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The toxicity of nanoparticles and their interaction with cells: an in vitro metabolomic perspective

Mohammad Awashra, Piotr Młynarz

Nanoscale Advances · 2023 · Review · CC BY

doi:10.1039/d2na00534d

Abstract

The interaction of nanoparticles (NPs) with cells depends on their physicochemical properties and can lead to cytotoxic events. Metabolomics reveals the molecular mechanisms of this nanotoxicity and avoids the misleading interferences with NPs.

Full text

From PubMed Central, U.S. National Library of Medicine, as current on Oct 22, 2026. Paragraph numbers are ours, so an answer can point at one.

Introduction

Nanomaterials are defined as materials with at least one dimension smaller than 100 nm, while nanotechnology is defined as the understanding and manipulation of matter at dimensions in the range of 1 to 100 nm, where unique phenomena enable novel applications. Nanotechnology introduces many potential health, environmental, and industrial benefits and its applications are widespread in daily life, transforming society. For example, its applications in the food industry range from agriculture to food processing and packaging. Furthermore, nanotechnology is applied in drug delivery, imaging, diagnostics, cosmetics, clothing, transportation, biofuels, and biosensors. Therefore, human exposure to nanomaterials (NMs) nowadays is highly probable. Depending on the type of product in which nanoparticles (NPs) are used, exposure may occur through inhalation, dermal, and oral pathways. Among them, inhalation is considered as the most significant exposure route for airborne NPs.

Due to their high surface to volume ratio, high reactivity, and tunable characteristic properties, NMs exhibit great benefits such as enhanced targeting and imaging techniques. However, NMs may also cause some potential risks to human health and the environment. Given that human and environmental exposure to NMs are inevitable, nanotoxicity research is attracting increasing attention. In the last decade, the number of research studies on the toxicity of different types of NMs has increased dramatically. NMs may affect human health in several ways such as inflammation and heart problems. Thus, to understand the mechanisms of these effects, more investigations in the nanotoxicology field are necessary at the cellular and sub-cellular levels. The scope of nanotoxicity depends on many parameters that are related to the NM itself such as its size, shape, chemical composition, and coating or the exposed cell type or tissue.

When exposed to NPs, the cell can be affected via several routes, including a decrease in cell viability and proliferation, inflammatory response, production of cytokines, oxidative stress, generation of reactive oxygen species (ROS), cell membrane damage, mitochondrial damage, cell cycle dysregulation, DNA damage, genotoxicity, lipid peroxidation, changes in cell morphology, apoptosis or necrosis, and metabolic changes. To study the cytotoxicity of NPs, many conventional assays and biomarkers are used. For example, the cell viability and proliferation can be investigated using tetrazolium-based assays such as MTT, MTS, and WST-1. Alternatively, the cell inflammatory response can be investigated by measuring inflammatory biomarkers, such as IL-8, IL-6, and tumor necrosis factor, using ELISA. Moreover, for cell membrane integrity, lactate dehydrogenase (LDH) and trypan blue exclusion assays can be used, and for cell metabolism, the Alamar Blue assay is frequently used. However, although these assays afford general information about the cytotoxicity of NPs, they do not give molecular information about the mechanism of their cytotoxicity. Moreover, NPs can interfere with the conventional assays, and thus the use of more than one assay is important. In general, most of the studies on the cytotoxicity of NPs mainly use the conventional (phenotypic) tests and assays. Alternatively, some studies used other techniques based on the change in epigenome, transcriptome, proteome, or metabolome (omics techniques) induced by NPs. These techniques are beneficial to study the effect of NPs on cells at the molecular level and explain the results of conventional essays. Among them, metabolomics is one of the most powerful bioanalytical strategies, enabling a picture of the metabolites of an organism in the course of a biological process to be obtained, which is the omics technique of interest in this review. The introduction of NPs in a cell line may cause a change in the levels of certain metabolites, which may give a clue on their effect on cells. During the past decade, many in vitro studies have used metabolomics to investigate the cytotoxicity of NPs on different cell lines.

In this review, the ways NPs and cells interact and the effects of the NP parameters on their interaction are discussed, followed by an overview of the cytotoxicity of different NPs in in vitro models, focusing on the use of metabolomics as a tool to identify the mechanisms and molecular information of their cytotoxicity.

Cellular uptake of NPs

The cytotoxic effects of NPs usually originate from their presence inside cells. However, many applications of NMs in biomedicine require their entry in the cell to achieve their goal. Therefore, to further understand the cytotoxicity mechanisms of NPs on the cell and its metabolism, it is important to first understand the cellular uptake mechanisms of NPs. This will also aid the design of environmentally safer NMs with enhanced cellular targeting and uptake properties for therapeutic purposes.

When immersed in a biological fluid, NPs are exposed to a different medium than that employed for their synthesis. This will force the NPs to interact with the surrounding medium, which may alter their physical and chemical properties. To stabilize themselves, NPs tend “to catch” the surrounding biomolecules (proteins, lipids, etc.) and form a biomolecular corona or protein corona (in case they are surrounded by proteins only), which may alter their identity.

NPs may be taken up by the cell in an energy-independent process, such as simple diffusion or translocation. However, most NP uptake pathways are energy dependent via endocytosis. Endocytosis is the formation of vesicles from the cell plasma membrane to take up substances such as particles, nutrients, and dead cells from the extracellular to the intracellular environment. Endocytosis is described in two categories, i.e., phagocytosis and pinocytosis.

Phagocytosis

Phagocytosis is the cellular uptake of particulates (0.5–10 μm) in the plasma-membrane envelope. It is known as a host defence mechanism, engulfing and internalizing cargos such as particles, dead cells, and cell debris. This mechanism is a ligand-induced process, where NPs are engulfed by adsorbing opsonins, followed by their interaction with complement receptors on the cell surface (see Fig. 1).

Pinocytosis

Pinocytosis is the cellular uptake of extracellular fluids and dissolved solutes. It can be divided into macropinocytosis, clathrin- and caveolae-independent endocytosis, and receptor-mediated endocytosis. The latter is classified as clathrin-dependent endocytosis and caveolae-dependent endocytosis based on the proteins involved in the pathway.

Macropinocytosis

This mechanism involves cytoskeleton rearrangements that induce the formation of membrane ruffles, which fold back, resulting in the formation of large intracellular vacuoles (0.1–5 μm) referred to as macropinosomes (see Fig. 2). Macropinocytosis is actin-dependent endocytosis, while it is independent of clathrin and membrane receptors.

Clathrin-mediated endocytosis

Clathrin-mediated endocytosis is the main process for the internalization of many NPs, which is used by all eukaryotic cells to internalize small particles and nutrients such as cholesterol. When the plasma membrane is rich in clathrin and ligand–receptor complexes start to form on the cell membrane surface, a cage of clathrin starts to form around the vesicle, resulting in vesicles with a diameter of 100–150 nm (see Fig. 2).

Caveolae-mediated endocytosis

Caveolae are bulb-shaped invaginations in the plasma membrane, which are 50–80 nm in size. These vesicles are coated by caveolin and cavin and detached from the membrane by dynamin, which is a 100 kDa GTPase (see Fig. 2).

Role of physicochemical properties of NPs in cellular uptake and cytotoxicity

It is important to consider the physicochemical properties (size, shape, surface functionalization, surface chemistry, chemical composition, concentration, etc.) of NPs in their design for biomedical or other applications. The interactions of NPs with the cell membrane and organelles can significantly be altered at the bio-nano interface by these physicochemical properties, consequently changing the cellular uptake and nanotoxicity of the NPs. Therefore, before starting to assess the biological responses of NPs, thorough and proper characterization of the physicochemical properties of their core and surface should be performed. In this part, we mainly focus on the effect of the size, shape, and surface chemistry of NPs on their cytotoxicity and cellular uptake (see Fig. 3). The effect of the NP core composition is not discussed here given that the surface characteristics are more important than the bulk characteristics in this context.

Size

The size of NPs plays an important role in both their cellular uptake and cytotoxicity. Thus, it is considered a key factor when designing NPs for biomedical application. Due to the fact that NPs possess a size between atoms and bulk materials, they lie on the critical transition zone between two different worlds. It is worthy to mention that the original (primary) size of NPs differs from their hydrodynamic size in biological media. This is mainly because of the formation of a biomolecular corona and the aggregation of the NPs. In this case, the aggregation of NPs can be prevented by manipulating the balance of attractive and repulsive forces. For instance, Fe3O4 NPs can be stabilized with citrate, preventing their aggregation due to electrostatic repulsion. However, due to the formation of a biomolecular corona and the different ionic strengths of biological solutions compared to water, NPs may have new surface identity. Wei et al. performed a cytotoxicity study on the different sizes of TiO2 (5 and 200 nm) and Al2O3 (10 and 50 nm) NPs and observed the formation of aggregates in solution form when the NPs were suspended in cell medium without serum, where the sizes of all the NPs became 8–388-fold larger than their original sizes due to the higher ionic strength of the medium compared to water. Upon the addition of serum, the hydrodynamic sizes of the NPs decreased to only 1.6–10 folds larger than their original sizes. This is because the formation of the protein corona around the NPs prevented them from aggregating due to steric repulsion. The authors found that the smaller NPs (in terms of primary size, rather than hydrodynamic size) for both TiO2 and Al2O3 had higher cytotoxicity and much greater decrease in cell metabolic activity.

When studying the NP–cell membrane interaction mechanism dependence on the size of NPs, it was found that it has a strong influence. Specifically, large NPs (>60 nm) may cause steric hindrance, which prevents their interaction with the cell membrane. Conversely, NPs smaller than the cut-off size of receptor diffusion (<30 nm) may not recruit enough cell membrane receptors in the interaction region to overcome the elastic recoil force, preventing membrane wrapping from occurring. Moreover, the membrane receptors are known to form clusters that are 10–50 nm in size. Thus, a 50 nm NP, for example, needs to interact with only one receptor cluster, while a 500 nm NP must interact with several clusters simultaneously. This makes the internalization of the 50 nm NP energetically more favourable than the 500 nm NP.

In general, smaller-sized NPs have been reported to have higher cellular uptake and higher cytotoxicity. For instance, Dong et al. reviewed 76 carefully chosen literature reports that included in vitro studies of the size-dependent cytotoxicity of amorphous silica NPs (aSiO2 NPs) and found that 76% of these papers showed that smaller-sized aSiO2 NPs exhibited greater cytotoxicity. However, it is important to consider that the cell type plays a role in this process given that it depends on the predominant pathway of cellular uptake in each different cell.

For some NPs, the higher the cellular uptake of NPs, the greater their cytotoxicity. Nonetheless, there are some exceptions, where the cytotoxicity of NPs is independent of their cellular uptake. In these cases, the cytotoxicity is induced by sources other than amount of toxicant, including the NP high surface area, instability, and ion release. Gliga et al. found that 10 nm silver NPs (AgNPs) are more toxic to the human lung BEAS-2B cell line than other NPs with higher uptake ratios due to the release of more Ag+.

Shape

The shape of NPs can be controlled by manipulating the experimental conditions during their synthesis, such as supersaturation, reducing agents, temperature, surfactants, and secondary nucleation. There are many different shapes and geometries of NPs, such as spherical, rod, flower, star, disc, cubic, prismatic, and needle-like structures. The aspect ratio (AR), which is the proportion between width and height of NPs, is used to compare different shapes of NPs. For example, spherical AuNPs have an AR of 1, while Au nanorods (AuNRs) have a higher AR.

It was proven that the cellular uptake and cytotoxicity of NPs are affected by the AR of NPs. Given that AuNPs are common in many biomedical applications, many studies investigated their shape-dependent cellular uptake and cytotoxicity. For instance, Woźniak et al. compared the in vitro cytotoxicity profiles of different shapes and sizes of bare (non-coated) AuNPs in cancer (HeLa) and normal (HEK293T) cell lines. They found that Au nanospheres (AuNS) and AuNRs had higher cytotoxicity than star-, flower- and prism-shaped AuNPs. However, the sizes of these different AuNPs shapes also differed. Specifically, the AuNSs and AuNRs had smaller sizes (10 nm and 38 × 16 nm, respectively), while the flower-, prism-, and star-shaped AuNPs had larger sizes (∼370 nm, ∼160 nm, and ∼240 nm, respectively). Thus, their sizes may also play a crucial role in this cytotoxicity tendency, given that smaller NPs are known to have higher cellular uptake and aggregation rate inside the cell, which explains the observed cytotoxicity.

Surface charge

NPs can have negative, positive, or neutral surface charge depending on their surface functional groups. The surface charge can affect the NP–cell membrane interactions, protein corona, and consequently the cellular uptake of NPs. Therefore, it is one of the most important physicochemical properties to control when designing NPs for biomedical applications. Generally, reports have shown that charged NPs have higher cellular uptake than neutral NPs. The cell membrane is negatively charged due to the anionic head group of phospholipids and the existence of some carbohydrates, such as sialic acid. Considering this, cationic NPs, in most nonphagocytic cells, are taken up by the cells to a greater extent than anionic NPs. However, in some cases, anionic NPs have greater cellular uptake in phagocytic cells. The surface charge of NPs can also tune their cellular uptake pathway. For instance, Untener et al. reported that positively charged AuNRs had a higher extent of internalization compared to their negatively charged counterparts. It was found that cationic AuNRs were taken up through macropinocytosis and clathrin-mediated endocytosis, while anionic AuNRs were internalized through macropinocytosis and caveolae-related mechanisms.

The cytotoxicity of NPs is also, as expected, affected by their surface charge. Similar to the dependence of the cellular uptake of NPs on their surface charge, in nonphagocytic cells, charged NPs were found to be more cytotoxic than their neutral counterparts, with the positively charged NPs, in most cases, being more cytotoxic than negatively charged NPs. Moreover, the surface charge of NPs does not only affect their cytotoxicity level but also their mechanisms. A study by Schaeublin et al. showed that although both charged and neutral AuNPs were taken up in similar amounts and caused cell morphology disruption and decreased cell viability through ROS generation in a human keratinocyte cell line (HaCaT) model, only charged NPs caused significant mitochondrial stress. This suggested that the surface charge of AuNPs can affect the mechanism of cell death. Further investigations on mitochondrial-mediated toxicity revealed that neutral AuNPs did not affect the mitochondrial outer membrane potential, which has a slight negative charge, and thus apoptosis was not initiated, and the authors suggested that necrosis may be the cell death mechanism in this case. However, charged AuNPs affected this membrane in different ways. On the one hand, cationic AuNPs accumulated on the mitochondrial outer membrane due to its slight negative charge, which eventually damaged the membrane and caused the release of apoptotic proteins such as caspase-3 inducing mitochondrial-mediated apoptosis. On the other hand, anionic AuNPs increased this slight negative charge on the outer membrane, which forced the mitochondria, trying to adjust this potential disruption, to release the positively charged calcium ions into the cytosol, inducing calcium-evoked apoptosis (see Fig. 4).

Hydrophobicity

It has been shown that the hydrophobicity of NPs can affect the protein binding, cellular uptake, and cytotoxicity of NPs. The hydrophobicity and hydrophilicity of NPs can originate from the core or the functionalities of the NPs. In a recent systematic simulation study, Li et al. showed that changing the spikes of virus-like NPs (VLP) significantly altered the cellular uptake efficiency, while the effect of the core hydrophobicity of VLP was secondary. This study reported that VLP with hydrophobic or amphiphilic spikes were internalized more efficiently than that with hydrophilic spikes.

Generally, when keeping the other properties of NPs such as surface charge constant, their hydrophobicity has a positive trend with their cytotoxicity. Muthukumarasamyvel et al. controlled the hydrophobicity of dicationic amphiphile-stabilized AuNPs by conjugating the dicationic functionality with different numbers and locations of H and OH groups. The authors observed increasing anticancer or cytotoxicity properties with an increase in the surface hydrophobicity of the NPs against A549 lung cancer cells.

Surface functionalization

Changing the ligands on the surface of NPs will mostly tune the previous parameters (surface charge and hydrophobicity), which affects the protein corona, cellular uptake, and cytotoxicity of the NPs. However, the specific functionalities on the surface of NPs can be useful for targeting purposes. Here, overexpressed or unique receptors on the cell membrane are targeted by functionalizing the NPs with a complementary aptamer, protein, or antibody, which can specifically bind to the cell receptors. Tao et al. targeted cervical cancer cells through folic acid (FA)-poly(ethylene glycol)-b-poly(lactide-co-glycolide) blended NPs, which enhanced the efficacy of cancer chemotherapy through the targeted-delivery of anticancer drugs.

Lund et al. showed that AuNPs functionalized with 50% PEG–NH2/50% glucose had an eighteen-fold higher internalization rate than NPs functionalized with either PEG–NH2 or glucose alone due to their different organization patterns. Alternatively, Yeh et al. studied the role of ligand coordination of two quantum dots (QDs) on their cytotoxicity. The authors found that monothiol-functionalized QDs had greater levels of cytotoxicity compared to dithiol-functionalized QDs in HeLa cell lines. However, the monothiol-functionalized QDs had a higher charge density, and thus it is difficult to tell if this tendency is solely related to the ligand coordination or charge density.

Studying the dependency of cellular uptake and cytotoxicity on a certain physicochemical property of NPs can be very complex. For instance, changing their surface charge may lead to a change in hydrophobicity, hydrodynamic size, and protein corona. Furthermore, this may be done by changing the functionalities and coating of the NPs.

Table 1 summarizes some recent studies exemplifying the effect of the physicochemical properties of NPs on their cellular uptake and cytotoxicity.

Cytotoxicity assessment

In vitro cytotoxicity of NPs is assessed using cell models. Although this assessment does not replace the in vivo evaluation of their cytotoxicity, it represents a screening bridge between the investigation of the quality and in vivo application of materials. Herein, we focus on the in vitro assessment of nanotoxicity. In the case of in vivo assessment, readers are encouraged to read the wholistic review by Kumar et al. Many in vitro assays are used to investigate or measure the cytotoxicity of NPs. These assays can be categorized to five main categories including cell viability and proliferation, ROS generation, cell stress, cell morphology phenotyping, and cell–NP uptake assays.Fig. 5 demonstrates some pathways of the effect of NPs on cells.

