Automation of the Ames Assay Scoring and Assessment of Water Samples for Mutagenicity

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The presence of these substances can result in mutagenic risk to the public, highlighting the need for surveillance. This study evaluated the mutagenic potential of source water and drinking water from two South African drinking water treatment plants. The study also investigated the high-throughput scoring of the Salmonella typhimurium Ames mutagenicity assay with frameshift and base-pair mutations. Two different scoring approaches were used including visual manual scoring and using the automatic image scanning platform. No mutagenic risk was detected for both TA98 and TA100 bacteria regardless of metabolic activation. Grab sampling may have missed any transient mutagenic events. Despite the limitations, automatic scanning of the microtiter plates ensured consistent, reliable and accurate results that can be reviewed. The outcomes of the study show effective mitigation of mutagenic risk by the treatment plants and deliver public reassurance of drinking water. The advantageous combination of automated scoring technologies, as demonstrated in this study, provides a scalable and standardized monitoring programme for mutagenic risk. Regulatory frameworks would benefit from a mutagenic risk monitoring programme, given the myriad health risks involved in exposure to environmental mutagens. Ames Salmonella mutagenicity test automatic imaging scanning platform microplate formats water samples automation high throughput Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Figure 8 HIGHLIGHTS OF THE STUDY Source and drinking water samples were taken at two different drinking water treatment plants. Water samples were analysed for mutagenicity using the Ames mutagenicity test using two bacterial strains TA98 and TA100. Two different scoring approaches were used to determine the number of revertants in the 96-well microtiter plate. Results showed that there was no mutagenicity of samples throughout the study period. The manual scoring approach relies heavily on the analysts ability to decipher different colours in the microtiter wells. The automatic image scanning platform tallies the number of revertants and classifies the results based on classifiers. Automation allows for high throughput of results; an audit trail; detection of subtle differences in colour. INTRODUCTION Historically, the Ames assay has been used to classify DNA-damaging effects in cells for approximately more than 500 agents worldwide (Kirkland and Fowler, 2010 ). However, the classification of these agents as either potential carcinogens, mutagens, or clastogens has resulted in only a slight decrease in cancer incidents (Rueff et al., 2019 ). The Ames method, originally developed and published by Bruce Ames in 1974, is now widely used to detect the mutagenicity of chemical compounds from freshwater sources, including rivers (Steyn et al., 2019 ; Zwart et al., 2020 ) and drinking water treatment plants (Rincón-Bedoya et al., 2013 ). The method makes use of the bacterial strains of Salmonella typhimurium , TA98 and TA100. These bacterial strains carry either a frameshift mutation or a base-pair mutation on the hisD3052 and hisG46 genes, respectively (Vasetska et al., 2025 ). The Ames method has been adapted from the plate incorporation method, which uses solid agar from which positive counts are determined by the growth of colonies, to the fluctuation method, which takes place in liquid form and scores results in 384-well microtiter plates (Flückiger-Isla; OECD, 2020; Rainer et al., 2021 ). Furthermore, the pH indicator allows for the indication of yellow positive wells that can be manually scored in the fluctuation method, the pH indicator dye changes to yellow as the solution's pH drops to roughly pKa = 5.2. This shift is caused by catabolism during metabolism, which stems from a lack of histidine in the mixture. Moreover, the pH indicator enables the identification and manual scoring of yellow positive wells (ISO 11350:2012). With the increasing requirement for advanced technology that is efficient and accurate, automation has become a key factor in various industries. One industry that greatly benefits from automation is the field of assay analysis. Assay analysis involves the testing and measurement of various substances and compounds to determine their concentrations or properties. Implementing automation in assay analysis not only saves time and reduces human error, but it also enhances productivity and allows for larger volumes of samples to be processed in a shorter amount of time (Holland and Davies, 2020 ; Rupp et al., 2024 ). Recently, a 96-well format of the traditional 384-well Ames method has been introduced. The 96-well method has a major advantage in that it uses a larger volume of sample, thereby increasing the likelihood of sensitivity in detecting mutagens present (Forsten et al., 2022 ; Calao-Ramos et al., 2023 ). Furthermore, the 96-well microplate version has a larger well size to better differentiate between the colours yellow and purple, and therefore the number of revertants in a study. Despite the larger wells, subjectivity still exists in colour differentiation of the positive and negative wells in a microplate. These shortfalls are a hindrance to the application of the fluctuation test in a high-throughput manner for testing laboratories. Automated methodologies can significantly enhance colour classification within the AMES fluctuation assay analysis. Colour classification is a critical step in Ames assay analysis, as it facilitates the scoring of mutagenicity in the test, and aids in ascertaining the presence or absence of particular substances or reactions (ISO 11350:2012). Traditionally, colour classification in the Ames fluctuation assay has been a manual and time-consuming process. To evaluate the microtiter plates, analysts would visually enumerate the 96-well microtiter plates and differentiate between the yellow (positive number of revertants) and purple (negative for reversion) wells. The enumeration of the revertant yellow wells is thus dependent on subjective differences between yellow and partially yellow (Jolibois et al., 2003 ; Large et al., 2022 ), yellow and turbid and cloudy wells (Calao–Ramos et al., 2023) to determine the mutagenicity results of the tests. To add to the difficulty, the challenge of scoring while being colour blind may exacerbate the outcome and viability of the results. Advances in the colour determination process have developed from the manual observation to the use of microplate readers and luminometers (Gee et al., 1998 ; Zwart et al., 2018 ; Shao et al., 2020 ; Zwart et al., 2020 ). However, the direct imaging and machine learning capabilities available today offer a pathway for high-accuracy-throughput analysis, along with the means to store and validate the results digitally. The application of such image analysis for bioassays has recently been validated in medical fields by Rossnerova et al. ( 2009 ), assessing chromosomal damage, Verma et al. ( 2016 ), assessing micronucleus dose response, and Wills et al. ( 2021 ) through automating flow cytometry assays. Similarly, educational training programs for the Ames fluctuation test have incorporated the practice of students photographing test plates to archive raw data. This can be helpful for instructors to score the test based on the subjectivity of the yellow and partially yellow colours of the revertants (Large et al., 2022 ). Moreover, for an accredited laboratory, high accuracy and audit trails are required (ISO 17025, 2017(E)). Therefore, the automatic scanning platform is proposed as a viable alternative to scoring plates manually. This will free up time for analysts to pursue other activities in the laboratory and has advantages such as providing an audit trail of the results including capturing an image that is not practicable for the manual scoring method. To the best of our knowledge scoring of Ames results for the 384-well and 96-well microtiter plate versions have always been done manually or through the use of microplate readers and no such work or research has been done on automating the process of scoring by using an imaging platform. Furthermore, the Ames fluctuation method has only been conducted on ad-hoc research purposes in South Africa, and the high-throughput of sample analysis has been overlooked. One of the main advantages of undergoing Ames testing is the ability to use the method for the determination of the mutagenicity of water samples. However, regulatory requirements for mutagenicity testing of water by drinking water treatment facilities are not often performed. Currently in South Africa, mutagenic testing has been limited to ad-hoc research spheres, including pharmacology and botany (Verschaeve and van Staden 2008 ; Abdillahi et al., 2012 ; Reid et al., 2006 ; Ndhlala et al., 2010 ; Ngubane et al., 2024 ). Freshwater mutagenicity potential has also been confined to rivers and their ecosystem health (Ansara-Ross et al., 2009 ; Steyn et al., 2019 ; Ijil et al., 2021 ). Despite the increasing application of the test on freshwater sources, the routine mutagenic testing of potable water from drinking water treatment plants is lacking. This may be due to many reasons, such as skill level to perform analysis, the lack of technology readily available, and given that performing these additional analyses on water samples can be time-consuming and expensive (Slabbert et al., 1998 ). However, knowing this information will provide vital information on the human health effects of drinking water with mutagenicity potential (Calao-Ramos et al., 2023 ). Despite these challenges, various researchers have studied the potential link between drinking water, specifically chlorinated compounds present in it, and the development of cancer in humans (Grellier et al., 2015 ; Shi et al., 2024 ; Xie et al., 2025 ). Transitioning to routine genotoxic analysis using effect-based methods such as the Ames test can serve as a rapid indicator for drinking water treatment plants to optimise their processes. It can further be used as a risk-based preventative tool for potable water suppliers and thus safeguard human health (Alias et al., 2022 ; Gameiro et al., 2025 ). More advanced countries have already begun enforcing genetic toxicity assessments in drinking water quality monitoring to complement traditional physicochemical assessments using standardised effect-based methods (ISO 11350:2012; OECD, 2020); however, South Africa still predominantly uses physicochemical techniques to evaluate drinking water safety. The aim of this study was therefore two-fold: 1) to elucidate mutagenicity using the Ames modified ISO test on water samples from two different drinking water treatment facilities, and 2) to assess