system control measurements revealed similar steps in the protocol that are critical to ensure overall robustness and reproducibility of the assay results within and between laboratories. These factors have also been taken into account in ISO 19007:2018(E) [37].

In contrast to previous interlaboratory studies in the nanosafety research field, we here demonstrated that the availability of an optimized SOP in combination with training and the active implementation of good practice in test performance enhanced the quality of intra-laboratory test results, and improved inter-laboratory variability. The ILC study has also highlighted that principles of GLP, including the use of verified instruments and registration of each step in the execution, may provide guidance to enhance quality of results from in vitro toxicity assays. The developed SOPs for cell culture, cell growth rate determination and the MTS assay for the cytotoxicity assessment of NMs, were compiled to cover the different categories of test facility activities, including equipment, media and reagents, test and control items, the consecutive steps of the experimental protocol in a chronological order and data analysis. Furthermore, the SOPs included acceptance criteria to monitor test performance, forms to record the laboratory performance and observations, and calculation templates for the reporting of the test data. Each of these categories contained sufficient and explicit detail to ensure proper execution. Based on feedback collected from the participating laboratories through an online survey, however, we found that despite the availability of SOPs, deviations in the execution were frequently reported, for example, in the verification of laboratory instruments or the addition of antibiotics to the cell culture medium. Additionally, basic laboratory practices, such as pipetting techniques, which are not normally made explicit in SOPs were identified as a potential source of variability, in addition to influences from differences in technical infrastructure. This overall complexity accounts for the conflicting results on NPs' toxicity in the reported literature, and calls for increasing awareness and proper training of nanosafety professionals. Based on the feedback received after the second tier, the MTS assay SOP was revised to account for the identified hidden sources of variability, and additional training was provided to ensure all participating laboratories were familiar with the procedures and carefully followed all steps as detailed in the SOP. The results of the second ILC on the MTS assay showed that this process makes it possible to obtain higher reproducibility across independent laboratories from academia, research institutions and industry. The NIEHS Nano GO Consortium [33] has previously investigated NMs in several bioassays, including the MTS assay. They also found substantial variations in the results between different laboratories, and further stated that "frequent communication was very helpful for achieving reproducible results within and among the laboratories". However, no further details regarding the improvement of reproducibility were given. As similar issues related to the quality in test performance discussed in our study are also present among different operators within the same laboratory, it may be valuable to implement a similar process using shared materials as described here within laboratories, to determine and align the reproducibility in performance by multiple operators. A representative selection of positive and negative control materials, as well as SOPs with quality acceptance criteria and statistical data analysis, will make it possible to judge whether the data generated by multiple operators, even within a single laboratory, are reliable or not.

As a secondary outcome of this ILC study, we confirmed the suitability of PS-COOH and PS-NH2 as negative and positive control nanomaterials, respectively, to validate the test performance of the MTS assay. The PS-COOH NPs are complementary to other suggested negative nano-sized control materials for in vitro cell viability assays, such as barium sulfate [44] and carboxylated nanodiamonds [45]. Positively charged PS-NH2 NPs have already been included as validated positive control nanomaterials in the ISO 19007:2018(E) standard [37]. Based on the dose-response data obtained for the PS-NH2 in our large ILC study comprising a representative sample of research laboratories, an EC50 value of 80.7 ± 22.8 µg/mL was obtained which is exactly within the commonly accepted biological variability window of CV <30%. This EC50 for PS-NH2 was higher than the consensus EC50 value of 52.6 µg/mL (95% confidence intervals 44.1 to 62.6 µg/mL) for the same NPs in the A549 cell line reported by Elliott et al. [34], which can be attributed to differences in cell stocks, serum sources, cell seeding density and exposure times (24 vs. 48 h, respectively). Nevertheless, variability in the EC50 values in both studies was in a similar range and well below 30%, confirming the suitability of 50-nm PS-NH2 as positive control NPs in the MTS cytotoxicity assay. However, it should be mentioned that, in both studies, PS-NH2 from Bangs Laboratories Inc. have been used, which were not available anymore from the supplier at the time of this report. Therefore, to enable standardization in nanosafety testing, stable and effective reference NPs from a secured source are urgently needed.