Cell viability and proliferation

Cell viability assays focus on investigating the cell metabolic activity and mitochondrial enzymes, such as lactate dehydrogenase (LDH), an enzyme that regulates pyruvate and lactate levels through nicotinamide adenine dinucleotide (NAD) oxidation. Tetrazolium salts can react with the mitochondrial dehydrogenase enzymes. This reaction leads to the cleavage of the tetrazolium ring and conversion of these salts into a colored formazan form, which can be detected using colorimetry-spectroscopy. The detected activity of these enzymes is an indication of the cell viability. One of the most commonly used tetrazolium salts for assessing the cytotoxicity of NPs is the 3-(4,5-dimethylthiazol-2-yl)-2,5-diphenyltetrazolium bromide (MTT) assay. The other tetrazolium salts used include 3-(4,5-dimethylthiazol-2-yl)-5-(3-carboxymethoxyphenyl)-2-(4-sulfo phenyl)-2H-tetrazolium (MTS), iodonitrotetrazolium (INT), and 4-[3-(4-iodophenyl)-2-(4-nitrophenyl)-2H-5-tetrazolio]-1,3-benzene disulfonate (WST-1), which different to the previous salts, produce the water-soluble formazan. Other colorimetric/fluorimetric cytotoxicity assays are also used, for example, neutral red, trypan blue, lactate dehydrogenase (LDH), mitochondrial membrane potential (MMP), and Alamar Blue (resazurin) assays.

Many types of interference between NPs and cell viability assays have been reported. One way is the adsorption of the mitochondrial activity-related proteins on the NP surfaces. This may lead to the enzyme denaturation, giving false results of the cell viability profiles. For instance, Stueker et al. used molecular dynamics simulation to investigate the effect of LDH enzyme binding on functionalized AuNPs. The authors observed that the dynamics of the side chains of the enzyme were largely constrained in all four active sites. Another way of interference is that the light absorbance spectra of the NPs can interfere with the absorption window of the assay, leading to false colorimetric measurements. For example, Díaz et al. reported that five NPs (magnetic iron/graphite, magnetite/silica, bare and poly(ethylene glycol)(PEG)–ylated silica, and magnetite/FAU zeolite) in culture medium after 72 h (in the absence of cells) showed absorbance at the same wavelength (525 nm) used in the MTT assay. This absorbance increases with the NP concentration, depending on their type. The third way of interference is that NPs may interact with the assay reagents. For instance, Hoshino et al. reported that cysteamine-coated quantum dots catalytically reduced MTT to formazan without cellular metabolism taking place (see Fig. 6).

ROS generation and oxidative stress

Reactive oxygen species (ROS) are a type of unstable molecule (free radicals) that contain oxygen and can easily react with the other molecules in cells. The ROS include the superoxide anion (O2˙−), hydrogen peroxide (H2O2), and hydroxyl radical (HO˙). ROS are normally produced by cells at certain levels to maintain regular metabolism and homeostasis, which are considered as critical signalling molecules in cell proliferation and survival. However, they may be produced through interactions with exogenous sources such as NPs. If this event produces excessive ROS that the cellular antioxidant defense system (enzymatic antioxidants such as glutathione (GSH) peroxidases) cannot handle, oxidative stress is triggered. This may lead to the destruction of organelles and bio-molecules, including triggering membrane damage, lipid peroxidation, DNA damage, protein damage, apoptosis, necrosis, and inflammatory response, leading to many diseases such as cancer, diabetes, neurodegenerative, and cornea diseases.

NPs can generate ROS by acting as a catalyst in ROS generation reactions. For instance, Higashi et al. reported the catalytic generation of ROS by AuNPs and showed that this reaction can be controlled by changing conditions such as the type, concentration, and pH of the NP solution.

ROS detection can be performed by the direct measurement of ROS levels or the measurement of their oxidative damage or other outcomes. Some direct methods for the detection of ROS are fluorescein-compound-based tests and electron paramagnetic resonance (EPR). The reactive fluorescein probes 2′,7′-difluorescein-diacetate (DCFH-DA) and dichlorodihydro-fluorescein diacetate (H2 DCFDA) are non-fluorescent; however, when they are exposed to the cell cytosol enzymes, they get hydrolysed. Then, the cellular ROS oxidize them into a highly fluorescent compound, dichlorofluorescein (DCF), yielding an optical ROS concentration-dependent response, which can be measured using fluorescence microscopy or flow cytometry. Alternatively, indirect approaches for the detection of ROS include many assays that depend on the stimulated oxidative effect of the ROS. One approach is by measuring the enzymatic or non-enzymatic antioxidants levels. Oxidative stress can also be assessed by measuring the oxidative damage of the cell biomolecules. These damaged biomolecules include proteins, lipids, and DNA and can be detected by measuring the protein carbonyl content, malondialdehyde levels, and 8-oxo-2′-deoxyguanosine (8-OdG) lesion, respectively. Other genotoxicity assays include the comet, Ames, micronucleus, and chromosome aberration assays.

During the course of measuring NP-induced ROS generation and oxidative stress, NP-assay interferences may occur. In colorimetric- and fluorimetric-dependent assays, NPs may interact with the final form of the dyes in a way that alters, by enhancing or reducing, the absorbance or fluorescence of the dye. For example, Aranda et al. observed the quenching effect of several NPs on the dye fluorescence emission in the DCFH-DA assay, which was correlated with the cellular uptake of the NPs. The authors suggested a threshold concentration of NPs at which their oxidative effect can be detected, and they proposed that changing the experimental conditions can reduce this interference. Conversely, Pfaller et al. reported the dye fluorescence enhancement of the DCFH-DA assay in the presence of Au or Fe2O3 NPs. This confirms that both scenarios (quenching and enhancement) may occur due to NP–probe interactions during colorimetric- and fluorimetric assays.

Inflammatory response

The inflammatory response induced by NPs in a cell line can be measured by detecting the produced inflammatory biomarkers. Macrophages and other cells release many cytokines, which play a crucial role in cell communication in the immune system by, for instance, promoting inflammation. Interleukins (ILs), such as IL-1β, IL-6, IL-8, and IL-10, in addition to other cytokines, such as tumor necrosis factor TNF-α and granulocyte-macrophage colony-stimulating factor (GM-CSF), play a central role in inflammation regulation. The expression of these biomarkers can be assayed to determine the inflammatory response caused by NPs. ELISA (enzyme-linked immunosorbent assay) or western blotting, and electrophoretic mobility shift assays (EMSAs) or real-time polymerase chain reaction (RT-PCR) systems are used for the measurement of cytokines and the related genetic expressions, respectively.

NPs were reported to induce an inflammatory response in different cell lines. Many studies used conventional assays to measure this response. However, these assays can also interfere with NPs during the measurement of inflammatory response in cell lines. Some inflammatory cytokines were reported to be adsorbed on the NP surface, causing interference (Fig. 7a). Guadagnini et al. investigated the interferences of different NPs with some in vitro cytotoxicity assays. The authors reported that Fe2O3, TiO2, and SiO2 NPs significantly adsorbed IL-6, IL-8, and GM-CSF cytokines on their surfaces at different levels at a NP concentration of 75 μg cm−2. Fig. 7b shows that all the studied NPs adsorbed the IL-8 cytokine except PLGA–PEO NPs, which surprisingly increased the apparent level of cytokines, probably due to the stabilization of the peptides and their protection from proteolysis. In the case of other NPs, the level of adsorption depends on the NPs and the cytokine studied. OC–Fe3O4 NPs are the most cytokine-adsorbing NPs tested given that cytokines could not be detected in the supernatants. Furthermore, Piret et al. observed a high inter-laboratory variability for the ELISA assay for IL1-β and TNF-α measurements and they suggested that testing of NP-cytotoxicity assay interferences should be always performed. Readers should kindly refer to ref. for more information about the interference between different assay and NPs and some solutions to this problem.

Apoptosis and necrosis

Apoptosis is a programmed cell death pattern, while necrosis is an unprogrammed cell death. Both patterns of cell death can be an outcome of NP treatment. Nickel ferrite (NiFe2O4), TiO2, Fe2O3, hydroxyapatite, and Ag NPs induced apoptosis in A549, BEAS-2B, ECV304, C6, and HepG2 cell lines, respectively. Alternatively, Reus et al. reported dose-dependent cell necrosis induced by SiO2 NPs in BALB/c 3T3 cell line. Apoptotic cell death is mostly non-inflammatory, while necrotic cell death can be inflammatory. Both pathways are extremes, and many cases are a complex combination of both. For instance, Kumar et al. observed that AgNPs caused cell death in L-929 fibroblast cell lines in association with both necrosis and apoptosis. The cell death pathway is controlled by many parameters such as the surface charge, concentration, and exposure time of NPs. Schaeublin et al. reported that charged AuNPs caused cell death through apoptosis, while neutral AuNPs caused it through necrosis (see Fig. 4).

Many assays are used to detect apoptosis and necrosis. Phosphatidylserine (PS) migration to the extracellular side of the cell membrane and caspase activation into initiator and effector enzymes are two events that accompany apoptosis and can be used as markers to detect it. Externalized PS on the surface of the cell can be detected using fluorescein isothiocyanate (FITC)-labelled Annexin-V. Annexin-V specifically binds to the exposed PS on the cell surface in the early apoptotic cells, and then can be measured via flow cytometry or fluorescent microscopy. Alternatively, the membrane-impermeable propidium iodide (PI) dye exclusion assay is used for the identification of cellular necrosis. PI binds to DNA in the nucleus and stains it only when the cell membrane integrity is lost (which is an event that accompanies necrosis). Thus, a combination of the above-mentioned assays can determine the pattern of cell death. For instance, Vafaei et al. used the Annexin V-FITC/PI staining kit to study the apoptotic efficacy of zinc-phosphate NPs (ZnPNPs) against the MCF-7 breast cancer cell line. The untreated cells with NPs showed a live cell (Annexin V-FITC−/PI−) percentage of 98.6%. Conversely, after exposure to ZnPNPs, the apoptotic cell (Annexin V-FITC+/PI−) ratio increased from 0.190% to 44.8% and the necrotic cell (Annexin V-FITC+/PI+) percentage increased to 1.34% (see Fig. 8).

Flow cytometry-based assays have negligible NP interferences. Bancos et al. reported that SiO2 NPs have low or no interference with flow cytometry assays. However, other colorimetric and fluorimetric-based assays face the same problems mentioned in the previous sections.

Metabolomics for the cytotoxicity assessment of NPs

In general, most studies on the cytotoxicity of NPs use the conventional (phenotypic) assays. However, many of these assays, as mentioned before, have been reported to interfere with the NPs because of their color, fluorescence, chemical activity, light scattering, etc. Thus, to precisely reveal the cytotoxicity of NPs, it is necessary to use a combination of more than two assays. This involves testing for NP interferences and eliminating them by changing experimental conditions or comparing the results of two similar tests, which is a complex and time-consuming process. However, many reports only used one or two cytotoxicity assays and ignored any potential interference with NPs. In addition, even though the conventional cytotoxicity assays can reveal that a certain cytotoxicity outcome happened, these assays are limited in terms of detecting the molecular information that caused this event.

The current toxicological assays need to be updated and new tools should be incorporated progressively in this field. A more advanced and emerging approach to study the toxicity of particles is the “omics” technique, which is based on the change in epigenome, transcriptome, proteome, genome, lipidome, and metabolome profiles introduced by internal or external stimuli. In increasing number of studies are using this approach to investigate the in vitro and in vivo toxicity induced by NPs. The determination of new targets and biomarkers for NP toxicity is one of the strengths of the omics technique. Moreover, the omics technique has high sensitivity, which is useful because of the low levels of environmental exposure to NPs that sometimes cannot be detected using the conventional assays. Another strength is that unlike the conventional assays, the omics technique has low or no interferences with NPs.

In the field of toxicology, the most related omics discipline is metabolomics. Metabolomics, one of the newest in the omics era, is an emerging field, which is broadly defined as the comprehensive measurement of all metabolites and low-molecular-weight molecules in a biological specimen (tissues, cells, fluids, or organisms), and is one of the most powerful bioanalytical strategies that allow a picture of the changes of metabolites levels of an organism to be obtained during the course of a biological process either as a footprint (analysis of extracellular metabolites) or fingerprint (analysis of intracellular metabolites). The detailed analysis of low molecular weight compounds provided by nuclear magnetic resonance (NMR) spectroscopy or mass spectrometry (MS), besides the analysis performed by the powerful chemometric software (MetaboAnalyst), provides an accurate and quick detection and comparison of many types of chemical entities including carbohydrates, amino acids, nucleotides, lipids, steroids, fatty acids, and their derivatives, which are produced by cell metabolism.

Currently, metabolomics is applied in many fields such as disease fingerprinting, biotechnology, environmental and plant research, toxicology and safety research, clinical medicine, and pharmacology. Our group has been investigating the metabolic changes in serum, urine, and feces induced by different diseases such as lung cancer and diabetes, or other stimuli such as kidney transplant. Due to the non-invasive sampling in the metabolomic approach, the relatively low number of metabolites (compared to transcripts and proteins), and good level of knowledge about the role of most metabolites, metabolomics provides a well-grounded and precise methodology to investigate the biochemical effects and toxicity of NPs, and it can present insight into the genotype and phenotype changes with a biological response. Moreover, single-cell metabolomics is achievable today, making it possible to determine phenotypic heterogeneity among individual cells. Many cellular activities such as intercellular signal transduction, energy transfer, cell proliferation, and differentiation occur at the metabolite molecular level and are regulated by the presence and level of specific metabolites. Furthermore, metabolites are the end result of the expression of functional genome, transcriptome, and proteome (see Fig. 9). This indicates that metabolomics can detect many NP cytotoxicity outcomes and reveal the molecular information behind these events even at low levels of NP exposure and with no interferences. Therefore, it is a great tool in nanotoxicology, which is being applied to reveal the effect and toxicity of NPs in many fields including environmental and agricultural fields and cancer research. Metabolomics can help in better understanding of the transition from in vitro to in vivo systems of NP toxicity and its effect given that it is applied in both types of experiment. Furthermore, metabolomics can be combined with other omics techniques to provide a more comprehensive understanding of the effects of NPs on cells.

When comparing NMR- and MS-based metabolomics, generally, NMR has lower sensitivity than MS, and thus it is considered more suitable to analyze extracellular metabolites (exometabolome), which is done by the analysis of the cell culture media. Alternatively, the more sensitive MS techniques are more suitable for the analysis of relatively low levels of intracellular metabolites (endometabolome), especially when isolated from a limited number of cells. However, both analysis techniques are complementary and should be used simultaneously to maximize the metabolic window.

This emerging technique has not yet been widely applied for the investigation of NP cytotoxicity in in vitro systems and more research needs to be done on different NPs and cell lines. In this section, we focus on the metabolic changes induced by different NPs in different cells in vitro. The workflow of a metabolomics experiment is demonstrated in Fig. 10. This review does not go into detail on the workflow of metabolomics. In this case, for a detailed demonstration of how metabolomic workflows generate data, the reader is directed to read the following reviews and book chapters.

AuNPs

Gold nanoparticles (AuNPs) are very common in the biomedical field. AuNPs have many unique properties such as ease of synthesis, tunable size, ease of surface modification, surface plasmon resonance (SPR), and X-ray attenuation. This makes them the center of attention in many applications, including the growing field of nanomedicine, biosensors, targeted drug delivery, radiation therapy, photothermal therapy, biomedical imaging, and cancer diagnostics and therapeutics.

Metabolomics is used in several studies to assess the cytotoxicity of AuNPs and reveal their molecular information. Au nanorods (AuNRs) are one example of AuNPs that have strong absorption in the near-infrared spectral region and can be used in tumor thermal therapy (hyperthermia), and also in targeted tumor therapy. Wang et al. observed, using conventional assays, that AuNRs have a unique influence on cell viability by causing the death of cancer cells (A549 cell line), while having negligible effect on normal cells (16HBE and MSC cell lines). The authors showed that AuNRs were released from the lysosome of cancer cells, and then translocated into the mitochondria, causing oxidative stress by the production of ROS. Alternatively, the normal cells had more intact lysosomes, and thus the AuNRs were not released in the cell cytoplasm. However, the molecular information during this cellular translocation was unclear. Later, the same group, used a metabonomic approach, a subset of metabolomics, by applying 1H NMR and multivariate data analysis, to study the metabolic change with time during the exposure of A595 and 16HBE cell lines to AuNRs. The authors found that both cell lines had intracellular disruption by the reduction of lactate levels and by causing oxidative stress. However, the normal cells resisted this oxidative stress by de novo GSH synthesis, unlike the cancer cells, which did not trigger this pathway, causing severe damage of their mitochondria (see Fig. 11). The metabonomic study further indicated the downregulation of nucleosides and nucleotides in the cancer cells, indicating cell death. Alternatively, the amino acid levels were upregulated in the normal cells, indicating cell stress. This study shows the usefulness of metabolomics in revealing the molecular information of the effect of NPs on cells, after conventional assays played the role of a general scanner for these effects.

Metabolomics can help in identifying biomarkers for NP cytotoxicity. For example, Xu et al. investigated the potential harmful effects of AuNRs on male reproduction by studying the metabolic change in spermatocyte-derived cells (GC-2) and Sertoli (TM-4) cell line after exposure to 10 nM of AuNRs. Employing metabolomics, the authors observed a strong downregulation in glycine levels in TM-4 cells, while there was no significant change in GC-2 cells. To identify what may accompany this reduction of glycine (potential biomarker), high content screening (HCS) and JC staining were used, and it was found that AuNRs decreased the membrane permeability and mitochondrial membrane potential of TM-4 cells. Moreover, the authors observed a disruption in the mRNA and protein levels of blood–testis barrier (BTB) factors using RT-PCR and western blot. Then, to confirm that glycine is a biomarker for these events, the authors repeated the experiments after adding glycine to the medium and noticed that the cells recovered from the previous harmful effects. This experiment reveals that glycine can be recognized as a biomarker to the changes in membrane permeability, mitochondrial membrane potential, and blood–testis barrier (BTB) factors in further similar experiments.

