Curr Microbiol DOI 10.1007/s00284-014-0752-z
Real-Time Cell Analysis for Monitoring Cholera Toxin-Induced Human Intestinal Epithelial Cell Response Julian Ye • Yun Luo • Weijia Fang • Junhang Pan • Zheng Zhang • Yanjun Zhang Zhiping Chen • Dazhi Jin
•
Received: 8 April 2014 / Accepted: 4 November 2014 Ó Springer Science+Business Media New York 2014
Abstract The pathogenic mechanism of Vibrio cholerae manifests as diarrhea and causes life-threatening dehydration. Here, we observe the human intestinal epithelialcells (HIEC) response to Cholera toxin (CT) by a real-time cell analysis (RTCA) platform, and disclose the difference from CT-induced cytotoxicity and others in HIEC. An HIEC cell of 1.0 9 105 cells/mL was characterized as the suitable concentration for each well. For experimentation, the assay requires an inoculation of CT dissolved in Dulbecco’s phosphate-buffered saline with 0.1 % gelatin for a period of 18–25 h. The dimensionless impedance cell index curve presented characteristic dose- and time-dependent drop responses at the first stage, and the CT-induced cytotoxicity was the most remarkable following exposure for 18–25 h (P = 0.0002). Following the obvious cytotoxic reaction, the CI curve gradually increased over time until the original CI value, indicating that self-recovery occurred. The CT-induced CI curve for HIEC was different from that induced by other toxins, including diphtheria and Clostridium difficile toxin. Collectively, these results suggest that the CT-induced cytotoxicity in HIEC was absolutely
Julian Ye and Yun Luo have contributed equally to this work.
Electronic supplementary material The online version of this article (doi:10.1007/s00284-014-0752-z) contains supplementary material, which is available to authorized users. J. Ye Y. Luo J. Pan Z. Zhang Y. Zhang Z. Chen D. Jin (&) Zhejiang Provincial Center for Disease Control and Prevention, Hangzhou 310051, Zhejiang, China e-mail: [email protected] W. Fang First Affiliated Hospital, School of Medicine, Zhejiang University, Hangzhou 310058, Zhejiang, China
different from that induced by C. difficile and other toxins because of the different pathogeneses that were correlated with the specific CI curve generated by the RTCA system. In summary, our data show that the assay described here is a convenient and rapid high-throughput tool for real-time monitoring of host cellular responses to CT on the basis of the characteristic CI curve.
Introduction Exposure to bacterial pathogens or their toxins triggers obvious alterations in cellular morphology and molecular pathology. The morphological changes include elongation, rounding, distention [21], and loss of cell–cell contact [22], all of which can disrupt the overall structural and/or functional integrity of the involved tissues and organs. Cholera toxin (CT), encoded by the clinically significant pathogen Vibrio cholera, is a heat-labile enterotoxin that causes a life-threatening acute diarrheal state in humans [8, 24]. CT is the major virulence determinant of V. cholera, and upon introduction to intestinal cells triggers massive electrolyte secretion and fluid efflux, which manifests as diarrhea [13]. The understanding of the pathogenic mechanisms of CT and other toxins-induced intestinal secretion had been disclosed in detail. When V. cholera colonises the small bowel, CT could provoke loss of water and electrolytes of intestinal cells. The Clostridium difficile toxin could impair the structure of the actin cytoskeleton in intestine epithelial cells [3, 4, 9, 19, 27]. So far, no simple and accurate assays have been developed for dynamically recording and monitoring these processes in real-time in a laboratory setting. Indeed, a wide array of accurate methods is available (and
123
J. Ye et al.: Real-Time Monitoring of CT-Induced Cell Response
well-established) for studying cellular responses to bacterial toxins. Colorimetric enzymatic assays are effective for assessing effects on cell viability and proliferation [2, 18], as are microscopy-based simple staining and enumeration approaches [22]. Meanwhile, enzyme-linked immunosorbent assays, as well as simple staining, are commonly used to assess pathogen-mediated effects on programmed cell death [14–16, 25]. However, all of these conventional assays are characterized as end-point cytotoxic methods, by which static physiologic states and isolated features are measured, but most toxin-induced responses of cells are dynamic, transient, or continuous. It is, therefore, necessary to develop a method capable of monitoring the dynamic processes of toxin-induced changes in intact cells, preferably in real-time. To this end, a real-time cell analysis (RTCA) system was designed for monitoring dynamic cellular alternations and shown as an effective tool for studying cytotoxicity, apoptosis, cell stress, cell invasion, and migration [1, 23, 29, 30]. This platform first measures change in electrical impedance and then transfers the data to a dimensionless cell index (CI) that allows for quantitative analysis of the cellular status and for continuous monitoring of cellular responses to toxicants in real-time. In this study, we used the RTCA platform to develop an assay for dynamic analysis of CT-induced responses in human intestinal epithelial cells (HIEC) over time. The CT-induced cytotoxicity in HIEC was compared with that induced by C. difficile and other toxins.
Materials and Methods Cell Culture, Toxins, and Antibodies HIEC (FHs74Int; American Type Culture Collection (ATCC), Manassas, VA, USA) were maintained in Hybri-Care medium (ATCC) supplemented with 10 % fetal bovine serum (FBS) (Cat. No. 12303C, Sigma–Aldrich Corp, St. Louis, MO) and 30 ng/mL epidermal growth factor (EGF) (Cat. No. E9644, Sigma–Aldrich Corp, St. Louis, MO), as previously described [12]. Culture conditions were 37 °C with an atmosphere of 5 % CO2 and 95 % relative humidity. Cells were seeded into 75 cm2 tissue culture flasks (Corning Inc., Lowell, MA, USA), grown to 70–90 % confluency (2–3 days). Purified CT (Cat. No. 100B) and diphtheria toxin (Cat. No. 150) were purchased from List Biological Laboratories (Campbell, CA, USA). C. difficile toxin B was purchased from EMD Chemicals, Inc. (Gibbstown, NJ, USA). Real-Time Cell Analysis The RTCA assay for monitoring cellular status was performed with the xCELLigenceTM RTCA system (ACEA
123
Biosciences, San Diego, CA, USA). The system is composed of three components: an electronic sensor analyzer; a 969 sensor device with 96-wells electronic microtiter plate evenly spaced apart at 9 mm, which is known as the ACEA 96-E-plate; a multiple plate (MP) device station, on which up to six 96-E-plates can be analyzed simultaneously in real-time. Approximately, 80 % of each well’s bottom surface area was integrated with a circle-on-line microelectrode sensor on glass slides, which had been fabricated as previously described [1]. These sensors monitored and analyzed electrical impedance corresponding to changes in the cellular morphology and kinetics in each well. For system operation, cells were first seeded into the wells of a 96-E-plate. Then, the plates were placed into the MP station, which was located inside the culturing CO2 incubator and connected to an electronic sensor analyzer by electrical cables. The accompanying software was used to carry out all automated liquid handling processes and impedance measurements. The dimensionless impedance-based CI was derived from the electrode impendence recordings as previously described [17, 29] and used to generate a characteristic RTCA curve by plotting over time [10, 23]. A CI value of zero indicated that no cells were attached to the electrodes. As such, an increase in cell growth, cell adhesion, or cell spreading would then result in a larger area of cell-electrode sensor contact, consequently producing an increased CI. The more cells attached to the electrode, the higher the CI value curve reached. The normalized cell index (nCI) was derived by normalizing the CI of interest to the last measurement time point taken prior to the experimental treatment being measured. The baseline normalized cell index (BnCI) represented all the nCI values of a blank control that had been normalized to zero for use as a baseline value for analysis [20]. HIEC Inoculation with CT For the RTCA assay, a 50-lL aliquot of Hybri-Care medium alone was added to wells of a 96-E-plate, and the electrical impedance measurement was taken for each well. Meanwhile, the HIEC was serially diluted to concentrations of 2.5 9 104, 5.0 9 104, 7.5 9 104, 1.0 9 105, 1.5 9 105, and 2.0 9 105 cells/mL in Hybri-Care medium. Then, a 100-lL aliquot of each of the HIEC dilutions was transferred to the well in the E-plate. After the cell growth and proliferation, purified CT was diluted tenfold with either growth medium alone, Dulbecco’s phosphate-buffered saline (DPBS) alone, DPBS with 0.1 % bovine serum album (BSA), or DPBS with 0.1 % gelatin. Fifteen microliter aliquots of the various CT solutions were added to HIEC-containing wells in the 96-E-plate at different time points. The cell response was measured every 3 min
J. Ye et al.: Real-Time Monitoring of CT-Induced Cell Response
for at least 70 h. A blank control was performed in parallel for each test. When the experiment was finished, the nCI for each experimental treatment was calculated. Furthermore, the growth curve of the blank control was reset as the reference baseline in order to easily analyze the dynamic results. The half maximum inhibitory concentration (IC50) of CT was calculated by the RTCA software.
extreme instability, indicated that cell concentrations were too high, and the confluency of the cells was too fast to monitor real cytotoxicity. For both, the cell growth remarkably decreased with no lag phase. Therefore, these two cell concentrations were excluded from further analysis of CT-induced cytotoxicity.
