Toxicology of Nanomaterials E-Book
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- Herausgeber: Wiley-VCH Verlag GmbH & Co. KGaA
- Kategorie: Fachliteratur
- Sprache: Englisch
This book provides the reader with a comprehensive view of analytical methods for nanotoxicology studies. After an introduction to nanomaterials and toxicological studies, the book discusses various characterization methods of nanomaterials and continues with the detection of nanoparticles in vivo as well as in vitro. A variety of techniques in molecular toxicology of nanomaterials is presented, followed by a detailed explanation of interaction between nanoparticles and biomacromolecules, including the structure-toxicity relationships of nanomaterials. Finally, the book concludes with the advantages and challenges of the analytical methods for nanotoxicology.
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Veröffentlichungsjahr: 2016
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Table of Contents
Cover
Title Page
Copyright
List of Contributors
Preface
Abbreviations
Chapter 1: Characterization of Nanomaterials in Nanotoxicological Analyses
1.1 Introduction
1.2 Size and Morphology of NMs
1.3 Composition and Structure
1.4 Surface Properties
1.5 Interactions between NMs and Biological Environments
1.6 Conclusions
References
Chapter 2: Quantitative Analysis of Metal-Based Nanomaterials in Biological Samples Using ICP-MS
2.1 Introduction
2.2 ICP-MS: A Power Tool for Element Analysis
2.3 Single-Particle ICP-MS: Theory and Application
2.4 Analysis of Nanoparticles by ICP-MS Hyphenate Techniques
2.5 Conclusion and Outlook
References
Chapter 3: Stable Isotopic Tracing of Nanomaterials In Vivo
3.1 Introduction
3.2 Development of Stable Isotope Labeling in Nanotechnology
3.3
13
C-Labeled Carbon Nanomaterials
3.4 Metal Stable Isotope Labeled Nanoparticles
3.5 Summary and Outlook
References
Chapter 4: Radiolabeling of Nanoparticles
4.1 Introduction
4.2 Radiolabeling of Nanomaterials
4.3 Summary and Outlook
References
Chapter 5: New Methods for Nanotoxicity Analyses: Synchrotron-Radiation-Based Techniques
5.1 Introduction
5.2 Speciation Transformation of NMs in Biological System by SR-Based Techniques
5.3 SR-Based Analytical Techniques for Understanding Nano–Bio Interactions
5.4 Conclusion and Prospects
References
Chapter 6: Imaging Techniques in Nanotoxicology Research1)
6.1 Introduction
6.2 Imaging Techniques for
In Vitro
Visualization and Quantification of Nanomaterials
6.3 Distribution and Quantification of Nanomaterials
In Vivo
6.4 Conclusions
References
Chapter 7: In Vivo Nanotoxicity Assays in Animal Models
7.1 Introduction
7.2 Laboratory Animal Models
7.3 Administration
7.4 Particokinetics
7.5
In Vivo
Toxicity of Nanomaterials
7.6 Recommendations
References
Chapter 8: In Vitro Testing Methods for Nanomaterials
8.1 Introduction
8.2 Preparation of Nanoparticle Suspensions
8.3 Cell Viability Assays
8.4 Oxidative Stress Assay
8.5 Inflammatory Assay
8.6 Summary and Outlook
References
Chapter 9: Localizing the Cellular Uptake of Nanomaterials
9.1 Introduction
9.2 Mechanism of Cellular Uptake of Nanomaterials
9.3 Methods to Determine Cellular Nanoparticle Uptake
In Vitro
9.4 Representative Cellular Uptake of Nanomaterials and Intracellular Location Determined with Different Methods
9.5 Summary and Outlook
References
Chapter 10: Methods and Techniques in Molecular Toxicology of Nanomaterials
10.1 Introduction
10.2 Gene Mutation Detection
10.3 Gene Expression Analysis
10.4 DNA Damage Detection
10.5 Chromosomal Aberration Analysis
10.6 Omics
10.7 Conclusions
References
Chapter 11: Analyses Methods for Nanoparticle Interaction with Biomacromolecules
11.1 Introduction
11.2 Biological Effects due to Nanoparticle–Biomolecule Interactions
11.3 Basic Methods to Understand NPs and Protein Interactions
11.4 Summary and Outlook
References
Chapter 12: “Omic” Techniques for Nanosafety
12.1 Introduction
12.2 Materials and Biological Models
12.3 Genomics Study for Nanosafety
12.4 Transcriptomics Study for the Biological Effects of ENMs
12.5 Proteomics Study for Nanosafety
12.6 Metabolomics Study for Nanosafety
12.7 Summary and Outlook
References
Chapter 13: Nanometallomics: New Approach on Analyzing Biological Effects of Metal-Related Nanomaterials1)
13.1 Introduction
13.2 Integrated Approaches on the ADME of Metal-Related Nanomaterials in Biological Systems
13.3 Interactions of Metal-Related Nanomaterials with Genes, Proteins, and Other Biomolecules
13.4 Conclusions
Acknowledgments
References
Chapter 14: Molecular Simulation Methods for Safety Analyses of Nanomaterials
14.1 Introduction
14.2 The Molecular Simulation Methods for Nanomaterials and Biological Systems
14.3 The Scientific Problems in Biological Effects of Nanomaterials Studied by Molecular Simulations
14.4 Summary and Outlook
Acknowledgments
References
Chapter 15: Ecotoxicity Analyses of Nanomaterials
15.1 Introduction
15.2 Transformation of ENMs in the Environment
15.3 Toxicity of ENMs in Terrestrial Ecosystem
15.4 Other Terrestrial Organisms
15.5 Aquatic Organisms
15.6 Challenges and Perspective
References
Index
End User License Agreement
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Guide
Cover
Table of Contents
Preface
Begin Reading
List of Illustrations
Chapter 2: Quantitative Analysis of Metal-Based Nanomaterials in Biological Samples Using ICP-MS
Figure 2.1 ICP-MS-based hyphenated systems [29].
Figure 2.2 Conceptual diagram for the single-particle inductively coupled plasma–mass spectrometry (SP-ICP-MS) method. Samples containing dissolved metals will produce a constant stream of charged ions after passing through the plasma. The detector will then have a relatively constant intensity versus time signal for each dwell time. Conversely, a sample containing inorganic nanoparticles at a sufficiently low concentration will create a pulse of charged ions after passing through the plasma. Here, a resulting spike in intensity versus time will occur for dwell times that contained nanoparticulate metal [36].
Figure 2.3 DNA hybridization assay with AuNP probes by using SP-ICPMS. The first step was to functionalize citrate-protected AuNPs with two sets of single-stranded DNA, probe 1 and probe 2. Then DNA targets were hybridized with AuNP–probe 1 and AuNP–probe 2 in buffer solution. The solution of AuNP aggregates was introduced into the plasma torch by the nebulizer and then AuNPs underwent desolvation, particle vaporization, atomization, and ionization in the ICP zone at approximately 6000–7000 K. Finally, the frequency and intensity of the 197Au + pulse signals were recorded by the electron multiplier detector [50].
Figure 2.4 Mass cytometry allows single-cell atomic mass spectrometry of heavy elemental (>100 Da) reporters. Schematic of ICP-MS-based analysis of cellular markers. An affinity product (e.g., antibody) tagged with a specific element binds to the cellular epitope. The cell is introduced into the ICP by droplet nebulization. Each cell is atomized, ionized, overly abundant ions removed, and the elemental composition of remaining heavy elements (reporters) is determined. Signals corresponding to each elemental tag are then correlated with the presence of the respective marker and analyzed using conventional cytometry platforms [60].
