Distribution of Paramagnetic Fe₂O₃/SiO₂⁻Core/Shell Nanoparticles in the Rat Lung Studied by Time-of-Flight Secondary Ion Mass Spectrometry: No Indication for Rapid Lipid Adsorption

Quadrant darkfield for label-free imaging of intracellular puncta

Significance

Imaging changes in subcellular structure is critical to understanding cell behavior but labeling can be impractical for some specimens and may induce artifacts. Although darkfield microscopy can reveal internal cell structures, it often produces strong signals at cell edges that obscure intracellular details. By optically eliminating the edge signal from darkfield images, we can resolve and quantify changes to cell structure without labeling.

Aim

We introduce a computational darkfield imaging approach named quadrant darkfield (QDF) to separate smaller cellular features from large structures, enabling label-free imaging of cell organelles and structures in living cells.

Approach

Using a programmable LED array as the illumination source, we vary the direction of illumination to encode additional information about the feature size within cells. This is possible due to the varying levels of directional scattering produced by features based on their sizes relative to the wavelength of light used.

Results

QDF successfully resolved small cellular features without interference from larger structures. QDF signal is more consistent during cell shape changes than traditional darkfield. QDF signals correlate with flow cytometry side scatter measurements, effectively differentiating cells by organelle content.

Conclusions

QDF imaging enhances the study of subcellular structures in living cells, offering improved quantification of organelle content compared with darkfield without labels. This method can be simultaneously performed with other techniques such as quantitative phase imaging to generate a multidimensional picture of living cells in real-time.

Quadrant darkfield (QDF) for label-free imaging of intracellular puncta

Significance

Measuring changes in cellular structure and organelles is crucial for understanding disease progression and cellular responses to treatments. A label-free imaging method can aid in advancing biomedical research and therapeutic strategies.

Aim

This study introduces a computational darkfield imaging approach named quadrant darkfield (QDF) to separate smaller cellular features from large structures, enabling label-free imaging of cell organelles and structures in living cells.

Approach

Using a programmable LED array as illumination source, we vary the direction of illumination to encode additional information about the feature size within cells. This is possible due to the varying level of directional scattering produced by features based on their sizes relative to the wavelength of light used.

Results

QDF successfully resolved small cellular features without interference from larger structures. QDF signal is more consistent during cell shape changes than traditional darkfield. QDF signals correlate with flow cytometry side scatter measurements, effectively differentiating cells by organelle content.

Conclusions

QDF imaging enhances the study of subcellular structures in living cells, offering improved quantification of organelle content compared to darkfield without labels. This method can be simultaneously performed with other techniques such as quantitative phase imaging to generate a multidimensional picture of living cells in real-time.

Identification of Zirconia Particle Uptake in Human Osteoblasts by ToF-SIMS Analysis and Particle-Size Effects on Cell Metabolism

As the use of zirconia-based nano-ceramics is rising in dentistry, the examination of possible biological effects caused by released nanoparticles on oral target tissues, such as bone, is gaining importance. The aim of this investigation was to identify a possible internalization of differently sized zirconia nanoparticles (ZrNP) into human osteoblasts applying Time-of-Flight Secondary Ion Mass Spectrometry (ToF-SIMS), and to examine whether ZrNP exposure affected the metabolic activity of the cells. Since ToF-SIMS has a low probing depth (about 5 nm), visualizing the ZrNP required the controlled erosion of the sample by oxygen bombardment. This procedure removed organic matter, uncovering the internalized ZrNP and leaving the hard particles practically unaffected. It was demonstrated that osteoblasts internalized ZrNP within 24 h in a size-dependent manner. Regarding the cellular metabolic activity, metabolization of alamarBlue by osteoblasts revealed a size- and time-dependent unfavorable effect of ZrNP, with the smallest ZrNP exerting the most pronounced effect. These findings point to different uptake efficiencies of the differently sized ZrNP by human osteoblasts. Furthermore, it was proven that ToF-SIMS is a powerful technique for the detection of zirconia-based nano/microparticles that can be applied for the cell-based validation of clinically relevant materials at the nano/micro scale.

