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. The altered surface functionality of protein corona reduced the cytotoxicity and cellular association of gold NPs in most cell types (e.g., monocytes, dendritic cells, B cells, natural killer (NK) cells, and T cells) and those immune cells selected different endocytosis pathways such as receptor-mediated endocytosis, phagocytosis, and micropinocytosis. However, even slight alterations in the major cell type (phagocytic cells and non-phagocytic cells) and T cell subsets (e.g., memory and naive T cells) resulted in significant protein corona-dependent variations in their cellular dose of gold NPs. Especially, naive T killer cells exhibited additional heterogeneity than memory T killer cells, with clusters exhibiting distinct cellular association patterns in single-cell contour plots. This multi-parametric analysis of mass cytometry data established a conceptual framework for a more holistic understanding of how the human immune system responds to external stimuli, paving the way for the application of precisely engineered NPs as promising tools of nanomedicine under various clinical settings, including targeted drug delivery and vaccine development.

Blood transcriptomics to facilitate diagnosis and stratification in pediatric rheumatic diseases - a proof of concept study.

BACKGROUND: Transcriptome profiling of blood cells is an efficient tool to study the gene expression signatures of rheumatic diseases. This study aims to improve the early diagnosis of pediatric rheumatic diseases by investigating patients' blood gene expression and applying machine learning on the transcriptome data to develop predictive models. METHODS: RNA sequencing was performed on whole blood collected from children with rheumatic diseases. Random Forest classification models were developed based on the transcriptome data of 48 rheumatic patients, 46 children with viral infection, and 35 controls to classify different disease groups. The performance of these classifiers was evaluated by leave-one-out cross-validation. Analyses of differentially expressed genes (DEG), gene ontology (GO), and interferon-stimulated gene (ISG) score were also conducted. RESULTS: Our first classifier could differentiate pediatric rheumatic patients from controls and infection cases with high area-under-the-curve (AUC) values (AUC = 0.8 ± 0.1 and 0.7 ± 0.1, respectively). Three other classifiers could distinguish chronic recurrent multifocal osteomyelitis (CRMO), juvenile idiopathic arthritis (JIA), and interferonopathies (IFN) from control and infection cases with AUC ≥ 0.8. DEG and GO analyses reveal that the pathophysiology of CRMO, IFN, and JIA involves innate immune responses including myeloid leukocyte and granulocyte activation, neutrophil activation and degranulation. IFN is specifically mediated by antibacterial and antifungal defense responses, CRMO by cellular response to cytokine, and JIA by cellular response to chemical stimulus. IFN patients particularly had the highest mean ISG score among all disease groups. CONCLUSION: Our data show that blood transcriptomics combined with machine learning is a promising diagnostic tool for pediatric rheumatic diseases and may assist physicians in making data-driven and patient-specific decisions in clinical practice.

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.e., 40-200 nm, positively charged) under various exposure conditions that reflect different situations of agglomeration, diffusion, and sedimentation in cell culture media, such as upright and inverted configurations with different media heights. Then, we correlated these nSSC values with the numbers of cellular Ag NPs determined by inductively coupled plasma mass spectrometry (ICPMS) as a well-established cross-validation method. The different core sizes of Ag NPs and the various exposure conditions tested in this study confirmed that the FCM-SSC intensities are highly correlated with their core sizes as well as the amount of cellular Ag NPs over a linear range up to ~80,000 Ag NPs/cell and ~23 nSSC, which is significantly broader than those of previous studies.

Predicting Cytotoxicity of Metal Oxide Nanoparticles using Isalos Analytics Platform.

A literature curated dataset containing 24 distinct metal oxide (MexOy) 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 MexOy 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 MexOy NPs. These were NP core size, hydrodynamic size, assay type, exposure dose, the energy of the MexOy conduction band (EC), the coordination number of the metal atoms on the NP surface (Avg. C.N. Me atoms surface) and the average force vector surface normal component of all metal atoms (v⟂ Me atoms surface). The significance and effect of these descriptors is discussed to demonstrate their direct correlation with cytotoxicity. The produced model has been made publicly available by the Horizon 2020 (H2020) NanoSolveIT project and will be added to the project's Integrated Approach to Testing and Assessment (IATA).

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. However, in most cases, building the optimal analytical workflow is not straightforward. A careful selection of the right tools must be carried out, since it will affect the downstream analyses and modelling approaches. Transcriptomics data preprocessing spans across multiple steps such as quality check, filtering, normalization, batch effect detection and correction. Currently, there is a lack of standard guidelines for data preprocessing in the TGx field. Defining the optimal tools and procedures to be employed in the transcriptomics data preprocessing will lead to the generation of homogeneous and unbiased data, allowing the development of more reliable, robust and accurate predictive models. In this review, we outline methods for the preprocessing of three main transcriptomic technologies including microarray, bulk RNA-Sequencing (RNA-Seq), and single cell RNA-Sequencing (scRNA-Seq). Moreover, we discuss the most common methods for the identification of differentially expressed genes and to perform a functional enrichment analysis. This review is the second part of a three-article series on Transcriptomics in Toxicogenomics.

