Skip to main content

Thank you for visiting nature.com. You are using a browser version with limited support for CSS. To obtain the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in Internet Explorer). In the meantime, to ensure continued support, we are displaying the site without styles and JavaScript.

High content analysis at single cell level identifies different cellular responses dependent on nanomaterial concentrations

Abstract

A mechanistic understanding of nanomaterial (NM) interaction with biological environments is pivotal for the safe transition from basic science to applied nanomedicine. NM exposure results in varying levels of internalized NM in different neighboring cells, due to variances in cell size, cell cycle phase and NM agglomeration. Using high-content analysis, we investigated the cytotoxic effects of fluorescent quantum dots on cultured cells, where all effects were correlated with the concentration of NMs at the single cell level. Upon binning the single cell data into different categories related to NM concentration, this study demonstrates, for the first time, that quantum dots activate both cytoprotective and cytotoxic mechanisms, resulting in a zero net result on the overall cell population, yet with significant effects in cells with higher cellular NM levels. Our results suggest that future NM cytotoxicity studies should correlate NM toxicity with cellular NM numbers on the single cell level, as conflicting mechanisms in particular cell subpopulations are commonly overlooked using classical toxicological methods.

Introduction

Bio-nano interactions are under continuous examination in order to further enhance the potential impact and safe use of NMs in biomedical applications1,2,3,4,5 to facilitate the move from pre-clinical to the clinical phase. Initially, the contribution of NM-related parameters was investigated using easily quantifiable measures such as cell viability and oxidative stress6,7. Subsequently, more mechanistic studies were being pursued, where more subtle effects such as the formation of protein coronas and the consequent effect of NMs on cellular homeostasis, the induction of lysosomal degradation pathways, such as autophagy and the intracellular degradation of NMs were being explored8,9,10. Many disparate data have been generated, however, the biological impact of a certain NM-related parameter remains somewhat elusive11. Various explanations have been suggested for this phenomenon, including, differences between cell types12, incubation conditions (NM concentration, time, type of culture medium)12,13, material properties (colloidal stability, charge, size, etc.)14,15 and the type of toxicity assays performed15. Other factors that can contribute to this variability are the lack of adequate NM characterization and/or interference of NMs with common toxicity assays15,16. Additionally, the interactions of nanosized materials with biological components is a highly complex field, where many parameters have to be taken into account, some of which have only recently been taken into consideration. Traditionally, the induction of reactive oxygen species (ROS) and loss of cell viability are studied as main parameters for determining NM cytotoxicity17,18. Recent studies have however shown that NMs can affect cell homeostasis through a wide range of different mechanisms, for instance by induction of autophagy9, intracellular degradation and loss of toxic ions19, binding important signaling molecules (ligands/receptors) and hereby affecting both intra- and intercellular communication20. An important factor in bio-nano interaction studies is the formation of the protein corona around NMs. The protein corona will determine how the NM will be presented to the cells when present in physiological conditions and hereby affect the final biological outcome of cellular NM exposure21. Recent studies have shown that the composition of the protein corona determines where the NMs will finally end up within the cells10. Therefore, various methods have been set up to enable quantitative profiling of the protein coronas22. Much work has been put into determining the physicochemical properties of NMs and how they influence the composition of the protein corona23,24,25. Recently, it has also been shown that temperature plays a vital role in determining protein corona composition and cellular NM uptake26.

To date, NM toxicity studies are commonly performed in a manner similar to chemical toxicity studies, where for every parameter tested, a biochemical assay is used, providing a single representative value for the entire cell population. Dose-response curves are then generated by exposing cultured cells to a wide range of concentrations of NMs or chemicals. For chemicals, this has been proven to be a suitable procedure, to test their reactivity on cells, as they typically cross membranes more easily. For NMs, this procedure is more questionable as toxicity is mostly linked to the intracellular presence of NMs, apart from more rare events such as interaction with cell surface receptors or plasma membrane permeabilisation27. Cellular NM levels can however vary greatly and are dependent on the efficiency of endocytotic NM uptake. Various groups have therefore stressed the importance of determining cellular NM concentrations to accurately determine NM toxicity28,29,30, as various NM-related parameters, such as the nature of the NM coating, can influence NM toxicity as a secondary effect caused only by altered cellular NM uptake levels28. However, the currently used methods still link cellular effects to the average cellular NM level for the entire cell population, based, for instance, on colorimetric or inductively coupled plasma-mass spectrometry assays. Cellular NM levels have been shown to vary widely, even between closely neighboring cells31. Therefore, even though calculating average cellular toxicity and NM uptake levels are, to date, the most accepted methods for analyzing NM exposure yet these methods do not provide a comprehensive overview of all the processes involved in the cells of a specific population, rather they provide an average effect elicited by the NMs.