Figure 2.5 Simultaneous identification and size characterization of ENMs in complex media by CE-ICP-MS. EOF = electro-osmotic flow [70].
Figure 2.6 Histogram showing the log-normal distribution of Au mass for the ablation of 70 cells. The two averages from single-cell analysis (the blue solid line) and cell digestion analysis (the red dot line) are 15 and 18 fg, respectively [75].
Chapter 3: Stable Isotopic Tracing of Nanomaterials In Vivo
Figure 3.1 Results of exposure of C
60
to
13
C-enriched carbon vapor. (a) Positive ions generated from vaporization of a target comprised of amorphous
13
C (99% atom
13
C) target mixed with C
60
under the same conditions, with mass-scale expansion of C
60
illustrating the occurrence of atom exchange events. (b) C
62
−C
70
mass region expanded to show growth of C
60
to larger fullerenes by successive
13
C incorporation events.
Figure 3.2 Characterization of
13
C-enriched C
60
. (a) MS of
13
C-enriched C
60
; (b) MS of unlabeled C
60
; (c) IR spectra; and (d) Raman spectra [29].
Figure 3.3
13
C NMR spectra of
13
C-labeled C
60
fullerenols; SSB indicates sideband. (a)
1
H−
13
C CP spectra at contact times of 2 ms and (b)
13
C MAS NMR spectrum of C
60
fullerenols [48].
Figure 3.4 Biodistribution of
13
C−C
60
in mice after intravenous(i.v.) administration (
n
= 4) [29].
Figure 3.5 (a) The biodistributions of pristine SWNTs (
13
C-SWNT). (Reprinted with permission from [28], Copyright (2007) American Chemical Society.); (b) the PEGylated SWNTs (
13
C-SWNT). (Reprinted from the cited paper [27] with permission from Wiley.); (c)
13
C-enriched carbon nanoparticles in mice at different time points postexposure via intravenous(i.v.) injection. (Reprinted with permission from [30], Copyright (2014) American Chemical Society.)
Figure 3.6 SWCNTs with different Raman colors and imaging. (a) Schematic SWNTs with three different isotope compositions (
13
C-SWNT,
12
C/
13
C-SWNT,
12
C-SWNT) conjugated with different targeting ligands and multicolor Raman imaging of live cells. (b) Solution-phase Raman spectra of the three SWNT conjugates under 785 nm laser excitation.
Figure 3.7 Raman spectra of the
12
C 1-layer graphene (LG),
13
C 1-LG, and
13
C/12C 2-LG (
13
C is on top,
12
C at bottom) samples.
Figure 3.8 Conceptual model. The exposure material is dispersed in artificial seawater (1), which results in the aggregation of the primary bulk and nano ZnO particles (2) and subsequent sedimentation (3). Dissolved aqueous
68
Zn is formed by dissolution of oxide precipitates or immediately after dispersion for
68
ZnCl
2
(4) and subsequently sorbed onto sediment particles (5).
Corophium volutator
feed on organic matter on the sediment surface or suspended particles by drawing this food into their U-shaped burrows. Uptake of the
68
Zn label from all forms of exposure material occurs via the dissolved state, directly from both the aqueous phase (6) and/or the intake of sediment with adsorbed Zn (7). The inset indicates food intake by
C. volutator
(8) and as the sediment and water pass through the alimentary canal, detoxification of Zn occurs through the formation of Zn-rich sphalerites in the hepatopancreas (9). The
C. volutator
are in a stage of metal accumulation for the duration of the exposure; therefore, defecation (10) does not include
68
Zn-rich sphalerites.
Chapter 4: Radiolabeling of Nanoparticles
Figure 4.1 Preparation of
125
I-SWNTols by chloramine-T method.
Figure 4.2 Autoradiographs of ceria NPs in cucumber leaves. (a) 7 nm ceria, third, fourth, fifth, and sixth leaves and (b) 25 nm ceria, first, fourth, and sixth leaves.
Chapter 5: New Methods for Nanotoxicity Analyses: Synchrotron-Radiation-Based Techniques
Figure 5.1 Example of SAXS diffractogram (experimental data on NM105 suspension sonicated at pH 2 as circles) illustrating the unified fit (solid red line) and its components, prevailing in each
q
-domain (dashed-dotted lines, see text for details). Insert of transmission electronic micrograph (credit P.-J. de Temmerman and J. Mast, CODA-CERVA) illustrating the gyration radius of primary particles (
R
g1
) and aggregates (
R
g2
) used in the model.
Figure 5.2 Stability regimes of BSA-Ag NPs in HNO
3
measured by absolute-intensity-calibrated USAXS. (a) In 50 mM HNO
3
, BSA-Ag NPs slowly dissolve with almost no agglomeration, as evidenced by the relatively flat, low-
q
scattering curve, and optical clarity of the solution (inset). (b) In 250 mM HNO
3
, BSA-Ag NPs simultaneously agglomerate and dissolve while the solution darkens. (c) In 500 mM HNO
3
, the BSA coating completely destabilizes, causing rapid clustering of principal particles into agglomerates, as seen in the large scattering intensities at low-
q
values and the dark turbid suspension. In this case, “steady” dissolution proceeds as long as the particles are stirred; otherwise, they sediment, as detected by an exponential decay in the scattering intensity and by visual inspection.
Figure 5.3 River biofilm exposed to 1 mg/l Cu nanoparticles for 5 min. (a) Cu image difference map (
I
931.3
eV–
I
925
eV) (red) overlayed on the gray-scale of the biology image difference map (
I
288.2
eV–
I
280
eV). The white box indicates the area of the detailed study. Detailed study of a cyanobacterial cell. (b) Color-coded composite map of protein (red), lipid (green), and polysaccharides (blue). (c) Overlay of the Cu image difference map from a on the lipid and protein component maps from (b) (red = Cu nanoparticles, green = lipid, blue = protein).
Figure 5.4 STXM mapping of the electronic structure of graphene. STXM images and data of CVD grown SLG on Cu after wet etching in HNO
3
and transfer. (a) Transmission-mode data is converted to OD by
I
/
I
0
, where
I
0
is measured in an empty scan region; summation of 0.3 eV energy steps is normalized to carbon at 320 eV. Corresponding white intensity describes the thickness and morphology of the graphene sheet. The scale bar is 1 µm and the total distance across the image is 4 µm. (b) Integrated C K-edge spectrum of entire image in (a). The electromagnetic field vector (
E
) and incident X-ray photon energy (
hN
) are at angle (
Q
). The angular difference between the pristine basal plane (blue P orbitals) and the asperity (red P orbitals) represents a degree of corrugation (
F
) of a rippled graphene sheet. (c) Isolated
C
K-edge spectra of each region displayed in (a), where spectra
D
and
F
displayed in the inset have the most prominent pre-edge features.
Figure 5.5 (A) Typical μ-XRF maps of P and Fe of macrophages unexposed or exposed to 50 µg/ml SWCNT for 24 h (a) and X-ray fluorescence spectra integrated exclusively over the scanned cells, normalized to the phosphorus signal (b). The inset represents zoomed areas around the positions of the Kα fluorescence peak of iron together with fits of its contribution (dashed lines) [34]. (B) X-ray microfluorescence spectra integrated over the whole murine macrophages exposed for 24 h to MWCNT suspensions at concentrations of 100 µg/ml [34]. (C) X-ray microfluorescence spectra integrated over the whole scanned area of murine macrophages exposed for 24 h to nonpurified SWCNT suspensions at 10 µg/ml [37].