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. Indeed, the publicly available omics datasets are constantly increasing together with a plethora of different methods that are made available to facilitate their analysis, interpretation and the generation of accurate and stable predictive models. In this review, we present the state-of-the-art of data modelling applied to transcriptomics data in TGx. We show how the benchmark dose (BMD) analysis can be applied to TGx data. We review read across and adverse outcome pathways (AOP) modelling methodologies. We discuss how network-based approaches can be successfully employed to clarify the mechanism of action (MOA) or specific biomarkers of exposure. We also describe the main AI methodologies applied to TGx data to create predictive classification and regression models and we address current challenges. Finally, we present a short description of deep learning (DL) and data integration methodologies applied in these contexts. Modelling of TGx data represents a valuable tool for more accurate chemical safety assessment. This review is the third part of a three-article series on Transcriptomics in Toxicogenomics.

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 enables the exploration of organisms' responses to environmental, chemical, and physical agents by observing the molecular alterations in more detail. Toxicogenomics integrates classical toxicology with omics assays, thus allowing the characterization of the mechanism of action (MOA) of chemical compounds, novel small molecules, and engineered nanomaterials (ENMs). Lack of standardization in data generation and analysis currently hampers the full exploitation of toxicogenomics-based evidence in risk assessment. To fill this gap, TGx methods need to take into account appropriate experimental design and possible pitfalls in the transcriptomic analyses as well as data generation and sharing that adhere to the FAIR (Findable, Accessible, Interoperable, and Reusable) principles. In this review, we summarize the recent advancements in the design and analysis of DNA microarray, RNA sequencing (RNA-Seq), and single-cell RNA-Seq (scRNA-Seq) data. We provide guidelines on exposure time, dose and complex endpoint selection, sample quality considerations and sample randomization. Furthermore, we summarize publicly available data resources and highlight applications of TGx data to understand and predict chemical toxicity potential. Additionally, we discuss the efforts to implement TGx into regulatory decision making to promote alternative methods for risk assessment and to support the 3R (reduction, refinement, and replacement) concept. This review is the first part of a three-article series on Transcriptomics in Toxicogenomics. These initial considerations on Experimental Design, Technologies, Publicly Available Data, Regulatory Aspects, are the starting point for further rigorous and reliable data preprocessing and modeling, described in the second and third part of the review series.

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. Mass cytometry reveals the heterogeneous biodistribution of the positively charged polyethylenimine-coated AgNPs in various cell types and finds that monocytes and B cells have higher association with the AgNPs than other populations. scRNA-seq data of these two cell types demonstrate that each type has distinct responses to AgNP treatment: NRF2-mediated oxidative stress is confined to B cells, whereas monocytes show Fcγ-mediated phagocytosis. Besides the between-population heterogeneity, analysis of single-cell dose-response relationships further reveals within-population diversity for the B cells and naïve CD4+ T cells. Distinct subsets having different levels of cellular responses with respect to their cellular AgNP doses are found. This study demonstrates that the complementary use of mass cytometry and scRNA-seq is helpful for gaining in-depth knowledge on the heterogeneous interactions between immune cells and NPs and can be incorporated into future toxicity assessments of nanomaterials.

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. Type I and Type II cells were moderately associated with AgNPs but as the cellular AgNP dose increased, Type I cells remained viable while Type II cells became less viable. Type III cells did not have high affinity with AgNPs but were, however, the least viable. Transmission electron microscopic images revealed that the biodistribution and the released Ag+ ions contributed to the distinct toxic effects of AgNPs in different populations. This single-cell dose-response analysis approach enabled the examination of how differently individual cells responded to different cellular NP doses and provided insights into nanotoxicity pathways at a single-cell level.

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. Then, classification models using four different algorithms, such as generalized linear model, support vector machine, random forest, and neural network, were developed and their performances were compared to find the best performing preprocessing methods as well as algorithms. The neural network model built using the balanced data set was identified as the model with best predictive performance, while applicability domain was defined using k-nearest neighbours algorithm. The analysis of relative attribute importance for the built neural network model identified dose, formation enthalpy, exposure time, and hydrodynamic size as the four most important attributes. As the presented model can predict the toxicity of the nanomaterials in consideration of various experimental conditions, it has the advantage of having a broader and more general applicability domain than the existing quantitative structure-activity relationship model.

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. Particularly, to improve completeness and quality of the extracted dataset, we adapted two preprocessing approaches: data gap-filling and physicochemical property based scoring. Performances of nano-SAR classification models revealed that the dataset with the highest score value resulted in the best predictivity with compromise in its applicability domain. The combination of physicochemical and toxicological attributes was proved to be more relevant to toxicity classification than quantum-mechanical attributes. Overall, by adapting these two preprocessing methods, we demonstrated that meta-analysis of nanotoxicity literatures could provide an effective alternative for the risk assessment of engineered nanomaterials.

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). On the basis of these comparisons, we have found linear correlations between the cellular Au NPs and the SSC intensities and used them to estimate the amount of Au NPs associated with HeLa cells. Once the correlations were found for specific cell lines and types of nanoparticles, this approach is useful for simple and quantitative estimation of the cellular Au NPs, without performing labor-intensive and complicated sample preparation procedures required for the ICPMS approach.