Averaging effects over a population cloaks distinct effects in multiple subpopulations. Many NM-elicited cellular responses can therefore be overlooked and only gross effects will be picked up. In order to provide a mechanistic understanding of how biological systems interact with NMs, it is therefore essential that individual cellular responses are collected. Hence, in this study, we employ a recently set up and validated methodology based on high-content imaging to evaluate a wide range of cellular responses to NM exposure11. We have modified the methodology to perform analysis on a single cell basis, where the consequences of exposure to fluorescent quantum dots (QDots) were correlated to the level of NM in the same cell. The data were then binned in 10 different categories, depending on the cellular QDot level, after which cellular parameters were analysed per category, to represent a substantial subcategory of the entire cellular population. At the same time, analysis was also performed without taking cellular NM levels into account, where the data provided by the two analysis methods can be immediately compared to one another.

Materials and Methods

Cell culture

Mouse embryonic fibroblasts (MEFs) and transgenic MEFs (MEF Atg5 KO and MEF Bax/Bak DKO) were grown in high glucose containing Dulbecco’s modified Eagle’s medium (DMEM), supplemented with 10% fetal calf serum, 1 mM sodium pyruvate, 2 mM L-Glutamine and 1% penicillin/streptomycin (Gibco, Invitrogen, Belgium). The MEF cells were passaged upon reaching 80% confluence and reseeded at a ratio of 1:5.

Mouse mesenchymal stem cells were maintained in high glucose containing Dulbecco’s modified Eagle’s medium (DMEM), supplemented with 10% fetal calf serum, 10% horse serum, 1 mM sodium pyruvate and 2 mM L-Glutamine (Gibco, Incitrogen, Belgium). Cells were passaged when reaching nearly 80% confluence and reseeded at a density of 100,000 cells/flask in 75 cm2 tissue culture flasks (Nunc, Belgium).

Cell nanoparticle interaction studies

For high-content imaging studies, all cell types were seeded at 7500 cells/well in a 24 well plate (Nunc, Belgium). Cells were allowed to attach overnight in a humidified atmosphere at 37 °C and 5% CO2, after which the cells were incubated with the carboxylated QDots for 24 h in full growth medium. For cellular exposure studies, cells were incubated with the QDots at 2.5, 5, 7.5, 10, 12.5, 15 or 20 nM. For intracellular QDot distribution studies, MSCs were incubated with the QDots at 10 nM, while MEF cells were incubated with 12.5 nM QDots. For mechanistic studies, MEF, MEF Atg 5 KO or MEF Bax/Bak DKO cells were used and incubated with the QDots for 24 h at 12.5 nM. MSCs were either exposed to the QDots directly (10 nM) or were co-incubated with autophagy inhibitor 3-methyladenine (3-MA; 1 mM; Sigma-Aldrich, Belgium) or pan-caspase inhibitor Z-VAD-fmk (100 μM, Bachem AG, Switzerland). Each experiment was performed in three independent repeats and data were analyzed using full data sets of the different repeats. The specific experimental procedures for the high-content imaging experiments are described in the Supplementary Information.

Statistical analysis

All data are expressed as the mean + standard error to the mean (SEM). For all experiments, any statistical significance between a single condition and untreated control cells were analyzed using the t-test statistical method.

Results

Average effects of quantum dots on cultured cells

The QDots used are commercially available CdSe/ZnS core/shell nanoparticles (Invitrogen, Belgium) with maximal emission at 655 nm, which were coated using a carboxylated amphiphilic polymer, generating negatively charged NPs (Supp Section S1). When exposing mouse mesenchymal stem cells (MSCs) and mouse embryonic fibroblasts (MEFs) to these QDots, a clear endosomal localization can be observed, as the QDots are efficiently endocytosed and do not merely adhere to the plasma membrane (Fig. 1a, Supp Fig. S2), which is in line with other reports on carboxylated QDots28. Next, MSCs and MEFs were exposed to a series of QDot concentrations and the effect on various parameters was evaluated (Fig. 1b–d). The data reveal significant concentration-dependent effects of the QDots on cell viability (Supp Fig. S3), cell membrane damage (Supp Fig. S4), autophagy induction (Supp Fig. S5), cell morphology (Supp Fig. S6), cell skewness (Supp Fig. S7), size of the mitochondrial network (Supp Fig. S8) and mitochondrial ROS (Supp Fig. S9), at concentrations of 15 nM or more. These values are in line with previous studies using carboxylated QDots28, suggesting that QDots appear to affect cell homeostasis through various mechanisms.

Figure 1
figure1

Effects of exposure to varying concentrations of QDots in MSCs and MEFs.