Figure 5.6 The 3 D reconstructed tomography images of Hela cells. (a) Control cells and (b) cells after incubation with TiO
2
NPs for 6 h (red color indicates the TiO
2
NPs.
Figure 5.7 Cellular uptake, accumulation, and exocytosis of AgNPs. The spatial distribution of AgNPs in a single cell captured by SR-TXM. Smaller colored spots indicate particles or vesicles on the surface or inside the cells. Green, yellow, and red colors indicate increasing gradients of X-ray absorption intensity by vesicles or aggregated particles. The larger red particles in the square blue frames are gold particles used as a reference for data reconstruction processing. The color bar indicates the related contrast signals from X-ray absorption of silver inside cells.
Figure 5.8 Fe distribution in mouse olfactory bulb (A) and brain regions (B) tested by SR-XRF. (a) Control group and (b) Fe
2
O
3
-NP group. ON: olfactory nerve layer, Gl: glomerular layer, Epl: external plexiform layer, Ipl: internal plexiform layer, GrO: granule cell layer of olfactory bulb, Md: medullary layer, GrA: granule cell layer of accessory olfactory bulb, and AOE: anterior olfactory nucleus external part.
Figure 5.9 Two-dimensional elemental maps for Zn, Se, and Ca, Zn, Se overlay for
D. magna
exposed to red MUA-coated CDSe/ZnS QDs. Vertical columns correspond to Zn, Se, and an overlay of Ca, Zn, and Se, respectively. Horizontal rows correspond to 12 and 24-h exposure time points.
Figure 5.10 The biodistribution of nutritional elements in eggs after parental exposure to MNPs. Synchrotron-radiation-based microbeam X-ray fluorescence (SR-μ-XRF) mappings of Fe, Ca, Zn, and Cu in
Drosophila
eggs of the control and UN-, CA-, and APTS-MNP- (300 µg/g) treated groups.
Figure 5.11
In situ
XAS analysis of intracellular Ti-rich regions. (a) μ-XRF image of a cross section of Caco-2 cells exposed for 24 h, on their apical pole, to 50 µg/ml of TiO
2
-NPs. Phosphorus (P) distribution map is depicted in green and titanium (Ti) distribution map is depicted in red. The area pointed out with an arrow was further analyzed by XAS. (b) XAS spectra of reference Ti-acetate and TiO
2
-anatase nanopowders (5, 12, and 25 nm) and of Ti-rich regions in Caco-2 cells exposed for 12 h (cells 12 h) or 24 h (cells 24 h) to 50 µg/ml of 12 nm-diameter anatase TiO
2
-NPs. (c) Focus on the pre-edge region (4972–4985 eV) and its deconvolution using an arctangent function and four Gaussian peaks (
A
1
,
A
2
,
A
3
,
B
). Solid line: recorded data; dashed line: fit. Panels indicate
A
2
/
A
3
, which is the ratio of intensity of
A
2
to intensity of
A
3
[52].
Figure 5.12
In situ
elemental analysis of the metabolization of QDs in
C. elegans
by XRF and XAS. (a) Scheme of XRF mapping of
C. elegans
; (b) mappings of an intact worm exposed to MEA-CdSe@ZnS for 24 h; and (c)
in situ
Se K edge microbeam X ray absorbance near-edge structure (μ-XANES) spectra of QDs within the digestive tract of
C. elegans
corresponded to points
A
,
B
, and
C
on XRF mappings. The beam size of μ-XRF mappings and μ-XAS spectra was 5 × 5 µm
2
.
Figure 5.13 Comparison of the C K-edge, N K-edge, and O K-edge XANES spectra of SWCNTs, the pristine streptavidin protein, and SWCNTs treated in streptavidin protein solutions at different concentrations: 15, 200, and 1000 µg/ml.
Figure 5.14 Analysis for bio–nano interaction at molecular level. SRCD spectra of protein–Ag NP complex (red) and free protein (black) collected with a low volume capacity 10 cm path length cell. There is a decrease of 6 °C in the thermal unfolding of human serum albumin upon interaction with silver NPs.
Figure 5.15 Integrated synchrotron radiation analytical techniques for nanotoxicological studies.
Chapter 6: Imaging Techniques in Nanotoxicology Research1)
Figure 6.1 Imaging techniques for cellular visualization of nanomaterials. (a,b) SEM [5]; (c,d) ESEM [6]; (e) EELS [7]; (f,g) STEM [8]; (h) TEM [9]; (i) EDX [10]; (j,k) PIXE [11]; (l) STXM; (m,n) μ-XRF[12]; (o) SIMS [13]; (p) LA-ICP-MS [14]; (q,r) confocal [15]; (s) multiphoton luminescence [16]; (t) dark-field microscopy [9]; and (u–y) AFM [[6, 17]].
Figure 6.2 Atomic force microscopy of silica NPs and carbon nanohorns in macrophages and red blood cells. (a) Schematic of the experimental set-up; (b) AFM image of macrophages exposed to SWCNH; (c) phase image of macrophages; (d) phase image of erythrocytes; (e) phase images of buried silica NPs in macrophages at different spatial resolution; and (f) influence of the driving frequencies on resulting force curves and phase images [17].
Figure 6.3 Commonly used imaging techniques arranged according to their spatial resolutions and sensitivities.
Chapter 7: In Vivo Nanotoxicity Assays in Animal Models
Figure 7.1 Schematic representation of zebrafish life cycle and embryonic development.
Figure 7.2 Automatic zebrafish manipulation and imaging platform.
Figure 7.3 (a) Schematic representation of
C. elegans
life cycle and (b) anatomy of an adult hermaphrodite.
Figure 7.4 Schematic representation of
Drosophila
life cycle.
Figure 7.5 Uptake and translocation routes of NMs.
Figure 7.6 Exposure chamber (breathing zone) for whole-body exposure (a) and nose-only exposure (b).
Figure 7.7 Schematic representation of inhalation exposure system for manufactured nanomaterials. Vertical view (a) and perspective view (b).
Figure 7.8 Diagram of the nebulizer NM delivery system.
Figure 7.9 Skin layers.
Figure 7.10 Predicted fractional deposition of inhaled particles in the nasopharyngeal, tracheobronchial, and alveolar regions of the human respiratory tract during nasal breathing [62].
Figure 7.11 Schematic representation of human and mouse placentae.
Chapter 9: Localizing the Cellular Uptake of Nanomaterials
Figure 9.1 Known pathways of cellular uptake of NPs.
Figure 9.2 The schematic of the spinning disk laser scanning confocal microscopy live-cell imaging system for temporal resolution cell imaging and cellular NP trajectories analysis (left part of picture was afforded by PerkinElmer Inc.). Live cells using a spinning disk laser confocal scanning microscope equipped with a cultivation chamber fitted with a temp control and CO
2
-control device. The cellular NPs trajectories were analyzed by professional imaging software Volocity. The circles in the live-cell image of fluorescent dots (right bottom) represent the areas from which the trajectories were generated in the right upper image.