the reproducibility of the traditional manual Ames scoring method and the scoring method generated using the automatic scanning platform. Although this study was unable to reject the null hypothesis, the data that was obtained from this study still adds value in making advancements in high-throughput automation of scoring of Ames mutagenicity test results. The findings of the study can be used by other facilities to increase efficiency and accuracy of data capturing. These efforts are crucial in improving the understanding of mutagenicity in the aquatic environment and furthering safety standards for drinking water. MATERIALS AND METHODS Sampling sites One-litre samples were collected from different sampling sites across the drinking water value chain from two water treatment plants in the Vaal Triangle, Gauteng, South Africa. These plants supply approximately 19 million consumers. Samples were taken biweekly over three months. The two water treatment plants were designated Plant 1 and Plant 2, respectively. The process units of the two plants utilize conventional treatment configurations, which include coagulation-flocculation, sedimentation, filtration, and disinfection. Source water, drinking water, and distributed water were collected in brown glass amber 1-litre bottles, sealed with a lid, placed in a cooler box, and transported to the laboratory. Sample preparation On the day of analysis, samples were sterile-filtered using a 0.22 µm sterile syringe filter. If immediate analysis was not possible, the samples were frozen in 50 mL tubes and defrosted in a refrigerator overnight before use in the assay. Bacterial rehydration and preparation The Ames Modified ISO kits were purchased from Environmental Bio Detection Products Incorporated. The manufacturer provides all necessary reagents, including positive controls, sterile water, exposure medium solution, and reversion medium solution. Reagent V was added to two respective vials of growth media, which were then added to the lyophilized TA100 and TA98 vials, respectively. The rehydrated bacterial suspension was incubated on a shaker at 200 RPM in a 37°C incubator (Incoshake). After 16 hours, the bacterial suspension was checked for turbidity. Once turbidity was observed, the overnight bacterial suspension was added to a cuvette. Subsequently, the optical density was read using a spectrophotometer (Evolution 300 UV-Vis, Thermofischer Scientific) and diluted to an optical density of 600nm or its respective working concentration for both bacteria using 1x exposure media. Sample addition to 24-well plates Sterile water was added to the designated negative control and positive control wells in a 24-well microtiter plate. For the TA100 bacterial strain, sodium azide (NaN 3 ) was used as a positive control in microtiter plates without metabolic activation. For the TA100 bacterial strain with S9 liver enzyme bioactivation (S9 mix), 2-amino anthracene (2-AA) positive control was added to the plate. For T98 bacteria 2-nitrofluorene(2-NF) was used as a positive control in microtiter plates with and without metabolic activation. As a sterility control, sterile water was added to the plate. Water samples were added undiluted to wells in triplicate. After the addition of the samples to the wells, the S9 mix was added to the wells of the S9-marked plates. Subsequently, exposure solution was added to all the wells with subsequent addition of diluted bacterial culture. No diluted bacterial culture was added to the sterile control wells. The plates were then sealed with the lid and incubated at 37°C for 100 minutes in an incubator. During the incubation period, the master mix reversion media was prepared in labelled conical tubes. Briefly, 40% D-Glucose, Bromocresol purple, D-Biotin, and 10X reversion solution were added to the conical tubes. Thereafter, sterile water was added to the reversion mixture prepared. After the exposure plate incubation, its well contents were added to the tubes. Each tube was vortexed and poured into a reservoir or a loading boat. Using a multichannel pipette, the reversion mix from the reagent boats was aliquoted into a 96-well plate. The 96-well plates were then sealed with lids and placed into Ziploc bags to prevent evaporation. The plates were incubated in a 37°C incubator for 3 days until scored. Quality control parameters To determine if a sample was positive, the following acceptance criteria were applied: for a negative control, the number of revertant wells had to be greater than or equal to zero and less than and equal to 15 (≥ 0 and ≤ 15). For the positive control, the number of revertants had to be greater than and equal to 25 (≥ 25). For the sterile control no revertant wells should have been present. Samples were considered positive when all the above quality control parameters were met and there was a 2-fold induction of the number of revertants above the baseline value. Scoring approaches The 96-well plates were scored both visually (manual method) (Fig. 1 ) and using the automatic scanning platform incorporating the Metafer Metasystems software platform (Fig. 2 ). Manual scoring of the 96-well plates The manual method consisted of placing the plates on a light box (Fig. 1 ) and then performing a manual count by scoring all the revertant wells. The results were recorded on a score sheet. Automated scoring of the 96-well plates The 96-well plates were scored using the automated plate loading capability, along with the automatic scanning and imaging acquisition via the Metafer Metasystems system. An Axio Imager Z1 (Carl Zeiss) microscope, equipped with the Metacyte module automated capture software and a Cool cube camera, was used to capture images. Images from each well of the 96-well microplates were captured using a 5x objective, focusing on one plane per Red Green Blue colour channel. A counterstain mask adjusted the mean colour intensity. The number of revertant wells was automatically totalled, and a report was generated. The Metafer Metasystems software scored unknown revertants in the 96-well plates, allowing the analyst to manually identify and correct results. An audit trail details the corrected wells where corrections were made. After scanning and scoring, a histogram of positive, negative, and unknown revertants (Fig. 3 ) was generated, followed by a report with the total number of revertants (Fig. 4 ). The determination of mutagenicity was automatically calculated after data integration and interfacing into the laboratory information management system. Statistical analyses The students t-test was used for the comparison between the number of revertant wells scored using the manual method and the number of revertant wells that were automatically calculated using the Metafer Metasystems software. Significance was accepted with p < 0.05. RESULTS Mutagenicity of water samples Drinking water samples were taken over a period of 3 months and analysed with the 96-well microtiter plate Ames mutagenicity test. Results of the analysis were scored using two different scoring approaches (Fig. 1 and Fig. 2 ). The relevant quality control parameters were used to identify if samples were positive. Furthermore, a sample was considered positive when the above quality control parameters are met and there was a 2-fold induction of the number of revertants above the baseline value. All quality control parameters for testing were met for all sampling occasions. No dilution of samples occurred and 100% of samples were used in the testing process. At this sample dilution, there was no evidence of a 2-fold induction of the number of revertants above the baseline value for all sampling occasions (p > 0.05) (Fig. 5 – 7 ). This was evident for both bacterial strains used in the study. There was also no evidence of mutagenicity of samples in the two bacterial strains with metabolic activation (Fig. 5 – 7 ). Evaluation of mutagenicity using the automated image scanning platform Ninety-six well microtiter plates were scored and tallied using an automated image scanning platform (Fig. 2 ). The automatic image scanning platform was able to analyse the results within 60 seconds for one 96-well microtiter plate, allowing a fast, efficient digital capture and tally of the number of revertants for all plates. Results of the study showed that the number of revertants in the control samples were tallied to be more and equal to zero and less than and equal to 15 (≥ 0 and ≤ 15). The Histogram for the positive control wells shows more than and equal to 25 revertants were tallied (Fig. 3 ). Acceptable results for the sterile control samples were also achieved with no evidence of revertant wells. The results, as shown in Fig. 4 , were reported without applying any corrections to the observed number of revertants. Evaluation of mutagenicity using the manual scoring approach The number of revertants for all 96-well microtiter plates was manually counted by visually inspecting the plates on a lightbox to enhance contrast and visibility (Fig. 1 ). Results were recorded and scored using standardized forms for each experiment. A comparison between manual scoring and the automated Metafer system revealed no statistically significant difference between the two methods (p > 0.05) (Fig. 8 ), indicating that both approaches produced comparable results in terms of mutagenicity evaluation. DISCUSSION Water samples contain a ubiquitous amount of chemicals, substances and compounds which may display mutagenic properties. The application of disinfection chemicals during the water treatment process results in the emergence of disinfection by-products (DBPs). DBPs have been associated with imminent health risks, inclusive of the development of cancer (Shi et al., 2024 ; Xie et al., 2025 ). Furthermore, research studies suggest that potable water is well recognised for its genotoxic activity (Tuomisto and Vartiainen, 1990 ; McDonald and Komulainen, 2007 ; Pellacani et al., 2005 ; Sujbert et al., 2006 ; Lundqvist et al., 2024 ). This highlighting the need for more robust monitoring to assess public health risks. To address this, current investigations utilise effect-based methods or bioassays including, the Ames mutagenicity assay for the detection of mutagenic potencies from water samples. The Ames assay offers a rapid, easy-to-use, cost-effective and sensitive method for determining DNA damage resulting from exposure to drinking water samples (Ceretti et al., 2016 ). The Ames assay is particularly sensitive to compounds that can result in mutagenicity, either inducing base pair mutations or frameshift mutations. Environmental mutagens including acridine dyes organochlorine pesticides and complex mixtures are well known to induce mutations (Tanaka et al., 1996 , Guan et al., 2017 ). These and other chemical pollutants are often introduced to freshwater resources through anthropogenic