ICP-MS-based Approach to Determine Nanoparticle Recovery After Hollow Fiber Flow Field Flow Fractionation.

Compared to the classical chemicals, nanoparticles (NPs) exhibit unique properties, which lead to challenges in sample preparation and analysis. Fractionation techniques and, in particular, hollow fiber flow field flow fractionation (HF5) have recently become popular in the characterization and quantification of nanomaterials, because of their fine fractionation capability in the nanoscale-range. When dealing with NPs, a great drawback during fractionation is the loss of particles in the fractionation devices, tubing and connectors. There is a need for studies to systematically explore and assess the quality of the fractionation process. A combination of two complementary mass-based setups was used to determine particle loss in HF5. Inductively coupled plasma mass spectrometry (ICP-MS) enabled the estimation of recovery rates for NPs after HF5 separation. Reciprocally, laser ablation ICP-MS (LA-ICP-MS) permitted the evaluation of particles retained on the hollow fiber. 15 nm Au-NPs in different concentrations were evaluated in this study and showed a recovery level for Au-NPs of 50 - 65% based on the applied concentrations after a complete HF5 separation run. Detection of sample deposition on the hollow fiber by LA-ICP-MS indicated a sample loss of about 8%. These findings are important for experiments relying on fractionation of low concentrated nanoparticulate samples.

Combinatory Effects of Cerium Dioxide Nanoparticles and Acetaminophen on the Liver-A Case Study of Low-Dose Interactions in Human HuH-7 Cells.

The interactions between pharmaceuticals and nanomaterials and its potentially resulting toxicological effects in living systems are only insufficiently investigated. In this study, two model compounds, acetaminophen, a pharmaceutical, and cerium dioxide, a manufactured nanomaterial, were investigated in combination and individually. Upon inhalation, cerium dioxide nanomaterials were shown to systemically translocate into other organs, such as the liver. Therefore we picked the human liver cell line HuH-7 cells as an in vitro system to investigate liver toxicity. Possible synergistic or antagonistic metabolic changes after co-exposure scenarios were investigated. Toxicological data of the water soluble tetrazolium (WST-1) assay for cell proliferation and genotoxicity assessment using the Comet assay were combined with an untargeted as well as a targeted lipidomics approach. We found an attenuated cytotoxicity and an altered metabolic profile in co-exposure experiments with cerium dioxide, indicating an interaction of both compounds at these endpoints. Single exposure against cerium dioxide showed a genotoxic effect in the Comet assay. Conversely, acetaminophen exhibited no genotoxic effect. Comet assay data do not indicate an enhancement of genotoxicity after co-exposure. The results obtained in this study highlight the advantage of investigating co-exposure scenarios, especially for bioactive substances.

Versatile Dual-Inlet Sample Introduction System for Multi-Mode Single Particle Inductively Coupled Plasma Mass Spectrometry Analysis and Validation.

Metal-containing nanoparticles (NP) can be characterized with inductively coupled plasma mass spectrometers (ICP-MS) in terms of their size and number concentration by using the single-particle mode of the instrument (spICP-MS). The accuracy of measurement depends on the setup, operational conditions of the instrument and specific parameters that are set by the user. The transport efficiency of the ICP-MS is crucial for the quantification of the NP and usually requires a reference material with homogenous size distribution and a known particle number concentration. Currently, NP reference materials are available for only a few metals and in limited sizes. If particles are characterized without a reference standard, the results of both size and particle number may be biased. Therefore, a dual-inlet setup for characterizing nanoparticles with spICP-MS was developed to overcome this problem. This setup is based on a conventional introduction system consisting of a pneumatic nebulizer (PN) for nanoparticle solutions and a microdroplet generator (µDG) for ionic calibration solutions. A new and flexible interface was developed to facilitate the coupling of µDG, PN and the ICP-MS system. The interface consists of available laboratory components and allows for the calibration, nanoparticle (NP) characterization and cleaning of the arrangement, while the ICP-MS instrument is still running. Three independent analysis modes are available for determining particle size and number concentration. Each mode is based on a different calibration principle. While mode I (counting) and mode III (µDG) are known from the literature, mode II (sensitivity), is used to determine the transport efficiency by inorganic ionic standard solutions only. It is independent of NP reference materials. The µDG based inlet system described here guarantees superior analyte sensitivities and, therefore, lower detection limits (LOD). The size dependent LODs achieved are less than 15 nm for all NP (Au, Ag, CeO2) investigated.

Artificial Intelligence and Machine Learning in Computational Nanotoxicology: Unlocking and Empowering Nanomedicine.

