Meta-analysis of cellular toxicity for cadmium-containing quantum dots.

ARTICLES PUBLISHED ONLINE: 29 FEBRUARY 2016 | DOI: 10.1038/NNANO.2015.338

Meta-analysis of cellular toxicity for cadmiumcontaining quantum dots Eunkeu Oh1,2†, Rong Liu3,4*†, Andre Nel4,5, Kelly Boeneman Gemill6, Muhammad Bilal4, Yoram Cohen3,4,7* and Igor L. Medintz6* Understanding the relationships between the physicochemical properties of engineered nanomaterials and their toxicity is critical for environmental and health risk analysis. However, this task is confounded by material diversity, heterogeneity of published data and limited sampling within individual studies. Here, we present an approach for analysing and extracting pertinent knowledge from published studies focusing on the cellular toxicity of cadmium-containing semiconductor quantum dots. From 307 publications, we obtain 1,741 cell viability-related data samples, each with 24 qualitative and quantitative attributes describing the material properties and experimental conditions. Using random forest regression models to analyse the data, we show that toxicity is closely correlated with quantum dot surface properties (including shell, ligand and surface modifications), diameter, assay type and exposure time. Our approach of integrating quantitative and categorical data provides a roadmap for interrogating the wide-ranging toxicity data in the literature and suggests that meta-analysis can help develop methods for predicting the toxicity of engineered nanomaterials. ecent studies1,2 have suggested that the adverse biological impacts of engineered nanomaterials (ENMs)3,4 are linked to their specific physicochemical properties5,6, and efforts are mounting to map the principles governing their toxicity7,8. Approaches to arrive at a generalization of ENM toxicity behaviour via either qualitative classification-based (banding) models9 or quantitative-structure–activity relationships (QSARs)10,11 have typically focused on developing a given model using a dataset(s) from an individual study or a small number of studies12,13 rather than considering the total body of published evidence. However, when resorting to data-mining and knowledge-extraction from literature data14, it is important to note that not all information is equally valuable15. Methodology is thus required to (1) assess the adequacy of the available literature data for unambiguous identification of physicochemical properties and experimental conditions (collectively referred to as attributes hereafter) relevant to the toxicity of a specific ENM; (2) develop robust data-driven models to correlate ENM toxicity with identified attributes; and (3) explore the compilation of published ENM data via similarity analysis and the identification of conditional attribute–toxicity dependences. Literature data-mining/knowledge-extraction, also known as meta-analysis16,17, has been successfully used in chemical toxicity studies. This approach has provided systematic reviews and critical appraisals18 of the relationships between physicochemical properties and experimental conditions on bioactivity (for example, toxicity)19. An initial meta-analysis effort regarding 136 types of carbon nanotube (CNT) from 17 publications investigated the correlation (via regression tree analysis and random forest) between pulmonary toxicity and 41 CNT attributes (focusing on impurities, physical dimensions and exposure characteristics)20. The above study concluded that metallic impurities, CNT width and aggregate size were properties relevant to correlating pulmonary toxicity in mice and rats.

R

In the present study, we performed a comprehensive and rigorous meta-analysis of the published data available from toxicity studies of Cd-containing quantum dots (QDs). QDs are currently undergoing intensive development for applications in medicine, biosensing and as probes/contrast agents21–23. QDs consist of binary combinations of semiconductors (for example, CdSe, CdTe) and display unique quantum confined properties, including, in particular, size-tunable photoluminescence. QDs are often coated with wider-bandgap materials (for example, ZnS) that act to protect the core, prevent Cd leaching and enhance photoluminescence. For biological applications, the prototypical QD platform consists of a common core/shell–ligand architecture (schematically illustrated in Fig. 1a) displaying a wide range of material/structural diversity (for example, core/shell configuration, surface solubilization chemistry and biofunctionalization)22,23. Although the unique benefits of QDs for bioapplications have been demonstrated repeatedly, there is still a vigorous debate about their potential toxicity24,25. Studies undertaken to evaluate QD potential toxicity have used a broad range of QD types and included classical cytotoxicity assays, and have examined the effects of QDs on cellular organelles and gene/protein expression, as well as their distribution, persistence and clearance in animal models24–27. It has been proposed that QD toxicity could be correlated to physicochemical properties such as core/shell materials, size, surface charge, nature of the surface ‘ligands’ (providing colloidal stability), the presence of other surface modifications, and interaction with various molecules (for example, proteins) present in biological media24–29. Other variables, including exposure period/concentration, exposure pathway and chemistry of the exposure medium, may also affect toxicity. Given that no single study to date has assessed the toxicity response associated with the above collective properties/ variables across the existing body of literature, a challenge exists in how to integrate quantitative/qualitative information from such