Figure 9.3 Confocal microscopic images show the subcellular localization of FITC-C
60
(C(COOH)
2
)
2
mainly in the lysosome. (a). FITC-C
60
(C(COOH)
2
)
2
(green fluorescence) were uptake by HeLa cells. (b). Punctate co-localization of FITC-C
60
(C(COOH)
2
)
2
with Lyso Tracker Red. (c). C
60
(C(COOH)
2
)
2
nanoparticles are not located in mitochondria. Bar: 10 μm.
Figure 9.4 Confocal microscopy study of the localization of PS NPs and tubulin in HeLa cells at different phases of mitosis. (a) Colocalization of COOH-PS NPs (green) with tubulin (CY-3-microtubulin antibody, red) after incubation for 24 h in fixed cells. (b) Colocalization of NH2-PS NPs (orange) with tubulin (TubulinTracker Green, Oregon Green 488 Taxol, bis-acetate) in live cells. The nuclei were stained with Hoechst 33342 (blue). Scale bar: 10 µm.
Figure 9.5 Uptake pathways and quantitative process of internalization and removal of Au NRs in A549, 16HBE, and MSC cells by ICP-MS after treated with Au NRs. (a, c) The process of cellular internalization and exclusion of Au NRs, respectively. (b) Uptake pathways for Au NRs in two types of cells using specific endocytosis inhibitors.
Chapter 10: Methods and Techniques in Molecular Toxicology of Nanomaterials
Figure 10.1 Schematic of widely applied techniques in molecular toxicology of nanomaterials.
Figure 10.2 (a) Ames test procedure of plate incorporation assay method. (b–d) TEM microphotographs of
Salmonella typhimurium
TA98 showing: (b) control cell, (c) internalization of ZnO NPs, and (d) internalization of TiO
2
NPs [80].
Figure 10.3 (a) The procedure of one-step and two-step RT-PCR. (b) Changes of genes expression from real-time RT-PCR analysis at 24 and 48 h Au NP treatment [116].
Figure 10.4 (a) Effects of Nano-Co (Co NPs) or Nano-TiO
2
(TiO
2
NPs) exposure on DNA double-strand breaks (DSBs) in A549 cells. (Reprinted with permission from [137], Copyright (2012), American Chemical Society.) (b) Immunofluorescence images of γ-H2AX after treatment with TiO
2
NPs. (c) Comparison of generation of γ-H2AX after treatment with TiO
2
NPs in different size detected by western blotting [138].
Figure 10.5 (a) Ag NP treated IMR-90 cells show acentric and centric fragments. (b) Arrow indicates acentric fragments. (c) Untreated cancer cells with no aberrations. (d) Ag NP treated U251 cells. White arrow points to a dicentric chromosome. (e) Acentric fragments. (f) Centric fragments. Red arrow points to a chromosome fragment [151]. (g) Fluorescence
in situ
Hybridization (FISH) analysis of control and Au NP treated MRC-5 lung fibroblasts (1 nM concentration and 72 h) [116]. (Copyright © 2011 Elsevier Ltd.) (h) The various possible fates of cultured cytokinesis-blocked cells following exposure to cytotoxic/genotoxic agents [152].
Chapter 11: Analyses Methods for Nanoparticle Interaction with Biomacromolecules
Figure 11.1 Surface hydrophobicity of Au NPs influencing the adsorption of serum proteins that determines the cellular uptake of NPs. (a) SDS-PAGE of serum proteins adsorbed on Au NPs. The lanes labeled with NP 4, NP 3, NP 2, and NP 1 correspond to the proteins adsorbed to the corresponding Au NPs that are incubated with 50% FBS for 6 h. The hydrophobicity index is shown as LogP square values, representing the hydrophobicity of the head groups. The values for four NPs are 0.63, 1.8, 2.9, and 3.65, respectively. (b) Uptake of four Au NPs inside HeLa cells within culture media containing three kinds of serum proteins (BSA, IgG, and Tf). The contents of three proteins are 25 mg/ml (BSA), 5 mg/ml (IgG), and 2 mg/ml (Tf). The protein mixture refers to a medium containing a mixture of all three proteins used at the same concentrations. (c) The relationship between Au NP surface hydrophobicity (LogP square values) and the amount of cellular uptake. HeLa cells that were exposed to Au NPs in the media supplemented with 10% FBS.
Figure 11.2 PEG backfilling preventing nonspecific adsorption of serum proteins on NPs that improves binding specificity of NPs to targeted cells. (a) Scheme of the backfilling strategy for mPEG docking with different chain lengths on OPSS-PEG-Herceptin-AF647-modifed Au NPs. (b) The specific binding efficiency of Herceptin-conjugated Au NPs to cells in media containing human serum. Two kinds of cells have high-level (SKBR3) or low-level (MCF7) expression of Herceptin-associated receptor, ErbB2-receptor. IF shows fluorescence intensity, and cell count means normalized cell number included in all events counted. Red and blue lines represent cells treated with or without competitive Herceptin molecules, respectively. (c) The binding specificity of NPs to targeted cells is dependent on serum-protein adsorption and the chain length of PEG for backfilling.
Figure 11.3 Superoxide-scavenging abilities in the CeO
2
NPs (nanoceria). (a) The antioxidant role of CeO
2
NPs mixed with the lysates of human bronchial epithelial cells, (b) the SOD mimetic activity of the CeO
2
NPs after the mixing of CuZn-SOD (final concentration of 1 U/ml) with CeO
2
NPs (0.033 nM) within 24 h, and (c) SOD mimetic activity for CeO
2
NPs. Effect of SOD/CeO
2
NPs on superoxide anions from KO
2
was determined by ESR measurement.
Figure 11.4 The characterization of protein corona on NPs in different physiological media. (a) Mean dynamic sizes of 50 μM FBS-coated Au NRs determined by DLS when incubated in PBS (pH at 7.2) and in artificial lysosomal fluid (ALF, pH at 4.5) during 60 min, respectively. (b) The visible and NIR absorption spectra of Au NRs before and after incubation with 10% fetal bovine serum (FBS) in PBS (Phosphate buffer solution) at different time intervals at 37 °C. (Reprinted with permission from [38]. Copyright © 2011 American Chemical Society.) (c) Zeta potentials of Au NRs dispersed in aqueous solution after being incubated with medium with (w/) or without (w/o) serum at 37 °C for 1 or 30 min and centrifuged. The inset shows reducing band intensity in SDS-PAGE for serum proteins recognized as serum albumin mainly in the supernatant separated from Au NRs and serum mixture after 2 h incubation. CTRL (control) represents the serum proteins without incubation with Au NRs [77].
Figure 11.5 The application of SAXS into the study of nano–protein interactions. SAXS data (scattering intensity
I
(
q
) versus length of scattering vector
q
) for, respectively, 1.0 mg/ml SC (open triangles) and 1.2 mg/ml SM particles (the silica NPs) and 1.0 mg/ml SC (closed triangles). The line is the sum of the SC and the SM scattering. (b) SAXS data for, respectively, 1.0 mg/ml BSA (open squares) and 1.2 mg/ml SM particles and 1.0 mg/ml BSA (closed squares). The line is the sum of the BSA and the SM scattering. Insets in both graphs show the residuals between the scattering intensities from the samples with SM + protein and the sum of the SM and protein scattering.
