A High-Throughput and Highly Automated Genotoxicity Screening Assay

The increasing number of compounds under development and chemicals in commerce that require safety assessments pose a serious challenge for regulatory agencies worldwide. In vitro screening using toxicogenomic biomarkers has been proposed as a first-tier screen in chemical assessments and has been endorsed internationally. We previously developed, evaluated, and validated an in vitro transcriptomic biomarker responsive to DNA-damageinducing (DDI) agents, namely TGx-DDI, for genotoxicity testing in human cells, and demonstrated the feasibility of using TGx-DDI in a medium-throughput, cell-based genotoxicity testing system by implementing this biomarker with the Nanostring nCounter system. In this current study, we took the advantage of Nanostring nCounter Plexset technology to develop a highly automated, multiplexed, and high-throughput genotoxicity testing assay, designated as the TGx-DDI Plexset assay, which can increase the screening efficiency by eight-fold compared to standard nCounter technology while decreasing the hands-on time. We demonstrated the high-throughput capability of this assay by eliminating concentration determination and RNA extraction steps without compromising the specificity and sensitivity of TGx-DDI. Thus, we propose that this simple, highly automated, multiplexed high-throughput pipeline can be widely used in chemical screening and assessment.


Introduction
Chemicals that are capable of inducing genetic changes, such as mutations and chromosome damage, are defined as genotoxic (GTX) compounds. Genotoxicity is initiated by the covalent binding of chemicals or their metabolites to DNA as well as by noncovalent binding that also perturbs DNA and chromatin structure. If not repaired, DNA damage can lead to genomic instability, and ultimately progress to cancer (Li et al., 2007;Birkett et al., 2019;Krewski et al., 2019;Brambilla et al., 2010). Therefore, genotoxicity testing is an essential part of the safety assessment for predicting the carcinogenic potential of all drugs and chemicals.
Humans are continuously exposed to potential chemical hazards throughout their daily lives, including industrial chemicals, food, medicine, and consumer products. For many chemicals, there is not sufficient toxicity information available for risk assessment (Judson et al., 2010). The inventory of the TSCA (Toxic Substances Control Act) has continued to grow since the initial reporting period and now lists more than 86,000 chemicals. The increasing number of compounds under development and chemicals in commerce that require safety assessments pose a serious challenge for regulatory agencies. The traditional toxicology tests are not feasible for screening the large backlog of chemicals due to high costs and the lengthy times required to conduct animal testing. Thus, there are compelling needs for developing high-throughput screening (HTS) assays to assist regulatory agencies in making informed decisions and policies regarding chemical safety.
The most commonly used in vitro genotoxicity testing batteries include the Ames test, mouse lymphoma assay (MLA), mammalian cell HPRT gene mutation assay, and in vitro chromosome damage assays consisting of micronucleus (MN) or chromosomal aberration (CA) assays. The specificity, sensitivity, and positive and negative predictivity of these assays were evaluated using data from a large database of over 700 rodent carcinogens and non-carcinogens (Kirkland et al., 2005). The mammalian cell-based testing batteries had high sensitivity (positive genotoxicity results with carcinogens) but low specificity (negative genotoxicity results for non-carcinogens), i.e., high "irrelevant-positive" results for tested chemicals particularly for ALTEX, accepted manuscript published September 28, 2021 doi:10.14573/altex.2102121 2 chromosome aberration/damage (CD) assays. Although Fowler et al. reported that with careful selection of cell types, e.g. instead of using long established, p53-defective rodent cell lines, using p53-competent human cells or cells line could reduce "irrelevantpositive" rate (Fowler et al., 2012a,b), a novel in vitro system with a balance between sensitivity and specificity for predicting genotoxicity is desirable. Taking advantage of a modern toxicogenomic approach, we developed, evaluated, and validated an in vitro transcriptomic biomarker responsive to DNA-damage-inducing (DDI) agents in human cells, namely TGx-DDI, for genotoxicity testing (Li et al., 2015(Li et al., , 2017. While TGx-DDI does not detect the mode of action of aneugens due to the fact that damages caused by aneugens are at the chromosomal but not the DNA level (Li et al., 2015;Allemang et al., 2021), the TGx-DDI biomarker can readily discriminate DDI agents from non-DDI agents. Importantly, this biomarker can differentiate compounds with irrelevant positive findings in CD assays from true DNA damaging agents (Li et al., 2017). The sensitivity and specificity of genotoxicity prediction by TGx-DDI were carefully validated in our laboratory and another laboratory using different technical platforms, such as microarray, nCounter, Tempo-seq, and qRT-PCR platforms (Buick et al., 2015(Buick et al., , 2020Cho et al., 2019;Li et al., 2017Li et al., , 2019Yauk et al., 2016). Moreover, we also demonstrated the feasibility of using TGx-DDI in a high-throughput compatible cell-based genotoxicity testing system by implementing this biomarker with the Nanostring nCounter system, which is much more efficient than using a microarray platform (Li et al., 2017). A standardized experimental workflow, which includes a concentration optimization step followed by either microarray or nCounter analysis has been proposed for TGx-DDI application in chemical testing (Li et al., 2015(Li et al., , 2017.