1

Optical Sciences Division, Code 5611, US Naval Research Laboratory, Washington, Washington DC 20375, USA. 2 Sotera Defense Solutions, Columbia, Maryland 21046, USA. 3 Institute of the Environment and Sustainability, University of California, Los Angeles, California 90095-1496, USA. 4 Center for Environmental Implications of Nanotechnology, University of California, Los Angeles, California 90095-7227, USA. 5 Department of Medicine, Division of NanoMedicine, University of California, Los Angeles, California 90095, USA. 6 Center for Bio/Molecular Science and Engineering, Code 6900, US Naval Research Laboratory, SW Washington, Washington DC 20375, USA. 7 Department of Chemical and Biomolecular Engineering, University of California, Los Angeles, California 90095-1592, USA. †These authors contributed equally to this work. * e-mail: [email protected]; [email protected]; [email protected]

NATURE NANOTECHNOLOGY | ADVANCE ONLINE PUBLICATION | www.nature.com/naturenanotechnology

© 2016 Macmillan Publishers Limited. All rights reserved

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DOI: 10.1038/NNANO.2015.338

Biomodifications: Peptide

Core: CdSe, CdTe Shell: ZnS, CdS Multishell(s)

Protein

Solubilizing ligand: Alkylthiol PEG/polyol

DNA, RNA

Amphiphiles Lipids, silica Polymer Charge: positive, negative, zwitterionic, neutral

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Figure 1 | QD structure, meta-analysis workflow and compiled QD data. a, Schematic of QD structure highlighting selected structural and physicochemical attributes used for characterization including core, shell and multishell constituents, ‘ligand’ (which imparts QD colloidal stability), presence of further surface modification, as well as surface charge. b, Workflow for analysis of literature QD toxicity data. QD toxicity data, together with their physicochemical properties and experimental condition parameters, were collected via manual literature data-mining. The compiled QD data were then preprocessed to normalize numeric attributes and to prune categorical attributes with excessive content. Regression models were then developed using the RF technique for both cell viability and IC50 with the most suitable attributes selected via exhaustive search. c, Number of QD data points extracted from each publication. Publications are identified by a publication reference ID (see Methods, refs 45–351). Data on the toxicity of QDs were compiled via a rigorous process of literature data-mining and analysed to identify key attributes of QDs that correlate with their reported toxicity.

complex and heterogeneous toxicity data to reliably derive predictable trends. Accordingly, in the present study, literature-mined QD toxicity data were used as a test platform to evaluate the feasibility of meta-analysis for achieving comprehensive ENM impact assessment. The analysis utilized the random forest (RF)30,31 approach to identify relevant QD data attributes, assess the consistency of reported literature QD toxicity data, and develop robust data-driven models of QD toxicity. In addition, network similarity analysis of the RF modelling 2

results was used to assess the heterogeneity of QD attribute–toxicity relationships as revealed in different published studies.

Compiled QD data and analysis workflow Literature data-mining of QD toxicity and associated knowledge extraction, together with model development, followed the workflow depicted in Fig. 1b with specifics as provided in the Methods. Extensive literature interrogation identified 307 publications




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Table 1 | Overview of literature QD data attributes (1,741 QD data points). 1. QD source In house Commercial

2 76% 24%

2. Core CdSe CdTe CdSeTe CdS CdHgTe …

8 63% 29% 3% 3% 1% 1%

3. Shell ZnS None CdS CdS/ZnS ZnSe CdZnS ZnTe …

14 50% 31% 7% 6% 2% 2% 1% 2%

4. QD diameter 1.4 to 20 × 80 (nm)* 5. Surface ligand Alkylthiol Amphiphilic polymer PEG Polyol Silica Amino acid Hydrophilic polymer

13 28% 14% 11% 8% 7% 6% 6%

Alkylthiol PEG Lipid Aminothiol Other hydrophilic Aminopolymer Other hydrophobic

10. Cell anatomical type Epithelial Fibroblast Endothelial Neuronal Monocyte Hepatocyte Monocyte macrophage Leukocyte Stem cell Progenitor stem cell Diatom Myoblast Erythrocyte …

29 53% 18% 5% 3% 3% 3% 2% 2% 2% 2% 1% 1% 1% 4%

11. Cell identification

135

12 64% 6% 6% 5% 4% 4% 3% 3% 2% 1% 1% 1%

12. Cell source species Human Mouse Rat Monkey Pig Cow Algae Rabbit Hamster Hydra …

103

13. Cell origin Cell line Primary

6% 5% 3% 3% 2% 1%

6. Ligand chemical

344

7. Surface charge Negative Neutral Positive Zwitterionic

4 41% 27% 26% 5%

8. Surface modification Unmodified Drug Delivery peptide Protein Polymer Amino acid Peptide Nucleic acid Mineral Antioxidant Toxin … 9. Surface modification chemical

14. Cell tissue/organ origin

45

18 69% 15% 6% 2% 2% 1% 1% 1% 1% 1% 1%

15. Assay type MTT MTS Alamar blue WST Trypan blue CCK8 BrdU TUNEL FACS LDH Live dead staining Hoechst staining SRB Colonigenic assay Haemolysis Proliferation Induction of autophagy Neutral red Crystal violet Luciferase assay …