Figure 11.6 The binding of BSA protein to the surface of Au NRs and its influence on cytotoxicity. (a) TEM images of BSA-adsorbed Au NRs. (b) The interfaces for BSA (
plane S
) via disulfides (yellow) to bind the Au (111) surface of Au NRs. (c) Various sulfur species in reference samples: Au−S, R−S (cysteine, thiol, Met), and R−S−S−R′ (cystine), shown as normalized S K-edge XANES spectra. (d) Chemical species of sulfur in cysteine, Met, and cystine after incubation with Au NRs. (e) Elemental mappings of Au, S, and Ca using μ-XRF to analyze internalized FBS/Au NRs in cells at different time intervals. The insets are cell images under a bright field. (f) LDH release from cells exposed to Au NRs and FBS-coated Au NRs for 24 h, which indicated the changed permeation of cell membrane after treatment. (g) Cytotoxicity was evaluated by alive–dead assay for cells exposed to CTAB/Au NRs and FBS/Au NRs after exposure for 12 and 24 h.
Chapter 12: “Omic” Techniques for Nanosafety
Figure 12.1 Major dose–response profiles for gene expression changes induced by 10 and 500 nm amorphous silica particles. Heat map profiles for the three major dose–response patterns of gene expression (2 h) identified by supervised hierarchical clustering are shown in (a–c). The centroid plots in (d–f) represent the corresponding overall average patterns of expression at three different doses of each particle for pattern a (d), pattern b (e), and pattern c (f).
Figure 12.2 Comparison of network analysis between GO and rGO by Pathway Studio.
Figure 12.3 Gene/protein expression profiles of biomarkers indicate difference of DNA damage repair pathways upon exposure to four types of ENMs ((a) TiO
2
-NPs, 50 µg/ml, (b) carbon black (CB), 5, 50 µg/ml for human cells), (c) single-walled carbon nanotube (SWCNT, 8 µg/ml for
E. coli
, 10 µg/ml/l for yeast and human cells), and (d) purified fullerene (C60, 50 µg/ml) across three species. The mean natural log of induction factor (ln
I
) indicates the magnitude of altered gene/protein expression (represented by a green-black-red color scale at bottom). Red spectrum colors indicate upregulation, green spectrum colors indicate downregulation. Values beyond ±2 are shown as ±2).
X
-axis bottom: for
E. coli
and yeast: testing time in minutes, the first data point shown is at 20 min after exposure due to data smoothing with moving average of every five data points; for human cells: testing time in hours.
Y
-axis left: clusters of genes/proteins by DNA damage repair pathways.
Y
-axis right for each species [43].
Figure 12.4 Transcriptomic analysis of gene regulation by SPIONPs. (a) Hierarchical clustering of significantly effected (compared to time matched controls) transcripts shows a higher preponderance of downregulation (green) by SPIONP exposures. Upregulation (red) was more prominent during earlier time points where the most highly induced mRNAs encoded inflammatory cytokines. (b) Highest ranking biological processes associated with transcripts upregulated by
in vivo
exposure to SPIO NPs. Cellular processes associated with inflammation and clearance of foreign bodies (cytokine production, cell migration, chemotaxis) were significantly up-regulated [24].
Figure 12.5 (a) Cartoon representation of the possible exchange/interaction scenarios at the bio–nano interface at the cellular level. (b) Schematic drawing of the structure of NP–protein complexes in plasma: the “core” nanoparticle is surrounded by the protein corona composed of an outer weakly interacting layer of protein (left, full red arrows) rapidly exchanging with a collection of free proteins and a “hard” slowly exchanging corona of proteins (right). Diagram is not to scale in representing the proportions of the different objects.
Figure 12.6 Bioinformatic classification of identified corona proteins according to their functions. Employing bioinformatics tools, proteins identified in the respective SiNPs corona were classified according to biological processes of the blood system (a). The relative percentages of the proteins compared to crude plasma are shown. A significant enrichment of plasma proteins involved in complement activation (b), lipoproteins (c), coagulation (d) as well as proteins grouped as “tissue leakage” (g) was evident in the corona. Although immunoglobulins (e), acute-phase response proteins (f), and serum albumin (h) were present in high amounts in the plasma, these proteins displayed a lower affinity for the SiNPs.
Figure 12.7 Protein coronas and their composition are established rapidly. (a) SDS-PAGE was used to visualize nanoparticle-bound plasma proteins. Molecular mass and time points are indicated. (b) Corona quantification (protein (fg) per particle at the indicated time points. Continuously increased protein binding was observed for AmSil30 and also, slightly, for nPsNPs, whereas pPsNPs showed decreased binding over time. Values are mean + s.d. from two independent experiments. (c,d) Classification of corona proteins identified on nine different nanoparticles by LC-MS according to their calculated molecular mass (c) or isoelectric point (d). Relative percentages are shown. (e) Plasma exposure time modulates protein abundance (averaged molecule number per nanoparticle) on the indicated silica nanoparticles. Compared with SiNP30, proteins bound to the larger SiNP140 in higher copy numbers. Relative numbers of proteins present at the indicated copy numbers per indicated nanoparticle are shown. (f) Dendrogram illustrating sample similarities between protein binding profiles, which shows that they are correctly kinetically classified and that significant changes in the corona composition occurred at early rather than at late exposure time periods.
Figure 12.8 Representative PCA score plots (PC1 versus PC2) derived from the
1
H NMR data of body fluid samples (plasma and urine) and tissue samples (extracted from brain, kidney, liver, lung, and spleen) from the corresponding groups of rats: C, control group; L, low-dose group; H, high-dose group; 0, 0 h post-dose; 6, 6 h post-dose; 24, 24 h post-dose; 48, 48 h post-dose [29].
Chapter 14: Molecular Simulation Methods for Safety Analyses of Nanomaterials
Figure 14.1 Four molecular simulation methods as classical molecular dynamics, first-principles, QM/MM, and reactive molecular dynamics in simulation scale–modeling scale coordinate.
Figure 14.2 Characteristic snapshots of the simulation interaction process of Gd@C
82
(OH)
22
binding to MMP-9 [28].
Figure 14.3 Interactions between BFG, Ig, Tf, BSA, and SWCNTs. (a,b) AFM images of proteins after incubation with SWCNTs for 10 min and 5 h. (c) Molecular modeling illustrations for proteins binding to SWCNTs after incubation d,e Locations and the interaction details of the most preferred binding sites on proteins for SWCNTs [41].
Figure 14.4 (a) RMSD of the backbone of the whole four-triplex bundle with reference to the initial conformation in Tetramer-0 (black), and Tetramer-Gd (red) systems. (b) Initial conformation of the four collagen triplexes [50].
Figure 14.5 The electric details during the transitions [83].
Chapter 15: Ecotoxicity Analyses of Nanomaterials
Figure 15.1 Pathway and transformation of nanomaterials in the environment.
Figure 15.2 Interactions between
E. coli
and CeO
2
NPs: in NS (a) and in PBS (b).
Figure 15.3 (a,e) TEM images of root cells and (b,f) Ce maps of rectangle area in (a) and (e) obtained by a ratio of 886 and 888 eV images. Color bar values are estimated from Ce absorption coefficients and X-ray absorption measurements (in g/cm
2
). The calculated surface densities are respectively between 1.1 × 10
−5
to 6.4 × 10
−5
and 2.4 × 10
−6
to 2.8 × 10
−5
g/cm
2
; (c,g) color-coded maps of Ce components in (b) and (f) derived from an STXM Ce M edge stack analysis. The order of Ce contents is as follows: green > red > yellow; blue color represents the non-Ce regions; panels (d) and (h) are respectively the XAFS spectra extracted from the image sequences of (c) and (g). The black line spectra above belong to the standard compounds and the colored spectra below belong to the root samples. The vertical red dotted lines indicate the characteristic peaks of CePO
4
and the dash lines indicate the characteristic peaks of CeO
2
NPs.
