Abstract Safety analysis of engineered nanomaterials (ENMs) presents a formidable challenge regarding environmental health and safety, due to their complicated and diverse physicochemical properties. Although large amounts of data have been published regarding the potential hazards of these materials, we still lack a comprehensive strategy for their safety assessment, which generates a huge workload in decision-making. Thus, an integrated approach is urgently required by government, industry, academia and all others who deal with the safe implementation of nanomaterials on their way to the marketplace. The rapid emergence and sheer number of new nanomaterials with novel properties demands rapid and high-content screening (HCS), which could be performed on multiple materials to assess their safety and generate large data sets for integrated decision-making. With this approach, we have to consider reducing and replacing the commonly used rodent models, which are expensive, time-consuming, and not amenable to high-throughput screening and analysis. In this review, we present a ‘Library Integration Approach’ for high-content safety analysis relevant to the ENMs. We propose the integration of compositional and property-based ENM libraries for HCS of cells and biologically relevant organisms to be screened for mechanistic biomarkers that can be used to generate data for HCS and decision analysis. This systematic approach integrates the use of material and biological libraries, automated HCS and high-content data analysis to provide predictions about the environmental impact of large numbers of ENMs in various categories. This integrated approach also allows the safer design of ENMs, which is relevant to the implementation of nanotechnology solutions in the pharmaceutical industry. analysis method, nanosafety assessment, library integration approach, high-content screening, nanomaterials INTRODUCTION The rapid development of the field of nanotechnology has promoted numerous engineered nanomaterials (ENMs) to actual or potential application, and the products enabled by ENMs are expected to eventually enter all industrial sectors and almost every aspect of our daily lives . As of May 2014, the Nanotechnology Consumer Products Inventory has recorded more than 1800 nanotechnology-based products on the market . It is estimated that the sale of nanotechnology-enabled products will grow to more than $4 trillion by 2018 . While exciting, this prospect has also raised concerns about the potential environmental, health and safety (EHS) impacts of ENMs [4–13]. Biomedical applications of ENMs have become one of the most prominent areas of research in nanoscience and technology. The rapid development of nanomedicine, such as the use of nanocarriers in theranostics and drug delivery [14–18], particularly underlines the urgent need for safety assessment of biomedical nanomaterials. In recent years, safety testing of nanomaterials has been widely explored , revealing potentially hazardous impacts of ENMs under a variety of experimental conditions [20–32]. However, the enormous variety of the large number of already available nanomaterials, which have diverse physicochemical properties, presents challenges with regard to safety testing by traditional methods, as well as regarding the toxicological analysis of new ENMs and other nano-enabled products on their way to the marketplace. Traditionally, potential adverse effects of drugs and chemicals are assessed through testing in rodent models, which have considerable limitations in terms of expense, time and the number of assessments that can be performed [33,34]. There are also important ethical considerations . Given the large number of new materials emerging in nanotechnology, it is of particular importance that rapid screening approaches are implemented in a relatively early stage of their development. This could be accomplished by using cells and simpler organisms, (e.g. bacteria, nematodes, zebrafish, fruit fly, Daphnia, yeast etc.), and more efficient technologies for the rapid analysis of the resulting large volumes of data. It is important to note that these new models are not intended to fully replace traditional approaches such as rodent testing, but to speed up the gathering of information that can be used for decision analysis and safer design. High-throughput screening (HTS) and high-content screening (HCS) are considered to be attractive solutions for the provision of safety profiles for nanomaterials . The major feature of HTS is its ability to rapidly assess key biological response outcomes in cells for a large number of substances at the same time. As a mature methodology in drug discovery , HTS has been adopted by many fields including toxicology . A number of HTS studies have already been carried out for ENMs [38–42] by leading research programs in the United States and Europe [33,43]. However, while data acquisition is enhanced compared to traditional toxicology approaches, the overall knowledge content generated by HTS is still limited. Moreover, most HTS to date has focused on mechanistic cellular responses, which must demonstrate a relationship to effects outcome in vivo in order to be predictive of a biological hazard at human or whole-organism level. To better deal with this limitation, HCS has been introduced to the field of nanosafety, which seeks to combine high-content analysis with HTS to enable the high-throughput acquisition of complex information such as phenotypic data [44,45]. This requires the development of HTS-suitable high-content image analysis techniques for cell-based screening, multi-parameter cell screening and phenotype recognition in organisms [46–48]. Significant attempts have been made to recognize major issues in nanosafety HTS and HCS, including material library construction, toxicity mechanism determination, and in-depth exploitation of collected data [33,38,49–52]. In early 2008, while commenting on the first generation of small-scale nanosafety HTS research, Service pointed out the importance of integrating different toxicological paradigms into experimental design . Feliu and Fadeel later highlighted the need for standardized and validated toxicity tests, novel model systems and miniaturized facilities . Nel and colleagues have played a pioneering role in framing a comprehensive strategy for HCS analysis of nanomaterial toxicities, and their strategy has been demonstrated in a series of insightful reviews in this issue [33,38,54]. Their research, which mainly focused on the adverse effects of various metal oxide nanoparticles, material library preparation, injury pathway-based toxicity analysis, predictive modeling and the consideration of actual circumstance of exposure, has been tentatively grouped together as a multidisciplinary approach. Cohen et al. have presented further discussion dedicated to the utilization of nanosafety screening results in an informative summary and suggested a comprehensive framework for the analysis and utilization of high-throughput data . Such exploration, while being primarily concerned only with HTS systems, has outlined a comprehensive vision for a high-throughput and high-content approach for nanosafety assessment. Our view, described here, is primarily based on the further development of established approaches, particularly the multidisciplinary one of Nel and colleagues. Much remains to be done to implement HCS and HTS for ENM safety assessment. This can be attributed to a number of factors: (i) the diversity of ENMs is potentially infinite, thus existing high-throughput studies lack comprehensive coverage of the territory of possible ENMs; (ii) the number of potentially relevant ENM properties that need to be monitored goes beyond the capacity of existing high-throughput systems; (iii) the variety of possible screening models for nanosafety from the cell to the organismal level is still expanding; (iv) these models and their corresponding assays provide numerous toxicity biomarkers, whose significance and relevance in determining the different EHS risks of ENMs are still to be assessed; and (v) their comparison, as well as the issue of how to select which one (or which combination) should be used, have been largely left out of the ongoing discussion. Thus, rapidly obtain reliable toxicological knowledge for the vast ENM family continues to be a significant challenge. Given the current obstacles that hinder the implementation of ENM risk evaluation, we describe a ‘Library Integration Approach’ (LIA) as a next step towards high-throughput analyses of nanomaterial toxicities. To promote the comprehensive and systematic understanding of numerous factors involved in nanosafety assessment, and to optimize the efficiency of analytical system design, we suggest that four fundamental libraries corresponding to the four key elements of toxicity evaluation be established as the framework for nanosafety HTS analysis: libraries of nanomaterials, nanomaterial properties, screening models and toxicity biomarkers. For nanosafety, HTS systems serving diverse purposes, with different assemblies of parameters in each library, would be included according to the particular type of exposure. With the assistance of high-throughput analysis and the use of predictive models based on HCS data, a network could be constructed between parameters of different libraries. Our hope is that by further organizing the potentially important factors in nanosafety assessment and sorting out possible links among them, this library approach may bring us closer to a more complete understanding of nanotoxicity. In the sections below, we discuss the key factors that contribute to nanosafety HCS, namely the four fundamental libraries, automatic instrumentation and data analysis (Fig. 1), with emphasis on the specific features and options of various biological models that are currently available. We also discuss the objective and requirements of each key step, summarize current progress, and identify existing problems or gaps in our knowledge. Figure 1. View largeDownload slide A library-based strategy for rapid high-content analysis of nanosafety. The libraries corresponding to key elements of nanosafety HCS include an ENM library, a nano-property library, a biological model library and a toxicity biomarker library. Figure 1. View largeDownload slide A library-based strategy for rapid high-content analysis of nanosafety. The libraries corresponding to key elements of nanosafety HCS include an ENM library, a nano-property library, a biological model library and a toxicity biomarker library. CONSTRUCTION OF FUNDAMENTAL LIBRARIES FOR NANOSAFETY HTS ANALYSIS Compositional ENM library In order to link a large number of nanoscale properties to interactions with complex biological systems, it is first necessary to gather knowledge about the composition and physicochemical properties of ENMs. Existing materials libraries may be used to establish a knowledge domain that can assist in the exploration of newly introduced materials instead of the traditional approach of testing one material at a time [55,56]. We have demonstrated that nanomaterial libraries should represent different compositions, as well as specific properties (e.g. shape, size, electronic properties, dissolution, surface functionalization, photoactivation, coating, crystallinity, etc.) that may act as key variables to render a particular composition hazardous. Although this approach can be used effectively to study the hazards of pristine nanomaterials, the evolution of hybrid materials, nanocomposites and nano-enabled products requires the dynamic adaptation of these strategies to keep up with the resulting complexity. In order to catalog and organize libraries according to toxicologically relevant features, a number of strategies should be applied (Fig. 2). For example, one could use spatial nanoscale dimensions, according to which ENMs can be classified into zero-dimensional (e.g. spherical nanoparticles and nanoclusters), one-dimensional (e.g. nanowires and nanotubes) and two-dimensional (e.g. graphene and nanofilms) materials. One can also use chemical composition to include categories such as: (i) metallic nanomaterials, i.e. nanostructures composed of noble (e.g. Au, Ag, Pt and Pd) or transitional metals (e.g. Cu, Zn, Co, Ni and Fe); (ii) nano metal oxides, such as TiO2, ZnO, CuO and CeO2 nanoparticles; (iii) semiconductor nanocrystals, such as CdTe, CdSe and other semiconductor quantum dots; (iv) polymeric nanomaterials (including dendrimers); (v) nanocarbons, including single-wall or multi-walled carbon nanotubes, fullerenes, metallofullerenes, graphene, carbon dots, etc.; (vi) nano ceramics, such as nano oxide ceramics, nano carbide ceramics, nano nitride ceramics, etc.; (vii) hybrids (e.g. core-shell combinations, alloys, doping and by self-assembly) of two or more type of materials; and so on. Each category has inherent characteristics that might lead to a biological hazard, which can be divided into universal (e.g. size, shape or surface charge) or category-specific (e.g. band gap in semiconductor crystals) properties. Controlled variations of such characteristics should be introduced into library databases in order to assess their relevance to nanosafety. In addition, potential exposure to ENMs is closely related to their practical application, hence it could also be helpful to classify ENMs according to their principal functional features (e.g. magnetic, fluorescent, nonlinear optical, semiconductor, superconductor and thermoelectric ENMs) or their usage (nanotechnology-enabled electrics, optics, biomedical materials, catalysts, energy materials, agricultural and environmental applications). Organizing ENMs according to their characteristics or practical applications (hence exposure scenarios) would also be helpful when linking an ENM library with other libraries discussed in the following sections. Figure 2. View largeDownload slide Construction of ENM library and nano-property library. Classifications that are relevant to ENM nature and exposure conditions can be used to organize the library ENMs. The ENM library further provides the basis for the nano-property library. Both intrinsic nano-properties acquired during manufacture and extrinsic nano-properties acquired under given exposure conditions should be included. Figure 2. View largeDownload slide Construction of ENM library and nano-property library. Classifications that are relevant to ENM nature and exposure conditions can be used to organize the library ENMs. The ENM library further provides the basis for the nano-property library. Both intrinsic nano-properties acquired during manufacture and extrinsic nano-properties acquired under given exposure conditions should be included. In early studies, HTS analysis was performed on libraries of metal nanoparticles , metal oxide nanoparticles, quantum dots, carbon nanomaterials and silica nanoparticles [39,57,58]. In addition, we have also generated combinatorial ENM libraries for HTS and HCS of metal oxide libraries, in which select dissolution and semiconductor properties were accentuated by doping or variation in particle shape, respectively [38,59,60]. We have also prepared carbon nanotube libraries, in which we introduced various surface functionalization, such as charge or the use of different coatings . The same has been accomplished for nano Ag and nano Au particles, with variations in shape, surface coating, size and surface functionalization [62–65]. These libraries need to be expanded to cover a wider range of materials to include many of the emerging commercial materials. This would expand our knowledge of characteristic toxicity mechanisms of ENMs and possible exposure circumstances, as well as make possible the development of well-defined ENM standards for toxicological tests. The effective creation of an ENM library requires reliable and readily available sources of chemicals and materials. Large-scale synthesis and processing technologies have already been reported for various ENMs, including metals and metal oxides, colloidal quantum dots and carbon nanomaterials [66–69]. However, the synthesis requirements of materials that will be used for safety screening in libraries may differ from those for the production of industrial materials in terms of volume and control of the required properties. Thus, libraries for nanosafety studies do not require large volumes of materials to be produced, but do have to be precise in terms of physicochemical properties and their combinatorial variation in order to generate structure activity relationship data that can reliably be used for a wide range of materials. The synthesis and acquisition of ENM libraries should be well coordinated and standardized to ensure that screening analysis reflects the correct property-activity relationships for materials from different sources. While many high-throughput approaches are able to fine-tune specific material characteristics [66,68,69], such as size, shape and surface modification, through the control of key synthesis parameters, the methodological approaches are often less well described. Examples of effective synthesis methods for combinatorial libraries include convergent synthesis of gold nanoparticle libraries with diverse metal core size and polymer coating by Gibson et al. [64,70], and particle libraries with a wide range of surface functionalities by Weissleder et al. [71,72]. Novel high-throughput synthesis techniques that are enabled by the development of automated instrumentation, such as microfluidic synthesis devices, are gaining acceptance. We have also described the synthesis of doped metal oxide nanoparticle libraries by flame spray pyrolysis, which were used to study the effects of semiconductor properties and particle dissolution