Cell Staining
To evaluate the potential influence of the fresh medium CT solvent on electrical impedance of HIEC, HIEC 1.0 9 105 cells/mL in medium alone or with tenfold serial dilutions of CT in medium were monitored by RTCA. Regardless of the presence of CT, the nCI value of all wells decreased substantially within the first approximately 12 min (from 0 to more than approximately -0.60; Fig. 2), indicating that this solvent had a non-specific influence on electrical impedance in the system. At the same time, cell status was not changed. Afterward, the curve of nCI values began to show a gradual increase, and dose-dependent responses became apparent. Meanwhile, only medium as the blank control was added in parallel with each experiment. When the incubator was opened, and CT and only medium were added to the wells, nothing was added to the blank control and the media were not changed. The results indicated changes in temperature and CO2 had no effect on nCI curve (Fig. 1S). Consequently, fresh medium was deemed unsuitable for use as a CT solvent, as it may mask the real reaction induced by CT. Meanwhile, the three other solvents (DPBS, DPBS with 0.1 % BSA, and DPBS with 0.1 % gelatin) were evaluated to determine their influence on the impedance measurements. None of the other three solvents influenced the electric impedance measurement, and addition of CT caused a dose-dependent cytotoxicity (Fig. 3a and Supplementary Fig. S2). Furthermore, we selected CT concentrations of 181, 18.1, 1.81, and 0.181 ng/mL to calculate the IC50 value for each solvent in order to evaluate their suitability. At a time point of 19.6 h, the degrees of cytotoxic effect induced by CT diluted in DPBS with 0.1 % gelatin were not statistically different from that of DPBS (R2 = 0.9283, P = 0.3115) and DPBS ? 0.1 % BSA (R2 = 0.9369, P = 0.7468). However, CT diluted in DPBS ? 0.1 % gelatin produced a significantly higher IC50 (2.41 9 10-9 g/mL, R2 = 0.995) than CT in DPBS (1.96 9 10-8 g/mL, R2 = 0.980) and in DPBS with 0.1 % BSA (1.24 9 10-8 g/mL, R2 = 0.999). The above results indicated that the different solvents affected CT-mediated cytotoxicity to different extents. Thus, DPBS with 0.1 % gelatin was selected as the CT solvent for use in all subsequent analysis. The HIEC responses induced by 181 ng/mL CT in DPBS ? 0.1 % gelatin were analyzed by microscopy. As shown in Fig. 3b, CT inoculation led to an obvious
The HIEC plated in the 96-E-plates were subsequently analyzed by cell staining and microscopy, to observe changes in cell morphology. Briefly, the plated cells were stained using the reagents and protocol from the REASTAINÒ Quick-Diff Kit by Reagena International Oy Ltd. (Toivala, Finland). The stained HIEC were observed under an inverted phase-contrast microscope equipped with a digital camera (Nikon Eclipse TE 2000-U system and accompanying ACT-1 software ver2.62; Nikon Corp., Tokyo, Japan). Data Analysis All data analysis was carried out in the Excel spreadsheet program (Microsoft Office 2003; Redmond, WA, USA) with the integrated statistical formulas. A P value of B0.05 was considered statistically significant.
Results Optimization of HIEC Concentration Various concentrations of HIEC were monitored continuously in real-time on the RTCA system. Comparative analysis of all the resultant cell growth curves indicated a general pattern for the concentrations between 2.5 9 104 and 1.0 9 105 cells/mL. A rapid and precipitous increase in CI occurred during the first 7 h after cell seeding, indicated that the initial cell attachment and cell spreading occurred. Then, cell growth entered the lag phase, which lasted up to the 20-h time point and produced a relatively steady CI curve (Fig. 1). Statistical analysis revealed that the maximal CI was closely related to the cell concentration. The highest CI value overall was 2.3 ± 0.11 and was produced by the 1.0 9 105 cells/mL concentration; the other three cell concentrations were 0.25 ± 0.02 (2.5 9 104 cells/mL), 0.87 ± 0.28 (5.0 9 104 cells/mL), and 0.98 ± 0.11 (7.5 9 104 cells/mL). Even though the other two cell concentrations tested (1.5 9 105 and 2.0 9 105 cells/mL) produced the highest CI values (*3.0 and 4.5, respectively) (Fig. 1), their cell growth curves showed
Screening of the Solvent
123
J. Ye et al.: Real-Time Monitoring of CT-Induced Cell Response 5.5
2.5×104 cells/mL 5.0×104 cells/mL 7.5×104 cells/mL 1.0×105 cells/mL 1.5×105 cells/mL 2.0×105 cells/mL
4.5
Cell Index
Fig. 1 Influence of HIEC concentration on the CI. Six cell concentrations were plated and continuously monitored in the RTCA system. The real-time cell growth curve was generated by plotting CI value over time. Red, 2.5 9 104 cells/mL; Green, 5.0 9 104 cells/mL; Blue, 7.5 9 104 cells/mL; Pink, 1.0 9 105 cells/mL; Light blue, 1.5 9 105 cells/mL; Dark green, 2.0 9 105 cells/mL (Color figure online)
3.5 2.5 1.5 0.5 -0.5 0.0
5.0
10.0
15.0
20.0
25.0
30.0
35.0
40.0
Time (in Hour)
Normalized Cell Index
1.8
1.81 μg/mL 181 ng/mL 18.1 ng/mL Only medium Blank control Non-changed media
1.6 1.4 1.2 1.0 0.8 0.6 0.4 0.2 20.26
Non-specific reaction 20.76
21.26
21.76
22.26
22.76
23.26
Time (in Hour)
Fig. 2 Dynamic monitoring of the effect of fresh medium and other solvents on electronic impedance measurement. Purified CT diluted tenfold with fresh medium produced a significant non-specific CI drop, which was not dependent upon CT. After nCI recovery, the dose-dependent responses became apparent. The values are presented
as mean (n = 3) ± SD. The vertical line indicated the time point at which the purified CT was added. Red, 1.81 lg/mL; Green, 181 ng/ mL; Blue, 18.1 ng/mL; Pink, only medium; Light blue, blank control; Orange, a control well that the media are not changed (Color figure online)
reduction in number of cells attached to the E-plate, however, no cytotoxicity appeared when no CT was added.
(P = 0.0002). The degree of cytotoxicity at 48 h addition of CT (0.9892 ± 0.0382) was also examined and found to be lower than those observed at 18, 22, and 25 h addition of CT. These data indicated that the time points of 18, 22, and 25 h were optimal for achieving the distinct CTmediated cytotoxicity.