29 56% 9% 4% 4% 4% 3% 2% 2% 2% 2% 1% 1% 1% 1% 1% 1% 1% 1% 1% 2%

16. Delivery type Passive Active

2 64% 36%

2 83% 17%

17. Exposure time 3.3 × 10−2 to 6.7 × 102 h

Cd concentration 18. pmol per cell: 1.3 × 10−4 to 1.9 × 104 19. mg l−1: 1.4 × 10−2 to 1.3 × 105 QD concentration –1 20. mg l : 2.9 × 10−2 to 1.7 × 105 21. nM: 1.1 × 10−1 to 7.6 × 105 22. No. QD per cell: 4.3 × 105 to 2.0 × 1013 23. amol per cell: 7.2 × 10−1 to 3.3 × 107 QD surface concentration 2 24. nM nm : 1.8 × 101 to 6.0 × 107 Cell viability† 0 to 100% IC50 range 1.3 × 10−2 to 1.7 × 105 mg l–1 8.0 × 10−2 to 7.6 × 105 nM

Attribute definitions are provided in Supplementary Table 1. * This value represents ‘rod-like’ QDs. † 111 values with more than 100% reported were normalized to 100%. Attributes are either numeric (4, 17–24) or categorical (1–3, 5–16). For categorical attributes, the total number of categories is provided to the right of the attribute name in bold, while the different corresponding categories are listed below with their percentage of occurrence in the entire dataset. For clarity, categories accounting for less than 1% of the data are pooled and are indicated as ‘…’. Also, categories are not listed if greater than 30 for a given attribute. Note that variable 18 is Cd concentration per cell, 21 is QD concentration in nM, 22 is number of QDs per cell and 23 is QD concentration exposed per cell.

containing relevant QD toxicity data (see sample dataset references in the Methods). Information extracted from the literature included QD cell viability data, attributes representing QD physicochemical properties and other QD relevant information, type of toxicity assay, nature of the cells used and experimental conditions (for the attribute list see Table 1). Literature data-mining yielded 1,741 QD cell viability (%) data samples. The distribution of data samples among the different publications is presented in Fig. 1c and, with the exception of one study, the data were not dominated by any particular source. Overall, the compiled data showed no observable trend in cell viability as a function of concentration for major QD types, indicating the need for more in-depth analysis (Supplementary Fig. 1). A total of 514 distinct IC50 values (defined here as the exposure concentration at which there is 50% cell death or inhibition of cell growth or other utilized toxicity metric) were also identified from 147 of the publications reporting toxicity data over a range of exposure concentrations (Fig. 2). The compiled QD data (Supplementary QD dataset file) were preprocessed to normalize the numeric attributes and to exclude categorical attributes of excessive categories. RF regression models30,32 (Supplementary Fig. 2) for cell viability and IC50 were then developed along with an exhaustive search to identify the most suitable model attributes (that is, QD properties and experimental conditions), followed by network similarity mapping of QD attribute–toxicity relationships. This approach served to evaluate both the body of evidence shared among different published studies and the significance of various attributes for correlating QD toxicity.

QD cell viability models RF regression models based on cell viability as toxicity metrics were developed initially using 17 QD data attributes identified from the

compiled literature (Table 1), in addition to exposure concentration as an additional attribute evaluated for seven different concentration metrics (attributes 18–24, Table 1). RF models with the various concentration metrics provided essentially the same prediction accuracy of R 2E632 ≈ 0.73 (coefficient of determination10,33 between the observed and predicted cell viability as determined by the 0.632 estimator34,35). Thus, the commonly reported QD mass concentration (mg l–1) was selected as the choice exposure concentration attribute (attribute 20, Table 1). The above RF model included the attributes ligand chemical, surface modification chemical, cell identification and cell tissue/organ origin, each containing an excessive number of categories (>30). This large number of categories per attribute can impair the RF model generalization capability. When the above four attributes were removed from the set, the RF model for cell viability with the remaining 14 attributes (that is, 1–5, 7, 8, 10, 12, 13, 15–17 and 20, Table 1) demonstrated only 4.1% decrease in R 2E632 (from 0.73 to 0.70). Given the tradeoff between increased model accuracy and the desire for increased generalization capability, the 14-attribute set was used in an exhaustive search for the most significant set of n attributes (evaluated for RF models with attribute number incremented from n = 2 up to 14). RF models require at least two initial attributes, and those were determined to be QD diameter and QD concentration, as these are most commonly reported in the literature (Fig. 3a). The six attributes that were subsequently identified as most significant for cell viability were QD diameter, QD concentration (mg l–1), surface ligand, exposure time, surface modification and assay type. A more approximate random permutation-based approach (see Methods) identified essentially the same attributes, with the exception of shell being identified instead of QD diameter (Supplementary Fig. 4a). For the RF model based on the exhaustive



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QD exposure dosage [nM] (increasing IC50 value →)

Figure 2 | IC50 values (red) and dosage range used (blue) for major QD core/shell structures. The IC50 values are sorted and plotted by increasing values to simplify the visual presentation. Plots of QD concentration ranges from studies that did not span a sufficient exposure concentration range to yield an IC50 are provided in Supplementary Fig. 5. Numbers in parentheses indicate the number of samples for that QD material. IC50 is defined here as the exposure concentration at which there is 50% cell death or inhibition of cell growth or other utilized toxicity metric.