(a) representative confocal micrographs of MSC (left) and MEF (right) cells that were transduced with CellLight Lysosomes-GFP (green) and subsequently exposed to 10 nM QDots (red) for 24 h. yellow/orange dots represent colocalized QDots and lyosomes. Scale bars = 25 μm. The area in the white rectangle is shown as a magnified view below the image. (b) Heat maps of high-content imaging-based data for MSCs and MEFs exposed to varying concentrations of QDots and analyzed for; cell viability, cell membrane damage, mitochondrial ROS, size of the mitochondrial network, area of the cell, skewness of the cell and level of autophagy. Data are shown as relative values after z-normalization compared to untreated control cells (=1) where the fold-change is indicated by the respective color-code. Data have been acquired for minimum 5000 cells/condition which were gathered from three independent experiments. (c,d) Representative InCell high-content images of control MSCs or MSCs exposed to the QDots at 10 or 20 nM for 24 h, after which the cells were stained for (c) β-actin (green) or (d) mitochondrial ROS (green). Cells were counterstained with Hoechst nuclear stain (blue). Scale bars = 100 μm, the area in the white rectangle is depicted in a magnified view below the original image.

Effects of quantum dots on cultured cells at the single cell level

When considering average results over the entire visualized population, our data reveal that the effects the QDots induce in MSCs at 10 nM are similar to those observed in MEFs at 12.5 nM. At these concentrations, no significant effects could be observed, apart from the induction of oxidative stress, which does not appear to induce any cell death at these levels due to natural cellular defense mechanisms32. Exposure of both cell types to the QDots (MSC at 10 nM; MEF at 12.5 nM) however resulted in a broad distribution in cellular QDot levels (Fig. 2), as is typical for in vitro cultured cells exposed to NMs31. NM levels between the two cell types were highly similar which explains the high similarity in their toxicity profiles. In order to evaluate the impact of cellular NM distribution on its resulting toxicity, the fluorescent properties of the QDots were exploited to determine relative cellular NM levels and consequently every cellular parameter was linked to a subpopulation of cells with a certain NM level. NM distribution in cells followed a near-Gaussian profile and cells were subdivided into 10 different categories, category 1 being the lowest NM level and category 10 the highest (Fig. 2c–f). The data reveal that the majority of cells are in the medium categories (c4–c7; 68%). The lower categories (c1–c3) represent merely 12% of the entire population while the higher categories (c8–c10) represent 20% of the population. Interestingly, for MSCs exposed to 10 nM QDots and MEFs exposed to 12.5 nM QDots, the distribution of cellular QDot levels is quite similar, suggesting that the high similarity in cellular effects under these conditions (Fig. 1) is due to the similar levels of intracellular QDots. In general, MEF cells appear to be slightly more resistant to QDot-elicited cytotoxic effects as a secondary mechanism caused by the intrinsic lower internalization efficiency of these cells.

Figure 2
figure2

Cellular distribution of cell-associated fluorescent quantum dots.

(a,b) Representative images of (a) MSCs exposed to 10 nM QDots for 24 h or (b) MEFs exposed to 12.5 nMQDots for 24 h. (c,d) Histograms presenting the number of (c) MSC or (d) MEF cells per category when the population is divided into 10 categories spanning the entire range of cellular QDot levels. (e,f) Histograms presenting the percentage of (e) MSC or (f) MEF cells per category for the total population analyzed.

Following this categorization, the results showed a clear impact of cellular NM levels on both the nature and degree of the cellular response (Fig. 3). For cells containing low to medium QDot levels, no significant effects were observed for any of the assessed parameters. However, at higher intracellular NM levels (categories 8–10), significant effects were observed, which, however, did not all correlate with intracellular NM levels. Mitochondrial stress and ROS (Supp Figs S09, S10), cell morphology and skewness (Supp Figs S11, S12) and autophagy (Supp Fig. S13) showed higher significance with an increase in intracellular QDot levels, whereas the highest levels of cell death and associated membrane damage were seen in categories 8 and 10, but less pronounced in category 9 (Supp Figs S14, S15).

Figure 3
figure3

Overview of cellular effects in view of cellular QDot concentration at “non-toxic” conditions.

(a) Heat maps of high-content imaging-based data for MSCs and MEFs exposed to QDots at 10 and 12.5 nM concentrations, respectively, at which slight but non-significant effects had been observed for nearly all the tested parameters based on the total cell population. Cells were analyzed for; cell viability, cell membrane damage, mitochondrial ROS, size of the mitochondrial network, area of the cell, skewness of the cell and level of autophagy. For every cell, the relative level of QDots was also calculated based on the cellular QDot intensity and area, after which cells were divided into 10 categories based on their cellular QDot levels, ranging from c1 (lowest) to c10 (highest). Observed cellular effects were grouped based on the different categories of intracellular QDot concentrations. The data are shown as relative values after z-normalization compared to untreated control cells (=1) where the fold-change is indicated by the respective color-code. Data were acquired for minimum 5000 cells/condition and were gathered from three independent experiments. (b,c) Representative InCell high-content images of (b) MSCs and (c) MEFs exposed to the QDots at 10 and 12.5 nM, respectively. Red: QDots, Blue: Hoechst nuclear stain, Green: dead cells (left), mitochondrial ROS (middle), β-actin (right). Scale bars = 100 μm, the area in the white rectangle is depicted in a magnified view below the original image.

