The use of manual microscopy for the scoring of chromosome damage in the in vitro micronucleus assay is often associated with user subjectivity. This level of subjectivity can be reduced by using automated platforms, which have added value of faster with high-throughput and multi-endpoint capabilities. However, there is a need to assess the reproducibility and sensitivity of these automated platforms compared with the gold standard of the manual scoring. The automated flow cytometry-based MicroFlow® and image analysis-based Metafer™ were used for dose response analyses in human lymphoblastoid TK6 cells exposed to the model clastogen, methyl methanesulfonate (MMS), aneugen, carbendazim, and the weak genotoxic carcinogen, ochratoxin A (OTA). Cells were treated for 4 or 30 h, with a 26- or 0-h recovery. Flow cytometry scoring parameters and the Metafer™ image classifier were investigated, to assess any potential differences in the micronucleus (MN) dose responses. Dose response data were assessed using the benchmark dose approach with chemical and scoring system set as covariate to assess reproducibility between endpoints. A clear increase in MN frequency was observed using the MicroFlow® approach on TK6 cells treated for 30 h with MMS, carbendazim and OTA. The MicroFlow®-based MN frequencies were comparable to those derived by using the Metafer™ and manual scoring platforms. However, there was a potential overscoring of MN with the MicroFlow® due to the cell lysis step and an underscoring with the Metafer™ system based on current image classifier settings. The findings clearly demonstrate that the MicroFlow® and Metafer™ MN scoring platforms are powerful tools for automated high-throughput MN scoring and dose response analysis.Electronic supplementary materialThe online version of this article (doi:10.1007/s00204-016-1903-8) contains supplementary material, which is available to authorized users.

The Phosphatidylinositol glycan class A (PIG-A) gene mutation assay phenotypically measures erythrocyte mutations, assessed here for their correlation to neoplastic progression in the gastro-oesophageal reflux disease (GORD)-Barrett’s metaplasia (BM)-oesophageal adenocarcinoma (OAC) model. Endoscopy patients underwent venipuncture and erythrocytes fluorescently stained for glycosyl phosphatidylinositol (GPI)–anchored proteins; CD55 and CD59. Using flow cytometry, GPI–anchor negative erythrocytes (mutants) were scored and compared amongst groups. The study enlisted 200 patients and 137 healthy volunteers. OAC patients had a three–fold increase in erythrocyte mutant frequency (EMF) compared to GORD patients (p < 0.001) and healthy volunteers (p < 0.001). In OAC patients, higher EMF was associated with worsening tumour staging (p = 0.014), nodal involvement (p = 0.019) and metastatic disease (p = 0.008). Chemotherapy patients demonstrated EMF’s over 19–times higher than GORD patients. Patients were further classified into groups containing those with non-neoplastic disease and those with high-grade dysplasia/cancer with 72.1% of cases correctly classified by high EMF. Within the non-neoplastic group, aspirin users had lower EMF (p = 0.001) and there was a positive correlation between body mass index (p = 0.03) and age (p < 0.001) and EMF. Smokers had EMF’s over double that of non-smokers (p = 0.011). Results suggest this test could help detect OAC and may be a useful predictor of disease progression.

Mutagens can be carcinogens, and traditionally, they have been identified in vitro using the Salmonella ‘Ames’ reverse mutation assay. However, prokaryotic DNA packaging, replication and repair systems are mechanistically very different to those in the humans we inevitably seek to protect. Therefore, for many years, mammalian cell line genotoxicity assays that can detect eukaryotic mutagens as well as clastogens and aneugens have been used. The apparent lack of specificity in these largely rodent systems, due partly to their mutant p53 status, has contributed to the use of animal studies to resolve data conflicts. Recently, silencing mutations at the PIG-A locus have been demonstrated to prevent glycophosphatidylinositol (GPI) anchor synthesis and consequentially result in loss of GPI-anchored proteins from the cell’s extracellular surface. The successful exploitation of this mutant phenotype in animal studies has triggered interest in the development of an analogous in vitro PIG-A mutation screening assay. This article describes the development of a robust assay design using metabolically active human cells. The assay includes viability and cell membrane integrity assessment and conforms to the future ideas of the 21st-century toxicology testing.

The p53 tumor suppressor protein plays an essential role in cellular integrity and inactivation of the TP53 gene by mutation is the most frequent alteration in human cancer. As loss of p53 function is associated with increased genetic instability, it is important in genotoxicity testing to explore the role of p53 competency. In vitro model systems for genotoxicity testing are sometimes prone to misleading positive results; some of this loss of predictivity may be caused by p53 inactivation in some cell models. To explore whether impaired p53 function plays a role in mutation sensitivity, TK6 cells (p53 competent) and NH32 cells (p53 deficient) were treated with two known genotoxicants, mitomycin C (MMC) and cytosine arabinoside (araC). Chromosomal damage was assessed in the low dose region by an automated micronucleus system and p53 activity was investigated by gene and protein expression analysis. Cell cycle progression studies were also assessed. Low levels of micronucleus and p53 induction were observed in TK6 cells treated with MMC. On the other hand, higher levels of micronucleus and p53 induction were shown in TK6 cells treated with araC and a G1/S arrest was observed after araC treatment. p53 deficient NH32 cells showed an increased sensitivity of micronucleus (MN) induction after araC treatment compared with TK6 cells and less of an active G1/S phase checkpoint. Thus, impaired p53 function sensitizes cells to genotoxicants and plays a central role in the DNA damage response. This data has clear importance for safety assessment of genotoxicity and shows how crucial p53 competence is.

The in vitro micronucleus assay is a globally significant method for DNA damage quantification used for regulatory compound safety testing in addition to inter-individual monitoring of environmental, lifestyle and occupational factors. However, it relies on time-consuming and user-subjective manual scoring. Here we show that imaging flow cytometry and deep learning image classification represents a capable platform for automated, inter-laboratory operation. Images were captured for the cytokinesis-block micronucleus (CBMN) assay across three laboratories using methyl methanesulphonate (1.25–5.0 μg/mL) and/or carbendazim (0.8–1.6 μg/mL) exposures to TK6 cells. Human-scored image sets were assembled and used to train and test the classification abilities of the “DeepFlow” neural network in both intra- and inter-laboratory contexts. Harnessing image diversity across laboratories yielded a network able to score unseen data from an entirely new laboratory without any user configuration. Image classification accuracies of 98%, 95%, 82% and 85% were achieved for ‘mononucleates’, ‘binucleates’, ‘mononucleates with MN’ and ‘binucleates with MN’, respectively. Successful classifications of ‘trinucleates’ (90%) and ‘tetranucleates’ (88%) in addition to ‘other or unscorable’ phenotypes (96%) were also achieved. Attempts to classify extremely rare, tri- and tetranucleated cells with micronuclei into their own categories were less successful (≤ 57%). Benchmark dose analyses of human or automatically scored micronucleus frequency data yielded quantitation of the same equipotent concentration regardless of scoring method. We conclude that this automated approach offers significant potential to broaden the practical utility of the CBMN method across industry, research and clinical domains. We share our strategy using openly-accessible frameworks.