search there was little change in prediction accuracy (step increase of R 2E632 of less than 3%) once the attribute number increased to above six (Fig. 3a). This behaviour could in part be due to scatter and/or quality of the cell viability data (Fig. 3b) from the different sources or may indicate the need for additional correlating attributes not addressed in the QD literature sources. RF models developed using subsets of these data were also quite similar (Supplementary Tables 2 to 5). The above results illustrate the significant challenge of establishing, based on compilation of heterogeneous literature data, a widely usable model that could correlate cell viability with QD properties and experimental attributes. To assess the heterogeneity of the QD cell viability data, a similarity network (Fig. 4) was established based on the proximity matrix30. This network quantifies attribute similarity according to the frequency with which QD samples appear in the same leaf node of a tree (Supplementary Fig. 2) in a RF model using the six attributes most relevant to cell viability. The network was further √ partitioned into 30 clusters as suggested by the rule k ≈ n/2, where k is the number of clusters and n is the total number of QDs (that is, 1,741)36, using hierarchical clustering analysis, to profile the major structures in the QD cell viability data. In the similarity network, QDs are represented as nodes (for the cluster assignment for each data point see Supplementary QD dataset file) and connected QDs are of proximity larger than the average within cluster proximity. The sparse connection of the similarity network (Fig. 4) demonstrates 4

the heterogeneity of the current QD toxicity studies, with some clusters having little (for example, C11, C27 and C29) or no connectivity (for example, C14, C28 and C30) to others, the latter clusters having little commonality in terms of QD properties or experimental conditions. For example, isolated QD clusters (C14, C28 and C30) are not connected to any other clusters and may possess distinct attribute–cell viability correlations. However, there are dense connections for QDs within these isolated clusters, indicating high similarity in their attribute–cell viability correlations; this can also be inferred from the low standard deviation of the cell viability in these clusters (Fig. 4). Through the network map one can assess if there is potential skewness of the overall model due to one or more specific data sources. For example, cluster C30, which has the highest average cell viability among all clusters, contains data from a single study (ref. 220 in the sample dataset references, see Methods).

QD IC50 models RF models for IC50 values were developed based on the same set of attributes used for cell viability analysis (that is, attributes 1–5, 7, 8, 10, 12, 13, 15–17 and 20, Table 1) with QD exposure concentration (attribute 20) omitted as this is not a relevant attribute for IC50. The IC50 (mg l–1) RF regression model, based on the above 13 attributes, demonstrated good performance of R 2E632 = 0.80 and R 2resub = 0.92. Generally, R 2resub ≥ 0.81 is indicative of good agreement between predicted and observed responses10,33. This is not surprising given



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a

0.80 0.75 IC50 (mg l−1)

R2E632

0.70 0.65

Cell viability (%)

0.60 0.55 0.50

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0.45

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e

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e

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3 2 1 0 −1

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0 1 2 3 4 Observed IC50 (log mg l−1)

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Figure 3 | RF analysis and models. a, RF prediction accuracy for the most suitable set of attributes identified via exhaustive search. Attributes were incrementally added to those previously selected by exhaustive search except for those already contained in the boxes (Supplementary Table 1). Therefore, the order that a given attribute was added by the exhaustive search also infers its importance to the correlation of QD toxicity (cell viability or IC50). b, RF model predicted versus observed cell viability (%). c, RF model predicted versus IC50 values (log mg l–1). The RF models for cell viability and IC50 demonstrated performances of R 2E632 = 0.68 and 0.77, respectively. In b and c, R 2E632 , R 2OOB and R 2resub identify the coefficient of determination (R2) assessed by the 0.632 estimator, out-of-bootstrap validation and resubstitution validation, respectively.

that IC50 is an intrinsic toxicity measure obtained from analysis of QD cell viability data integrated over an experimental concentration range. The above RF regression accuracy demonstrates that IC50 is reasonably correlated with these 13 attributes (Fig. 3a). Moreover, the good performance of the IC50 RF model suggests that QD IC50 data compiled from diverse publications can provide valuable information enabling both the identification and generalization of the collective dependence of toxicity on QD properties and experimental conditions. Exhaustive search for the most significant attribute subset (of size n = 2 to 13) for IC50 demonstrated a behaviour similar to that of viability, but with a higher RF model prediction accuracy (Fig. 3a). The top six significant attributes were QD diameter, surface ligand, shell, assay type, exposure time and surface modification, which were also identified as the top ranked attributes by the random permutation approach (Supplementary Fig. 4b). With the above six attributes, the RF model prediction accuracy was R 2E632 = 0.77, which increased at a level of 80% of 6