Huang et al. observed that spherical AuNPs (20 nm) were not cytotoxic against the human dermal fibroblast (HDF) cell line. The authors combined bioinformatics with metabolomics to determine the molecular information of this toxicity resistance. Firstly, they detected that 29, 30 and 27 metabolites were differentially expressed in HDFs after 4, 8, and 24 h treatment with AuNPs, respectively. Among them, only six metabolites were determined to be key metabolites using bioinformatics techniques including expression pattern analysis and metabolic pathway analysis using MetaboAnalyst online tool. The key metabolic pathway was identified to be the GSH pathway with GSH as the key metabolite. Subsequently, these results were verified and it was found that the increase in GSH levels after AuNP treatment may be the reason behind the toxicity resistance behaviour of the cells, given that GSH can trigger an oxidative stress protection mechanism that helps in avoiding cytotoxicity. This reveals that GSH can be considered as a biomarker for oxidative stress resistance.

Lindeque et al. used MS metabolomics to study the effect of citrate-, poly-(sodium styrene sulfonate)-, and poly-vinylpyrrolidone (PVP)-capped AuNPs on the intracellular metabolites of HepG2 cells. Surprisingly, after 3 h of treatment, a holistic depletion of intracellular metabolites was observed for all the capped AuNPs. Usually, metabolic changes result in the upregulation of the metabolite levels because of secondary pathways, clearance issues, and reduced enzyme functionality. Firstly, the authors suggested that a loss of cell membrane integrity happened, but the exometabolomic data, measured using the NMR technique, was not consistent with this reasoning. Subsequently, they hypothesized that the AuNPs bind to the intracellular metabolites with or without replacing the surface coatings.

Gioria et al. combined proteomics and metabolomics to gain a further understanding of the effects of two sizes, i.e., 5 and 30 nm, of AuNPs on the human colon adenocarcinoma Caco-2 cell line. The proteome and metabolome are directly interconnected and influence each other given that the protein levels can change the metabolic profile of a cell system and vice versa. Genomics and transcriptomics were excluded from this study due to their restricted value given that they provide limited information about phenotyping. The authors used liquid chromatography high-resolution tandem mass spectrometry (LC-HRMS/MS) and two-dimensional gel electrophoresis (2DE) to obtain qualitative and quantitative data of de-regulated metabolites and proteins, respectively. Subsequently, the data was combined and interpreted using systems biology analysis. After 72 h of exposure to AuNPs, 61 proteins and 35 metabolites in the cell extract were identified to be up-/down-regulated. The internalization mechanism was found to be endocytosis due to the downregulation of the SH3GL1 and EAA1 proteins, which are involved in the endocytic pathway. The smaller-sized AuNPs caused a greater number of de-regulated proteins and metabolites due to their higher internalization in the cells. Concerning metabolomics, the metabolite propionylcarnitine (C-3 carnitine) and glycine levels increased upon exposure to AuNPs, which indicates apoptosis. This study further reported the accumulation of GSH in both 5 and 30 nm AuNP-treated cells, which indicates that an anti-oxidative mechanism occurred as a self-defense system against oxidative stress. These results were confirmed using fluorescence microscopy analysis, where the over-expression of Annexin-V and nuclear fragmentation induced by AuNPs were evident, emphasizing that apoptosis occurred.

Omics technology together with complementary methods not only offer a promising tool in nanotoxicology to understand the molecular mechanisms of NP toxicity, but they also enhance the development and design of nano-drugs. For instance, Ali et al. combined MS-based metabolomics and proteomics results through network analysis to better understand the molecular mechanism of AuNR photo-thermal therapy in the human oral squamous cell carcinoma (HSC-3) cell line. The results showed an upregulation in phenylalanine, which is considered an outcome of apoptosis pathways, indicating the good photo-thermal therapy efficiency of the AuNRs. Table 2 summarizes the studies that used the metabolomics technique to assess the effect of AuNPs in vitro on different cell lines.

AgNPs

Silver nanoparticles (AgNPs) have various interesting biological properties and are known for their well-reported antibacterial activity. They have a wide range of applications including cosmetics, textiles, and biomedical products. Also, their therapeutic application as antiviral and anticancer drugs is expected to be further expanded. Regarding the use of AgNPs as potential drug carriers for cancer therapy from proteogenomic and metabolomic perspectives, the reader is directed to the review by Raja et al. AgNPs have been shown to influence different cells causing apoptosis, lipid peroxidation, and DNA damage.

One of the advantages of metabolomics is that it is capable of detecting early biochemical events and metabolic changes even during the absence of a significant cytotoxic response by conventional assays. Carrola et al. studied the effect of citrate-stabilized 30 nm AgNPs on the human epidermis keratinocyte (HaCaT) cell line after 48 h of exposure at two concentrations, i.e., 40 μg mL−1 (close to IC50 = 38.7 ± 2.5 μg mL−1) and 10 μg mL−1 (no significant cell viability loss). Using NMR-based metabolomics, the authors observed that most metabolic changes happened at the lower concentration, which allowed the detection of early biochemical events, including upregulated GSH-based antioxidant protection, downregulated tricarboxylic acid (TCA) cycle activity, energy depletion, and cell membrane modification. In a similar study, NMR metabolomics was used to assess the metabolic effects of two types of coated AgNPs towards the human hepatoma (HepG2) cell line and significant metabolome changes were observed at a subtoxic concentration of AgNPs. These changes include energy production, antioxidant defence system, protein degradation, and lipid metabolism pathways, suggesting that the cells have metabolism-mediated protective mechanisms against AgNPs. In the third study by this group, they investigated the effect of size and surface chemistry of AgNPs on the metabolic change caused in the HaCaT cell line. The authors used citrate-coated AgNPs with a diameter of 10, 30, and 60 nm, and 30 nm AgNPs coated with citrate, polyethylene glycol (PEG), or bovine serum albumin (BSA). It was found that the largest NPs and the PEG-coated NPs had the least impact on cell metabolism and viability, which is the expected tendency, as mentioned before in Section 3. Furthermore, Carrola et al. used NMR metabolomics to characterize the responses of RAW 264.7 macrophages to subtoxic concentrations of AgNPs (30 nm) and ionic silver (Ag+). They observed that the exposure to AgNPs caused a downregulation in intracellular glucose utilization, possibly due to the reprogramming of the TCA cycle towards anaplerotic fuelling and production of anti-inflammatory metabolites. Also, an upregulation in the synthesis of GSH was observed, enabling the cells to control the ROS levels. In contrast, macrophages exposed to Ag+ at equivalent subtoxic concentrations showed reduced metabolic activity, lower ability to counterbalance ROS generation, and alterations in membrane lipids. This indicates that the ionic form of silver has a greater effect on the cells and is one of the sources of AgNP cytotoxicity.

Huang et al. compared the effect of AgNPs and AuNPs, and showed that while AuNPs had no cytotoxicity, AgNPs induced grade 1 cytotoxicity after HDF cells were exposed to them for 72 h. Using metabolomics, the citrate cycle pathway was determined to be the key metabolic pathways in the AgNP-treated cells with malic acid as the key metabolite. Thus, the mechanism of AgNP cytotoxicity is by the upregulation of citric acid content, which indicated the inhibition of malic acid synthesis, influencing the production of ATP (mitochondrial dysfunction) and inhibiting cell proliferation, leading to cytotoxicity (see Fig. 12). Conversely, AuNPs were not cytotoxic due to the triggering of the antioxidant defence system by the upregulation of GSH. Kim et al. used high-resolution magic angle spinning (HR-MAS) NMR-based metabolomics to study the cytotoxicity of AgNPs against human Chang liver cells. The authors observed the depletion of GSH, lactate, taurine, and glycine levels, while most amino acids, choline analogues, and pyruvate were upregulated by the AgNPs. It is probable that the downregulation of GSH induced the conversion of lactate and taurine to pyruvate.

The effect of AgNPs was also studied on non-mammalian cells such as yeast and unicellular alga. Babele et al. studied the effect of 1.0 mg L−1 of 50–100 nm-sized AgNPs, prepared using aqueous gooseberry extract, on yeast Saccharomyces cerevisiae cells. Untargeted 1H NMR-based metabolomics revealed a several-fold increase or decrease in the levels of 55 different metabolites, including the ones involved in amino acid metabolism, glycolysis, and tricarboxylic acid (TCA) cycle, organic acids, nucleotide metabolism, urea cycle, and lipids metabolism. The authors noticed a reduced level of GSH, which indicates that oxidative stress occurred, leading to the strong cytotoxicity of AgNPs to the yeast cells. Qu et al. investigated the effect of AgNPs on the performance of Chlorella vulgaris F1068 unicellular green alga in phosphorus assimilation (phosphorus removal by algae-based biotechnology). Using MS-based metabolomics, the authors observed the inhibition of algal assimilation. AgNPs disturbed the metabolic responses related to the phosphorus assimilation by reducing the levels of guanine, glutamine, alanine, and aspartic acid and increasing the levels of succinic acid. The NPs also inhibited phospholipid metabolism by the downregulation of glycerol-3-phosphate and myo-inositol and upregulation of serine. Furthermore, GSH metabolism was affected by the NPs, which induced oxidative stress in the alga cells (upregulation of glycine). Cao et al. showed that the effect of AgNPs on Chlorella pyrenoidosa can be altered by the number of repeated exposures. In this study, NP single exposure had a greater impact on the C. pyrenoidosa metabolome than repeated exposure. Table 3 summarizes the studies that used the metabolomics technique to assess the effect of AgNPs in vitro on different cell lines.

TiO2 NPs

Micro-titania (titanium oxide, TiO2) particles are known as biologically inert in humans, enabling their use in many products such as cosmetics and pharmaceuticals. Nano-titania (TiO2 NPs) are also used as additives in many products such as sunscreen products, paints, printing ink, rubber, paper, sugar, cement, toothpaste, film, biomedical ceramics, implanted biomaterials, antimicrobial plastic packaging, and self-cleaning sanitary ceramicss. However, TiO2 NPs can enter the body via inhalation, ingestion, and dermal contact and they have been shown to exert significant toxic effects, such as cell metabolic change, chronic pulmonary inflammation, and pro-inflammatory effects in cells. Raja et al. reviewed the microenvironmental influence of TiO2 NP-induced mechanical stimuli on tumor cells and showed using the omics analysis that the exposure of cancer cells to TiO2 NPs caused gene mutations, protein alterations, and metabolite changes.

Chen et al. observed mitochondrial dysfunction caused by TiO2 NPs in a macrophage (RAW) cell line and primary mouse bone marrow-derived macrophages (BMDM) using a combination of metabolomics, lipidomics, and proteomics. The targeted UPLC-MS-based metabolomic analysis revealed a significant upregulation in the production of COX-2 metabolites including PGD2, PGE2, and 15dPGJ2, indicating an inflammatory response in macrophages. The authors also used GC-MS-based metabolic flux analysis, which is a technique that uses MS to track the fate of stable isotope tracers (e.g., 13C-glucose and 15N-glutamine), allowing the investigation of the contribution of specific metabolic pathways to the prevailing levels of specific metabolites, to measure the metabolic flux in the tricarboxylic acid (TCA) cycle using 13C-labelled glutamine. They observed a downregulation in TCA cycle metabolism and ATP production caused by significant mitochondrial dysfunction after the exposure of macrophages to TiO2 NPs. In a similar study, Tucci et al. studied the response of the human keratinocyte HaCaT cell line after exposure to 10–100 nm TiO2 NPs and found that the NPs were only present in the phagosomes of the cells without their internalization in any other cytoplasmic organelle. Specifically, “268” metabolites were detected using GC/LC-MS-based metabolomics, of which 85 metabolites were found to be significantly altered at 100 μg mL−1 dose of NPs. As stated in other studies, TiO2 NPs have shown significant and rapid effects on mitochondrial function by altering energy metabolism and anabolic pathways. However, they did not affect the cell cycle phase distribution or cell death.

Jin et al. used GC/TOFMS-based metabolomics to study the metabolic changes in L929 cells and their corresponding culture media induced by 5 nm-TiO2 NPs. At concentrations higher than 100 μg mL−1, the NPs caused a depletion in the cellular carbohydrate metabolism (the major biochemical metabolism pathway) after causing energy metabolism disruption, pentose phosphate pathway inhibition, nicotinamide metabolism block, mitochondria damage, and oxidative stress activation. Bo, Jin, Liu et al. again used GC/TOFMS-based metabolomics to study the change in amino acid levels in L929 cells after they were exposed to TiO2 NPs. The study revealed that seven metabolic pathways among the regulated pathways were significantly altered including 12 amino acids, i.e., l-α-alanine, β-alanine, glycine, l-aspartate, l-methionine, l-cysteine, glutamate, l-pyroglutamate, l-asparagine, l-glutamine, S-adenosyl methionine, and l-lysine.

In dental science, the use of TiO2 NPs as an additive to glass ionomer cements is known to improve their mechanical and antibacterial properties. However, the study by Garcia-Contreras et al. showed that these NPs may induce pro-inflammation in human gingival fibroblast (HGF) cells. Nevertheless, the molecular mechanism of the pro-inflammatory action of TiO2 NPs on these cells was still unclear. MS metabolomics was used to reveal the mechanism of this pro-inflammatory action by the treatment of HGF cells with IL-1b alone or in combination with TiO2 NPs. A total of 109 metabolites was successfully identified and quantified by CE/TOFMS. Most amino acids levels were downregulated at high concentrations of TiO2 NPs, while ophthalmate, α-aminoadipate, kynurenine, and β-alanine were upregulated. The activation of the urea cycle, polyamine, S-adenosylmethionine, and GSH synthetic pathways was stronger than that of the other pathways. The intracellular levels of urea cycle metabolites were downregulated significantly in the presence of both IL-1b/TiO2 NPs. In conclusion, ornithine was downregulated, which led to an immediate decline in putrescine. That latter is used to synthesize spermidine, which has anti-inflammatory properties. Thus, the reduction of this polyamine level accelerated the inflammation in HGF cells upon exposure to a combination of IL-1b/TiO2 NPs.

Kitchin et al. studied the effect of four different TiO2 NPs (in addition to two CeO2 NPs) on human liver HepG2 cells. Using LC/GC MS-based metabolomics, five out of the six NPs were found to cause a significant downregulation in GSH concentration. The authors observed a decrease in the GSH system in GSH precursors (glutamate and cysteine), GSH itself, and GSH metabolites (the gamma-glutamyl condensation products, glutamine, alanine, valine, 5-oxoproline, and cysteine–GSH). Among the 265 metabolites detected, the reduction in GSH was the largest deregulation. This indicates that the NPs are acting via an oxidative stress mode, which is a consistent biochemical effect of NPs.

Metabolomics can help to better understand the transition from in vitro to in vivo systems of NPs toxicity given that it can be applied in both types of experiment. For example, Cui et al. employed LC-MS-based metabolomics to investigate the effect of four metal oxide NPs, including TiO2 NPs, in vitro on human bronchial epithelial (BEAS-2B) cell line, and in vivo on mouse model after lung exposure. Their study showed that in vitro metabolomic findings can effectively reveal the biochemical effects in vivo, and that LC-MS-based metabolomics is sensitive enough to detect the tiny metabolomic changes when conventional cytotoxicity assays cannot detect any significant effect. Fig. 13a shows the workflow of this study. BEAS-2B cells were exposed to the four studied NPs, and then the metabolomics experiment was performed in vitro. This was followed by validation in vitro by enzymatic assays, in vivo using a mouse model after lung exposure to respective NPs, and finally by cellular function assays. The TiO2 NPs significantly altered the metabolic pathways of sphingosine-1-phosphate, fatty acid oxidation, folate cycle, inflammation/redox, and lipid metabolism, inducing inflammation. In addition, this effect was dose-dependent for some metabolites. Fig. 13b shows the altered metabolites and effect of the four studied metal oxide (MOx) NPs and their numbers, respectively.

Metabolomics is also applied in many in vivo nanotoxicity studies. For instance, Chen et al. performed three recent studies of TiO2 NP toxicity in vivo using MS-based metabolomics, once in rats by feces metabolite analysis, and then screened for urine and serum biomarkers in human workers exposed to these NPs in factories. This group also performed another metabolomics study using rat serum after subchronic oral exposure of TiO2 NPs. Han et al. used MS-based metabolomics to study the influence of TiO2 NPs on the fecal metabolome in rats after oral administration for 90 days. Åslund et al. used NMR-based metabolomics to assess the effects of 5 nm-TiO2 NPs on Eisenia fetida earthworms and observed metabolic changes related to oxidative stress. Eight years later, Zhu et al. used transcriptomics besides metabolomics to investigate the same effect of TiO2 NPs on the same earthworm and noticed that the antioxidant system and metabolic profiles of the earthworms were significantly affected. Ratnasekhar et al. used MS-based metabolomics to investigate the effects of TiO2 NPs on the soil nematode Caenorhabditis elegans. The results indicated the disruption of the tricarboxylic acid (TCA) cycle, arachidonic acid metabolism, and glyoxylate dicarboxylate metabolism pathways. For more about the in vivo metabolic effects of NPs including Ag, TiO2, and carbon-based NPs on organisms (plants, aquatic, and terrestrial invertebrates), the reader is kindly referred to the chapter by Farré and Jha.

Metabolomics reveals the global responses that cannot be observed by conventional toxicity endpoints, leading to an effective assessment of the effects of NPs in the environment, in vivo, and in vitro. Metabolomics has also been used to reveal the metabolite corona that is surrounding TiO2 NPs.Table 4 summarizes the studies that used the metabolomics technique to assess the effect of TiO2 NPs in vitro on different cells.

SiO2 NPs

The annual global production of SiO2 NPs is reported to exceed 1.5 million tons, making SiO2 NPs one of the most widely used NPs in the industrial manufacturing, drug delivery, cancer therapy, and biotechnological fields. This widespread is due to their biocompatibility, stability, and other unique properties compared with their bulk.