pollution. Monitoring efforts in countries like Brazil have shown varying degrees of water contamination from both point and diffuse pollution (Vargas et al., 1993 ; Pereira et al., 2007 ; Tagliari et al., 2004 ; Lemos et al., 2009 ). A range of mutagenic potencies, from low and moderate mutagenic, were found in studies assessing the mutagenicity of surface and treated water (Watanabe et al., 2002; Ohe et al., 2004 ; Warren et al., 2015 ). While such research is common in other parts of Asia, Europe and South America, African countries face several challenges that hinder routine mutagenicity testing. These include insufficient infrastructure, lack of regulatory emphasis and resource constraints (Ibor et al., 2023 ; Tyhali and Forbes, 2023 ). Given these limitations, this study aimed to evaluate the mutagenic potential of source and drinking water samples from two South African drinking water treatment plants. In addition, this study also aimed to elucidate or optimise the best scoring approaches for Ames assays for high-throughput and high-accuracy results. Mutagenic potential of water samples To estimate the mutagenicity potential of the source water, drinking water and distribution water from the two different water treatment plants, a 96-well microplate format of the Ames test was employed. Samples were tested with Salmonella typhimurium strains TA98 and TA100, with and without metabolic activation. A positive result indicates the potential of the presence of mutagens, whilst a negative result suggests a reassuring absence of these substances (Zeiger and Mortelmans, 1999 ). No significant mutagenic activity was observed in either strain regardless of metabolic activation, with induction factors remaining below the threshold of a 2-fold increase (Fig. 5 – 7 ). These results were consistent across all samples over the three-month monitoring period. Although, there are a host of bacterial strains that can be used in Ames testing, only the two bacterial strains were selected in this study. These two bacterial strains were selected for their relevance in detecting the mutagenicity of environmental water samples and their potential as indicators of public health risks (Guan et al., 2017 ). Given this, the results of this study are limited to the bacterial strain-specific genetic endpoints of frameshift and base-pair mutations. The absence of experimental effects suggests a potential lack of mutagens in the analysed samples. However, the lack of mutagenicity detected may be associated with the grab sampling approach. Grab samples are notoriously known as insufficient to fully provide information on effects. This suggests that instances of mutagens or mutagenic effects present at other times may not have been captured by grab sampling. Despite this, using a single-point sampling approach, researchers Gameiro et al. ( 2025 ) and Calao-Ramos et al. ( 2023 ) detected positive mutagenic results in raw water and final consumer-destined water in Brazil and Colombia, respectively. Even so, the 2-year effect-based monitoring of water from source to consumer displayed fluctuations in genotoxic activity in source-water and consumer water supplies (Lundqvist et al., 2024 ). Given these findings, an increased frequency of analysis would increase the probability of detecting mutagenic potencies from water resources. By implementing long-term monitoring programs, drinking water treatment plants can optimise and integrate advanced treatment processes in place of conventional chlorination to reduce mutagenicity risk of the supplied water (Takanashi et al., 2011 ). A comprehensive sampling programme or composite programme should be actioned to assess the mutagenicity of drinking water sources. This approach supports a risk-based strategy for potable water supplies, prioritising the protection of human health. A limitation of this study is the lack of extraction methods to concentrate potential mutagenic substances. Concentrating drinking water samples may enhance the detection of organic compounds responsible for mutagenicity when using the Ames test. However, studies have shown that each concentration method has its own limitations, as no single concentration method or adsorbent can cover all the chemicals in a sample. Lah et al. ( 2005 ) found negative mutagenic results from both concentrated and non-concentrated water samples. Additionally, Xue et al. ( 2021 ) found no detectable mutagenicity in concentrated drinking water organic samples. Nonetheless, this study showed that mixed exposure to water samples results in negative mutagenic outcomes, although the study was limited to base-pair mutations and frameshift mutations. Analogous methods of analysis for genotoxicities such as the Comet assay, the SOS assay and immunofluorescence assays can be used to assess additional genotoxicity endpoints and thereby broaden detection and understanding of genotoxicity from mixed exposures. Previous studies have shown that chlorinated chemicals such as Trihalomethanes (TCM) and approximately 600 disinfection byproducts are possibly carcinogenic (Grellier et al., 2015 ; Ceretti et al., 2016 ). A recent study by Xu et al. ( 2025 ) showed results of tumorigenesis from mice exposed to known DBPs in their drinking water. Comparative studies have highlighted the variability of disinfection by-product formation and removal efficiency in the water purification process (Grellier et al., 2015 ). The adverse health risks posed to human health by the uptake of these DBPs from the water systems have been investigated by Kumari and Gupta ( 2022 ). Because of the established risk, water treatment plants should constantly optimise their pre-chlorination and disinfection chlorine dosages during the disinfection process. However, this should not be at the expense of the disinfection process, as there is a higher risk of pathogenic harm from inadequate disinfection if compared with the DBPs risk (NHMRC, NRMMC, 2025). Public health and regulatory implications The absence of mutagenicity of samples may signify that the water treatment plant processes effectively removed any mutagens. However, mutagens may still be present in water samples, although at levels below the detection threshold. No detectable mutagenic risks were also found in environmental and drinking water samples (Meier, 1988 ; Liu et al., 1999 ; Warren et al., 2015 ). Interestingly, these findings are encouraging from a public health standpoint. This is particularly important when it comes to complying to established requirements set by drinking water standard of South Africa (SANS 241, 2015) and international regulations by the World Health Organisation (WHO, 2017). The results of the investigation further suggest that conventional water treatment processes followed by chloramination for final disinfection, effectively diminish environmental pollutants. Contrasting studies have detected mutagenicity in drinking water samples from similar treatment process unit configurations (Xue et al., 2021 ). Nevertheless, the outcomes of this analysis support successful mitigation of mutagenic risks by the treatment plants, it is crucial to consider additional factors. Seasonal variations, changes in water composition, and potential interactions between disinfection chemicals and natural organic matter all play a role in the presence of compounds in water. The results of these studies signify the need for government-enforced regulation of these carcinogenic and genotoxic compounds in drinking water. To aid this effort, increased support should be given to water treatment suppliers to assist their adoption and implementation of methods applicable to detecting and monitoring these mutagenic potencies. Effect-based methods such as the Ames mutagenicity test used in this study are useful as a genotoxic screening tool for environmental water and potable water supplies by agencies worldwide (ISO 11350:2012; OECD, 2020). Currently, there is a scarcity of these regulations in Africa. This is despite authors such as Rossum et al. ( 1982 ), Rossum et al. ( 1990 ), Agwa et al. ( 2017 ), Bariweni et al. ( 2023 ), and Mhlongo et al. ( 2024 ) finding positive results in drinking water supplies from water treatment plants and groundwater sources. Moreover, in South Africa, the SANS241 document has no provision for toxicity testing on a routine basis. This is alarming given the common use of chlorine products in the disinfection process and the known harm associated with DBPs to human life. The current study's protocol and automation of a bioassay offer a pathway for monitoring mutagenic potencies in drinking water. This can further be implemented by more laboratories in the country to better understand the mutagenic potential of drinking water, as well as optimising the drinking water treatment chlorination process. Scoring approaches The Metafer software and system analyses microplates using a monochromatic camera and high-resolution digital photography. Yellow and purple coloured samples are differentiated using pre-established classifiers. The Metafer systems automated feeder and image-processing algorithms allow for high-throughput sample screening. It also facilitates data storage which is easily retrievable and allows for automatic interfacing with the laboratory management system. Classifier sensitivity, equipment expenses, and accessibility issues for laboratories with limited resources are some of the system's drawbacks. Furthermore, although automation is ideal, it is important to emphasise that proper calibration and training of classifiers are important to ensure correct interpretation of results. Traditionally the Ames mutagenicity test was performed in an agar plate but recent advancements in technology, have made the 384 well and 96-well microplate versions possible. There are several advantages in using the 96-well microplate version as larger volumes of samples can be analysed. This results in a higher degree of a mutagenic response. Ninety-six well microplates also have wider wells and analysts are able to decipher and score the differences between yellow and purple without difficulty. Human differences in colour perception also exist, which makes manual scoring difficult, affecting the accuracy and consistency in scoring. Although this study did not show this aspect as there was no significant difference between visual and automatic detection of the number of revertants in wells. However, in cases where individuals experience colour vision deficiency, also known as colour blindness, the use of the automatic scanning platform may be more appropriate. Additionally, this study showed that using automation increased scoring efficiency and consistency of Ames results, thereby reducing subjectivity while maintaining accuracy. CONCLUSIONS AND FUTURE DIRECTIONS Furthermore, this study showed a critical challenge in manual scoring