Perturbational profiling of nanomaterial biologic activity

Abstract

Our understanding of the biologic effects (including toxicity) of nanomaterials is incomplete. In vivo animal studies remain the gold standard; however, widespread testing remains impractical, and the development of in vitro assays that correlate with in vivo activity has proven challenging. Here, we demonstrate the feasibility of analyzing in vitro nanomaterial activity in a generalizable, systematic fashion. We assessed nanoparticle effects in a multidimensional manner, using multiple cell types and multiple assays that reflect different aspects of cellular physiology. Hierarchical clustering of these data identifies nanomaterials with similar patterns of biologic activity across a broad sampling of cellular contexts, as opposed to extrapolating from results of a single in vitro assay. We show that this approach yields robust and detailed structure-activity relationships. Furthermore, a subset of nanoparticles were tested in mice, and nanoparticles with similar activity profiles in vitro exert similar effects on monocyte number in vivo. These data suggest a strategy of multidimensional characterization of nanomaterials in vitro that can inform the design of novel nanomaterials and guide studies of in vivo activity.

Fig. 1.

Determining biologic activity of nanoparticles. The heat map displays Z scores for each nanoparticle (in columns) under 64 different conditions (in rows, all combinations of four doses × four cell types × four assays). Wedge shapes indicate increasing nanoparticle dose. AO, aorta endothelial cell; SM, vascular smooth muscle; HEP, hepatocyte; MP, monocyte/macrophage; Apo, apoptosis assay; Mito, mitochondrial potential assay; Red, reducing equivalents assay; ATP, ATP content assay.

Fig. 2.

Comparison of nanoparticle activity profiles in the complete dataset vs. data subsets. (a) Correlation between complete dataset and subsets that include varying numbers (and combinations) of assays. Columns and rows are named according to the number (1, 2, 3, or 4) and names of the assays included in the subset. (b) Scatter plots of Pearson correlations between the complete dataset and subsets that include one, two, or three assays. Each point represents the correlation value between the complete dataset and a specific combination of assays. (c) Correlation between complete dataset and subsets that include varying numbers (and combinations) of cell types. (d) Scatter plot of Pearson correlations between the complete dataset and subsets that include one, two, or three cell types. Assay and cell type abbreviations are the same as in Fig. 1.

Fig. 3.

Hierarchical clustering of nanoparticles based on activity profiles. (a) Heat map depicting hierarchical clustering of nanoparticle biological activity in the entire dataset. Z scores are depicted in each cell; nanoparticle labels are color-coded to reflect their underlying platform (Table S1). Numbers adjacent to nodes in the dendrogram indicate the correlation value for that node, with the correlation scale along the left. (b) Dendrogram of a subcluster containing all three quantum dots (NP 49–NP51) (see Table 1 and Table S1). (c) Dendrogram of hierarchical clustering of two nanoparticles in our dataset approved for human use (NP24, Feridex IV, and NP25, Ferrum Hausmann) and four MION-based nanoparticles (NP45–NP48) (see Table 1 and Table S1).

Fig. 4.

Structure–activity relationships based on activity profiles. (a) Heat map showing results of consensus clustering for NP26, NP27, NP31, and NP32 (Table 1). The color of each cell and the number within each cell reflect the fraction of iterative clustering runs in which two particles cluster together. (b) Heat map showing results of consensus clustering for eight nanoparticles that bear different basic peptides on their surface (Table 1).

Fig. 5.

In vitro activity profiles for three commonly used nanoparticles (CLIO-NH₂, Feridex IV, and Qdot-NH₂-PEG) across all experimental conditions (combinations of dose, cell type, and assay). (a) Z score profile for CLIO-NH₂ and Feridex IV, showing similar Z scores across all conditions. (b) Z score profiles for Feridex IV vs. Qdot-NH₂-PEG (Upper) and CLIO-NH₂ vs. Qdot-NH₂-PEG (Lower). Divergent Z scores between nanoparticles are observed only with the apoptosis and reducing equivalents assays (orange).

Fig. 6.

Effect of intravenously administered nanoparticles on monocyte fractions in vivo. (a) FACS analysis on representative individual spleen or blood samples after nanoparticle treatment. The fraction of cells that are monocytes (defined as CD11b^hi [CD90/B220/CD49b/NK1.1/Ly-6G)^lo cells] is indicated. (b) Monocyte fractions observed in the spleen after treatment with nanoparticles. (c) Monocyte fractions observed in peripheral blood. Values shown are the mean and standard deviation from measurements on three mice.