Determination of Time Point for Addition of CT Cholera toxin (CT) inoculation was performed at the different time points of 2, 5, 7, 9, 12, 18, 22, and 25 h from cell seeding, in order to determine which induced the most obvious cytotoxic response. As indicated in Fig. 4a, the CT-mediated cytotoxic effect was time-dependent, increasing steadily over time with the most remarkable levels occurring at 18, 22, and 25 h addition of CT. Linear regression analysis of the data yielded the formula: y = 0.0548x - 0.203, where y was the difference of nCI of blank control to the minimal nCI induced by CT and x was the time of CT addition (R2 = 0.9828, P = 0.005). Furthermore, the results indicated that the cellular change was not clear at the beginning of cell growth (Fig. 4b). Statistical analysis indicated that the degrees of cytotoxicity at the time points of 2, 5, 7, 9, and 12 h were significantly lower than those at the later time points of 18, 22, and 25 h
123
Analysis of Specific Intact Cell Responses by CT As shown in Fig. 4a, 1.81 lg/mL CT exposure initially induces an immediate drop in nCI, which is followed by a step-wise increase. After more than 30 h of exposure, the nCI value recovered to the original level, indicating the HIEC had an initial capability for cell attachment and cell spreading. After that, the nCI value continuously increased and exceeded the original level. The observed reactions were then confirmed as specific to CT induction by monitoring the responses in the presence of anti-cholera toxin neutralization antibody. Diphtheria toxin and C. difficile toxin B were used to further evaluate the specificity of intact cell responses by CT. After the above toxins were
J. Ye et al.: Real-Time Monitoring of CT-Induced Cell Response
Normalized Cell Index
1.0 0.9 0.8 0.7 0.6 0.5 0.4 0.3 0.2 0.1 17.11
181 ng/mL 18.1 ng/mL 1.81 ng/mL 181 pg/mL Blank control
18.11
19.11
20.11
21.11
22.11
Time (in Hour)
B
A
1.4
Normalized Cell Index
Fig. 4 RTCA-mediated dynamic monitoring of CTinduced cytotoxicity over time. a Purified CT (1.81 lg/mL in DPBS with 0.1 % gelatin) was added to HIEC at the time points of 18 (Red), 22 (Green), or 25 (Blue) h, respectively. The last CI measurement taken before CT addition was normalized to one, and all values of the nCI for the blank control (Pink) were reset to zero to serve as the baseline normalized CI (nCI). b The value of nCI decrease was calculated as in Fig. 2. Time points plotted were (left to right): 2, 5, 7, 9, 12, 18, 22, and 25 h. The values are presented as mean (n = 3) ± SD. Linear regression analysis of this data provided a formula: y = 0.0548x - 0.203 (R2 = 0.9828, P = 0.005) (Color figure online)
A 1.1
1.2
(1)
(2)
CT
No CT
18 hr 22 hr 25 hr Blank control
1.0 0.8 0.6 0.4 0.2 0.0 0.0
10.0
20.0
30.0
40.0
50.0
60.0
70.0
Time (in Hour)
B The value of cell index decrease
Fig. 3 RTCA-mediated dynamic monitoring of dosedependent CT-induced FHs74Int cell responses. a Purified CT tenfold dilutions in DPBS ? 0.1 % gelatin. The vertical line indicates the time point that the purified CT was added. The individual curves are representative of triplicate experiments. Pink, 181 ng/mL; Light blue, 18.1 ng/mL; Red, 1.81 ng/mL; Green, 181 pg/mL; Blue, blank control. b The value of CI decrease was calculated by subtracting the maximum nCI from the corresponding minimum nCI. The values are presented as mean (n = 3) ± SD. The IC50 values of three solvents were calculated by the integrated software. 1, 181 ng/mL; 2, 18.1 ng/mL; 3, 1.81 ng/mL; 4, 181 pg/mL; 5, blank control. c Stained microscopic images of HIEC incubated with 181 ng/ mL purified CT (1) or no CT (2) growing on a representative 96-E-plate (Color figure online)
1.4 1.2 1 0.8 0.6 0.4 0.2 0 -0.2
0
5
10
15
20
25
30
Time point (h)
123
J. Ye et al.: Real-Time Monitoring of CT-Induced Cell Response
added, respectively, to the HIEC at an inoculation time of 18 h, no response was observed for the cells exposed to diphtheria toxin (i.e., the CI curve continued to increase; Fig. 5), the HIEC had no change on cell morphology. Exposure to the C. difficile toxin B led to a CI curve that was similar curve to that for CT exposure; an obvious doseand time-dependent drop appeared, and the nCI values were close to zero. However, at more than 50 h after the CT inoculation, no recovery of the nCI value was observed, indicating that the cytotoxicity could not be recovered by itself, and the HIEC lost growth capability including attachment and spreading. Taken together, the data collected from the RTCA system suggested that CT induces specific responses in HIEC, and absolutely different from C. difficile and other toxin-induced cytotoxicity in HIEC.
Discussion CT introduction into HIEC leads to massive secretion of water and electrolytes. During this process, the host cell undergoes a serial of stress reactions that influence not only its morphology and physiology but also the immunologic and inflammatory status of neighboring cells and tissues. Due to the severe clinical symptoms associated with CT, extensive research efforts have aimed to determine the toxin’s structure, biological function, and pathogenic interplay with human cells and signaling pathways [13, 27]. It has been shown that CT can induce morphological changes in a wide range of mammalian cell types [6] [5]. However, the dynamic reactions that occur in host cells after CT exposure have not yet been reported. Our previous studies have provided a RTCA assay for quantitative detection of CT through screening of four cell lines [11]. However, the cytotoxicity of intact HIEC cells induced by CT was not shown in detail. Here, we demonstrate, for the first time, that the RTCA assay can dynamically monitor cell proliferation and host cell responses induced by CT in real-time, over a day-long
C. difficile toxin B 181 ng/mL C. difficile toxin B 18.1 ng/mL C. difficile toxin B 1.81 ng/mL Diphtheria toxin 1.81μg/mL Blank control
Normalized Cell Index
1.7 1.5 1.3 1.1 0.9 0.7 0.5 0.3 0.1 -0.1 0.0
period. When the cells attached to the bottom surface area of wells in an E-plate are reduced, the nCI value is decreased (as shown by measurement of electronic impedance). Specifically, in this study, the RTCA-derived data showed that when HIEC were inoculated with CT, a specific change occurred in nCI which reflected the changes in intact cells. Thereafter, the nCI increased gradually over time, until the original level was reached, indicating that HIEC may have a self-regulated recovery mechanism. We also disclosed the HIEC cytotoxicity that was induced by C. difficile and other toxins. The results indicated that C. difficile toxin could lead to continuous CI curve drop and did not recover up to the original level, and no any change occurred by diphtheria toxin. As the references shown, the pathogenic mechanisms and targets were absolutely different between CT and C. difficile toxin. The different pathogeneses were also reflected by the RTCA data (Figs. 4, 5). CT results in the massive secretion of water and electrolytes, so after CT invades HIEC, HIEC trigger a recovery mechanism by itself. On the basis of the above principle, the RTCA system record CT-induced HIEC responses through the pathogenic stages of initial proliferation, CT exposure, and CI curve drop to recovery, and showed a specific CI curve as Fig. 4 indicated. However, C. difficile toxin changes the formation of microtubulebased protrusions on the surface of the intestine epithelial cells and leads to cell death. Therefore, a characteristic CI curve generated, in which CI value did not increase again after C. difficile toxin resulted in dramatic drop of CI curve. Together with all the above results, the different pathogeneses according to the different toxins were correlated with the CI curve generated by the RTCA system. Therefore, after various bacterial toxins with different pathogeneses are tested by this platform, we should identify which type of tested toxin is in the well on the basis of characteristic CI curves in the future. We notably found potential confounding influences of different solvents, including their ability to disrupt the electrical impedance of the cells and to induce cytotoxicity.