of Ames test results, as human variability in colour perception can affect the reliability of results. Automation is an important tool for extensive drinking water monitoring programs because it reduces human error, improves consistency, and enables high-throughput screening. Although this study did not show conclusive evidence of mutagenicity of drinking water samples, it showed the possibility of mitigation of mutagenic compounds by drinking water treatment processes. The knowledge gained in this study also shows the use of a larger scale microplate version of the Ames test that can offer increased sample capacity, higher sensitivity and more precise scoring, reducing the variability with other versions of the Ames method. Future research should look into pollution events and cycles, and the concentration of drinking water samples. Additionally, this study highlights the role of automation in scoring of Ames test microplates and its practical application in large-scale drinking water quality monitoring. Declarations Ethics approval and consent to participate Not applicable. Consent for publication All authors and co-authors consent to publication of this manuscript. Availability of data and material The raw data for this study was generated at a central, large-scale facility. The derived data supporting the findings, besides being available in the article itself, will be available from the corresponding author (R. Hendricks) upon request. Competing interests The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper. Funding The authors would like to thank Rand Water for providing funding to undertake the study. Author contributions Rahzia Hendricks conceived and designed the study; performed data collection and analysis; and developed the methodology and software. Rahzia Hendricks wrote the initial draft of the manuscript. Rahzia Hendricks and Hlakae Leseba provided extensive revisions and editing, ensuring the final version met the journal's requirements. Clinical Trial Declaration Clinical trial number: not applicable. References Abdillahi, H. S., Verschaeve, L., Finnie, J. F., & van Staden, J. (2012). Mutagenicity, antimutagenicity and cytotoxicity evaluation of South African Podocarpus species. Journal of Ethnopharmacology, 132 (3), 728–738. https://doi.org/10.1016/j.jep.2011.11.044 Agwa, O. K., Eze, N. J., & Okpokwasili, G. C. (2017). Mutagenic potentials of potable water from ground sources. The Open Biotechnology Journal, 11 , 81–88. https://doi.org/10.2174/1874070701711010081 Alias, C., Feretti, D., Benassi, L., Zerbini, I., Zani, C., & Sorlini, S. (2022). 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J., de Boer, J., Kool, J., & Hamers, T. (2018). Development of a luminescent mutagenicity test for high-throughput screening of aquatic samples. Toxicology in Vitro, 46 , 350–360. https://doi.org/10.1016/j.tiv.2017.09.005 Additional Declarations No competing interests reported. Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-7883227","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":533677843,"identity":"d44b0fce-afe7-4ae5-bc9a-ac3ea3b9fa7e","order_by":0,"name":"Rahzia Hendricks","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA9ElEQVRIiWNgGAWjYDACCSDmAZL8DGAGKVokGyBaJIjVwsBgcIBYLeazm489eLvHQt749uFnD9622dXxSx9g/PCBoU4elxaZO8fSDec8kzDcdi7N3HBuW7KEZF8Cs+QMBjbDBlzuksgxk+Y5IJFgdobBTJp3G7OEwRkGNmYeBh5G3Fryv4G1GPewfwNqqZewh2iRsMdjCxtYiwEPD8iWwxIGPGAtBom4taSZSc45IGE44wxPmeTcf8clZ5xhbJacYZCQjFtL8jOJNwfq5Pl72LdJvDlTzc/fw3zww4eKOltcWrABkMcNSFA/CkbBKBgFowADAAAmFkcqBf5OQgAAAABJRU5ErkJggg==","orcid":"","institution":"Rand Water, Scientific Services division","correspondingAuthor":true,"prefix":"","firstName":"Rahzia","middleName":"","lastName":"Hendricks","suffix":""},{"id":533677844,"identity":"583e1d43-436f-4cfe-8bfe-bdc7e446aa7b","order_by":1,"name":"Hlakae Leseba","email":"","orcid":"","institution":"Rand Water, Scientific Services division","correspondingAuthor":false,"prefix":"","firstName":"Hlakae","middleName":"","lastName":"Leseba","suffix":""}],"badges":[],"createdAt":"2025-10-17 06:23:23","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-7883227/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-7883227/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":96270462,"identity":"0aac915e-c00b-4a85-a43a-d9ad4548d62a","added_by":"auto","created_at":"2025-11-19 09:17:36","extension":"jpeg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":310030,"visible":true,"origin":"","legend":"\u003cp\u003eManual counting approach of scoring the 96-well plates. \u0026nbsp;Plates are placed on the light box and a manual count of the yellow versus purple wells/ number of revertants are counted by the analyst. \u0026nbsp;Results are recorded on a standardised form.\u003c/p\u003e","description":"","filename":"image2.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-7883227/v1/fcae4529e1eff44f9d6ebfc8.jpeg"},{"id":96363500,"identity":"6a9784c8-4df7-4493-ab5e-2c55294b1c4e","added_by":"auto","created_at":"2025-11-20 10:07:10","extension":"jpeg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":283656,"visible":true,"origin":"","legend":"\u003cp\u003ePlates are placed in the automatic feeder and then the robotic arm places the 96-well plates on the Microscope stage. \u0026nbsp;The plates are scanned or scored for the number of revertants using the Metafer Metasystems software programme.\u003c/p\u003e","description":"","filename":"image3.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-7883227/v1/126f71d3521baceda16f07f7.jpeg"},{"id":96270468,"identity":"a8a6dac4-8db1-48a3-8f78-7145bc91e3c5","added_by":"auto","created_at":"2025-11-19 09:17:36","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":251358,"visible":true,"origin":"","legend":"\u003cp\u003eThe Metafer Metasystems interface with the histogram data for the number of revertants in the positive control wells (39 revertant wells) and the sterile control wells (0 number of revertants wells).\u003c/p\u003e","description":"","filename":"image4.png","url":"https://assets-eu.researchsquare.com/files/rs-7883227/v1/f9e03199175172f6a4e3f676.png"},{"id":96364395,"identity":"1acd8e4c-2203-4cf3-a686-619cbbb40670","added_by":"auto","created_at":"2025-11-20 10:09:16","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":116111,"visible":true,"origin":"","legend":"\u003cp\u003eThe corresponding report layout of the results for the positive control and sterile control wells shown in Figure 4. If unknown wells are corrected this is also indicated in the table in the report format, which provides an audit trial of results.\u003c/p\u003e","description":"","filename":"image5.png","url":"https://assets-eu.researchsquare.com/files/rs-7883227/v1/e8adb9aadfbc6141bb63b188.png"},{"id":96270463,"identity":"e445a9b5-ecdb-4b6d-ad2b-1b2ce5d20811","added_by":"auto","created_at":"2025-11-19 09:17:36","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":18305,"visible":true,"origin":"","legend":"\u003cp\u003eDiagrams a and c show TA98 and TA100 bacterial strains without S9 mixture (no metabolic activation) taken bi-weekly in month 1. \u0026nbsp;Diagrams b and d show TA98 and TA100 bacterial strains with S9 mixture (with metabolic activation) taken bi-weekly in month 2. Results show that no samples were above the 2-fold induction limit for mutagenicity for either treatment or either sampling date.\u003c/p\u003e","description":"","filename":"image6.png","url":"https://assets-eu.researchsquare.com/files/rs-7883227/v1/4e67efc0f620258503ea9f50.png"},{"id":96270461,"identity":"61ff8013-865d-4998-a28f-4d6a745246eb","added_by":"auto","created_at":"2025-11-19 09:17:36","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":16691,"visible":true,"origin":"","legend":"\u003cp\u003eDiagrams a and c show TA98 and TA100 bacterial strains without S9 mixture (no metabolic activation) taken bi-weekly in month 3. \u0026nbsp;Diagrams b and d show TA98 and TA100 bacterial strains with S9 mixture (with metabolic activation) taken bi-weekly in month 4. Results show that no samples were above the 2-fold induction limit for mutagenicity for either treatment or either sampling date.\u003c/p\u003e","description":"","filename":"image7.png","url":"https://assets-eu.researchsquare.com/files/rs-7883227/v1/14f4b083e27776aa17f6b876.png"},{"id":96364362,"identity":"5b1ad18f-6ea8-45f2-bb2e-e9cc9b141cbf","added_by":"auto","created_at":"2025-11-20 10:09:14","extension":"png","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":15378,"visible":true,"origin":"","legend":"\u003cp\u003eDiagrams a and c show TA98 and TA100 bacterial strains without S9 mixture (no metabolic activation) taken bi-weekly in month 5. \u0026nbsp;Diagrams b and d show TA98 and TA100 bacterial strains with S9 mixture (with metabolic activation) taken bi-weekly in month 6. Results show that no samples were above the 2-fold induction limit for mutagenicity for either treatment or either sampling date.\u003c/p\u003e","description":"","filename":"image8.png","url":"https://assets-eu.researchsquare.com/files/rs-7883227/v1/c6e51c2d405b1430c33d4c87.png"},{"id":96270466,"identity":"1a33947a-6669-4aa3-b780-6ed278a7d886","added_by":"auto","created_at":"2025-11-19 09:17:36","extension":"png","order_by":8,"title":"Figure 8","display":"","copyAsset":false,"role":"figure","size":12881,"visible":true,"origin":"","legend":"\u003cp\u003eNo significant difference in results of the two different scoring approaches automatic versus manual scoring were ascertained (P≥0.05).\u003c/p\u003e","description":"","filename":"image9.png","url":"https://assets-eu.researchsquare.com/files/rs-7883227/v1/5c022de0e93478610a21c299.png"},{"id":98622030,"identity":"c9150f04-d847-40b6-a592-bd1de81d8a09","added_by":"auto","created_at":"2025-12-19 16:42:15","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1772020,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7883227/v1/271f4533-d693-438f-bcbc-f05fc04b4084.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"\u003cp\u003eAutomation of the Ames Assay Scoring and Assessment of Water Samples for Mutagenicity\u003c/p\u003e","fulltext":[{"header":"HIGHLIGHTS OF THE STUDY","content":"\u003cul\u003e\n \u003cli\u003eSource and drinking water samples were taken at two different drinking water treatment plants.\u0026nbsp;\u003c/li\u003e\n \u003cli\u003eWater samples were analysed for mutagenicity using the Ames mutagenicity test using two bacterial strains TA98 and TA100.\u0026nbsp;\u003c/li\u003e\n \u003cli\u003eTwo different scoring approaches were used to determine the number of revertants in the 96-well microtiter plate.\u0026nbsp;\u003c/li\u003e\n \u003cli\u003eResults showed that there was no mutagenicity of samples throughout the study period.\u0026nbsp;\u003c/li\u003e\n \u003cli\u003eThe manual scoring approach relies heavily on the analysts ability to decipher different colours in the microtiter wells.