Automated image analysis of cytokinesis-blocked micronuclei: an adapted protocol and a validated scoring procedure for biomonitoring

Abstract

Micronuclei (MN) frequencies in peripheral blood lymphocytes have been used worldwide as a biomarker of chromosomal damage for genotoxicity testing and biomonitoring studies. Automation of MN analysis would provide faster and more reliable results with minimizing subjective MN identification. We developed an automated facility for the scoring of the in vitro MN cytokinesis-block assay for biomonitoring on Giemsa-stained slides, fulfilling the following criteria: applicable to the cytokinesis-block micronucleus methodology, discriminating between mono-, bi- and polynucleated cells, MN scoring according to HUMN scoring criteria, false-negative MN rate <10% and false-positive (FP) MN rate <1%. We first adapted the slide preparation protocol to obtain an optimal cell density and dispersion, which is important for image analysis. We developed specific algorithms starting from the cell as a detection unit. The whole detection and scoring process was separated into two distinct steps: in the first step, the cells and nuclei are detected; then, in the second step, the MN are searched for in the detected cells. Since the rate of FP MN obtained by the automatic analysis was in the range of 0.5-1.5%, an interactive visual validation step was introduced, which is not time consuming and allows quality control. Validation of the automated scoring procedure was undertaken by comparing the results of visual and automated scoring of micronucleated mono- and binucleated cells in human lymphocytes induced by two clastogens (ionizing radiation and methyl methane-sulphonate), two aneugens (nocodazole and carbendazim) and one apoptogen (staurosporine). Although the absolute MN frequencies obtained with automated scoring were lower as compared to those detected by visual scoring, a clear dose response for MNBN frequencies was observed with the automated scoring system, indicating that it is able to produce biologically relevant and reliable results. These observations, together with its ability to detect cells, nuclei and MN in accordance with the HUMN scoring criteria, confirm the usability of the automated MN analysis system for biomonitoring.