Iron oxide nanoparticles oxidize transformed RAW 264.7 macrophages into foam cells: Impact of pulmonary surfactant component dipalmitoylphosphatidylcholine

Iron oxide nanoparticles (IONPs) are one of the most important components in airborne particulate matter that originally generated from traffic emission, iron ore mining, coal combustion and melting of engine fragments. Once IONPs entered respiratory tract and deposit in the alveoli, they may interact with pulmonary surfactant (PS) that distributed in the alveolar lining. Thereafter, it is necessary to investigate the interaction of inhaled IONPs and PS, which helps the understanding of health risk of respiratory health induced by IONPs. Using dipalmitoyl phosphatidylcholine (DPPC), the major components of PS, as a lipid model, we explored the interaction of DPPC with typical IONPs, Fe₃O₄ NPs and amino-functionalized analogue (Fe₃O₄-NH₂ NPs). DPPC was readily adsorbed on the surface of both IONPs. Although DPPC corona depressed the cellular uptake of IONPs, IONPs@DPPC complexes caused higher cytotoxicity toward RAW 264.7 macrophages, compared to pristine IONPs. Mechanistic studies have shown that IONPs react with intracellular hydrogen peroxide, which promotes the Fenton reaction, to generate hydroxyl radicals. Iron ions could oxidize lipids to form lipid peroxides, and lipid hydroperoxides will decompose to generate hydroxyl radicals, which further promote cellular oxidative stress, lipid accumulation, foam cell formation, and the release of inflammatory factors. These findings demonstrated the phenomenon of coronal component oxidation, which contributed to IONPs-induced cytotoxicity. This study offered a brand-new toxicological mechanism of IONPs at the molecular level, which is helpful for further understanding the adverse effects of IONPs.

Multimodal Imaging Mass Spectrometry to Identify Markers of Pulmonary Arterial Hypertension in Human Lung Tissue Using MALDI-ToF, ToF-SIMS, and Hybrid SIMS

Pulmonary arterial hypertension (PAH) is a rare and deadly disease affecting roughly 15-60 people per million in Europe with a poorly understood pathology. There are currently no diagnostic tools for early detection nor does a curative treatment exist. The lipid composition of arteries in lung tissue samples from human PAH and control patients were investigated using matrix-assisted laser desorption ionization (MALDI) imaging mass spectrometry (IMS) combined with time-of-flight secondary ion mass spectrometry (TOF-SIMS) imaging. Using random forests as an IMS data analysis technique, it was possible to identify the ion at m/z 885.6 as a marker of PAH in human lung tissue. The m/z 885.6 ion intensity was shown to be significantly higher around diseased arteries and was confirmed to be a diacylglycerophosphoinositol PI(C18:0/C20:4) via MS/MS using a novel hybrid SIMS instrument. The discovery of a potential biomarker opens up new research avenues which may finally lead to a better understanding of the PAH pathology and highlights the vital role IMS can play in modern biomedical research.

An in-depth multi-omics analysis in RLE-6TN rat alveolar epithelial cells allows for nanomaterial categorization

BACKGROUND

Nanomaterials (NMs) can be fine-tuned in their properties resulting in a high number of variants, each requiring a thorough safety assessment. Grouping and categorization approaches that would reduce the amount of testing are in principle existing for NMs but are still mostly conceptual. One drawback is the limited mechanistic understanding of NM toxicity. Thus, we conducted a multi-omics in vitro study in RLE-6TN rat alveolar epithelial cells involving 12 NMs covering different materials and including a systematic variation of particle size, surface charge and hydrophobicity for SiO₂ NMs. Cellular responses were analyzed by global proteomics, targeted metabolomics and SH2 profiling. Results were integrated using Weighted Gene Correlation Network Analysis (WGCNA).

RESULTS

Cluster analyses involving all data sets separated Graphene Oxide, TiO2_NM105, SiO2_40 and Phthalocyanine Blue from the other NMs as their cellular responses showed a high degree of similarities, although apical in vivo results may differ. SiO2_7 behaved differently but still induced significant changes. In contrast, the remaining NMs were more similar to untreated controls. WGCNA revealed correlations of specific physico-chemical properties such as agglomerate size and redox potential to cellular responses. A key driver analysis could identify biomolecules being highly correlated to the observed effects, which might be representative biomarker candidates. Key drivers in our study were mainly related to oxidative stress responses and apoptosis.

CONCLUSIONS

Our multi-omics approach involving proteomics, metabolomics and SH2 profiling proved useful to obtain insights into NMs Mode of Actions. Integrating results allowed for a more robust NM categorization. Moreover, key physico-chemical properties strongly correlating with NM toxicity were identified. Finally, we suggest several key drivers of toxicity that bear the potential to improve future testing and assessment approaches.