Mechanistic understanding of quantum dot-induced cytotoxic effects at single cell level

NM-mediated autophagy has been described to be a general cellular response to exposure to a wide range of NMs, including QDots9. The physiological impact of NM-induced autophagy remains however somewhat unclear, as some studies have indicated clear cytoprotective effects by impeding cellular apoptosis signaling, while other studies showed direct autophagy-mediated cell death33,34. The induction of autophagy was therefore selected as the potential key mechanism to explain the lower correlation of intracellular NM levels and cell death. We tested two transgenic MEF cell lines, displaying either compromised autophagy, because of the deletion of the essential autophagy gene Atg5 (i.e. MEF-Atg5 KO) or mitochondrial apoptosis due to the double deficiency in pro-apoptotoc Bax and Bak (i.e. MEF-Bax/Bak DKO), a defect known to blunt apoptosis to a variety of stress signals35,36. Along with these genetic approaches, we chemically inhibited autophagy (3-methyladenine (3-MA)) and apoptosis (Z-VAD-fmk) in MSCs. Figure 4 reveals clear involvement of apoptosis and autophagy in QDot-mediated cellular response. Inhibition of apoptosis did not affect the levels of cellular autophagy (Supp Fig. S16), whereas inhibition of autophagy resulted in a clear increase in cellular apoptosis levels at higher intracellular QDot concentrations (Supp Fig. S17). Interestingly, inhibition of either mechanism could not prevent cell death, yet in apoptosis-deficient cells, cell death only occurred at the highest intracellular QDot concentration, whereas in autophagy-deficient cells, cell death induction followed a clear concentration-dependent profile (Supp Fig. S18).

Figure 4
figure4

Overview of conflicting mechanisms involved in general cellular response.

(a) Heat maps of high-content imaging-based data for MSCs, MSCs treated with 3-MA or Z-VAD-fmk or MEFs, MEF Atg5 KO or MEF Bax/Bak DKO exposed to QDots at 10 nM (MSC cells) and 12.5 nM (MEF cells), respectively. Cells were analyzed for; cell viability, autophagy and apoptosis. For every cell, the relative level of QDots was also calculated based on the cellular QDot intensity and area, after which cells were divided into 10 categories based on their cellular QDot levels, ranging from c1(lowest) to c10 (highest). After analysis, the cellular effects were grouped based on the different categories for cellular QDot concentrations. The data are shown as relative values after z-normalization compared to untreated control cells (=1) where the fold-change is indicated by the respective color-code. Data were acquired for minimum 5000 cells/condition and were gathered from three independent experiments. (bd) Representative InCell high-content images of MSCs, MSCs treated with 3-MA or MSC Z-VAD-fmk exposed to QDots (10 nM) for 24 h, after which the cells were stained for (b) cell death, (c) autophagy (LC3) and (d) apoptosis (active caspase-3). Red: QDots, Blue: Hoechst nuclear stain, Green: (b) dead cells, (c) LC3 (d) active caspase-3. Scale bars = 100 μm, the area in the white rectangle is depicted in a magnified view below the original image.

Discussion

In the present study, we have demonstrated a clear impact of intracellular NM concentration and NM distribution throughout the entire cell population on the cellular response to NM exposure. Upon using biochemical assays that commonly generate mean values for an entire cell population, more subtle effects that are only present in a certain subpopulation of the cells are normally overlooked, although they can have serious consequences and limitations for the final use of the cells. Biochemical assays are often used as they are generally easy to perform, amenable to high-throughput and quantitative. Imaging-based methods provide visual information on the impact of NMs and are therefore generally qualitative. Some parameters, such as cell morphology can however only be properly studied using visual analysis. To overcome these limitations, several research groups have turned to the use of high content imaging37. One of the main advantages of high content imaging is the automated generation of a high number of images that can then be analyzed and turned into quantitative information over thousands of cells per condition38. The use of high content imaging has gained high interest from many fields, including cell biology and drug discovery as a powerful quantitative method that enables the generation of multidimensional (multiple parameters) and hierarchical data (i.e. in this study: all parameters linked to cellular QDot levels)38. Additional advantages of high content imaging are the ability to discriminate between real and off-target effects, non-specific fluorescence (i.e. interference of NMs), or the ability to study additional parameters (i.e. NM distribution).

