A new imaging platform (iScreen) allows for the concurrent assessment of micronucleus induction and genotoxic mode of action in human A375 cells

Visualization strategies to aid interpretation of high-dimensional genotoxicity data

This article describes a range of high-dimensional data visualization strategies that we have explored for their ability to complement machine learning algorithm predictions derived from MultiFlow® assay results. For this exercise, we focused on seven biomarker responses resulting from the exposure of TK6 cells to each of 126 diverse chemicals over a range of concentrations. Obviously, challenges associated with visualizing seven biomarker responses were further complicated whenever there was a desire to represent the entire 126 chemical data set as opposed to results from a single chemical. Scatter plots, spider plots, parallel coordinate plots, hierarchical clustering, principal component analysis, toxicological prioritization index, multidimensional scaling, t-distributed stochastic neighbor embedding, and uniform manifold approximation and projection are each considered in turn. Our report provides a comparative analysis of these techniques. In an era where multiplexed assays and machine learning algorithms are becoming the norm, stakeholders should find some of these visualization strategies useful for efficiently and effectively interpreting their high-dimensional data.

Comparative analysis of micronucleus induction and DNA damage biomarkers in TK6 and A375 cells using flow cytometry

Previously, we introduced an alternative adherent A375 cell line for clastogenicity and aneugenicity testing using a high content imaging platform. To further characterize the performance of A375 cells, we investigated the sensitivity and specificity of A375 and TK6 cells by directly comparing micronucleus (MN) induction, cytotoxicity (relative cell counts, viability, and apoptosis), clastogenicity (γH2AX), and aneuploidy markers (pH 3, MPM-2, and polyploidy) using flow cytometric methods. We evaluated 14 compounds across different mechanisms (non-genotoxic apoptosis inducers, clastogens, and aneugens with either tubulin binding or aurora kinase inhibiting phenotypes) at 4-h and 24-h post treatment. Both aneugens and clastogens tested positive for micronucleus induction in both cell lines. Apoptosis continued to be a confounding factor for flow cytometry-based micronuclei assessment in TK6 cells as evidenced by positive responses by the three cytotoxicants. Conversely, A375 cells were not affected by apoptosis-related false positive signals and did not produce a positive response in the in vitro micronucleus assay. Benchmark dose response (BMD) analysis showed that the induction of micronuclei and biomarkers occurred at similar concentrations in both cell lines for clastogens and aneugens. By showing that A375 cells have similar sensitivity to TK6 cells but a greater specificity, these results provide additional support for A375 cells to be used as an alternative adherent cell line for in vitro genetic toxicology assessment.

Early detection of genotoxic hepatocarcinogens in rats using γH2AX and Ki-67: prediction by machine learning

Direct DNA double-strand breaks result in phosphorylation of H2AX, a variant of the histone H2 protein. Phosphorylated H2AX (γH2AX) may be a potential indicator in the evaluation of genotoxicity and hepatocarcinogenicity. In this study, γH2AX and Ki-67 were detected in the short-term responses (24 h after chemical administration) to classify genotoxic hepatocarcinogens (GHs) from non-GH chemicals. One hundred and thirty-five 6-week-old Crl: CD(SD) (SPF) male rats were treated with 22 chemicals including 11 GH and 11 non-GH, sacrificed 24 h later, and immunostained with γH2AX and Ki-67. Positivity rates of these markers were measured in the 3 liver ZONEs 1-3; portal, lobular, and central venous regions. These values were input into 3 machine learning models-Naïve Bayes, Random Forest, and k-Nearest Neighbor to classify GH and non-GH using a 10-fold cross-validation method. All 11 and 10 out of 11 GH caused significant increase in γH2AX and Ki-67 levels, respectively (P < .05). Of the 3 machine learning models, Random Forest performed the best. GH were identified with 95.0% sensitivity (76/80 GH-treated rats), 90.9% specificity (50/55 non-GH-treated rats), and 90.0% overall correct response rate using γH2AX staining, and 96.2% sensitivity (77/80), 81.8% specificity (45/55), and 90.4% overall correct response rate using Ki-67 labeling. Random Forest model using γH2AX and Ki-67 could independently predict GH in the early stage with high accuracy.