\u0026nbsp;\u003c/li\u003e\n \u003cli\u003eThe automatic image scanning platform tallies the number of revertants and classifies the results based on classifiers.\u0026nbsp;\u003c/li\u003e\n \u003cli\u003eAutomation allows for high throughput of results; an audit trail; detection of subtle differences in colour.\u0026nbsp;\u003c/li\u003e\n\u003c/ul\u003e"},{"header":"INTRODUCTION","content":"\u003cp\u003eHistorically, the Ames assay has been used to classify DNA-damaging effects in cells for approximately more than 500 agents worldwide (Kirkland and Fowler, \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2010\u003c/span\u003e). However, the classification of these agents as either potential carcinogens, mutagens, or clastogens has resulted in only a slight decrease in cancer incidents (Rueff et al., \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). The Ames method, originally developed and published by Bruce Ames in 1974, is now widely used to detect the mutagenicity of chemical compounds from freshwater sources, including rivers (Steyn et al., \u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Zwart et al., \u003cspan citationid=\"CR68\" class=\"CitationRef\"\u003e2020\u003c/span\u003e) and drinking water treatment plants (Rinc\u0026oacute;n-Bedoya et al., \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e2013\u003c/span\u003e). The method makes use of the bacterial strains of \u003cem\u003eSalmonella typhimurium\u003c/em\u003e, TA98 and TA100. These bacterial strains carry either a frameshift mutation or a base-pair mutation on the hisD3052 and hisG46 genes, respectively (Vasetska et al., \u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). The Ames method has been adapted from the plate incorporation method, which uses solid agar from which positive counts are determined by the growth of colonies, to the fluctuation method, which takes place in liquid form and scores results in 384-well microtiter plates (Fl\u0026uuml;ckiger-Isla; OECD, 2020; Rainer et al., \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Furthermore, the pH indicator allows for the indication of yellow positive wells that can be manually scored in the fluctuation method, the pH indicator dye changes to yellow as the solution's pH drops to roughly pKa\u0026thinsp;=\u0026thinsp;5.2. This shift is caused by catabolism during metabolism, which stems from a lack of histidine in the mixture. Moreover, the pH indicator enables the identification and manual scoring of yellow positive wells (ISO 11350:2012).\u003c/p\u003e\u003cp\u003eWith the increasing requirement for advanced technology that is efficient and accurate, automation has become a key factor in various industries. One industry that greatly benefits from automation is the field of assay analysis. Assay analysis involves the testing and measurement of various substances and compounds to determine their concentrations or properties. Implementing automation in assay analysis not only saves time and reduces human error, but it also enhances productivity and allows for larger volumes of samples to be processed in a shorter amount of time (Holland and Davies, \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Rupp et al., \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e2024\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eRecently, a 96-well format of the traditional 384-well Ames method has been introduced. The 96-well method has a major advantage in that it uses a larger volume of sample, thereby increasing the likelihood of sensitivity in detecting mutagens present (Forsten et al., \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Calao-Ramos et al., \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Furthermore, the 96-well microplate version has a larger well size to better differentiate between the colours yellow and purple, and therefore the number of revertants in a study. Despite the larger wells, subjectivity still exists in colour differentiation of the positive and negative wells in a microplate. These shortfalls are a hindrance to the application of the fluctuation test in a high-throughput manner for testing laboratories.\u003c/p\u003e\u003cp\u003eAutomated methodologies can significantly enhance colour classification within the AMES fluctuation assay analysis. Colour classification is a critical step in Ames assay analysis, as it facilitates the scoring of mutagenicity in the test, and aids in ascertaining the presence or absence of particular substances or reactions (ISO 11350:2012). Traditionally, colour classification in the Ames fluctuation assay has been a manual and time-consuming process. To evaluate the microtiter plates, analysts would visually enumerate the 96-well microtiter plates and differentiate between the yellow (positive number of revertants) and purple (negative for reversion) wells. The enumeration of the revertant yellow wells is thus dependent on subjective differences between yellow and partially yellow (Jolibois et al., \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2003\u003c/span\u003e; Large et al., \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2022\u003c/span\u003e), yellow and turbid and cloudy wells (Calao\u0026ndash;Ramos et al., 2023) to determine the mutagenicity results of the tests. To add to the difficulty, the challenge of scoring while being colour blind may exacerbate the outcome and viability of the results. Advances in the colour determination process have developed from the manual observation to the use of microplate readers and luminometers (Gee et al., \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e1998\u003c/span\u003e; Zwart et al., \u003cspan citationid=\"CR69\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Shao et al., \u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Zwart et al., \u003cspan citationid=\"CR68\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). However, the direct imaging and machine learning capabilities available today offer a pathway for high-accuracy-throughput analysis, along with the means to store and validate the results digitally. The application of such image analysis for bioassays has recently been validated in medical fields by Rossnerova et al. (\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e2009\u003c/span\u003e), assessing chromosomal damage, Verma et al. (\u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e2016\u003c/span\u003e), assessing micronucleus dose response, and Wills et al. (\u003cspan citationid=\"CR62\" class=\"CitationRef\"\u003e2021\u003c/span\u003e) through automating flow cytometry assays. Similarly, educational training programs for the Ames fluctuation test have incorporated the practice of students photographing test plates to archive raw data. This can be helpful for instructors to score the test based on the subjectivity of the yellow and partially yellow colours of the revertants (Large et al., \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Moreover, for an accredited laboratory, high accuracy and audit trails are required (ISO 17025, 2017(E)). Therefore, the automatic scanning platform is proposed as a viable alternative to scoring plates manually. This will free up time for analysts to pursue other activities in the laboratory and has advantages such as providing an audit trail of the results including capturing an image that is not practicable for the manual scoring method. To the best of our knowledge scoring of Ames results for the 384-well and 96-well microtiter plate versions have always been done manually or through the use of microplate readers and no such work or research has been done on automating the process of scoring by using an imaging platform. Furthermore, the Ames fluctuation method has only been conducted on ad-hoc research purposes in South Africa, and the high-throughput of sample analysis has been overlooked.\u003c/p\u003e\u003cp\u003eOne of the main advantages of undergoing Ames testing is the ability to use the method for the determination of the mutagenicity of water samples. However, regulatory requirements for mutagenicity testing of water by drinking water treatment facilities are not often performed. Currently in South Africa, mutagenic testing has been limited to ad-hoc research spheres, including pharmacology and botany (Verschaeve and van Staden \u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e2008\u003c/span\u003e; Abdillahi et al., \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2012\u003c/span\u003e; Reid et al., \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2006\u003c/span\u003e; Ndhlala et al., \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2010\u003c/span\u003e; Ngubane et al., \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Freshwater mutagenicity potential has also been confined to rivers and their ecosystem health (Ansara-Ross et al., \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2009\u003c/span\u003e; Steyn et al., \u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Ijil et al., \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Despite the increasing application of the test on freshwater sources, the routine mutagenic testing of potable water from drinking water treatment plants is lacking. This may be due to many reasons, such as skill level to perform analysis, the lack of technology readily available, and given that performing these additional analyses on water samples can be time-consuming and expensive (Slabbert et al., \u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e1998\u003c/span\u003e). However, knowing this information will provide vital information on the human health effects of drinking water with mutagenicity potential (Calao-Ramos et al., \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Despite these challenges, various researchers have studied the potential link between drinking water, specifically chlorinated compounds present in it, and the development of cancer in humans (Grellier et al., \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2015\u003c/span\u003e; Shi et al., \u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Xie et al., \u003cspan citationid=\"CR64\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). Transitioning to routine genotoxic analysis using effect-based methods such as the Ames test can serve as a rapid indicator for drinking water treatment plants to optimise their processes. It can further be used as a risk-based preventative tool for potable water suppliers and thus safeguard human health (Alias et al., \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Gameiro et al., \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). More advanced countries have already begun enforcing genetic toxicity assessments in drinking water quality monitoring to complement traditional physicochemical assessments using standardised effect-based methods (ISO 11350:2012; OECD, 2020); however, South Africa still predominantly uses physicochemical techniques to evaluate drinking water safety.