Recognition of chemical compounds in contaminated water using time-dependent multiple dose cellular responses

Functional non-parametric mixed effects models for cytotoxicity assessment and clustering

A multitude of natural and synthetic chemicals are present in our environment.Through the study of a compound's cytotoxicity, researchers can carefully set regulations regarding how much of a certain chemical in the ambient environment is tolerable. In the past, research has focused on point measurements such as the LD50. Instead, we consider entire time-dependent cellular response curves through the application of functional mixed effects models. We identify differences in such curves corresponding to the chemical's mode of action-i.e. how the compound attacks human cells. Through such analysis, we identify curve features to be used for cluster analysis via application of both k-means and self organizing maps. The data is analyzed by making use of functional principal components as a data driven basis and separately by considering B-splines for identifying local-time features. Our analysis can be used to drastically speed up future cytotoxicity research.

A Novel Edge Effect Detection Method for Real-Time Cellular Analyzer Using Functional Principal Component Analysis

Real-time cellular analyzer (RTCA) has been generally applied to test the cytotoxicity of chemicals. However, several factors impact the experimental quality. A non-negligible factor is the abnormal time-dependent cellular response curves (TCRCs) of the wells located at the edge of the E-plate which is defined as edge effect. In this paper, a novel statistical analysis is proposed to detect the edge effect. First, TCRCs are considered as observations of a random variable in a functional space. Then, functional principal component analysis (FPCA) is adopted to extract the principal component (PC) functions of the TCRCs, and the first and second PCs of these curves are selected to distinguish abnormal TCRCs. The average TCRC of the inner wells with the same culture environment is set as the standard. If the distance between the scoring point of the standard curve and one designated scoring point exceeds the defined threshold, the corresponding TCRC of the designated point should be removed automatically. The experimental results demonstrate the effectiveness of the proposed algorithm. This method can be used as a standard method to resolve general time-dependent series issues.

Protective Role of Agrimonia eupatoria L. in Heavy Metal Induced Nephrotoxicity

The aim of this study was to evaluate the potential protective role of Agrimonia eupatoria L. in heavy metal induced nephrotoxicity. Rabbit kidney epithelial cells (RK13) were used as the model cell line. They were exposed to three different heavy metal compounds: cadmium chloride dihydrate CdCl2.2H2O (15 and 20 mg.l−1), potassium dichromate K2Cr2O7 (1, 10 mg.l−1), and zinc sulfate heptahydrate ZnSO4.7H2O (50, 150 mg.l−1) simultaneously with agrimony (ethanolic extract, 100 mg.l−1). The cell response was recorded using the xCELLigence system or real-time cell analysis (RTCA) as a cell index (CI) and expressed as cell adherence (%) compared to control cells without treatment. The potential nephroprotective effects were recorded in cells treated with chromium (1 a 10 mg.l−1) and agrimony, where the cell adherence increased from 95.11 ± 11.25 % and 7.24 ± 0.33 % to 103.26 ± 1.23 % and 68.54 ± 4.89 % (P < 0.05) respectfully and also with a combination of agrimony and zinc (150 mg.l−1), where the adherence increased from 57.45 ± 1.98 % to 95.4 ± 6.95 %. During the cell exposure to cadmium in combination with agrimony, the protective effect was not recorded; the adherence of cells was even decreased (P < 0.05).

Edge effect detection for real-time cellular analyzer using statistical analysis

The Smirnov test is used to detect the edge effect, which can help technicians rapidly screen valid time-dependent cellular response curves (TCRCs) in the real time cellular analyzers (RTCA).

Machine learning algorithms for mode-of-action classification in toxicity assessment

Background

Real Time Cell Analysis (RTCA) technology is used to monitor cellular changes continuously over the entire exposure period. Combining with different testing concentrations, the profiles have potential in probing the mode of action (MOA) of the testing substances.

