Online Monitoring of Bacterial Growth with an Electrical Sensor

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Online monitoring of bacterial growth with electrical sensor Xuzhi Zhang, Xiaoyu Jiang, Qianqian Yang, Xiaochun Wang, Yan Zhang, Jun Zhao, Keming Qu, and Chuan Zhao Anal. Chem., Just Accepted Manuscript • DOI: 10.1021/acs.analchem.8b01214 • Publication Date (Web): 24 Apr 2018 Downloaded from http://pubs.acs.org on April 24, 2018

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Analytical Chemistry

Online monitoring of bacterial growth with electrical sensor Xuzhi Zhang1, Xiaoyu Jiang1,2, Qianqian Yang1,2, Xiaochun Wang1, Yan Zhang1, Jun Zhao1, Keming Qu1* and Chuan Zhao3* 1

Yellow Sea Fisheries Research Institute, Chinese Academy of Fishery Sciences; Laboratory for

Marine Fisheries Science and Food Production Processes, Qingdao National Laboratory for Marine Science and Technology, Qingdao 266071, China 2

College of Marine Sciences, Shanghai Ocean University, Shanghai 201306, China

3

School of Chemistry, The University of New South Wales, Sydney, NSW 2052, Australia

* Corresponding author: Keming Qu, Yellow Sea Fisheries Research Institute, Chinese Academy of Fishery Sciences; Laboratory for Marine Fisheries Science and Food Production Processes, Qingdao National Laboratory for Marine Science and Technology, 106 Nanjing Rd, Qingdao 266071, China. Email address: [email protected]; Tel: +86-532-858m 13271; Fax: +86-532-85811514. Chuan Zhao, School of Chemistry, The University of New South Wales, Kensington Campus, Sydney, NSW 2052, Australia. E-mail address: [email protected]; Tel: +61-2-93851000

Abstract: Herein, we developed an automatic electrical bacterial growth sensor (EBGS) based on multichannel capacitively coupled contactless conductivity detector (C4D). Using the EBGS, up to eight culture samples of E. coli in disposable tubes were online monitored simultaneously with a non-invasive manner. Growth curves with high resolution (on the order of second timescale) were generated by plotting normalized apparent conductivity value against incubation time. The characteristic data of E. coli growth (e.g. growth rate) obtained here were more accurate than those obtained with optical density and contact conductivity methods. And the correlation coefficient of the regression line (r) for quantitative determination of viable bacteria was 0.9977. Moreover, it also could be used for other tasks, such as the investigation of toxic/stress effects from chemicals and antimicrobial susceptibility testing. All of these performances required neither auxiliary devices nor additional chemicals and biomaterials. Taken together, this strategy has advantages of simplicity, accuracy, reproducibility, affordability, versatility and miniaturization, liberating the users greatly from financial and labor costs.

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Insight into the nature of bacterial growth, including the speed of proliferation, stress and/or toxic effect of chemicals, as well as physiological activities, are of fundamental significance in many fields, such as medical research, clinical diagnosis, food safety, fermentation industry and environmental monitoring.1-3 For example, at present, growth-based measurement is still considered the ‘gold standard’ method for antimicrobial susceptibility testing,4,5 and is employed to study the revolution of life at gene level.6 Various techniques for characterizing bacterial growth have been established. Among them plate counting1,2,7,8 and PCR2,9,10 are popular because of relatively accurate data. However, in these offline methods periodic manual sampling from the incubation medium is usually required, which can be labor-intensive, and risks disturbing the incubation environment, creating false sense.10,11 To address this problem, a few online monitoring methods have been developed. Among them, optics-based patterns, including turbidity,6,7,12-14 imaging,15,16 fluorescence,2,17,18 diffraction19 and reflectance5 are popular, and are even capable of determining morphology of bacteria down to single-cell resolution.15,18 With automatic instrumentations based on these patterns, fast, continuous, non-invasive and high-throughput measurement can be realized. However, turbid or colored samples and impurities in the medium are all undesirable in practical applications.7 Thus, it is extremely complicated to employ them in some cases (e.g. the investigation of nanoparticle effects on bacterial growth),20 despite Qiu et al14 have provided a marvelous suggestion to lower the negative effect at the expense of intensive labor. Besides, expertise is required for the optimization of image acquisition conditions for various microscopy platforms necessary to observe dynamic changes accurately,2 and the number of individual cells that can be continuously monitored with microscopy is limited.21 These challenges limit the application of optical approaches in basic laboratories. The appearance of new imaging approaches, such as measuring changes in rotational frequency of magnetic beads, which is proportional to cell mass,10,21 has suggested another possible technique to characterize bacterial growth. However, more work is needed for practical application to address these problems. Recently, studies have demonstrated that isothermal microcalorimetry is useful in investigating bacterial growth by measuring cumulative heat.10,22 It also offers online monitoring, and in comparison with optics-based patterns, is less affected by turbidity and color. However, the instrument is expensive and bulky. Another niche technique, i.e., in situ determination of bacterial growth by multiple headspace extraction gas chromatography,11 has the same merits and challenges as isothermal microcalorimetry. As is well known, electrical and electrochemical devices are more easily miniaturized, and cheaper