10.0
20.0
30.0
40.0
50.0
Time (in Hour)
Fig. 5 Diphtheria toxin and C. difficile toxin B induced HIEC over a 70-h period. The vertical line indicated the time point at which the purified toxin was added. Pink, C. difficile toxin B 181 ng/mL; Blue,
123
C. difficile toxin B 18.1 ng/mL; Light blue, C. difficile toxin B 1.81 ng/mL; Dark blue, diphtheria toxin 1.81 lg/mL; Red, blank control (Color figure online)
J. Ye et al.: Real-Time Monitoring of CT-Induced Cell Response
DPBS with 0.1 % gelatin was the least disruptive and cytotoxic solvent for this test. We also noted that a physical parameter (i.e., agitation by gentle stirring) also influenced the monitoring capabilities of the system. Thus, even though opening the incubator resulting in changes in temperature and CO2 had no effect on nCI, we still presumed that the physical properties of the system (movement, temperature) must be carefully controlled during the experiment to ensure accuracy of the RTCA system when first establishing the RTCA system for previously untested cell types. In our study, we used the RTCA system to monitor host cell responses induced by CT in real-time. This impedance-based system has been previously employed to analyze cellular functions during meningococcal infection [22], to measure the poxvirus titration [28], to evaluate neutralization antibodies to the 2009 Influenza A (H1N1) virus [26], and to detect C. difficile toxin or CT in clinical samples [11, 20] and experimentally infected piglets [7]. Compared to the classical end-point assays, this technology was able to read out a large amount of continuous, real-time data reflecting the intact process of cell status alteration. After seeding into 96-E-plate, the HIEC were monitored in real-time for up to 70 h, including prior to and after CT and other toxins treatment. An important finding in this study was that after the initial rapid drop in nCI induced by CT, the nCI started to increase and reached the original level. However, when other toxins including C. difficile and diphtheria toxin were treated with HIEC, their characteristic CI curves were distinctly different from CT-induced CI curve. Further studies should investigate the characteristic CI curves induced by different toxins by use of this RTCA assay. In summary, we monitored HIEC responses induced by CT in real-time using the RTCA assay and described the process in intact cells by plotting the nCI curve. We also disclosed the difference from CT-induced cytotoxicity and others in HIEC, and different pathogeneses were correlated with the specific CI curve generated by the RTCA system. Therefore, our data show that the assay described here is a convenient and rapid high-throughput tool for real-time monitoring of host cellular responses to CT on the basis of the characteristic CI curve. Acknowledgments This work was supported in part by the program for Zhejiang Leading Team of Science and Technology Innovation (2011R50021-21).
References 1. Atienza JM, Zhu J, Wang X, Xu X, Abassi Y (2005) Dynamic monitoring of cell adhesion and spreading on microelectronic sensor arrays. J Biomol Screen 10:795–805
2. Bernas T, Dobrucki J (2002) Mitochondrial and nonmitochondrial reduction of MTT: interaction of MTT with TMRE, JC-1, and NAO mitochondrial fluorescent probes. Cytometry 47:236–242 3. Chinnapen DJ, Chinnapen H, Saslowsky D, Lencer WI (2007) Rafting with cholera toxin: endocytosis and trafficking from plasma membrane to ER. FEMS Microbiol Lett 266:129–137 4. De Haan L, Hirst TR (2004) Cholera toxin: a paradigm for multifunctional engagement of cellular mechanisms (Review). Mol Membr Biol 21:77–92 5. Donta ST (1974) Differentiation between the steroidogenic effects of cholera enterotoxin and adrenocorticotropin through use of a mutant adrenal cell line. J Infect Dis 129:728–731 6. Guerrant RL, Brunton LL, Schnaitman TC, Rebhun LI, Gilman AG (1974) Cyclic adenosine monophosphate and alteration of Chinese hamster ovary cell morphology: a rapid, sensitive in vitro assay for the enterotoxins of Vibrio cholerae and Escherichia coli. Infect Immun 10:320–327 7. He X, Wang J, Steele J, Sun X, Nie W, Tzipori S, Feng H (2009) An ultrasensitive rapid immunocytotoxicity assay for detecting Clostridium difficile toxins. J Microbiol Methods 78:97–100 8. Holmes RK, Twiddy EM (1983) Characterization of monoclonal antibodies that react with unique and cross-reacting determinants of cholera enterotoxin and its subunits. Infect Immun 42:914–923 9. Holmgren J (1981) Actions of cholera toxin and the prevention and treatment of cholera. Nature 292:413–417 10. Irelan JT, Wu MJ, Morgan J, Ke N, Xi B, Wang X, Xu X, Abassi YA (2011) Rapid and quantitative assessment of cell quality, identity, and functionality for cell-based assays using real-time cellular analysis. J Biomol Screen 16:313–322 11. Jin D, Luo Y, Zheng M, Li H, Zhang J, Stampfl M, Xu X, Ding G, Zhang Y, Tang YW (2013) Quantitative detection of Vibrio cholera toxin by real-time and dynamic cytotoxicity monitoring. J Clin Microbiol 51:3968–3974 12. Kanwar JR, Kanwar RK (2009) Gut health immunomodulatory and anti-inflammatory functions of gut enzyme digested high protein micro-nutrient dietary supplement-Enprocal. BMC Immunol 10:7 13. Kaper JB, Morris JG Jr., Levine MM (1995) Cholera. Clin Microbiol Rev 8:48–86 14. Krysko DV, Vanden Berghe T, D’Herde K, Vandenabeele P (2008) Apoptosis and necrosis: detection, discrimination and phagocytosis. Methods 44:205–221 15. Krysko DV, Vanden Berghe T, Parthoens E, D’Herde K, Vandenabeele P (2008) Methods for distinguishing apoptotic from necrotic cells and measuring their clearance. Methods Enzymol 442:307–341 16. Lockshin RA, Zakeri Z (2004) Apoptosis, autophagy, and more. Int J Biochem Cell Biol 36:2405–2419 17. Martinez-Serra J, Gutierrez A, Munoz-Capo S, Navarro-Palou M, Ros T, Amat JC, Lopez B, Marcus TF, Fueyo L, Suquia AG, Gines J, Rubio F, Ramos R, Besalduch J (2014) xCELLigence system for real-time label-free monitoring of growth and viability of cell lines from hematological malignancies. Onco Targets Ther 7:985–994 18. Mosmann T (1983) Rapid colorimetric assay for cellular growth and survival: application to proliferation and cytotoxicity assays. J Immunol Methods 65:55–63 19. Moss J, Vaughan M (1979) Activation of adenylate cyclase by choleragen. Annu Rev Biochem 48:581–600 20. Ryder AB, Huang Y, Li H, Zheng M, Wang X, Stratton CW, Xu X, Tang YW (2010) Assessment of Clostridium difficile infections by quantitative detection of tcdB toxin by use of a real-time cell analysis system. J Clin Microbiol 48:4129–4134 21. Sears CL, Kaper JB (1996) Enteric bacterial toxins: mechanisms of action and linkage to intestinal secretion. Microbiol Rev 60:167–215
123
J. Ye et al.: Real-Time Monitoring of CT-Induced Cell Response 22. Slanina H, Konig A, Claus H, Frosch M, Schubert-Unkmeir A (2011) Real-time impedance analysis of host cell response to meningococcal infection. J Microbiol Methods 84:101–108 23. Solly K, Wang X, Xu X, Strulovici B, Zheng W (2004) Application of real-time cell electronic sensing (RT-CES) technology to cell-based assays. Assay Drug Dev Technol 2:363–372 24. Spangler BD (1992) Structure and function of cholera toxin and the related Escherichia coli heat-labile enterotoxin. Microbiol Rev 56:622–647 25. Tasdemir E, Galluzzi L, Maiuri MC, Criollo A, Vitale I, Hangen E, Modjtahedi N, Kroemer G (2008) Methods for assessing autophagy and autophagic cell death. Methods Mol Biol 445:29–76 26. Tian D, Zhang W, He J, Liu Y, Song Z, Zhou Z, Zheng M, Hu Y (2012) Novel, real-time cell analysis for measuring viral cytopathogenesis and the efficacy of neutralizing antibodies to the 2009 influenza A (H1N1) virus. PLoS One 7:e31965
123
27. Vanden Broeck D, Horvath C, De Wolf MJ (2007) Vibrio cholerae: cholera toxin. Int J Biochem Cell Biol 39:1771–1775 28. Witkowski PT, Schuenadel L, Wiethaus J, Bourquain DR, Kurth A, Nitsche A (2010) Cellular impedance measurement as a new tool for poxvirus titration, antibody neutralization testing and evaluation of antiviral substances. Biochem Biophys Res Commun 401:37–41 29. Xing JZ, Zhu L, Jackson JA, Gabos S, Sun XJ, Wang XB, Xu X (2005) Dynamic monitoring of cytotoxicity on microelectronic sensors. Chem Res Toxicol 18:154–161 30. Yu N, Atienza JM, Bernard J, Blanc S, Zhu J, Wang X, Xu X, Abassi YA (2006) Real-time monitoring of morphological changes in living cells by electronic cell sensor arrays: an approach to study G protein-coupled receptors. Anal Chem 78:35–43
Real-Time Cell Analysis for Monitoring Cholera Toxin-Induced Human Intestinal Epithelial Cell Response Julian Ye • Yun Luo • Weijia Fang • Junhang Pan • Zheng Zhang • Yanjun Zhang Zhiping Chen • Dazhi Jin
•
Received: 8 April 2014 / Accepted: 4 November 2014 Ó Springer Science+Business Media New York 2014
Abstract The pathogenic mechanism of Vibrio cholerae manifests as diarrhea and causes life-threatening dehydration. Here, we observe the human intestinal epithelialcells (HIEC) response to Cholera toxin (CT) by a real-time cell analysis (RTCA) platform, and disclose the difference from CT-induced cytotoxicity and others in HIEC. An HIEC cell of 1.0 9 105 cells/mL was characterized as the suitable concentration for each well. For experimentation, the assay requires an inoculation of CT dissolved in Dulbecco’s phosphate-buffered saline with 0.1 % gelatin for a period of 18–25 h. The dimensionless impedance cell index curve presented characteristic dose- and time-dependent drop responses at the first stage, and the CT-induced cytotoxicity was the most remarkable following exposure for 18–25 h (P = 0.0002). Following the obvious cytotoxic reaction, the CI curve gradually increased over time until the original CI value, indicating that self-recovery occurred. The CT-induced CI curve for HIEC was different from that induced by other toxins, including diphtheria and Clostridium difficile toxin. Collectively, these results suggest that the CT-induced cytotoxicity in HIEC was absolutely
Julian Ye and Yun Luo have contributed equally to this work.