the QDs with either lipid, amphiphilic polymer, or aminothiol surface ligand are associated with IC50 < 25 mg l–1 (highly toxic), while >80% of QDs with a polyol surface ligand demonstrated IC50 ≥ 25 mg l–1 (less toxic) (Fig. 5). On the other hand, alkylthiol as the prevalent surface ligand category (accounting for ∼34% of the IC50 data) did not have a noticeable correlation with IC50 (Fig. 5). However, if one also considers QD diameter >5 nm then ∼87% of QDs with alkylthiol surface ligand are associated with IC50 ≥ 25 mg l–1 (Fig. 5). Clearly, there is a complex coupling of attributes in terms of their impact on observed toxicity, hence the challenge of arriving at generalized relationships from heterogeneous literature-compiled datasets. As the above conditional dependence was extracted from a single decision tree, it cannot be considered to be robust (or representative) of the conditional dependence that would be obtained by considering the complete set of RF trees. Accordingly, using a clustering-based approach (see Methods), a number of robust conditional attribute–IC50 dependences were extracted from the compiled QD samples. The first extracted conditional dependence: ‘If (surface_ligand in {aminothiol, hydrophilicpolymer, lipid, silica}, assay type in {fluoresceinretentionassay, mtt, rtces, wst}, surface modification in {drug, toxin, unmodified}), then IC50 ≤ 38.6 mg l–1’ is supported by ∼80% of the 77 QD samples from 22 different studies that satisfy its condition. Potential toxicity contributors to



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alkylthiol

No. of QD samples

140 120 100

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20 0 c hili op ydr bic erh ho op oth ydr erh oth

g pe d er lipi lym po ino am ca sili ol thi ino am d aci ino g am lpe er hio lym o ylt alk licp i er iph lym ph cpo am hili rop hyd l lyo po l hio ylt alk

Surface ligand

Figure 5 | Conditional dependence of QD IC50 on surface ligand and/or QD diameter. The conditional dependence is illustrated via the distribution of the number of QD samples with respect to surface ligand and the distribution of the number of QDs with surface ligand = alkylthiol with respect to QD diameter. If one considers QD diameter > 5 nm (inset) then the lower toxicity of IC50 ≥ 25 mg l–1 will be expected for ∼87% of QDs with the alkylthiol surface ligand. Note that ligand types appear here and in the text as used in the computer code, meaning all lower case without spacing or dashes.

this include a high percentage of samples with poorly stable surface ligands, toxic surface modifications and active delivery. The second conditional dependence: ‘If (QD diameter in [3.12, 5.11] nm, surface_modification in {aminoacid, antioxidant, drug, nucleicacid, peptide}, exposure time = 48 h), then IC50 in [39.4, 175] mg l–1’ is supported by 80% of the 64 QD samples that satisfy the condition. It is noted that >95% of the above 64 QD samples were from four studies (refs 164, 190, 268 and 325 in the sample dataset references, see Methods) that appear to be from the same research group. Examining the QD samples that support that above conditional dependence for IC50 suggests that the extended 48 h exposure period, small diameter and the specific surface modifiers may have all contributed to either active cellular QD uptake and/or an increased toxicity response. The third conditional dependence: ‘If (assay type = crystalviolet), then IC50 >1,585 mg l–1’ was extracted from a single study (ref. 193 in the sample dataset references, see Methods). The extraction of such a biased conditional dependence (of limited generality) signifies that it is important to evaluate the biological interpretation of conditional dependences identified from machine learning approaches. Importantly, the above identified dependences directly suggest specific QD attributes meriting further study for causative relationships with cytotoxicity. A discussion of specific details associated with the above dependences is provided in Supplementary Results and Discussion (‘Conditional Attribute–IC50 Dependences’).

any ENM to date. The adequacy, consistency and applicability of literature data for QD toxicity were assessed using RF models. These models demonstrated a prediction accuracy of R 2E632 = 0.68 for cell viability, with a higher accuracy of R 2E632 = 0.77 for IC50. Attribute significance, evaluated in conjunction with RF model development, indicated that the QD-induced toxicity response correlated primarily with key intrinsic QD properties (such as QD diameter, surface ligand, shell and surface modification), although other attributes (such as exposure time, exposure concentration (for cell viability model only), assay type, cell anatomical type and cell origin) also had measurable relevance. Finding that QD surface properties and size are dominant correlating attributes for QD toxicity was conclusively demonstrated by our analysis of the compiled literature data. We also note that the identification of QD surface shell (absent in 31% of samples) as an attribute of measurable significance in correlating toxicity is consistent with studies that have raised concern regarding the cellular environment having direct access to the Cd-containing core24–26,37,40. Intriguing questions arise from how surface modification correlates with QD toxicity, as almost all modified QD materials (in the dataset) were actively delivered to cells; thus, is it just the modification itself, a specific subset of modification types, or the fact that active delivery may result in far more material achieving cellular uptake than passive exposure? Overall, this work suggests that information derived from literature data-mining can provide guidance regarding key ENM attributes (for example, QD physicochemical properties and experimental conditions) that should be characterized and reported in ENM toxicity studies. Similar approaches should be applicable to other ENMs (for example, gold, silver, iron oxide, silicon and carbon nanoparticles) assuming dosage concentrations can be appropriately extracted. Data from the ‘growing body of evidence’ for these materials can be continuously added to a dataset that can also be subjected to other analyses. To facilitate this, studies providing extensive physicochemical characterization along with detailed dose–response toxicity data (that is, IC50 determination) for each ENM will clearly be most desirable. Identifying key attributes to ENM toxicity as well as the conditional attribute– toxicity dependence will certainly help focus future research so as to elucidate the underlying toxicity mechanisms involved and help in the development of nanomaterials that are safe-by-design. Even with the availability of large datasets, it is imperative that reliance on compiled literature data for the formulation of rational environmental and health regulatory policies must rigorously consider (1) the value of the published information and (2) suitable modelling approaches to correlate ENM toxicity with the relevant attributes (for example, intrinsic ENM properties and experimental conditions).

