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Quaternary ammonium compounds (QACs) are widely used as active agents in disinfectants, antiseptics, and preservatives. Despite being in use since the 1940s, there remain multiple open questions regarding their detailed mode-of-action and the mechanisms, including phenotypic heterogeneity, that can make bacteria less susceptible to QACs. To facilitate studies on resistance mechanisms towards QACs, we synthesized a fluorescent quaternary ammonium compound, namely N-dodecyl-N,N-dimethyl-[2-[(4-nitro-2,1,3-benzoxadiazol-7-yl)amino]ethyl]azanium-iodide (NBD-DDA). NBD-DDA is readily detected by flow cytometry and fluorescence microscopy with standard GFP/FITC-settings, making it suitable for molecular and single-cell studies. As a proof-of-concept, NBD-DDA was then used to investigate resistance mechanisms which can be heterogeneous among individual bacterial cells. Our results reveal that the antimicrobial activity of NBD-DDA against Escherichia coli, Staphylococcus aureus and Pseudomonas aeruginosa is comparable to that of benzalkonium chloride (BAC), a widely used QAC, and benzyl-dimethyl-dodecylammonium chloride (BAC12), a mono-constituent BAC with alkyl-chain length of 12 and high structural similarity to NBD-DDA. Characteristic time-kill kinetics and increased tolerance of a BAC tolerant E. coli strain against NBD-DDA suggest that the mode of action of NBD-DDA is similar to that of BAC. As revealed by confocal laser scanning microscopy (CLSM), NBD-DDA is preferentially localized to the cell envelope of E. coli, which is a primary target of BAC and other QACs. Leveraging these findings and NBD-DDA‘s fluorescent properties, we show that reduced cellular accumulation is responsible for the evolved BAC tolerance in the BAC tolerant E. coli strain and that NBD-DDA is subject to efflux mediated by TolC. Overall, NBD-DDA’s antimicrobial activity, its fluorescent properties, and its ease of detection render it a powerful tool to study resistance mechanisms of QACs in bacteria and highlight its potential to gain detailed insights into its mode-of-action.
Stability, dissolution, and cytotoxicity of NaYF4‑upconversion nanoparticles with different coatings
(2022)
Upconversion nanoparticles (UCNPs) have attracted considerable attention owing to their unique photophysical properties. Their utilization in biomedical applications depends on the understanding of their transformations under physiological conditions and their potential toxicity. In this study, NaYF4: Yb,Er UCNPs, widely used for luminescence and photophysical studies, were modified with a set of four different coordinatively bound surface ligands, i.e., citrate, alendronate (AA), ethylendiamine tetra(methylene phosphonate) (EDTMP), and poly(maleic anhydride-alt-1-octadecene) (PMAO), as well as silica coatings with two different thicknesses. Subsequently, the aging-induced release of fluoride ions in water and cell culture media and their cytotoxic profile to human keratinocytes were assessed in parallel to the cytotoxic evaluation of the ligands, sodium fluoride and the lanthanide ions. The cytotoxicity studies of UCNPs with different surface modifications demonstrated the good biocompatibility of EDTMP-UCNPs and PMAO-UCNPs, which is in line with the low amount of fluoride ions released from these samples. An efficient prevention of UCNP dissolution and release of cytotoxic ions, as well as low cytotoxicity was also observed for UCNPs with a sufficiently thick silica shell. Overall, our results provide new insights into the
understanding of the contribution of surface chemistry to the stability, dissolution behavior, and cytotoxicity of UCNPs. Altogether, the results obtained are highly important for future applications of UCNPs in the life sciences and bioimaging studies.
Imaging in the shortwave-infrared region (SWIR, λ = 1000–2500 nm) has the potential to enable deep tissue imaging with high resolution. Critical to the development of these Methods is the identification of low molecular weight, biologically compatible fluorescent probes that emit beyond 1000 nm.
Exchanging the bridging oxygen atom on the xanthene scaffold (C10’ position) with electron withdrawing groups has been shown to lead to significant redshifts in absorbance and emission. Guided by quantum chemistry computational modeling studies, we investigated the installation of a Ketone bridge at the C10’ position. This simple modification extends the absorbance maxima to 860 nm and the emission beyond 1000 nm, albeit with reduced photon output. Overall, These studies demonstrate that broadly applied xanthene dyes can be extended into the SWIR range.
Risk assessment of nanomaterials requires not only standardized toxicity studies but also validated methods for nanomaterial surface characterization with known uncertainties. In this context, a first bilateral interlaboratory comparison on Surface group quantification of nanomaterials is presented that assesses different reporter-free and labeling methods for the quantification of the total and accessible number of amine functionalities on commercially available silica nanoparticles that are widely used in the life sciences. The overall goal of this comparison is the identification of optimum methods as well as achievable measurement uncertainties and the comparability of the results across laboratories. We also examined the robustness and ease of implementation of the applied analytical methods and discussed method-inherent limitations. In summary, this comparison presents a first step toward the eventually required standardization of methods for surface group quantification.