Electronic supplementary material The online version of this article (doi:10.1007/s00284-014-0752-z) contains supplementary material, which is available to authorized users. J. Ye Y. Luo J. Pan Z. Zhang Y. Zhang Z. Chen D. Jin (&) Zhejiang Provincial Center for Disease Control and Prevention, Hangzhou 310051, Zhejiang, China e-mail: [email protected] W. Fang First Affiliated Hospital, School of Medicine, Zhejiang University, Hangzhou 310058, Zhejiang, China
different from that induced by C. difficile and other toxins because of the different pathogeneses that were correlated with the specific CI curve generated by the RTCA system. In summary, our data show that the assay described here is a convenient and rapid high-throughput tool for real-time monitoring of host cellular responses to CT on the basis of the characteristic CI curve.
Introduction Exposure to bacterial pathogens or their toxins triggers obvious alterations in cellular morphology and molecular pathology. The morphological changes include elongation, rounding, distention [21], and loss of cell–cell contact [22], all of which can disrupt the overall structural and/or functional integrity of the involved tissues and organs. Cholera toxin (CT), encoded by the clinically significant pathogen Vibrio cholera, is a heat-labile enterotoxin that causes a life-threatening acute diarrheal state in humans [8, 24]. CT is the major virulence determinant of V. cholera, and upon introduction to intestinal cells triggers massive electrolyte secretion and fluid efflux, which manifests as diarrhea [13]. The understanding of the pathogenic mechanisms of CT and other toxins-induced intestinal secretion had been disclosed in detail. When V. cholera colonises the small bowel, CT could provoke loss of water and electrolytes of intestinal cells. The Clostridium difficile toxin could impair the structure of the actin cytoskeleton in intestine epithelial cells [3, 4, 9, 19, 27]. So far, no simple and accurate assays have been developed for dynamically recording and monitoring these processes in real-time in a laboratory setting. Indeed, a wide array of accurate methods is available (and
123
J. Ye et al.: Real-Time Monitoring of CT-Induced Cell Response
well-established) for studying cellular responses to bacterial toxins. Colorimetric enzymatic assays are effective for assessing effects on cell viability and proliferation [2, 18], as are microscopy-based simple staining and enumeration approaches [22]. Meanwhile, enzyme-linked immunosorbent assays, as well as simple staining, are commonly used to assess pathogen-mediated effects on programmed cell death [14–16, 25]. However, all of these conventional assays are characterized as end-point cytotoxic methods, by which static physiologic states and isolated features are measured, but most toxin-induced responses of cells are dynamic, transient, or continuous. It is, therefore, necessary to develop a method capable of monitoring the dynamic processes of toxin-induced changes in intact cells, preferably in real-time. To this end, a real-time cell analysis (RTCA) system was designed for monitoring dynamic cellular alternations and shown as an effective tool for studying cytotoxicity, apoptosis, cell stress, cell invasion, and migration [1, 23, 29, 30]. This platform first measures change in electrical impedance and then transfers the data to a dimensionless cell index (CI) that allows for quantitative analysis of the cellular status and for continuous monitoring of cellular responses to toxicants in real-time. In this study, we used the RTCA platform to develop an assay for dynamic analysis of CT-induced responses in human intestinal epithelial cells (HIEC) over time. The CT-induced cytotoxicity in HIEC was compared with that induced by C. difficile and other toxins.
Materials and Methods Cell Culture, Toxins, and Antibodies HIEC (FHs74Int; American Type Culture Collection (ATCC), Manassas, VA, USA) were maintained in Hybri-Care medium (ATCC) supplemented with 10 % fetal bovine serum (FBS) (Cat. No. 12303C, Sigma–Aldrich Corp, St. Louis, MO) and 30 ng/mL epidermal growth factor (EGF) (Cat. No. E9644, Sigma–Aldrich Corp, St. Louis, MO), as previously described [12]. Culture conditions were 37 °C with an atmosphere of 5 % CO2 and 95 % relative humidity. Cells were seeded into 75 cm2 tissue culture flasks (Corning Inc., Lowell, MA, USA), grown to 70–90 % confluency (2–3 days). Purified CT (Cat. No. 100B) and diphtheria toxin (Cat. No. 150) were purchased from List Biological Laboratories (Campbell, CA, USA). C. difficile toxin B was purchased from EMD Chemicals, Inc. (Gibbstown, NJ, USA). Real-Time Cell Analysis The RTCA assay for monitoring cellular status was performed with the xCELLigenceTM RTCA system (ACEA
123
Biosciences, San Diego, CA, USA). The system is composed of three components: an electronic sensor analyzer; a 969 sensor device with 96-wells electronic microtiter plate evenly spaced apart at 9 mm, which is known as the ACEA 96-E-plate; a multiple plate (MP) device station, on which up to six 96-E-plates can be analyzed simultaneously in real-time. Approximately, 80 % of each well’s bottom surface area was integrated with a circle-on-line microelectrode sensor on glass slides, which had been fabricated as previously described [1]. These sensors monitored and analyzed electrical impedance corresponding to changes in the cellular morphology and kinetics in each well. For system operation, cells were first seeded into the wells of a 96-E-plate. Then, the plates were placed into the MP station, which was located inside the culturing CO2 incubator and connected to an electronic sensor analyzer by electrical cables. The accompanying software was used to carry out all automated liquid handling processes and impedance measurements. The dimensionless impedance-based CI was derived from the electrode impendence recordings as previously described [17, 29] and used to generate a characteristic RTCA curve by plotting over time [10, 23]. A CI value of zero indicated that no cells were attached to the electrodes. As such, an increase in cell growth, cell adhesion, or cell spreading would then result in a larger area of cell-electrode sensor contact, consequently producing an increased CI. The more cells attached to the electrode, the higher the CI value curve reached. The normalized cell index (nCI) was derived by normalizing the CI of interest to the last measurement time point taken prior to the experimental treatment being measured. The baseline normalized cell index (BnCI) represented all the nCI values of a blank control that had been normalized to zero for use as a baseline value for analysis [20]. HIEC Inoculation with CT For the RTCA assay, a 50-lL aliquot of Hybri-Care medium alone was added to wells of a 96-E-plate, and the electrical impedance measurement was taken for each well. Meanwhile, the HIEC was serially diluted to concentrations of 2.5 9 104, 5.0 9 104, 7.5 9 104, 1.0 9 105, 1.5 9 105, and 2.0 9 105 cells/mL in Hybri-Care medium. Then, a 100-lL aliquot of each of the HIEC dilutions was transferred to the well in the E-plate. After the cell growth and proliferation, purified CT was diluted tenfold with either growth medium alone, Dulbecco’s phosphate-buffered saline (DPBS) alone, DPBS with 0.1 % bovine serum album (BSA), or DPBS with 0.1 % gelatin. Fifteen microliter aliquots of the various CT solutions were added to HIEC-containing wells in the 96-E-plate at different time points. The cell response was measured every 3 min
J. Ye et al.: Real-Time Monitoring of CT-Induced Cell Response
for at least 70 h. A blank control was performed in parallel for each test. When the experiment was finished, the nCI for each experimental treatment was calculated. Furthermore, the growth curve of the blank control was reset as the reference baseline in order to easily analyze the dynamic results. The half maximum inhibitory concentration (IC50) of CT was calculated by the RTCA software.
extreme instability, indicated that cell concentrations were too high, and the confluency of the cells was too fast to monitor real cytotoxicity. For both, the cell growth remarkably decreased with no lag phase. Therefore, these two cell concentrations were excluded from further analysis of CT-induced cytotoxicity.