Although SiO2 NPs have been shown to have different cytotoxic effects on cells, the molecular mechanism of this cytotoxicity still needs to be explored using novel analytical techniques, such as metabolomics. Huang et al. used MS-based metabolomics to reveal the molecular information of the effect of SiO2 NPs on the human fetal lung fibroblast MRC-5 cell line. The authors observed NP dose-dependent changes in the metabolic profiles of the cells. As the dose increased, there was a downregulation in the amino acid and GSH levels together with an upregulation in urea and phospholipid concentrations, causing oxidative stress and energy metabolism disturbance. Feng et al. used NMR-based metabolomics to study the effects of 0.01 or 1.0 mg mL−1 of hydrophilic SiO2 NPs on the human cervical adenocarcinoma (HeLa) cell line. They studied both the intracellular and extracellular metabolome changes. In the early stage of NP exposure, no clear dose-effect of the HeLa cell metabolome was observed, which implied that the cellular stress-response regulated the metabolic variations in the HeLa cells. Afterwards, the NPs induced cell membrane modification, catabolism of carbohydrate and protein, and a stress response. The toxicological effects induced by high-dosage SiO2 NPs could be derived from the elevated levels of ATP and ADP, the utilization of glucose and amino acids and the production of metabolic end-products such as glutamate, glycine, lysine, methionine, phenylalanine, and valine. Irfan et al. used conventional assays and NMR-based extracellular metabolomics to study the effect of fumed SiO2 NPs on human lung A549 cells. The authors observed an upregulation in the extracellular glucose, lactate, phenylalanine, histidine, and tyrosine levels in a time- and NP dose-dependent manner. There was also an increase in intracellular ROS and cell membrane damage at 4 h and a loss of cell viability after 48 h observed by conventional assays.

A few metabolomics studies compared the in vitro and in vivo outcomes of SiO2 NP treatments. For instance, Chatterjee et al. used NMR-based untargeted-metabolomics to study the effect of amorphous SiO2 NPs on the human hepatoma HepG2 cell line and mice liver (Fig. 14). Firstly, this study determined the altered metabolites in the cells and mice liver using OPLS-DA analysis (Fig. 14a and d, respectively). Subsequently, the selected significantly altered metabolites were determined (Fig. 14b and e), followed by pathway analysis using the MetaboAnalyst 3.0 software (Fig. 14c and f). In both in vitro and in vivo systems, the perturbation of GSH metabolism and the depletion of the GSH pool were detected after aSiO2 NP treatment. Moreover, the in vitro results were further supported by the in vivo data, specifically for metabolite profiling and pathway analysis, were there were 8 common altered metabolic pathways in the two systems. This study revealed that the major causes of aSiO2 NP-mediated hepatotoxicity were the suppression of GSH metabolism and oxidative stress. In a similar study, Bannuscher et al. studied the responses of rat lung epithelial cells (RLE-6TN) and alveolar macrophages (NR8383) (in vitro) to four well-selected SiO2 NPs, differing in structure, size, and surface charge, and compared the results to in vivo responses in rat lung tissues. The authors observed a cell-specific time- and concentration-dependent changes in vitro and identified several biomarker candidates such as Asp, Asn, Ser, Pro, spermidine, putrescine, and LysoPCaC16:1 in vitro, and then verified them in vivo.

It was proven that SiO2 NP exposure inevitably induces damage to the respiratory system, however, knowledge of its mode of action and metabolic interactions with the cells is limited. Zhao et al. performed a study to reveal the molecular information of the metabolic responses of the lung bronchial epithelial BEAS2B cell line after SiO2 NP exposure, using MS-based metabolomics. They revealed that even with low cytotoxicity, SiO2 NPs still caused global metabolism disruption. Specifically, five metabolic pathways were significantly perturbed; in particular, oxidative stress, as confirmed by GSH depletion, mitochondrial dysfunction-related GSH metabolism, and pantothenate and coenzyme A (CoA) biosynthesis. The identified key metabolites were GSH, glycine, beta-alanine, cysteine, cysteinyl-glycine, and pantothenic acid. Oxidative DNA damage and cell membrane disintegration were detected by observing elevated 8-oxo-2′-deoxyguanosine (8-OdG) and decreased phospholipids levels.

Several studies compared the effect of SiO2 NPs on cells to other NPs using metabolomics and other omics techniques. For example, Karkossa et al. used targeted metabolomics and global proteomics to compare the effect of SiO2 NPs with different particle sizes, surface charges, and hydrophobicity to the effect of TiO2, graphene oxide (GO), phthalocyanine blue, phthalocyanine green, and Mn2O3 NPs on RLE-6TN alveolar epithelial cells. Alternatively, Cui et al. used MS-based metabolomics to reveal the significantly altered metabolites and metabolic pathways in human bronchial epithelial cells and a mouse model exposed to four different types of metal oxide NPs (SiO2, ZnO, TiO2, and CeO2) at both high (25 μg mL−1) and low (12.5 μg mL−1) doses (see Fig. 13). Table 5 summarizes the studies that used metabolomics technique to assess the effect of SiO2 NPs in vitro on different cells.

ZnO NPs

Zinc oxide NPs are gaining increasing attention due to their unique properties, especially their optical and electronic properties. Also, they can be prepared using a variety of methods and in a range of different morphologies. This makes them the third highest global production volume among metal-containing NMs and excellent for a broad range of applications, including optoelectronic devices (light-emitting diodes (LEDs), laser diodes, solar cells, and photodetectors), electronic devices (transistors), and active compounds in sunscreens, drug delivery, biomedical engineering, food additives, and cosmetics. It was shown that human exposure to these engineered NPs can cause health problems for both consumers and industry workers, making it important to further investigate in their toxicity and improve their safety when they are used and produced.

The respiratory tract is the primary route of exposure to airborne NPs such as ZnO. Thus, it is common to use the bronchial epithelial BEAS-2B cell line as an in vitro model to study the toxicity of these NPs. For instance, Lim et al. used this cell line to perform an MS-based metabolomics study to reveal the effect of ZnO NPs on the respiratory system. The authors revealed ROS-mediated cell death associated with mitochondrial dysfunction and interference in regulating energy metabolism. This was concluded after observing a significant decrease in the levels of amino acids (valine, tryptophan, lysine, proline, threonine, glycine, serine, glutamic acid, and aspartic acid) and TCA intermediate metabolites (citrate) (Fig. 15a). These results indicate that ZnO NPs can be seriously harmful to human health if they were inhaled.

Although Zn is a key micronutrient for plants, a high dose of this metal is toxic to plants either in the nano or other forms. Salehi et al. used UHPLC-QTOF metabolomics to study the effect of ZnO NPs and bulk ZnSO4 on bean plants (Phaseolus vulgaris L). The results indicated the unique NP-related toxic effects of ZnO in beans compared to the ionic forms of Zn. Two similar studies of the effect of ZnO NPs have been done on tomato and cucumber. Wan et al. performed a metabolomics analysis to reveal the effect of ZnO NPs on salt tolerance in the Sophora alopecuroides plant. Moreover, He et al. elucidated toxicodynamic differences at the molecular scale between ZnO NPs and ZnCl2 in Enchytraeus crypticus, a model species in soil ecotoxicology, using non-targeted metabolomics. They found that the number of altered metabolites after Zn2+ exposure was larger than the number of altered metabolites after ZnO NP exposure, indicating the higher toxicity of the Zn ionic form (Fig. 15b and c). For more information about nanotechnology in agriculture and the effect of metallic-, metal oxide-, and carbon-based-NPs on plants, the reader is advised to read the review by Paramo et al. and review by Majumdar et al.

The toxic effects of a NP may be reduced by applying co-exposure with another NP. For instance, Wu et al. studied the combined effects of graphene oxide (GO) and ZnO NPs on human A549 cells using NMR-based metabolomics. PLS-DA analysis showed that the control and GO-alone exposure groups overlapped, indicating a low effect of 10 mg L−1 GO on the metabolome profiles. In contrast, ZnO NP-alone exposure significantly altered the metabolome profiles in A549 cells. A total of 14 altered metabolites was shared in the ZnO NP-alone and the co-exposure with GO groups. However, the levels of fold changes of the 14 shared metabolites were lower in the co-exposure group than that in the ZnO NP-alone group. This tendency indicates that GO alleviated the toxicity induced by ZnO NPs in the cellular metabolism by reducing or blocking their internalization in the cells (Fig. 15d–f). Table 6 summarizes the studies that used metabolomics technique to assess the effect of ZnO NPs in vitro on different cells.

Cobalt ferrite (CoFe2O4) NPs

Cobalt ferrite (CoFe2O4) NPs have interesting properties, such as mechanical hardness, excellent chemical stability, high anisotropy, superparamagnetism, and coercivity. Oliveira et al. studied the cytotoxic effect, cellular uptake, and metabolomic effect of CoFe2O4 NPs on the HeLa and HaCaT cell lines. This study revealed, using NMR-based metabolomics, that although the uptake of NPs at 2 mg mL−1 caused low cytotoxicity, it significantly impacted the cell metabolism. Both cell lines shared stress-related metabolic changes such as upregulation in alanine and creatine. A downregulation in fumarate level was present in HeLa cells treated with the NPs. Given that this metabolite is associated with cell proliferation and tumor growth, it was concluded that CoFe2O4 NPs can inhibit tumorigenesis.

Copper oxide (CuO) NPs

Copper oxide (CuO) NPs have been used in heat transfer fluids, semiconductors, and intrauterine contraceptive devices. Human exposure to CuO NPs is rapidly increasing, and thus reliable toxicity test systems are urgently needed. It was shown that CuO NPs are more toxic than their microparticles (MPs). To reveal the mechanism of this toxicity, Murgia et al. used MS-based metabolomics to the study the effect of CuO micro- and nano-particles against human bone marrow mesenchymal stem cells (hBMMSCs). It was found that the MPs increased the levels of serine, glyceric acid, and succinic acid, while glutamine was the only discriminant metabolite for the class of samples treated with NPs. This proves that ROS formation is the active mode of action in NP treatment, providing the first step toward the understanding of the mechanism of toxicity of CuO NP-treated cells. Wang et al. compared the effect of CuO NPs, MPs, and Cu ions on microalga Chlorella vulgaris after 5 days exposure using global metabolomics. A total of 75 differentiated metabolites was identified. Most metabolic pathways perturbed after CuO NP exposure were shared by that after CuO MP and Cu ion exposure. Only one difference between metabolic responses to particles and that to ions was observed, which is the accumulation of fatty acid oxidation products, i.e., particles caused higher fold changes at 1 mg L−1 and lower fold changes at 10 mg L−1 compared with ions. This indicates the significant role of dissolved Cu ions on the toxicity of CuO NPs and MPs. Kruszka et al. compared the effect of Cu and CuO NPs on the secondary metabolism of Hypericum perforatum L. cell suspension cultures and found that metal NPs induce higher metabolic changes than their counterpart metal oxide NPs. Table 7 summarizes the studies that used the metabolomics technique to assess the effect of other metal/metal oxide NPs in vitro on different cells.

Graphene

Graphene has attracted significant attention due to its unique and novel properties, which has promising applications in different fields, including biomedical engineering, tissue engineering, and biosensors. However, graphene-based drug delivery systems and other biomedical applications are associated with challenges related to the safety of carbon NMs for clinical use. Many groups have investigated the cytotoxicity of graphene. In this case, although the conventional in vitro toxicity assays of graphene yielded contradictory results, Jiao et al. used the metabolomics approach to investigate the metabolic responses on graphene-treated HepG2 and detected twelve metabolites as potential biomarkers. The authors also determined three KEGG pathways including arginine and proline metabolism, purine metabolism, and glycophospholipid metabolism.

Adamson et al. studied the metabolic change caused in macrophages by graphene nanoplatelets. The number of compounds changed following exposure to graphene was determined to be both concentration and time dependent. The identified metabolites are related to several metabolism pathways, such as GSH metabolism, pantothenate and CoA biosynthesis, sphingolipid metabolism, purine metabolism, arachidonic acid metabolism and others. Graphene oxide (GO) also has some biomedical applications but a greater understanding of its cytotoxicity and efficiency as a drug carrier is needed. Raja et al. used NMR-based metabolomics to assess the metabolic effect of GO nanosheets on MCF-7 breast cancer cells. The treatment affected arginine metabolism, proline metabolism, and aminoacyl-tRNA biosynthesis, including anabolism and catabolism. Moreover, GO increased the number of metabolic disturbances in cancer steroids in a dose-dependent manner.

Carbon black NPs (CBNPs)

Carbon black NPs (CBNPs) are the core component of fine particulate matter in the atmosphere, which make its exposure to the respiratory system easy. It was reported that CBNPs can induce inflammation, oxidative stress, and changes in cell signalling and gene expression in mammalian cells and organs. Hou et al. used MS-based metabolomics to reveal this mechanism in A549 cells. Their study identified a total of 32 differential metabolites between the CBNP exposure and control groups. The pathway analysis showed that the metabolic changes were involved in tricarboxylic acid (TCA) cycle, alanine, aspartate, glutamate, and histidine metabolism. This suggests that CBNPs act by affecting the normal process of energy metabolism and disturbing several vital signalling pathways in the cells, finally leading to cell apoptosis. Other studies performed in vivo experiments and assessed the effect of carbon-based NMs on the ecosystem by studying some models such as earthworms. For instance, Xu et al. studied the impacts of three carbon NMs, i.e., carbon black (CB), reduced graphene oxide (RGO), and single-wall carbon nanotubes (SWCNTs), on Eisenia fetida, an early warning soil invertebrate for pollution events. They concluded that the soil environmental risk of C-NMs was related to their particle morphology, contributing to a comprehensive understanding of nano-agriculture. Table 8 summarizes the studies that used the metabolomics technique to assess the effect of other C-based NPs in vitro on different cells.

Polymeric NPs

Polymeric NPs such as polystyrene (PS) are gaining considerable attention because of their growing accumulation in the environment and the high probability of human and animal exposure. Therefore, more research must be done to increase our understanding of their potential effects. Kim et al. studied the metabolic effects of PS NPs on human epithelial colorectal cells (Caco-2). The authors designed two methods to investigate the exposure of Caco-2 cells to NPs, where the first is by exposing cells to a high concentration of 50 nm PS NPs for 24 h (acute), and the second is by exposing them to a relatively lower concentration for over 1.5 months (chronic). The biological assays were performed using specific NP concentrations, which were 10 and 80 μg mL−1 for the acute model and 0.1 μg mL−1 for the chronic exposure model. After acute exposure, untargeted metabolic profiling was performed and the change in lipid metabolic pathways determined, including steroid and arachidonic acid metabolism. Alternatively, chronic exposure induced relatively minor changes. However, there was still a potential effect on fatty acid biosynthesis, indicating that acute and chronic exposure to PS NPs may disturb lipid homeostasis. They also confirmed the changes in the expression levels of lipid transcriptional regulatory factor coding genes, namely, sterol regulatory element-binding transcription factors 1 and 2. The total fatty acid composition was further studied to verify metabolic disturbance by chronic exposure. Su et al. investigated the effect of poly(l-lactic acid) (PLLA) nanofibers on PC12 cell differentiation at the metabolic level. Many differential metabolites were identified and two pathways and three metabolites critical to PC12 cell differentiation were influenced by the nanofibers. Table 9 summarizes the studies that used the metabolomics technique to assess the effect of other polymeric NPs in vitro on different cells.

Conclusions

Currently, it is obvious for many researchers that nanotechnology provides countless benefits, and consequently its demand is increasing daily. It is very important to assess the safety of every NP that is being produced and to test its beneficial and disadvantageous effects. Understanding the interactions between NPs with cells and how NPs are internalized in cells are the first step to assess their toxicity. Here, not only the type of the NP matters, but also its physicochemical properties such as size, shape, and surface properties. It was proven that NPs with different properties have different effect on cells. Some sizes of NPs are not toxic, but others are severely harmful to cells. In general, conventional assays are the most used strategy to assess the effect of NPs on cells. However, these assays were found to interfere with NPs, giving false results in some cases, and they are unable to reveal the molecular information of the toxicity or effect of NPs. Thus, an increasing number of researchers are heading towards the use of other analytical techniques. Metabolomics is a powerful technique that provides a full picture of the toxicity of NPs by analyzing the functionality of an existing living system by measuring the metabolic change induced by NPs. Unlike conventional assays, this tool does not interfere with NPs and provides information at the molecular level about the toxicity of NPs. Furthermore, it forms an additional bridge that connects the in vitro with the in vivo models, as proven by several references. It was shown in this review that NPs can harm the cell through different ways, including cell viability and proliferation perturbation, inflammatory response, oxidative stress, ROS generation, and cell death via apoptosis or necrosis. Moreover, using metabolomics, NPs were shown to perturb the metabolic pathways of cells, including the TCA cycle, DNA and protein synthesis, glycolysis, glutathione, and amino acid pathways. Thus far, metabolomics has been used in many studies to assess the effects of different NPs on living organisms. However, more research needs to be done to identify and validate specific biomarkers of the effects of NPs on cells. Reaching this point will introduce a huge step in determining the toxicity of NPs and how to avoid or multiply this toxicity. This will help in designing better NPs for biomedical applications and producing safer NPs for industrial applications. Nevertheless, long-term targeted studies should also be performed to fill many gaps in this field. Also, the combination of metabolomics with other techniques is required in some cases to provide a bigger picture on the events occurring in the cell.

Author contributions

Mohammad Awashra: literature search, writing, figures designing and creating. Piotr Mlynarz: review idea, reviewing, and editing.

Conflicts of interest

There are no conflicts to declare.

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    Kumar V., Sharma N., Maitra S. S. In vitro and in vivo toxicity assessment of nanoparticles. Int. Nano Lett. 2017;7(4):243-256.

    Not in our library10.1007/s40089-017-0221-3

  95. 95.

    Veranth J. M., in Nanoscience and Nanotechnology, John Wiley & Sons, Inc., Hoboken, NJ, USA, 2008, pp. 261–286

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  96. 96.

