Phenotypic Landscape of Immune Cells in Sepsis: Insights from High-Dimensional Mass Cytometry.

Understanding the sepsis-induced immunological response can be facilitated by identifying phenotypic changes in immune cells at the single-cell level. Mass cytometry, a novel multiparametric single-cell analysis technique, offers considerable benefits in characterizing sepsis-induced phenotypic changes in peripheral blood mononuclear cells. Here, we analyzed peripheral blood mononuclear cells from 20 sepsis patients and 10 healthy donors using mass cytometry and employing 23 markers. Both manual gating and automated clustering approaches (PhenoGraph) were used for cell identification, complemented by uniform manifold approximation and projection (UMAP) for dimensionality reduction and visualization. Our study revealed that patients with sepsis exhibited a unique immune cell profile, marked by an increased presence of monocytes, B cells, and dendritic cells, alongside a reduction in natural killer (NK) cells and CD4/CD8 T cells. Notably, significant changes in the distributions of monocytes and B and CD4 T cells were observed. Clustering with PhenoGraph unveiled the subsets of each cell type and identified elevated CCR6 expression in sepsis patients' monocyte subset (PG#5), while further PhenoGraph clustering on manually gated T and B cells discovered sepsis-specific CD4 T cell subsets (CCR4low CD20low CD38low) and B cell subsets (HLA-DRlow CCR7low CCR6high), which could potentially serve as novel diagnostic markers for sepsis.

Automated machine learning in nanotoxicity assessment: A comparative study of predictive model performance.

Computational modeling has earned significant interest as an alternative to animal testing of toxicity assessment. However, the process of selecting an appropriate algorithm and fine-tuning hyperparameters for the developing of optimized models takes considerable time, expertise, and an intensive search. The recent emergence of automated machine learning (autoML) approaches, available as user-friendly platforms, has proven beneficial for individuals with limited knowledge in ML-based predictive model development. These autoML platforms automate crucial steps in model development, including data preprocessing, algorithm selection, and hyperparameter tuning. In this study, we used seven previously published and publicly available datasets for oxides and metals to develop nanotoxicity prediction models. AutoML platforms, namely Vertex AI, Azure, and Dataiku, were employed and performance measures such as accuracy, F1 score, precision, and recall for these autoML-based models were then compared with those of conventional ML-based models. The results demonstrated clearly that the autoML platforms produced more reliable nanotoxicity prediction models, outperforming those built with conventional ML algorithms. While none of the three autoML platforms significantly outperformed the others, distinctions exist among them in terms of the available options for choosing technical features throughout the model development steps. This allows users to select an autoML platform that aligns with their knowledge of predictive model development and its technical features. Additionally, prediction models constructed from datasets with better data quality displayed, enhanced performance than those built from datasets with lower data quality, indicating that future studies with high-quality datasets can further improve the performance of those autoML-based prediction models.

Upconversion Nanomaterials in Bioimaging and Biosensor Applications and Their Biological Response.

In recent decades, upconversion nanomaterials (UCNMs) have attracted considerable research interest because of their unique optical properties, such as large anti-Stokes shifts, sharp emissions, non-photobleaching, and long lifetime. These unique properties make them ideal candidates for unified applications in biomedical fields, including drug delivery, bioimaging, biosensing, and photodynamic therapy for specific cancers. This review describes the general mechanisms of upconversion, synthesis methods, and potential applications in biology and their biological responses. Additionally, the biological toxicity of UCNMs is explained and summarized with the associated intracellular association mechanisms. Finally, the prospects and future challenges of UCNMs at the clinical level in biological applications are described, along with a summary of opportunity for biological as well as clinical applications of UCNMs.

Selective optosensing of iron(III) ions in HeLa cells using NaYF4:Yb3+/Tm3+ upconversion nanoparticles coated with polyepinephrine.

Novel polyepinephrine-modified NaYF4:Yb,Tm upconversion luminescent nanoparticles (UCNP@PEP) were prepared via the self-polymerization of epinephrine on the surfaces of the UCNPs for selective sensing of Fe3+ inside a cell and for intracellular imaging. The proposed UCNP@PEP probe is a strong blue light emitter (λmax = 474 nm) upon exposure to an excitation wavelength of 980 nm. The probe was used for detecting Fe3+ owing to the complexation reaction between UCNP@PEP and Fe3+, resulting in reduced upconversion luminescence (UCL) intensity. The proposed probe has a detection limit of 0.2 µM and a good linear range of 1-10 µM for sensing Fe3+ ions. Moreover, the UCNP@PEP probe displays high cell viability (90%) and is feasible for intracellular imaging. The ability of the probe to sense Fe3+ in a human serum sample was tested and shows promising output for diagnostic purposes. The prepared UCNP@PEP probe was characterized by using UV-visible (UV-Vis) absorption spectrometry, fluorescence (FL) spectrometry, field emission scanning electron microscopy (FE-SEM), X-ray photoelectron spectroscopy (XPS), and Fourier transform infrared spectroscopy (FT-IR).

Identification of Ca-rich dense granules in human platelets using scanning transmission X-ray microscopy.

Whole-mount (WM) platelet preparation followed by transmission electron microscopy (TEM) observation is the standard method currently used to assess dense granule (DG) deficiency (DGD). However, due to the electron-density-based contrast mechanism in TEM, other granules such as α-granules might cause false DG detection. Here, scanning transmission X-ray microscopy (STXM) was used to identify DGs and minimize false DG detection of human platelets. STXM image stacks of human platelets were collected at the calcium (Ca) L2,3 absorption edge and then converted to optical density maps. Ca distribution maps, obtained by subtracting the optical density maps at the pre-edge region from those at the post-edge region, were used to identify DGs based on the Ca richness. DGs were successfully detected using this STXM method without false detection, based on Ca maps for four human platelets. Spectral analysis of granules in human platelets confirmed that DGs contain a richer Ca content than other granules. The Ca distribution maps facilitated more effective DG identification than TEM which might falsely detect DGs. Correct identification of DGs would be important to assess the status of platelets and DG-related diseases. Therefore, this STXM method is proposed as a promising approach for better DG identification and diagnosis, as a complementary tool to the current WM TEM approach.