List of Tables
Chapter 3: Stable Isotopic Tracing of Nanomaterials In Vivo
Table 3.1 Stable-isotope-labeled nanomaterials and their structure and nanobiological effects
Table 3.2 Natural Zn isotope abundances and isotopic enrichment and approximate cost of commercially available enriched Zn isotopes [88]
Chapter 4: Radiolabeling of Nanoparticles
Table 4.1 SuiTable isotopes for radiotracer research
Table 4.2 An overview of radiolabeling of iron oxide NPs for multimodal imaging
Chapter 7: In Vivo Nanotoxicity Assays in Animal Models
Table 7.1 Main features of animal models widely used in nanotoxicology
Chapter 8: In Vitro Testing Methods for Nanomaterials
Table 8.1
In vitro
methods and in nanotoxicology studies
Chapter 12: “Omic” Techniques for Nanosafety
Table 12.1 KEGG pathway analysis of gene expression data from
Daphnia magna
exposed to Ag NWs identified the enrichments of different biological pathways.
a
Edited by Yuliang Zhao, Zhiyong Zhang, and Weiyue Feng
Toxicology of Nanomaterials
Editors
Prof. Yuliang Zhao
Chinese Academy of Sciences
Center for Nanosciences and Technology
19B Yuquan Road
100049 Beijing
China
Prof. Zhiyong Zhang
Chinese Academy of Sciences
Key Laboratory of Biomed Effects of Nanomaterials
19B Yuquan Road
100049 Beijing
China
Prof. Weiyue Feng
Chinese Academy of Sciences
Key Laboratory of Biomed Effects of Nanomaterials
19B Yuquan Road
100049 Beijing
China
Cover
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List of Contributors
Xueling Chang
Institute of High Energy Physics
CAS Key Laboratory for Biomedical Effects of Nanomaterials and Nanosafety
Chinese Academy of Sciences
Beijing 100049
China
Chunying Chen
CAS Key Laboratory for Biomedical Effects of Nanomaterials and Nanosafety National Center for Nanoscience and Technology
Beijing 100190
China
Weiyue Feng
Institute of High Energy Physics CAS Key Laboratory for Biomedical Effects of Nanomaterials and Nanosafety Chinese Academy of Sciences
Beijing 100049
China
Yuxi Gao
Institute of High Energy Physics CAS Key Laboratory for Biomedical Effects ofNanomaterials and Nanosafety Chinese Academy of Sciences
Beijing 100049
China
Zhanjun Gu
Institute of High Energy Physics CAS Key Laboratory for Biomedical Effects of Nanomaterials and Nanosafety Chinese Academy of Sciences
Beijing 100049
China
Xiao He
Institute of High Energy Physics CAS Key Laboratory for Biomedical Effects of Nanomaterials and Nanosafety Chinese Academy of Sciences
Beijing 100049
China
Chenchen Li
Shanghai University
Institute of Nanochemistry and Nanobiology
Shanghai 200444
China
Wei Li
Institute of High Energy Physics CAS Key Laboratory for Biomedical Effects of Nanomaterials and Nanosafety Chinese Academy of Sciences
Beijing 100049
China
and
Wuhan Institute of Virology Chinese Academy of Sciences
Wuhan 430071
China
Yu-Feng Li
Institute of High Energy Physics CAS Key Laboratory for Biomedical Effects of Nanomaterials and Nanosafety Chinese Academy of Sciences
Beijing 100049
China
Xueying Liu
Institute of High Energy Physics CAS Key Laboratory for Biomedical Effects of Nanomaterials and Nanosafety Chinese Academy of Sciences
Beijing 100049
China
Yuhui Ma
Institute of High Energy Physics CAS Key Laboratory for Biomedical Effects of Nanomaterials and Nanosafety Chinese Academy of Sciences
Beijing 100049
China
Bing Wang
Institute of High Energy Physics CAS Key Laboratory for Biomedical Effects of Nanomaterials and Nanosafety Chinese Academy of Sciences
Beijing 100049
China
Liming Wang
Institute of High Energy Physics CAS Key Laboratory for Biomedical Effects of Nanomaterials and Nanosafety Chinese Academy of Sciences
Beijing 100049
China
Meng Wang
Institute of High Energy Physics CAS Key Laboratory for Biomedical Effects of Nanomaterials and Nanosafety Chinese Academy of Sciences
Beijing 100049
China
Yanli Wang
Shanghai University
Institute of Nanochemistry and Nanobiology
Shanghai 200444
China
Liang Yan
Institute of High Energy Physics CAS Key Laboratory for Biomedical Effects of Nanomaterials and Nanosafety Chinese Academy of Sciences
Beijing 100049
China
Peng Zhang
Institute of High Energy Physics CAS Key Laboratory for Biomedical Effects of Nanomaterials and Nanosafety Chinese Academy of Sciences
Beijing 100049
China
Zhiyong Zhang
Institute of High Energy Physics CAS Key Laboratory for Biomedical Effects of Nanomaterials and Nanosafety Chinese Academy of Sciences
Beijing 100049
China
Feng Zhao
Institute of High Energy Physics CAS Key Laboratory for Biomedical Effects of Nanomaterials and Nanosafety Chinese Academy of Sciences
Beijing 100049
China
Jiating Zhao
Institute of High Energy Physics CAS Key Laboratory for Biomedical Effects of Nanomaterials and Nanosafety Chinese Academy of Sciences
Beijing 100049
China
Lina Zhao
Institute of High Energy Physics CAS Key Laboratory for Biomedical Effects of Nanomaterials and Nanosafety Chinese Academy of Sciences
Beijing 100049
China
Yuliang Zhao
Institute of High Energy Physics CAS Key Laboratory for Biomedical Effects of Nanomaterials and Nanosafety Chinese Academy of Sciences
Beijing 100049
China
Preface
After more than 30 years of basic and applied research, nanotechnology is coming to play a big role in almost all of our lives, ranging from industry, food, and agriculture to biomedicine, and so on. Nanomaterials are known as the most important bases of nanotechnology and possess more novel and unique physicochemical properties than bulk materials. So, the impacts of their unpredictable behaviors on human health and the environment undoubtedly cause public concern. The understanding of the safety and potential hazards of engineered nanomaterials (ENMs), that is, nanotoxicity, has witnessed an explosion in the past decade and become one of key issues in nanotechnology, in particularly, the sustainable development of nanotechnology.
The study of toxicology of nanomaterials, unlike the classic one for those ordinary chemical compounds, should be approached by many ways, as multiparameters associated with the size, shape, chemical composition, crystalline structure, aspect ratio, surface property (chemical modification, surface charge, surface area, biological/chemical activity, etc.), agglomeration, concentration, and so on, likely combine to contribute to the overall toxicity. To obtain the whole picture, the advanced methods with integrated techniques for quantitatively monitoring the biological responses with material-specific or exposure-route-specific are needed. Moreover, it is expected that some new techniques, such as synchrotron-radiation-based analytical techniques, high-throughput “omic” techniques, in situ, and in vivo image techniques, as well as computational biology are involved for the exploration of exposure, early effect, differentially sensitive targets, and molecular mechanisms of ENMs in biological systems and, furthermore, trigger revolutionary research to understand the complex reactions of nanomaterials occurring at a nano–bio interface of biological or environmental systems.
