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. 2011 Feb 15;45(4):1695-702.
doi: 10.1021/es103606x. Epub 2011 Jan 20.

Self-organizing map analysis of toxicity-related cell signaling pathways for metal and metal oxide nanoparticles

Robert Rallo  1 , Bryan France, Rong Liu, Sumitra Nair, Saji George, Robert Damoiseaux, Francesc Giralt, Andre Nel, Kenneth Bradley, Yoram Cohen
Affiliations

Self-organizing map analysis of toxicity-related cell signaling pathways for metal and metal oxide nanoparticles

Robert Rallo et al. Environ Sci Technol. .

Abstract

The response of a murine macrophage cell line exposed to a library of seven metal and metal oxide nanoparticles was evaluated via High Throughput Screening (HTS) assay employing luciferase-reporters for ten independent toxicity-related signaling pathways. Similarities of toxicity response among the nanoparticles were identified via Self-Organizing Map (SOM) analysis. This analysis, applied to the HTS data, quantified the significance of the signaling pathway responses (SPRs) of the cell population exposed to nanomaterials relative to a population of untreated cells, using the Strictly Standardized Mean Difference (SSMD). Given the high dimensionality of the data and relatively small data set, the validity of the SOM clusters was established via a consensus clustering technique. Analysis of the SPR signatures revealed two cluster groups corresponding to (i) sublethal pro-inflammatory responses to Al2O3, Au, Ag, SiO2 nanoparticles possibly related to ROS generation, and (ii) lethal genotoxic responses due to exposure to ZnO and Pt nanoparticles at a concentration range of 25-100 μg/mL at 12 h exposure. In addition to identifying and visualizing clusters and quantifying similarity measures, the SOM approach can aid in developing predictive quantitative-structure relations; however, this would require significantly larger data sets generated from combinatorial libraries of engineered nanoparticles.

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Figures

Figure 1
Figure 1
Depiction of the experimental set-up for the HTS assay and the data generation. Layout of a 384-well HTS plate (left) showing the location of untreated samples (i.e., cell not exposed to eNMs) and the four replicate wells for each nanoparticle concentration. Four different plates were employed to measure the activity of each of the 10 pathways (right) at different exposure times (3, 6, 12 and 24 h).
Figure 2
Figure 2
Structure and principal elements of the SOM used the current work. The gray plane (left) represents the SOM grid composed by 40 units arranged in a hexagonal configuration of 8×5 units. Subsequent color slices correspond to the visualization of each of the 40 component planes (c-planes) corresponding to each signaling pathway at each exposure time (3 h – 24 h). The plane on the right side depicts the clustering of similar SOM units based on the distance matrix.
Figure 3
Figure 3
Clustering of nanoparticles according to their signaling pathway response profile using the SOM. (a) Visualization of the consensus index for each SOM unit. (b) Clusters obtained from the SOM distance matrix and its corresponding consensus index.
Figure 4
Figure 4
Component planes corresponding to the biological response (up-regulation/down-regulation) of the 10 cell signaling pathways for the RAW 264.7 macrophage cells at 3 h, 6 h, 12 h and 24 h of exposure to the seven nanoparticles (Table 1).
Figure 5
Figure 5
Sub-lethal pro-inflammatory signatures at 3h, 6h, 12h and 24h of exposure corresponding to SPR profiles in Cluster V. The error bars indicate the within-cluster variability of each signaling pathway.
Figure 6
Figure 6
Lethal genotoxic signatures of ZnO and Pt. The error bars indicate the within-cluster variability of each signaling pathway. (a) Response signatures at 3h, 6h, 12h and 24h of exposure corresponding to SPR profiles in Cluster II, (b) Response signatures at 3h, 6h, 12h and 24h of exposure corresponding to SPR profiles in Cluster I.
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