via high-throughput approaches . Due to the easy synthesis of metal and metal oxide nanoparticles, most HCS and HTS studies have been conducted on them (Table 1). Regrettably, many of the reported nanosafety HTS systems rely on individually purchased materials and relatively small material collections, rather than a systematic library synthesis approach. While there is a need to screen all materials by rapid throughput approaches, for the purposes of knowledge generation and producing reference materials for ENM libraries, it is also necessary to use well-formulated and precisely controlled methods. Table 1. An overview of selected, high-throughput or high-content nanosafety studies. Nanomaterials Screening models Toxicity paradigms of HTS Read-out Reference CdSe/ZnS QDs Human cell line Gene expression Gene chip  Fe3O4, CdSe QDs Human cell lines Apoptosis, MMP, ATP content, cellular reducing equivalents Fluorescence plate reader  Al2O3, CeO2, Co3O4, TiO2, ZnO, CuO, SiO2, Fe3O4, WO3 Human cell line Cell membrane permeability High-content epifluorescence microscopy  Ag, Au, Pt, Al2O3, Fe3O4, SiO2, ZnO Human cell line Metabolic activity Plate luminometer  Ag, Au, Pt, Al2O3, SiO2, ZnO, QDs (CdSe/ZnS, CdSe) Human cell line MSF, MMP, EIC, CMD High-content epifluorescence microscopy  Ag0, Al2O3, CeO2, Fe0, Fe2O3, HfO2,Mn2O3, SiO2, TiO2, ZnO, ZrO2 Human cell line Cell viability, morphology, adhesion degree Microelectrodes for electrical impedance measurement  Functionalized polystyrene Human cell lines Nuclear morphology, lysosomal acidification, MMP, EIC, CMD High-content epifluorescence microscopy  Ag, SiO2, ZnO, Fe2O3, CeO2 Human cell lines DNA damage Agar electrophoresis coupled with fluorescence microscope  Ag, SiO2 Human cell line and primary cell Cytokinesis-block micronuclei High-content epifluorescence microscopy  Functionalized Ag Human and murine cell lines Cell morphology, cell viability, CMD, ROS, mitochondrial viability, autophagy High-content epifluorescence microscopy  Fe3O4, Fe2O3 Murine cell line Cell viability, DNA damage, ROS High-content epifluorescence microscopy  Polystyrene E. coli ROS, growth inhibition Absorbance plate reader  Aminofullerene, nCb, nC60, UV-irradiated nC60, fullerol, SWCNT, MWCNTs, GO, RGO, graphene, Ag, SiO2, TiO2 C. elegans Body size, locomotion, life span Stereomicroscopy  Al2O3, CeO2, CoO, Co3O4, Cr2O3, CuO, Fe2O3, Fe3O4, Gd2O3, HfO2, In2O3, La2O3, Mn2O3, NiO, Ni2O3, Sb2O3, SiO2, SnO2, TiO2, WO2, Y2O3, Yb2O3, ZnO, ZrO2 Zebrafish embryos Morphological abnormalities Bright-field high-content imaging  CuO Zebrafish embryos Hatching dynamics, mortality, morphological abnormalities Bright-field high-content imaging  Nanomaterials Screening models Toxicity paradigms of HTS Read-out Reference CdSe/ZnS QDs Human cell line Gene expression Gene chip  Fe3O4, CdSe QDs Human cell lines Apoptosis, MMP, ATP content, cellular reducing equivalents Fluorescence plate reader  Al2O3, CeO2, Co3O4, TiO2, ZnO, CuO, SiO2, Fe3O4, WO3 Human cell line Cell membrane permeability High-content epifluorescence microscopy  Ag, Au, Pt, Al2O3, Fe3O4, SiO2, ZnO Human cell line Metabolic activity Plate luminometer  Ag, Au, Pt, Al2O3, SiO2, ZnO, QDs (CdSe/ZnS, CdSe) Human cell line MSF, MMP, EIC, CMD High-content epifluorescence microscopy  Ag0, Al2O3, CeO2, Fe0, Fe2O3, HfO2,Mn2O3, SiO2, TiO2, ZnO, ZrO2 Human cell line Cell viability, morphology, adhesion degree Microelectrodes for electrical impedance measurement  Functionalized polystyrene Human cell lines Nuclear morphology, lysosomal acidification, MMP, EIC, CMD High-content epifluorescence microscopy  Ag, SiO2, ZnO, Fe2O3, CeO2 Human cell lines DNA damage Agar electrophoresis coupled with fluorescence microscope  Ag, SiO2 Human cell line and primary cell Cytokinesis-block micronuclei High-content epifluorescence microscopy  Functionalized Ag Human and murine cell lines Cell morphology, cell viability, CMD, ROS, mitochondrial viability, autophagy High-content epifluorescence microscopy  Fe3O4, Fe2O3 Murine cell line Cell viability, DNA damage, ROS High-content epifluorescence microscopy  Polystyrene E. coli ROS, growth inhibition Absorbance plate reader  Aminofullerene, nCb, nC60, UV-irradiated nC60, fullerol, SWCNT, MWCNTs, GO, RGO, graphene, Ag, SiO2, TiO2 C. elegans Body size, locomotion, life span Stereomicroscopy  Al2O3, CeO2, CoO, Co3O4, Cr2O3, CuO, Fe2O3, Fe3O4, Gd2O3, HfO2, In2O3, La2O3, Mn2O3, NiO, Ni2O3, Sb2O3, SiO2, SnO2, TiO2, WO2, Y2O3, Yb2O3, ZnO, ZrO2 Zebrafish embryos Morphological abnormalities Bright-field high-content imaging  CuO Zebrafish embryos Hatching dynamics, mortality, morphological abnormalities Bright-field high-content imaging  Abbreviations: nCb, nano-carbon black; QD, quantum dot; SWCNT, single-walled carbon nanotube; MWCNT, multi-walled carbon nanotube; GO, graphene oxide; RGO, reduced graphene oxide; MSF, mitochondrial superoxide formation; MMP, loss of mitochondrial membrane potential; EIC, elevated intracellular calcium; CMD, cellular membrane damage; ROS, reactive oxygen species. View Large Table 1. An overview of selected, high-throughput or high-content nanosafety studies. Nanomaterials Screening models Toxicity paradigms of HTS Read-out Reference CdSe/ZnS QDs Human cell line Gene expression Gene chip  Fe3O4, CdSe QDs Human cell lines Apoptosis, MMP, ATP content, cellular reducing equivalents Fluorescence plate reader  Al2O3, CeO2, Co3O4, TiO2, ZnO, CuO, SiO2, Fe3O4, WO3 Human cell line Cell membrane permeability High-content epifluorescence microscopy  Ag, Au, Pt, Al2O3, Fe3O4, SiO2, ZnO Human cell line Metabolic activity Plate luminometer  Ag, Au, Pt, Al2O3, SiO2, ZnO, QDs (CdSe/ZnS, CdSe) Human cell line MSF, MMP, EIC, CMD High-content epifluorescence microscopy  Ag0, Al2O3, CeO2, Fe0, Fe2O3, HfO2,Mn2O3, SiO2, TiO2, ZnO, ZrO2 Human cell line Cell viability, morphology, adhesion degree Microelectrodes for electrical impedance measurement  Functionalized polystyrene Human cell lines Nuclear morphology, lysosomal acidification, MMP, EIC, CMD High-content epifluorescence microscopy  Ag, SiO2, ZnO, Fe2O3, CeO2 Human cell lines DNA damage Agar electrophoresis coupled with fluorescence microscope  Ag, SiO2 Human cell line and primary cell Cytokinesis-block micronuclei High-content epifluorescence microscopy  Functionalized Ag Human and murine cell lines Cell morphology, cell viability, CMD, ROS, mitochondrial viability, autophagy High-content epifluorescence microscopy  Fe3O4, Fe2O3 Murine cell line Cell viability, DNA damage, ROS High-content epifluorescence microscopy  Polystyrene E. coli ROS, growth inhibition Absorbance plate reader  Aminofullerene, nCb, nC60, UV-irradiated nC60, fullerol, SWCNT, MWCNTs, GO, RGO, graphene, Ag, SiO2, TiO2 C. elegans Body size, locomotion, life span Stereomicroscopy  Al2O3, CeO2, CoO, Co3O4, Cr2O3, CuO, Fe2O3, Fe3O4, Gd2O3, HfO2, In2O3, La2O3, Mn2O3, NiO, Ni2O3, Sb2O3, SiO2, SnO2, TiO2, WO2, Y2O3, Yb2O3, ZnO, ZrO2 Zebrafish embryos Morphological abnormalities Bright-field high-content imaging  CuO Zebrafish embryos Hatching dynamics, mortality, morphological abnormalities Bright-field high-content imaging  Nanomaterials Screening models Toxicity paradigms of HTS Read-out Reference CdSe/ZnS QDs Human cell line Gene expression Gene chip  Fe3O4, CdSe QDs Human cell lines Apoptosis, MMP, ATP content, cellular reducing equivalents Fluorescence plate reader  Al2O3, CeO2, Co3O4, TiO2, ZnO, CuO, SiO2, Fe3O4, WO3 Human cell line Cell membrane permeability High-content epifluorescence microscopy  Ag, Au, Pt, Al2O3, Fe3O4, SiO2, ZnO Human cell line Metabolic activity Plate luminometer  Ag, Au, Pt, Al2O3, SiO2, ZnO, QDs (CdSe/ZnS, CdSe) Human cell line MSF, MMP, EIC, CMD High-content epifluorescence microscopy  Ag0, Al2O3, CeO2, Fe0, Fe2O3, HfO2,Mn2O3, SiO2, TiO2, ZnO, ZrO2 Human cell line Cell viability, morphology, adhesion degree Microelectrodes for electrical impedance measurement  Functionalized polystyrene Human cell lines Nuclear morphology, lysosomal acidification, MMP, EIC, CMD High-content epifluorescence microscopy  Ag, SiO2, ZnO, Fe2O3, CeO2 Human cell lines DNA damage Agar electrophoresis coupled with fluorescence microscope  Ag, SiO2 Human cell line and primary cell Cytokinesis-block micronuclei High-content epifluorescence microscopy  Functionalized Ag Human and murine cell lines Cell morphology, cell viability, CMD, ROS, mitochondrial viability, autophagy High-content epifluorescence microscopy  Fe3O4, Fe2O3 Murine cell line Cell viability, DNA damage, ROS High-content epifluorescence microscopy  Polystyrene E. coli ROS, growth inhibition Absorbance plate reader  Aminofullerene, nCb, nC60, UV-irradiated nC60, fullerol, SWCNT, MWCNTs, GO, RGO, graphene, Ag, SiO2, TiO2 C. elegans Body size, locomotion, life span Stereomicroscopy  Al2O3, CeO2, CoO, Co3O4, Cr2O3, CuO, Fe2O3, Fe3O4, Gd2O3, HfO2, In2O3, La2O3, Mn2O3, NiO, Ni2O3, Sb2O3, SiO2, SnO2, TiO2, WO2, Y2O3, Yb2O3, ZnO, ZrO2 Zebrafish embryos Morphological abnormalities Bright-field high-content imaging  CuO Zebrafish embryos Hatching dynamics, mortality, morphological abnormalities Bright-field high-content imaging  Abbreviations: nCb, nano-carbon black; QD, quantum dot; SWCNT, single-walled carbon nanotube; MWCNT, multi-walled carbon nanotube; GO, graphene oxide; RGO, reduced graphene oxide; MSF, mitochondrial superoxide formation; MMP, loss of mitochondrial membrane potential; EIC, elevated intracellular calcium; CMD, cellular membrane damage; ROS, reactive oxygen species. View Large Combinatorial libraries with systematic accentuation of nanomaterial properties The potentially hazardous properties of each given nanomaterial depend on the intrinsic properties of the material as well as their dynamic modification during contact with biological media [10,22,25,73,74]. Thus, in addition to the intrinsic properties acquired during manufacture, such as composition, size, shape, morphology, crystallinity, and photonic, electronic, and magnetic properties, ENMs may display new properties when they encounter biological or environmental matrices under specific exposure conditions (Fig. 2). These acquired properties, e.g. double layer formation, aggregation, changes in colloidal stability, dissolution, surface adsorption and the acquisition of a protein corona, are often less well controlled than the intrinsic properties, and may lead to a significant change in biological behavior and variability during toxicity analysis. To reduce the uncertainty during HCS analysis, it is important that these properties are well characterized and controlled, or at least accounted for. Proper ENM characterization is essential for the appropriate and effective interpretation of high-content analysis results. The most common analytical techniques for ENM characterization include analysis of: (i) compositional properties, e.g. as determined by Raman spectroscopy, infrared spectroscopy, mass spectrometry, X-ray photoelectron spectroscopy and inductively coupled plasma; (ii) morphological properties of nanomaterials, e.g. from transmission electron microscopy, scanning electron microscopy, atomic force microscopy, Brunauer–Emmett–Teller surface area analysis and pore size measurements; (iii) crystallographic properties, e.g. from X-ray diffraction data; (iv) photonic, electronic, or magnetic properties, e.g. by UV-visible, fluorescence spectroscopy, magnetic susceptibility measurements, etc.; and (v) nanomaterial behavior in solution or colloidal suspension, e.g. equivalent diameter from dynamic light scattering and surface potential measurements. In addition to the characterization of primary materials, it is also necessary to characterize ENMs under biologically relevant exposure conditions and use them under conditions that imitate the environment of the potential exposure, e.g. disperse ENMs in serum or in albumin solutions, especially when interactions with the blood or systemic circulation of the particles are concerned. Unsurprisingly, certain properties of ENMs are more relevant to the hazardous effects of specific materials. For example, it has been reported that for carbon nanotubes, their aspect ratio and length are correlated with lysosome damage, while surface modifications determine pro-inflammatory and pro-fibrogenic effects by affecting their cellular uptake and processing . Nel et al. have also demonstrated that the cytotoxicity of silica nanoparticles relates to the synthesis temperature, the structure of the silica ring and to the display of H-bonded hydroxyl groups . To uncover these relationships, it is necessary to construct combinatorial ENM libraries, in which potential key properties are systematically accentuated in order to determine their contribution to the framework of other nanoscale properties, which contribute to the overall outcome. While some success has been achieved for commercial material compositions, the great diversity of existing nanomaterials and the wide range of unique nanoscale properties present an important challenge. Theoretical simulation and statistical analyses have been useful for library selection. For instance, developing nano structure-activity relationships (nano-SARs) has opened up the possibility of delineating dependent and independent property variations, which in turn can help to predict toxicologically relevant properties that may not otherwise be uncovered. Hence, nano-SARs may help to establish a parameter system for materials libraries [75,76]. An informative discussion of nanomaterial modeling was presented by Barnard et al. in 2012 . To fill current knowledge gaps, this approach may be used to generate various hypotheses to be tested by HCS experiments. Considering the urgent need for systematic organization of safety-related information, the knowledge presented in a nano-property library would be increasingly useful. Biological model library Firstly, the most crucial step in the workflow of high-content nanosafety screening is the selection of an appropriate platform to carry out specific measurements. The success of nanosafety evaluation greatly depends on the successful establishment of a biological model library. Nanotechnology-related products are becoming ubiquitous and are being disseminated through various channels, such as production and consumption in the food chain. Thus, multiple species, ecosystems and urban environments are inevitably exposed to ENMs. As a consequence, a biological model library should include models for the whole life cycles of ENMs. While each screening model has its own limitations, multilevel models with different sensitivities can maximize the comprehensiveness and accuracy of nanosafety evaluation of ENMs. Secondly, models should be suitable for nanosafety evaluation. In theory, all models that have been successfully used in traditional toxicity screening should also be applicable for the screening of nanomaterials because they are well tested, reliable and stable. However, testing of the applicability of each screening model prior to use is also essential. Ultimately, to deal with the unique features and new toxicity mechanisms of ENMs, new screening models will also have to be introduced into this library. Thirdly, the models should be amenable to HCS. Certain screening models may not work in a high-content setting if they cannot be coupled with automatic instrumentation and high-throughput inspection methods. In addition to model cells, microorganisms and whole-organism systems, such as zebrafish [38,78], will have to be added to the biological model library to strengthen future HCS systems. After the identities of nanomaterials are determined, appropriate screening models will have to be selected according to various criteria, including the fate of the ENM, the target of risk assessment and the particular ENM properties, which may affect the suitability of specific screening models. EHS assessment of ENMs should take into account all of the species living in the local environment, rather than only the human species . Each biological platform has its own unique features and different manifestations of biological outcomes. Thus, the responsiveness of a particular model to potentially hazardous properties could be target-specific. Therefore, the selection of a screening model should be made according to the specific requirements of each nanomaterial and its realistic function or exposure environment. Because no screening model is perfect and no single toxic test shows uniform sensitivity to all nanomaterials, a battery of methods with different sensitivity profiles is recommended to assure a reliable and unbiased result. This HCS strategy is relatively new in toxicological screening and offers significant advantages over the classical single readout HTS measurement in terms of specificity and sensitivity. To achieve the adequate evaluation of the full-scale toxicological situation of a given nanomaterial, outcomes from all relevant screening models that reflect the different aspects and levels of its toxicity should eventually be integrated for comprehensive EHS risk assessment. Some biological models are proven effective and have already been applied for screening, including high-content studies of nanosafety (Fig. 1). The cell model is the most widely used for screening in nanosafety research, and the HCS technique for this system is well developed. Whole-organism models, such as zebrafish, fruit fly, and Caenorhabditis elegans, provide another level of physiological relevance. All these models have unique advantages over rodent models with regard to assessment of the human health risk of nanomaterials. Meanwhile, to cope with the inevitable release of engineered nanoparticles into the environment, high-content nanosafety studies using other available environmental model organisms should also be implemented to gain a comprehensive