We also stress that for this study, in order to focus—as a first step—on the sources of variability related to cell toxicity testing, we selected model PS NPs behaving well in terms of stability and dispersion, thus, many of the reproducibility issues highlighted in our study are not nano-specific. Reproducibility in in vitro nanotoxicity testing goes far beyond this simplistic view, as real nanoparticles, such as, for instance, metal oxides, introduce many more challenges related to their intrinsic (medium independent) and extrinsic (medium dependent) physical and chemical properties, which affect their dispersion and stability in cell culture medium, and—as a consequence of this—their fate and transport into cells, and, thus, the dose delivered to cells as a function of exposure time [58,59]. For example, surface affinity, which is dependent on particle and medium parameters, may cause the agglomeration or aggregation of nanomaterials, whereas particle size and density can affect the diffusion and sedimentation of NPs, thus affecting their in vitro exposure. Furthermore, especially for partially soluble NPs, such as metal and metal oxides, the rate of release of ions is depending on system parameters (e.g., pH, ligands present, flow conditions, etc.) and can greatly influence their in vitro toxicity potential. To allow for the correct interpretation of in vitro assay data using NPs closer to real applications, as opposed to model NPs with optimal dispersibility, standard protocols for the dispersion of nanomaterials in complex media, dispersion characterization and dosimetry, as described by Deloid et al. [59] and in ISO/TR 16196:2016 [60], should be adopted to ensure meaningful and reproducible quantification of in vitro delivered dose. The standardization of characterization methods to monitor the physicochemical properties before and after NPs' dispersion is also work in progress, e.g., by the OECD Series on the Safety of Manufactured Nanomaterials [58] and ISO/TC229 (Nanotechnologies), and has been subject of several ILC studies. Remarkably, in ILC studies in which

model nanoparticles as polystyrene, gold or silica nanospheres have been used, for example those looking at size distribution measurements using nanoparticle tracking analysis [61], dynamic light scattering and centrifugal particle sedimentation [49], similar issues in reproducibility compared to our study have been reported, even for simple dispersions in buffer. They concluded that SOPs are indispensable for obtaining reliable and comparable NP size data, and should be tailored to the specific test system, sufficiently detailed and verified by a larger number of laboratories, to enable reproducible performance [49,61].

Other NP types than polystyrene have already been applied by others in the MTS assay, such as titanium dioxide, zinc oxide and multi-walled carbon nanotubes [33]. In addition to selecting the best protocol for NPs' dispersion and verification of the dispersion stability in complete CCM, as indicated above, these will require a case-by-case consideration of interference of the NPs with the optical density measurements. Although, initially, a number of laboratories observed optical interference of PS-NH2 with the MTS test read-out in our study (tier 2), we showed that, after good practice training and diligent application of the optimized SOP, this bias was resolved. This may be attributed to detailed instructions on NP dispersion in serum-containing medium and the use of the same serum batch by all laboratories. In addition, we introduced in the SOP a transfer of MTS medium to a new plate before optical read-out to avoid high absorbance contribution of the cells and NPs remaining in the wells. In this respect, our SOP deviates from the ILC study of Elliott et al. [34] and the ISO 19007:2018(E) standard [37], in which the average background absorbance level of the NP doses in culture medium are subtracted from each absorbance value of NP-exposed cells. In contrast, we recommend using an alternative cytotoxicity assay, based on a different optical read-out principle, in case NP interference is observed, as the correct assessment of the issue and adjustment is hampered by multiple factors, such as NP agglomeration, NP adherence to the cell surface or assay plate, cell-dependent NP uptake kinetics, etc.

Finally, it remains to be investigated whether the MTS assay SOP developed and tested by multiple laboratories in our study can be transferred to other cell types. Based on our experience, the SOP works optimally for adherent cell types growing in monolayers. The SOP can be adapted for use with suspension cells by introducing centrifugation steps to pellet the cells at the bottom of the wells before medium changes.

In conclusion, the experience reported in this study overall clearly indicates that the approach followed in this study, including inter-laboratory comparison studies using shared materials and detailed SOPs, together with training of the participants, can be used to optimize and generate robust SOPs, and to obtain reproducible data on NP cytotoxicity within and across independent laboratories.