Methods Methods and any associated references are available in the online version of the paper. Received 18 September 2014; accepted 16 December 2015; published online 29 February 2016

References 1.

Conclusions Addressing the toxicity of a given ENM requires defining the key material properties that contribute to toxicity at the most basic level—the cell. A meta-analysis has been developed here for the assembly and generalization of published EMN cellular toxicity data using Cd-containing QDs as a model system. More than 300 publications were mined, generating 1,741 QD toxicity data samples (each with 24 qualitative/quantitative attributes) and providing perhaps the most comprehensive compiled dataset for

2. 3.

4. 5.

Colvin, V. L. The potential environmental impact of engineered nanomaterials. Nature Biotechnol. 21, 1166–1170 (2003). Nel, A., Xia, T., Madler, L. & Li, N. Toxic potential of materials at the nanolevel. Science 311, 622–627 (2006). Ray, P. C., Yu, H. T. & Fu, P. P. Toxicity and environmental risks of nanomaterials: challenges and future needs. J. Environ. Sci. Health C 27, 1–35 (2009). Kahru, A. & Dubourguier, H. C. From ecotoxicology to nanoecotoxicology. Toxicology 269, 105–119 (2010). Brunner, T. J. et al. In vitro cytotoxicity of oxide nanoparticles. Comparison to asbestos, silica, and the effect of particle solubility. Environ. Sci. Technol. 40, 4374–4381 (2006).



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Acknowledgements I.L.M. acknowledges the Naval Research Laboratory Nanosciences Institute and the Defense Threat Reduction Agency Joint Science and Technology Office Military Interdepartmental Purchase Request no. B112582M. This study is also based on work supported by the National Science Foundation and the Environmental Protection Agency under Cooperative Agreement no. DBI-0830117. Any opinions, findings and conclusions or recommendations expressed in this material are those of the author(s) and do not necessarily reflect the views of the National Science Foundation or the Environmental Protection Agency. This work has not been subjected to EPA review and no official endorsement should be inferred. Computational cluster support by the UCLA WaTeR center is also acknowledged.

Author contributions I.L.M., Y.C., R.L., E.O. and A.N. conceived the study. E.O., K.B.G. and I.L.M. searched the literature, extracted data, identified attributes and prepared data for analysis. E.O. developed the methodology for converting QD concentrations. R.L., M.B. and Y.C. analysed the data and developed the reported models and attribute significance. I.L.M., E.O., Y.C. and R.L. co-wrote the paper with input from all authors.

Additional information Supplementary information is available in the online version of the paper. Reprints and permissions information is available online at www.nature.com/reprints. Correspondence and requests for materials should be addressed to R.L., Y.C. and I.L.M.

Competing financial interests

The authors declare no competing financial interests.