understanding of the EHS risk posed by ENMs. Organisms that are part of the food chain in natural ecological systems, such as bacteria, algae, ciliates, crustaceans, yeast, nematodes, fish and plants, are ideal candidates for nanosafety studies [57,59,80]. In addition to the above-mentioned nematode (e.g. C. elegans) and fish (e.g. zebrafish) models that have already been employed by HTS research, other organisms from terrestrial or aquatic ecosystems have long been used as standard biological models for ecotoxicological assessment. The characteristics of representative screening model candidates for high-content evaluation of nanosafety are summarized in Table 2. Although ENM-relevant HCS systems are still in their infancy, emerging methods, where culture is performed in microplates, coupled with the development of robotics, are opening up new possibilities for these powerful models. A number of studies have recently focused on potential EHS effects of ENMs in the representative screening models discussed below. We expect that when coupled with HTS and/or HCS, as well as other emerging technologies (e.g. toxicogenomic methods ), these models could accelerate the process of identifying potentially ‘hazardous’ ENMs that, at a later stage, should undergo more stringent study in classic rodent animal models . Typical nanosafety studies with high-throughput or high-content characteristics that have used these models are summarized in Table 1. The significance of biological screening models will become more apparent in high-throughput nano-EHS risk assessment with the rapid expansion of the nanotechnology industry. Table 2. Characteristics of some model organism candidates for nanosafety HCS. Zebrafish D. melanogaster C. elegans Ceriodaphnia dubia S. cerevisiae P. subcapitata E. coli Generation time 3–4 months 10–12 d 3–5 d 3–4 d 120 min 20–24 h 20 min Average embryo size 1 mm 100 μm 50 μm 400 μm Average adult size 6 cm 3 mm 1 mm 1 mm 4–20 μm 8–14 μm 1–3 μm Growth conditions Liquid medium Solid medium Solid or liquid medium Liquid medium Solid or liquid medium Liquid medium Solid or liquid medium Average offspring number 200 90 300 5–20 3–15 4–16 2 Storage +++ ++ ++++ ++++ ++++ ++++ ++++ Strains available + +++ ++++ +++ ++++ +++ ++++ Available sorting equipment Eggs to embryos Eggs to embryos Eggs to adults Eggs to adults Whole life cycle Whole life cycle Whole life cycle Culture in microplate Eggs to larvae Eggs to embryos Eggs to adults Eggs to adults Whole life cycle Whole life cycle Whole life cycle Cost per assay Medium Low Low Low Low Low Low Screening throughput Low to medium Low to medium Medium to high Medium to high High High High Automation applicability +++ +++ +++ +++ +++ +++ +++ HCS applicability +++ +++ +++ +++ +++ +++ +++ Current advancement of HCS technologies ++ + +++ +/− ++++ +/− ++++ Typical application fields Reproductive toxicology, developmental toxicology and aquatic ecotoxicology Genetic toxicology and ecotoxicology Neurotoxicology, genetic toxicology and ecotoxicology Aquatic ecotoxicology Ecotoxicology Aquatic ecotoxicology Ecotoxicology Reference [62,94–102] [108–110] [26,57,116,117] [118,184–186] [134–138] [187–189] [51,120,143–145] Zebrafish D. melanogaster C. elegans Ceriodaphnia dubia S. cerevisiae P. subcapitata E. coli Generation time 3–4 months 10–12 d 3–5 d 3–4 d 120 min 20–24 h 20 min Average embryo size 1 mm 100 μm 50 μm 400 μm Average adult size 6 cm 3 mm 1 mm 1 mm 4–20 μm 8–14 μm 1–3 μm Growth conditions Liquid medium Solid medium Solid or liquid medium Liquid medium Solid or liquid medium Liquid medium Solid or liquid medium Average offspring number 200 90 300 5–20 3–15 4–16 2 Storage +++ ++ ++++ ++++ ++++ ++++ ++++ Strains available + +++ ++++ +++ ++++ +++ ++++ Available sorting equipment Eggs to embryos Eggs to embryos Eggs to adults Eggs to adults Whole life cycle Whole life cycle Whole life cycle Culture in microplate Eggs to larvae Eggs to embryos Eggs to adults Eggs to adults Whole life cycle Whole life cycle Whole life cycle Cost per assay Medium Low Low Low Low Low Low Screening throughput Low to medium Low to medium Medium to high Medium to high High High High Automation applicability +++ +++ +++ +++ +++ +++ +++ HCS applicability +++ +++ +++ +++ +++ +++ +++ Current advancement of HCS technologies ++ + +++ +/− ++++ +/− ++++ Typical application fields Reproductive toxicology, developmental toxicology and aquatic ecotoxicology Genetic toxicology and ecotoxicology Neurotoxicology, genetic toxicology and ecotoxicology Aquatic ecotoxicology Ecotoxicology Aquatic ecotoxicology Ecotoxicology Reference [62,94–102] [108–110] [26,57,116,117] [118,184–186] [134–138] [187–189] [51,120,143–145] Abbreviations: D. melanogaster, Drosophila melanogaster; C. elegans, Caenorhabditis elegans; S. cerevisiae, Saccharomyces cerevisiae; P. subcapitata, Pseudokirchneriella subcapitata; E. coli, Escherichia coli; +, degree of robustness for given parameter. View Large Table 2. Characteristics of some model organism candidates for nanosafety HCS. Zebrafish D. melanogaster C. elegans Ceriodaphnia dubia S. cerevisiae P. subcapitata E. coli Generation time 3–4 months 10–12 d 3–5 d 3–4 d 120 min 20–24 h 20 min Average embryo size 1 mm 100 μm 50 μm 400 μm Average adult size 6 cm 3 mm 1 mm 1 mm 4–20 μm 8–14 μm 1–3 μm Growth conditions Liquid medium Solid medium Solid or liquid medium Liquid medium Solid or liquid medium Liquid medium Solid or liquid medium Average offspring number 200 90 300 5–20 3–15 4–16 2 Storage +++ ++ ++++ ++++ ++++ ++++ ++++ Strains available + +++ ++++ +++ ++++ +++ ++++ Available sorting equipment Eggs to embryos Eggs to embryos Eggs to adults Eggs to adults Whole life cycle Whole life cycle Whole life cycle Culture in microplate Eggs to larvae Eggs to embryos Eggs to adults Eggs to adults Whole life cycle Whole life cycle Whole life cycle Cost per assay Medium Low Low Low Low Low Low Screening throughput Low to medium Low to medium Medium to high Medium to high High High High Automation applicability +++ +++ +++ +++ +++ +++ +++ HCS applicability +++ +++ +++ +++ +++ +++ +++ Current advancement of HCS technologies ++ + +++ +/− ++++ +/− ++++ Typical application fields Reproductive toxicology, developmental toxicology and aquatic ecotoxicology Genetic toxicology and ecotoxicology Neurotoxicology, genetic toxicology and ecotoxicology Aquatic ecotoxicology Ecotoxicology Aquatic ecotoxicology Ecotoxicology Reference [62,94–102] [108–110] [26,57,116,117] [118,184–186] [134–138] [187–189] [51,120,143–145] Zebrafish D. melanogaster C. elegans Ceriodaphnia dubia S. cerevisiae P. subcapitata E. coli Generation time 3–4 months 10–12 d 3–5 d 3–4 d 120 min 20–24 h 20 min Average embryo size 1 mm 100 μm 50 μm 400 μm Average adult size 6 cm 3 mm 1 mm 1 mm 4–20 μm 8–14 μm 1–3 μm Growth conditions Liquid medium Solid medium Solid or liquid medium Liquid medium Solid or liquid medium Liquid medium Solid or liquid medium Average offspring number 200 90 300 5–20 3–15 4–16 2 Storage +++ ++ ++++ ++++ ++++ ++++ ++++ Strains available + +++ ++++ +++ ++++ +++ ++++ Available sorting equipment Eggs to embryos Eggs to embryos Eggs to adults Eggs to adults Whole life cycle Whole life cycle Whole life cycle Culture in microplate Eggs to larvae Eggs to embryos Eggs to adults Eggs to adults Whole life cycle Whole life cycle Whole life cycle Cost per assay Medium Low Low Low Low Low Low Screening throughput Low to medium Low to medium Medium to high Medium to high High High High Automation applicability +++ +++ +++ +++ +++ +++ +++ HCS applicability +++ +++ +++ +++ +++ +++ +++ Current advancement of HCS technologies ++ + +++ +/− ++++ +/− ++++ Typical application fields Reproductive toxicology, developmental toxicology and aquatic ecotoxicology Genetic toxicology and ecotoxicology Neurotoxicology, genetic toxicology and ecotoxicology Aquatic ecotoxicology Ecotoxicology Aquatic ecotoxicology Ecotoxicology Reference [62,94–102] [108–110] [26,57,116,117] [118,184–186] [134–138] [187–189] [51,120,143–145] Abbreviations: D. melanogaster, Drosophila melanogaster; C. elegans, Caenorhabditis elegans; S. cerevisiae, Saccharomyces cerevisiae; P. subcapitata, Pseudokirchneriella subcapitata; E. coli, Escherichia coli; +, degree of robustness for given parameter. View Large Cell Cells, especially various human cell lines, are still the most popular HCS models in modern toxicology studies due to their known origin, fast proliferation rate, simple growth conditions, low cost and the many well-established protocols for biochemical and genetic analysis. Cell lines have also been used as powerful tools for drug toxicity screening in the pharmaceutical industry for decades. A wide variety of cell-based assays are used that reflect the diversity of physiological responses in vivo. A wealth of knowledge about molecular and cellular injury mechanisms can also be utilized to identify potentially harmful nanomaterials without needing to rely primarily on animal models. The prohibitive cost, time-consuming processes and ethical questions associated with the use of traditional animal experiments also favor the use of cell-based in vitro alternative models for preliminary nanosafety testing. To date, cell-based HTS systems have been developed  in 384-, 1536-, and even 3456-well plate formats. The whole workflow of HCS for cytotoxicity is a routine procedure in the pharmaceutical industry, and can be applied to nanosafety screening. Different human cell lines, representing almost all of the different cell types in the body, have been successfully cultivated. The ability to select different cell types has been a great advantage in evaluating the potential hazards of nanomaterials for different exposure scenarios. Several major types of cells, including phagocytic, endothelial, neural, epithelial, hepatic and red blood cells, in addition to various cancer cell lines, have widely been used for in vitro nanosafety assessment. The selected cell types are ones that are generally involved in the major routes of cell–nanomaterial interaction and are considered to represent typical features of related organs. Most toxicological research on nanomaterials to date has been based on these reliable cell models. Our existing knowledge regarding the routes of exposure and biodistribution of nanomaterials is sufficient to enable the selection of appropriate cell lines for specific purposes. Recently, the potential applications of human stem cells, such as human embryonic stem cells (hESCs) and induced pluripotent stem cells, as new models for nanosafety studies have also been reported [81,83]. Stem cells possess the ability to differentiate into diverse cell types and self-renew indefinitely, making them superior models to primary cells or immortalized cell lines. In the future, the use of hESCs may partially replace that of embryonic cells and tissues obtained from pregnant animals for embryotoxicity and teratogenecity assessment. However, before stem cells can become the mainstream models for toxicological research, key issues of genetic stability have to be addressed. Better understanding of stem cell biology and development may also create new opportunities for in vitro nanosafety screening. Although the number of studies to date is still far below that of conventional low-throughput toxicity studies, HCS- and HTS-based toxicological evaluation of nanomaterials is gradually attracting more and more attention [34,50,53,56]. Since 2006, several cell-based nanosafety studies employing different methods in the true sense of high-throughput, and even high-content, have been reported (Table 1). HTS studies to date have included the examination of: (i) the effects of silica-coated quantum dots by employing high-throughput genome expression analysis and high-content cellomics measurements based on image analysis [48,84]; (ii) cytotoxic effects and SARs of 50 different nanomaterials concluded from hierarchical clustering of experimental data ; (iii) a nano-SAR approach for the cytotoxicity HTS of metal oxide nanoparticles ; (iv) a novel impedance-based real-time cell analyzer for rapidly screening nanoparticle toxicity [86,87]; and (v) high-content nanosafety evaluation of a batch of commercial nanoparticles by applying multi-parametric automated screening platforms that incorporate several cytotoxicity testing methods . These pioneering works have promoted HCS-based, rapid evaluation of the nanosafety of ENMs from concept to reality, providing flexible methods and broadening the outlook for the establishment of effective HCS systems for nanosafety studies. It is worth noting that in vitro cell culture systems often inadequately represent the physiological environment and the multitude of cellular responses that are present in an in vivo setting. Three-dimensional (3D) cell culture models, such as microtissue spheroids, are emerging as attractive approaches for the study of complex nano-bio interactions and their use in nanosafety assessment has been attempted [88,89]; however, further improvement should be made to promote the integration of 3D cell models into the evaluation frame of HCS. In cell-based assays, the determination of meaningful endpoints that can reflect the associated in vivo toxicity remains a challenge because of the common issue of disconnection between in vitro and in vivo results and the inability of cell assays to predict biocompatibility with materials . Therefore, cell models are recommended for use only in the initial stages of nanosafety screening to increase the throughput of screening with minimal resource expenditure. Zebrafish Zebrafish (Danio rerio), a tropical freshwater species, has long been recognized as a tractable vertebrate model organism with an impressive range of applications, from toxicology to ecotoxicology. Compared to invertebrate model organisms, such as Drosophila and nematodes, zebrafish display more complex biological behaviors that are closer to those of mammals, and thus allow testing of the effects of nanoparticles on overall viability, organ and tissue function, development and genetics. A high degree of genetic and physiological similarity between humans and zebrafish translates into highly conserved, fundamental cellular and molecular pathways that are involved in responses to toxicants or stress. Indeed, studies have shown that many toxin-induced responses in zebrafish, such as endocrine disruption, reproductive toxicity, behavioral defects, teratogenesis, carcinogenesis, cardiotoxicity, ototoxicity and liver toxicity, are effective early warning signs that predict effects on mammalian health and environmental safety . Fish are the main vertebrate test organisms in ecotoxicology studies. To avoid the unnecessary use of animals whenever possible and reduce unnecessary testing costs, fish are used to check the potential acute aquatic toxicity of substances prior to testing in mammals . Although it cannot completely replace mammals, the zebrafish model remains a useful and cost-effective alternative to some mammalian models in nanosafety assessment. Zebrafish have many attractive features that facilitate nanosafety HCS. The early zebrafish embryo is less than 1 mm in diameter, allowing several embryos to fit easily into a single well of a 384-well plate . The high fecundity (200–300 new progeny per week per pair) and variety of strains make zebrafish ideal for mass screening in a single assay, with lower infrastructure costs than rodents. Further, the embryos and larvae of zebrafish are transparent, allowing real-time observation of morphological and functional defects in the internal organs in vivo. Additionally, important information, such as methods, anatomical descriptions, developmental processes and mutant phenotypes, along with numerous mutant and transgenic lines, can be easily obtained from several open databases and organizations . Taken together, these features make the zebrafish model a highly versatile system for toxicogenomic screening of nanomaterials. In recent years, the toxicities of various ENMs, such as metals, metallic oxides, quantum dots, dendrimers, carbon nanotubes and fullerenes, have been studied in the zebrafish model [47,73–80]. Most of these studies have focused on the development of zebrafish embryos and explored nanotoxicological mechanisms through the examination of abnormal changes in morphology, the hatching rate and the mortality rate of embryos [62,94–99]. These studies have also demonstrated that phenotype-based toxicity assays are the most important methods for nanosafety screening in both wild-type and transgenic zebrafish. Several groups have analyzed lipid peroxidation levels, the histopathology of gill tissue and specific gene expression profiles to assess the safety of nanomaterials in larval or adult zebrafish [100–102]. Though small in quantity, HTS and HCS nanosafety analysis methods have also been successfully applied in the zebrafish model. Phenotypic defects are the major evaluation biomarkers of zebrafish-based HTS. Lin et al. used of high-content imaging to speed up the hazard ranking of transition metal oxide nanomaterials in zebrafish embryos . They then assessed the impact of dissolvable CuO, ZnO, Cr2O3 and NiO nanoparticles on zebrafish embryo hatching using a robotic HTS platform and automated image analysis . Additionally, they developed a novel phenotype recognition modeling approach and specialized equipment for HTS of ENMs safety . Zebrafish have also proved to be useful for HCS of toxicity, allowing the safety of nanoparticles to be assessed more quickly, less expensively and at an earlier stage of development. Drosophila melanogaster The fruit fly Drosophila melanogaster is a classic invertebrate animal model, that has contributed more to the laying down of the foundations of modern genetics than any other organism . Because of its homology to many basic biological, physiological and neurological properties in mammals, D. melanogaster is well studied and has been used for development, signal transduction, cell biology, pharmacological and toxicological research for over 100 years [93,104]. This long history has provided us with exhaustive biological information, and thousands of Drosophila strains are available from several constantly updated databases and stock centers . Thus, this widely distributed invertebrate insect is also an excellent experimental model for nanosafety studies. The potential utility of Drosophila melanogaster in large-scale screening has already been demonstrated. It has a rapid life cycle of about 2 weeks, and a single fertile mating pair can produce hundreds of genetically identical offspring within 10–12 d. In addition, their simple diet makes the flies easy to maintain. Drosophila melanogaster has four developmental stages (embryo, larva, pupa and adult) and not all of them can be applied to HTS due to specific constraints . However, large-scale patterning of embryos would be a very useful tool for high-content nanosafety studies; the high-content technique affords a powerful capability to monitor pattern formation and morphogenesis in large numbers of embryos at multiple time points and in diverse genetic backgrounds. Drosophila melanogaster embryos are about 100 μm in diameter, allowing manipulation with automated pipetting . However, it should be noted that the D. melanogaster embryo is surrounded by a thick cuticle, which acts a physical barrier against the penetration of surrounding substances, including some ENMs, and may bring the risk of false negative results in nanosafety screening. Several nanosafety screens have taken advantage of this model organism [107–110]. There are also efforts underway to promote the development of HTS and HCS in D. melanogaster . The large-scale patterning of embryos is a very useful tool for the high-content study of morphogenesis in embryos, and microfluidic systems provide innovative ways to precisely control animal position for imaging. Dagani et al. have developed a method for the immobilization of embryos in the middle of microfluidic channels via a capillary force-induced self-assembly technique in an oil-adhesion pad under a minimal potential-energy principle . Based on passive hydrodynamics, Chung et al. have developed a microfluidic embryo-trap array that can be used to rapidly order and vertically orient hundreds of D. melanogaster embryos within minutes. This platform was used in a quantitative, high-throughput analysis of multiple morphogen gradients in the dorsoventral patterning system through the statistical analysis of images of vertically oriented embryos . Until now, safety HCS of nanomaterials has not commonly used D. melanogaster; however, increasing technological innovation and development will certainly make greater use of this model system. Caenorhabditis elegans The nematode C. elegans is an abundant animal in soil, aquatic and sediment ecosystems, and plays important roles in the maintenance of environmental quality, leading to their wide use in ecotoxicological studies . As an invertebrate model, C. elegans has led to landmark discoveries on many molecular mechanisms, such as cell death, ageing, development and neuronal function, and is a widely-appreciated, powerful platform for the study of important toxicological mechanisms relating to human health [93,115]. Fully described biological and physiological maps, together with mature and convenient research approaches, have led to the increased use of C. elegans in the areas of neurotoxicology, genetic toxicology and environmental toxicology, as well as high-throughput safety assessment . Although there are some drawbacks, such as relatively fewer gene homologs in mammals, the lack of many key organs and the limited permeability of the thick cuticle, C. elegans is still a powerful whole-organism model for the preliminary risk assessment of nanomaterials. Caenorhabditis elegans has some remarkable characteristics that make it amenable to high-content, toxicological screening and has been extensively used in large-scale genetic screens. Caenorhabditis elegans embryos are about 50 μm in length and reach about 1 mm as adults with a simple anatomy. Their small size allows automated manipulation in multi-well microplates through all developmental stages. They can be simply cultivated with a diet of Escherichia coli in liquid medium or stored long-term via cryopreservation. The whole life span is about 2–3 weeks, with a reproductive cycle of about 3 d at 20°C, and cycle changes can be controlled through the culture temperature, which makes experimental design more flexible. Moreover, the whole body of C. elegans is optically transparent, enabling real-time observation of morphological and functional changes in the living organism with or without the aid of fluorescent reporters of gene expression. Recently, the potentially harmful effects of many nanomaterials, such as metals [116–119], metallic oxides [57,120] and carbon nanomaterials [26,57,121,122], were widely studied in the C. elegans model. Most of these studies were based on traditional toxicity evaluation methods that detect the change of some basic physiological events, such as morphology, growth, movement, feeding, reproduction or the oxidative stress response. These parameters are also the main readouts of HCS in this transparent organism. With the development of new technologies, including rapid sorting and high-throughput digital imaging, C. elegans have also been used to screening small molecules for bioactivity and target identification [115,123]. However, HCS of nanosafety is still under development. Recently, Roh et al. detected changes in the nematode transcription profile following silver nanoparticle exposure using whole-genome microarrays and subsequently analyzed reproductive toxicity-related genes by quantitative real-time polymerase chain reaction . This ‘exotoxicogenomic’ strategy may represent a new screening paradigm to complement the traditional assessment criteria for nanosafety HTS, and even HCS, in C. elegans. Daphnia Crustaceans are the most abundant and ecologically important group of invertebrates in aquatic ecosystems, working as shredders that accelerate the degradation of organic material and nutrient recycling. Crustaceans also play important roles in regulatory toxicity testing as a part of the basic set of organisms that are required for the assessment of risk to both aquatic and terrestrial environments . Daphnia is the most commonly used crustacean model in regulatory chemical testing. Well-accepted testing methods with Daphnia have been included in several guidelines and international standards for acute and chronic toxicity studies , buttressing their suitability for nanosafety evaluation. The transparent body, small size, short generation time, large brood size, ease of laboratory manipulation and well-characterized genome facilitate HCS of nanomaterials with this model organism . By October 2011, 24% of all nanoecotoxico-logical data obtained were based on Daphnia studies . To date, most ecotoxicological tests on manufactured nanomaterials have used Daphnia magna as the test organism. Among them, the most studied nanomaterials were titanium dioxide  and silver [127,128], followed by carbon nanomaterials such as carbon nanotubes [129,130] and fullerenes . Based on standardized ecotoxicology methods, acute and chronic toxicities of nanomaterials were revealed in terms of typically assessed endpoints, such as growth, sensitivity, mortality, movement, reproduction, genetic toxicity, energy reserves and body distribution. However, HCS of nanosafety has not yet been reported. Along with the increasing comprehension of the importance of nanoecotoxicology and the rapid development of methodologies, future efforts will certainly promote nanosafety HCS in this unique model. Yeast Yeasts are unicellular eukaryotes that make up a significant proportion of the soil microbial community and play significant roles in the processes of substance circulation and energy exchange in the ecosystem. On account of yeast’s versatile genetic malleability and abundant mutant strain availability, it has long seen scientific use in genetics and molecular biology. The similarities regarding cellular structure, functional organization and basic molecular mechanisms with cells of higher organisms have made yeast a valuable tool for the toxicological evaluation of chemicals such as heavy metals, anticancer drugs, herbicides and food preservatives . Yeast has a short generation time and can be easily cultured in microplates. Yeast-based assays developed for HTS have been conducted extensively in the pharmaceutical and biotechnology industries . Thus, yeast presents a useful and simple system for high-content measurement of the transfer and potential risk of nanomaterials in the environment. The number of nanosafety studies based on yeast models to date is small [134–137]. The application of yeast in nanoecotoxicity research also remains underdeveloped, which is not in line with the long history of HTS techniques used in this model . Most recently, Kasemets et al. elucidated the toxicity mechanism of CuO nanoparticles in S. cerevisiae BY4741 wild-type and nine isogenic single-gene deletion mutants as model systems, demonstrating a high-throughput test format combining systematic genome-wide mutant collections of yeast and a mechanism-based phenotypic profiling approach for the cost-efficient screening of nanosafety . Algae The aquatic ecosystem is likely to be the ultimate destination for most nanomaterials released into the environment. The evaluation of their various effects upon phytoplankton is a critical step in predicting the potential impact of materials on the aquatic food network and even entire ecosystems. As one main category of biota in aquatic ecosystems, algae are present throughout fresh water and marine ecosystems and are particularly susceptible to most types of contamination associated with anthropogenic pollution, including nanomaterials. Therefore, algae are often used for the monitoring of water quality and hazard assessment of wastewaters and are included in several international standards for ecotoxicity tests. Because algae are at the bottom of the aquatic food chain and filter the aqueous environment in search of food, they will be important in ecotoxicological research regarding nanomaterials, especially in long-term, low level exposure studies of chronic endpoints and bioaccumulation . Moreover, their small size, rapid growth, and well-defined and easy readout test methods give algae the potential to be amenable to high-content toxicity screening, although specific techniques still need to be developed. Although current data regarding the safety of nanomaterials in algae models is still limited, the number of studies in this field has grown rapidly over the past decade [125,139]. A series of algae, such as Chattonella marina, Chlorella sp., Chlorella vulgaris, Chlamydomonas reinhardtii, Desmodesmus subspicatus, Dunaliella tertiolecta, Isochrysis galbana, Microcystis aeruginosa, Pavlova lutheri and Pseudokirchneriella subcapitata [140–142], have been used to study the ecotoxicity of nanoparticles, including a focus on various engineered metallic oxide nanomaterials. Standard chemical toxicity test methods were applied to detect the toxicological effects of nanomaterials by testing growth inhibition, photosynthesis inhibition, cell membrane damage, cyto-ultrastructure disorder, reactive oxygen species (ROS) production, lipid peroxidation and stress response gene expression, and have been proven to be highly effective. However, high-content nanoecotoxicological data are still rare. Future studies should consider ENM characteristics and their specific interactions with algae to develop more efficient methods, including high-content methods, and expand the application of the model. Bacteria Bacteria are universal prokaryotic microorganisms in both terrestrial and aquatic ecosystems, and play fundamental roles in biogeochemical processes, from primary productivity to nutrient cycling and waste decomposition, which influence and control nanomaterial fate and behavior. Bacteria are conventional toxicity test models because they have a high surface-to-volume ratio, which makes them very sensitive to low concentrations of toxic substances. In addition, bacteria exhibit convenient ecotoxicity endpoints of survival, reproductive capacity and mutation . Furthermore, their rapid growth, inexpensive culture conditions and amenability to low-volume platforms make bacteria ideal model organisms for HTS for nanosafety studies . Consequently, the study of nanosafety in bacteria is important for the EHS risk assessment of nanoparticles in the environment, which may provide warning signs in terms of possible risks to higher organisms in the food chain. Most nanosafety research based on bacteria models to date has been carried out through traditional microbiological toxicity assays detecting morphological damage, reduction in growth, metabolic activity and ROS production [120,143,144]. In recent years, several high-throughput assays have been applied to nanosafety in bacteria. By exploiting a genome-wide collection of single-gene deletion mutants, the toxicological pathways of cationic polystyrene nanomaterials were studied in E. coli, and the assay was validated for quantitative high-throughput toxicological analysis for nanomaterials . Reyes et al. investigated the toxicity of zinc-containing nanomaterials using a time-resolved HTS methodology in an arrayed E. coli genome-wide knockout library . These studies exhibit the utility of bacterial models in high-throughput, and even high-content, nanoecotoxicological assessment. Toxicity biomarker library After choosing proper screening models, one should select appropriate toxicological pathways to be examined and with a readout suitable for HCS to carry out risk evaluation of ENMs. Toxicity biomarkers are specific quantitative tools for the assessment of various nanotoxicities that form the basis of nanosafety evaluation [146,147]. A well-established toxicity biomarker library together with comprehensive ENM, nano-property and biological model libraries form the framework of the complex network of nanosafety HTS. Similar to the other three libraries, the content of the toxicity biomarker library should be as rich as possible. Because any particular approach and specific biomarker can only provide limited information on a specific toxicity phenomenon, nanotoxicity assays should always be supplemented with other complimentary assays that cover relevant injury and toxicological mechanisms. Moreover, the responsiveness of screening models may be different even to the same nanomaterial, due to the development and differentiation stages of the models. To achieve high efficiency, it is often necessary to combine related models and multiple toxicity assays to improve the predictive power of evaluations. This HCS strategy has been receiving growing acceptance in nanosafety research [46,48,57]. Only with the construction of a series of sub-libraries that cover all valid screening models and different injury mechanisms, can multiple levels of nanotoxicities from the cell to the whole organism be accurately reflected. Characteristics of current HCS technologies require that detection methods are relatively simple and that biomarkers are directly detectable and easy to analyze. Traditional toxicological evaluation approaches are based on plate readers, which have long been used in the pharmaceutical industry, and are regarded to be effective methods for high-content analysis of toxicity. Utilizing existing HCS equipment with automated sample treatment, these approaches are able to meet the dual requirements of toxicity detection and downstream data analysis. Such systems are capable of investigating various toxicity biomarkers, with readouts of: (i) luminescence and fluorescence for indicating various injury mechanisms in cells, microorganism and whole-organism models ; (ii) abnormal changes or defects of morphology mainly in whole-organism models; and (iii) relatively new genomic and proteomic analysis platforms . In addition to traditional optical and morphological biomarkers for toxicity analysis, the potential of several new techniques in promoting rapid screening methods for nanosafety has been demonstrated, such as impedance-based real-time cell analysis [86,87] and band gap energy levels of ENMs . The future development of novel instrumentation and techniques could also significantly expand the range of toxicity biomarkers and realize currently ‘virtually impossible’ applications for HCS. For instance, microfluidic systems, in addition to cell HCS , may provide innovative tools for large-scale patterning by precisely controlling embryos for high-throughput studies of morphogenesis [112,113]. Models and test batteries widely used within standard EHS assessment frameworks are generally considered appropriate for nano-EHS research. Many toxicity mechanisms have been identified and validated for nanosafety [81,144,151–155]. Several toxicity biomarkers have also been successfully applied for high-content evaluation of nanosafety (Table 1). We have developed a multi-parametric in vitro HCS assay of ENMs based on oxidative stress to assess and predict nanosafety by ROS, intracellular calcium flux, mitochondrial membrane depolarization and membrane damage . The hierarchical oxidative stress effects are often outcomes of ROS generation, and are presented as antioxidant defense (Tier 1), pro-inflammatory (Tier 2) and cytotoxic (Tier 3) responses . Each tier of oxidative stress