**Supplementary Materials:** The following are available online at http://www.mdpi.com/2079-4991/10/8/1430/s1, Figure S1: Dosing Plate Layout for the MTS assay, Figure S2: Growth curve of the A549 cell line, Figure S3: Dose-response curves of cytotoxicity assessment using the MTS assay (Tier 2) from two individual laboratories, Figure S4: Intra- and inter-laboratory biases for percentage cell survival determined using the MTS assay based on all (valid and non-valid) runs of all laboratories (tier 2), Figure S5: Intra- and inter-laboratory biases for percentage cell survival determined using the MTS assay based on valid runs of laboratories after training in quality aspects of assay performance (tier 3), Figure S6: Intra- and inter-laboratory biases for percentage cell survival determined using the MTS assay based on valid runs of laboratories after training in quality aspects of assay performance (tier 3), Figure S7: Intra- and inter-laboratory biases for EC30 and EC50 values determined using the MTS assay based on valid runs of laboratories after training in quality aspects of assay performance (tier 3), Figure S8: Comparison of intra-laboratory bias in cytotoxicity assessment using the MTS assay before (tier 2) and after training in quality aspects of assay performance (tier 3), Standard Operating Procedure for A549 cell culturing, Standard Operating Procedure for assessment of A549 cell growth rate and viability, Calculation template for assessment of A549 cell growth rate and viability, Standard Operating Procedure for the MTS cytotoxicity assay, Calculation template for the MTS cytotoxicity assay.

**Author Contributions:** Conceptualization, I.N., A.H. (Andrea Haase), A.S. and K.A.D.; methodology, I.N., A.H. (Andrea Haase), S.A., A.J. and A.S.; coordination of ILC studies and training, I.N., A.H. (Andrea Haase) and A.S.; performance of ILC studies and data reporting, I.N., A.H. (Andrea Haase), S.A., L.R., A.J., C.R., A.L., J.-P.P., O.T., B.T., G.L., A.C.G., S.C., S.D., C.W., T.L.-F., Ã.G.-F., M.D., A.H. (Anna Huk), V.S., N.K., M.N., M.S., S.M. (Stefania Meschini), M.G.A., N.L., M.R., M.V., F.B., J.T., T.B., S.M. (Silvia Milani), J.R., A.S., K.A.D.; statistical data analysis and proficiency testing, H.W.; writing—original draft preparation, I.N., A.H. (Andrea Haase) and A.S.;

writing—review and editing, I.N., A.H. (Andrea Haase), S.A., L.R., A.J., H.W., C.R., A.L., J.-P.P., O.T., B.T., G.L., A.C.G., S.C., S.D., C.W., T.L.-F., Ã.G.-F., M.D., A.H. (Anna Huk), V.S., N.K., M.N., M.S., S.M. (Stefania Meschini), M.G.A., N.L., M.R., M.V., F.B., J.T., T.B., S.M. (Silvia Milani), J.R., A.S., K.A.D.; visualization of results, I.N., H.W.; funding acquisition, K.A.D. All authors have read and agreed to the published version of the manuscript.

**Funding:** This research received financial support from the EC FP7 project QualityNano (INFRA-2010-1.1.31- 262163). The contribution of LIST was supported by the Fonds National de la Recherche of Luxembourg within the project NANION (FNR/12/SR/4009651). J.-P. Piret and O. Toussaint acknowledge the DG06 (Direction générale opérationnelle de l'Economie, de l'Emploi et de la Recherche) of the Walloon Region of Belgium for the project complement 'QNano' (2011-14) n◦1117448. O. Toussaint, who unfortunately deceased in 2016, was a senior Research Associate from the Belgian F.R.S.-FNRS. The contribution of NILU was also supported by the NorNANoREG project (Norwegian Research Council, 239199/070). All authors are grateful to their institutions for supporting this work.

**Acknowledgments:** The authors would like to thank Siegfried Hofman (VITO) for advice concerning statistical methods for proficiency testing of laboratories and Christelle Saout (UNamur) for expert discussions on SOP development. We are grateful to Magda Marchetti (ISS), Pavel Rossner (IEM), Zuzana Novakova (IEM), Lisa Wollenberg (BfR), Dorit Mattern (KIT), Maximilian Kohnle (KIT), Kelly Blazy (INERIS) and Christelle Gamez (INERIS) for their excellent technical assistance.

**Conflicts of Interest:** The authors declare no conflict of interest.

#### **Abbreviations**

The following abbreviations are used in the manuscript:

#### **References**

© 2020 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).

