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.

Keywords: integrated omics; machine learning; mechanobiology; nanoparticle; nanotoxicity.

Figure 1

The metabotranscriptomic network and evaluation of ROS generation, glucose metabolism disorder, and ATP synthesis in 1.0 µg/µL MNPs@SiO₂(RITC)-treated cells. Red and green areas indicate upregulated and downregulated factors, respectively, compared with those of the untreated control group. OAs; organic acids, AAs; amino acids, FAs: fatty acids. The data were reproduced from our previous studies [18,28].

Figure 2

Overview of the ML approaches in the integration of multi-omics data. Multi-omics data could be integrated using both statistical and ML methods, where statistical approaches are used as a preprocessing step. ML approaches are classified into supervised and unsupervised algorithms, which could be utilized in the integration of omics to reduce the data dimensionality for building novel networks and pathways. SVM: support vector machine; KNN: K-nearest neighbor; HDBSCAN: hierarchical density-based spatial clustering of applications with noise.

Figure 3

Analysis of the mechanism network of NP-treated cells using metabotranscriptomics (a), traction force (b), and rigidity sensing (c). Metabotranscriptomic network was analyzed in cells treated with MNPs@SiO₂(RITC) in previous reports [21,28]. Submicron elastomeric micropillars were used to measure the traction force (F) and directionality parameter (γ) in MNPs@SiO₂(RITC)-treated cells. The data were reproduced from our previous study [32,33].