Cell Staining
To evaluate the potential influence of the fresh medium CT solvent on electrical impedance of HIEC, HIEC 1.0 9 105 cells/mL in medium alone or with tenfold serial dilutions of CT in medium were monitored by RTCA. Regardless of the presence of CT, the nCI value of all wells decreased substantially within the first approximately 12 min (from 0 to more than approximately -0.60; Fig. 2), indicating that this solvent had a non-specific influence on electrical impedance in the system. At the same time, cell status was not changed. Afterward, the curve of nCI values began to show a gradual increase, and dose-dependent responses became apparent. Meanwhile, only medium as the blank control was added in parallel with each experiment. When the incubator was opened, and CT and only medium were added to the wells, nothing was added to the blank control and the media were not changed. The results indicated changes in temperature and CO2 had no effect on nCI curve (Fig. 1S). Consequently, fresh medium was deemed unsuitable for use as a CT solvent, as it may mask the real reaction induced by CT. Meanwhile, the three other solvents (DPBS, DPBS with 0.1 % BSA, and DPBS with 0.1 % gelatin) were evaluated to determine their influence on the impedance measurements. None of the other three solvents influenced the electric impedance measurement, and addition of CT caused a dose-dependent cytotoxicity (Fig. 3a and Supplementary Fig. S2). Furthermore, we selected CT concentrations of 181, 18.1, 1.81, and 0.181 ng/mL to calculate the IC50 value for each solvent in order to evaluate their suitability. At a time point of 19.6 h, the degrees of cytotoxic effect induced by CT diluted in DPBS with 0.1 % gelatin were not statistically different from that of DPBS (R2 = 0.9283, P = 0.3115) and DPBS ? 0.1 % BSA (R2 = 0.9369, P = 0.7468). However, CT diluted in DPBS ? 0.1 % gelatin produced a significantly higher IC50 (2.41 9 10-9 g/mL, R2 = 0.995) than CT in DPBS (1.96 9 10-8 g/mL, R2 = 0.980) and in DPBS with 0.1 % BSA (1.24 9 10-8 g/mL, R2 = 0.999). The above results indicated that the different solvents affected CT-mediated cytotoxicity to different extents. Thus, DPBS with 0.1 % gelatin was selected as the CT solvent for use in all subsequent analysis. The HIEC responses induced by 181 ng/mL CT in DPBS ? 0.1 % gelatin were analyzed by microscopy. As shown in Fig. 3b, CT inoculation led to an obvious
The HIEC plated in the 96-E-plates were subsequently analyzed by cell staining and microscopy, to observe changes in cell morphology. Briefly, the plated cells were stained using the reagents and protocol from the REASTAINÒ Quick-Diff Kit by Reagena International Oy Ltd. (Toivala, Finland). The stained HIEC were observed under an inverted phase-contrast microscope equipped with a digital camera (Nikon Eclipse TE 2000-U system and accompanying ACT-1 software ver2.62; Nikon Corp., Tokyo, Japan). Data Analysis All data analysis was carried out in the Excel spreadsheet program (Microsoft Office 2003; Redmond, WA, USA) with the integrated statistical formulas. A P value of B0.05 was considered statistically significant.
Results Optimization of HIEC Concentration Various concentrations of HIEC were monitored continuously in real-time on the RTCA system. Comparative analysis of all the resultant cell growth curves indicated a general pattern for the concentrations between 2.5 9 104 and 1.0 9 105 cells/mL. A rapid and precipitous increase in CI occurred during the first 7 h after cell seeding, indicated that the initial cell attachment and cell spreading occurred. Then, cell growth entered the lag phase, which lasted up to the 20-h time point and produced a relatively steady CI curve (Fig. 1). Statistical analysis revealed that the maximal CI was closely related to the cell concentration. The highest CI value overall was 2.3 ± 0.11 and was produced by the 1.0 9 105 cells/mL concentration; the other three cell concentrations were 0.25 ± 0.02 (2.5 9 104 cells/mL), 0.87 ± 0.28 (5.0 9 104 cells/mL), and 0.98 ± 0.11 (7.5 9 104 cells/mL). Even though the other two cell concentrations tested (1.5 9 105 and 2.0 9 105 cells/mL) produced the highest CI values (*3.0 and 4.5, respectively) (Fig. 1), their cell growth curves showed
Screening of the Solvent
123
J. Ye et al.: Real-Time Monitoring of CT-Induced Cell Response 5.5
2.5×104 cells/mL 5.0×104 cells/mL 7.5×104 cells/mL 1.0×105 cells/mL 1.5×105 cells/mL 2.0×105 cells/mL
4.5
Cell Index
Fig. 1 Influence of HIEC concentration on the CI. Six cell concentrations were plated and continuously monitored in the RTCA system. The real-time cell growth curve was generated by plotting CI value over time. Red, 2.5 9 104 cells/mL; Green, 5.0 9 104 cells/mL; Blue, 7.5 9 104 cells/mL; Pink, 1.0 9 105 cells/mL; Light blue, 1.5 9 105 cells/mL; Dark green, 2.0 9 105 cells/mL (Color figure online)
3.5 2.5 1.5 0.5 -0.5 0.0
5.0
10.0
15.0
20.0
25.0
30.0
35.0
40.0
Time (in Hour)
Normalized Cell Index
1.8
1.81 μg/mL 181 ng/mL 18.1 ng/mL Only medium Blank control Non-changed media
1.6 1.4 1.2 1.0 0.8 0.6 0.4 0.2 20.26
Non-specific reaction 20.76
21.26
21.76
22.26
22.76
23.26
Time (in Hour)
Fig. 2 Dynamic monitoring of the effect of fresh medium and other solvents on electronic impedance measurement. Purified CT diluted tenfold with fresh medium produced a significant non-specific CI drop, which was not dependent upon CT. After nCI recovery, the dose-dependent responses became apparent. The values are presented
as mean (n = 3) ± SD. The vertical line indicated the time point at which the purified CT was added. Red, 1.81 lg/mL; Green, 181 ng/ mL; Blue, 18.1 ng/mL; Pink, only medium; Light blue, blank control; Orange, a control well that the media are not changed (Color figure online)
reduction in number of cells attached to the E-plate, however, no cytotoxicity appeared when no CT was added.
(P = 0.0002). The degree of cytotoxicity at 48 h addition of CT (0.9892 ± 0.0382) was also examined and found to be lower than those observed at 18, 22, and 25 h addition of CT. These data indicated that the time points of 18, 22, and 25 h were optimal for achieving the distinct CTmediated cytotoxicity.