Advances in nanomedicine, coupled with novel methods of creating advanced materials at the nanoscale, have opened new perspectives for the development of healthcare and medical products. Special attention must be paid toward safe design approaches for nanomaterial-based products. Recently, artificial intelligence (AI) and machine learning (ML) gifted the computational tool for enhancing and improving the simulation and modeling process for nanotoxicology and nanotherapeutics. In particular, the correlation of in vitro generated pharmacokinetics and pharmacodynamics to in vivo application scenarios is an important step toward the development of safe nanomedicinal products. This review portrays how in vitro and in vivo datasets are used in in silico models to unlock and empower nanomedicine. Physiologically based pharmacokinetic (PBPK) modeling and absorption, distribution, metabolism, and excretion (ADME)-based in silico methods along with dosimetry models as a focus area for nanomedicine are mainly described. The computational OMICS, colloidal particle determination, and algorithms to establish dosimetry for inhalation toxicology, and quantitative structure-activity relationships at nanoscale (nano-QSAR) are revisited. The challenges and opportunities facing the blind spots in nanotoxicology in this computationally dominated era are highlighted as the future to accelerate nanomedicine clinical translation.

Tackling Complex Analytical Tasks: An ISO/TS-Based Validation Approach for Hydrodynamic Chromatography Single Particle Inductively Coupled Plasma Mass Spectrometry.

Nano-carrier systems such as liposomes have promising biomedical applications. Nevertheless, characterization of these complex samples is a challenging analytical task. In this study a coupled hydrodynamic chromatography-single particle-inductively coupled plasma mass spectrometry (HDC-spICP-MS) approach was validated based on the technical specification (TS) 19590:2017 of the international organization for standardization (ISO). The TS has been adapted to the hyphenated setup. The quality criteria (QC), e.g., linearity of the calibration, transport efficiency, were investigated. Furthermore, a cross calibration of the particle size was performed with values from dynamic light scattering (DLS) and transmission electron microscopy (TEM). Due to an additional Y-piece, an online-calibration routine was implemented. This approach allows the calibration of the ICP-MS during the dead time of the chromatography run, to reduce the required time and enhance the robustness of the results. The optimized method was tested with different gold nanoparticle (Au-NP) mixtures to investigate the characterization properties of HDC separations for samples with increasing complexity. Additionally, the technique was successfully applied to simultaneously determine both the hydrodynamic radius and the Au-NP content in liposomes. With the established hyphenated setup, it was possible to distinguish between different subpopulations with various NP loads and different hydrodynamic diameters inside the liposome carriers.

The Vitamin A and D Exposure of Cells Affects the Intracellular Uptake of Aluminum Nanomaterials and its Agglomeration Behavior: A Chemo-Analytic Investigation.

Aluminum (Al) is extensively used for the production of different consumer products, agents, as well as pharmaceuticals. Studies that demonstrate neurotoxicity and a possible link to Alzheimer's disease trigger concern about potential health risks due to high Al intake. Al in cosmetic products raises the question whether a possible interaction between Al and retinol (vitamin A) and cholecalciferol (vitamin D3) metabolism might exist. Understanding the uptake mechanisms of ionic or elemental Al and Al nanomaterials (Al NMs) in combination with bioactive substances are important for the assessment of possible health risk associated. Therefore, we studied the uptake and distribution of Al oxide (Al2O3) and metallic Al0 NMs in the human keratinocyte cell line HaCaT. Possible alterations of the metabolic pattern upon application of the two Al species together with vitamin A or D3 were investigated. Time-of-flight secondary ion mass spectrometry (ToF-SIMS) imaging and inductively coupled plasma mass spectrometry (ICP-MS) were applied to quantify the cellular uptake of Al NMs.

Aluminum and aluminum oxide nanomaterials uptake after oral exposure - a comparative study.

The knowledge about a potential in vivo uptake and subsequent toxicological effects of aluminum (Al), especially in the nanoparticulate form, is still limited. This paper focuses on a three day oral gavage study with three different Al species in Sprague Dawley rats. The Al amount was investigated in major organs in order to determine the oral bioavailability and distribution. Al-containing nanoparticles (NMs composed of Al0 and aluminum oxide (Al2O3)) were administered at three different concentrations and soluble aluminum chloride (AlCl3·6H2O) was used as a reference control at one concentration. A microwave assisted acid digestion approach followed by inductively coupled plasma mass spectrometry (ICP-MS) analysis was developed to analyse the Al burden of individual organs. Special attention was paid on how the sample matrix affected the calibration procedure. After 3 days exposure, AlCl3·6H2O treated animals showed high Al levels in liver and intestine, while upon treatment with Al0 NMs significant amounts of Al were detected only in the latter. In contrast, following Al2O3 NMs treatment, Al was detected in all investigated organs with particular high concentrations in the spleen. A rapid absorption and systemic distribution of all three Al forms tested were found after 3-day oral exposure. The identified differences between Al0 and Al2O3 NMs point out that both, particle shape and surface composition could be key factors for Al biodistribution and accumulation.

Improved validation for single particle ICP-MS analysis using a pneumatic nebulizer / microdroplet generator sample introduction system for multi-mode nanoparticle determination.