could induce a wide range of damage cascades, and requires different sets of assays and biomarkers for detection. Universal injury biomarkers are generally associated with specific screening models and unique biomarkers closely matching the model's inherent characteristics. In general, current cytotoxicity (a core event of oxidative stress) assays mainly include phenotypic, functional and reporter gene assays for cellular processes. The most common categories of toxicity metrics include cell viability (cell necrosis or apoptosis), membrane perturbation, organelle damage, protein damage or misfolding, the inflammatory response, frustrated phagocytosis, disturbance of essential cell signaling, an influence on the cellular electron transfer chains and abnormal gene expression [22,26,90,157,158]. In the case of whole organisms, most studies of nanosafety to date have focused on the changes in some basic physiological events, such as morphology, growth, movement, feeding and reproduction, together with the oxidative stress response and defective expression of stress response genes and proteins. For bacteria and yeast, traditional microbiological toxicity assays, morphological damage, reduction in growth, metabolic activity and ROS production are the major common evaluation biomarkers of nanotoxicities [122,123]. In nanosafety assessment with ecological screening models, typical endpoints include biomarkers used for standard chemical toxicity test methods and standard ecotoxicology methods, such as growth inhibition, photosynthesis inhibition, mortality, reproduction and genetic toxicity. The reasonable integration of screening models and toxicity biomarkers of the libraries discussed above may provide fundamental systemic data for the development of nano-SARs when combined with the ENM and ENM property libraries. With the better understanding of toxicity mechanisms and the continuous improvement of microscopic imaging and biological labeling, more and more toxicity biomarkers will be developed and applied to the high-content evaluation of nanosafety. INSTRUMENTATION AND DATA ANALYSIS FOR NANOSAFETY ASSESSMENT To fully exploit high-throughput nanosafety assays, in addition to appropriate library construction and experimental design, powerful tools are needed to support data collection and analysis. An effective HCS set-up should enable scientists to take measurements in a high-throughput and high-content setting, and manipulate the whole HCS process through robotic control (Fig. 3a). To achieve this goal, HCS instrumentation should integrate an appropriate read-out on an automated platform. Figure 3. View largeDownload slide Framework of data collection and analysis in nanosafety HCS and representative examples. (a) Basic scheme of nanosafety HTS and HCS data analysis. Using a computer-driven, automated high-throughput system, high-content data are collected, processed and interpreted. Predictive models (nano-SARs) based on HCS results provide fundamental information for quantitative prediction of nanotoxicity and for risk assessment of ENMs. (b) An example of automated data collection in nanosafety HCS in intact zebrafish embryos. The embryo manipulation and imaging procedure (left) is carried out by a robotic system. The embryos were automatically picked and placed in a 96-well plate, onto which were added suspensions of metal oxide nanoparticles (NPs). During the exposure, four-quadrant high-content images of hatching embryos (representative examples given on the right) were automatically captured at 24 h intervals. Reprinted with permission from . Copyright © 2013 WILEY-VCH Verlag GmbH & Co. (c) SOM clusters of toxicological responses of RAW264.7 cells exposed to seven metal and metal oxide nanoparticles. The use of SOM or other clustering tools may significantly reduce the amount of data required in a HCS study. Reprinted with permission from . Copyright © 2011 American Chemical Society. (d) An example of nano-SAR based on a data set of 44 iron oxide core nanoparticles, with spin-lattice relativity and zeta potentials as descriptors. P(T|x)-P(N|x) is the difference between the probabilities of an ENM being bioactive and being inactive. Reprinted with permission from . Copyright © 2013 WILEY-VCH Verlag GmbH & Co. Figure 3. View largeDownload slide Framework of data collection and analysis in nanosafety HCS and representative examples. (a) Basic scheme of nanosafety HTS and HCS data analysis. Using a computer-driven, automated high-throughput system, high-content data are collected, processed and interpreted. Predictive models (nano-SARs) based on HCS results provide fundamental information for quantitative prediction of nanotoxicity and for risk assessment of ENMs. (b) An example of automated data collection in nanosafety HCS in intact zebrafish embryos. The embryo manipulation and imaging procedure (left) is carried out by a robotic system. The embryos were automatically picked and placed in a 96-well plate, onto which were added suspensions of metal oxide nanoparticles (NPs). During the exposure, four-quadrant high-content images of hatching embryos (representative examples given on the right) were automatically captured at 24 h intervals. Reprinted with permission from . Copyright © 2013 WILEY-VCH Verlag GmbH & Co. (c) SOM clusters of toxicological responses of RAW264.7 cells exposed to seven metal and metal oxide nanoparticles. The use of SOM or other clustering tools may significantly reduce the amount of data required in a HCS study. Reprinted with permission from . Copyright © 2011 American Chemical Society. (d) An example of nano-SAR based on a data set of 44 iron oxide core nanoparticles, with spin-lattice relativity and zeta potentials as descriptors. P(T|x)-P(N|x) is the difference between the probabilities of an ENM being bioactive and being inactive. Reprinted with permission from . Copyright © 2013 WILEY-VCH Verlag GmbH & Co. Most of the early read-out systems developed for high-throughput toxicity analysis were designed to simultaneously detect a single signal from multiple samples, mainly in classic cell assays. For example, plate reader-based devices have been employed to measure absorbance, fluorescence and luminescence signals, as well as the less commonly used time-resolved fluorescence and fluorescence polarization, in early high-throughput nanosafety studies [33,51]. Fluorescence microscopy-based facilities for high-throughput cell imaging are also available . Other related interests include the development of new multi-well measuring methods for cell assays [160,161]. However, in order to go beyond the traditional low-content measurement methods and meet the needs of high-content analysis, platforms capable of the collection and analysis of high complexity data are necessary. High-content platforms allowing simultaneous multichannel analysis have already been reported for fluorescence-based cell imaging [59,162]. These approaches can also be adapted for microorganism models, including the assays of ecotoxicity discussed in the previous section. For those assays that involve relatively complex whole organisms (worms, flies, fish, etc.), image analysis systems that recognize morphological abnormalities are of particular importance. To satisfy the corresponding need, bright-field high-content imaging systems can be employed, with fluorescence imaging used as a supplement . Computerized phenotype recognition is often coupled with an imaging system to analyze the acquired images. Such an automated phenotype recognition imaging system has been employed in nanosafety HCS in the zebrafish model to assess the hatching status of embryos (Fig. 3b) . Through further technical and instrumental development, systems that generate data with higher complexity, such as 3D imaging and 3D cell culture, may also be integrated into HCS platforms if useful. Platform automation affords the possibility of precisely controlling experimental parameters, to treat large numbers of samples rapidly and to accelerate analysis by incorporating sample handling, data acquisition and data processing to measure toxicity. Automated HCS facilities with the capacity to wash, seed and stain cells, prepare working solutions, add solutions to cells, scan plates for data collection and carry out other experimental actions have been established [48,59]. A further hope is that, coupled with novel automated methods for nanomaterial library synthesis and characterization, nanosafety screening can eventually be carried out within an all-in-one HCS facility. A good example of the future development of nanosafety HCS instrumentation was demonstrated by the evaluation of nanosized metal oxide safety by Nel et al. . By integrating a flame spray pyrolysis facility, serving as an in-house material source, with an automated platform for both in vitro and in vivo screening measurements and toxicity assessment into a HCS analysis workflow, they were able to obtain toxicological data of metal oxide nanoparticles at various levels. It must be noted that the cost of manufacturing and maintaining such an automated platform rises dramatically with the complexity of the integrated assays. Hence, the miniaturization and simplification of current systems for measurement and data collection will be an important mission. One of the tools that has been considered favorable for future nanosafety HCS platforms is microfluidic technology, which enables the miniaturization and integrated manipulation of devices for diverse elements of HTS and HCS analyses, e.g. material synthesis, screening model management and toxicity testing [163,164]. With improved integration between the various components, microfluidic nanotoxicity analysis systems may support high-content nanosafety evaluation. Nanosafety screening seeks not only to assess the safety of the examined nanoparticles, but also to predict the impacts of nanomaterials on human health and/or the environment. Due to the complicated nature of the potential adverse effects of nanomaterials, multi-level information is needed to accomplish this goal. HCS is capable of acquiring toxicological data on a large-scale; yet appropriate data analysis tools are still needed to ‘translate’ these measurements into information to establish toxicological predictive models. Figure 3a describes a basic scheme for the collection, processing and analysis of HCS data. Data sets collected by an HCS facility will first have to be ‘pre-processed’ to ensure data quality. For data collected as numerical information (e.g. absorbance or fluorescence intensity), replicate measurements, quality control to reduce random errors and normalization of the raw data is required to minimize variability between plates . Statistical tools can then be applied to identify outliers, distinguish negative and positive hits, and reduce the initial dimensions of data sets [85,165,166]. However, such processing steps must be carried out with caution to minimize bias and false identification . Discussions of useful statistical methods for HCS data pre-processing and the accompanying issues can be found elsewhere [165,167]. When data are acquired as graphic information, i.e. images that need to be analyzed as a whole to extract morphological features, raw images may first undergo a quality enhancement process to improve the image contrast and suppress noise. Image segmentation and edge detection techniques can then be used to enable the recognition of desired objects, such as cells or embryos. A set of descriptors of color, texture or other features can then be calculated from the segmented images. For phenotype recognition, these descriptors are optimized and used to develop a recognition model for new images. Further analysis of HCS data, such as clustering and ranking analysis, is then applied to extract and visualize toxicological information [50,168]. Such analyses aim to uncover possible relationships between parameters (environmental and material) and toxicity, correlations among responses from different assays and to classify ENMs according to their toxicological effects or physicochemical properties. Some of the proposed clustering tools, e.g. heat mapping [39,57], self-organizing maps (SOM) [50,57] and sensing systems [169–171], have already advanced nanosafety HTS or HCS research. A good example is the toxicity analysis of metal and metal oxide nanoparticles reported by Rallo et al. in 2011 (Fig. 3c), where the authors highlighted how the application of SOM significantly reduced the amount of data required . Information extracted from data analysis is the foundation of ENM hazard ranking and EHS risk assessment, and predictive modeling approaches, such as nano-SARs, are considered to be the crucial ‘bridge’ for this connection [49,172]. Nano-SARs require proper sets of quantitative parameters (properties of nanoparticles and of the environment in which they are expected to show toxicity) and large nanosafety databases that cover an acceptable range for each important parameter. HCS can act as a powerful data source to establish these relationships. Modeling techniques, including statistical (e.g. multiple linear regression), clustering (e.g. random forest) and machine learning methods (e.g. support vector machines and artificial neural networks), are commonly used in nano-SAR development, as summarized by Winkler et al. . Most of the published toxicity nano-SAR studies to date have examined metals or metal oxides [75,85,144], probably due to their abundant accessible toxicity data. Among these models, one based on HTS data was reported by Liu et al. , who developed a nano-SAR model considering the cytotoxicity of nano metal oxides. This study was supported by HTS of nine different nanomaterials. Another nano-SAR model, predicting the cytotoxicity of nano metal oxides, was presented by Liu et al.  (Fig. 3d). They derived their conclusions from HTS of 44 nanomaterials. HTS-based nano-SARs for other ENMs include the two SARs reported by Fourches et al. , who modelled the cytotoxicity data obtained by the HTS data of Shaw et al.  via a support vector machine classifier. Currently, nano-SAR modeling for nanosafety is still far from being well developed, which is primarily due to the insufficient quality and coverage of experimental data . With the development of further HCS methods and facilities, it will be possible to provide much richer data than the early HTS methods. We hope that nano-SARs will be increasingly helpful in data analysis for all classes of nanosafety HCS research and that, in time, they will become a powerful approach for the setting up of quantitative structure-activity models for ENMs. EHS risks expected from nanomaterials manifest under complicated and diverse circumstances. For example, adverse effects on the human body can occur through inhalation, skin contact or oral ingestion; those against the environment can arise in various types of ecosystems. To accomplish quantitative risk evaluation, characteristics of different exposure conditions should also be integrated into HCS studies. Although different screening models and conditions can be chosen according to the impact to be addressed, for instance the use of mammalian sera or cell lines [39,48,86,177] for impacts on human and ecotoxicity-related models (e.g. soil creatures  or fish ), HCS systems can only provide a highly simplified imitation of the actual exposure conditions. Conceptual approaches, such as the dynamic energy budget (DEB) theory, are therefore essential to understand the relationships between the impacts of nanomaterials on an ecosystem and the molecular or organismal level results provided by HTS or HCS [179,180]. DEB theory seeks to describe the principles of how organisms acquire and use energy to participate in biological activities. This has been employed by scientists at UC CEIN (University of California, Center for Environmental Implications of Nanotechnology) to model and predict the effects of nanomaterials on population growth. Given the unfavorable duration and cost of ecological and clinical experiments, the establishment of new concepts to analyze important exposure circumstances will be key to assess the risks created by the rapidly expanding presence of commercial nanomaterials. LIA FOR EHS RISK ASSESSMENT OF ENMs While we suggest that the creation of fundamental libraries for nanosafety HCS analysis may aid the resolution of complex nano-EHS issues, it should be noted that success of the LIA relies on strong networking between the libraries (Fig. 4). The first priority is to establish an ENM library that registers the manufacture and use of nanotechnology-based products. Current knowledge of toxicological characteristics of ENMs will aid the selection and control of library parameters. Such an ENM library will lay the foundation for a nano-property library that will preferably collect all properties of indexed nanomaterials. These properties, including both the intrinsic physical and chemical properties acquired during manufacture, and the extrinsic ones that appear only at nano-bio interfaces, will have to be catalogued to facilitate further predictive modeling. The toxicities of nanomaterials in the established ENM library will then be evaluated using the models in the screening model library, whose selection should both be based on the purpose of toxicity monitoring and correspond to the organisms or environmental systems involved in the relevant exposure scenarios. To deal with the multiple aspects (e.g. growth inhibition, metabolic disorders and reproductive abnormities) of nanosafety, a library of toxicity biomarkers organized according to different toxicity mechanisms and screening models can be created. Both the handling of model organisms and detection of toxicity should be assisted by automated HCS platforms. For