Results

In this paper, we present machine learning approaches for MOA assessment. Computational tools based on artificial neural network (ANN) and support vector machine (SVM) are developed to analyze the time-concentration response curves (TCRCs) of human cell lines responding to tested chemicals. The techniques are capable of learning data from given TCRCs with known MOA information and then making MOA classification for the unknown toxicity. A novel data processing step based on wavelet transform is introduced to extract important features from the original TCRC data. From the dose response curves, time interval leading to higher classification success rate can be selected as input to enhance the performance of the machine learning algorithm. This is particularly helpful when handling cases with limited and imbalanced data. The validation of the proposed method is demonstrated by the supervised learning algorithm applied to the exposure data of HepG2 cell line to 63 chemicals with 11 concentrations in each test case. Classification success rate in the range of 85 to 95 % are obtained using SVM for MOA classification with two clusters to cases up to four clusters.

Conclusions

Wavelet transform is capable of capturing important features of TCRCs for MOA classification. The proposed SVM scheme incorporated with wavelet transform has a great potential for large scale MOA classification and high-through output chemical screening.

Electronic supplementary material

The online version of this article (doi:10.1186/s13040-016-0098-0) contains supplementary material, which is available to authorized users.

Pattern recognition for cytotoxicity mode of action (MOA) of chemicals by using a high-throughput real-time cell analyzer

Slope and entropy are extracted from the relative normalized cell index collected from RTCA. Then the median value is selected to denote the main mode of actin (MoA) of chemical. Hierarchical cluster is used for pattern recognition of MoA.

Real-time monitoring of cadmium toxicity in rabbit kidney cells

The aim of this study was to investigate the toxic effect of the metal salt cadmium chloride dihydrate on the rabbit kidney cell line using the xCELLigence system or real-time cell analyser (RTCA), and to compare this relatively new method with standard biological cytotoxicity assays. This system provides real-time monitoring of cell behaviour and proliferative activity during the whole time of experiment. Moreover, after 24 h exposure of cells to cadmium, colorimetric 3-[4,5-dimethylthiazol-2-yl]-2,5-difenyl tetrazolium bromide (MTT) test was used to measure the metabolic activity and cytotoxicity was determined by measurement of lactate dehydrogenase (LDH) leaked from damaged cells. We found that renal cells exposed to lower concentrations (5–10 mg·l-1) of cadmium tend to grow similarly to control cells, however, cell index was significantly different (P < 0.05) after 24 h. With increasing concentration of cadmium (15–50 mg·l-1) significantly lower proliferative (P < 0.05) and metabolic activity (P < 0.05) of cells was observed and cytotoxicity increased simultaneously (P < 0.001). In addition, we found that the real-time monitoring of the cell response was significantly correlated with commonly used biological methods for toxicity measurement, for MTT assay R2 was 0.9448 (P < 0.01) and for LDH assay R2 was 0.9466 (P < 0.01), respectively. The present study is the first report when combination of RTCA, MTT assay and LDH test was used for cadmium nephrotoxicity assessment. In all these methods, the toxic effect of cadmium on rabbit kidney cells increased in a concentration-dependent manner.

Analysis of inter-/intra-E-plate repeatability in the real-time cell analyzer

Over the past decade, the real-time cell analyzer (RTCA) has provided a good tool to the cell-based in vitro assay. Unlike the traditional systems that label the target cells with luminescence, fluorescence, or light absorption, RTCA monitors cell properties using noninvasive and label-free impedance measuring. However, realization of the maximum value of RTCA for applications will require assurance of within-experiment repeatability, day-to-day repeatability, and robustness to variations in conditions that might occur from different experiments. In this article, the performance and variability of RTCA is evaluated and a novel repeatability index (RI) is proposed to analyze the intra-/inter-E-plate repeatability of RTCA. The repeatability assay involves six cell lines and two media (water [H2O] and dimethyl sulfoxide [DMSO]). First, six cell lines are exposed to the media individually, and time-dependent cellular response curves characterized as a cell index (CI) are recorded by RTCA. Then, the variations along sampling time and among repeated tests are calculated and RI values are obtained. Finally, a discriminating standard is set up to evaluate the degree of repeatability. As opposed to the standardized methodologies, it is shown that the presented index can give the quantitative evaluation for repeatability of RTCA within E-plate and variation on different days.