    Sayes C. M., Reed K. L., Warheit D. B. Assessing toxicology of fine and nanoparticles: comparing in vitro measurements to in vivo pulmonary toxicity profiles. Toxicol. Sci. 2007;97(1):163-180.

    Not in our library10.1093/toxsci/kfm018

  97. 97.

    Han X., Gelein R., Corson N., Wade-Mercer P., Jiang J., Biswas P., Finkelstein J. N., Elder A., Oberdörster G. Validation of an LDH assay for assessing nanoparticle toxicity. Toxicology. 2011;287(1–3):99-104.

    Not in our library10.1016/j.tox.2011.06.011

  98. 98.

    Stueker O., Ortega V. A., Goss G. G., Stepanova M. Understanding interactions of functionalized nanoparticles with proteins: a case study on lactate dehydrogenase. Small. 2014;10(10):2006-2021.

    Not in our library10.1002/smll.201303639

  99. 99.

    Drasler B., Sayre P., Steinhäuser K. G., Petri-Fink A., Rothen-Rutishauser B. In vitro approaches to assess the hazard of nanomaterials. NanoImpact. 2017;8:99-116.

    Not in our library10.1016/j.impact.2017.08.002

  100. 100.

    Díaz B., Sánchez-Espinel C., Arruebo M., Faro J., de Miguel E., Magadán S., Yagüe C., Fernández-Pacheco R., Ibarra M. R., Santamaría J., González-Fernández Á. Assessing methods for blood cell cytotoxic responses to inorganic nanoparticles and nanoparticle aggregates. Small. 2008;4(11):2025-2034.

    Not in our library10.1002/smll.200800199

  101. 101.

    Hoshino A., Fujioka K., Oku T., Suga M., Sasaki Y. F., Ohta T., Yasuhara M., Suzuki K., Yamamoto K. Physicochemical properties and cellular toxicity of nanocrystal quantum dots depend on their surface modification. Nano Lett. 2004;4(11):2163-2169.

    Not in our library10.1021/nl048715d

  102. 102.

    Ray P. D., Huang B. W., Tsuji Y. Reactive oxygen species (ROS) homeostasis and redox regulation in cellular signaling. Cell. Signalling. 2012;24:981-990.

    Not in our library10.1016/j.cellsig.2012.01.008

  103. 103.

    He L., He T., Farrar S., Ji L., Liu T., Ma X. Antioxidants maintain cellular redox homeostasis by elimination of reactive oxygen species. Cell. Physiol. Biochem. 2017;44(2):532-553.

    Not in our library10.1159/000485089

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  105. 105.

    Higashi Y., Mazumder J., Yoshikawa H., Saito M., Tamiya E. Chemically regulated ROS generation from gold nanoparticles for enzyme-free electrochemiluminescent immunosensing. Anal. Chem. 2018;90(9):5773-5780.

    Not in our library10.1021/acs.analchem.8b00118

  106. 106.
  107. 107.

    Kalyanaraman B., Darley-Usmar V., Davies K. J. A., Dennery P. A., Forman H. J., Grisham M. B., Mann G. E., Moore K., Roberts L. J., Ischiropoulos H. Measuring reactive oxygen and nitrogen species with fluorescent probes: challenges and limitations. Free Radical Biol. Med. 2012;52:1-6.

    Not in our library10.1016/j.freeradbiomed.2011.09.030

  108. 108.

    Whongsiri P., Phoyen S., Boonla C. Oxidative stress in urothelial carcinogenesis: Measurements of protein carbonylation and intracellular production of reactive oxygen species. Methods Mol. Biol. 2018;1655:109-117.

    Not in our library10.1007/978-1-4939-7234-0_9

  109. 109.

    Jeszka-Skowron M., Podgórski T. and Czarczyńska-Goślińska B., Determination of Antioxidant Biomarkers in Biological Fluids, Springer, Cham, 2021,

    Not in our library10.1007/978-3-030-61879-7_11

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    Fantel A. G. Reactive oxygen species in developmental toxicity: review and hypothesis. Teratology. 1996;53:196-217.

    Not in our library10.1002/(sici)1096-9926(199603)53:3<196::aid-tera7>3.0.co;2-2

  111. 111.

    Ohkawa H., Ohishi N., Yagi K. Assay for lipid peroxides in animal tissues by thiobarbituric acid reaction. Anal. Biochem. 1979;95(2):351-358.

    Not in our library10.1016/0003-2697(79)90738-3

  112. 112.

    Pylväs M., Puistola U., Laatio L., Kauppila S., Karihtala P. Elevated serum 8-OHdG is associated with poor prognosis in epithelial ovarian cancer. Anticancer Res. 2011;31(4):1411-1415.

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  113. 113.

    Toyokuni S., Tanaka T., Hattori Y., Nishiyama Y., Yoshida A., Uchida K., Hiai H., Ochi H., Osawa T. Quantitative immunohistochemical determination of 8-hydroxy-2’- deoxyguanosine by a monoclonal antibody N45.1: its application to ferric nitrilotriacetate-induced renal carcinogenesis model. Lab. Invest. 1997;76(3):365-374.

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  114. 114.

    Chiou C.-C., Chang P.-Y., Chan E.-C., Wu T.-L., Tsao K.-C., Wu J. T. Urinary 8-hydroxydeoxyguanosine and its analogs as DNA marker of oxidative stress: development of an ELISA and measurement in both bladder and prostate cancers. Clin. Chim. Acta. 2003;334:87-94.

    Not in our library10.1016/s0009-8981(03)00191-8

  115. 115.

    Golbamaki N., Rasulev B., Cassano A., Marchese Robinson R. L., Benfenati E., Leszczynski J., Cronin M. T. D. Genotoxicity of metal oxide nanomaterials: review of recent data and discussion of possible mechanisms. Nanoscale. 2015;7:2154-2198.

    Not in our library10.1039/c4nr06670g

  116. 116.

    Aranda A., Sequedo L., Tolosa L., Quintas G., Burello E., Castell J. v., Gombau L. Dichloro-dihydro-fluorescein diacetate (DCFH-DA) assay: a quantitative method for oxidative stress assessment of nanoparticle-treated cells. Toxicol. in Vitro. 2013;27(2):954-963.

    Not in our library10.1016/j.tiv.2013.01.016

  117. 117.

    Pfaller T., Colognato R., Nelissen I., Favilli F., Casals E., Ooms D., Leppens H., Ponti J., Stritzinger R., Puntes V., Boraschi D., Duschl A., Oostingh G. J. The suitability of different cellular in vitro immunotoxicity and genotoxicity methods for the analysis of nanoparticle-induced events. Nanotoxicology. 2010;4(1):52-72.

    Not in our library10.3109/17435390903374001

  118. 118.

    Kinoshita S., Akira S., Kishimoto T. A member of the C/EBP family, NF-IL6 beta, forms a heterodimer and transcriptionally synergizes with NF-IL6. Proc. Natl. Acad. Sci. U. S. A. 1992;89:1473-1476.

    Not in our library10.1073/pnas.89.4.1473

  119. 119.

    Yu F., Zhang X., Gao L., Xue H., Liu L., Wang S., Chen S., Huang L. LncRNA loc105377478 promotes NPs-Nd2O3-induced inflammation in human bronchial epithelial cells through the ADIPOR1/NF-κB axis. Ecotoxicol. Environ. Saf. 2021;208:111609.

    Not in our library10.1016/j.ecoenv.2020.111609

  120. 120.

    Tada-Oikawa S., Eguchi M., Yasuda M., Izuoka K., Ikegami A., Vranic S., Boland S., Tran L., Ichihara G., Ichihara S. Functionalized surface-charged sio2 nanoparticles induce pro-inflammatory responses, but are not lethal to caco-2 cells. Chem. Res. Toxicol. 2020;33(5):1226-1236.

    Not in our library10.1021/acs.chemrestox.9b00478

  121. 121.

    Lin H., Fu G., Yu Q., Wang Z., Zuo Y., Shi Y., Zhang L., Gu Y., Qin L., Zhou T. Carbon black nanoparticles induce HDAC6-mediated inflammatory responses in 16HBE cells. Toxicol. Ind. Health. 2020;36(10):759-768.

    Not in our library10.1177/0748233720947214

  122. 122.

    Guadagnini R., Halamoda Kenzaoui B., Walker L., Pojana G., Magdolenova Z., Bilanicova D., Saunders M., Juillerat-Jeanneret L., Marcomini A., Huk A., Dusinska M., Fjellsbo L. M., Marano F., Boland S. Toxicity screenings of nanomaterials: Challenges due to interference with assay processes and components of classic in vitro tests. Nanotoxicology. 2015;9(S1):13-24.

    Not in our library10.3109/17435390.2013.829590

  123. 123.

    Piret J. P., Bondarenko O. M., Boyles M. S. P., Himly M., Ribeiro A. R., Benetti F., Smal C., Lima B., Potthoff A., Simion M., Dumortier E., Leite P. E. C., Balottin L. B., Granjeiro J. M., Ivask A., Kahru A., Radauer-Preiml I., Tischler U., Duschl A., Saout C., Anguissola S., Haase A., Jacobs A., Nelissen I., Misra S. K., Toussaint O. Pan-European inter-laboratory studies on a panel of in vitro cytotoxicity and pro-inflammation assays for nanoparticles. Arch. Toxicol. 2017;91(6):2315-2330.

    Not in our library10.1007/s00204-016-1897-2

  124. 124.

    Elmore S. Apoptosis: A Review of Programmed Cell Death. Toxicol. Pathol. 2007;35:495-516.

    Not in our library10.1080/01926230701320337

  125. 125.

    Ruffolo P. R. The pathogenesis of necrosis. i. correlated light and electron microscopic observations of the myocardial necrosis induced by the intravenous injection of papain. Am. J. Pathol. 1964;45:741-756.

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  126. 126.

    Ahamed M., Akhtar M. J., Siddiqui M. A., Ahmad J., Musarrat J., Al-Khedhairy A. A., AlSalhi M. S., Alrokayan S. A. Oxidative stress mediated apoptosis induced by nickel ferrite nanoparticles in cultured A549 cells. Toxicology. 2011;283(2–3):101-108.

    Not in our library10.1016/j.tox.2011.02.010

  127. 127.

    Park E. J., Yi J., Chung K. H., Ryu D. Y., Choi J., Park K. Oxidative stress and apoptosis induced by titanium dioxide nanoparticles in cultured BEAS-2B cells. Toxicol. Lett. 2008;180(3):222-229.

    Not in our library10.1016/j.toxlet.2008.06.869

  128. 128.

    Zhu M. T., Wang Y., Feng W. Y., Wang B., Wang M., Ouyang H., Chai Z. F. Oxidative stress and apoptosis induced by iron oxide nanoparticles in cultured human umbilical endothelial cells. J. Nanosci. Nanotechnol. 2010;10:8584-8590.

    Not in our library10.1166/jnn.2010.2488

  129. 129.

    Xu J., Xu P., Li Z., Huang J., Yang Z. Oxidative stress and apoptosis induced by hydroxyapatite nanoparticles in C6 cells. J. Biomed. Mater. Res. 2012;100 A(3):738-745.

    Not in our library10.1002/jbm.a.33270

  130. 130.

    Xue Y., Zhang T., Zhang B., Gong F., Huang Y., Tang M. Cytotoxicity and apoptosis induced by silver nanoparticles in human liver HepG2 cells in different dispersion media. J. Appl. Toxicol. 2016;36(3):352-360.

    Not in our library10.1002/jat.3199

  131. 131.

    Reus T. L., Marcon B. H., Paschoal A. C. C., Ribeiro I. R. S., Cardoso M. B., Dallagiovanna B., de Aguiar A. M. Dose-dependent cell necrosis induced by silica nanoparticles. Toxicol. in Vitro. 2020;63:104723.

    Not in our library10.1016/j.tiv.2019.104723

  132. 132.

    Fiers W., Beyaert R., Declercq W., Vandenabeele P. More than one way to die: apoptosis, necrosis and reactive oxygen damage. Oncogene. 1999;18:7719-7730.

    Not in our library10.1038/sj.onc.1203249

  133. 133.

    Kumar G., Degheidy H., Casey B. J., Goering P. L. Flow cytometry evaluation of in vitro cellular necrosis and apoptosis induced by silver nanoparticles. Food Chem. Toxicol. 2015;85:45-51.

    Not in our library10.1016/j.fct.2015.06.012

  134. 134.

    Vermes I., Haanen C., Steffens-Nakken H., Reutellingsperger C. A novel assay for apoptosis Flow cytometric detection of phosphatidylserine expression on early apoptotic cells using fluorescein labelled Annexin V. J. Immunol. Methods. 1995;184(1):39-51.

    Not in our library10.1016/0022-1759(95)00072-i

  135. 135.

    Vafaei S., Sadat Shandiz S. A., Piravar Z. Zinc-phosphate nanoparticles as a novel anticancer agent: an in vitro evaluation of their ability to induce apoptosis. Biol. Trace Elem. Res. 2020;198(1):109-117.

    Not in our library10.1007/s12011-020-02054-6

  136. 136.

    Bancos S., Tsai D.-H., Hackley V., Weaver J. L., Tyner K. M. Evaluation of viability and proliferation profiles on macrophages treated with silica nanoparticles in vitro via plate-based, flow cytometry, and coulter counter assays. ISRN Nanotechnol. 2012;2012:454072.

    Not in our library10.5402/2012/454072

  137. 137.
  138. 138.

    Naseer B., Srivastava G., Qadri O. S., Faridi S. A., Islam R. U., Younis K. Importance and health hazards of nanoparticles used in the food industry. Nanotechnol. Rev. 2018;7(6):623-641.

    Not in our library10.1515/ntrev-2018-0076

  139. 139.

    Ramirez T., Daneshian M., Kamp H., Bois F. Y., Clench M. R., Coen M., Donley B., Fischer S. M., Ekman D. R., Fabian E., Guillou C., Heuer J., Hogberg H. T., Jungnickel H., Keun H. C., Krennrich G., Krupp E., Luch A., Noor F., Peter E., Riefke B., Seymour M., Skinner N., Smirnova L., Verheij E., Wagner S., Hartung T., van Ravenzwaay B., Leist M. T4 Report* Metabolomics in toxicology and preclinical research. ALTEX. 2013;30(2):209-225.

    Not in our library10.14573/altex.2013.2.209

  140. 140.
  141. 141.

    Idle J. R., Gonzalez F. J. Metabolomics. Cell Metab. 2007;6:348-351.

    Not in our library10.1016/j.cmet.2007.10.005

  142. 142.

    Gomez-Casati D. F., Zanor M. I., Busi M. v. Metabolomics in plants and humans: applications in the prevention and diagnosis of diseases. BioMed Res. Int. 2013;2013:792527.

    Not in our library10.1155/2013/792527

  143. 143.

    Masutin V., Kersch C., Schmitz-Spanke S. A systematic review: metabolomics-based identification of altered metabolites and pathways in the skin caused by internal and external factors. Exp. Dermatol. 2022;700-714.

    Not in our library10.1111/exd.14529

  144. 144.

    Deja S., Porebska I., Kowal A., Zabek A., Barg W., Pawelczyk K., Stanimirova I., Daszykowski M., Korzeniewska A., Jankowska R., Mlynarz P. Metabolomics provide new insights on lung cancer staging and discrimination from chronic obstructive pulmonary disease. J. Pharm. Biomed. Anal. 2014;100:369-380.

    Not in our library10.1016/j.jpba.2014.08.020

  145. 145.

    Deja S., Barg E., Młynarz P., Basiak A., Willak-Janc E. 1H NMR-based metabolomics studies of urine reveal differences between type 1 diabetic patients with high and low HbAc1 values. J. Pharm. Biomed. Anal. 2013;83:43-48.

    Not in our library10.1016/j.jpba.2013.04.017

  146. 146.

    Mika A., Wojtowicz W., Ząbek A., Młynarz P., Chmielewski M., Sledzinski T., Stepnowski P. Application of nuclear magnetic resonance spectroscopy for the detection of metabolic disorders in patients with moderate kidney insufficiency. J. Pharm. Biomed. Anal. 2018;149:1-8.

    Not in our library10.1016/j.jpba.2017.10.037

  147. 147.

    Bu Q., Yan G., Deng P., Peng F., Lin H., Xu Y., Cao Z., Zhou T., Xue A., Wang Y., Cen X., Zhao Y. L. NMR-based metabonomic study of the sub-acute toxicity of titanium dioxide nanoparticles in rats after oral administration. Nanotechnology. 2010;21(12):125105.

    Not in our library10.1088/0957-4484/21/12/125105

  148. 148.

    Grintzalis K., Lawson T. N., Nasser F., Lynch I., Viant M. R. Metabolomic method to detect a metabolite corona on amino-functionalized polystyrene nanoparticles. Nanotoxicology. 2019;13(6):783-794.

    Not in our library10.1080/17435390.2019.1577510

  149. 149.

    Lanekoff I., Sharma V. v., Marques C. Single-cell metabolomics: where are we and where are we going? Curr. Opin. Biotechnol. 2022;75:102693.

    Not in our library10.1016/j.copbio.2022.102693

  150. 150.

    Liu Y., Cheng Y., Chen T., Zhang Y., Wang X., Zhao A., Jia W., Bo Y., Jin C. GC/TOFMS Analysis of Endogenous Metabolites in Mouse Fibroblast Cells and Its Application in TiO2 Nanoparticle-Induced Cytotoxicity Study. Chromatographia. 2012;75:1301-1310.

    Not in our library10.1007/s10337-012-2315-4

  151. 151.

    Steuer A. E., Brockbals L., Kraemer T. Metabolomic strategies in biomarker research-new approach for indirect identification of drug consumption and sample manipulation in clinical and forensic toxicology? Front. Chem. 2019;7.

    Not in our library10.3389/fchem.2019.00319

  152. 152.

    Deng C., Tang Q., Yang Z., Dai Z., Cheng C., Xu Y., Chen X., Zhang X., Su J. Effects of iron oxide nanoparticles on phenotype and metabolite changes in hemp clones (Cannabis sativa L.). Front. Environ. Sci. Eng. 2022;16:134.