Stochastic Electrochemical Cytometry of Human Platelets via a Particle Collision Approach.

The quantitative analysis of human platelets is important for the diagnosis of various hematologic and cardiovascular diseases. In this article, we present a stochastic particle impact electrochemical (SPIE) approach for human platelets with fixation (F-HPs). Carboxylate-functionalized polystyrene particles (PSPs) are studied as well as a standard platform of SPIE-F-HPs. For SPIE-PSPs (or F-HPs), [Fe(CN)6]4- was used as the redox mediator, and electro-oxidation of [Fe(CN)6]4- to [Fe(CN)6]3- was conducted on a Pt ultramicroelectrode (UME) by applying a constant potential, where the corresponding oxidation current is mass-transfer-controlled. When PSPs (or F-HPs) are introduced into aqueous solution with [Fe(CN)6]4-, sudden current drops (SCDs) were observed, which resulted from the partial blockage of a Pt UME by collision of an individual PSP (or F-HP). For SPIE-PSPs (or F-HPs), we found that it is essential to enhance the migration of PSPs (F-HPs) toward a Pt UME by maximizing the steady state current associated with electro-oxidation of [Fe(CN)6]4-. This was accomplished by increasing its concentration to the solubility limit. We successfully measured the concentration of F-HPs dispersed in aqueous solution containing [Fe(CN)6]4- with a minimum detectable concentration of 0.1 fM, and the size distribution of F-HPs was also estimated from the obtained idrop distribution based on the SPIE analysis, where idrop stands for the magnitude of the current drop of each SCD. Lastly, we revealed that HPs without the fixation process (WF-HPs) are difficult to quantitatively analyze by SPIE because of their transient activation process, which results in changes from their spherical shape. The observed difficulty was also confirmed by finite element analysis, which shows that idrop can be significantly increased, as an elongated WF-HP is adsorbed on the edge of an UME.

Fluorescence Optosensing of Triclosan by Upconversion Nanoparticles with Potassium Permanganate.

It is greatly significant to develop a simple and rapid sensing method for triclosan (TCS) because it is a widely used and a chronically toxic compound that adversely affects biological organisms and human health. This paper presents the design and development of a novel simple optosensor that uses carboxylic group-functionalized NaYF4:Yb3+/Er3+ upconversion nanoparticles (UCNPs) coated with potassium permanganate (KMnO4). The sensor enables the rapid, non-autofluorescence, sensitive, and selective detection of TCS based on the "turn off-on fluorescence" technique through fluorescence resonance energy transfer. Under an near-infrared radiation excitation (980 nm), the "turn-off fluorescence" process involves the transfer of fluorescence resonance energy between the UCNPs and KMnO4, whereas the "turn-on fluorescence" process occurs when KMnO4 is reduced in the presence of TCS. TCS was detected by recovering the green emission of UCNPs. Under optimized conditions, the resulting sensor offered an excellent response to TCS with 0.2 µM of a limit of detection. The developed sensor showed higher selectivity to TCS than other phenolic compounds. Moreover, the analytical performance of the proposed probe was practically demonstrated to successfully monitor trace levels of TCS in samples of tap water and personal care products. The developed simple and sensitive method may offer a new approach for determining TCS in environmental applications.

One-step synthesis of NaLu80-xGdxF4:Yb183+/Er23+(Tm3+) upconversion nanoparticles for in vitro cell imaging.

Upconversion nanoparticles (UCNPs) possess a unique type of photoluminescence (PL) in which lower-energy excitation is converted into higher-energy emission via multi-photon absorption processes. In this work, we have used a facile one-step hydrothermal method promoted water solubility to synthesis NaLuGdF4:Yb3+/Er3+(Tm3+) UCNPs coated with malonic acid (MA). Scanning electron microscopy images and X-ray diffraction patterns reveal sphere-shaped UCNPs with an average size of ~80nm crystallized in the cubic NaLuF4 structure. The characteristic vibrations of cubic UCNPs have been taken into account by using Fourier-transform infrared spectroscopy. Based on PL studies, we have determined an optimal concentration of Gd3+ doping. The dependence of upconversion PL intensity on Gd3+ concentration is discussed via the results of magnetization measurements, which is related to the coupling/uncoupling of Gd3+ ions. Particularly, our study reveals that carboxyl-functionalized NaLuGdF4:Yb3+/Er3+(Tm3+) UCNPs have a relatively high cell viability with HeLa cells.

Phospholipase A2-Responsive Phosphate Micelle-Loaded UCNPs for Bioimaging of Prostate Cancer Cells.

We report the effective synthesis of biocompatible upconversion nanoparticles (UCNP)-loaded phosphate micelles and successful delivery of UCNPs to prostate cancer cells via secreted phospholipase A2 (sPLA-2) enzyme cleavage of the loaded micelles for the first time. The activity of the (sPLA-2) enzyme toward the synthesized micelles was investigated and confirmed by LC-MS. TEM results showed that the micelles have a size distribution of 80 to 150 nm, whereas UCNP-loaded micelles range from 200 to 350 nm, indicating the successful loading of UCNPs. The selective release of UCNPs to prostate cancer cells rather than other cells, specifically cervical cancer cells, was observed and confirmed by a range of bioimaging studies. Moreover, cytotoxicity assays confirmed the biocompatibility of the UCNP-loaded micelles.