hazards above the molecular level, platforms capable of high-content detection and analysis are considered to be essential. The results generated by high-throughput and high-content instrumentation will have to be processed and analyzed with appropriate statistical and computational methods to deduce the toxicity of the screened materials. Figure 4. View largeDownload slide Schematic illustration of the library integration approach for EHS risk assessment of ENMs. Guided by fate monitoring of ENMs, a network closely woven with different parameters of four libraries, combined with automatic HCS instrumentation and powerful data analysis, helps to give an adequate evaluation of the full-scale EHS situation of ENMs. Reasonable choices of different biological models and a battery of nanotoxicity biomarkers with different sensitivity profiles will be crucial in the whole workflow. Figure 4. View largeDownload slide Schematic illustration of the library integration approach for EHS risk assessment of ENMs. Guided by fate monitoring of ENMs, a network closely woven with different parameters of four libraries, combined with automatic HCS instrumentation and powerful data analysis, helps to give an adequate evaluation of the full-scale EHS situation of ENMs. Reasonable choices of different biological models and a battery of nanotoxicity biomarkers with different sensitivity profiles will be crucial in the whole workflow. An example that can help to illustrate the application of LIA in nanosafety HCS studies is the work carried out at UC CEIN concerning metal oxide nanoparticles, which was reviewed in previous sections [38,47,181]. Nel et al. have set up a nano metal oxide library, and organized its content according to the varying metal elements. The systematic construction of such library was realized by the development of a novel high-throughput synthesis platform . The resulting collection of nano-sized metal oxides was characterized to obtain intrinsic properties such as primary size and ‘in-solution’ properties, such as size and dissolution kinetics . Due to the environmental and ecological focus of the research, zebrafish embryos and larvae were chosen as the screening model, and their abnormalities during hatching and development were accordingly captured and analyzed by an HCS imaging system . Using zebrafish embryos as model and a phenotype recognition system to assess hatching abnormalities, 24 metal oxide standards were screened in an early phase of the work, among which four (CuO, ZnO, Cr2O3 and NiO) were demonstrated as hazardous for the hatching of zebrafish embryos. Such hazard has also been reported to be mechanistically linked to the inhibition of a hatching enzyme, metalloprotease ZHE 1 . Another part of the study applied transgenic zebrafish larvae, in which expression of fluorescent proteins was promoted by oxidative stress responses, to screen the stress induction effects of metal oxides . This pioneering example confirms that the major elements of nanosafety HCS analysis, represented by the four libraries in the LIA approach, should be considered as parts of an integrated scheme as discussed above, and that nanosafety HCS should be closely combined with toxicological pathway knowledge. Moreover, their study has also confirmed the relationship between oxidative stress generation and the band gap energy of these nano metal oxides, demonstrating the importance of predictive models in correlating toxicity data with ENM properties. Nano-SARs will provide fundamental toxicological information for more targeted and efficient library construction in the future, and eventually the necessary guidance for more efficient health and environmental monitoring, as well as the safer design, manufacture, use and disposal of nanotechnology-based products in the future. PERSPECTIVES HCS is currently the most effective methodology available to rapidly detect potential adverse impacts of nanomaterials on human health and the environment. Such highly efficient screening technology, based on a next-generation framework of data analysis and subsequent confirmation coupled with detailed functional studies, can vastly increase our knowledge base for biosafety evaluation. As a relatively underdeveloped area in nanotechnology, HCS evaluation of nanosafety is urgently needed. Continuous improvement and the introduction of new comprehensive evaluation strategies, such as genomic and proteomic methodologies, will further the field. In addition, sufficient information about the toxicological mechanisms and real-life environmental exposure effects of nanomaterials is critical for the progression of nanosafety HCS studies. All of these elements should be coupled with the further development of screening and analysis technology or instruments, to enable high-throughput analysis of data of even higher complexity and of different types, such as 3D imaging or real-time dynamic imaging results. It should also be recognized that it is impossible to consider all of the EHS risks, including chronic exposure situations and other real exposure scenarios, from nanosafety HCS data alone. Therefore, a sophisticated research network consisting of both comprehensive HCS systems, which are used for initial hazard ranking and high-level prioritization, and animal models appropriate for systematic long-term toxicological studies will be needed for EHS risk evaluation of nanomaterials. Previous studies, illustrated by the multidisciplinary investigation performed at UC CEIN, have already shaped the strategy and framework of HCS-based safety assessments. We speculate that HCS-based large-scale and rapid evaluation of ENMs will pave the way for the efficient biosafety assessment of new nanomaterials and secure the application of nanotechnology in every aspect of industry. FUNDING This work was supported by the National Natural Science Foundation of China (NSFC) (91543127, 11435002, 31300823, 31571021 and 31470969), NSFC Innovation Team (11621505), Frontier Research Program of Chinese Academy of Sciences (QYZDJ-SSW-SLH022), and National Natural Science Funds for Distinguished Young Scholar (31325010). REFERENCES 1. De Volder MFL, Tawfick SH, Baughman RH et al. Carbon nanotubes: present and future commercial applications. Science 2013; 339: 535– 9. https://doi.org/10.1126/science.1222453 Google Scholar CrossRef Search ADS PubMed 2. The Project on Emerging Nanotechnologies. Consumer Products Inventory. http://www.nanotechproject.org/cpi/products/ (23 May 2014, date last accessed). 3. Aitken RJ, Chaudhry MQ, Boxall ABA et al. Manufacture and use of nanomaterials: current status in the UK and global trends. Occup Med 2006; 56: 300– 6. https://doi.org/10.1093/occmed/kql051 Google Scholar CrossRef Search ADS 4. Maynard AD, Aitken RJ, Butz T et al. Safe handling of nanotechnology. Nature 2006; 444: 267– 9. https://doi.org/10.1038/444267a Google Scholar CrossRef Search ADS PubMed 5. Nel A, Xia T, Madler L et al. Toxic potential of materials at the nanolevel. Science 2006; 311: 622. https://doi.org/10.1126/science.1114397 Google Scholar CrossRef Search ADS PubMed 6. Nel AE, Madler L, Velegol D et al. Understanding biophysicochemical interactions at the nano–bio interface. Nat Mater 2009; 8: 543– 57. https://doi.org/10.1038/nmat2442 Google Scholar CrossRef Search ADS PubMed 7. Stern ST, McNeil SE. Nanotechnology safety concerns revisited. Toxicol Sci 2008; 101: 4– 21. https://doi.org/10.1093/toxsci/kfm169 Google Scholar CrossRef Search ADS PubMed 8. Oberdorster G, Ferin J, Gelein R et al. Role of the alveolar macrophage in lung injury: studies with ultrafine particles. Environ Health Perspect 1992; 97: 193– 9. https://doi.org/10.1289/ehp.9297193 Google Scholar CrossRef Search ADS PubMed 9. Wang H, Wang J, Deng X et al. Biodistribution of carbon single-wall carbon nanotubes in mice. J Nanosci Nanotech 2004; 4: 1019– 24. https://doi.org/10.1166/jnn.2004.146 Google Scholar CrossRef Search ADS 10. Jia G, Wang H, Yan L et al. Cytotoxicity of carbon nanomaterials: single-wall nanotube, multi-wall nanotube, and fullerene. Environ Sci Technol 2005; 39: 1378– 83. https://doi.org/10.1021/es048729l Google Scholar CrossRef Search ADS PubMed 11. Zhao Y, Nalwa HS. Nanotoxicology . Los Angeles: American Scientific Publishers, 2007. 12. Umair M, Javed I, Rehman M et al. Nanotoxicity of inert materials: the case of gold, silver and iron. J Pharm Pharm Sci 2016; 19: 161– 80. https://doi.org/10.18433/J31021 Google Scholar CrossRef Search ADS PubMed 13. Jain A, Ranjan S, Dasgupta N et al. Nanomaterials in food and agriculture: an overview on their safety concerns and regulatory issues. Crit Rev Food Sci Nutr 2016; doi: 10.1080/10408398.2016.1160363. https://doi.org/10.1080/10408398.2016.1160363 14. Ikoba U, Peng H, Li H et al. Nanocarriers in therapy of infectious and inflammatory diseases. Nanoscale 2015; 7: 4291– 305. https://doi.org/10.1039/C4NR07682F Google Scholar CrossRef Search ADS PubMed 15. Peng H, Wang C, Xu X et al. An intestinal Trojan horse for gene delivery. Nanoscale 2015; 7: 4354– 60. https://doi.org/10.1039/C4NR06377E Google Scholar CrossRef Search ADS PubMed 16. Peng H, Liu X, Wang G et al. Polymeric multifunctional nanomaterials for theranostics. J Mater Chem B 2015; 3: 6856– 70. https://doi.org/10.1039/C5TB00617A Google Scholar CrossRef Search ADS 17. Du D, Chang N, Sun S et al. The role of glucose transporters in the distribution of p-aminophenyl-alpha-d-mannopyranoside modified liposomes within mice brain. J Control Release 2014; 182: 99– 110. https://doi.org/10.1016/j.jconrel.2014.03.006 Google Scholar CrossRef Search ADS PubMed 18. Liu M, Li M, Sun S et al. The use of antibody modified liposomes loaded with AMO-1 to deliver oligonucleotides to ischemic myocardium for arrhythmia therapy. Biomaterials 2014; 35: 3697– 707. https://doi.org/10.1016/j.biomaterials.2013.12.099 Google Scholar CrossRef Search ADS PubMed 19. Zhao F, Meng H, Yan L et al. Nanosurface chemistry and dose govern the bioaccumulation and toxicity of carbon nanotubes, metal nanomaterials and quantum dots in vivo. Sci Bull 2015; 60: 3– 20. https://doi.org/10.1007/s11434-014-0700-0 Google Scholar CrossRef Search ADS 20. Buzea C, Pacheco II, Robbie K. Nanomaterials and nanoparticles: sources and toxicity. Biointerphases 2007; 2: MR17– 71. https://doi.org/10.1116/1.2815690 Google Scholar CrossRef Search ADS PubMed 21. Chen XX, Cheng B, Yang YX et al. Characterization and preliminary toxicity assay of nano-titanium dioxide additive in sugar-coated chewing gum. Small 2013; 9: 1765– 74. https://doi.org/10.1002/smll.201201506 Google Scholar CrossRef Search ADS PubMed 22. Li Y, Zhou Y, Wang HY et al. Chirality of glutathione surface coating affects the cytotoxicity of quantum dots. Angew Chem Int Ed 2011; 50: 5860– 4. https://doi.org/10.1002/anie.201008206 Google Scholar CrossRef Search ADS 23. Meng H, Chen Z, Xing G et al. Ultrahigh reactivity provokes nanotoxicity: explanation of oral toxicity of nano-copper particles. Toxicol Lett 2007; 175: 102– 10. https://doi.org/10.1016/j.toxlet.2007.09.015 Google Scholar CrossRef Search ADS PubMed 24. Wiesner MR, Lowry GV, Alvarez P et al. Assessing the risks of manufactured nanomaterials. Environ Sci Technol 2006; 40: 4336– 45. https://doi.org/10.1021/es062726m Google Scholar CrossRef Search ADS PubMed 25. Yang H, Sun C, Fan Z et al. Effects of gestational age and surface modification on materno-fetal transfer of nanoparticles in murine pregnancy. Sci Rep 2012; 2: 847. https://doi.org/10.1038/srep00847 Google Scholar CrossRef Search ADS PubMed 26. Zhang W, Wang C, Li Z et al. Unraveling stress-induced toxicity properties of graphene oxide and the underlying mechanism. Adv Mater 2012; 24: 5391– 7. https://doi.org/10.1002/adma.201202678 Google Scholar CrossRef Search ADS PubMed 27. Wang B, Feng WY, Wang TC et al. Acute toxicity of nano- and micro-scale zinc powder in healthy adult mice. Toxicol Lett 2006; 161: 115– 23. https://doi.org/10.1016/j.toxlet.2005.08.007 Google Scholar CrossRef Search ADS PubMed 28. Wang J, Chen C, Liu Y et al. Potential neurological lesion after nasal instillation of TiO2 nanoparticles in the anatase and rutile crystal phases. Toxicol Lett 2008; 183: 72– 80. https://doi.org/10.1016/j.toxlet.2008.10.001 Google Scholar CrossRef Search ADS PubMed 29. Wang J, Zhou G, Chen C et al. Acute toxicity and biodistribution of different sized titanium dioxide particles in mice after oral administration. Toxicol Lett 2007; 168: 176– 85. https://doi.org/10.1016/j.toxlet.2006.12.001 Google Scholar CrossRef Search ADS PubMed 30. Chen Z, Meng H, Xing G et al. Acute toxicological effects of copper nanoparticles in vivo. Toxicol Lett 2006; 163: 109– 20. https://doi.org/10.1016/j.toxlet.2005.10.003 Google Scholar CrossRef Search ADS PubMed 31. Felix LC, Ede JD, Snell DA et al. Physicochemical properties of functionalized carbon-based nanomaterials and their toxicity to fishes. Carbon 2016; 104: 78– 89. https://doi.org/10.1016/j.carbon.2016.03.041 Google Scholar CrossRef Search ADS 32. Canesi L, Corsi I. Effects of nanomaterials on marine invertebrates. Sci Total Environ 2016; 565: 933– 40. https://doi.org/10.1016/j.scitotenv.2016.01.085 Google Scholar CrossRef Search ADS PubMed 33. Damoiseaux R, George S, Li M et al. No time to lose—high throughput screening to assess nanomaterial safety. Nanoscale 2011; 3: 1345– 60. https://doi.org/10.1039/c0nr00618a Google Scholar CrossRef Search ADS PubMed 34. Service R. Nanotechnology: can high-speed tests sort out which nanomaterials are safe? Science 2008; 321: 1036– 7. https://doi.org/10.1126/science.321.5892.1036 Google Scholar CrossRef Search ADS PubMed 35. Purchase IF. Ethical review of regulatory toxicology guidelines involving experiments on animals: the example of endocrine disrupters. Toxicol Sci 1999; 52: 141– 7. https://doi.org/10.1093/toxsci/52.2.141 Google Scholar CrossRef Search ADS PubMed 36. Pereira DA, Williams JA. Origin and evolution of high throughput screening. Br J Pharmacol 2007; 152: 53– 61. https://doi.org/10.1038/sj.bjp.0707373 Google Scholar CrossRef Search ADS PubMed 37. Bhogal N, Grindon C, Combes R et al. Toxicity testing: creating a revolution based on new technologies. Trends Biotechnol 2005; 23: 299– 307. https://doi.org/10.1016/j.tibtech.2005.04.006 Google Scholar CrossRef Search ADS PubMed 38. Lin S, Zhao Y, Ji Z et al. Zebrafish high-throughput screening to study the impact of dissolvable metal oxide nanoparticles on the hatching enzyme, ZHE1. Small 2013; 9: 1776– 85. https://doi.org/10.1002/smll.201202128 Google Scholar CrossRef Search ADS PubMed 39. Shaw SY, Westly EC, Pittet MJ et al. Perturbational profiling of nanomaterial biologic activity. Proc Natl Acad Sci USA 2008; 105: 7387– 92. https://doi.org/10.1073/pnas.0802878105 Google Scholar CrossRef Search ADS PubMed 40. Vecchio G, Fenech M, Pompa PP et al. Lab-on-a-chip-based high-throughput screening of the genotoxicity of engineered nanomaterials. Small 2014; 10: 2721– 34. https://doi.org/10.1002/smll.201303359 Google Scholar CrossRef Search ADS PubMed 41. Watson C, Ge J, Cohen J et al. High-throughput screening platform for engineered nanoparticle-mediated genotoxicity using CometChip technology. ACS Nano 2014; 8: 2118– 33. https://doi.org/10.1021/nn404871p Google Scholar CrossRef Search ADS PubMed 42. Jung SK, Qu XL, Aleman-Meza B et al. Multi-endpoint, high-throughput study of nanomaterial toxicity in Caenorhabditis elegans. Environ Sci Technol 2015; 49: 2477– 85. https://doi.org/10.1021/es5056462 Google Scholar CrossRef Search ADS PubMed 43. Judson RS, Houck KS, Kavlock RJ et al. In vitro screening of environmental chemicals for targeted testing prioritization: the ToxCast project. Environ Health Perspect 2010; 118: 485– 92. https://doi.org/10.1289/ehp.0901392 Google Scholar CrossRef Search ADS PubMed 44. Harris G, Palosaari T, Magdolenova Z et al. Iron oxide nanoparticle toxicity testing using high-throughput analysis and high-content imaging. Nanotoxicology 2015; 9: 87– 94. https://doi.org/10.3109/17435390.2013.816797 Google Scholar CrossRef Search ADS PubMed 45. Manshian BB, Pfeiffer C, Pelaz B et al. High-content imaging and gene expression approaches to unravel the effect of surface functionality on cellular interactions of silver nanoparticles. ACS Nano 2015; 9: 10431– 44. https://doi.org/10.1021/acsnano.5b04661 Google Scholar CrossRef Search ADS PubMed 46. Jan E, Byrne SJ, Cuddihy M et al. High-content screening as a universal tool for fingerprinting of cytotoxicity of nanoparticles. ACS Nano 2008; 2: 928– 38. https://doi.org/10.1021/nn7004393 Google Scholar CrossRef Search ADS PubMed 47. Lin S, Zhao Y, Xia T et al. High content screening in zebrafish speeds up hazard ranking of transition metal oxide nanoparticles. ACS Nano 2011; 5: 7284– 95. https://doi.org/10.1021/nn202116p Google Scholar CrossRef Search ADS PubMed 48. Zhang T, Stilwell JL, Gerion D et al. Cellular effect of high doses of silica-coated quantum dot profiled with high throughput gene expression analysis and high content cellomics measurements. Nano Lett 2006; 6: 800– 8. https://doi.org/10.1021/nl0603350 Google Scholar CrossRef Search ADS PubMed 49. Cohen Y, Rallo R, Liu R et al. In silico analysis of nanomaterials hazard and risk. Acc Chem Res 2013; 46: 802– 12. https://doi.org/10.1021/ar300049e Google Scholar CrossRef Search ADS PubMed 50. Rallo R, France B, Liu R et al. Self-organizing map analysis of toxicity-related cell signaling pathways for metal and metal oxide nanoparticles. Environ Sci Technol 2011; 45: 1695– 702. https://doi.org/10.1021/es103606x Google Scholar CrossRef Search ADS PubMed 51. Reyes VC, Li M, Hoek EM et al. Genome-wide assessment in Escherichia coli reveals time-dependent nanotoxicity paradigms. ACS Nano 2012; 6: 9402– 15. https://doi.org/10.1021/nn302815w Google Scholar CrossRef Search ADS PubMed 52. Collins AR, Annangi B, Rubio L et al. High throughput toxicity screening and intracellular detection of nanomaterials. Wiley Interdiscip Rev Nanomed Nanobiotechnol 2016; doi: 10.1002/wnan.1413. 