In the present study, we focused on developing a new highly-automated and high-throughput genotoxicity testing assay by integrating TGx-DDI with Nanostring nCounter Plexset™ technology, designated as TGx-DDI Plexset assay, which can increase the screening efficiency eight-fold compared to standard nCounter Codeset technology, which is referred as the Codeset method hereafter, while decreasing the hands-on time. Different from the Codeset method, which involves the measurement of colored barcode tag for one sample in each nCounter assay, the Plexset technology combines eight samples, which are barcoded differently, in one assay. In this study, we first evaluated the robustness, reproducibility, sensitivity, and specificity of the TGx-DDI Plexset assay to show that results are in concordance with the previously used approaches. Next, a panel of 17 chemicals with known mechanisms of action were analyzed to demonstrate the versatility of TGx-DDI Plexset assay. Since this assay can directly measure gene expression from cell lysate, the concentration setting experiment and RNA isolation step are eliminated; and a simple, high automated HTS pipeline is proposed for the safety assessment of chemical agents.
Currently, the U.S. Environmental Protection Agency (EPA) ToxCast program and the NIEHS Tox21 collaboration have been using in vitro HTS assays to screen chemicals for potential health effects and minimize the required animal-based toxicity tests. A battery of HTS assays, including both cell-free and cell-based assays, has been evaluated to survey a broad spectrum of bioactivities, such as cytotoxicity, cell growth, enzymatic activity, transcription factor activity, gene induction, and high-content imaging of cells, which are used as predictors of toxicity in prioritizing chemicals for more in-depth testing (Dix et al., 2007;Judson et al., 2010;Shukla et al., 2010). Unlike other in vitro assays that usually assess one specific endpoint, the TGx-DDI biomarker measures transcriptomic changes that reflect the complex cellular responses to the genotoxic stress, which provides additional mechanistic information. Thus, the TGx-DDI Plexset assay with high sensitivity and specificity will provide a novel and highly efficient approach for genotoxicity screening and human health risk assessment.

Material and Methods
Cell Culture and Treatment TK6 cells, a spontaneously transformed human lymphoblastoid cell line, were grown in RPMI 1640 medium supplemented with 10% FBS as described previously (Li et al., 2015). The cell culture density was maintained at a concentration of 1-10 × 10 5 cells/ml. On the day before the treatment, exponentially growing TK6 cells were seeded in a 96-well plate at a density of 5 × 10 4 cells/well. All chemicals were purchased from Sigma-Aldrich, and stocks and were prepared according to the manufacturer's instructions. Cells were treated with the indicated chemical agent for 4 h over a broad dosage range (2µM to 1mM) and its corresponding vehicle control (H2O, DMSO, or EtOH) for 4 h, rinsed to remove the drug, and then lysed in 10 µl of RNA lysis buffer (Qiagen) to make a lysate containing 5,000 cells/µl. For ionizing radiation (IR) exposure, cells were irradiated at a dose of 4 Gy using a gamma ray ( 137 Cs) irradiator. For agents requiring metabolic activation, treatment of TK6 cells included S9 rat liver extract as described previously (Buick et al., 2015) with a modification. In brief, cells were exposed to agent in the presence of 1% 5,6 benzoflavone-/phenobarbital-induced rat liver S9 (BF/PB-induced S9) (Moltox, Boone, NC) with NADPH generating system cofactors for 3h, rinsed to remove the drug and S9, incubated in fresh RPMI for additional 4h, and then lysed in RNA lysis buffer as described above. All treatments were performed in triplicate.