The results obtained in this study are in line with previous reports on the toxicity of CdSe/ZnS QDots, coated with amphiphilic polymers, where clear effects on cell viability could be observed at concentrations ranging from about 2–20 nM28,39,40. This wide range of safe upper concentrations is mainly linked with the generation of disparate data due to differences in cell types, QDot surface chemistry and analysis method used. For cadmium-containing QDots, their toxicity has been mainly associated with the presence of free Cd2+ ions, that are known toxic agents. The presence of Cd2+ ions can result from remnants of cadmium after the synthesis and purification, where in a hydrophilic oxidative environment, some Cd2+ will also leach from the QDot surface, as a result of the inequilibrium of Cd2+ ions in solution and within the QDots. In a cellular environment, the QDots will be exposed to the degradative acidic microenvironment of the endosomes in which they will locate, which will further increase the loss of Cd2+ ions due to acid etching of the surface19,28,41. The present work shows that the second mechanism is more predominant in affecting cellular viability, as toxicity is clearly linked to intracellular QDot levels and thus Cd2+ ions being released intraendosomally. If toxicity was caused by free Cd2+ ions in solution, then any toxic effects would be more homogeneously spread and not linked so closely to the intracellular QDot levels. This of course also depends on the quality and purity of the QDot sample, where “aged” QDots or QDots that have been poorly purified and contain high Cd2+ levels in the stock suspension, may be more prone to toxicity induced by the higher levels of free Cd2+ ions42.

A clear difference between the two cell types in terms of their ability to deal with NM exposure has been observed. The MSCs and MEFs show similar effects when exposed to 10 and 12.5 nM QDots, respectively. This has then be shown to result in highly similar intracellular levels of the QDots, thus showing that the intracellular level is more meaningful in terms of comparing different cell types than the concentration of NMs used for initial cell exposure. To date, nearly all studies however express NM toxicity in terms of exposure concentrations, which, as shown here, is less meaningful. The difference in cellular NM concentrations between different cell types can be due to a lower intrinsic endocytic capacity of one cell type compared to the other, or a smaller surface area and hence less potential interaction area for NMs with the cell surface. Other studies have made similar observations, where the concept of “cell vision” has been introduced as a key factor in nano-bio interactions. In short, “cell vision” signifies the intrinsic capacity of a particular cell type to deal with NMs, which is determined by the cell-specific uptake and defense mechanisms43,44.

For mechanistic studies, the overall effect of the NMs will dominate, which requires high NM concentrations. By linking cellular responses to the intracellular NM concentration on the single cell level, we have shown that at apparent sub-cytotoxic NM incubation concentrations, various conflicting mechanisms are evoked, such as apoptosis and autophagy. Taken together, these data suggest that apoptosis occurs at medium to high intracellular QDot concentrations, but is then impeded by autophagy at higher concentrations, resulting in a partial recovery of cell viability. At the highest intracellular QDot concentrations, autophagy levels were too high, resulting in autophagy-mediated cell death. One possibility is that the level of Cd2+ ions present in the QDot incubation media, are responsible for the induction of apoptosis as the release of Cd2+ has been shown to be a major cause of QDot toxicity41. Particularly for cell-internalized QDots, degradation has been observed to occur at a higher rate, resulting in the generation of free Cd2+ ions. Cd2+ ions are known to be highly toxic and a sustained cellular exposure to high levels of these ions (partially derived from the initial incubation mixture and partially from intracellular QDot dissolution) will result in cell death, through the process of apoptosis. The precise mechanism underlying the induction of autophagy remains somewhat unclear, but oxidative stress-induced mitochondrial damage, which is known to result in a particular form of autophagy, called mitophagy45, could be involved. Autophagy in itself is known to be a cytoprotective mechanism46 and induction of autophagy is known to inhibit apoptosis signaling47. Many different types of NMs, including various types of Cd2+− containing ones, have been shown to induce autophagy, which either results in cytotoxicity or cytoprotection33,34,48. Our findings reveal a clear induction of apoptosis for higher levels of cellular NMs, which is however inhibited by the concurrent induction of autophagy. Together, this results in variable levels of cell death, where at low levels of autophagy, apoptosis is more pronounced and cell viability decreases. When autophagy levels increase, apoptosis is more inhibited, resulting in a recovery of cell viability. At high autophagy levels, cell viability again decreases, although apoptosis is still strongly inhibited. These data suggest that at the highest levels of autophagy, cell death occurs through direct autophagy-mediated signaling49.

These findings can explain the high level of disparate data that have been generated on NM toxicity, where small differences in cell types, exposure conditions or NM properties can result in changes in cellular NM levels and hereby affect the balance between the different cellular responses. We have, for the first time, demonstrated a robust method to evaluate NM toxicity on high cell numbers with single cell resolution, allowing to delineate cellular responses in specific cellular subpopulations. The data here demonstrate the clear need for correlating cellular responses with intracellular NM concentration to enhance our understanding of NM toxicity and the precise mechanisms by which NMs can interact with biological components. Additionally, the findings reveal that data obtained from average population analyses overestimate the safe concentrations of NMs, due to the added cellular variability. Although single cell analysis was achieved, binning the data into the different categories provided a nicer and more achievable overview of substantial parts of the entire cell population, as revealed by the data in this study where significant effects are observed in nearly 20% of the entire population, but this goes unnoticed in the entire cell population. This knowledge is imperative for the future progress and clinical translation of nanotechnology and the methodology demonstrated here can serve as a template for future nanotoxicological analyses.

