Current opinion in toxicology最新文献

Recent developments of novel single-cell analysis techniques have rapidly advanced the fields of immunotoxicology and immunometabolism. Single-cell analyses enable the characterization of immune cells, unraveling heterogeneity, and population dynamics in response to cellular perturbations, including toxicant insults and changes in cellular metabolism. This review provides an overview of current technologies and recent discoveries, illustrating an emerging role of single-cell analyses in the field of immunotoxicology and immunometabolism. Various single-cell techniques, including flow cytometry, mass cytometry, multiplexed imaging, and sequencing, together with their applications to studying immunotoxicology and immunometabolism are discussed. This review emphasizes the potential for single-cell analyses to revolutionize our understanding of immune cell heterogeneity, uncover novel cellular therapeutic targets, and pave the way for novel mechanistic insights.

Exposure to environmental chemicals has been associated with increased risks for various diseases, but our understanding of their molecular targets and how they drive disease progression remains limited. Environmental toxicants can trigger a multitude of effects on the epigenome, transcriptome, proteome, and other molecular entities in individual cells and tissues. The recent advances in high throughput single cell multiomics technologies are enabling a deeper understanding of these complex molecular alterations and interactions underlying exposure mode of action at a single cell resolution. Accompanying the increased capacity to generate single cell multiomics data is the rapid advancement in computational tools for data analysis of individual omics layers, multimodal data integration and molecular network modeling. Recent applications of single cell omics technologies and analytical methods have enabled the elucidation of cell type specific genes and pathways affected by various environmental exposures. Further adoption of advanced single cell multiomics methodologies in the molecular toxicology field promises a more comprehensive understanding of the regulatory networks within and between cell types underlying the perturbations in physiological systems and disease risks posed by environmental toxicants.

The discovery of cancer-specific therapeutics and determining their sensitivity is a critical step in preventing drug-induced toxicity. Drug sensitivity varies among cancer patients due to intra-tumor heterogeneity. It demands rational drug design, target identification, and novel treatment modalities. This review discusses the use of network medicine in targeted therapy and AI-based drug response prediction for personalized cancer therapy. The network medicine is successfully implemented to integrate multiple omics data to identify the disease modules in cancer. The cancer-specific disease modules are utilized for drug screening and targeted therapy. Additionally, the model developed using AI, and genomic data shows superior performance and also reveals relationships between the genomic variability of cancer and their response to drugs. There is significant promise for network medicine and AI to handle large-scale omics data, leading to the identification of a novel cancer-specific treatment strategy and improved patient care.

The application and analysis of single-cell transcriptomics in toxicology presents unique challenges. These include identifying cell sub-populations sensitive to perturbation; interpreting dynamic shifts in cell type proportions in response to chemical exposures; and performing differential expression analysis in dose–response studies spanning multiple treatment conditions. This review examines these challenges while presenting best practices for critical single cell analysis tasks. This covers areas such as cell type identification; analysis of differential cell type abundance; differential gene expression; and cellular trajectories. Towards enhancing the use of single-cell transcriptomics in toxicology, this review aims to address key challenges in this field and offer practical analytical solutions. Overall, applying appropriate bioinformatic techniques to single-cell transcriptomic data can yield valuable insights into cellular responses to toxic exposures.

Reproduction is a remarkably intricate process involving the interaction of multiple cell types and organ systems unfolding over long periods of time and that culminates with the production of gametes. The initiation of germ cell development takes place during embryogenesis but only completes decades later in humans. The complexity inherent to reproduction and its study has long hampered our ability to decipher how environmental agents disrupt this process. Single-cell approaches provide an opportunity for a deeper understanding of the action of toxicants on germline function and analyze how the response to their exposure is differentially distributed across tissues and cell types. In addition to single-cell RNA sequencing, other single-cell or nucleus level approaches such as ATAC-sequencing and multi-omics have expanded the strategies that can be implemented in reproductive toxicological studies to include epigenomic and the nuclear transcriptomic data. Here we will discuss the current state of single-cell technologies and how they can best be utilized to advance reproductive toxicological studies. We will then discuss case studies in two model organisms (Caenorhabditis elegans and rat) studying different environmental exposures (alcohol and e-cigarettes respectively) to highlight the value of single-cell and single-nucleus approaches for reproductive biology and reproductive toxicology.

Human exposure to the metal lead (Pb) is prevalent and associated with adverse neurodevelopmental and neurodegenerative outcomes. Pb disrupts normal brain function by inducing oxidative stress and neuroinflammation, altering cellular metabolism, and displacing essential metals. Prior studies on the molecular impacts of Pb have examined bulk tissues, which collapse information across all cell types, or in targeted cells, which are limited to cell autonomous effects. These approaches are unable to represent the complete biological implications of Pb exposure because the brain is a cooperative network of highly heterogeneous cells, with cellular diversity and proportions shifting throughout development, by brain region, and with disease. New technologies are necessary to investigate whether Pb and other environmental exposures alter cell composition in the brain and whether they cause molecular changes in a cell-type-specific manner. Cutting-edge, single-cell approaches now enable research resolving cell-type-specific effects from bulk tissues. This article reviews existing Pb neurotoxicology studies with genome-wide molecular signatures and provides a path forward for the field to implement single-cell approaches with practical recommendations.

The utilization of transcriptomic studies identifying profiles of gene expression, especially in toxicogenomics, has catapulted next-generation sequencing to the forefront of reproductive toxicology. An innovative yet underutilized RNA sequencing technique emerging into this field is single-cell RNA sequencing (scRNA-seq), which provides sequencing at the individual cellular level of gonad tissue. ScRNA-seq provides a novel and unique perspective for identifying distinct cellular profiles, including identification of rare cell subtypes. The specificity of scRNA-seq is a powerful tool for reproductive toxicity research, especially for translational animal models including zebrafish. Studies to date not only have focused on ‘tissue atlassing’ or characterizing what cell types make up different tissues but have also begun to include toxicant exposure as a factor that this review aims to explore. Future scRNA-seq studies will contribute to understanding exposure-induced outcomes; however, the trade-offs with traditional methods need to be considered.

The European Chemicals Strategy for Sustainability requests to include a mixture assessment factor (MAF) into the safety assessment of chemicals, in order to account for the elevated risks of chemical mixtures. This text first reflects on the conceptual background of the MAF, and then provides an overview of current stakeholder positions and of the studies attempting to quantify an appropriate size of the MAF.

Stakeholders from industry, civil society organizations (NGOs), and regulatory authorities have already put forth statements regarding the perceived advantages and disadvantages of the MAF approach, sometimes without providing detailed arguments. A consensus seems to emerge that the so-called MAF_factor is not a suitable instrument, due to its indiscriminatory nature that penalizes even chemicals that contribute only marginally to the mixture risk. Members of the larger MAF_ceiling family, in particular the MAF_exact, overcome this limitation and are therefore suggested as the way forward.