TGx-DDI nCounter Plexset Assay
The nCounter Plexset™ technology is based on proven nCounter technology to ensure highly multiplexed digital analysis. Methodological details of the nCounter technology have been published previously (Geiss et al., 2008 3 that enable direct digital counting and multiplexing capabilities. The details of the nCounter Plexset technology can be found in Nanostring PlexSet Reagents for Gene Expression user manual 1 . In this study, each set includes the TGx-DDI gene set and eight housekeeping genes-G6PD, GUSB, HPRT1, LDHA, NONO, PGK1, PPIH, and TFRC-selected based on stability and detectable expression levels. The optimized sequences for genes in the TGx-DDI panel were custom designed and manufactured by NanoString, and Probe A and probe B pair were designed by Nanostring and manufactured by IDT. The hybridization protocol followed the Nanostring PlexSet Reagents for Gene Expression user manual. Data collection was performed on a nCounter Digital Analyzer, where each fluorescent barcode was individually resolved and counted. The counts are separated (demultiplexed) by nSolver Analysis (version 4.0) for further analysis. Normalized data were subjected to bioinformatics analysis. The nCounter Plexset assay was performed with cell lysate containing 5,000 cells/µl.

Bioinformatics Analyses
Gene expression data were exported from nSolver Analysis (version 4.0). Posterior Probability Analysis (PA) for test agents was performed given the classifier as described by Tibshirani et al. (2002) and implemented in the pamr package for R. By determining the extent of gene expression changes for each of the biomarker genes from DDI and non-DDI centroids, a DDI call was based on p > 0.9 of the compound being in that class, and vice versa for a non-DDI call. Two-dimensional clustering (2DC) was performed using Euclidean distances with average linkage by Genesis 2 . Principal Component Analysis (PCA) was performed using the prcomp function (Venables and Ripley, 2002) in R Bioconductor. Category assignment was determined by the position of the test agents in the tree structure of the dendrogram generated by 2DC, or in the PCA plot. A chemical was considered unclassified if it did not meet these criteria. An agent was classified as DDI if it gave a positive call in any one of the TGx-DDI biomarker analyses described above (2DC, PCA, or PA prediction), and was classified as non-DDI if it did not meet any of these criteria.

Technical performance evaluation of TGx-DDI Plexset assay
In order to develop a high-throughput testing system, we integrated nCounter Plexset technology with our TGx-DDI transcriptomic biomarker to minimize the hands-on time and to increase the testing capacity. One full set of the nCounter Plexset assay includes 8 PlexSets, A through H that contains unique barcode for each set. Each set is comprised of the identical genes, i.e., the 64 genes in TGx-DDI and eight housekeeping genes that were selected based on stability and expression level (Li et al., 2017). To demonstrate the technical reproducibility and robustness of the nCounter Plexset assay and cell culture conditions, we conducted a series of experiments. We first evaluated the reproducibility of the assay and cell culture conditions using two DDI agents, IR and bleomycin, and one non-DDI agent, caffeine, to treat TK6 cells. In this experiment, TK6 cells were treated with these three agents using previous exposure conditions along with a concurrent vehicle control (H2O) in eight independent replicates. Each replicate was then hybridized to the individual Plexset (Plexset A to H in Fig.1) to assess the reproducibility of nCounter Plexset assay. As shown in Figure 1A, the scatter plot of gene counts for each treatment showed high consistency among each replicate/Plexset. Two-dimensional clustering of each treatment/Plexset using fold-changes induced by treatments also demonstrated that Plexset results clustered with each other in a single treatment ( Figure 1B), indicating the high reproducibility among individual Plexsets within the full set.
We then assessed the robustness of nCounter Plexset assay by comparing the results of training set agents in TK6 cells to those using total RNA and the standard nCounter codeset (Figure 2). Using the same gene and treatment order as the previous nCounter assay, a similar gene expression profile was observed between the Plexset and standard codeset assays ( Figure 2A). Moreover, as anticipated, DDI and non-DDI agents could readily cluster with other agents in the same category ( Figure 2B). By applying our three-pronged analyses for classification as described in our previous study (Li et al., 2017), all compounds were classified into the correct category based on the nCounter Plexset assay results. The classification results of the three-prong analyses and overall classification results are listed in Table 1. Taken together, these experiments demonstrated that the TGx-DDI Plexset assay can generate precise and reproducible data comparable to the standard codeset assay, rendering it suitable for highthroughput genotoxicity testing.