Additional Information

How to cite this article: Manshian, B. B. et al. High content analysis at single cell level identifies different cellular responses dependent on nanomaterial concentrations. Sci. Rep. 5, 13890; doi: 10.1038/srep13890 (2015).

References

  1. Hansen, S. F., Maynard, A., Baun, A. & Tickner, J. A. Late lessons from early warnings for nanotechnology. Nature Nanotech. 3, 444–447 (2008).

    CAS  ADS  Google Scholar 

  2. Cho, E. C., Zhang, Q. & Xia, Y. N. The effect of sedimentation and diffusion on cellular uptake of gold nanoparticles. Nature Nanotech. 6, 385–391 (2011).

    CAS  ADS  Google Scholar 

  3. Kim, J. A., Aberg, C., Salvati, A. & Dawson, K. A. Role of cell cycle on the cellular uptake and dilution of nanoparticles in a cell population. Nature Nanotech. 7, 62–68 (2012).

    CAS  ADS  Google Scholar 

  4. Alkilany, A. M. & Murphy, C. J. Toxicity and cellular uptake of gold nanoparticles: what we have learned so far? J Nanopart. Res. 12, 2313–2333 (2010).

    CAS  PubMed  PubMed Central  Google Scholar 

  5. Medina, C., Santos-Martinez, M. J., Radomski, A., Corrigan, O. I. & Radomski, M. W. Nanoparticles: pharmacological and toxicological significance. Brit. J. Pharmacol. 150, 552–558 (2007).

    CAS  Google Scholar 

  6. Braydich-Stolle, L., Hussain, S., Schlager, J. J. & Hofmann, M. C. In vitro cytotoxicity of nanoparticles in mammalian germline stem cells. Toxicol. Sci. 88, 412–419 (2005).

    CAS  PubMed  PubMed Central  Google Scholar 

  7. Lai, J. C. et al. Exposure to titanium dioxide and other metallic oxide nanoparticles induces cytotoxicity on human neural cells and fibroblasts. Int. J.Nanomed. 3, 533–545 (2008).

    CAS  Google Scholar 

  8. Lynch, I., Salvati, A. & Dawson, K. A. Protein-nanoparticle interactions what does the cell see? Nature Nanotech. 4, 546–547 (2009).

    CAS  ADS  Google Scholar 

  9. Peynshaert, K. et al. Exploiting intrinsic nanoparticle toxicity: the pros and cons of nanoparticle-induced autophagy in biomedical research. Chem. Rev. 114, 7581–7609 (2014).

    CAS  PubMed  Google Scholar 

  10. Walkey, C. D. et al. Protein corona fingerprinting predicts the cellular interaction of gold and silver nanoparticles. ACS Nano 8, 2439–2455 (2014).

    CAS  PubMed  Google Scholar 

  11. Manshian, B. B. et al. High-content imaging and gene expression analysis to study cell-nanomaterial interactions: The effect of surface hydrophobicity. Biomaterials 35, 9941–9950 (2014).

    CAS  PubMed  Google Scholar 

  12. Kim, I. Y., Joachim, E., Choi, H. & Kim, K. Toxicity of silica nanoparticles depends on size, dose and cell type. Nanomedicine 11, 1407–1416 (2015).

    CAS  PubMed  Google Scholar 

  13. Maiorano, G. et al. Effects of cell culture media on the dynamic formation of protein-nanoparticle complexes and influence on the cellular response. ACS Nano 4, 7481–7491 (2010).

    CAS  PubMed  Google Scholar 

  14. Yoon, D. et al. Agglomeration, sedimentation and cellular toxicity of alumina nanoparticles in cell culture medium. J. Nanopart. Res. 13, 2543–2551 (2011).

    CAS  ADS  Google Scholar 

  15. Rivera Gil, P., Oberdorster, G., Elder, A., Puntes, V. & Parak, W. J. Correlating physico-chemical with toxicological properties of nanoparticles: the present and the future. ACS Nano 4, 5527–5531 (2010).

    CAS  PubMed  PubMed Central  Google Scholar 

  16. Ong, K. J. et al. Widespread nanoparticle-assay interference: implications for nanotoxicity testing. Plos One 9, (2014).

  17. Lin, W., Huang, Y. W., Zhou, X. D. & Ma, Y. In vitro toxicity of silica nanoparticles in human lung cancer cells. Toxicol. Appl. Pharmacol. 15, 252–259 (2006).