This study reports on the development of a single-particle (sp) inductively coupled plasma mass spectrometry (ICP-MS) technique suitable for the multi-mode determination of nanoparticle (NP) metal mass fraction and number concentration. The described technique, which is based on a dual inlet system consisting of a pneumatic nebulizer (PN) and a microdroplet generator (MDG), allows for the sequential introduction of ionic metal calibrant solutions and nanoparticle suspensions via all combinations of the two inlets; thus allowing for a combination of three independent modes of analysis. A novel interface, assembled using standard analytical components (a demountable quartz ICP-MS torch, flexible non-conducting silicon tubing and various connectors), was used to interface the dual inlet system to an ICP-MS. The interface provided improved functionality, compared to a previous design. It is now possible to conveniently exchange and introduce standard solutions and samples via all inlet combinations, analyze them, and also wash the sample inlet systems while the whole setup is still connected to an operating ICP-MS. This setup provided seamless and robust operation in a total of three analysis modes, i.e. three ways to independently determine the metal mass fraction and NP number concentration. All three analyses modes could be carried out within a single analytical run lasting approximately 20 min. The unique feature of the described approach is that each analysis mode is based on a different calibration principle, thus constituting an independent way to determine metal mass fractions and nanoparticle number concentrations. Conducting the three independent state-of-the-art analysis, within a single analytical run, improves substantially the validation capabilities of sp-ICP-MS for NP analysis. To assess the technique's analytical performance, Au, Ag and CeO2 nanoparticles were analyzed. The determined average diameters for Au (56.7 ± 1.5 nm), Ag (72.8 ± 3.4 nm) and CeO2 (69.0 ± 6.4 nm) NPs were in close agreement for all three modes of analysis, as well as with the values provided by suppliers' for Au and Ag NPs (56.0 ± 0.5 for Au, 74.6 ± 3.8 nm for Ag). However, the determined average value for CeO2 was much higher than the expected 28.4 ± 10.4 nm, possibly due to NP agglomeration and the inability to detect NPs existing within the lower size range. The determined NP number concentrations, using analysis modes -I and -II, gave recoveries between 91 and 100% for the Au and Ag NP number concentrations. Whereas analysis mode -III showed a recovery of 70-88% for the same materials. Because of the polydispersity, the small size and polyhedral shape of the CeO2 NPs it was not possible to make NP number concentration comparisons for this material.

Morphology-Based Distinction Between Healthy and Pathological Cells Utilizing Fourier Transforms and Self-Organizing Maps.

The appearance and the movements of immune cells are driven by their environment. As a reaction to a pathogen invasion, the immune cells are recruited to the site of inflammation and are activated to prevent a further spreading of the invasion. This is also reflected by changes in the behavior and the morphological appearance of the immune cells. In cancerous tissue, similar morphokinetic changes have been observed in the behavior of microglial cells: intra-tumoral microglia have less complex 3-dimensional shapes, having less-branched cellular processes, and move more rapidly than those in healthy tissue. The examination of such morphokinetic properties requires complex 3D microscopy techniques, which can be extremely challenging when executed longitudinally. Therefore, the recording of a static 3D shape of a cell is much simpler, because this does not require intravital measurements and can be performed on excised tissue as well. However, it is essential to possess analysis tools that allow the fast and precise description of the 3D shapes and allows the diagnostic classification of healthy and pathogenic tissue samples based solely on static, shape-related information. Here, we present a toolkit that analyzes the discrete Fourier components of the outline of a set of 2D projections of the 3D cell surfaces via Self-Organizing Maps. The application of artificial intelligence methods allows our framework to learn about various cell shapes as it is applied to more and more tissue samples, whilst the workflow remains simple.

Cell shape characterization and classification with discrete Fourier transforms and self-organizing maps.

Cells in their natural environment often exhibit complex kinetic behavior and radical adjustments of their shapes. This enables them to accommodate to short- and long-term changes in their surroundings under physiological and pathological conditions. Intravital multi-photon microscopy is a powerful tool to record this complex behavior. Traditionally, cell behavior is characterized by tracking the cells' movements, which yields numerous parameters describing the spatiotemporal characteristics of cells. Cells can be classified according to their tracking behavior using all or a subset of these kinetic parameters. This categorization can be supported by the a priori knowledge of experts. While such an approach provides an excellent starting point for analyzing complex intravital imaging data, faster methods are required for automated and unbiased characterization. In addition to their kinetic behavior, the 3D shape of these cells also provide essential clues about the cells' status and functionality. New approaches that include the study of cell shapes as well may also allow the discovery of correlations amongst the track- and shape-describing parameters. In the current study, we examine the applicability of a set of Fourier components produced by Discrete Fourier Transform (DFT) as a tool for more efficient and less biased classification of complex cell shapes. By carrying out a number of 3D-to-2D projections of surface-rendered cells, the applied method reduces the more complex 3D shape characterization to a series of 2D DFTs. The resulting shape factors are used to train a Self-Organizing Map (SOM), which provides an unbiased estimate for the best clustering of the data, thereby characterizing groups of cells according to their shape. We propose and demonstrate that such shape characterization is a powerful addition to, or a replacement for kinetic analysis. This would make it especially useful in situations where live kinetic imaging is less practical or not possible at all. © 2017 International Society for Advancement of Cytometry.