DOI: 10.1038/NNANO.2015.338

Methods

Workflow for meta-analysis of QD toxicity data. The present study followed the workflow depicted in Fig. 1b. The first step consisted of extracting and compiling literature QD toxicity data (cell viability and IC50 values), together with their physicochemical properties and experimental conditions (referred to as QD data attributes; Table 1). Categorical or discrete attributes provide non-continuous variable information (for example, surface ligand, core type). Compiled data were pre-processed to normalize (via log transform) numeric attributes (including exposure time, various concentration metrics and IC50 values) into reasonable spanned ranges while identifying and removing categorical attributes of excessive categories. Based on the pre-processed QD data, RF regression models were developed for both cell viability and IC50 values, together with exhaustive search to identify the most relevant sets of attributes (that is, from sets of 2 up to the total number of attributes in the selected initial pool). RF is an advanced ‘ensemble learning’ approach to develop data-driven models based on the aggregation of decision trees developed using bootstrap samples of training data30. RF has proven suitable for robust meta-analysis of highly complex and heterogeneous literature data20,30. RF regression was employed for model development and attribute assessment given the following advantages: (1) capability of coping with the heterogeneity of compiled QD data that contains both categorical and numerical attributes; (2) robustness with respect to data noise; (3) ability to provide internal estimates of model predictive performance via out-of-bootstrap (OOB) validation30, which, together with resubstitution validation, provides a synthetic model performance estimator34; (4) internal assessment of attribute significance as a variable for correlating QD toxicity; and (5) suitability for quantifying similarity in QD attribute-cell viability correlations based on the frequency that the QDs appear in the same leaf node of decision trees (Supplementary Fig. 2)30. Data compilation. The literature search and data extraction/conversion followed the workflow (Supplementary Fig. 3) described in the Supplementary Methods. Briefly, data from a given publication were selected for inclusion in the dataset provided that the QD contained Cd and information was provided regarding: (1) core/shell structure; (2) surface ligand and further (bio)modification(s); (3) QD physicochemical parameters (for example, size, emission wavelength, surface charge); (4) cellular delivery mechanisms; (5) delivery data to enable determination of dosage per cell/exposure time; and (6) quantifiable metrics of cytoviability/ toxicity evaluated in a eukaryotic cell system such as a primary or transformed cell line. The above criteria provided for similar cellular structure among the tested cells and were critical because different toxicity assays were reported in the studies, thus requiring the consideration of gross cellular toxicity/cytoviability in similar anatomical systems (that is, eukaryotic cells) when assessing a body of evidence regarding toxicological information. An extensive literature search initially identified ∼1,100 papers. However, only 307 papers satisfied all selection criteria45–351. Each publication was manually examined and relevant QD physicochemical characteristics, experimental conditions and cytotoxicity data were extracted (Supplementary QDs dataset file), yielding 1,741 data samples with 24 attributes (Table 1). Note that toxicity data collected under illuminated conditions where QDs purposely generate reactive oxygen species (ROS) were specifically excluded. Toxicity data encompassed 29 different assays, of which the 3-(4,5-dimethylthiazol-2-yl)-2,5-diphenyltetrazolium bromide (MTT) and related 3-(4,5-dimethylthiazol-2-yl)-5-(3-carboxymethoxyphenyl)-2-(4-sulphophenyl)-2H-tetrazolium (MTS) and water-soluble tetrazolium salts (WST) assays were most prevalent (∼69% of the evaluated studies; Table 1)352. Toxicity data in the various studies were categorized as (1) single point (one QD concentration/exposure time, 34% of total studies); (2) multiple data points from a range of experimental conditions but where IC50 values were not reached, or those reporting over 50% viability (37%); (3) multiple data points from a range of experimental conditions with an IC50 value (29%, Fig. 2); and (4) datasets that did not demonstrate cell viability above 50% (1%). The ranges of exposure concentrations where IC50 was not attained or could not be extracted are provided in Supplementary Fig. 5. RF regression. To robustly handle the high complexity and excessive heterogeneity (that is, both numerical and categorical attributes) of the compiled QD toxicity data while assessing model performance and attribute importance at the same time, an advanced machine learning technique known as random forest (RF)30–32 was used in the present work. RF, as an extension of the well-known decision tree technique43,44, has proven suitable for modelling literature data (for example, meta-analysis) due to its favourable performance and robustness20. Decision trees are typically recursively constructed by choosing an attribute at each step that best partitions (splits) the data (according to criteria such as information gain, gain ratio, gini index, least standard deviation/squared error; Supplementary Fig. 2)43,44. In RF, a bootstrap sample30–32 is first drawn from the original data (that is, a sample drawn with replacement), which is then used to build a decision tree with a random subset of attributes selected for each tree split30–32. The process is repeated according to a prescribed number of rounds until the prediction (aggregated from each of the built decision trees) is within a target convergence tolerance (Supplementary Fig. 2). In the present work, the R package ‘randomForest’32 was used to develop the RF models. According to the recommended rule30–32 for RF model development, the number of repeat rounds was set to 500, and m/3 attributes were selected for each tree split (where m identifies the