    Google Scholar 

  18. Liu, S., Xu, L., Zhang, T., Ren, G. & Yang. Z. Oxidative stress and apoptosis induced by nanosized titanium dioxide in PC12 cells. Toxicology 267, 172–177 (2010).

    CAS  PubMed  Google Scholar 

  19. Soenen, S. J., Rejman, J., Parak, W. J. & Manshian, B. (Intra) cellular stability of inorganic nanoparticles: effects on cytotoxicity, particle functionality and biomedical applications. Chem. Rev. 115, 2109–2135 (2015).

    CAS  PubMed  Google Scholar 

  20. Comfort, K. K., Maurer, E. I., Braydich-Stolle, L. K. & Hussain S. M. Interference of silver, gold and iron oxide nanoparticles on epidermal growth factor signal transduction in epithelial cells. ACS Nano 5, 10000–10008 (2011).

    CAS  PubMed  Google Scholar 

  21. Nazarenus, M. et al. In vitro interaction of colloidal nanoparticles with mammalian cells: What have we learned thus far? Beilstein J. Nanotechnol. 5, 1477–1490 (2014).

    PubMed  PubMed Central  Google Scholar 

  22. Docter, D. et al. Quantitative profiling of the protein coronas that form around nanoparticles. Nature Protoc. 9, 2030–2044 (2014).

    CAS  Google Scholar 

  23. Lundqvist, M. et al. The evolution of the protein corona around nanoparticles: a test study. ACS Nano 5, 7503–7509 (2011).

    CAS  PubMed  Google Scholar 

  24. Rocker, C., Potzl, M., Zhang, F., Parak, W. J. & Nienhaus, G. U. A quantitative fluorescence study of protein monolayer formation on colloidal nanoparticles. Nat. Nanotechnol. 4, 577–580 (2009).

    ADS  PubMed  Google Scholar 

  25. Mahmoudi, M. et al. Irreversible changes in protein conformation due to interaction with superparamagnetic iron oxide nanoparticles. Nanoscale 3, 1127–1138 (2011).

    CAS  ADS  PubMed  Google Scholar 

  26. Mahmoudi, M. et al. Temperature: The “ignored” factor at the nanobio interface. ACS Nano 7, 6555–6562 (2013).

    CAS  PubMed  Google Scholar 

  27. Turabekova, M. et al. Immunotoxicity of nanoparticles: a computational study suggests that CNTs and C60 fullerenes might be recognized as pathogens by Toll-like receptors. Nanoscale 6, 3488–3495 (2014).

    CAS  ADS  PubMed  Google Scholar 

  28. Soenen, S. J., Demeester, J., De Smedt, S. C. & Braeckmans, K. The cytotoxic effects of polymer-coated quantum dots and restrictions for live cell applications. Biomaterials 33, 4882–4888 (2012).

    CAS  PubMed  Google Scholar 

  29. Chang, E., Thekkek, N., Yu, W. W., Colvin, V. L. & Drezek, R. Evaluation of quantum dot cytotoxicity based on intracellular uptake. Small 2, 1412–1417 (2006).

    CAS  PubMed  Google Scholar 

  30. Ketkar-Atre et al. Variability in contrast agent uptake by different but similar stem cell types. Int. J. Nanomed. 8, 4577–4591 (2013).

    Google Scholar 

  31. Summers, H. D. et al. Statistical analysis of nanoparticle dosing in a dynamic cellular system. Nature Nanotech. 6, 170–174 (2011).

    CAS  ADS  Google Scholar 

  32. Nel, A., Xia, T., Madler, L. & Li, N. Toxic potential of materials at the nanolevel. Science 311, 622–627 (2006).

    CAS  ADS  PubMed  Google Scholar 

  33. Andon, F. T. & Fadeel, B. Programmed cell death: molecular mechanisms and implications for safety assessment of nanomaterials. Acc. Chem. Res. 46, 733–742 (2013).

    CAS  PubMed  Google Scholar 

  34. Luo, Y. H. et al. Cadmium-based quantum dot induced autophagy formation for cell survival via oxidative stress. Chem. Res. Toxicol. 26, 662–673 (2013).

    CAS  PubMed  Google Scholar 

  35. Lin, C. W., Jan, M. S. & Kuo, J. H. Autophagy-related gene expression analysis of wild-type and atg5 gene knockout mouse embryonic fibroblast cells treated with polyethylenimine. Mol. Pharm. 11, 3002–3008 (2014).

    CAS  PubMed  Google Scholar 

  36. Liu, J. et al. A diterpenoid derivate compound targets selenocysteine of thioredoxin reductases and induces Bax/Bak-independent apoptosis. Free Radic. Biol. Med. 63, 485–494 (2013).

    CAS  ADS  PubMed  Google Scholar 

  37. Brayden, D. J., Cryan, S. A., Dawson, K. A., O’Brien, P. J. & Simpson, J. C. High-content analysis for drug delivery and nanoparticle applications. Drug Discovery Today (2015), 10.1016/j.drudis.2015.04.001.