    Not in our library10.1007/s11783-022-1569-9

  153. 153.

    Bouyahya A., el Omari N., Hakkour M., el Menyiy N., Benali T., Kulikov D., Karpukhin M., Shariati M. A., Venkidasamy B., Thiruvengadam M., Chamkhi I. A review on transcriptomic and metabolomic responses of plants to nanopollution. Environ. Sci. Pollut. Res. 2022;29:22913-22929.

    Not in our library10.1007/s11356-022-18659-4

  154. 154.

    Chen Z., Guo Z., Niu J., Xu N., Sui X., Kareem H. A., Hassan M. U., Yan M., Zhang Q., Wang Z., Mi F., Kang J., Cui J., Wang Q. Phytotoxic effect and molecular mechanism induced by graphene towards alfalfa (Medicago sativa L.) by integrating transcriptomic and metabolomics analysis. Chemosphere. 2022;290:133368.

    Not in our library10.1016/j.chemosphere.2021.133368

  155. 155.

    Kumar D., Mutreja I., Chitcholtan K., Sykes P. Cytotoxicity and cellular uptake of different sized gold nanoparticles in ovarian cancer cells. Nanotechnology. 2017;28(47):475101.

    Not in our library10.1088/1361-6528/aa935e

  156. 156.

    Shim W., Paik M. J., Nguyen D. T., Lee J. K., Lee Y., Kim J. H., Shin E. H., Kang J. S., Jung H. S., Choi S., Park S., Shim J. S., Lee G. Analysis of changes in gene expression and metabolic profiles induced by silica-coated magnetic nanoparticles. ACS Nano. 2012;6(9):7665-7680.

    Not in our library10.1021/nn301113f

  157. 157.

    Kou L., Jiang X., Tang Y., Xia X., Li Y., Cai A., Zheng H., Zhang H., Ganapathy V., Yao Q., Chen R. Resetting amino acid metabolism of cancer cells by ATB0,+-targeted nanoparticles for enhanced anticancer therapy. Bioact. Mater. 2022;9:15-28.

    Not in our library10.1016/j.bioactmat.2021.07.009

  158. 158.

    Shin T. H., Lee D. Y., Lee H.-S., Park H. J., Jin M. S., Paik M.-J., Manavalan B., Mo J.-S., Lee G. Integration of metabolomics and transcriptomics in nanotoxicity studies. BMB Rep. 2018;51:14-20.

    Not in our library10.5483/bmbrep.2018.51.1.237

  159. 159.

    Machuca A., Garcia-Calvo E., Anunciação D. S., Luque-Garcia J. L. Integration of transcriptomics and metabolomics to reveal the molecular mechanisms underlying rhodium nanoparticles-based photodynamic cancer therapy. Pharmaceutics. 2021;13(10):1629.

    Not in our library10.3390/pharmaceutics13101629

  160. 160.

    Shin T. H., Nithiyanandam S., Lee D. Y., Kwon D. H., Hwang J. S., Kim S. G., Jang Y. E., Basith S., Park S., Mo J. S., Lee G. Analysis of nanotoxicity with integrated omics and mechanobiology. Nanomaterials. 2021;11(9):2385.

    Not in our library10.3390/nano11092385

  161. 161.

    Lv M., Huang W., Chen Z., Jiang H., Chen J., Tian Y., Zhang Z., Xu F. Metabolomics techniques for nanotoxicity investigations. Bioanalysis. 2015;7:1527-1544.

    Not in our library10.4155/bio.15.83

  162. 162.

    Ahmad F., Wang X., Li W. Toxico-metabolomics of engineered nanomaterials: progress and challenges. Adv. Funct. Mater. 2019;29:1904268.

    Not in our library10.1002/adfm.201904268

  163. 163.

    Feng W. Omic” Techniques for Nanosafety. Toxicol. Nanomater. 2016.

    Not in our library10.1002/9783527689125.ch12

  164. 164.

    Schnackenberg L. K., Sun J., Beger R. D. Metabolomics techniques in nanotoxicology studies. Methods Mol. Biol. 2012;926:141-156.

    Not in our library10.1007/978-1-62703-2-1_10

  165. 165.

    Farré M. and Jha A. N., in Environmental Metabolomics, Elsevier, 2020, pp. 259–281

    Not in our library

  166. 166.
  167. 167.

    Boisselier E., Astruc D. Gold nanoparticles in nanomedicine: preparations, imaging, diagnostics, therapies and toxicity. Chem. Soc. Rev. 2009;38(6):1759-1782.

    Not in our library10.1039/b806051g

  168. 168.

    Wang L., Liu Y., Li W., Jiang X., Ji Y., Wu X., Xu L., Qiu Y., Zhao K., Wei T., Li Y., Zhao Y., Chen C. Selective targeting of gold nanorods at the mitochondria of cancer cells: Implications for cancer therapy. Nano Lett. 2011;11(2):772-780.

    Not in our library10.1021/nl103992v

  169. 169.

    Zhang L., Wang L., Hu Y., Liu Z., Tian Y., Wu X., Zhao Y., Tang H., Chen C., Wang Y. Selective metabolic effects of gold nanorods on normal and cancer cells and their application in anticancer drug screening. Biomaterials. 2013;34(29):7117-7126.

    Not in our library10.1016/j.biomaterials.2013.05.043

  170. 170.

    Ramsden J. J., Metabolomics and Metabonomics, in Computational Biology, 2009, pp. 1–6

    Not in our library

  171. 171.

    Xu B., Chen M., Ji X., Mao Z., Zhang X., Wang X., Xia Y. Metabolomic profiles delineate the potential role of glycine in gold nanorod-induced disruption of mitochondria and blood-testis barrier factors in TM-4 cells. Nanoscale. 2014;6(14):8265-8273.

    Not in our library10.1039/c4nr01035c

  172. 172.

    Huang Y., Lü X., Chen R., Chen Y. Comparative study of the effects of gold and silver nanoparticles on the metabolism of human dermal fibroblasts. Regener. Biomater. 2020;7(2):221-232.

    Not in our library10.1093/rb/rbz051

  173. 173.

    Lindeque J. Z., Matthyser A., Mason S., Louw R., Taute C. J. F. Metabolomics reveals the depletion of intracellular metabolites in HepG2 cells after treatment with gold nanoparticles. Nanotoxicology. 2018;12(3):251-262.

    Not in our library10.1080/17435390.2018.1432779

  174. 174.

    Reinecke C. J., Koekemoer G., van der Westhuizen F. H., Louw R., Lindeque J. Z., Mienie L. J., Smuts I. Metabolomics of urinary organic acids in respiratory chain deficiencies in children. Metabolomics. 2012;8(2):264-283.

    Not in our library10.1007/s11306-011-0309-0

  175. 175.

    Gioria S., Vicente J. L., Barboro P., la Spina R., Tomasi G., Urbán P., Kinsner-Ovaskainen A., François R., Chassaigne H. A combined proteomics and metabolomics approach to assess the effects of gold nanoparticles in vitro. Nanotoxicology. 2016;10(6):736-748.

    Not in our library10.3109/17435390.2015.1121412

  176. 176.

    Ali M. R. K., Wu Y., Han T., Zang X., Xiao H., Tang Y., Wu R., Fernández F. M., El-Sayed M. A. Simultaneous Time-Dependent Surface-Enhanced Raman Spectroscopy, Metabolomics, and Proteomics Reveal Cancer Cell Death Mechanisms Associated with Gold Nanorod Photothermal Therapy. J. Am. Chem. Soc. 2016;138(47):15434-15442.

    Not in our library10.1021/jacs.6b08787

  177. 177.

    Liu Z., Wang L., Zhang L., Wu X., Nie G., Chen C., Tang H., Wang Y. Metabolic characteristics of 16HBE and A549 cells exposed to different surface modified gold nanorods. Adv. Healthcare Mater. 2016;5(18):2363-2375.

    Not in our library10.1002/adhm.201600164

  178. 178.

    Dahabiyeh L. A., Mahmoud N. N., Al-Natour M. A., Safo L., Kim D. H., Khalil E. A., Abu-Dahab R. Phospholipid-gold nanorods induce energy crisis in mcf-7 cells: Cytotoxicity evaluation using lc-ms-based metabolomics approach. Biomolecules. 2021;11(3):364.

    Not in our library10.3390/biom11030364

  179. 179.

    Herance J. R., García H., Gutiérrez-Carcedo P., Navalón S., Pineda-Lucena A., Palomino-Schätzlein M. A translational approach to assess the metabolomic impact of stabilized gold nanoparticles by NMR spectroscopy. Analyst. 2019;144(4):1265-1274.

    Not in our library10.1039/c8an01827h

  180. 180.

    Schätzlein M. P., Becker J., Schulze-Sünninghausen D., Pineda-Lucena A., Herance J. R., Luy B. Rapid two-dimensional ALSOFAST-HSQC experiment for metabolomics and fluxomics studies: application to a 13C-enriched cancer cell model treated with gold nanoparticles. Anal. Bioanal. Chem. 2018;410(11):2793-2804.

    Not in our library10.1007/s00216-018-0961-6

  181. 181.
  182. 182.

    Paris D., Melck D., Longo A., Napoletano S., Carotenuto G., Nicolais L., Motta A., Vitale E. Metabolic response of SH-SY5Y cells to gold nanoparticles by NMR-based metabolomics analyses. Biomed. Phys. Eng. Express. 2016;2(4):045003.

    Not in our library10.1088/2057-1976/2/4/045003

  183. 183.

    Rai M. K., Deshmukh S. D., Ingle A. P., Gade A. K. Silver nanoparticles: The powerful nanoweapon against multidrug-resistant bacteria. J. Appl. Microbiol. 2012;112:841-852.

    Not in our library10.1111/j.1365-2672.2012.05253.x

  184. 184.
  185. 185.

    Wei L., Lu J., Xu H., Patel A., Chen Z. S., Chen G. Silver nanoparticles: Synthesis, properties, and therapeutic applications. Drug Discovery Today. 2015;20(5):595-601.

    Not in our library10.1016/j.drudis.2014.11.014

  186. 186.

    Raja G., Jang Y. K., Suh J. S., Kim H. S., Ahn S. H., Kim T. J. Microcellular environmental regulation of silver nanoparticles in cancer therapy: a critical review. Cancers. 2020;12:664.

    Not in our library10.3390/cancers12030664

  187. 187.

    Krzyzanowski D., Kruszewski M., Grzelak A. Differential action of silver nanoparticles on abcb1 (Mdr1) and abcc1 (mrp1) activity in mammalian cell lines. Materials. 2021;14(12):3383.

    Not in our library10.3390/ma14123383

  188. 188.

    Paciorek P., Zuberek M., Grzelak A. Products of lipid peroxidation as a factor in the toxic effect of silver nanoparticles. Materials. 2020;13(11):2460.

    Not in our library10.3390/ma13112460

  189. 189.

    Sikorska K., Gradzka I., Wasyk I., Brzóska K., Stepkowski T. M., Czerwińska M., Kruszewski M. K. The impact of Ag nanoparticles and CdTe quantum dots on expression and function of receptors involved in amyloid-β uptake by BV-2 microglial cells. Materials. 2020;13(14):3227.

    Not in our library10.3390/ma13143227

  190. 190.

    Rajan R., Huo P. P., Chandran K., Manickam Dakshinamoorthi B., il Yun S., Liu B. A review on the toxicity of silver nanoparticles against different biosystems. Chemosphere. 2022;292:133397.

    Not in our library10.1016/j.chemosphere.2021.133397

  191. 191.

    Carrola J., Bastos V., Ferreira De Oliveira J. M. P., Oliveira H., Santos C., Gil A. M., Duarte I. F. Insights into the impact of silver nanoparticles on human keratinocytes metabolism through NMR metabolomics. Arch. Biochem. Biophys. 2016;589:53-61.

    Not in our library10.1016/j.abb.2015.08.022

  192. 192.

    Carrola J., Pinto R. J. B., Nasirpour M., Freire C. S. R., Gil A. M., Santos C., Oliveira H., Duarte I. F. NMR metabolomics reveals metabolism-mediated protective effects in liver (HepG2) cells exposed to subtoxic levels of silver nanoparticles. J. Proteome Res. 2018;17(4):1636-1646.

    Not in our library10.1021/acs.jproteome.7b00905

  193. 193.

    Carrola J., Bastos V., Jarak I., Oliveira-Silva R., Malheiro E., Daniel-da-Silva A. L., Oliveira H., Santos C., Gil A. M., Duarte I. F. Metabolomics of silver nanoparticles toxicity in HaCaT cells: structure–activity relationships and role of ionic silver and oxidative stress. Nanotoxicology. 2016;10(8):1105-1117.

    Not in our library10.1080/17435390.2016.1177744

  194. 194.

    Carrola J., Bastos V., Daniel-da-Silva A. L., Gil A. M., Santos C., Oliveira H., Duarte I. F. Macrophage Metabolomics Reveals Differential Metabolic Responses to Subtoxic Levels of Silver Nanoparticles and Ionic Silver. Eur. J. Inorg. Chem. 2020;2020(19):1867-1876.

    Not in our library10.1002/ejic.202000095

  195. 195.

    Kim S., Kim S., Lee S., Kwon B., Choi J., Hyun J. W., Kim S. Characterization of the effects of silver nanoparticles on liver cell using HR-MAS NMR spectroscopy. Bull. Korean Chem. Soc. 2011;32(6):2021-2026.

    Not in our library10.5012/bkcs.2011.32.6.2021

  196. 196.

    Babele P. K., Singh A. K., Srivastava A. Bio-inspired silver nanoparticles impose metabolic and epigenetic toxicity to saccharomyces cerevisiae. Front. Pharmacol. 2019;10.

    Not in our library10.3389/fphar.2019.01016

  197. 197.

    Qu R., Xie Q., Tian J., Zhou M., Ge F. Metabolomics reveals the inhibition on phosphorus assimilation in Chlorella vulgaris F1068 exposed to AgNPs. Sci. Total Environ. 2021;770(20):145362.

    Not in our library10.1016/j.scitotenv.2021.145362

  198. 198.

    Cao M., Huang X., Wang F., Zhang Y., Zhou B., Chen H., Yuan R., Ma S., Geng H., Xu D., Yan C., Xing B. Transcriptomics and metabolomics revealed the biological response of Chlorella pyrenoidesato single and repeated exposures of AgNPs at different concentrations. Environ. Sci. Technol. 2021;55(23):15776-15787.

    Not in our library10.1021/acs.est.1c04059

  199. 199.

    Dekkers S., Williams T. D., Zhang J., Zhou J., Vandebriel R. J., de La Fonteyne L. J. J., Gremmer E. R., He S., Guggenheim E. J., Lynch I., Cassee F. R., de Jong W. H., Viant M. R. Multi-omics approaches confirm metal ions mediate the main toxicological pathways of metal-bearing nanoparticles in lung epithelial A549 cells. Environ. Sci.: Nano. 2018;5(6):1506-1517.

    Not in our library10.1039/c8en00071a

  200. 200.

    Cao J., Qin X., Li Z. Synthesis of Silver Nanoparticles from the Polysaccharide of Farfarae Flos and Uncovering Its Anticancer Mechanism Based on the Cell Metabolomic Approach. J. Proteome Res. 2022;21:172-181.

    Not in our library10.1021/acs.jproteome.1c00668

  201. 201.

    Wang P., Zhang B., Zhang H., He Y., Ong C. N., Yang J. Metabolites change of Scenedesmus obliquus exerted by AgNPs. J. Environ. Sci. 2019;76:310-318.

    Not in our library10.1016/j.jes.2018.05.017

  202. 202.
  203. 203.

    Zhang J. L., Zhou Z. P., Pei Y., Xiang Q. Q., Chang X. X., Ling J., Shea D., Chen L. Q. Metabolic profiling of silver nanoparticle toxicity in Microcystis aeruginosa. Environ. Sci.: Nano. 2018;5(11):2519-2530.

    Not in our library10.1039/c8en00738a

  204. 204.

    Hart G. A., Hesterberg T. W. In vitro toxicity of respirable-size particles of diatomaceous earth and crystalline silica compared with asbestos and titanium dioxide. J. Occup. Environ. Med. 1998;40(1):29-42.

    Not in our library10.1097/00043764-199801000-00008

  205. 205.

    Bernard B. K., Osheroff M. R., Hofmann A., Mennear J. H. Toxicology and carcinogenesis studies of dietary titanium dioxide-coated mica in male and female fischer 344 rats. J. Toxicol. Environ. Health. 1990;29(4):417-429.

    Not in our library10.1080/15287399009531402

  206. 206.

    Tucci P., Porta G., Agostini M., Dinsdale D., Iavicoli I., Cain K., Finazzi-Agró A., Melino G., Willis A. Metabolic effects of TIO2 nanoparticles, a common component of sunscreens and cosmetics, on human keratinocytes. Cell Death Dis. 2013;4(3):e549.

    Not in our library10.1038/cddis.2013.76

  207. 207.

    Oberdörster G., Ferin J., Gelein R., Soderholm S. C., Finkelstein J. Role of the alveolar macrophage in lung injury: studies with ultrafine particles. Environ. Health Perspect. 1992;97:193-199.

    Not in our library10.1289/ehp.9297193

  208. 208.

    Peters K., Unger R. E., Kirkpatrick C. J., Gatti A. M., Monari E. Effects of nano-scaled particles on endothelial cell function in vitro: studies on viability, proliferation and inflammation. J. Mater. Sci.: Mater. Med. 2004;15:321-325.

    Not in our library10.1023/b:jmsm.0000021095.36878.1b

  209. 209.

    Raja G., Cao S., Kim D. H., Kim T. J. Mechanoregulation of titanium dioxide nanoparticles in cancer therapy. Mater. Sci. Eng., C. 2020;107:110303.

    Not in our library10.1016/j.msec.2019.110303

  210. 210.

    Chen Q., Wang N., Zhu M., Lu J., Zhong H., Xue X., Guo S., Li M., Wei X., Tao Y., Yin H. TiO2 nanoparticles cause mitochondrial dysfunction, activate inflammatory responses, and attenuate phagocytosis in macrophages: a proteomic and metabolomic insight. Redox Biol. 2018;15:266-276.