*Article*

## **Attachment E**ffi**ciency of Nanomaterials to Algae as an Important Criterion for Ecotoxicity and Grouping**

**Kerstin Hund-Rinke 1,\* , Tim Sinram 1 , Karsten Schlich 1 , Carmen Nickel 2 , Hanna Paula Dickehut 3 , Matthias Schmidt 3 and Dana Kühnel 3**

Received: 6 March 2020; Accepted: 20 May 2020; Published: 27 May 2020

**Abstract:** Engineered nanomaterials (ENMs) based on CeO2 and TiO2 differ in their effects on the unicellular green alga *Raphidocelis subcapitata* but these effects do not reflect the physicochemical parameters that characterize such materials in water and other test media. To determine whether interactions with algae can predict the ecotoxicity of ENMs, we studied the attachment of model compounds (three subtypes of CeO2 and five subtypes of TiO2) to algal cells by light microscopy and electron microscopy. We correlated our observations with EC50 values determined in growth inhibition assays carried out according to the Organisation for Economic Co-operation and Development (OECD) test guideline 201. Light microscopy revealed distinct patterns of ENM attachment to algal cells according to the type of compound, with stronger interactions leading to greater toxicity. This was confirmed by electron microscopy, which allowed the quantitative assessment of particle attachment. Our results indicate that algal extracellular polymeric substances play an important role in the attachment of ENMs, influencing the formation of agglomerates. The attachment parameters in short-term tests predicted the toxicity of CeO2 and TiO2 ENMs and can be considered as a valuable tool for the identification of sets of similar nanoforms as requested by the European Chemicals Agency in the context of grouping and read-across.

**Keywords:** nanotoxicology; European Chemicals Agency (ECHA); ecotoxicology; nanoparticles; aggregation; *Raphidocelis subcapitata*

#### **1. Introduction**

Engineered nanomaterials (ENMs) show great variation in size, shape, crystalline structure, and surface modifications. According to the European Chemicals Agency (ECHA), grouping and read-across approaches can be applied to reduce the number of tests required for the risk assessment of ENMs [1]. ENM groups with analogous sets of physicochemical properties enable reasonable hazard predictions without additional testing, thus saving time and costs. Most concepts for the prediction of ENM properties focus on toxicity in humans [2,3]. Insight into ecotoxicity and grouping has been gained in systematic studies that generated ecotoxicological data for seven chemical species (Ag, ZnO, TiO2, CeO2, Cu, Fe, and SiO2) with 25 modifications [4,5]. Given the focus on regulatory applications, ecotoxicity was based on the Organisation for Economic Co-operation and Development (OECD) test guidelines 201 (algae), 202 (daphnids), and 236 (fish embryos). The studies considered reactivity, ion release, and morphology as properties indicating ecotoxicity. Nevertheless, it was difficult to

separate subtypes of the same chemical species with this grouping approach. The ecotoxicity of TiO2 and CeO2 showed particularly broad ranges of subtype-dependent EC50 values (TiO2 = 0.2–126.9 mg/L, CeO2 = 8.5–99 mg/L). This suggests that additional parameters are needed to improve grouping, such as the adsorption of ENMs to algae [5]. The attachment of nanomaterials to green algae has already been reported [6–9] but these studies have not systematically addressed the attachment of different subtypes of the same ENM (and the relationship with ecotoxicity) or the quantity of ENMs attached to the algal cells.

We therefore investigated the attachment of ENMs to algae in order to determine whether this parameter can improve the results of ecotoxicological grouping. We focused on ENMs based on three subtypes of CeO2 and five subtypes of TiO2 differing in their ecotoxicological impact on algae, representing a subset of nanomaterials that have been comprehensively tested for aquatic and terrestrial ecotoxicity [4,5]. We studied the interaction between the ENMs and algal cells by light microscopy and quantified their behavior by scanning electron microscopy (SEM) with energy-dispersive X-ray spectroscopy (EDX).

#### **2. Materials and Methods**

#### *2.1. Selection of Nanomaterials*

The eight ENMs (three subtypes of CeO2 and five subtypes of TiO2) have been characterized in detail, and their physicochemical properties (and the corresponding analytical methods) are described in the supporting information of two publications [4,5]. The ENMs considered herein originated mainly from the program "Testing a Representative set of Manufactured Nanomaterials" initiated by the OECD Working Party on Manufactured Nanomaterials [10,11]. Selected characteristics of the ENMs are listed in Table 1. Each ENM was used at a concentration of 100 mg/L to determine the agglomerate size, zeta-potential, and reactivity.

**Table 1.** Selected physicochemical characteristics of the eight engineered nanomaterials (ENMs) investigated in this study (for further details see the supporting information in two earlier publications [4,5]).