This dataset accompanies the following publication, first published in Scientific Reports (www.nature.com/articles/s41598-021-84287-6):
B. Ruehle, J. Krumrey, V.-D. Hodoroaba, Scientific Reports, Workflow towards Automated Segmentation of Agglomerated, Non-Spherical Particles from Electron Microscopy Images using Artificial Neural Networks, DOI: 10.1038/s41598-021-84287-6
It contains electron microscopy micrographs of TiO2 particles, the corresponding segmentation masks, and their classifications into different categories depending on their visibility/occlusion. Please refer to the publication and its supporting information for more details on the acquisition and contents of the dataset, as well as the GitHub repository at https://github.com/BAMresearch/automatic-sem-image-segmentation
We present a workflow for obtaining fully trained artificial neural networks that can perform automatic particle segmentations of agglomerated, non-spherical nanoparticles from electron microscopy images “from scratch”, without the need for large training data sets of manually annotated images. This is achieved by using unsupervised learning for most of the training dataset generation, making heavy use of generative adversarial networks and especially unpaired image-to-image translation via cycle-consistent adversarial networks. The whole process only requires about 15 minutes of hands-on time by a user and can typically be finished within less than 12 hours when training on a single graphics card (GPU). After training, SEM image analysis can be carried out by the artificial neural network within seconds, and the segmented images can be used for automatically extracting and calculating various other particle size and shape descriptors.
We present a workflow for obtaining fully trained artificial neural networks that can perform automatic particle segmentations of agglomerated, non-spherical nanoparticles from scanning electron microscopy images “from scratch”, without the need for large training data sets of manually annotated images. The whole process only requires about 15 minutes of hands-on time by a user and can typically be finished within less than 12 hours when training on a single graphics card (GPU). After training, SEM image analysis can be carried out by the artificial neural network within seconds. This is achieved by using unsupervised learning for most of the training dataset generation, making heavy use of generative adversarial networks and especially unpaired image-to-image translation via cycle-consistent adversarial networks. We compare the segmentation masks obtained with our suggested workflow qualitatively and quantitatively to state-of-the-art methods using various metrics. Finally, we used the segmentation masks for automatically extracting particle size distributions from the SEM images of TiO2 particles, which were in excellent agreement with particle size distributions obtained manually but could be obtained in a fraction of the time.
In this contribution different ways are explored with the aim to generate suitable training data for ‘non-ideal’ samples using various approaches, e.g., computer-generated images or unsupervised learning algorithms such as generative adversarial networks (GANs). We used these data to train simple CNNs to produce segmentation masks of SEM images and tested the trained networks on real SEM images of complex nanoparticle samples. The novel use of CNN for the automated analysis of the size of nanoparticles of complex shape and with a high degree of agglomeration has proved to be a promising tool for the evaluation of particle size distribution on a large number of constituent particles. Further development and validation of the preliminary model, respectively larger training and validation data sets are necessary.
We studied the dissolution behavior of β NaYF4:Yb(20%), Er(2%) UCNP of two different sizes in biologically relevant media i.e., water (neutral pH), phosphate buffered saline (PBS), and Dulbecco’s modified Eagle medium (DMEM) at different temperatures and particle concentrations. Special emphasis was dedicated to assess the influence of different surface functionalizations, particularly the potential of mesoporous and microporous silica shells of different thicknesses for UCNP stabilization and protection. Dissolution was quantified electrochemically using a fluoride ion selective electrode (ISE) and by inductively coupled plasma optical emission spectrometry (ICP OES). In addition, dissolution was monitored fluorometrically. These experiments revealed that a thick microporous silica shell drastically decreased dissolution. Our results also underline the critical influence of the chemical composition of the aqueous environment on UCNP dissolution. In DMEM, we observed the formation of a layer of adsorbed molecules on the UCNP surface that protected the UCNP from dissolution and enhanced their fluorescence. Examination of this layer by X ray photoelectron spectroscopy (XPS) and mass spectrometry (MS) suggested that mainly phenylalanine, lysine, and glucose are adsorbed from DMEM. These findings should be considered in the future for cellular toxicity studies with UCNP and other nanoparticles and the design of new biocompatible surface coatings.
Organic and inorganic nanoparticles (NPs) are increasingly used as drug carriers, fluorescent sensors, and multimodal labels in the life and material sciences. These applications require knowledge of the chemical nature, total number of surface groups, and the number of groups accessible for subsequent coupling of e.g., antifouling ligands, targeting bioligands, or sensor molecules. To establish the concept of catch-and-release assays, cleavable probes were rationally designed from a quantitatively cleavable disulfide moiety and the optically detectable reporter 2-thiopyridone (2-TP). For quantifying surface groups on nanomaterials, first, a set of monodisperse carboxy-and amino-functionalized, 100 nm-sized polymer and silica NPs with different surface group densities was synthesized. Subsequently, the accessible functional groups (FGs) were quantified via optical spectroscopy of the cleaved off reporter after its release in solution. Method validation was done with inductively coupled plasma optical emission spectroscopy (ICP-OES) utilizing the sulfur atom of the cleavable probe. This comparison underlined the reliability and versatility of our probes, which can be used for surface group quantification on all types of transparent, scattering, absorbing and/or fluorescent particles. The correlation between the total and accessible number of FGs quantified by conductometric titration, qNMR, and with our cleavable probes, together with the comparison to results of conjugation studies with differently sized biomolecules reveal the potential of catch-and-release reporters for surface analysis. Our findings also underline the importance of quantifying particularly the accessible amount of FGs for many applications of NPs in the life sciences.