Determination of Time Point for Addition of CT Cholera toxin (CT) inoculation was performed at the different time points of 2, 5, 7, 9, 12, 18, 22, and 25 h from cell seeding, in order to determine which induced the most obvious cytotoxic response. As indicated in Fig. 4a, the CT-mediated cytotoxic effect was time-dependent, increasing steadily over time with the most remarkable levels occurring at 18, 22, and 25 h addition of CT. Linear regression analysis of the data yielded the formula: y = 0.0548x - 0.203, where y was the difference of nCI of blank control to the minimal nCI induced by CT and x was the time of CT addition (R2 = 0.9828, P = 0.005). Furthermore, the results indicated that the cellular change was not clear at the beginning of cell growth (Fig. 4b). Statistical analysis indicated that the degrees of cytotoxicity at the time points of 2, 5, 7, 9, and 12 h were significantly lower than those at the later time points of 18, 22, and 25 h
123
Analysis of Specific Intact Cell Responses by CT As shown in Fig. 4a, 1.81 lg/mL CT exposure initially induces an immediate drop in nCI, which is followed by a step-wise increase. After more than 30 h of exposure, the nCI value recovered to the original level, indicating the HIEC had an initial capability for cell attachment and cell spreading. After that, the nCI value continuously increased and exceeded the original level. The observed reactions were then confirmed as specific to CT induction by monitoring the responses in the presence of anti-cholera toxin neutralization antibody. Diphtheria toxin and C. difficile toxin B were used to further evaluate the specificity of intact cell responses by CT. After the above toxins were
J. Ye et al.: Real-Time Monitoring of CT-Induced Cell Response
Normalized Cell Index
1.0 0.9 0.8 0.7 0.6 0.5 0.4 0.3 0.2 0.1 17.11
181 ng/mL 18.1 ng/mL 1.81 ng/mL 181 pg/mL Blank control
18.11
19.11
20.11
21.11
22.11
Time (in Hour)
B
A
1.4
Normalized Cell Index
Fig. 4 RTCA-mediated dynamic monitoring of CTinduced cytotoxicity over time. a Purified CT (1.81 lg/mL in DPBS with 0.1 % gelatin) was added to HIEC at the time points of 18 (Red), 22 (Green), or 25 (Blue) h, respectively. The last CI measurement taken before CT addition was normalized to one, and all values of the nCI for the blank control (Pink) were reset to zero to serve as the baseline normalized CI (nCI). b The value of nCI decrease was calculated as in Fig. 2. Time points plotted were (left to right): 2, 5, 7, 9, 12, 18, 22, and 25 h. The values are presented as mean (n = 3) ± SD. Linear regression analysis of this data provided a formula: y = 0.0548x - 0.203 (R2 = 0.9828, P = 0.005) (Color figure online)
A 1.1
1.2
(1)
(2)
CT
No CT
18 hr 22 hr 25 hr Blank control
1.0 0.8 0.6 0.4 0.2 0.0 0.0
10.0
20.0
30.0
40.0
50.0
60.0
70.0
Time (in Hour)
B The value of cell index decrease
Fig. 3 RTCA-mediated dynamic monitoring of dosedependent CT-induced FHs74Int cell responses. a Purified CT tenfold dilutions in DPBS ? 0.1 % gelatin. The vertical line indicates the time point that the purified CT was added. The individual curves are representative of triplicate experiments. Pink, 181 ng/mL; Light blue, 18.1 ng/mL; Red, 1.81 ng/mL; Green, 181 pg/mL; Blue, blank control. b The value of CI decrease was calculated by subtracting the maximum nCI from the corresponding minimum nCI. The values are presented as mean (n = 3) ± SD. The IC50 values of three solvents were calculated by the integrated software. 1, 181 ng/mL; 2, 18.1 ng/mL; 3, 1.81 ng/mL; 4, 181 pg/mL; 5, blank control. c Stained microscopic images of HIEC incubated with 181 ng/ mL purified CT (1) or no CT (2) growing on a representative 96-E-plate (Color figure online)
1.4 1.2 1 0.8 0.6 0.4 0.2 0 -0.2
0
5
10
15
20
25
30
Time point (h)
123
J. Ye et al.: Real-Time Monitoring of CT-Induced Cell Response
added, respectively, to the HIEC at an inoculation time of 18 h, no response was observed for the cells exposed to diphtheria toxin (i.e., the CI curve continued to increase; Fig. 5), the HIEC had no change on cell morphology. Exposure to the C. difficile toxin B led to a CI curve that was similar curve to that for CT exposure; an obvious doseand time-dependent drop appeared, and the nCI values were close to zero. However, at more than 50 h after the CT inoculation, no recovery of the nCI value was observed, indicating that the cytotoxicity could not be recovered by itself, and the HIEC lost growth capability including attachment and spreading. Taken together, the data collected from the RTCA system suggested that CT induces specific responses in HIEC, and absolutely different from C. difficile and other toxin-induced cytotoxicity in HIEC.
Discussion CT introduction into HIEC leads to massive secretion of water and electrolytes. During this process, the host cell undergoes a serial of stress reactions that influence not only its morphology and physiology but also the immunologic and inflammatory status of neighboring cells and tissues. Due to the severe clinical symptoms associated with CT, extensive research efforts have aimed to determine the toxin’s structure, biological function, and pathogenic interplay with human cells and signaling pathways [13, 27]. It has been shown that CT can induce morphological changes in a wide range of mammalian cell types [6] [5]. However, the dynamic reactions that occur in host cells after CT exposure have not yet been reported. Our previous studies have provided a RTCA assay for quantitative detection of CT through screening of four cell lines [11]. However, the cytotoxicity of intact HIEC cells induced by CT was not shown in detail. Here, we demonstrate, for the first time, that the RTCA assay can dynamically monitor cell proliferation and host cell responses induced by CT in real-time, over a day-long
C. difficile toxin B 181 ng/mL C. difficile toxin B 18.1 ng/mL C. difficile toxin B 1.81 ng/mL Diphtheria toxin 1.81μg/mL Blank control
Normalized Cell Index
1.7 1.5 1.3 1.1 0.9 0.7 0.5 0.3 0.1 -0.1 0.0
period. When the cells attached to the bottom surface area of wells in an E-plate are reduced, the nCI value is decreased (as shown by measurement of electronic impedance). Specifically, in this study, the RTCA-derived data showed that when HIEC were inoculated with CT, a specific change occurred in nCI which reflected the changes in intact cells. Thereafter, the nCI increased gradually over time, until the original level was reached, indicating that HIEC may have a self-regulated recovery mechanism. We also disclosed the HIEC cytotoxicity that was induced by C. difficile and other toxins. The results indicated that C. difficile toxin could lead to continuous CI curve drop and did not recover up to the original level, and no any change occurred by diphtheria toxin. As the references shown, the pathogenic mechanisms and targets were absolutely different between CT and C. difficile toxin. The different pathogeneses were also reflected by the RTCA data (Figs. 4, 5). CT results in the massive secretion of water and electrolytes, so after CT invades HIEC, HIEC trigger a recovery mechanism by itself. On the basis of the above principle, the RTCA system record CT-induced HIEC responses through the pathogenic stages of initial proliferation, CT exposure, and CI curve drop to recovery, and showed a specific CI curve as Fig. 4 indicated. However, C. difficile toxin changes the formation of microtubulebased protrusions on the surface of the intestine epithelial cells and leads to cell death. Therefore, a characteristic CI curve generated, in which CI value did not increase again after C. difficile toxin resulted in dramatic drop of CI curve. Together with all the above results, the different pathogeneses according to the different toxins were correlated with the CI curve generated by the RTCA system. Therefore, after various bacterial toxins with different pathogeneses are tested by this platform, we should identify which type of tested toxin is in the well on the basis of characteristic CI curves in the future. We notably found potential confounding influences of different solvents, including their ability to disrupt the electrical impedance of the cells and to induce cytotoxicity.