53. Feliu N, Fadeel B. Nanotoxicology: no small matter. Nanoscale 2010; 2: 2514– 20. https://doi.org/10.1039/c0nr00535e Google Scholar CrossRef Search ADS PubMed 54. Nel A. Implementation of alternative test strategies for the safety assessment of engineered nanomaterials. J Intern Med 2013; 274: 561– 77. https://doi.org/10.1111/joim.12109 Google Scholar CrossRef Search ADS PubMed 55. Meng H, Xia T, George S et al. A predictive toxicological paradigm for the safety assessment of nanomaterials. ACS Nano 2009; 3: 1620– 7. https://doi.org/10.1021/nn9005973 Google Scholar CrossRef Search ADS PubMed 56. Thomas CR, George S, Horst AM. Nanomaterials in the environment: from materials to high-throughput screening to organisms. ACS Nano 2011; 5: 13– 20. https://doi.org/10.1021/nn1034857 Google Scholar CrossRef Search ADS PubMed 57. George S, Xia T, Rallo R et al. Use of a high-throughput screening approach coupled with in vivo zebrafish embryo screening to develop hazard ranking for engineered nanomaterials. ACS Nano 2011; 5: 1805– 17. https://doi.org/10.1021/nn102734s Google Scholar CrossRef Search ADS PubMed 58. Zhang H, Dunphy DR, Jiang X et al. Processing pathway dependence of amorphous silica nanoparticle toxicity: colloidal vs pyrolytic. J Am Chem Soc 2012; 134: 15790– 804. https://doi.org/10.1021/ja304907c Google Scholar CrossRef Search ADS PubMed 59. George S, Pokhrel S, Xia T et al. Use of a rapid cytotoxicity screening approach to engineer a safer zinc oxide nanoparticle through iron doping. ACS Nano 2010; 4: 15– 29. https://doi.org/10.1021/nn901503q Google Scholar CrossRef Search ADS PubMed 60. Ji Z, Wang X, Zhang H et al. Designed synthesis of CeO2 nanorods and nanowires for studying toxicological effects of high aspect ratio nanomaterials. ACS Nano 2012; 6: 5366– 80. https://doi.org/10.1021/nn3012114 Google Scholar CrossRef Search ADS PubMed 61. Li R, Wang X, Ji Z et al. Surface charge and cellular processing of covalently functionalized multiwall carbon nanotubes determine pulmonary toxicity. ACS Nano 2013; 7: 2352– 68. https://doi.org/10.1021/nn305567s Google Scholar CrossRef Search ADS PubMed 62. George S, Lin S, Ji Z et al. Surface defects on plate-shaped silver nanoparticles contribute to its hazard potential in a fish gill cell line and zebrafish embryos. ACS Nano 2012; 6: 3745– 59. https://doi.org/10.1021/nn204671v Google Scholar CrossRef Search ADS PubMed 63. Schaeublin NM, Braydich-Stolle LK, Schrand AM et al. Surface charge of gold nanoparticles mediates mechanism of toxicity. Nanoscale 2011; 3: 410– 20. https://doi.org/10.1039/c0nr00478b Google Scholar CrossRef Search ADS PubMed 64. Alkilany AM, Lohse SE, Murphy CJ. The gold standard: gold nanoparticle libraries to understand the nano-bio interface. Acc Chem Res 2013; 46: 650– 61. https://doi.org/10.1021/ar300015b Google Scholar CrossRef Search ADS PubMed 65. Zhao R, Wang H, Ji T et al. Biodegradable cationic ε-poly-L-lysine-conjugated polymeric nanoparticles as a new effective antibacterial agent. Sci Bull 2015; 60: 216– 26. https://doi.org/10.1007/s11434-014-0704-9 Google Scholar CrossRef Search ADS 66. Chan EM, Xu C, Mao AW et al. Reproducible, high-throughput synthesis of colloidal nanocrystals for optimization in multidimensional parameter space. Nano Lett 2010; 10: 1874– 85. https://doi.org/10.1021/nl100669s Google Scholar CrossRef Search ADS PubMed 67. Tung VC, Allen MJ, Yang Y et al. High-throughput solution processing of large-scale graphene. Nat Nanotech 2009; 4: 25– 9. https://doi.org/10.1038/nnano.2008.329 Google Scholar CrossRef Search ADS 68. Marchand P, Makwana NM, Tighe CJ et al. High-throughput synthesis, screening, and scale-up of optimized conducting indium tin oxides. ACS Comb Sci 2016; 18: 130– 7. https://doi.org/10.1021/acscombsci.5b00166 Google Scholar CrossRef Search ADS PubMed 69. Luan B, Friedrich T, Zhai J et al. A library of AuNPs modified by RAFT polymers of different charge and chain length: high throughput synthesis and synchrotron XFM imaging using a zebrafish larvae model. RSC Adv 2016; 6: 23550– 63. https://doi.org/10.1039/C6RA02801B Google Scholar CrossRef Search ADS 70. Gibson MI, Danial M, Klok H-A. Sequentially modified, polymer-stabilized gold nanoparticle libraries: convergent synthesis and aggregation behavior. ACS Comb Sci 2011; 13: 286– 97. https://doi.org/10.1021/co100099r Google Scholar CrossRef Search ADS PubMed 71. Weissleder R, Kelly K, Sun EY et al. Cell-specific targeting of nanoparticles by multivalent attachment of small molecules. Nat Biotechnol 2005; 23: 1418– 23. https://doi.org/10.1038/nbt1159 Google Scholar CrossRef Search ADS PubMed 72. Sun EY, Josephson L, Kelly KA et al. Development of nanoparticle libraries for biosensing. Bioconjugate Chem 2006; 17: 109– 13. https://doi.org/10.1021/bc050290e Google Scholar CrossRef Search ADS 73. Zhu M, Nie G, Meng H et al. Physicochemical properties determine nanomaterial cellular uptake, transport, and fate. Acc Chem Res 2013; 46: 622– 31. https://doi.org/10.1021/ar300031y Google Scholar CrossRef Search ADS PubMed 74. Qiu Y, Liu Y, Wang L et al. Surface chemistry and aspect ratio mediated cellular uptake of Au nanorods. Biomaterials 2010; 31: 7606– 19. https://doi.org/10.1016/j.biomaterials.2010.06.051 Google Scholar CrossRef Search ADS PubMed 75. Burello E, Worth AP. A theoretical framework for predicting the oxidative stress potential of oxide nanoparticles. Nanotoxicology 2011; 5: 228– 35. https://doi.org/10.3109/17435390.2010.502980 Google Scholar CrossRef Search ADS PubMed 76. Toropova AP, Toropov AA, Maksudov SK. QSPR modeling mineral crystal lattice energy by optimal descriptors of the graph of atomic orbitals. Chem Phys Lett 2006; 428: 183– 6. https://doi.org/10.1016/j.cplett.2006.06.084 Google Scholar CrossRef Search ADS 77. Barnard A, Li CM, Zhou RH et al. Modelling of the nanoscale. Nanoscale 2012; 4: 1042– 3. https://doi.org/10.1039/c2nr90005j Google Scholar CrossRef Search ADS PubMed 78. Pardo-Martin C, Chang TY, Koo BK et al. High-throughput in vivo vertebrate screening. Nat Meth 2010; 7: 634– 6. https://doi.org/10.1038/nmeth.1481 Google Scholar CrossRef Search ADS 79. Kahru A, Dubourguier H-C. From ecotoxicology to nanoecotoxicology. Toxicology 2010; 269: 105– 19. https://doi.org/10.1016/j.tox.2009.08.016 Google Scholar CrossRef Search ADS PubMed 80. Ma Y, He X, Zhang P et al. Phytotoxicity and biotransformation of La2O3 nanoparticles in a terrestrial plant cucumber (Cucumis sativus). Nanotoxicology 2011; 5: 743– 53. https://doi.org/10.3109/17435390.2010.545487 Google Scholar CrossRef Search ADS PubMed 81. Kathawala MH, Xiong S, Richards M et al. Emerging in vitro models for safety screening of high-volume production nanomaterials under environmentally relevant exposure conditions. Small 2013; 9: 1504– 20. https://doi.org/10.1002/smll.201201452 Google Scholar CrossRef Search ADS PubMed 82. Brandish PE, Chiu CS, Schneeweis J et al. A cell-based ultra-high-throughput screening assay for identifying inhibitors of D-amino acid oxidase. J Biomol Screen 2006; 11: 481– 7. https://doi.org/10.1177/1087057106288181 Google Scholar CrossRef Search ADS PubMed 83. Senut MC, Zhang Y, Liu F et al. Size-dependent toxicity of gold nanoparticles on human embryonic stem cells and their neural derivatives. Small 2016; 12: 631– 46. https://doi.org/10.1002/smll.201502346 Google Scholar CrossRef Search ADS PubMed 84. Zhang WB, Gao CY. Recent advances in cell imaging and cytotoxicity of intracellular stimuli-responsive nanomaterials. Sci Bull 2015; 60: 1973– 9. https://doi.org/10.1007/s11434-015-0952-3 Google Scholar CrossRef Search ADS 85. Liu R, Rallo R, George S et al. Classification NanoSAR development for cytotoxicity of metal oxide nanoparticles. Small 2011; 7: 1118– 26. https://doi.org/10.1002/smll.201002366 Google Scholar CrossRef Search ADS PubMed 86. Otero-Gonzalez L, Sierra-Alvarez R, Boitano S et al. Application and validation of an impedance-based real time cell analyzer to measure the toxicity of nanoparticles impacting human bronchial epithelial cells. Environ Sci Technol 2012; 46: 10271– 8. Google Scholar PubMed 87. Shah P, Zhu X, Zhang X et al. Microelectromechanical system-based sensing arrays for comparative in vitro nanotoxicity assessment at single cell and small cell-population using electrochemical impedance spectroscopy. ACS Appl Mater Interfaces 2016; 8: 5804– 12. https://doi.org/10.1021/acsami.5b11409 Google Scholar CrossRef Search ADS PubMed 88. Chia SL, Tay CY, Setyawati MI et al. Biomimicry 3D gastrointestinal spheroid platform for the assessment of toxicity and inflammatory effects of zinc oxide nanoparticles. Small 2015; 11: 702– 12. https://doi.org/10.1002/smll.201401915 Google Scholar CrossRef Search ADS PubMed 89. Tay CY, Muthu MS, Chia SL et al. Reality check for nanomaterial-mediated therapy with 3D biomimetic culture systems. Adv Funct Mater 2016; 26: 4046– 65. https://doi.org/10.1002/adfm.201600476 Google Scholar CrossRef Search ADS 90. Jones CF, Grainger DW. In vitro assessments of nanomaterial toxicity. Adv Drug Deliv Rev 2009; 61: 438– 56. https://doi.org/10.1016/j.addr.2009.03.005 Google Scholar CrossRef Search ADS PubMed 91. Zon LI, Peterson RT. In vivo drug discovery in the zebrafish. Nat Rev Drug Discov 2005; 4: 35– 44. https://doi.org/10.1038/nrd1606 Google Scholar CrossRef Search ADS PubMed 92. Baun A, Hartmann N, Grieger K et al. Ecotoxicity of engineered nanoparticles to aquatic invertebrates: a brief review and recommendations for future toxicity testing. Ecotoxicology 2008; 17: 387– 95. https://doi.org/10.1007/s10646-008-0208-y Google Scholar CrossRef Search ADS PubMed 93. Giacomotto J, Segalat L. High-throughput screening and small animal models, where are we? Br J Pharmacol 2010; 160: 204– 16. https://doi.org/10.1111/j.1476-5381.2010.00725.x Google Scholar CrossRef Search ADS PubMed 94. Harper SL, Carriere JL, Miller JM et al. Systematic evaluation of nanomaterial toxicity: utility of standardized materials and rapid assays. ACS Nano 2011; 5: 4688– 97. https://doi.org/10.1021/nn200546k Google Scholar CrossRef Search ADS PubMed 95. Nallathamby PD, Lee KJ, Xu X-HN. Design of stable and uniform single nanoparticle photonics for in vivo dynamics imaging of nanoenvironments of zebrafish embryonic fluids. ACS Nano 2008; 2: 1371– 80. https://doi.org/10.1021/nn800048x Google Scholar CrossRef Search ADS PubMed 96. Zhu X, Zhu L, Duan Z et al. Comparative toxicity of several metal oxide nanoparticle aqueous suspensions to Zebrafish (Danio rerio) early developmental stage. J Environ Sci Health Part A Tox Hazard Subst Environ Eng 2008; 43: 278– 84. https://doi.org/10.1080/10934520701792779 Google Scholar CrossRef Search ADS 97. Cheng J, Flahaut E, Cheng SH. Effect of carbon nanotubes on developing zebrafish (Danio rerio) embryos. Environ Toxicol Chem 2007; 26: 708– 16. https://doi.org/10.1897/06-272R.1 Google Scholar CrossRef Search ADS PubMed 98. King-Heiden TC, Wiecinski PN, Mangham AN et al. Quantum dot nanotoxicity assessment using the zebrafish embryo. Environ Sci Technol 2009; 43: 1605– 11. https://doi.org/10.1021/es801925c Google Scholar CrossRef Search ADS PubMed 99. Usenko CY, Harper SL, Tanguay RL. In vivo evaluation of carbon fullerene toxicity using embryonic zebrafish. Carbon 2007; 45: 1891– 8. https://doi.org/10.1016/j.carbon.2007.04.021 Google Scholar CrossRef Search ADS PubMed 100. Henry TB, Menn F-M, Fleming JT et al. Attributing effects of aqueous C60 nano-aggregates to tetrahydrofuran decomposition products in larval zebrafish by assessment of gene expression. Environ Health Perspect 2007; 115: 1059. https://doi.org/10.1289/ehp.9757 Google Scholar CrossRef Search ADS PubMed 101. Griffitt RJ, Weil R, Hyndman KA et al. Exposure to copper nanoparticles causes gill injury and acute lethality in zebrafish (Danio rerio). Environ Sci Technol 2007; 41: 8178– 86. https://doi.org/10.1021/es071235e Google Scholar CrossRef Search ADS PubMed 102. Ispas C, Andreescu D, Patel A et al. Toxicity and developmental defects of different sizes and shape nickel nanoparticles in zebrafish. Environ Sci Technol 2009; 43: 6349– 56. https://doi.org/10.1021/es9010543 Google Scholar CrossRef Search ADS PubMed 103. Arias AM. Drosophila melanogaster and the development of biology in the 20th century. Methods Mol Biol 2008; 420: 1– 25. https://doi.org/10.1007/978-1-59745-583-1_1 Google Scholar CrossRef Search ADS PubMed 104. Bier E. Drosophila, the golden bug, emerges as a tool for human genetics. Nat Rev Genet 2005; 6: 9– 23. https://doi.org/10.1038/nrg1503 Google Scholar CrossRef Search ADS PubMed 105. Matthews KA, Kaufman TC, Gelbart WM. Research resources for Drosophila: the expanding universe. Nat Rev Genet 2005; 6: 179– 93. https://doi.org/10.1038/nrg1554 Google Scholar CrossRef Search ADS PubMed 106. Lessman CA. The developing zebrafish (Danio rerio): a vertebrate model for high-throughput screening of chemical libraries. Birth Defects Res C Embryo Today 2011; 93: 268– 80. https://doi.org/10.1002/bdrc.20212 Google Scholar CrossRef Search ADS PubMed 107. Ghosh M, Sonkar SK, Saxena M et al. Carbon nano-onions for imaging the life cycle of Drosophila melanogaster. Small 2011; 7: 3170– 7. https://doi.org/10.1002/smll.201101158 Google Scholar CrossRef Search ADS PubMed 108. Leeuw TK, Reith RM, Simonette RA et al. Single-walled carbon nanotubes in the intact organism: near-IR imaging and biocompatibility studies in drosophila. Nano Lett 2007; 7: 2650– 4. https://doi.org/10.1021/nl0710452 Google Scholar CrossRef Search ADS PubMed 109. Liu X, Vinson D, Abt D et al. Differential toxicity of carbon nanomaterials in drosophila: larval dietary uptake is benign, but adult exposure causes locomotor impairment and mortality. Environ Sci Technol 2009; 43: 6357– 63. https://doi.org/10.1021/es901079z Google Scholar CrossRef Search ADS PubMed 110. Vecchio G, Galeone A, Brunetti V et al. Mutagenic effects of gold nanoparticles induce aberrant phenotypes in Drosophila melanogaster. Nanomed Nanotechnol Biol Med 2012; 8: 1– 7. https://doi.org/10.1016/j.nano.2011.11.001 Google Scholar CrossRef Search ADS 111. Wlodkowic D, Khoshmanesh K, Akagi J et al. Wormometry-on-a-chip: innovative technologies for in situ analysis of small multicellular organisms. Cytometry 2011; 79: 799– 813. https://doi.org/10.1002/cyto.a.21070 Google Scholar CrossRef Search ADS PubMed 112. Dagani GT, Monzo K, Fakhoury JR et al. Microfluidic self-assembly of live Drosophila embryos for versatile high-throughput analysis of embryonic morphogenesis. Biomed Microdevices 2007; 9: 681– 94. https://doi.org/10.1007/s10544-007-9077-z Google Scholar CrossRef Search ADS PubMed 113. Chung K, Kim Y, Kanodia JS et al. A microfluidic array for large-scale ordering and orientation of embryos. Nat Meth 2011; 8: 171– 6. https://doi.org/10.1038/nmeth.1548 Google Scholar CrossRef Search ADS 114. Leung MC, Williams PL, Benedetto A et al. Caenorhabditis elegans: an emerging model in biomedical and environmental toxicology. Toxicol Sci 2008; 106: 5– 28. https://doi.org/10.1093/toxsci/kfn121 Google Scholar CrossRef Search ADS PubMed 115. Burns AR, Kwok TC, Howard A et al. High-throughput screening of small molecules for bioactivity and target identification in Caenorhabditis elegans. Nat Protoc 2006; 1: 1906– 14. https://doi.org/10.1038/nprot.2006.283 Google Scholar CrossRef Search ADS PubMed 116. Gao Y, Liu N, Chen C et al. Mapping technique for biodistribution of elements in a model organism, Caenorhabditis elegans, after exposure to copper nanoparticles with microbeam synchrotron radiation X-ray fluorescence. J Anal At Spectrom 2008; 23: 1121– 4. https://doi.org/10.1039/b802338g Google Scholar CrossRef Search ADS 117. Kim B, Han G, Toley BJ et al. Tuning payload delivery in tumour cylindroids using gold nanoparticles. Nat Nanotech 2010; 5: 465– 72. https://doi.org/10.1038/nnano.2010.58 Google Scholar CrossRef Search ADS 118. Roh J, Sim SJ, Yi J et al. Ecotoxicity of silver nanoparticles on the soil nematode Caenorhabditis elegans using functional ecotoxicogenomics. Environ Sci Technol 2009; 43: 3933– 40. https://doi.org/10.1021/es803477u Google Scholar CrossRef Search ADS PubMed 119. Zhang H, He X, Bai W et al. Ecotoxicological assessment of lanthanum with Caenorhabditis elegans in liquid medium. Metallomics 2010; 2: 806– 10. https://doi.org/10.1039/c0mt00059k Google Scholar CrossRef Search ADS PubMed 120. Zhang H, He X, Zhang Z et al. Nano-CeO2 exhibits adverse effects at environmental relevant concentrations. Environ Sci Technol 2011; 45: 3725– 30. https://doi.org/10.1021/es103309n Google Scholar CrossRef Search ADS PubMed 121. Zanni E, De Bellis G, Bracciale MP et al. Graphite nanoplatelets and Caenorhabditis elegans: insights from an in vivo model. Nano Lett 2012; 12: 2740– 4. https://doi.org/10.1021/nl204388p Google Scholar CrossRef Search ADS PubMed 122. Mohan N, Chen C, Hsieh H et al. In vivo imaging and toxicity assessments of fluorescent nanodiamonds in Caenorhabditis elegans. Nano Lett 2010; 10: 3692– 9. https://doi.org/10.1021/nl1021909 Google Scholar CrossRef Search ADS PubMed 123. Kwok TCY, Ricker N, Fraser R et al. A small-molecule screen in C. elegans yields a new calcium channel antagonist. Nature 2006; 441: 91– 5. https://doi.org/10.1038/nature04657 Google Scholar CrossRef Search ADS PubMed 124. Colbourne JK, Pfrender ME, Gilbert D et al. The ecoresponsive genome of Daphnia pulex. Science 2011; 331: 555– 61. https://doi.org/10.1126/science.1197761 Google Scholar CrossRef Search ADS PubMed 125. Kahru A, Ivask A. Mapping the dawn of nanoecotoxicological research. Acc Chem Res 2013; 46: 823– 33. https://doi.org/10.1021/ar3000212 Google Scholar CrossRef Search ADS