1 Based on the BET (information provided by the manufacturers). 2 Determined in the Organisation for Economic Co-operation and Development (OECD) test medium [12] used for the growth test with algae. The pH was not adjusted but reached values between 7.0 and 7.4 (values>1.3 indicate reactivity). The values are presented as sample-to-blank ratios (*n*=3).

 3 Measurement of hydroxyl radicals generated after UV irradiation via Fenton-type reactions in the presence of hydrogen peroxide and 5,5-dimethyl-1-pyrroline-*N*-oxide (DMPO) [13,14]. SEM=scanning electron microscopy, TEM=transmission electron microscopy, DLS=dynamic light scattering, BET=Brunauer, Emmett and Teller specific surface area, UV=ultraviolet.

#### *2.2. Preparation of Suspensions*

The ENM suspensions were prepared as previously described [4]. Briefly, a stock suspension of each ENM (1 mg/mL) was prepared in ultrapure water by sonicating for 10 min using a cup horn (Bandelin, Germany) with a final energy input of 0.6 W/mL. A specific amount of the stock suspension was then applied to the test medium to achieve the target concentration for subsequent tests.

#### *2.3. Algal Growth Inhibition Test*

Growth of the green alga *Raphidocelis subcapitata* was measured as set out in the OECD test guideline 201 [12]. The growth rate was calculated by measuring chlorophyll fluorescence in vitro [15], with four replicates per test concentration and eight replicates for the control. Five to six test concentrations with a spacing factor of 2–3 were prepared. Furthermore, growth inhibition was compared in high-ionic-strength Grimme–Broadman (GB) medium and OECD medium (Table 2).

**Table 2.** Composition of Grimme–Broadman (GB) medium [16] and OECD medium [12].

1 Composed of FeSO3; \* 7 H2O and Na2EDTA \* 2 H2O.

#### *2.4. Microscopy*

Particle attachment to algae was observed by light and electron microscopy, the former for the rapid and inexpensive screening of the ENMs and the latter for more detailed tests at the single-cell level. Image evaluation was then used to estimate the coverage of cells by ENM particles, but this was labor-intensive and only a few individual cells could be analyzed per sample, reducing the statistical power. Image analysis was also unable to account for particles attached underneath the cells. The advantages and disadvantages of each method are summarized in Table 3.

#### 2.4.1. Attachment of ENMs to Algae by Light Microscopy

We carried out a short-term test and a growth inhibition test, and in each case observed the algae by light microscopy to investigate their interactions with the ENMs. For the short-term test, an algal culture was incubated in OECD medium (Table 2) until the cell density reached 3–4 million cells/mL. We then transferred 90 mL of this culture to a clean, sterile 250-mL Schott Duran Erlenmeyer flask and added 10 mL of the ENM stock dispersion to achieve a final concentration of 100 mg/L. The flask was then incubated for 3 h under the same conditions as the algal growth inhibition tests before removing samples for analysis. In addition, 3-h spike tests were carried out with fixed concentrations of NM-212

(100 mg/L) and varying algal cell densities (3,000,000, 1,400,000, 700,000 and 175,000 cells/mL). For microscopic analysis during the growth test, algae incubated with selected ENM concentrations were analyzed at the test end.

**Table 3.** Advantages and disadvantages of light and electron microscopy for the observation of particle–cell interactions.

We pipetted 20–50 µL of algal culture (growth tests and spike tests) onto a clean microscope slide and placed a cover glass on top. The slide was air dried at room temperature until the liquid under the cover glass had partially evaporated. We then sealed the edges to prevent further drying (which would cause the cells to shrivel and prevent detailed observation). To avoid the influence of physical or chemical parameters on the attached nanoparticles, we avoided reagents and protocols that might affect the structural integrity of the sample (e.g., paraformaldehyde or heat fixation). The algae were then observed using a Leica Primo Star (Leica Microsystems, Wetzlar, Germany) equipped with three Plan Achromat oil immersion objectives (10×, 40×, and 100×) as well as filters for phase-contrast microscopy. Only objects in the aqueous phase were considered. All images were captured using an AxioCam Erc 5s with an additional 10× lens (Carl Zeiss, Jena, Germany) for final magnifications of 100×, 400×, and 1000×.