    Not in our library10.1016/j.redox.2017.12.011

  211. 211.

    Radenkovic S., Vuckovic I., Lanza I. R. Metabolic flux analysis: moving beyond static metabolomics. Trends Biochem. Sci. 2020;45:545-546.

    Not in our library10.1016/j.tibs.2020.02.011

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    Jin C., Liu Y., Sun L., Chen T., Zhang Y., Zhao A., Wang X., Cristau M., Wang K., Jia W. Metabolic profiling reveals disorder of carbohydrate metabolism in mouse fibroblast cells induced by titanium dioxide nanoparticles. J. Appl. Toxicol. 2013;33(12):1442-1450.

    Not in our library10.1002/jat.2808

  213. 213.

    Bo Y., Jin C., Liu Y., Yu W., Kang H. Metabolomic analysis on the toxicological effects of TiO2 nanoparticles in mouse fibroblast cells: from the perspective of perturbations in amino acid metabolism. Toxicol. Mech. Methods. 2014;24(7):461-469.

    Not in our library10.3109/15376516.2014.939321

  214. 214.

    Garcia-Contreras R., Scougall-Vilchis R. J., Contreras-Bulnes R., Kanda Y., Nakajima H., Sakagami H. Induction of prostaglandin E2 production by TiO2 nanoparticles in human gingival fibroblast. In Vivo. 2014;28(2):217-222.

    Not in our library

  215. 215.

    Garcia-Contreras R., Sugimoto M., Umemura N., Kaneko M., Hatakeyama Y., Soga T., Tomita M., Scougall-Vilchis R. J., Contreras-Bulnes R., Nakajima H., Sakagami H. Alteration of metabolomic profiles by titanium dioxide nanoparticles in human gingivitis model. Biomaterials. 2015;57:33-40.

    Not in our library10.1016/j.biomaterials.2015.03.059

  216. 216.

    Kitchin K. T., Grulke E., Robinette B. L., Castellon B. T. Metabolomic effects in HepG2 cells exposed to four TiO2 and two CeO2 nanomaterials. Environ. Sci.: Nano. 2014;1(5):466-477.

    Not in our library10.1039/c4en00096j

  217. 217.

    Cui L., Wang X., Sun B., Xia T., Hu S. Predictive metabolomic signatures for safety assessment of metal oxide nanoparticles. ACS Nano. 2019;13(11):13065-13082.

    Not in our library10.1021/acsnano.9b05793

  218. 218.

    Jia S., Setyawati M. I., Liu M., Xu T., Loo J., Yan M., Gong J., Chotirmall S. H., Demokritou P., Ng K. W., Fang M. Association of nanoparticle exposure with serum metabolic disorders of healthy adults in printing centers. J. Hazard. Mater. 2022;432:128710.

    Not in our library10.1016/j.jhazmat.2022.128710

  219. 219.

    Zhang Q., Chang X., Wang X., Zhan H., Gao Q., Yang M., Liu H., Li S., Sun Y. A metabolomic-based study on disturbance of bile acids metabolism induced by intratracheal instillation of nickel oxide nanoparticles in rats. Toxicol. Res. (Camb). 2021;10(3):579-591.

    Not in our library10.1093/toxres/tfab039

  220. 220.

    Chen Z., Han S., Zhou D., Zhou S., Jia G. Effects of oral exposure to titanium dioxide nanoparticles on gut microbiota and gut-associated metabolism: in vivo. Nanoscale. 2019;11(46):22398-22412.

    Not in our library10.1039/c9nr07580a

  221. 221.

    Chen Z., Han S., Zhang J., Zheng P., Liu X., Zhang Y., Jia G. Exploring urine biomarkers of early health effects for occupational exposure to titanium dioxide nanoparticles using metabolomics. Nanoscale. 2021;13(7):4122-4132.

    Not in our library10.1039/d0nr08792k

  222. 222.

    Chen Z., Han S., Zhang J., Zheng P., Liu X., Zhang Y., Jia G. Metabolomics screening of serum biomarkers for occupational exposure of titanium dioxide nanoparticles. Nanotoxicology. 2021;15(6):832-849.

    Not in our library10.1080/17435390.2021.1921872

  223. 223.

    Chen Z., Han S., Zhou D., Zheng P., Zhou S., Jia G. Serum metabolomic signatures of Sprague-Dawley rats after oral administration of titanium dioxide nanoparticles. NanoImpact. 2020;19:100236.

    Not in our library10.1016/j.impact.2020.100236

  224. 224.

    Han S., Chen Z. J., Zhou D., Zheng P., Zhang J. H., Jia G. Effects of titanium dioxide nanoparticles on fecal metabolome in rats after oral administration for 90 days. Beijing Daxue Xuebao, Yixueban. 2020;52(3):457-463.

    Not in our library10.19723/j.issn.1671-167x.2020.03.010

  225. 225.

    Whitfield Åslund M. L., McShane H., Simpson M. J., Simpson A. J., Whalen J. K., Hendershot W. H., Sunahara G. I. Earthworm sublethal responses to titanium dioxide nanomaterial in soil detected by 1H NMR metabolomics. Environ. Sci. Technol. 2012;46(2):1111-1118.

    Not in our library10.1021/es202327k

  226. 226.

    Zhu Y., Wu X., Liu Y., Zhang J., Lin D. Integration of transcriptomics and metabolomics reveals the responses of earthworms to the long-term exposure of TiO2 nanoparticles in soil. Sci. Total Environ. 2020;719:137492.

    Not in our library10.1016/j.scitotenv.2020.137492

  227. 227.

    Ratnasekhar C., Sonane M., Satish A., Mudiam M. K. R. Metabolomics reveals the perturbations in the metabolome of Caenorhabditis elegans exposed to titanium dioxide nanoparticles. Nanotoxicology. 2015;9(8):994-1004.

    Not in our library10.3109/17435390.2014.993345

  228. 228.

    Kurepa J., Shull T. E., Smalle J. A. Metabolomic analyses of the bio-corona formed on TiO2 nanoparticles incubated with plant leaf tissues. J. Nanobiotechnol. 2020;18(1):28.

    Not in our library10.1186/s12951-020-00592-8

  229. 229.

    Chetwynd A. J., Zhang W., Thorn J. A., Lynch I., Ramautar R. The nanomaterial metabolite corona determined using a quantitative metabolomics approach: a pilot study. Small. 2020;16(21):2000295.

    Not in our library10.1002/smll.202000295

  230. 230.

    Bannuscher A., Hellack B., Bahl A., Laloy J., Herman H., Stan M. S., Dinischiotu A., Giusti A., Krause B.-C., Tentschert J., Roşu M., Balta C., Hermenean A., Wiemann M., Luch A., Haase A. Metabolomics profiling to investigate nanomaterial toxicity in vitro and in vivo. Nanotoxicology. 2020;14:807-826.

    Not in our library10.1080/17435390.2020.1764123

  231. 231.

    Karkossa I., Bannuscher A., Hellack B., Bahl A., Buhs S., Nollau P., Luch A., Schubert K., von Bergen M., Haase A. An in-depth multi-omics analysis in RLE-6TN rat alveolar epithelial cells allows for nanomaterial categorization. Part. Fibre Toxicol. 2019;16(1):38.

    Not in our library10.1186/s12989-019-0321-5

  232. 232.

    Karkossa I., Bannuscher A., Hellack B., Wohlleben W., Laloy J., Stan M. S., Dinischiotu A., Wiemann M., Luch A., Haase A., von Bergen M., Schubert K. Nanomaterials induce different levels of oxidative stress, depending on the used model system: comparison of in vitro and in vivo effects. Sci. Total Environ. 2021;801:149538.

    Not in our library10.1016/j.scitotenv.2021.149538

  233. 233.

    Pathakoti K., Manubolu M., min Hwang H. Mechanistic insights into TiO2 and ZnO nanoparticle-induced metabolic changes in Escherichia coli under solar simulated light irradiation. Water, Air, Soil Pollut. 2020;231(1):16.

    Not in our library10.1007/s11270-019-4388-2

  234. 234.

    Planchon M., Leger T., Spalla O., Huber G., Ferrari R. Metabolomic and proteomic investigations of impacts of titanium dioxide nanoparticles on Escherichia coli. PLoS One. 2017;12(6):e0178437.

    Not in our library10.1371/journal.pone.0178437

  235. 235.

    Zhang Z., Liang Z. C., Zhang J. H., Tian S. L., le Qu J., Tang J. N., de Liu S. Nano-sized TiO2 (nTiO2) induces metabolic perturbations in Physarum polycephalum macroplasmodium to counter oxidative stress under dark conditions. Ecotoxicol. Environ. Saf. 2018;154:108-117.

    Not in our library10.1016/j.ecoenv.2018.02.012

  236. 236.

    Kumar N., Mittal A., Yadav M., Sharma S., Kumar T., Chakraborty R., Sengupta S., Chauhan N. S. Photocatalytic TiO2/CdS/ZnS nanocomposite induces Bacillus subtilis cell death by disrupting its metabolism and membrane integrity. Indian J. Microbiol. 2021;61(4):487-496.

    Not in our library10.1007/s12088-021-00973-z

  237. 237.

    Feng J., Li J., Wu H., Chen Z. Metabolic responses of HeLa cells to silica nanoparticles by NMR-based metabolomic analyses. Metabolomics. 2013;9(4):874-886.

    Not in our library10.1007/s11306-013-0499-8

  238. 238.

    Huang S. M., Zuo X., Li J. J. E., Li S. F. Y., Bay B. H., Ong C. N. Metabolomics studies show dose-dependent toxicity induced by SiO2 nanoparticles in MRC-5 human fetal lung fibroblasts. Adv. Healthcare Mater. 2012;1(6):779-784.

    Not in our library10.1002/adhm.201200114

  239. 239.

    Irfan A., Cauchi M., Edmands W., Gooderham N. J., Njuguna J., Zhu H. Assessment of Temporal Dose-Toxicity Relationship of Fumed Silica Nanoparticle in Human Lung A549 Cells by Conventional Cytotoxicity and 1H-NMR-Based Extracellular Metabonomic Assays. Toxicol. Sci. 2014;138:354-364.

    Not in our library10.1093/toxsci/kfu009

  240. 240.

    Chatterjee N., Jeong J., Yoon D., Kim S., Choi J. Global metabolomics approach in in vitro and in vivo models reveals hepatic glutathione depletion induced by amorphous silica nanoparticles. Chem.-Biol. Interact. 2018;293:100-106.

    Not in our library10.1016/j.cbi.2018.07.013

  241. 241.

    Zhao X., Abulikemu A., Lv S., Qi Y., Duan J., Zhang J., Chen R., Guo C., Li Y., Sun Z. Oxidative stress- and mitochondrial dysfunction-mediated cytotoxicity by silica nanoparticle in lung epithelial cells from metabolomic perspective. Chemosphere. 2021;275:129969.

    Not in our library10.1016/j.chemosphere.2021.129969

  242. 242.

    Kitchin K. T., Stirdivant S., Robinette B. L., Castellon B. T., Liang X. Metabolomic effects of CeO2, SiO2 and CuO metal oxide nanomaterials on HepG2 cells. Part. Fibre Toxicol. 2017;14(1):50.

    Not in our library10.1186/s12989-017-0230-4

  243. 243.

    Bannuscher A., Karkossa I., Buhs S., Nollau P., Kettler K., Balas M., Dinischiotu A., Hellack B., Wiemann M., Luch A., von Bergen M., Haase A., Schubert K. A multi-omics approach reveals mechanisms of nanomaterial toxicity and structure–activity relationships in alveolar macrophages. Nanotoxicology. 2020;14(2):181-195.

    Not in our library10.1080/17435390.2019.1684592

  244. 244.

    Saborano R., Wongpinyochit T., Totten J. D., Johnston B. F., Seib F. P., Duarte I. F. Metabolic reprogramming of macrophages exposed to silk, poly(lactic-co-glycolic acid), and silica nanoparticles. Adv. Healthcare Mater. 2017;6(14):1601240.

    Not in our library10.1002/adhm.201601240

  245. 245.

    Zhu Y., Zhang Y., Li Y., Guo C., Fan Z., Li Y., Yang M., Zhou X., Sun Z., Wang J. Integrative proteomics and metabolomics approach to elucidate metabolic dysfunction induced by silica nanoparticles in hepatocytes. J. Hazard. Mater. 2022;434:128820.

    Not in our library10.1016/j.jhazmat.2022.128820

  246. 246.

    Djurišić A. B., Chen X., Leung Y. H., Man Ching Ng A. ZnO nanostructures: Growth, properties and applications. J. Mater. Chem. 2012;22(14):6526-6535.

    Not in our library10.1039/c2jm15548f

  247. 247.

    Lim S. L., Ng C. T., Zou L., Lu Y., Chen J., Bay B. H., Shen H. M., Ong C. N. Targeted metabolomics reveals differential biological effects of nanoplastics and nanoZnO in human lung cells. Nanotoxicology. 2019;13(8):1117-1132.

    Not in our library10.1080/17435390.2019.1640913

  248. 248.

    Keller A. A., Vosti W., Wang H., Lazareva A. Release of engineered nanomaterials from personal care products throughout their life cycle. J. Nanopart. Res. 2014;16(7):2489.

    Not in our library10.1007/s11051-014-2489-9

  249. 249.

    Heim J., Felder E., Tahir M. N., Kaltbeitzel A., Heinrich U. R., Brochhausen C., Mailänder V., Tremel W., Brieger J. Genotoxic effects of zinc oxide nanoparticles. Nanoscale. 2015;7(19):8931-8938.

    Not in our library10.1039/c5nr01167a

  250. 250.

    Salehi H., de Diego N., Chehregani Rad A., Benjamin J. J., Trevisan M., Lucini L. Exogenous application of ZnO nanoparticles and ZnSO4 distinctly influence the metabolic response in Phaseolus vulgaris L. Sci. Total Environ. 2021;778:146331.

    Not in our library10.1016/j.scitotenv.2021.146331

  251. 251.

    Li S., Liu J., Wang Y., Gao Y., Zhang Z., Xu J., Xing G. Comparative physiological and metabolomic analyses revealed that foliar spraying with zinc oxide and silica nanoparticles modulates metabolite profiles in cucumber (Cucumis sativus L.). Food Energy Secur. 2021;10(1):e269.

    Not in our library10.1002/fes3.269

  252. 252.

    Sun L., Wang Y., Wang R., Wang R., Zhang P., Ju Q., Xu J. Physiological, transcriptomic, and metabolomic analyses reveal zinc oxide nanoparticles modulate plant growth in tomato. Environ. Sci.: Nano. 2020;7(11):3587-3604.

    Not in our library10.1039/d0en00723d

  253. 253.

    Wan J., Wang R., Bai H., Wang Y., Xu J. Comparative physiological and metabolomics analysis reveals that single-walled carbon nanohorns and ZnO nanoparticles affect salt tolerance in: Sophora alopecuroides. Environ. Sci.: Nano. 2020;7(10):2968-2981.

    Not in our library10.1039/d0en00582g

  254. 254.

    He E., Qiu R., Cao X., Song L., Peijnenburg W. J. G. M., Qiu H. Elucidating toxicodynamic differences at the molecular scale between ZnO nanoparticles and ZnCl2 in Enchytraeus crypticus via nontargeted metabolomics. Environ. Sci. Technol. 2020;45(6):3487-3498.

    Not in our library10.1021/acs.est.0c00663

  255. 255.

    Paramo L. A., Feregrino-Pérez A. A., Guevara R., Mendoza S., Esquivel K. Nanoparticles in agroindustry: applications, toxicity, challenges, and trends. Nanomaterials. 2020;10:1654.

    Not in our library10.3390/nano10091654

  256. 256.

    Majumdar S., Keller A. A. Omics to address the opportunities and challenges of nanotechnology in agriculture. Crit. Rev. Environ. Sci. Technol. 2021;51(22):2595-2636.

    Not in our library10.1080/10643389.2020.1785264

  257. 257.

    Wu B., Wu J., Liu S., Shen Z., Chen L., Zhang X. X., Ren H. q. Combined effects of graphene oxide and zinc oxide nanoparticle on human A549 cells: bioavailability, toxicity and mechanisms. Environ. Sci.: Nano. 2019;6(2):635-645.

    Not in our library10.1039/c8en00965a

  258. 258.

    Kumar Babele P. Zinc oxide nanoparticles impose metabolic toxicity by de-regulating proteome and metabolome in Saccharomyces cerevisiae. Toxicol. Rep. 2019;6:64-73.

    Not in our library10.1016/j.toxrep.2018.12.001

  259. 259.

    Amiri S., Shokrollahi H. The role of cobalt ferrite magnetic nanoparticles in medical science. Mater. Sci. Eng., C. 2013;33:1-8.

    Not in our library10.1016/j.msec.2012.09.003

  260. 260.

    Oliveira A. B. B., de Moraes F. R., Candido N. M., Sampaio I., Paula A. S., de Vasconcellos A., Silva T. C., Miller A. H., Rahal P., Nery J. G., Calmon M. F. Metabolic effects of cobalt ferrite nanoparticles on cervical carcinoma cells and nontumorigenic keratinocytes. J. Proteome Res. 2016;15(12):4337-4348.

    Not in our library10.1021/acs.jproteome.6b00411

  261. 261.

    Aruoja V., Dubourguier H. C., Kasemets K., Kahru A. Toxicity of nanoparticles of CuO, ZnO and TiO2 to microalgae Pseudokirchneriella subcapitata. Sci. Total Environ. 2009;407(4):1461-1468.

    Not in our library10.1016/j.scitotenv.2008.10.053

  262. 262.

    Murgia A., Mancuso L., Manis C., Caboni P., Cao G. GC-MS metabolomics analysis of mesenchymal stem cells treated with copper oxide nanoparticles. Toxicol. Mech. Methods. 2016;26(8):611-619.

    Not in our library10.1080/15376516.2016.1220654

  263. 263.