3.2
TGx-DDI Plexset assay evaluation To validate the sensitivity and specificity of DDI prediction of the TGx-DDI nCounter Plexset assay, we consulted with members of the National Toxicology Program, including the Genetic Toxicology Group in the Biomolecular Screening Branch, for advice on compound selection, and based on literature search (Kirkland et al., 2016) and in vitro genotoxicity profiles, we investigated 17 compounds with different characteristics. These compounds were categorized into three groups ( Table 2). The criteria of classification were explained in detail previously (Kirkland et al., 2016). In brief, Group 1 included compounds that are positive in both Ames and Chromosome damage (CD) assays, referred as "true positives" in Table 2. Five out of six agents in this group need metabolic activation to produce metabolites that directly interact with DNA. Therefore, this group of agents serves as a  (Li et al., 2017), and visualized using Genesis 2 ; right is heatmap of TGx-DDI expression analysis using cell lysate and nCounter Plexset. The orders of genes (vertical order) and chemicals (horizontal order) are same in both panels. (B) Heatmap of TGx-DDI expression analysis using previously tested chemicals and nCounter Plexset assay. All chemicals were classified as DDI or non-DDI using the same approach used in previous studies (Li et al., 2015(Li et al., , 2017 .

ALTEX, accepted manuscript published September 28, 2021 doi:10.14573/altex.2102121
6 positive control for detection of direct DNA-reactive mechanism. Agents in Group 2 are negative in both Ames and CD assays, referred as "true negatives". This group of agents serves as the negative control for testing the assay. Group 3 was comprised of compounds that should be negative in in vitro mammalian cell genotoxicity tests but were reported to induce positive results typically in chromosomal damage assays, often at high concentrations (Kirkland et al., 2016). Compounds in this group usually are negative in vivo and are negative in Ames assay, and thus considered as "irrelevant positives".
Tab. 1: The classification results of previously tested agents using nCounter Plexset assay and three-prong analysis Previously tested training agents were used to treat TK6 cells, cell lysates were used to perform TGx-DDI Plexset assay. Threepronged analysis was performed to evaluate the accuracy of TGx-DDI Plexset.

Tab. 2: Groups of test compounds
Group 1 is for true positives compounds, with both Ames and chromosome damage (CD) positive; Group 2 is for true negatives, with both Ames and CD negative; Group 3 is for irrelevant positives, with Ames negative and CD positive. Y: metabolic activation is required; N: metabolic activation is not required. In our previous study, the nCounter TGx-DDI assay used isolated RNA and the standard codeset (Li et al., 2017) following concentration determination. In order to achieve high-throughput capability, we eliminated the concentrationdetermination and RNA preparation steps and measured the gene count directly from crude cell lysate. In this assay, cytotoxicity was determined over a broad dosage range (from 2µM to 1mM) using a standard MTT assay (Fig. S1 3 ). Four concentrations were selected to proceed to the TGx-DDI Plexset assay for each agent based on the cytotoxicity results. Three low concentrations, 2µM, 16µM and 125µM, were consistent among all agents. The top concentration of each agent was determined according to the cytotoxicity. If there was no cytotoxicity, a concentration of 1mM was used; otherwise, the top concentration is set to induce the decline of cell survival to 45% +/-5%. Figure 3 shows the TGx-DDI heatmap for compounds in all three groups. 2DC, PCA and PA results using TGx-DDI biomarker are shown by colored boxes above each heatmap. Yellow and blue represent positive and negative results, respectively, and grey indicates indeterminable result. The three-pronged analysis strategy was used to minimize the chance of false negatives. As described in our previous study (Li et al., 2017), a compound was classified as DDI if it was determined positive in any of the three classification analyses and was classified as non-DDI if all analyses produced negative results. 2DC results and the PCA plot for each treatment are shown in Fig. S2 3 . The classification of Group 1 agents is shown in Figure 3A. In this group, benzo[a]pyrene (BaP) was tested in parallel as a positive control for S9 activation. A concentrationdependent response was observed for BaP. It was classified as DDI when cells were treated with BaP at the concentration of 10 µg/ml. As shown in Fig. 3A, Group 1 compounds that require metabolic activation were classified as DDI in the presence of S9. Without metabolic activation, 2-acetylaminofluorene (2-AAF) and 2,4-diaminotoluene (2,4-DAT) were also classified as DDI at the highest concentration of 1mM. Moreover, cyclophosphamide and PhIP were classified as non-DDI at the lowest concentration in the presence of S9. Interestingly, 7,12-dimethylbenzanthracene (DMBA) was classified as non-DDI at the lowest concentration but was predicted as DDI at other concentrations with or without S9 presence. Lastly, 4-chloroaniline, which does not need metabolic activation, was classified as non-DDI at the three lower concentrations but classified as DDI at the highest concentration. Figure 3B shows the classification of Group 2 compounds. All compounds in this group were classified as non-DDI at all concentrations. The prediction of Group 3 compounds is shown in Figure 3C. Four out of six agents in this group were classified as non-DDI agents at all concentrations and only one compound tertiary-butyl hydroquinone (TBHQ) was classified as DDI at all concentrations. Additionally, ethyl acrylate gave non-DDI calls at the three lower concentrations but was classified as DDI at highest concentration.