#### 2.4.2. Attachment of ENMs to Algae by Electron Microscopy (SEM-EDX)

The algal cells were exposed to 0.1 or 1 mg/L CeO2 NM-212 for 24 h in OECD or GB medium as described above for the growth inhibition test. For each concentration, we collected 5–6 samples of 10 mL. Each algal–ENM suspension was centrifuged for 5 min at 4400× *g* in a Heraeus Megafuge (Heraeus Institute, Hanau, Germany). The supernatant was discarded, and the pellet gently resuspended in 1 mL 4% paraformaldehyde in sodium cacodylate buffer, pH 7.4 (Electron Microscopy Sciences, Hatfield, PA, USA) for 15 h at 4 ◦C. The cells were then deposited onto polycarbonate filters (0.2 µm pore size) and washed for 15 min in sodium cacodylate buffer, before gradual dehydration (transfer to 30% ethanol in MilliQ water followed by the dropwise addition of 96% ethanol over 30 min to increase the final ethanol concentration to 93%). The filters were then transferred to absolute ethanol and critical point dried using a Leica EM CPD 300 device. In preparation for SEM, the samples were sputter-coated with a 30-nm gold–palladium (90/10) layer using a Leica EM SCD 500 instrument and mounted onto SEM stubs.

Samples were observed under a Zeiss Merlin VP Compact field-emitting SEM (Carl Zeiss Microscopy, Oberkochen, Germany) with a Bruker ((Bruker Nano Analytics, Berlin, Germany)) QUANTAX FlatQUAD X-ray spectrometer. The electron acceleration voltage was set to 10 kV and the beam current to ~300 pA, which achieved the ionization of cerium while allowing for cell surface imaging with an Everhard-Thornley secondary electron detector. Cerium maps were obtained from spatially resolved EDX data using the Ce L-alpha line.

The coverage of algal cells with ENMs was determined from overlays of SEM and EDX images generated using the Correlia Plugin for ImageJ/Fiji [17,18]. Based on these overlays, Fiji tools were used to calculate the area of each image covered with algae and the area covered with cerium as well as algae. From these values, we were able to calculate the coverage as a percentage. In each of the 5–6 samples collected from the two different media (GB and OCED) and the two different ENM concentrations (0.1 and 1 mg/L), we analyzed 2–7 individual cells by EDX in order to determine the mean coverage.

#### *2.5. Statistical Evaluation*

Statistical analysis and the calculation of EC50 values in the algal growth inhibition tests were carried out using ToxRat Professional v3.3 (ToxRat Solutions, Alsdorf, Germany). Dose-response functions were determined by linear regression (probit model) and EC50 confidence limits were based on Fieller's theorem.

#### **3. Results**

## *3.1. Attachment of CeO2*

#### 3.1.1. Light Microscopy

CeO2 NM-212 showed strong attachment to algal cells during spike experiments after a contact period of 3 h (Figures 1 and 2). There was mostly no direct contact with the cell wall, but instead the ENMs attached to a transparent sheath around the individual algal cells. We also observed the attachment of NM-212 to a transparent structure, with a shape similar to the algal cells. The formation of agglomerates was highly dependent on the concentration ratio between the algal cells and nanoparticles. The 3-h spike experiments showed that small agglomerates (~0.1 mm) formed at the highest and lowest cell densities (3,000,000 and 175,000 cells/mL), whereas larger agglomerates (0.5–2 mm) formed at the intermediate cell densities (1,400,000 and 700,000 cells/mL). Although the appearance of the agglomerates was dependent on cell density, the attachment of ENMs to individual algal cells was similar across all spike experiments. Even algal cells embedded within thicker and larger agglomerates featured the same gap between the cell surface and attached nanoparticles. These transparent structures were not observed in the controls without NM-212, indicating they were induced by the presence of the ENM.

**Figure 1.** Nanoparticle attachment to transparent, sheath-like structures around algal cells (red arrows). This phase-contrast image (1000× magnification) was captured after 3 h incubation with 100 mg/L CeO2 NM-212.

The attachment of ENMs to the algal cells was observed in the algal growth inhibition tests at all test concentrations after 72 h. As described for the spike experiments, the size of the agglomerates was highly dependent on the concentration ratio between the algal cells and nanoparticles (Figures S1–S3). The size of agglomerates, as determined by microscopy, initially increased in line with the ENM test concentration (2.5–10 mg/L) but decreased again at the highest value (40 mg/L). The distinct shell-like

attachment, which we identified during spike experiments, was less pronounced during the growth inhibition tests perhaps due to the lower ENM concentrations. CeO2 NM-211 showed attachment behavior comparable to CeO2 NM-212 (Figure S4).