PubMed 126. Tan C, Fan WH, Wang WX. Role of titanium dioxide nanoparticles in the elevated uptake and retention of cadmium and zinc in Daphnia magna. Environ Sci Technol 2012; 46: 469– 76. https://doi.org/10.1021/es202110d Google Scholar CrossRef Search ADS PubMed 127. Zhao CM, Wang WX. Importance of surface coatings and soluble silver in silver nanoparticles toxicity to Daphnia magna. Nanotoxicology 2012; 6: 361– 70. https://doi.org/10.3109/17435390.2011.579632 Google Scholar CrossRef Search ADS PubMed 128. Zhao CM, Wang WX. Comparison of acute and chronic toxicity of silver nanoparticles and silver nitrate to Daphnia magna. Environ Toxicol Chem 2011; 30: 885– 92. https://doi.org/10.1002/etc.451 Google Scholar CrossRef Search ADS PubMed 129. Kim KT, Edgington AJ, Klaine SJ et al. Influence of multiwalled carbon nanotubes dispersed in natural organic matter on speciation and bioavailability of copper. Environ Sci Technol 2009; 43: 8979– 84. https://doi.org/10.1021/es900647f Google Scholar CrossRef Search ADS PubMed 130. Petersen EJ, Pinto RA, Mai DJ et al. Influence of polyethyleneimine graftings of multi-walled carbon nanotubes on their accumulation and elimination by and toxicity to Daphnia magna. Environ Sci Technol 2011; 45: 1133– 8. https://doi.org/10.1021/es1030239 Google Scholar CrossRef Search ADS PubMed 131. Brausch KA, Anderson TA, Smith PN et al. The effect of fullerenes and functionalized fullerenes on Daphnia magna phototaxis and swimming behavior. Environ Toxicol Chem 2011; 30: 878– 84. https://doi.org/10.1002/etc.442 Google Scholar CrossRef Search ADS PubMed 132. Kasemets K, Ivask A, Dubourguier H-C et al. Toxicity of nanoparticles of ZnO, CuO and TiO2 to yeast Saccharomyces cerevisiae. Toxicol in Vitro 2009; 23: 1116– 22. https://doi.org/10.1016/j.tiv.2009.05.015 Google Scholar CrossRef Search ADS PubMed 133. Barberis A, Gunde T, Berset C et al. Yeast as a screening tool. Drug Discov Today Technol 2005; 2: 187– 92. https://doi.org/10.1016/j.ddtec.2005.05.022 Google Scholar CrossRef Search ADS PubMed 134. Garcia-Saucedo C, Field JA, Otero-Gonzalez L et al. Low toxicity of HfO2, SiO2, Al2O3 and CeO2 nanoparticles to the yeast, Saccharomyces cerevisiae. J Hazard Mater 2011; 192: 1572– 9. https://doi.org/10.1016/j.jhazmat.2011.06.081 Google Scholar CrossRef Search ADS PubMed 135. Hadduck AN, Hindagolla V, Contreras AE et al. Does aqueous fullerene inhibit the growth of Saccharomyces cerevisiae or Escherichia coli? Appl Environ Microbiol 2010; 76: 8239– 42. https://doi.org/10.1128/AEM.01925-10 Google Scholar CrossRef Search ADS PubMed 136. Han X, Lai L, Tian F et al. Toxicity of CdTe quantum dots on yeast Saccharomyces cerevisiae. Small 2012; 8: 2680– 9. https://doi.org/10.1002/smll.201200591 Google Scholar CrossRef Search ADS PubMed 137. Nomura T, Miyazaki J, Miyamoto A et al. Exposure of the yeast Saccharomyces cerevisiae to functionalized polystyrene latex nanoparticles: influence of surface charge on toxicity. Environ Sci Technol 2013; 47: 3417– 23. Google Scholar CrossRef Search ADS PubMed 138. Kasemets K, Suppi S, Kunnis-Beres K et al. Toxicity of CuO nanoparticles to yeast saccharomyces cerevisiae BY4741 wild-type and its nine isogenic single-gene deletion mutants. Chem Res Toxicol 2013; 26: 356– 67. https://doi.org/10.1021/tx300467d Google Scholar CrossRef Search ADS PubMed 139. Handy RD, van den Brink N, Chappell M et al. Practical considerations for conducting ecotoxicity test methods with manufactured nanomaterials: what have we learnt so far? Ecotoxicology 2012; 21: 933– 72. https://doi.org/10.1007/s10646-012-0862-y Google Scholar CrossRef Search ADS PubMed 140. Georgantzopoulou A, Balachandran YL, Rosenkranz P et al. Ag nanoparticles: size- and surface-dependent effects on model aquatic organisms and uptake evaluation with NanoSIMS. Nanotoxicology 2012; 7: 1168– 78. https://doi.org/10.3109/17435390.2012.715312 Google Scholar CrossRef Search ADS PubMed 141. He X, Kuang Y, Li Y et al. Changing exposure media can reverse the cytotoxicity of ceria nanoparticles for Escherichia coli. Nanotoxicology 2012; 6: 233– 40. https://doi.org/10.3109/17435390.2011.569097 Google Scholar CrossRef Search ADS PubMed 142. Manzo S, Miglietta M, Rametta G et al. Toxic effects of ZnO nanoparticles towards marine algae Dunaliella tertiolecta. Sci Total Environ 2013; 445–446: 371– 6. https://doi.org/10.1016/j.scitotenv.2012.12.051 Google Scholar CrossRef Search ADS PubMed 143. Horst AM, Vukanti R, Priester JH et al. An assessment of fluorescence- and absorbance-based assays to study metal-oxide nanoparticle ROS production and effects on bacterial membranes. Small 2013; 9: 1753– 64. https://doi.org/10.1002/smll.201201455 Google Scholar CrossRef Search ADS PubMed 144. Puzyn T, Rasulev B, Gajewicz A et al. Using nano-QSAR to predict the cytotoxicity of metal oxide nanoparticles. Nat Nanotech 2011; 6: 175– 8. https://doi.org/10.1038/nnano.2011.10 Google Scholar CrossRef Search ADS 145. Ivask A, Suarez E, Patel T et al. Genome-wide bacterial toxicity screening uncovers the mechanisms of toxicity of a cationic polystyrene nanomaterial. Environ Sci Technol 2012; 46: 2398– 405. https://doi.org/10.1021/es203087m Google Scholar CrossRef Search ADS PubMed 146. Klaper R, Arndt D, Bozich J et al. Molecular interactions of nanomaterials and organisms: defining biomarkers for toxicity and high-throughput screening using traditional and next-generation sequencing approaches. Analyst 2014; 139: 882– 95. https://doi.org/10.1039/C3AN01644G Google Scholar CrossRef Search ADS PubMed 147. Li JX. Nanotechnology-based platform for early diagnosis of cancer. Sci Bull 2015; 60: 488– 90. https://doi.org/10.1007/s11434-014-0720-9 Google Scholar CrossRef Search ADS 148. Nel A, Xia T, Meng H et al. Nanomaterial toxicity testing in the 21st century: use of a predictive toxicological approach and high-throughput screening. Acc Chem Res 2013; 46: 607– 21. https://doi.org/10.1021/ar300022h Google Scholar CrossRef Search ADS PubMed 149. Zhang H, Ji Z, Xia T et al. Use of metal oxide nanoparticle band gap to develop a predictive paradigm for oxidative stress and acute pulmonary inflammation. ACS Nano 2012; 6: 4349– 68. https://doi.org/10.1021/nn3010087 Google Scholar CrossRef Search ADS PubMed 150. Kim MJ, Lee SC, Pal S et al. High-content screening of drug-induced cardiotoxicity using quantitative single cell imaging cytometry on microfluidic device. Lab Chip 2011; 11: 104– 14. https://doi.org/10.1039/C0LC00110D Google Scholar CrossRef Search ADS PubMed 151. Chen Z, Meng H, Xing G et al. Age-related differences in pulmonary and cardiovascular responses to SiO2 nanoparticle inhalation: nanotoxicity has susceptible population. Environ Sci Technol 2008; 42: 8985– 92. https://doi.org/10.1021/es800975u Google Scholar CrossRef Search ADS PubMed 152. Ge C, Du J, Zhao L et al. Binding of blood proteins to carbon nanotubes reduces cytotoxicity. Proc Natl Acad Sci USA 2011; 108: 16968– 73. https://doi.org/10.1073/pnas.1105270108 Google Scholar CrossRef Search ADS PubMed 153. He X, Zhang Z, Zhang H et al. Neurotoxicological evaluation of long-term lanthanum chloride exposure in rats. Toxicol Sci 2008; 103: 354– 61. https://doi.org/10.1093/toxsci/kfn046 Google Scholar CrossRef Search ADS PubMed 154. Zhu M, Li Y, Shi J et al. Exosomes as extrapulmonary signaling conveyors for nanoparticle-induced systemic immune activation. Small 2012; 8: 404– 12. https://doi.org/10.1002/smll.201101708 Google Scholar CrossRef Search ADS PubMed 155. Zhu M, Tian X, Song X et al. Nanoparticle-induced exosomes target antigen-presenting cells to initiate Th1-type immune activation. Small 2012; 8: 2841– 8. https://doi.org/10.1002/smll.201200381 Google Scholar CrossRef Search ADS PubMed 156. Xia T, Malasarn D, Lin S et al. Implementation of a multidisciplinary approach to solve complex nano EHS problems by the UC Center for the environmental implications of nanotechnology. Small 2013; 9: 1428– 43. https://doi.org/10.1002/smll.201201700 Google Scholar CrossRef Search ADS PubMed 157. Guo BY, Zeng T, Wu HC. Recent advances of DNA sequencing via nanopore-based technologies. Sci Bull 2015; 60: 287– 95. https://doi.org/10.1007/s11434-014-0707-6 Google Scholar CrossRef Search ADS 158. Xu JJ. Visualizing nanopore blinkings in parallel: a high-throughput nanopore array potential for ultra-rapid DNA sequencing. Sci Bull 2015; 60: 2067– 8. https://doi.org/10.1007/s11434-015-0943-4 Google Scholar CrossRef Search ADS 159. Taylor RJ, Falconnet D, Niemistö A et al. Dynamic analysis of MAPK signaling using a high-throughput microfluidic single-cell imaging platform. Proc Natl Acad Sci USA 2009; 106: 3758– 63. https://doi.org/10.1073/pnas.0813416106 Google Scholar CrossRef Search ADS PubMed 160. Parker GJ. Development of high throughput screening assays using fluorescence polarization: nuclear receptor-ligand-binding and kinase/phosphatase assays. J Biomol Screen 2000; 5: 77– 88. https://doi.org/10.1177/108705710000500204 Google Scholar CrossRef Search ADS PubMed 161. Wodnicka M, Guarino RD, Hemperly JJ et al. Novel fluorescent technology platform for high throughput cytotoxicity and proliferation assays. J Biomol Screen 2000; 5: 141– 52. https://doi.org/10.1177/108705710000500306 Google Scholar CrossRef Search ADS PubMed 162. Moffat J, Grueneberg DA, Yang XP et al. A lentiviral RNAi library for human and mouse genes applied to an arrayed viral high-content screen. Cell 2006; 124: 1283– 98. https://doi.org/10.1016/j.cell.2006.01.040 Google Scholar CrossRef Search ADS PubMed 163. van Midwoud PM, Verpoorte E, Groothuis GMM. Microfluidic devices for in vitro studies on liver drug metabolism and toxicity. Integr Biol 2011; 3: 509– 21. https://doi.org/10.1039/c0ib00119h Google Scholar CrossRef Search ADS 164. Giridharan V, Yun Y, Hajdu P et al. Microfluidic platforms for evaluation of nanobiomaterials: a review. J Nanomater 2012; 2012: 789841. https://doi.org/10.1155/2012/789841 Google Scholar CrossRef Search ADS 165. Birmingham A, Selfors LM, Forster T et al. Statistical methods for analysis of high-throughput RNA interference screens. Nat Meth 2009; 6: 569– 75. https://doi.org/10.1038/nmeth.1351 Google Scholar CrossRef Search ADS 166. Saeys Y, Inza I, Larrañaga P. A review of feature selection techniques in bioinformatics. Bioinformatics 2007; 23: 2507– 17. https://doi.org/10.1093/bioinformatics/btm344 Google Scholar CrossRef Search ADS PubMed 167. Malo N, Hanley J, Cerquozzi S et al. Statistical practice in high-throughput screening data analysis. Nat Biotechnol 2006; 24: 167– 75. https://doi.org/10.1038/nbt1186 Google Scholar CrossRef Search ADS PubMed 168. Eisen MB, Spellman PT, Brown PO et al. Cluster analysis and display of genome-wide expression patterns. Proc Natl Acad Sci USA 1998; 95: 14863– 8. https://doi.org/10.1073/pnas.95.25.14863 Google Scholar CrossRef Search ADS PubMed 169. Zhao YL. Sensing system for mimicking cancer cell–drug interaction. Sci Bull 2015; 60: 1218– 9. https://doi.org/10.1007/s11434-015-0835-7 Google Scholar CrossRef Search ADS 170. Jiang YN, Guo W. Nanopore-based sensing and analysis: beyond the resistive-pulse method. Sci Bull 2015; 60: 491– 502. https://doi.org/10.1007/s11434-015-0739-6 Google Scholar CrossRef Search ADS 171. Zhang J, Dong L, Yu SH. A selective sensor for cyanide ion (CN-) based on the inner filter effect of metal nanoparticles with photoluminescent carbon dots as the fluorophore. Sci Bull 2015; 60: 785– 91. https://doi.org/10.1007/s11434-015-0764-5 Google Scholar CrossRef Search ADS 172. Chen G, Peijnenburg WJ, Kovalishyn V et al. Development of nanostructure–activity relationships assisting the nanomaterial hazard categorization for risk assessment and regulatory decision-making. RSC Adv 2016; 6: 52227– 35. https://doi.org/10.1039/C6RA06159A Google Scholar CrossRef Search ADS 173. Winkler DA, Mombelli E, Pietroiusti A et al. Applying quantitative structure–activity relationship approaches to nanotoxicology: current status and future potential. Toxicology 2013; 313: 15– 23. https://doi.org/10.1016/j.tox.2012.11.005 Google Scholar CrossRef Search ADS PubMed 174. Liu R, Rallo R, Weissleder R et al. Nano-SAR development for bioactivity of nanoparticles with considerations of decision boundaries. Small 2013; 9: 1842– 52. https://doi.org/10.1002/smll.201201903 Google Scholar CrossRef Search ADS PubMed 175. Fourches D, Pu D, Tassa C et al. Quantitative nanostructure-activity relationship modeling. ACS Nano 2010; 4: 5703– 12. https://doi.org/10.1021/nn1013484 Google Scholar CrossRef Search ADS PubMed 176. Tantra R, Oksel C, Puzyn T et al. Nano(Q)SAR: challenges, pitfalls and perspectives. Nanotoxicology 2015; 9: 636– 42. https://doi.org/10.3109/17435390.2014.952698 Google Scholar CrossRef Search ADS PubMed 177. Rogers E, Hsieh S, Organti N et al. A high throughput in vitro analytical approach to screen for oxidative stress potential exerted by nanomaterials using a biologically relevant matrix: human blood serum. Toxicol in Vitro 2008; 22: 1639– 47. https://doi.org/10.1016/j.tiv.2008.06.001 Google Scholar CrossRef Search ADS PubMed 178. Bar-Ilan O, Albrecht RM, Fako VE et al. Toxicity assessments of multisized gold and silver nanoparticles in zebrafish embryos. Small 2009; 5: 1897– 910. https://doi.org/10.1002/smll.200801716 Google Scholar CrossRef Search ADS PubMed 179. Klanjscek T, Nisbet RM, Priester JH et al. Modeling physiological processes that relate toxicant exposure and bacterial population dynamics. PLoS One 2012; 7: e26955. https://doi.org/10.1371/journal.pone.0026955 Google Scholar CrossRef Search ADS PubMed 180. Muller EB, Nisbet RM, Berkley HA. Sublethal toxicant effects with dynamic energy budget theory: model formulation. Ecotoxicology 2010; 19: 48– 60. https://doi.org/10.1007/s10646-009-0385-3 Google Scholar CrossRef Search ADS PubMed 181. Liu R, Lin S, Rallo R et al. Automated phenotype recognition for zebrafish embryo based in vivo high throughput toxicity screening of engineered nano-materials. PLoS One 2012; 7: e35014. https://doi.org/10.1371/journal.pone.0035014 Google Scholar CrossRef Search ADS PubMed 182. Anguissola S, Garry D, Salvati A et al. High content analysis provides mechanistic insights on the pathways of toxicity induced by amine-modified polystyrene nanoparticles. PLoS One 2014; 9: e108025. https://doi.org/10.1371/journal.pone.0108025 Google Scholar CrossRef Search ADS PubMed 183. Muller EB, Lin SJ, Nisbet RM. Quantitative adverse outcome pathway analysis of hatching in zebrafish with CuO nanoparticles. Environ Sci Technol 2015; 49: 11817– 24. https://doi.org/10.1021/acs.est.5b01837 Google Scholar CrossRef Search ADS PubMed 184. Hu J, Wang D, Forthaus BE et al. Quantifying the effect of nanoparticles on As(V) ecotoxicity exemplified by nano-Fe2O3 (magnetic) and nano-Al2O3. Environ Toxicol Chem 2012; 31: 2870– 6. https://doi.org/10.1002/etc.2013 Google Scholar CrossRef Search ADS PubMed 185. Li M, Czymmek KJ, Huang CP. Responses of Ceriodaphnia dubia to TiO2 and Al2O3 nanoparticles: a dynamic nano-toxicity assessment of energy budget distribution. J Hazard Mater 2011; 187: 502– 8. https://doi.org/10.1016/j.jhazmat.2011.01.061 Google Scholar CrossRef Search ADS PubMed 186. Pakrashi S, Dalai S, Humayun A et al. Ceriodaphnia dubia as a potential bio-indicator for assessing acute aluminum oxide nanoparticle toxicity in fresh water environment. PLoS One 2013; 8: e74003. https://doi.org/10.1371/journal.pone.0074003 Google Scholar CrossRef Search ADS PubMed 187. Youn S, Wang R, Gao J et al. Mitigation of the impact of single-walled carbon nanotubes on a freshwater green algae: Pseudokirchneriella subcapitata. Nanotoxicology 2012; 6: 161– 72. https://doi.org/10.3109/17435390.2011.562329 Google Scholar CrossRef Search ADS PubMed 188. Manier N, Bado-Nilles A, Delalain P et al. Ecotoxicity of non-aged and aged CeO2 nanomaterials towards freshwater microalgae. Environ Pollut 2013; 180: 63– 70. https://doi.org/10.1016/j.envpol.2013.04.040 Google Scholar CrossRef Search ADS PubMed 189. Fu L, Hamzeh M, Dodard S et al. Effects of TiO2 nanoparticles on ROS production and growth inhibition using freshwater green algae pre-exposed to UV irradiation. Environ Toxicol Pharmacol 2015; 39: 1074– 80. https://doi.org/10.1016/j.etap.2015.03.015 Google Scholar CrossRef Search ADS PubMed © The Author(s) 2017. Published by Oxford University Press on behalf of China Science Publishing & Media Ltd. This article is published and distributed under the terms of the Oxford University Press, Standard Journals Publication Model (https://academic.oup.com/journals/pages/about_us/legal/notices)
National Science Review – Oxford University Press
Published: Nov 6, 2017
It’s your single place to instantly
discover and read the research
that matters to you.
Enjoy affordable access to
over 18 million articles from more than
15,000 peer-reviewed journals.
All for just $49/month
Query the DeepDyve database, plus search all of PubMed and Google Scholar seamlessly
Save any article or search result from DeepDyve, PubMed, and Google Scholar... all in one place.
Get unlimited, online access to over 18 million full-text articles from more than 15,000 scientific journals.
Read from thousands of the leading scholarly journals from SpringerNature, Elsevier, Wiley-Blackwell, Oxford University Press and more.
All the latest content is available, no embargo periods.
“Hi guys, I cannot tell you how much I love this resource. Incredible. I really believe you've hit the nail on the head with this site in regards to solving the research-purchase issue.”Daniel C.
“Whoa! It’s like Spotify but for academic articles.”@Phil_Robichaud
“I must say, @deepdyve is a fabulous solution to the independent researcher's problem of #access to #information.”@deepthiw
“My last article couldn't be possible without the platform @deepdyve that makes journal papers cheaper.”@JoseServera