    Wang L., Huang X., Sun W., Too H. Z., Laserna A. K. C., Li S. F. Y. A global metabolomic insight into the oxidative stress and membrane damage of copper oxide nanoparticles and microparticles on microalga Chlorella vulgaris. Environ. Pollut. 2020;258:113647.

    Not in our library10.1016/j.envpol.2019.113647

  264. 264.

    Kruszka D., Selvakesavan R. K., Kachlicki P., Franklin G. Untargeted metabolomics analysis reveals the elicitation of important secondary metabolites upon treatment with various metal and metal oxide nanoparticles in Hypericum perforatum L. cell suspension cultures. Ind. Crops Prod. 2022;178:114561.

    Not in our library10.1016/j.indcrop.2022.114561

  265. 265.

    Boyles M. S. P., Ranninger C., Reischl R., Rurik M., Tessadri R., Kohlbacher O., Duschl A., Huber C. G. Copper oxide nanoparticle toxicity profiling using untargeted metabolomics. Part. Fibre Toxicol. 2016;13(1):49.

    Not in our library10.1186/s12989-016-0160-6

  266. 266.

    Chavez Soria N. G., Aga D. S., Atilla-Gokcumen G. E. Lipidomics reveals insights on the biological effects of copper oxide nanoparticles in a human colon carcinoma cell line. Mol. Omics. 2019;15(1):30-38.

    Not in our library10.1039/c8mo00162f

  267. 267.

    Krause B. C., Kriegel F. L., Tartz V., Jungnickel H., Reichardt P., Singh A. V., Laux P., Shemis M., Luch A. Combinatory effects of cerium dioxide nanoparticles and acetaminophen on the liver—a case study of low-dose interactions in human HuH-7 cells. Int. J. Mol. Sci. 2021;22(13):6866.

    Not in our library10.3390/ijms22136866

  268. 268.

    Taylor N. S., Merrifield R., Williams T. D., Chipman J. K., Lead J. R., Viant M. R. Molecular toxicity of cerium oxide nanoparticles to the freshwater alga Chlamydomonas reinhardtii is associated with supra-environmental exposure concentrations. Nanotoxicology. 2016;10(1):32-41.

    Not in our library10.3109/17435390.2014.1002868

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    Shin T. H., Ketebo A. A., Lee D. Y., Lee S., Kang S. H., Basith S., Manavalan B., Kwon D. H., Park S., Lee G. Decrease in membrane fluidity and traction force induced by silica-coated magnetic nanoparticles. J. Nanobiotechnol. 2021;19(1):21.

    Not in our library10.1186/s12951-020-00765-5

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    Li X., Zhang C., Zhang X., Wang S., Meng Q., Wu S., Yang H., Xia Y., Chen R. An acetyl-L-carnitine switch on mitochondrial dysfunction and rescue in the metabolomics study on aluminum oxide nanoparticles. Part. Fibre Toxicol. 2016;13(1):4.

    Not in our library10.1186/s12989-016-0115-y

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  273. 273.

    Guo C., Weber R. J. M., Buckley A., Mazzolini J., Robertson S., Delgado-Saborit J. M., Rappoport J. Z., Warren J., Hodgson A., Sanderson P., Chipman J. K., Viant M. R., Smith R. Environmentally relevant iron oxide nanoparticles produce limited acute pulmonary effects in rats at realistic exposure levels. Int. J. Mol. Sci. 2021;22(2):556.

    Not in our library10.3390/ijms22020556

  274. 274.

    Ye M., Wang L., Wu Z., Liu W. Metabolomic profiling of ZrO 2 nanoparticles in MC3T3-E1 cells. IET Nanobiotechnol. 2021;15:687-697.

    Not in our library10.1049/nbt2.12067

  275. 275.

    Yang Q., Zhang L., Ben A., Wu N., Yi Y., Jiang L., Huang H., Yu Y. Effects of dispersible MoS2 nanosheets and Nano-silver coexistence on the metabolome of yeast. Chemosphere. 2018;198:216-225.

    Not in our library10.1016/j.chemosphere.2018.01.140

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    Wang A., Jin Q., Xu X., Miao A., White J. C., Gardea-Torresdey J. L., Ji R., Zhao L. High-throughput screening for engineered nanoparticles that enhance photosynthesis using mesophyll protoplasts. J. Agric. Food Chem. 2020;68(11):3382-3389.

    Not in our library10.1021/acs.jafc.9b06429

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  278. 278.

    Yu Y., Wu N., Yi Y., Li Y., Zhang L., Yang Q., Miao W., Ding X., Jiang L., Huang H. Dispersible MoS2 nanosheets activated TGF-β/Smad pathway and perturbed the metabolome of human dermal fibroblasts. ACS Biomater. Sci. Eng. 2017;3(12):3261-3272.

    Not in our library10.1021/acsbiomaterials.7b00575

  279. 279.

    Yuan P., Hu X., Zhou Q. The nanomaterial-induced bystander effects reprogrammed macrophage immune function and metabolic profile. Nanotoxicology. 2020;14(8):1137-1155.

    Not in our library10.1080/17435390.2020.1817598

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    Chatzimitakos T. G., Stalikas C. D. Qualitative Alterations of Bacterial Metabolome after Exposure to Metal Nanoparticles with Bactericidal Properties: A Comprehensive Workflow Based on 1H NMR, UHPLC-HRMS, and Metabolic Databases. J. Proteome Res. 2016;15(9):3322-3330.

    Not in our library10.1021/acs.jproteome.6b00489

  281. 281.

    Nazari P., Dowlatabadi-Bazaz R., Mofid M. R., Pourmand M. R., Daryani N. E., Faramarzi M. A., Sepehrizadeh Z., Shahverdi A. R. The antimicrobial effects and metabolomic footprinting of carboxyl-capped bismuth nanoparticles against helicobacter pylori. Appl. Biochem. Biotechnol. 2014;172(2):570-579.

    Not in our library10.1007/s12010-013-0571-x

  282. 282.

    Li X., Qin Y., Kong L., Yan X., Zhang W., Martyniuk C. J., Wang X., Yan B. Metabolomic and bioenergetic responses of human hepatocellular carcinoma cells following exposure to commercial copper hydroxide nanopesticide. Environ. Sci.: Nano. 2022;9:589-605.

    Not in our library10.1039/d1en00790d

  283. 283.

    Choudhury S. R., Mandal A., Ghosh M., Basu S., Chakravorty D., Goswami A. Investigation of antimicrobial physiology of orthorhombic and monoclinic nanoallotropes of sulfur at the interface of transcriptome and metabolome. Appl. Microbiol. Biotechnol. 2013;97(13):5965-5978.

    Not in our library10.1007/s00253-013-4789-x

  284. 284.

    Zhang D., Li X., Xie X., Zheng W., Li A., Liu Y., Liu X., Zhang R., Deng C., Cheng J., Yang H., Gong M. Exploring the Biological Effect of Biosynthesized Au–Pd Core–Shell Nanoparticles through an Untargeted Metabolomics Approach. ACS Appl. Mater. Interfaces. 2021;13:59633-59648.

    Not in our library10.1021/acsami.1c14850

  285. 285.

    Zhang D., Zheng W., Li X., Li A., Ye N., Zhang L., Liu Y., Liu X., Zhang R., Wang M., Cheng J., Yang H., Gong M. Investigating the effect of Ti3C2 (MXene) nanosheet on human umbilical vein endothelial cells via a combined untargeted and targeted metabolomics approach. Carbon. 2021;178:810-821.

    Not in our library10.1016/j.carbon.2021.04.023

  286. 286.

    Jiao G., Li X., Zhang N., Qiu J., Xu H., Liu S. Metabolomics study on the cytotoxicity of graphene. RSC Adv. 2014;4(84):44712-44717.

    Not in our library10.1039/c4ra06312k

  287. 287.

    Adamson S. X. F., Wang R., Wu W., Cooper B., Shannahan J. Metabolomic insights of macrophage responses to graphene nanoplatelets: Role of scavenger receptor CD36. PLoS One. 2018;13(11):e0207042.

    Not in our library10.1371/journal.pone.0207042

  288. 288.

    Raja G., Selvaraj V., Suk M., Suk K. T., Kim T. J. Metabolic phenotyping analysis of graphene oxide nanosheets exposures in breast cancer cells: metabolomics profiling techniques. Process Biochem. 2021;104:39-45.

    Not in our library10.1016/j.procbio.2021.02.016

  289. 289.

    Hou L., Guan S., Jin Y., Sun W., Wang Q., Du Y., Zhang R. Cell metabolomics to study the cytotoxicity of carbon black nanoparticles on A549 cells using UHPLC-Q/TOF-MS and multivariate data analysis. Sci. Total Environ. 2020;698:134122.

    Not in our library10.1016/j.scitotenv.2019.134122

  290. 290.

    Xu K., Wang X., Lu C., Liu Y., Zhang D., Cheng J. Toxicity of three carbon-based nanomaterials to earthworms: Effect of morphology on biomarkers, cytotoxicity, and metabolomics. Sci. Total Environ. 2021;777:146224.

    Not in our library10.1016/j.scitotenv.2021.146224

  291. 291.

    Frontiñán-Rubio J., Victoria Gómez M., Martín C., González-Domínguez J. M., Durán-Prado M., Vázquez E. Differential effects of graphene materials on the metabolism and function of human skin cells. Nanoscale. 2018;10(24):11604-11615.

    Not in our library10.1039/c8nr00897c

  292. 292.

    Zhang D., Zhang L., Zheng W., Wu F., Cheng J., Yang H., Gong M. Investigating biological effects of multidimensional carboxylated carbon-based nanomaterials on human lung A549 cells revealed via non-targeted metabolomics approach. Nanotechnology. 2021;32(1):015704.

    Not in our library10.1088/1361-6528/abb55b

  293. 293.

    Cicuéndez M., Flores J., Oliveira H., Portolés M. T., Vallet-Regí M., Vila M., Duarte I. F. Metabolomic response of osteosarcoma cells to nanographene oxide-mediated hyperthermia. Mater. Sci. Eng., C. 2018;91:340-348.

    Not in our library10.1016/j.msec.2018.05.057

  294. 294.

    Hu X., Ouyang S., Mu L., An J., Zhou Q. Effects of graphene oxide and oxidized carbon nanotubes on the cellular division, microstructure, uptake, oxidative stress, and metabolic profiles. Environ. Sci. Technol. 2015;49(18):10825-10833.

    Not in our library10.1021/acs.est.5b02102

  295. 295.

    Chatterjee N., Yang J., Yoon D., Kim S., Joo S. W., Choi J. Differential crosstalk between global DNA methylation and metabolomics associated with cell type specific stress response by pristine and functionalized MWCNT. Biomaterials. 2017;115:167-180.

    Not in our library10.1016/j.biomaterials.2016.11.005

  296. 296.

    Chatterjee N., Yang J., Kim S., Joo S. W., Choi J. Diameter size and aspect ratio as critical determinants of uptake, stress response, global metabolomics and epigenetic alterations in multi-wall carbon nanotubes. Carbon. 2016;108:529-540.

    Not in our library10.1016/j.carbon.2016.07.031

  297. 297.

    Phuyal S., Kasem M., Knittelfelder O., Sharma A., Fonseca D. d. M., Vebraite V., Shaposhnikov S., Slupphaug G., Skaug V., Zienolddiny S. Characterization of the proteome and lipidome profiles of human lung cells after low dose and chronic exposure to multiwalled carbon nanotubes. Nanotoxicology. 2018;12(2):138-152.

    Not in our library10.1080/17435390.2018.1425500

  298. 298.

    Du C., Zhang B., He Y., Hu C., Ng Q. X., Zhang H., Ong C. N., ZhifenLin. Biological effect of aqueous C60 aggregates on Scenedesmus obliquus revealed by transcriptomics and non-targeted metabolomics. J. Hazard. Mater. 2017;324:221-229.

    Not in our library10.1016/j.jhazmat.2016.10.052

  299. 299.

    Pink M., Verma N., Rettenmeier A. W., Schmitz-Spanke S. Integrated proteomic and metabolomic analysis to assess the effects of pure and benzo[a]pyrene-loaded carbon black particles on energy metabolism and motility in the human endothelial cell line EA.hy926. Arch. Toxicol. 2014;88(4):913-934.

    Not in our library10.1007/s00204-014-1200-3

  300. 300.

    Zanin H., Hollanda L. M., Ceragioli H. J., Ferreira M. S., Machado D., Lancellotti M., Catharino R. R., Baranauskas V., Lobo A. O. Carbon nanoparticles for gene transfection in eukaryotic cell lines. Mater. Sci. Eng., C. 2014;39(1):359-370.

    Not in our library10.1016/j.msec.2014.03.016

  301. 301.

    Varol I., Kaplan O., Erdoğar N., Öncül S., Nielsen T. T., Ercan A., Bilensoy E., Çelebier M. Q-TOF LC/MS-based untargeted metabolomics approach to evaluate the effect of folate-conjugated cyclodextrins on triple-negative breast cancer cells. Curr. Pharm. Anal. 2021;17(10):1272-1281.

    Not in our library10.2174/1573412917999201020205745

  302. 302.

    Yang Y., Liang H., Zeng P., Fu W., Yu J., Chen L., Chai D., Wen Y., Chen A., Zhang Y. Synthesis, characterization, and anti-hepatocellular carcinoma effect of glycyrrhizin-coupled bovine serum albumin-loaded luteolin nanoparticles. Pharmacogn. Mag. 2022;18(77):216-225.

    Not in our library10.4103/pm.pm_34_21

  303. 303.

    Kim S. J., Long N. P., Jung C. W., Anh N. H., Min J. E., Kim H. M., Kwon S. W. Exposure to nano-polystyrene induces metabolic alteration in lipid homeostasis in Caco-2. Environ. Sci.: Nano. 2021;8(5):1408-1424.

    Not in our library10.1039/d1en00145k

  304. 304.

    Su X., Huang Y., Chen R., Zhang Y., He M., Lü X. Metabolomics analysis of poly(l-lactic acid) nanofibers’ performance on PC12 cell differentiation. Regener. Biomater. 2021;8(4):rbab031.

    Not in our library10.1093/rb/rbab031

  305. 305.

    Feng L. J., Li J. W., Xu E. G., Sun X. D., Zhu F. P., Ding Z., Tian H., Dong S. S., Xia P. F., Yuan X. Z. Short-term exposure to positively charged polystyrene nanoparticles causes oxidative stress and membrane destruction in cyanobacteria. Environ. Sci.: Nano. 2019;6(10):3072-3079.

    Not in our library10.1039/c9en00807a

  306. 306.

    Al-Natour M. A., Alazzo A., Ghaemmaghami A. M., Kim D. H., Alexander C. LC-MS metabolomics comparisons of cancer cell and macrophage responses to methotrexate and polymer-encapsulated methotrexate. Int. J. Pharm.: X. 2019;1:100036.

    Not in our library10.1016/j.ijpx.2019.100036

  307. 307.

    Yaşacan M., Erikçi A., Eylem C. C., Çiftçi S. Y., Nemutlu E., Ulubayram K., Eroğlu İ. Polymeric nanoparticle versus liposome formulations: comparative physicochemical and metabolomic studies as l-carnitine delivery systems. AAPS PharmSciTech. 2020;21(8):308.

    Not in our library10.1208/s12249-020-01852-4

  308. 308.

    Magrì D., Veronesi M., Sánchez-Moreno P., Tolardo V., Bandiera T., Pompa P. P., Athanassiou A., Fragouli D. PET nanoplastics interactions with water contaminants and their impact on human cells. Environ. Pollut. 2021;271:116262.

    Not in our library10.1016/j.envpol.2020.116262

  309. 309.

    Al-Malki A. L., Bakkar A., Huwait E. A., Barbour E. K., Abulnaja K. O., Kumosani T. A., Moselhy S. S. Strigol1/albumin/chitosan nanoparticles decrease cell viability, induce apoptosis and alter metabolomics profile in HepG2 cancer cell line. Biomed. Pharmacother. 2021;142:111960.

    Not in our library10.1016/j.biopha.2021.111960

  310. 310.

    de Castro F., Vergaro V., Benedetti M., Baldassarre F., del Coco L., Dell’anna M. M., Mastrorilli P., Fanizzi F. P., Ciccarella G. Visible Light-Activated Water-Soluble Platicur Nanocolloids: Photocytotoxicity and Metabolomics Studies in Cancer Cells. ACS Appl. Bio Mater. 2020;3(10):6836-6851.

    Not in our library10.1021/acsabm.0c00766

  311. 311.

    Ercan A., Çelebier M., Varan G., Öncül S., Nenni M., Kaplan O., Bilensoy E. Global omics strategies to investigate the effect of cyclodextrin nanoparticles on MCF-7 breast cancer cells. Eur. J. Pharm. Sci. 2018;123:377-386.

    Not in our library10.1016/j.ejps.2018.07.060

  312. 312.

    Yu J., Chen J., Zhao H., Gao J., Li Y., Li Y., Xue J., Dahan A., Sun D., Zhang G., Zhang H. Integrative proteomics and metabolomics analysis reveals the toxicity of cationic liposomes to human normal hepatocyte cell line L02. Mol. Omics. 2018;14(5):362-372.

    Not in our library10.1039/c8mo00132d

  313. 313.

    Elmi T., Shafiee Ardestani M., Hajialiani F., Motevalian M., Mohamadi M., Sadeghi S., Zamani Z., Tabatabaie F. Novel chloroquine loaded curcumin based anionic linear globular dendrimer G2: A metabolomics study on Plasmodium falciparum in vitro using 1H NMR spectroscopy. Parasitology. 2020;147(7):747-759.

    Not in our library10.1017/s0031182020000372

  314. 314.

    Catala A., Dzieciatkowska M., Wang G., Gutierrez-Hartmann A., Simberg D., Hansen K. C., D’Alessandro A., Catalano C. E. Targeted intracellular delivery of Trastuzumab using designer phage lambda nanoparticles alters cellular programs in human breast cancer cells. ACS Nano. 2021;15(7):11789-11805.

    Not in our library10.1021/acsnano.1c02864

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