\u003c/p\u003e\u003cp\u003eThe aim of this study was therefore two-fold: 1) to elucidate mutagenicity using the Ames modified ISO test on water samples from two different drinking water treatment facilities, and 2) to assess the reproducibility of the traditional manual Ames scoring method and the scoring method generated using the automatic scanning platform. Although this study was unable to reject the null hypothesis, the data that was obtained from this study still adds value in making advancements in high-throughput automation of scoring of Ames mutagenicity test results. The findings of the study can be used by other facilities to increase efficiency and accuracy of data capturing. These efforts are crucial in improving the understanding of mutagenicity in the aquatic environment and furthering safety standards for drinking water.\u003c/p\u003e"},{"header":"MATERIALS AND METHODS","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\u003ch2\u003eSampling sites\u003c/h2\u003e\u003cp\u003eOne-litre samples were collected from different sampling sites across the drinking water value chain from two water treatment plants in the Vaal Triangle, Gauteng, South Africa. These plants supply approximately 19\u0026nbsp;million consumers. Samples were taken biweekly over three months. The two water treatment plants were designated Plant 1 and Plant 2, respectively. The process units of the two plants utilize conventional treatment configurations, which include coagulation-flocculation, sedimentation, filtration, and disinfection. Source water, drinking water, and distributed water were collected in brown glass amber 1-litre bottles, sealed with a lid, placed in a cooler box, and transported to the laboratory.\u003c/p\u003e\u003c/div\u003e\n\u003ch3\u003eSample preparation\u003c/h3\u003e\n\u003cp\u003eOn the day of analysis, samples were sterile-filtered using a 0.22 \u0026micro;m sterile syringe filter. If immediate analysis was not possible, the samples were frozen in 50 mL tubes and defrosted in a refrigerator overnight before use in the assay.\u003c/p\u003e\n\u003ch3\u003eBacterial rehydration and preparation\u003c/h3\u003e\n\u003cp\u003eThe Ames Modified ISO kits were purchased from Environmental Bio Detection Products Incorporated. The manufacturer provides all necessary reagents, including positive controls, sterile water, exposure medium solution, and reversion medium solution. Reagent V was added to two respective vials of growth media, which were then added to the lyophilized TA100 and TA98 vials, respectively. The rehydrated bacterial suspension was incubated on a shaker at 200 RPM in a 37\u0026deg;C incubator (Incoshake). After 16 hours, the bacterial suspension was checked for turbidity. Once turbidity was observed, the overnight bacterial suspension was added to a cuvette. Subsequently, the optical density was read using a spectrophotometer (Evolution 300 UV-Vis, Thermofischer Scientific) and diluted to an optical density of 600nm or its respective working concentration for both bacteria using 1x exposure media.\u003c/p\u003e\n\u003ch3\u003eSample addition to 24-well plates\u003c/h3\u003e\n\u003cp\u003eSterile water was added to the designated negative control and positive control wells in a 24-well microtiter plate. For the TA100 bacterial strain, sodium azide (NaN\u003csub\u003e3\u003c/sub\u003e) was used as a positive control in microtiter plates without metabolic activation. For the TA100 bacterial strain with S9 liver enzyme bioactivation (S9 mix), 2-amino anthracene (2-AA) positive control was added to the plate. For T98 bacteria 2-nitrofluorene(2-NF) was used as a positive control in microtiter plates with and without metabolic activation. As a sterility control, sterile water was added to the plate. Water samples were added undiluted to wells in triplicate. After the addition of the samples to the wells, the S9 mix was added to the wells of the S9-marked plates. Subsequently, exposure solution was added to all the wells with subsequent addition of diluted bacterial culture. No diluted bacterial culture was added to the sterile control wells. The plates were then sealed with the lid and incubated at 37\u0026deg;C for 100 minutes in an incubator. During the incubation period, the master mix reversion media was prepared in labelled conical tubes. Briefly, 40% D-Glucose, Bromocresol purple, D-Biotin, and 10X reversion solution were added to the conical tubes. Thereafter, sterile water was added to the reversion mixture prepared. After the exposure plate incubation, its well contents were added to the tubes. Each tube was vortexed and poured into a reservoir or a loading boat. Using a multichannel pipette, the reversion mix from the reagent boats was aliquoted into a 96-well plate. The 96-well plates were then sealed with lids and placed into Ziploc bags to prevent evaporation. The plates were incubated in a 37\u0026deg;C incubator for 3 days until scored.\u003c/p\u003e\n\u003ch3\u003eQuality control parameters\u003c/h3\u003e\n\u003cp\u003eTo determine if a sample was positive, the following acceptance criteria were applied: for a negative control, the number of revertant wells had to be greater than or equal to zero and less than and equal to 15 (\u0026ge;\u0026thinsp;0 and \u0026le;\u0026thinsp;15). For the positive control, the number of revertants had to be greater than and equal to 25 (\u0026ge;\u0026thinsp;25). For the sterile control no revertant wells should have been present. Samples were considered positive when all the above quality control parameters were met and there was a 2-fold induction of the number of revertants above the baseline value.\u003c/p\u003e\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e\u003ch2\u003eScoring approaches\u003c/h2\u003e\u003cp\u003eThe 96-well plates were scored both visually (manual method) (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e) and using the automatic scanning platform incorporating the Metafer Metasystems software platform (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e\u003c/div\u003e\n\u003ch3\u003eManual scoring of the 96-well plates\u003c/h3\u003e\n\u003cp\u003eThe manual method consisted of placing the plates on a light box (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e) and then performing a manual count by scoring all the revertant wells. The results were recorded on a score sheet.\u003c/p\u003e\n\u003ch3\u003eAutomated scoring of the 96-well plates\u003c/h3\u003e\n\u003cp\u003eThe 96-well plates were scored using the automated plate loading capability, along with the automatic scanning and imaging acquisition via the Metafer Metasystems system. An Axio Imager Z1 (Carl Zeiss) microscope, equipped with the Metacyte module automated capture software and a Cool cube camera, was used to capture images. Images from each well of the 96-well microplates were captured using a 5x objective, focusing on one plane per Red Green Blue colour channel. A counterstain mask adjusted the mean colour intensity. The number of revertant wells was automatically totalled, and a report was generated. The Metafer Metasystems software scored unknown revertants in the 96-well plates, allowing the analyst to manually identify and correct results. An audit trail details the corrected wells where corrections were made. After scanning and scoring, a histogram of positive, negative, and unknown revertants (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e) was generated, followed by a report with the total number of revertants (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e). The determination of mutagenicity was automatically calculated after data integration and interfacing into the laboratory information management system.\u003c/p\u003e\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e\u003ch2\u003eStatistical analyses\u003c/h2\u003e\u003cp\u003eThe students t-test was used for the comparison between the number of revertant wells scored using the manual method and the number of revertant wells that were automatically calculated using the Metafer Metasystems software. Significance was accepted with p\u0026thinsp;\u0026lt;\u0026thinsp;0.05.\u003c/p\u003e\u003c/div\u003e"},{"header":"RESULTS","content":"\u003cdiv id=\"Sec13\" class=\"Section2\"\u003e\u003ch2\u003eMutagenicity of water samples\u003c/h2\u003e\u003cp\u003eDrinking water samples were taken over a period of 3 months and analysed with the 96-well microtiter plate Ames mutagenicity test. Results of the analysis were scored using two different scoring approaches (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e and Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). The relevant quality control parameters were used to identify if samples were positive. Furthermore, a sample was considered positive when the above quality control parameters are met and there was a 2-fold induction of the number of revertants above the baseline value.\u003c/p\u003e\u003cp\u003eAll quality control parameters for testing were met for all sampling occasions. No dilution of samples occurred and 100% of samples were used in the testing process. At this sample dilution, there was no evidence of a 2-fold induction of the number of revertants above the baseline value for all sampling occasions (p\u0026thinsp;\u0026gt;\u0026thinsp;0.05) (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e\u0026ndash;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003e). This was evident for both bacterial strains used in the study. There was also no evidence of mutagenicity of samples in the two bacterial strains with metabolic activation (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e\u0026ndash;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003e).\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec14\" class=\"Section2\"\u003e\u003ch2\u003eEvaluation of mutagenicity using the automated image scanning platform\u003c/h2\u003e\u003cp\u003eNinety-six well microtiter plates were scored and tallied using an automated image scanning platform (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). The automatic image scanning platform was able to analyse the results within 60 seconds for one 96-well microtiter plate, allowing a fast, efficient digital capture and tally of the number of revertants for all plates. Results of the study showed that the number of revertants in the control samples were tallied to be more and equal to zero and less than and equal to 15 (\u0026ge;\u0026thinsp;0 and \u0026le;\u0026thinsp;15). The Histogram for the positive control wells shows more than and equal to 25 revertants were tallied (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). Acceptable results for the sterile control samples were also achieved with no evidence of revertant wells. The results, as shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e, were reported without applying any corrections to the observed number of revertants.