Use of a high throughput screening approach coupled with in vivo zebrafish embryo screening to develop hazard ranking of engineered nanomaterials
Because of concerns about the safety of a growing number of engineered nanomaterials (ENM), it is necessary to develop high-throughput screening and in silico data transformation tools that can speed up in vitro hazard ranking. Here, we report the use of a multiparametric, automated screening assay that incorporates sublethal and lethal cellular injury responses to perform high-throughput analysis of a batch of commercial metal/metal oxide nanoparticles (NP) with the inclusion of a quantum dot (QD1). The responses chosen for tracking cellular injury through automated epifluorescence microscopy included ROS production, intracellular calcium flux, mitochondrial depolarization, and plasma membrane permeability. The z-score transformed high volume data set was used to construct heat maps for in vitro hazard ranking as well as showing the similarity patterns of NPs and response parameters through the use of self- organizing maps (SOM). Among the materials analyzed, QD1 and nano-ZnO showed the most prominent lethality, while Pt, Ag, SiO2, Al2O3, and Au triggered sublethal effects but without cytotoxicity. In order to compare the in vitro with the in vivo response outcomes in zebrafish embryos, NPs were used to assess their impact on mortality rate, hatching rate, cardiac rate, and morphological defects. While QDs, ZnO, and Ag induced morphological abnormalities or interfered in embryo hatching, Pt and Ag exerted inhibitory effects on cardiac rate. Ag toxicity in zebrafish differed from the in vitro results, which is congruent with this material’s designation as extremely dangerous in the environment. Interestingly, while toxicity in the initially selected QD formulation was due to a solvent (toluene), supplementary testing of additional QDs selections yielded in vitro hazard profiling that reflect the release of chalcogenides. In conclusion, the use of a high-throughput screening, in silico data handling and zebrafish testing may constitute a paradigm for rapid and integrated ENM toxicological screening.
George S, Xia T, Rallo R, et al. (2011) Use of a high throughput screening approach coupled with in vivo zebrafish embryo screening to develop hazard ranking of engineered nanomaterial. ACS Nano, 5(3):1805-1817
Self-Organizing Map Analysis of Toxicity-related Cell Signaling Pathways for Metal and Metal Oxide Nanoparticles
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 ex- posed 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 clus- tering 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.
Rallo R, France B, Liu R, Nair S, George S, Damoiseaux R, Giralt F, et al. (2011) Self-Organizing Map Analysis of Toxicity-related Cell Signaling Pathways for Metal and Metal Oxide Nanoparticles. Environmental Science and Technology, 45(4): 1695-1702
Conscious worst case definition for risk assessment, part II A methodological case study for pesticide risk assessment
This paper illustrates, by a case study, how to apply the conceptual Worst-Case Definition (WCD) model, developed in the methodological paper in the current journal, by Sørensen et al. (2010-this issue). The case is about eco-toxicological risk assessment of pesticides under Danish conditions. Cumulative aspects are included on a conceptual basis as elements of the worst-case conditions. This defines factors that govern the risk assessment, including location in time and space of risk “hotspots”. Two pillars of concern drive the conceptual modelling: (1) What to protect (denoted Protected Units (PUs)) and (2) the reason for increased risk level (denoted Causes of Risks (CRs)). Both PUs and CRs are analysed using hierarchical procedures that facilitate a complete listing of concrete factors governing increased risk for adverse effect due to agricultural usage of pesticide. The factors governing pesticide risk are combined in a context that combines the protection of relevant groupings of organisms with the factors for increased risk level for each of these. Identification of the most important relations between defined types of PUs and CRs is illustrated using expert knowledge. Existing databases are used to form spatial distributed risk indicators as estimators for a selection of important relations between PUs and CRs. This paper illustrates how the WCD model can break down the complex issue of uncertainty into fractions that are more open for evaluations. Finally, it shows application of risk indicators in a multi-criterion analysis using respectively self organizing mapping and partial order technique in a comparative analysis that highlights critical aspects of uncertainty, due to the ambiguity between single risk indicator rankings.
Sorensen, P.B., Giralt F., Rallo R., Espinosa G., Munier B., Gyldenkaerne S., Thomsen M. (2010) Conscious worst case definition for risk assessment, part II A methodological case study for pesticide risk assessment. Science of the Total Environment,408:3860-3870
The protection of personal privacy is paramount, and consequently many efforts have been devoted to the study of data protection techniques. Governments, statistical agencies and corporations must protect the privacy of the individuals while guaranteeing the right of the society to knowledge. Microaggregation is one of the most promising solutions to deal with this praiseworthy task. However, its high computational cost prevents its use with large amounts of data. In this article we propose a new microaggregation algorithm that uses self-organizing maps to scale down the computational costs while maintaining a reasonable loss of information.
Solanas, A., Gavalda, A., Rallo, R. (2009) Micro-SOM: A Linear-Time Multivariate Microaggregation Algorithm Based on Self-Organizing Maps. ICANN, Lecture Notes in Computer Science, 5768:525-535
A predictive Fuzzy ARTMAP neural system and two hybrid networks, each combining a dynamic unsupervised classifier with a different kind of supervised mechanism, were applied to develop virtual sensor systems capable of inferring the properties of manufactured products from real process variables. A new method to construct dynamically the unsupervised layer was developed. A sensitivity analysis was carried out by means of self-organizing maps to select the most relevant process features and to reduce the number of input variables into the model. The prediction of the melt index (MI) or quality of six different LDPE grades produced in a tubular reactor was taken as a case study. The MI inferred from the most relevant process variables measured at the beginning of the process cycle deviated 5% from on-line MI values for single grade neural sensors and 7% for composite neural models valid for all grades simultaneously.
Rallo R, Ferré-Giné J, Arenas A, Giralt F (2002) Neural Virtual Sensor for the Inferential Prediction of Product Quality from Process Variables. Computers and Chemical Engineering (26) 12, 1735-54