Lack of functional TCR-epitope interaction is associated with herpes zoster through reduced downstream T cell activation.

The role of T cell receptor (TCR) diversity in infectious disease susceptibility is not well understood. We use a systems immunology approach on three cohorts of herpes zoster (HZ) patients and controls to investigate whether TCR diversity against varicella-zoster virus (VZV) influences the risk of HZ. We show that CD4 T cell TCR diversity against VZV glycoprotein E (gE) and immediate early 63 protein (IE63) after 1-week culture is more restricted in HZ patients.

Effects of Immune Cell Heterogeneity and Protein Corona on the Cellular Association and Cytotoxicity of Gold Nanoparticles: A Single-Cell-Based, High-Dimensional Mass Cytometry Study.

Understanding how nanoparticles (NPs) interact with biological systems is important in many biomedical research areas. However, the heterogeneous nature of biological systems, including the existence of numerous cell types and multitudes of key environmental factors, makes these interactions extremely challenging to investigate precisely. Here, using a single-cell-based, high-dimensional mass cytometry approach, we demonstrated that the presence of protein corona has significant influences on the cellular associations and cytotoxicity of gold NPs for human immune cells, and those effects vary significantly with the types of immune cells and their subsets.

Quantitative Estimation of Cell-Associated Silver Nanoparticles using the Normalized Side Scattering Intensities of Flow Cytometry.

Quantification of cellular nanoparticles (NPs) is one of the most important steps in studying NP-cell interactions. Here, a simple method for the estimation of cell-associated silver (Ag) NPs in lung cancer cells (A549) is proposed based on their side scattering (SSC) intensities measured by flow cytometry (FCM). To estimate cellular Ag NPs associated with A549 cells over a broad range of experimental conditions, we measured the normalized SSC intensities (nSSC) of A549 cells treated with Ag NPs with five different core sizes (i.

Predicting Cytotoxicity of Metal Oxide Nanoparticles using Isalos Analytics Platform.

A literature curated dataset containing 24 distinct metal oxide (MeO) nanoparticles (NPs), including 15 physicochemical, structural and assay-related descriptors, was enriched with 62 atomistic computational descriptors and exploited to produce a robust and validated in silico model for prediction of NP cytotoxicity. The model can be used to predict the cytotoxicity (cell viability) of MeO NPs based on the colorimetric lactate dehydrogenase (LDH) assay and the luminometric adenosine triphosphate (ATP) assay, both of which quantify irreversible cell membrane damage. Out of the 77 total descriptors used, 7 were identified as being significant for induction of cytotoxicity by MeO NPs.

Transcriptomics in Toxicogenomics, Part II: Preprocessing and Differential Expression Analysis for High Quality Data.

Preprocessing of transcriptomics data plays a pivotal role in the development of toxicogenomics-driven tools for chemical toxicity assessment. The generation and exploitation of large volumes of molecular profiles, following an appropriate experimental design, allows the employment of toxicogenomics (TGx) approaches for a thorough characterisation of the mechanism of action (MOA) of different compounds. To date, a plethora of data preprocessing methodologies have been suggested.

Transcriptomics in Toxicogenomics, Part I: Experimental Design, Technologies, Publicly Available Data, and Regulatory Aspects.

The starting point of successful hazard assessment is the generation of unbiased and trustworthy data. Conventional toxicity testing deals with extensive observations of phenotypic endpoints in vivo and complementing in vitro models. The increasing development of novel materials and chemical compounds dictates the need for a better understanding of the molecular changes occurring in exposed biological systems.

Transcriptomics in Toxicogenomics, Part III: Data Modelling for Risk Assessment.

Transcriptomics data are relevant to address a number of challenges in Toxicogenomics (TGx). After careful planning of exposure conditions and data preprocessing, the TGx data can be used in predictive toxicology, where more advanced modelling techniques are applied. The large volume of molecular profiles produced by omics-based technologies allows the development and application of artificial intelligence (AI) methods in TGx.

Mass Cytometry and Single-Cell RNA-seq Profiling of the Heterogeneity in Human Peripheral Blood Mononuclear Cells Interacting with Silver Nanoparticles.

Understanding the interactions between nanoparticles (NPs) and human immune cells is necessary for justifying their utilization in consumer products and biomedical applications. However, conventional assays may be insufficient in describing the complexity and heterogeneity of cell-NP interactions. Herein, mass cytometry and single-cell RNA-sequencing (scRNA-seq) are complementarily used to investigate the heterogeneous interactions between silver nanoparticles (AgNPs) and primary immune cells.

Heterogeneity in Biodistribution and Cytotoxicity of Silver Nanoparticles in Pulmonary Adenocarcinoma Human Cells.

Cellular association of nanoparticles (NPs) and their resultant cytotoxicity are heterogeneous in nature and can be influenced by the variances in NPs' properties, cell types, and status. However, conventional in vitro assays typically consider the administered NP dose and the averaged cellular responses based on the assumption of a uniform distribution of monodisperse NPs in homogeneous cells, which might be insufficient to describe the complex nature of cell-NP interactions. Here, using flow cytometry, we report observations of the heterogeneity in the cellular association of silver nanoparticles (AgNPs) in A549 cells, which resulted in distinct dose-response relationships and cytotoxicity.

Towards a generalized toxicity prediction model for oxide nanomaterials using integrated data from different sources.

A generalized toxicity classification model for 7 different oxide nanomaterials is presented in this study. A data set extracted from multiple literature sources and screened by physicochemical property based quality scores were used for model development. Moreover, a few more preprocessing techniques, such as synthetic minority over-sampling technique, were applied to address the imbalanced class problem in the data set.

Toxicity Classification of Oxide Nanomaterials: Effects of Data Gap Filling and PChem Score-based Screening Approaches.

Development of nanotoxicity prediction models is becoming increasingly important in the risk assessment of engineered nanomaterials. However, it has significant obstacles caused by the wide heterogeneities of published literature in terms of data completeness and quality. Here, we performed a meta-analysis of 216 published articles on oxide nanoparticles using 14 attributes of physicochemical, toxicological and quantum-mechanical properties.

Flow Cytometry-Based Quantification of Cellular Au Nanoparticles.

There has been a great deal of research regarding the cellular association of nanoparticles (NPs), although there are only a few methods available yet for the quantitative measurements of cellular NPs. In this study, we propose a simple and quantitative method to estimate the cellular uptake of Au NPs into cervical cancer cells (HeLa) based on their side scattering (SSC) intensities measured by flow cytometry (FCM). We have compared SSC intensities of HeLa cells exposed to eight different types of Au NPs (40-100 nm size, with positive or negative surface charge) with the amount of cellular Au NPs measured by inductively coupled plasma mass spectrometry (ICPMS).