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec15\" class=\"Section2\"\u003e\u003ch2\u003eEvaluation of mutagenicity using the manual scoring approach\u003c/h2\u003e\u003cp\u003eThe number of revertants for all 96-well microtiter plates was manually counted by visually inspecting the plates on a lightbox to enhance contrast and visibility (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). Results were recorded and scored using standardized forms for each experiment. A comparison between manual scoring and the automated Metafer system revealed no statistically significant difference between the two methods (p\u0026thinsp;\u0026gt;\u0026thinsp;0.05) (Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003e), indicating that both approaches produced comparable results in terms of mutagenicity evaluation.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e"},{"header":"DISCUSSION","content":"\u003cp\u003eWater samples contain a ubiquitous amount of chemicals, substances and compounds which may display mutagenic properties. The application of disinfection chemicals during the water treatment process results in the emergence of disinfection by-products (DBPs). DBPs have been associated with imminent health risks, inclusive of the development of cancer (Shi et al., \u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Xie et al., \u003cspan citationid=\"CR64\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). Furthermore, research studies suggest that potable water is well recognised for its genotoxic activity (Tuomisto and Vartiainen, \u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e1990\u003c/span\u003e; McDonald and Komulainen, \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2007\u003c/span\u003e; Pellacani et al., \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2005\u003c/span\u003e; Sujbert et al., \u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e2006\u003c/span\u003e; Lundqvist et al., \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). This highlighting the need for more robust monitoring to assess public health risks.\u003c/p\u003e\u003cp\u003eTo address this, current investigations utilise effect-based methods or bioassays including, the Ames mutagenicity assay for the detection of mutagenic potencies from water samples. The Ames assay offers a rapid, easy-to-use, cost-effective and sensitive method for determining DNA damage resulting from exposure to drinking water samples (Ceretti et al., \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). The Ames assay is particularly sensitive to compounds that can result in mutagenicity, either inducing base pair mutations or frameshift mutations. Environmental mutagens including acridine dyes organochlorine pesticides and complex mixtures are well known to induce mutations (Tanaka et al., \u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e1996\u003c/span\u003e, Guan et al., \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). These and other chemical pollutants are often introduced to freshwater resources through anthropogenic pollution.\u003c/p\u003e\u003cp\u003eMonitoring efforts in countries like Brazil have shown varying degrees of water contamination from both point and diffuse pollution (Vargas et al., \u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e1993\u003c/span\u003e; Pereira et al., \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2007\u003c/span\u003e; Tagliari et al., \u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e2004\u003c/span\u003e; Lemos et al., \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2009\u003c/span\u003e). A range of mutagenic potencies, from low and moderate mutagenic, were found in studies assessing the mutagenicity of surface and treated water (Watanabe et al., 2002; Ohe et al., \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2004\u003c/span\u003e; Warren et al., \u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e2015\u003c/span\u003e). While such research is common in other parts of Asia, Europe and South America, African countries face several challenges that hinder routine mutagenicity testing. These include insufficient infrastructure, lack of regulatory emphasis and resource constraints (Ibor et al., \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Tyhali and Forbes, \u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e2023\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eGiven these limitations, this study aimed to evaluate the mutagenic potential of source and drinking water samples from two South African drinking water treatment plants. In addition, this study also aimed to elucidate or optimise the best scoring approaches for Ames assays for high-throughput and high-accuracy results.\u003c/p\u003e\u003cdiv id=\"Sec17\" class=\"Section2\"\u003e\u003ch2\u003eMutagenic potential of water samples\u003c/h2\u003e\u003cp\u003eTo estimate the mutagenicity potential of the source water, drinking water and distribution water from the two different water treatment plants, a 96-well microplate format of the Ames test was employed. Samples were tested with \u003cem\u003eSalmonella typhimurium\u003c/em\u003e strains TA98 and TA100, with and without metabolic activation. A positive result indicates the potential of the presence of mutagens, whilst a negative result suggests a reassuring absence of these substances (Zeiger and Mortelmans, \u003cspan citationid=\"CR67\" class=\"CitationRef\"\u003e1999\u003c/span\u003e). No significant mutagenic activity was observed in either strain regardless of metabolic activation, with induction factors remaining below the threshold of a 2-fold increase (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e–\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003e). These results were consistent across all samples over the three-month monitoring period. Although, there are a host of bacterial strains that can be used in Ames testing, only the two bacterial strains were selected in this study. These two bacterial strains were selected for their relevance in detecting the mutagenicity of environmental water samples and their potential as indicators of public health risks (Guan et al., \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). Given this, the results of this study are limited to the bacterial strain-specific genetic endpoints of frameshift and base-pair mutations.\u003c/p\u003e\u003cp\u003eThe absence of experimental effects suggests a potential lack of mutagens in the analysed samples. However, the lack of mutagenicity detected may be associated with the grab sampling approach. Grab samples are notoriously known as insufficient to fully provide information on effects. This suggests that instances of mutagens or mutagenic effects present at other times may not have been captured by grab sampling. Despite this, using a single-point sampling approach, researchers Gameiro et al. (\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2025\u003c/span\u003e) and Calao-Ramos et al. (\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2023\u003c/span\u003e) detected positive mutagenic results in raw water and final consumer-destined water in Brazil and Colombia, respectively. Even so, the 2-year effect-based monitoring of water from source to consumer displayed fluctuations in genotoxic activity in source-water and consumer water supplies (Lundqvist et al., \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Given these findings, an increased frequency of analysis would increase the probability of detecting mutagenic potencies from water resources. By implementing long-term monitoring programs, drinking water treatment plants can optimise and integrate advanced treatment processes in place of conventional chlorination to reduce mutagenicity risk of the supplied water (Takanashi et al., \u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e2011\u003c/span\u003e). A comprehensive sampling programme or composite programme should be actioned to assess the mutagenicity of drinking water sources. This approach supports a risk-based strategy for potable water supplies, prioritising the protection of human health.\u003c/p\u003e\u003cp\u003eA limitation of this study is the lack of extraction methods to concentrate potential mutagenic substances. Concentrating drinking water samples may enhance the detection of organic compounds responsible for mutagenicity when using the Ames test. However, studies have shown that each concentration method has its own limitations, as no single concentration method or adsorbent can cover all the chemicals in a sample. Lah et al. (\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2005\u003c/span\u003e) found negative mutagenic results from both concentrated and non-concentrated water samples. Additionally, Xue et al. (\u003cspan citationid=\"CR66\" class=\"CitationRef\"\u003e2021\u003c/span\u003e) found no detectable mutagenicity in concentrated drinking water organic samples. Nonetheless, this study showed that mixed exposure to water samples results in negative mutagenic outcomes, although the study was limited to base-pair mutations and frameshift mutations. Analogous methods of analysis for genotoxicities such as the Comet assay, the SOS assay and immunofluorescence assays can be used to assess additional genotoxicity endpoints and thereby broaden detection and understanding of genotoxicity from mixed exposures.\u003c/p\u003e\u003cp\u003ePrevious studies have shown that chlorinated chemicals such as Trihalomethanes (TCM) and approximately 600 disinfection byproducts are possibly carcinogenic (Grellier et al., \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2015\u003c/span\u003e; Ceretti et al., \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). A recent study by Xu et al. (\u003cspan citationid=\"CR65\" class=\"CitationRef\"\u003e2025\u003c/span\u003e) showed results of tumorigenesis from mice exposed to known DBPs in their drinking water. Comparative studies have highlighted the variability of disinfection by-product formation and removal efficiency in the water purification process (Grellier et al., \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2015\u003c/span\u003e). The adverse health risks posed to human health by the uptake of these DBPs from the water systems have been investigated by Kumari and Gupta (\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Because of the established risk, water treatment plants should constantly optimise their pre-chlorination and disinfection chlorine dosages during the disinfection process. However, this should not be at the expense of the disinfection process, as there is a higher risk of pathogenic harm from inadequate disinfection if compared with the DBPs risk (NHMRC, NRMMC, 2025).