10.0
20.0
30.0
40.0
50.0
Time (in Hour)
Fig. 5 Diphtheria toxin and C. difficile toxin B induced HIEC over a 70-h period. The vertical line indicated the time point at which the purified toxin was added. Pink, C. difficile toxin B 181 ng/mL; Blue,
123
C. difficile toxin B 18.1 ng/mL; Light blue, C. difficile toxin B 1.81 ng/mL; Dark blue, diphtheria toxin 1.81 lg/mL; Red, blank control (Color figure online)
J. Ye et al.: Real-Time Monitoring of CT-Induced Cell Response
DPBS with 0.1 % gelatin was the least disruptive and cytotoxic solvent for this test. We also noted that a physical parameter (i.e., agitation by gentle stirring) also influenced the monitoring capabilities of the system. Thus, even though opening the incubator resulting in changes in temperature and CO2 had no effect on nCI, we still presumed that the physical properties of the system (movement, temperature) must be carefully controlled during the experiment to ensure accuracy of the RTCA system when first establishing the RTCA system for previously untested cell types. In our study, we used the RTCA system to monitor host cell responses induced by CT in real-time. This impedance-based system has been previously employed to analyze cellular functions during meningococcal infection [22], to measure the poxvirus titration [28], to evaluate neutralization antibodies to the 2009 Influenza A (H1N1) virus [26], and to detect C. difficile toxin or CT in clinical samples [11, 20] and experimentally infected piglets [7]. Compared to the classical end-point assays, this technology was able to read out a large amount of continuous, real-time data reflecting the intact process of cell status alteration. After seeding into 96-E-plate, the HIEC were monitored in real-time for up to 70 h, including prior to and after CT and other toxins treatment. An important finding in this study was that after the initial rapid drop in nCI induced by CT, the nCI started to increase and reached the original level. However, when other toxins including C. difficile and diphtheria toxin were treated with HIEC, their characteristic CI curves were distinctly different from CT-induced CI curve. Further studies should investigate the characteristic CI curves induced by different toxins by use of this RTCA assay. In summary, we monitored HIEC responses induced by CT in real-time using the RTCA assay and described the process in intact cells by plotting the nCI curve. We also disclosed the difference from CT-induced cytotoxicity and others in HIEC, and different pathogeneses were correlated with the specific CI curve generated by the RTCA system. Therefore, our data show that the assay described here is a convenient and rapid high-throughput tool for real-time monitoring of host cellular responses to CT on the basis of the characteristic CI curve. Acknowledgments This work was supported in part by the program for Zhejiang Leading Team of Science and Technology Innovation (2011R50021-21).
References 1. Atienza JM, Zhu J, Wang X, Xu X, Abassi Y (2005) Dynamic monitoring of cell adhesion and spreading on microelectronic sensor arrays. J Biomol Screen 10:795–805
2. Bernas T, Dobrucki J (2002) Mitochondrial and nonmitochondrial reduction of MTT: interaction of MTT with TMRE, JC-1, and NAO mitochondrial fluorescent probes. Cytometry 47:236–242 3. Chinnapen DJ, Chinnapen H, Saslowsky D, Lencer WI (2007) Rafting with cholera toxin: endocytosis and trafficking from plasma membrane to ER. FEMS Microbiol Lett 266:129–137 4. De Haan L, Hirst TR (2004) Cholera toxin: a paradigm for multifunctional engagement of cellular mechanisms (Review). Mol Membr Biol 21:77–92 5. Donta ST (1974) Differentiation between the steroidogenic effects of cholera enterotoxin and adrenocorticotropin through use of a mutant adrenal cell line. J Infect Dis 129:728–731 6. Guerrant RL, Brunton LL, Schnaitman TC, Rebhun LI, Gilman AG (1974) Cyclic adenosine monophosphate and alteration of Chinese hamster ovary cell morphology: a rapid, sensitive in vitro assay for the enterotoxins of Vibrio cholerae and Escherichia coli. Infect Immun 10:320–327 7. He X, Wang J, Steele J, Sun X, Nie W, Tzipori S, Feng H (2009) An ultrasensitive rapid immunocytotoxicity assay for detecting Clostridium difficile toxins. J Microbiol Methods 78:97–100 8. Holmes RK, Twiddy EM (1983) Characterization of monoclonal antibodies that react with unique and cross-reacting determinants of cholera enterotoxin and its subunits. Infect Immun 42:914–923 9. Holmgren J (1981) Actions of cholera toxin and the prevention and treatment of cholera. Nature 292:413–417 10. Irelan JT, Wu MJ, Morgan J, Ke N, Xi B, Wang X, Xu X, Abassi YA (2011) Rapid and quantitative assessment of cell quality, identity, and functionality for cell-based assays using real-time cellular analysis. J Biomol Screen 16:313–322 11. Jin D, Luo Y, Zheng M, Li H, Zhang J, Stampfl M, Xu X, Ding G, Zhang Y, Tang YW (2013) Quantitative detection of Vibrio cholera toxin by real-time and dynamic cytotoxicity monitoring. J Clin Microbiol 51:3968–3974 12. Kanwar JR, Kanwar RK (2009) Gut health immunomodulatory and anti-inflammatory functions of gut enzyme digested high protein micro-nutrient dietary supplement-Enprocal. BMC Immunol 10:7 13. Kaper JB, Morris JG Jr., Levine MM (1995) Cholera. Clin Microbiol Rev 8:48–86 14. Krysko DV, Vanden Berghe T, D’Herde K, Vandenabeele P (2008) Apoptosis and necrosis: detection, discrimination and phagocytosis. Methods 44:205–221 15. Krysko DV, Vanden Berghe T, Parthoens E, D’Herde K, Vandenabeele P (2008) Methods for distinguishing apoptotic from necrotic cells and measuring their clearance. Methods Enzymol 442:307–341 16. Lockshin RA, Zakeri Z (2004) Apoptosis, autophagy, and more. Int J Biochem Cell Biol 36:2405–2419 17. Martinez-Serra J, Gutierrez A, Munoz-Capo S, Navarro-Palou M, Ros T, Amat JC, Lopez B, Marcus TF, Fueyo L, Suquia AG, Gines J, Rubio F, Ramos R, Besalduch J (2014) xCELLigence system for real-time label-free monitoring of growth and viability of cell lines from hematological malignancies. Onco Targets Ther 7:985–994 18. Mosmann T (1983) Rapid colorimetric assay for cellular growth and survival: application to proliferation and cytotoxicity assays. J Immunol Methods 65:55–63 19. Moss J, Vaughan M (1979) Activation of adenylate cyclase by choleragen. Annu Rev Biochem 48:581–600 20. Ryder AB, Huang Y, Li H, Zheng M, Wang X, Stratton CW, Xu X, Tang YW (2010) Assessment of Clostridium difficile infections by quantitative detection of tcdB toxin by use of a real-time cell analysis system. J Clin Microbiol 48:4129–4134 21. Sears CL, Kaper JB (1996) Enteric bacterial toxins: mechanisms of action and linkage to intestinal secretion. Microbiol Rev 60:167–215
123
J. Ye et al.: Real-Time Monitoring of CT-Induced Cell Response 22. Slanina H, Konig A, Claus H, Frosch M, Schubert-Unkmeir A (2011) Real-time impedance analysis of host cell response to meningococcal infection. J Microbiol Methods 84:101–108 23. Solly K, Wang X, Xu X, Strulovici B, Zheng W (2004) Application of real-time cell electronic sensing (RT-CES) technology to cell-based assays. Assay Drug Dev Technol 2:363–372 24. Spangler BD (1992) Structure and function of cholera toxin and the related Escherichia coli heat-labile enterotoxin. Microbiol Rev 56:622–647 25. Tasdemir E, Galluzzi L, Maiuri MC, Criollo A, Vitale I, Hangen E, Modjtahedi N, Kroemer G (2008) Methods for assessing autophagy and autophagic cell death. Methods Mol Biol 445:29–76 26. Tian D, Zhang W, He J, Liu Y, Song Z, Zhou Z, Zheng M, Hu Y (2012) Novel, real-time cell analysis for measuring viral cytopathogenesis and the efficacy of neutralizing antibodies to the 2009 influenza A (H1N1) virus. PLoS One 7:e31965
123
27. Vanden Broeck D, Horvath C, De Wolf MJ (2007) Vibrio cholerae: cholera toxin. Int J Biochem Cell Biol 39:1771–1775 28. Witkowski PT, Schuenadel L, Wiethaus J, Bourquain DR, Kurth A, Nitsche A (2010) Cellular impedance measurement as a new tool for poxvirus titration, antibody neutralization testing and evaluation of antiviral substances. Biochem Biophys Res Commun 401:37–41 29. Xing JZ, Zhu L, Jackson JA, Gabos S, Sun XJ, Wang XB, Xu X (2005) Dynamic monitoring of cytotoxicity on microelectronic sensors. Chem Res Toxicol 18:154–161 30. Yu N, Atienza JM, Bernard J, Blanc S, Zhu J, Wang X, Xu X, Abassi YA (2006) Real-time monitoring of morphological changes in living cells by electronic cell sensor arrays: an approach to study G protein-coupled receptors. Anal Chem 78:35–43
