Application of the Cytokinesis-Block Micronucleus Assay for High-Dose Exposures Using Imaging Flow Cytometry.

The cytokinesis-block micronucleus assay is a well-established method to assess radiation-induced genetic damage in human cells. This assay has been adapted to imaging flow cytometry (IFC), allowing automated analysis of many cells, and eliminating the need to create microscope slides. Furthermore, to improve the efficiency of assay performance, a small-volume method previously developed was employed. Irradiated human blood samples were cultured, stained, and analyzed by IFC to produce images of the cells. Samples were run using both manual and 96-well plate automated acquisition. Multiple parameter-based image features were collected for each sample, and the results were compared to confirm that these acquisition methods are functionally identical. This paper details the multi-parametric analysis developed and the resulting calibration curves up to 10 Gy. The calibration curves were created using a quadratic random coefficient model with Poisson errors, as well as a logistic discriminant function. The curves were then validated with blinded, irradiated samples, using relative bias and relative mean square error. Overall, the accuracy of the dose estimates was adequate for triage dosimetry (within 1 Gy of the true dose) over 90% of the time for lower doses and about half the time for higher doses, with the lowest success rate between 5 and 6 Gy where the calibration curve reached its peak and there was the smallest change in MN/BNC with dose. This work describes the application of a novel multi-parametric analysis that fits the calibration curves and allows dose estimates up to 10 Gy, which were previously limited to 4 Gy. Furthermore, it demonstrates that the results from samples acquired manually and with the autosampler are functionally similar.

Adverse outcome pathways and linkages to transcriptomic effects relevant to ionizing radiation injury.

BACKGROUND: In the past three decades, a large body of data on the effects of exposure to ionizing radiation and the ensuing changes in gene expression has been generated. These data have allowed for an understanding of molecular-level events and shown a level of consistency in response despite the vast formats and experimental procedures being used across institutions. However, clarity on how this information may inform strategies for health risk assessment needs to be explored. An approach to bridge this gap is the adverse outcome pathway (AOP) framework. AOPs represent an illustrative framework characterizing a stressor associated with a sequential set of causally linked key events (KEs) at different levels of biological organization, beginning with a molecular initiating event (MIE) and culminating in an adverse outcome (AO). Here, we demonstrate the interpretation of transcriptomic datasets in the context of the AOP framework within the field of ionizing radiation by using a lung cancer AOP (AOP 272: https://www.aopwiki.org/aops/272) as a case example. METHODS: Through the mining of the literature, radiation exposure-related transcriptomic studies in line with AOP 272 related to lung cancer, DNA damage response, and repair were identified. The differentially expressed genes within relevant studies were collated and subjected to the pathway and network analysis using Reactome and GeneMANIA platforms. Identified pathways were filtered (p < .001, ≥3 genes) and categorized based on relevance to KEs in the AOP. Gene connectivities were identified and further grouped by gene expression-informed associated events (AEs). Relevant quantitative dose-response data were used to inform the directionality in the expression of the genes in the network across AEs. RESULTS: Reactome analyses identified 7 high-level biological processes with multiple pathways and associated genes that mapped to potential KEs in AOP 272. The gene connectivities were further represented as a network of AEs with associated expression profiles that highlighted patterns of gene expression levels. CONCLUSIONS: This study demonstrates the application of transcriptomics data in AOP development and provides information on potential data gaps. Although the approach is new and anticipated to evolve, it shows promise for improving the understanding of underlying mechanisms of disease progression with a long-term vision to be predictive of adverse outcomes.