Using toxcast™ data to reconstruct dynamic cell state trajectories and estimate toxicological points of departure

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Abstract

BACKGROUND:: High-content imaging (HCI) allows simultaneous measurement of multiple cellular phenotypic changes and is an important tool for evaluating the biological activity of chemicals. OBJECTIVES:: Our goal was to analyze dynamic cellular changes using HCI to identify the “tipping point” at which the cells did not show recovery towards a normal phenotypic state. METHODS:: HCI was used to evaluate the effects of 967 chemicals (in concentrations ranging from 0.4 to 200 μM) on HepG2 cells over a 72-hr exposure period. The HCI end points included p53, c-Jun, histone H2A.x, α-tubulin, histone H3, alpha tubulin, mitochondrial membrane potential, mitochondrial mass, cell cycle arrest, nuclear size, and cell number. A computational model was developed to interpret HCI responses as cell-state trajectories. RESULTS:: Analysis of cell-state trajectories showed that 336 chemicals produced tipping points and that HepG2 cells were resilient to the effects of 334 chemicals up to the highest concentration (200 μM) and duration (72 hr) tested. Tipping points were identified as concentration-dependent transitions in system recovery, and the corresponding critical concentrations were generally between 5 and 15 times (25th and 75th percentiles, respectively) lower than the concentration that produced any significant effect on HepG2 cells. The remaining 297 chemicals require more data before they can be placed in either of these categories. CONCLUSIONS:: These findings show the utility of HCI data for reconstructing cell state trajectories and provide insight into the adaptation and resilience of in vitro cellular systems based on tipping points. Cellular tipping points could be used to define a point of departure for risk-based prioritization of environmental chemicals. CITATION:: Shah I, Setzer RW, Jack J, Houck KA, Judson RS, Knudsen TB, Liu J, Martin MT, Reif DM, Richard AM, Thomas RS, Crofton KM, Dix DJ, Kavlock RJ. 2016. Using ToxCast™ data to reconstruct dynamic cell state trajectories and estimate toxicological points of departure. Environ Health Perspect 124:910–919; http://dx.doi.org/10.1289/ehp.1409029

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APA

Shah, I., Woodrow Setzer, R., Jack, J., Houck, K. A., Judson, R. S., Knudsen, T. B., … Kavlock, R. J. (2016). Using toxcastTM data to reconstruct dynamic cell state trajectories and estimate toxicological points of departure. Environmental Health Perspectives, 124(7), 910–919. https://doi.org/10.1289/ehp.1409029

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