Toxicology of Nanomaterials focuses on topics describing the current tools and methods that have been developed to study nanomaterial effects on biological and environmental systems, including the following: Characterization of Nanomaterials in Nanotoxicological Analyses (Ma Yuhui); Quantitative Analysis of Metal-Based Nanomaterials in Biological Samples Using inductively coupled plasma–mass spectrometry (ICP-MS) (Wang Meng); Stable Isotopic Tracing of Nanomaterials In Vivo (Chang Xueling, Zhao Yuliang); Radiolabeling of Nanoparticles (Zhang Zhiyong); New Methods for Nanotoxicity Analyses: Synchrotron-Radiation-Based Techniques (Wang Bing, Feng Weiyue); Imaging Techniques in Nanotoxicology Research (Yan Liang, Li Yufeng, Gu Zhanjun); In Vivo Nanotoxicity Assays in Animal Models (He Xiao); In Vitro Testing Methods for Nanomaterials (Zhao Feng, Liu Xueying); Localizing cellular uptake of nanomaterials (Li wei); Methods and Techniques in Molecular Toxicology of Nanomaterials (Wang Yanli, Li Chenchen, Chen Chunying); Analyses Methods for Nanoparticle Interaction with Biomacromolecules (Wang Liming, Chen Chunying); Omics Techniques in Nanosafety (Feng Weiyue); Nanometallomics: New Approach on Analyzing Biological Effects of Metal-Related Nanomaterials (Li Yufeng, Zhao Jiating, Gao Yuxi, Chen Chunying); Molecular Simulation Methods for safety Analyses of Nanomaterials (Zhao Lina): Ecotoxicity Analyses of Nanomaterials (Zhang Peng). Excepting Yanli Wang, all the other authors are from Chinese Academy of Sciences Key Laboratory for Biomedical Effects of Nanomaterials and Nanosafety. Please note that the book is not possible to describe detailed principles of all the aforementioned analyses methods, but describes how to apply these methods in the study of nanotoxicology.
The outcomes from more than 10 years of nanosafety research have shown that the interactions between nanomaterials and cells, animals, humans, or the environment are remarkably complex. Thus, this book also intends to give the state-of-art information on multidisciplinary techniques from biology, chemistry, and physics that enables the study of nanotoxicology. The book is designed to benefit researchers who plan to investigate nanotoxicology, nanomedicines, nanobiotechnology, and biomedical nanomaterials, nanochemistry, nanobioanalytical sciences, and so on, in particularly, to understand how the physical, chemical, and other properties of nanomaterials influence their biological/environmental behaviors and interactions and thus determine the ultimate impacts on health and the environment, and to design/synthesize/manufacture safer nanomaterials in various applications.
Beijing June, 2016
Yuliang Zhao, Zhiyong Zhang, and Weiyue Feng
Abbreviations
2-DGE
two-dimensional gel electrophoresis
ADME
absorption, distribution, metabolism, and excretion
AES
auger electron spectroscopy
AFM
atomic force microscope
AMBER
assisted model building with energy refinement
AMs
alveolar macrophages
AMU
atomic mass unit
ARDRA
amplified ribosomal DNA restriction analysis
BALF
bronchoalveolar lavage fluid
BBB
blood–brain barrier
BFG
bovine fibrinogen
BO
bond order
BSA
bovine serum albumin
C2
molecule of two C atoms
CAT
catalase
CD
circular dichroism
CE
capillary electrophoresis
CE-MS
capillary electrophoresis–mass spectrometry
CHARMM
chemistry Harvard macromolecular mechanics
CMAP
cross-term map
CNS
central nervous system
CTAB
cetyltrimethylammonium bromide
CT-SPECT
computed tomography coregistered with single-photon emission computerized tomography
Cyt c
cytochrome c
DCFH-DA
dichlorodihydrofluorescein diacetate
DCS
differential centrifugal sedimentation
DFT
density function theory
DGGE
denaturing gradient gel electrophoresis
DLS
dynamic light scattering
DMEM
Dulbecco's modified eagle's medium
DOTA
1,4,7,10-tetraazacyclododecane-tetraacetic acid
dsDNA
double-strand DNA
EDX or EDS
energy-dispersive X-ray spectroscopy
EE
electron equilibration
EELS
electron energy-loss spectroscopy
EGP
effective group potentials
ELISA
enzyme-linked immunosorbent assay
ENM
engineered nanomaterial
EPR
electron paramagnetic resonance
ESCA
electron spectroscopy for chemical analysis
ESEM
environmental scanning electronic microscopy
ESI-MS
electrospray ionization–mass spectrometry
EXAFS
extended X-ray absorption fine structure
FBS
fetal bovine serum
FCM
flow cytometry
FFF
field flow fractionation
FISH
fluorescence in situ hybridization
FTIR
Fourier-transform infrared spectroscopy
GC
gas chromatography
GC-MS
gas chromatography–mass spectrometry
GE
gel electrophoresis
GGA
generalized gradient approximation
GHO
generalized hybrid orbital
GO
graphene oxide
GROMOS
Groningen molecular simulation
GPx
glutathione peroxidase
HCS
high-content screening
HDC
hydrodynamic chromatography
HPLC
high-performance liquid chromatography
HPRT
hypoxanthine-guanine phosphoribosyltransferase
HSA
human serum albumin
ICP-MS
inductively coupled plasma–mass spectrometry
ICP-OES
inductively coupled plasma–optical emission spectrometer
Ig
gamma globulin
IgG
immunoglobulin G
IR
infrared spectroscopy
IRMS
isotope ratio mass spectrometry
ITC
isothermal titration calorimetry
LA-ICP-MS
laser ablation–inductively coupled plasma–mass spectrometry
LC-MS
liquid chromatography–mass spectroscopy
LCSM
laser confocal scanning microscopy
LDH
lactate dehydrogenase
LEIS
low-energy ion scattering
LSCF
local self-consistent field
LSDA
local spin density approximation
MALDI-TOF-MS
matrix-assisted laser desorption/ionization time-of-flight mass spectrometry
MD
molecular dynamics
MDA
malondialdehyde
MFM
multiphoton fluorescence microscope
MMP-9
matrix metalloproteinases-9
MN
micronucleus
MRI
magnetic resonance imaging
MTT
3-(4,5-dimethylthiazol-2-yl)-2,5-diphenyltetrazolium bromide
MWCNTs
multiwalled carbon nanotubes
NAA
neutron activation analysis
NADH
nicotinamide adenine dinucleotide
NADPH
nicotinamide adenine dinucleotide phosphate
NanoXRF
SRXRF with the nanosized spatial resolution
NEGF
nonequilibrium Green's function
NM
nanomaterial
NMR
nuclear magnetic resonance
NOM
natural organic matters
NOTA
1,4,7-triazacyclononane-1,4,7-triacetic acid
NP
nanoparticle
OPLS-AA
optimized potentials for liquid simulations-all atom
PAA
poly(acrylic acid)
PC
protein corona
PCR-DGGE
polymerase chain reaction–denaturing gradient gel electrophoresis
PET
positron emission tomography
PLFA
phospholipid fatty acid
PME
particle mesh Ewald
PMF
potential of mean force
PVP
polyvinyl pyrrolidone
QDs
quantum dots
QEq
Q Equilibration
QM/MM
quantum mechanics/molecular mechanics
qPCR
quantitative polymerase chain reaction
REMD
replica-exchange molecular dynamics
RES
reticuloendothelial systems
REST
replica exchange with solvent tempering
rGO
reduced graphene oxide
RMSD
root mean square deviation
RMSF
root mean square fluctuation
ROS
reactive oxygen species
RT-PCR
reverse transcription polymerase chain reaction
SAXS
small-angle X-ray scattering
SBO
sum of π-bond order
SCE
sister chromatid exchange
SCGE
single-cell gel electrophoresis assay
SDS-PAGE
sodium dodecylsulphate–polyacrylamide gel electrophoresis
SEC
size-exclusion chromatography
SEM
scanning electron microscope
SERS
surface-enhanced Raman spectroscopy
SIEMN
inhalation exposure for manufactured nanomaterials
SIMS
secondary ion mass spectroscopy
SOD
superoxide dismutase
SPECT
single-photon emission computerized tomography
sp-ICP-MS
single-particle inductively coupled plasma–mass spectrometry
SPIONs
superparamagnetic iron oxide nanoparticles
SQUID
superconducting quantum interference device
SR
synchrotron radiation
SRXRF
synchrotron radiation X-ray fluorescence
STM
scanning tunneling microscopy
STXM
