Analysis of Nanotoxicity with Integrated Omics and Mechanobiology

Abstract

Nanoparticles (NPs) in biomedical applications have benefits owing to their small size. However, their intricate and sensitive nature makes an evaluation of the adverse effects of NPs on health necessary and challenging. Since there are limitations to conventional toxicological methods and omics analyses provide a more comprehensive molecular profiling of multifactorial biological systems, omics approaches are necessary to evaluate nanotoxicity. Compared to a single omics layer, integrated omics across multiple omics layers provides more sensitive and comprehensive details on NP-induced toxicity based on network integration analysis. As multi-omics data are heterogeneous and massive, computational methods such as machine learning (ML) have been applied for investigating correlation among each omics. This integration of omics and ML approaches will be helpful for analyzing nanotoxicity. To that end, mechanobiology has been applied for evaluating the biophysical changes in NPs by measuring the traction force and rigidity sensing in NP-treated cells using a sub-elastomeric pillar. Therefore, integrated omics approaches are suitable for elucidating mechanobiological effects exerted by NPs. These technologies will be valuable for expanding the safety evaluations of NPs. Here, we review the integration of omics, ML, and mechanobiology for evaluating nanotoxicity.

Cerebrospinal Fluid Metabolome in Parkinson’s Disease and Multiple System Atrophy

“…Metabolomics is the comprehensive study of metabolic changes and the large-scale study of metabolites in human cells, tissues, and organs, including fluids [52]. Compared to other omics technologies, metabolomics directly reflects biological processes, such as the regulation of enzyme activity, cellular signaling, energy metabolism and conversion, and interactions with other organisms [53][54][55]. Primary metabolites such as amino acids, fatty acids, organic acids, carbohydrates, and vitamins are essential for growth, development, and reproduction, and are required for maintaining the physiological functions of the human body.…”

“…Proteomics has been applied for the investigation of neurodegenerative diseases [93] because it permits understanding molecular processes, compositions, sizes, and charges of related proteins [55]. There have been analytical limitations with the use of CSF samples of patients with neurodegenerative diseases using classical analytical techniques, such as Western blotting and two-dimensional (2D) gel electrophoresis based on pI.…”

“…The metabolic profile is an endpoint of biological metabolism and closely reflects the corresponding phenotype [54,55]. However, metabolites cannot be amplified; therefore, metabolomics has relatively lower coverage than transcriptomics and genomics [101].…”

“…Machine learning is used for handling large-scale datasets, including integrated omics data [105]. Machine learning is divided into supervised learning, unsupervised learning, and reinforcement learning [55,110]. Supervised learning typically includes support vector machine (SVM), K-nearest neighbor (KNN) [111].…”

“…Unsupervised learning involves training with unlabeled data and grouping them into similar groups [112]. In the unsupervised learning clustering algorithm, there are K-means, K-medoids, and hierarchical density-based spatial clustering of applications with noise (HDBSCAN) clustering [55,110]. In addition, principal component analysis (PCA) facilitates data analysis by reducing the dimensions of the data distribution [113].…”