  38. Singh, S., Carpenter, A. E. & Genovesio, A. Increasing the content of high-content screening: An overview. J. Biomol. Screen. 19, 640–650 (2014).

    PubMed  PubMed Central  Google Scholar 

  39. Kirchner, C. et al. Cytotoxicity of colloidal CdSe and CdSe/ZnS nanoparticles. Nano Lett. 5, 331–338 (2005).

    CAS  ADS  PubMed  Google Scholar 

  40. Soenen, S. J. et al. The effect of nanoparticle degradation on amphiphilic polymer-coated quantum dot toxicity: the importance of particle functionality assessment in toxicology. Acta Biomater. 10, 732–741 (2014).

    CAS  PubMed  Google Scholar 

  41. Derfus, A. M., Chan, W. C. W. & Bhatia, S. N. Probing the cytotoxicity of semiconductor quantum dots. Nano Lett. 4, 11–18 (2004).

    CAS  ADS  PubMed  Google Scholar 

  42. Gagné, F., Maysinger, D., André, C. & Blaise, C. Cytotoxicity of aged cadmium-telluride quantum dots to rainbow trout hepatocytes. Nanotox. 2, 113–120 (2008).

    Google Scholar 

  43. Mahmoudi, M. et al. Cell “vision”: complementary factor of protein corona in nanotoxicology. Nanoscale 4, 5461–5468 (2012).

    CAS  ADS  PubMed  Google Scholar 

  44. Xue, J. et al. An assessment of the impact of SiO2 nanoparticles of different sizes on the rest/wake behavior and the developmental profile of zebrafish larvae. Small 9, 3161–3168 (2013).

    CAS  PubMed  Google Scholar 

  45. Mishra, P. & Chan, D. C. Mitochondrial dynamics and inheritance during cell division, development and disease. Nat. Rev. Mol. Cell. Bio. 15, 634–646 (2014).

    CAS  Google Scholar 

  46. Yang, Z., Goronzy, J. J. & Weyand, C. M. Autophagy in autoimmune disease. J. Mol. Med. (Berl.) 93, 707–717 (2015).

    CAS  Google Scholar 

  47. Liu, D., Gao, M., Yang, Y., Qi, Y. U., Wu, K. & Zhao, S. Inhibition of autophagy promotes cell apoptosis induced by the proteasome inhibitor MG-132 in human esophageal squamous cell carcinoma EC9706 cells. Oncol. Lett. 9, 2278–2282 (2015).

    CAS  PubMed  PubMed Central  Google Scholar 

  48. Wang, Q. W. et al. Cadmium-induced autophagy promotes survival of rat cerebral cortical neurons by activating class III phosphoinositide 3-kinase/beclin-1/B-cell lymphoma 2 signaling pathways. Mol. Med. Rep. 12, 2912–2918 (2015).

    CAS  PubMed  Google Scholar 

  49. Pi, H. et al. SIRT3-SOD2-mROS-dependent autophagy in cadmium-induced hepatotoxicity and salvage by melatonin. Autophagy 11, 1037–1051 (2015).

    CAS  PubMed  PubMed Central  Google Scholar 

Download references

Acknowledgements

This work was supported by the FWO-Vlaanderen (G0584.12N to P.A., 1.2.354.14N to S.J.S), the Flemish agency for Innovation by Science and Technology (IWT SBO MIRIAD/130065) and the KU Leuven program financing IMIR (PF 2010/017). S.J.S. is supported by the FWO-Vlaanderen.

Author information

Affiliations

Authors

Contributions

B.B.M. and S.J.S. conceived, designed and performed the experiments. S.M. Contributed high-content imaging analysis tools. P.A. contributed to designing mechanistic cell studies. U.H. contributed reagents/materials and aided in analyzing the data. All authors contributed to the writing of the manuscript.

Ethics declarations

Competing interests

The authors declare no competing financial interests.

Electronic supplementary material

Rights and permissions

This work is licensed under a Creative Commons Attribution 4.0 International License. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in the credit line; if the material is not included under the Creative Commons license, users will need to obtain permission from the license holder to reproduce the material. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

Manshian, B., Munck, S., Agostinis, P. et al. High content analysis at single cell level identifies different cellular responses dependent on nanomaterial concentrations. Sci Rep 5, 13890 (2015). https://doi.org/10.1038/srep13890

Download citation

Further reading

Comments

By submitting a comment you agree to abide by our Terms and Community Guidelines. If you find something abusive or that does not comply with our terms or guidelines please flag it as inappropriate.

Search

Quick links

Nature Briefing

Sign up for the Nature Briefing newsletter — what matters in science, free to your inbox daily.

Get the most important science stories of the day, free in your inbox. Sign up for Nature Briefing