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number of total attributes) with squared error (that is, node variance/impurity measure) used as the tree splitting criterion. The prediction performance of the RF model for QD toxicity was internally estimated in conjunction with the model development process. In RF model development, for each sampling round, only ∼63.2% of original data (that is, bootstrap samples)30–32 were used for tree construction (that is, decision tree building), while the remainder (that is, out-of-bootstrap or out-of-bag (OOB) samples) were used as a validation set to test the model prediction accuracy. It has been shown that the averaged OOB accuracy across all the rounds of a constructed tree is an accurate estimate of model prediction accuracy32. In the present work, the coefficient of determination R 2 (that is, 1 – MSE/Var( y), where y denotes the observation variable (that is, cell viability or IC50 value) and MSE identifies the mean squared error of the model prediction) was used as the metric for OOB prediction accuracy. Based on OOB accuracy, the model performance estimate known as the 0.632 estimator was used, which is defined as 0.632 × OOB accuracy + 0.368 × resubstitution accuracy34,35. In RF model development, the importance of each individual attribute was also assessed using a random permutation-based approach30–32. In this approach, the increase in OOB error (per MSE) for a given attribute after its permutation quantifies the attribute importance; the premise is that the permutation of an important attribute will lead to a significant decrease in RF model performance. Although the above permutation-based approach can identify top-ranked attributes, the combination of such attributes may not necessarily be the globally optimal attribute subset. The reason for this is that the permutation-based approach does not account for possible dependence and complementary information provided by multiple attributes in the composite dataset. To identify the most significant attribute subset, an exhaustive attribute search/selection was conducted in conjunction with RF model development. Given that there were only 14 attributes remaining after attribute pruning, it was feasible to identify the most suitable attribute set of a given size (from 2 attributes per set up to 14) using an exhaustive search. RF-based similarity network analysis. To quantify the proximity/similarity among the QDs, in RF model development the frequency that two QDs end in the same leaf (terminal) node of the regression trees was recorded (Supplementary Fig. 2). The frequency was then summed for all the built decision trees as a similarity (or proximity) matrix. QDs that frequently appear in the same leaf node can be considered as having similar attribute–toxicity correlations, as they more frequently share the same decision pathways (from root to leaf nodes, Supplementary Fig. 2). Such a matrix provides a measure of the internal structure of the QD data, quantifying the similarity (or conversely heterogeneity) among QDs with consideration of both the attributes and toxicity (that is, the attribute–toxicity correlation). To further profile the main structure in the QD cell viability data, hierarchical clustering analysis44 was conducted based on the obtained similarity matrix. The number of clusters, k, was set to 30 in the hierarchical clustering, √ following a rule of thumb that k ≈ n/2 (where n = 1,741 identifies the total 36 number of QDs) . A similarity network map was then established by connecting QDs of proximity larger than the average within cluster proximity to depict the overall proximity/similarity among the QD dataset. Extraction of conditional dependences from RFs. To extract the conditional dependences implied in the RF models, an approach based on clustering analysis of the RF proximity matrix was developed in the present work. It is noted that, for a single decision tree, the QDs in the same leaf node satisfy the same conditions at each branching node and have similar toxicity (for example, cell viability or IC50). In other words, for a single decision tree, each pathway from root to a leaf node represents a conditional dependence (correlation) between QD data attributes and toxicity (Supplementary Fig. 2). However, extraction of conditional dependence from a RF composed of a large number of decision trees presents a fundamental challenge as it is less likely to possess common pathways for all trees. On the other hand, conditional dependences extracted from RF are more robust than those identified from a single decision tree. As mentioned above, the RF proximity matrix quantifies the internal structure in QD samples in terms of both data attributes and toxicity (that is, the attribute– toxicity correlation). Therefore, clustering analysis was conducted using RF proximity to identify QD samples with similar attribute–toxicity correlations; the identified clusters were then evaluated to establish the conditional dependence with a reasonable level of generality and accuracy. The generality of a conditional dependence can be quantified as the number of QD samples clustered in the same group. However, clustering analysis of the proximity matrix obtained for a RF regression model usually identifies a large number of small clusters, which would then lead to a conditional dependence √of limited generality. Therefore, in the present work the number of clusters k ≈ n/6 (where n = 514 identifies the total sample of QD IC50 data) specified for the clustering analysis was larger than the typical recommended value36, so as to identify larger clusters. Clusters identified from the RF proximity were evaluated based on the IC50 distribution of the QD samples in each cluster. Only clusters with an IC50 distribution significantly different from the entire QD samples were kept as candidates for conditional dependence extraction (that is, the clusters with a similar

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IC50 distribution to the entire QD samples are less informative). For each of these candidate clusters, an initial conditional attribute–toxicity dependence was established by collecting each attribute distribution/range of the QD samples in the cluster as antecedent (that is, the ‘If’ term) and the IC50 range as consequent (that is, the ‘Then’ term). It is noted that the number of conditions in the initial conditional dependences is identical to the number of attributes of QD samples. However, some of the attribute conditions are ‘dummy’ conditions, which are either implied by other attribute conditions or irrelevant to the IC50 distribution. Dummy conditions were thus excluded from the initial conditional dependences via backward elimination, which removes at each step the attribute condition that leads to the least change in the IC50 distribution (for example, by comparing the 0.8 quantile of the toxicity distribution).

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Polymersomes containing quantum dots for cellular imaging.

Toxicity of quantum dots on respiratory system.

Designing quantum dots for solotronics.

Synthesis of Luminescent Graphene Quantum Dots with High Quantum Yield and Their Toxicity Study.

Extremely high brightness from polymer-encapsulated quantum dots for two-photon cellular and deep-tissue imaging.

Multifunctional graphene quantum dots for simultaneous targeted cellular imaging and drug delivery.

Polymer-coated quantum dots.

Colloidal Double Quantum Dots.

Core Concept: Quantum dots.

Quantum dots: Electrifying cavities.

c mice.

The in vitro and in vivo toxicity of graphene quantum dots.

Genome-wide approach in Arabidopsis thaliana to assess the toxicity of cadmium sulfide quantum dots.

Stability and toxicity of ZnO quantum dots: interplay between nanoparticles and bacteria.

Sperm DNA-mediated reduction of nonspecific fluorescence during cellular imaging with quantum dots.

Cellular distribution and cytotoxicity of graphene quantum dots with different functional groups.

In vivo biodistribution and toxicity of intravesical administration of quantum dots for optical molecular imaging of bladder cancer.

Colloidal quantum dots intraband photodetectors.

Silicon quantum dots: surface matters.

Optophononics with coupled quantum dots.

Reproductive toxicity and gender differences induced by cadmium telluride quantum dots in an invertebrate model organism.

Quantum Optical Signature of Plasmonically Coupled Nanocrystal Quantum Dots.

ZnS Quantum Dots and Quantum Rods.

Hematopoiesis toxicity induced by CdTe quantum dots determined in an invertebrate model organism.