**Figure 2.** Nanoparticle attachment to transparent, algal-shaped structures with (red arrow) and without (yellow arrow) a corresponding algal cell. This phase-contrast image (1000× magnification) was captured after 3 h incubation with 100 mg/L CeO2 NM-212.

In contrast, CeO2 NM-213 showed much weaker attachment to *R. subcapitata* cells during both the spike experiments and algal growth inhibition tests. Despite this weak interaction, a transparent sheath-like structure was again observed around the algal cells, preventing direct contact between the ENM and cell surface (Figure 3). Transparent, algae-shaped structures were also observed with few or no particles attached (Figure 4).

**Figure 3.** Nanoparticle attachment to transparent, sheath-like structures around algal cells (red arrow). This phase-contrast image (1000× magnification) was captured after 3 h incubation with 100 mg/L CeO2 NM-213.

As described for the spike experiments, only a few particles attached to the algal cells during the growth inhibition tests after 72 h (Figures S5–S8). Accordingly, the shell-like structures around the algal cells were less obvious, although many transparent algae-shaped structures featuring a small number of attached particles were observed following the test period, especially at the higher test concentrations.

**Figure 4.** Transparent, algae-shaped structures (red arrow). This phase-contrast image (1000× magnification) was captured after 3 h incubation with 100 mg/L CeO2 NM-213.

#### 3.1.2. SEM-EDX Analysis

We studied the attachment of CeO2 NM-212 to individual *R. subcapitata* cells by electron microscopy. Given the observed differences in toxicity (Section 3.3), we also compared particle attachment following incubation in two different media: GB and OECD. In both cases, we observed the attachment of CeO2 NM-212 particles to algal cells and this was confirmed by EDX (Figure 5). Control cells that were not exposed to the ENM are shown in Figure S9. Interestingly, EDX analysis showed that not all particles associated with the algae were composed of cerium, but that iron and sodium were also present. The iron particles were deposited on the algae exclusively when the cells were incubated in GB medium and given that no iron-containing chemicals were used during sample preparation we conclude that iron in the GB medium precipitated onto the cells. Sodium precipitation was occasionally observed in the samples incubated in OECD medium. Furthermore, most cells were surrounded by filamentous web-like envelopes, probably the shrunken remains of extracellular polymeric structures (EPS) visible by light microscopy. Fewer of these structures were associated with the control cells (Figure S9). These network-like structures could also be artifacts generated during sample preparation.

The coverage of algal cells with NM-212 particles was quantified from the overlay of SEM and EDX images. This showed no significant difference between the GB and OECD media at an ENM concentration of 0.1 mg/L (2–3% coverage in each case), but a trend towards higher coverage in OECD medium (14% in OECD vs. 2.4% in GB) at an ENM concentration of 1 mg/L (Figure 6).

#### *3.2. Attachment of TiO2*

The analysis of TiO2 ENMs by light microscopy revealed that, like CeO2 NM-211 and NM-212, the TiO2 particles formed agglomerates that attached to the algal cells (Figures S10–S20). However, these particles formed heterogeneous agglomerates that differed in terms of compactness and the manner of attachment. Specifically, they primarily formed shell-like single layers of compact agglomerates around the algal cells but also formed loose agglomerations of cells and particles. Like the non-doped TiO2 particles (Figure S10), the Eu-doped TiO2 particles densely covered the algal cells and formed compact agglomerates up to 1 mm in diameter, as well as loose agglomerations with less-ordered attachments (Figures S11 and S12). In contrast, the Fe-doped TiO2 particles formed only shell-like single layers of compact agglomerates (Figure S13). The gap between the TiO2 NM-105 particles and algal cells was wider and the shell-like structure was less dense compared to the doped particles (Figure S14). NM-104 particles attached to the algae sparingly and formed a fragmented rather than a

complete compact shell, and the width of the gap varied from indistinguishable up to a clearly defined space (Figure S15).

**Figure 5.** SEM-EDX (energy-dispersive X-ray spectroscopy) images of *Raphidocelis subcapitata* cells following exposure to CeO2 NM-212 at two concentrations in two media, GB (**A**,**B**) and OECD (**C**,**D**). Some cells appear to be covered by network-like structures (potentially extracellular polymeric substances or artifacts that appear during sample preparation). Optically identical particulate assemblies are associated with the cells and, based on EDX analysis, contain not only cerium (green), but also sodium (blue) and iron (red).