Discussion
The objective of this study was to validate the specificity and versatility of this high-throughput genotoxicity testing assay for assessment of genotoxic hazard and de-risking compounds with irrelevant in vitro positive chromosome damage findings. In our previous study, we demonstrated the feasibility of using the TGx-DDI biomarker and nCounter system in a high-throughput cellbased genotoxicity testing system (Li et al., 2017). An ideal high-throughput assay requires 1) high degree of multiplex, 2) high accuracy, 3) automated workflow to reduce human errors and lessen the labor cost, and 4) low cost so that large scale screening is affordable. In order to achieve the above goals, we integrated NanoString nCounter Plexset technology with our TGx-DDI biomarker to develop a high-throughput TGx-DDI testing assay, named the TGx-DDI Plexset assay. NanoString's nCounter Plexset technology enables direct, multiplexed, high-throughput detection of gene expression levels that does not involve reverse transcription or require amplification. By using nCounter Plexset technology, in which 8 sets of reagents, Plexset A to Plexset H in a full set are included, we can test up to 96 samples per nCounter run. We first assessed the robustness and reproducibility of TGx-DDI Plexset assay and demonstrated that the TGx-DDI Plexset assay can generate comparable, robust, and reproducible data. Due to the cost and low throughput of standard transcriptomic methods such as microarray and RNAseq, it is not always feasible to test multiple concentrations when a large number of compounds are subjected to toxicity screening. Thus, a concentration optimization procedure had been applied to determine the concentration for the transcriptomic assay (Li et al., 2015(Li et al., , 2017. We here demonstrated the high-throughput capability of this assay by eliminating concentration determination and RNA extraction steps for the classification of DDI and non-DDI agents. In our previous study, we developed a TGx-DDI biomarker based on experimental and analytical protocol for distinguishing DDI agents from non-DDI agent and evaluated its performance across different platforms (Li et al., 2017;Buick et al., 2015). Moreover, by integrating the TGx-DDI biomarker with nCounter technology, we demonstrated the feasibility of developing a highly automated high-throughput genotoxicity testing system (Li et al., 2017). The nCounter Plexset technology provides an excellent technical platform for this purpose since it can analyze up to 96 samples per nCounter run which is eight times more than Codeset technology. Therefore, the robustness, reproducibility, and sensitivity of TGx-DDI Plexset assay needed to be assessed. The results showed that the nCounter Plexset assay can generate comparable, robust, and reproducible data for each Plexset (Figure 1). Moreover, the output of the TGx-DDI Plexset assay is comparable to that of standard nCounter assay ( Figure 2 and Table 1), demonstrating that this multiplexed technology can increase testing capacity without compromising sensitivity. Hence, the TGx-DDI Plexset assay is an excellent system for automated HTS assay.