\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec18\" class=\"Section2\"\u003e\u003ch2\u003ePublic health and regulatory implications\u003c/h2\u003e\u003cp\u003eThe absence of mutagenicity of samples may signify that the water treatment plant processes effectively removed any mutagens. However, mutagens may still be present in water samples, although at levels below the detection threshold. No detectable mutagenic risks were also found in environmental and drinking water samples (Meier, \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e1988\u003c/span\u003e; Liu et al., \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e1999\u003c/span\u003e; Warren et al., \u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e2015\u003c/span\u003e). Interestingly, these findings are encouraging from a public health standpoint. This is particularly important when it comes to complying to established requirements set by drinking water standard of South Africa (SANS 241, 2015) and international regulations by the World Health Organisation (WHO, 2017). The results of the investigation further suggest that conventional water treatment processes followed by chloramination for final disinfection, effectively diminish environmental pollutants. Contrasting studies have detected mutagenicity in drinking water samples from similar treatment process unit configurations (Xue et al., \u003cspan citationid=\"CR66\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Nevertheless, the outcomes of this analysis support successful mitigation of mutagenic risks by the treatment plants, it is crucial to consider additional factors. Seasonal variations, changes in water composition, and potential interactions between disinfection chemicals and natural organic matter all play a role in the presence of compounds in water.\u003c/p\u003e\u003cp\u003eThe results of these studies signify the need for government-enforced regulation of these carcinogenic and genotoxic compounds in drinking water. To aid this effort, increased support should be given to water treatment suppliers to assist their adoption and implementation of methods applicable to detecting and monitoring these mutagenic potencies. Effect-based methods such as the Ames mutagenicity test used in this study are useful as a genotoxic screening tool for environmental water and potable water supplies by agencies worldwide (ISO 11350:2012; OECD, 2020). Currently, there is a scarcity of these regulations in Africa. This is despite authors such as Rossum et al. (\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e1982\u003c/span\u003e), Rossum et al. (\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e1990\u003c/span\u003e), Agwa et al. (\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2017\u003c/span\u003e), Bariweni et al. (\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2023\u003c/span\u003e), and Mhlongo et al. (\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2024\u003c/span\u003e) finding positive results in drinking water supplies from water treatment plants and groundwater sources. Moreover, in South Africa, the SANS241 document has no provision for toxicity testing on a routine basis. This is alarming given the common use of chlorine products in the disinfection process and the known harm associated with DBPs to human life. The current study's protocol and automation of a bioassay offer a pathway for monitoring mutagenic potencies in drinking water. This can further be implemented by more laboratories in the country to better understand the mutagenic potential of drinking water, as well as optimising the drinking water treatment chlorination process.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec19\" class=\"Section2\"\u003e\u003ch2\u003eScoring approaches\u003c/h2\u003e\u003cp\u003eThe Metafer software and system analyses microplates using a monochromatic camera and high-resolution digital photography. Yellow and purple coloured samples are differentiated using pre-established classifiers. The Metafer systems automated feeder and image-processing algorithms allow for high-throughput sample screening. It also facilitates data storage which is easily retrievable and allows for automatic interfacing with the laboratory management system. Classifier sensitivity, equipment expenses, and accessibility issues for laboratories with limited resources are some of the system's drawbacks. Furthermore, although automation is ideal, it is important to emphasise that proper calibration and training of classifiers are important to ensure correct interpretation of results.\u003c/p\u003e\u003cp\u003eTraditionally the Ames mutagenicity test was performed in an agar plate but recent advancements in technology, have made the 384 well and 96-well microplate versions possible. There are several advantages in using the 96-well microplate version as larger volumes of samples can be analysed. This results in a higher degree of a mutagenic response. Ninety-six well microplates also have wider wells and analysts are able to decipher and score the differences between yellow and purple without difficulty. Human differences in colour perception also exist, which makes manual scoring difficult, affecting the accuracy and consistency in scoring. Although this study did not show this aspect as there was no significant difference between visual and automatic detection of the number of revertants in wells. However, in cases where individuals experience colour vision deficiency, also known as colour blindness, the use of the automatic scanning platform may be more appropriate. Additionally, this study showed that using automation increased scoring efficiency and consistency of Ames results, thereby reducing subjectivity while maintaining accuracy.\u003c/p\u003e\u003c/div\u003e"},{"header":"CONCLUSIONS AND FUTURE DIRECTIONS","content":"\u003cp\u003eFurthermore, this study showed a critical challenge in manual scoring of Ames test results, as human variability in colour perception can affect the reliability of results. Automation is an important tool for extensive drinking water monitoring programs because it reduces human error, improves consistency, and enables high-throughput screening. Although this study did not show conclusive evidence of mutagenicity of drinking water samples, it showed the possibility of mitigation of mutagenic compounds by drinking water treatment processes. The knowledge gained in this study also shows the use of a larger scale microplate version of the Ames test that can offer increased sample capacity, higher sensitivity and more precise scoring, reducing the variability with other versions of the Ames method. Future research should look into pollution events and cycles, and the concentration of drinking water samples. Additionally, this study highlights the role of automation in scoring of Ames test microplates and its practical application in large-scale drinking water quality monitoring.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll authors and co-authors consent to publication of this manuscript.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and material\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe raw data for this study was generated at a central, large-scale facility. The derived data supporting the findings, besides being available in the article itself, will be available from the corresponding author (R. Hendricks) upon request.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors would like to thank Rand Water for providing funding to undertake the study.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003cstrong\u003eAuthor contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eRahzia Hendricks conceived and designed the study; performed data collection and analysis; and developed the methodology and software.\u0026nbsp;Rahzia Hendricks wrote the initial draft of the manuscript. Rahzia Hendricks and Hlakae Leseba provided extensive revisions and editing, ensuring the final version met the journal\u0026apos;s requirements.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eClinical Trial Declaration\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eClinical trial number: not applicable.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eAbdillahi, H. S., Verschaeve, L., Finnie, J. F., \u0026amp; van Staden, J. (2012). Mutagenicity, antimutagenicity and cytotoxicity evaluation of South African \u003cem\u003ePodocarpus\u003c/em\u003e species. \u003cem\u003eJournal of Ethnopharmacology, 132\u003c/em\u003e(3), 728\u0026ndash;738. https://doi.org/10.1016/j.jep.2011.11.044\u003c/li\u003e\n\u003cli\u003eAgwa, O. K., Eze, N. J., \u0026amp; Okpokwasili, G. C. (2017). 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The presence of these substances can result in mutagenic risk to the public, highlighting the need for surveillance. This study evaluated the mutagenic potential of source water and drinking water from two South African drinking water treatment plants. The study also investigated the high-throughput scoring of the \u003cem\u003eSalmonella typhimurium\u003c/em\u003e Ames mutagenicity assay with frameshift and base-pair mutations. Two different scoring approaches were used including visual manual scoring and using the automatic image scanning platform. No mutagenic risk was detected for both TA98 and TA100 bacteria regardless of metabolic activation. Grab sampling may have missed any transient mutagenic events. Despite the limitations, automatic scanning of the microtiter plates ensured consistent, reliable and accurate results that can be reviewed. The outcomes of the study show effective mitigation of mutagenic risk by the treatment plants and deliver public reassurance of drinking water. The advantageous combination of automated scoring technologies, as demonstrated in this study, provides a scalable and standardized monitoring programme for mutagenic risk. Regulatory frameworks would benefit from a mutagenic risk monitoring programme, given the myriad health risks involved in exposure to environmental mutagens.\u003c/p\u003e","manuscriptTitle":"Automation of the Ames Assay Scoring and Assessment of Water Samples for Mutagenicity","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-11-19 09:17:31","doi":"10.21203/rs.3.rs-7883227/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"9eb50108-446a-4feb-8f1d-fada54903478","owner":[],"postedDate":"November 19th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2025-12-11T16:38:54+00:00","versionOfRecord":[],"versionCreatedAt":"2025-11-19 09:17:31","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7883227","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7883227","identity":"rs-7883227","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}