**Figure 6.** Coverage of the algal surface with cerium from NM-212 particles as determined by SEM-EDX image overlay analysis. We analyzed 5–6 samples per medium (GB and OECD), each sample comprised 2–7 cells. The data are means ± standard deviations. One-tailed unpaired *t*-test (Microsoft Excel 2013) revealed significant differences (marked by \*) between the concentrations (*p* = 0.033 in GB medium, *p* = 0.044 in OECD medium) as well as between the media at 1 mg/L (*p* = 0.045).

Microscopic analysis during the growth inhibition tests revealed the formation of a shell-like layer for the highest test concentration of the Eu-doped TiO2 particles (18 mg/L) and large but loose agglomerates at a lower concentration (2 mg/L) near the EC50 value (Figures S16 and S17). In contrast, the NM-104 agglomerates formed loose and open structures at the lowest test concentration of 7.5 mg/L (Figures S18–S20). Although most algal cells were incorporated into agglomerates, the status of individual cells was dependent on the ENM concentration, with few if any surface particles at the lowest test concentration (7.5 mg/L) or at 30 mg/L, but all algal cells incorporated into agglomerates at the highest test concentration of 120 mg/L.

#### *3.3. Growth Inhibition Tests*

For all three CeO2 ENMs and two of the five TiO2 ENMs, we carried out growth tests based on OECD test guideline 201 [12] and compared our results to earlier experiments [5]. We also compared the toxicity of CeO2 NM-212, Eu-doped TiO2, and non-doped TiO2 in the two media and found that all three ENMs were more toxic in OECD medium than GB medium (Table 4).

**Table 4.** EC50 values determined in growth tests (OECD medium) with the green alga *Raphidocelis subcapitata* 1 .

1 Values in brackets = confidence interval. 2 Tests carried out as described in Section 2 and results presented in the supporting information of a previous study [5]. 3 n.d. = not determined. 4 Data from this study (Fraunhofer Institute for Molecular Biology and Applied Ecology). 5 Data from this study (Helmholtz Centre for Environmental Research).

We compared the results generated by the two laboratories involved in this study (Fraunhofer Institute for Molecular Biology and Applied Ecology and Helmholtz Centre for Environmental Research) and also compared our results to published data [5]. In most cases, there was less than a five-fold variation. The ecotoxicity of the three CeO2 ENMs differed by a factor of 100, with NM-213 showing the lowest toxicity. The ecotoxicity of the TiO2 ENMs also differed by a factor of 100, with the non-doped and Eu-doped ENMs showing the highest toxicity and NM-104 the lowest.

#### *3.4. Relationship between Attachment Behavior and Ecotoxicity*

We observed a clear relationship between ecotoxicity and attachment efficiency for the three CeO2 ENMs (Figure 7). NM-211 and NM-212 were highly toxic and also showed a great propensity for attachment to algal cells, whereas NM-213 was much less toxic and formed few agglomerations. In contrast, the relationship between ecotoxicity and attachment was more complex for the TiO2 ENMs (Figure 7). There was no clear division between the particles that favored and disfavored interactions with algae, but rather a gradual change from strong to weak attachment. The Fe-doped ENM showed the strongest attachment, forming shell-like structures in compact agglomerates with most algal cells, followed by the Eu-doped and non-doped ENMs (mostly shell-like structures in compact agglomerates but some looser agglomerates), NM-105 (loose shell-like structures, gaps between algae and particles), and finally NM-104 (shell-like fragments, large gaps between algae and particles). The Eu-doped and non-doped ENMs showed the highest toxicity, followed by the Fe-doped ENM and NM-105, and finally NM-104. Furthermore, there was no obvious relationship between the crystalline structure of TiO2 (rutile or rutile/anatase) and ecotoxicity.

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EC50: 8.5 mg/L EC50: 11 mg/L EC50: 99 mg/L **CeO2 NM-211 CeO2 NM-212 CeO2 NM-213**

EC50: 0.2–0.91 mg/L EC50: 0.38 mg/L EC50: 3.6 mg/L **Eu-doped TiO2 (rutile) Non-doped TiO2 (anatase, rutile) Fe-doped TiO2 (rutile)**

**TiO2 NM-105 (anatase, rutile) TiO2 NM-104 (rutile)**

#### **4. Discussion**

#### *4.1. Attachment Behavior*