To validate TGx-DDI Plexset assay, we assessed 17 agents in three groups with broad in vitro genotoxicity profiles. Our previous TGx-DDI based on experimental protocol requires a concentration-determination step before the microarray or  Fig. 3: Prediction of chemical toxicity using nCounter Plexset assay Nineteen chemicals were grouped based on toxicology profile (Table 1). Group 1 includes chemicals shown positive in both Ames and Chromosome Aberration (CA) assays. Five out of six testing chemicals require metabolic activation; Group 2 chemicals are both Ames and CA negative chemicals; Group 3 chemicals are Ames assay negative and CA positive chemicals. (A)-(C) Heatmaps of group 1-3 chemicals. The TGx-DDI transcriptomic biomarker panel from previous study (Li et al., 2017) is on the left, one the right is the prediction of each compound. The results of 2DC, PA, PCA, and overall are displayed above each heat map. Three methods were used to predict DDI positivity (yellow), and the overall prediction (Bottom) is based on positive results with any of these three methods. Yellow and blue indicate positive and negative findings, grey indicates indeterminable, respectively.  (Li et al., 2017;Buick et al., 2015). In this HTS assay, TK6 cells were treated with these agents over a wide concentration range to eliminate qRT-PCR based concentration-determination process (Li et al., 2017). Following our data analysis workflow, the data from TGx-DDI Plexset assay were interpreted using three statistical approaches, 2DC, PA, and PCA, to ensure robust data analysis and minimize false negatives (Li et al., 2017). Figure 3 summarizes the results of TGx-DDI Plexset assay for these 17 agents. As shown in Figure 3A, consistent with their in vitro genotoxicity profiles, five out of six agents that require metabolic activation in Group 1 were classified as DDI in the presence of S9 at various concentrations, and 4-Chloroaniline that does not need metabolic activation was also classified as DDI at the highest concentration. In addition, all agents in Group 2 were classified as non-DDI at all concentrations, which is consistent with the findings of in vitro CA and Ames assays ( Figure 3B). Without the concentration-determination step by which we selected only one concentration based on the induction of three indicator genes (ATF3, CDKN1A, and GADD45), this HTS assay enables us to assess the genotoxicity of agents in a dynamic range so that we could classify agents in a concentration-dependent manner. For example, 2-AAF and 2,4-DAT were classified as DDI at 1mM without S9 activation ( Figure 3A). If only one concentration was tested, this information might have been missed. As the positive control of S9 activation in this assay, BaP was classified as DDI only at the concentration of 10 µg/ml. In our previous study using total RNA to measure gene expression levels, BaP was classified as DDI at both 1.4 µg/ml and 10 µg/ml (Li et al., 2017). This discrepancy could be due to the S9 activation time. In the previous study, cells were exposed to agents in the presence of S9 for 4hr, then recovered in fresh medium for additional 4hr, and then were collected for RNA isolation (Li et al., 2017). In this assay, we eliminated the RNA isolation step and instead measured gene expression levels directly from crude cell lysates, which requires a higher percentage of viable cells. Although variations in cell number can be normalized, big differences in the number of viable cells may compromise the precision of results. In a time course experiment of BaP testing the cytotoxicity of S9, the MTT assay of BaP-treated cells showed that there was no difference between 2hr and 3hr treatments, but S9 became more cytotoxic at 4hr (Fig. S3 3 ). Therefore, we modified our previous protocol by reducing S9 exposure to 3hr to compensate for the cytotoxicity of S9. Four out of six agents in Group 3 were classified as non-DDI at all concentrations, while TBHQ was classified as DDI at all concentrations ( Figure 3C). TBHQ is a phenolic antioxidant used in foods and cosmetics industries. Many antioxidants have been shown to act as either antioxidant or pro-oxidant under certain conditions, which could be caused by the generation of reactive oxygen intermediates (Black, 2002;Fujisawa et al., 2002) and this activity could potentially explain the DDI exhibited by TBHQ. Moreover, ethyl acrylate was classified as non-DDI at 3 lower concentrations and as DDI at 1mM concentration. It has been showed that ethyl acrylate has potential for genotoxicity in mammalian cells through clastogenic mechanism and is "possibly carcinogenic to humans" (EFSA Panel on Food Contact Materials et al., 2017;Suh et al., 2018). An increase in sister chromatid exchanges (SCE) and chromosomal aberrations (CA) in Chinese hamster ovary (CHO) cells with or without metabolic activation were reported (Moore et al., 1988;Loveday et al., 1990;Tennant et al., 1987). Our results showed that ethyl acrylate only induce DNA-reactive genotoxicity at very high concentrations, which demonstrated that our assay could readily de-risk irrelevant findings for a single agent with this multi-concentration assay and add significant value to the current genotoxicity testing battery.
Overall, these results demonstrated that TGx-DDI Plexset assay could assess genotoxicity in a simple and rapid way with a high-throughput capacity by combining dose optimization and transcriptomic analysis into one assay. The ability to include a wide range of concentrations is another strength and can highlight positive effects only seen at very high irrelevant concentrations. Not only has this assay reduced hands-on time and allowed a more automated workflow, but it also enhanced specificity without compromising sensitivity, fulfilling the criteria for an ideal high-throughput testing assay mentioned earlier.
As a transcriptomic biomarker that reflects the cellular responses to genotoxicity, we proposed that using TGx-DDI assay to provide additional mechanistic information to augment current genotoxicity hazard assessment as stated previously (Li et al., 2017). The high-throughput potential developed in the current study will facilitate and expedite the genotoxicity screening for chemical agents.