scanning transmission X-ray microscopy
SWCNTs
single-walled carbon nanotubes
TEM
transmission electron microscope
TETA
1,4,8,11-tetraazacyclotetradecane-
N
,
N
′,
N
″,
N
′″-tetraacetic acid
Tf
transferrin
TOF-SIMS
time-of-flight secondary-ion mass spectrometry
T-RFLP
terminal restriction fragment length polymorphism
UV-Vis
ultraviolet–visible spectrophotometry
XANES
X-ray absorption near-edge structure spectra
XAS
X-ray absorption spectroscopy
XPS
X-ray photoelectron spectroscopy
XRD
X-ray diffraction
XRF
X-ray fluorescence
γ-H2AX IF
γ-H2AX immunofluorescence assay
Chapter 1Characterization of Nanomaterials in Nanotoxicological Analyses
Yuhui Ma
1.1 Introduction
In accordance with the European Commission's Recommendation, “Nanomaterial” is defined as a natural, incidental, or manufactured material containing particles, in an unbound state or as an aggregate or as an agglomerate and where, for 50% or more of the particles in the number size distribution, one or more external dimensions are in the size range 1–100 nm [1]. Nanomaterials (NMs) have attracted great attention because of their unique physical, chemical, and mechanical properties that differ from those of bulk solids and molecules, which enabled them to be widely used in the fields of electronics, chemical industry, medicine, machinery, energy, and so on. With the widespread applications of NMs, the environmental and health impacts of these materials have caused the attention of scientific community, regulatory agencies, environmentalists, industry representatives, and the public. They all agree that more efforts are required to ensure the responsible and safe development of new nanotechnologies. Characterization of NMs is a key aspect in this effort because physicochemical properties of NMs are important factors determining their biological effects and environmental fate. However, there is no universal agreement upon the minimum set of characteristics, although certain common properties are included in most recommendations. Particle characterization is an essential aspect of any attempt to assess potential biological effects of nanoparticulate systems. The thorough characterization of NMs is a daunting task, especially in the context of a complex biological environment. The characteristics of NMs should be measured under conditions as close to the point of application as possible. For toxicology studies, this should include, if possible, the biological environment. For example, if in vitro cell studies are being conducted, the particle size should be measured in cell culture media or at least under the same pH and ionic strength conditions.
Physicochemical properties are the basis for understanding the biological effects of test materials. In this chapter, we emphasize and illustrate the major characterization parameters, including size and size distribution, shape, agglomeration state, crystal structure, chemical composition, surface area, surface chemistry, and surface charge, which should be investigated before, during, and after administration. In addition, the available analytical techniques, methods, and procedures are evaluated to be capable of detecting and quantifying NMs during in vivo/in vitro studies. These topics provide a comprehensive review of more adequate characterization techniques, methods, and procedures.
1.2 Size and Morphology of NMs
1.2.1 Transmission Electron Microscopy (TEM)
TEM has become one of the most powerful characterization tools in NM research, which provides direct images and information such as the size, shape, morphology, agglomeration state, and crystalline structure of particles at a spatial resolution down to the level of atomic dimensions (<1 nm) [2]. In the conventional TEM mode, an incident electron beam is transmitted through a very thin foil specimen, during which the incident electrons interacting with specimen are transformed to unscattered electrons, elastically scattered electrons, or inelastically scattered electrons [3]. The magnification of TEM is mainly determined by the ratio of the distance between objective lens and the specimen and the distance between objective lens and its image plane. The scattered or unscattered electrons are focused by a series of electromagnetic lenses and then projected on a screen to generate an electron diffraction (ED), amplitude-contrast image, a phase-contrast image, or a shadow image of varying darkness according to the density of unscattered electrons [3]. In addition to the high spatial resolution of TEM, one should ensure that enough particles are examined to provide statistically valid representation of the full size or shape distribution. This can be very difficult and time-consuming and may require the image analysis of literally thousands of individual particles. There are many commercial automated image analysis systems and computer software packages that are used for this purpose. Although TEM is a useful characterization tool, a wide variety of analytical techniques can be coupled with TEM for different applications; for example, energy-dispersive spectroscopy (EDS), ED, or electron energy-loss spectroscopy (EELS) may be useful for determining additional characterization parameters such as chemical composition and speciation at the atomic scale.
However, there are certain drawbacks accompanying the advantages of TEM. Since a high vacuum and thin sample section are required for electron-beam penetration in TEM measurement, care should be taken to validate the system used against standardized materials and sample preparation [4]. The representativeness of the sample depends on their dispersion, so it is necessary to select the appropriate disperse conditions to achieve a uniform dispersion of the particles. It should also be noted that electron microscopy normally provides only two-dimensional images, so care must be taken to avoid bias introduced by orientation effects. High-resolution microscopy is subject to artifacts caused by sample preparation or special analysis conditions.
1.2.2 Scanning Electron Microscopy (SEM)
SEM is a surface imaging method in which the incident electron beam scans across the sample surface and interacts with the sample to generate signals reflecting the topographic detail of the specimen surface [4, 5]. The incident electrons cause emissions of elastic scattering of electrons, referring to backscattered electrons, low-energy secondary electrons, and cathodoluminescence from the atoms on the sample surface or near-surface material. Among these emissions, detection of the secondary electrons is the most common mode in SEM and can achieve resolution smaller than 1 nm [5]. It does not require electron-beam penetration in SEM measurement, so it can be used for bulk samples, except for soft biological tissues, which contain large amounts of water.
The size, size distribution, and shape of NMs can be directly acquired from SEM. For conducting materials, the sample preparation is simple, with the size and weight of samples being required for different SEM sample rooms. While for many biological samples with poor electrical conductivity or even insulator, the surface of specimens should be coated by spraying an ultrathin layer of electrically conducting material, such as gold, silver, or other precious metals [4]. When the size of the particles was below 10 nm, the sample cannot be sprayed by gold, for the size of this coating is about 8 nm. The carbon evaporation coating is an alternative method. In short, the samples for SEM should be dry and conductive, as well as the surface structure should be well preserved without deformation or contamination.