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than instruments based on optics, calorimetry and chromatography. Studies on online monitoring of bacterial growth with electrochemical techniques have been reported frequently.23 In particular, impedimetric approaches, which measure electrochemical impedance, represent a very promising choice,23-25 being label free, and less costly than potentiometric and amperometric patterns, and not affected by the presence of colored compounds. However, electrode deterioration and nonspecific binding are unavoidable, as the working electrodes must be in galvanic contact with the medium. This phenomenon results in erratic measurements that decrease the accuracy.26 To address this challenge, some breakthroughs have been made by employing a wireless remote query resonant-circuit sensor.26-28 The elimination of any physical connection between the measurement sensor and the culture medium facilitates aseptic operation and avoids electrode deterioration, making the platform ideally suited for online monitoring. There is only one shortcoming—a set of electronic components are placed in the test medium. The requirement of cleanup and sterilization complicates the whole operation. Capacitively coupled contactless conductivity detection (C4D) is a particular type of conductivity-based detector where the electrodes are not in direct contact with the measured solution.29 The magnitude of the detected signal is proportional to the concentration and mobility of the ionic charge carriers within the solution.29-33 It not only shares the advantages of common electrical and electrochemical techniques—such

as instrumental

simplicity,

affordability,

rapid response,

non-transparent requirement and easy miniaturization, but also is free of polarization and passivation risks.29,31 In addition, it has an advantage over the wireless remote query resonant-circuit sensor, in that nothing is placed in the test solution. Currently, research and application of C4D are in the foreground of capillary electrophoresis fields.29,30 In the last few years successful applications of this technique to real-time monitoring biochemistry reaction31 and titrations32,33 have been achieved. Herein we have constructed a device, which was based on a developed multichannel C4D and named as electrical bacterial growth sensor (EBGS), to thoroughly solve problems faced by conventional methods for online monitoring bacterial growth, as well as to realize automated high-throughput measurement while equaling the precision of manual plate counting methods1,8 and PCR,9,10 using Escherichia coli (E. coli) as a model organism.

EXPERIMENTAL SECTION Design and fabrication of the EBGS. There are two major considerations to design the EBGS. Firstly, we must guarantee bacterial growth at a predetermined temperature. Meanwhile, stability and



uniformity of the temperature inside the sensor cavity are also critical factors for obtaining identical response between every C4D channel because that solution conductivity changes 2%/°C change in temperature.29,31 The other consideration is to obtain accurate differentiation of conductivity changes online. To meet these needs, a set of temperature control device was developed, as well as an eight-channel C4D prototype. The details are showed in Supplementary Information. Characterization of E. coli growth with the EBGS. Cultures of E. coli (ATCC 25922) were performed according to the method reported by Lin et al12 with minor modifications (Supplementary Information). To characterize the growth, disposable glass tubes were used as culture vessels. Inoculated LB medium (1.3 mL), in which the concentrations of E. coli and NaCl were 1.18×104 CFU/mL and 2%, respectively, was loaded into a culture tube with a sterile injector. Then we sealed the vessel with parafilm. Meanwhile, a control sample without inoculation was also prepared similarly. The culture tubes were inserted into two channels of the EBGS, simultaneously. After 120 s of incubation (to balance the temperature inside and outside of the tubes), collection of the apparent conductivity values was initiated. The pre-set parameters were excitation frequency of 2.0 MHz, excitation amplitude of 16 V and recording rate of per 10 second. The details of controlled experiments, i.e. optical density (OD), plate counting and contact conductivity measurements, are showed in Supplementary Information. Demonstration of versatility. Using the EBGS, quantitative determination of viable E. coli, investigation of toxic/stress effects of chemicals on bacterial growth and antimicrobial susceptibility testing were also performed. The details are showed in Supplementary Information.

RESULTS AND DISCUSSION Characterization of the EBGS. Fig. 1 shows the working principle, structure and a picture of head stage with eight bacterial culture tubes inserted. In each channel of C4D, a couple of copper cylinders (internal diameter=5.01 mm; thickness=0.4 mm; length=20.0 mm) was used as actuator electrode and pick-up electrode, respectively, spacing in a distance of 46.0 mm. An AC voltage was applied to the actuator electrodes, to be capacitively coupled into the electrolyte. The two electrodes, the insulating tube and the electrolyte solution formed two coupling capacitances C1 and C2, as well as a stray capacitance arising from direct capacitive coupling between the two electrodes through air (C3). The solution in the tube was equivalent to a resistor R,30,31 which was used to show the conductivity. The growth of bacteria at a desired temperature transformed uncharged or weakly charged substrates, e.g.


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yeast, peptone, and sugar into highly charged end products, such as amino acids, aldehydes, ketones, acids, and other metabolites, causing an alteration in ionic concentration, increasing the conductivity of the medium.35 Thus, a growth curve of apparent conductivity value against incubation time could be generated by online monitoring the process of bacterial growth with the C4D. Figure 1. Online monitoring bacterial growth with the EBGS. (A) The growth of bacteria transforms uncharged or weakly charged substrates into highly charged end products, increasing the conductivity of the medium. (B) The practical equivalent circuit of each unit of C4D. (C) Photograph of the head stage of the eight-channel C4D, with eight bacterial culture tubes inserted in. (D) Schematic of the EBGS, consisting of two key functional modules (i.e. temperature control and conductivity data collection), which are both fully software controlled. 1—Bacterial culture tube; 2—mini electronic fan; 3—temperature sensor; 4—thermoelectric cooler; 5—actuator electrode and 6—pick-up electrode. The other properties of the EBGS were also characterized. To eliminate the effect of systematic variation between different culture tubes, an algorithm is presented. Of note, unlike employing OD measurement,1 here no blank well was needed for zero the monitoring. These details are all showed in Supplementary Information. Characterization of E. coli growth with the EBGS. Fig. 2A shows a typical curve of E. coli growth in LB medium, which is generated by plotting normalized apparent conductivity value (NACV, seen in Supplementary Information) against incubation time over a period around 45000 s. An S-shaped curve, similar to those obtained with OD,6,7,14 isothermal calorimetry,10 conductivity34 or impedance method,25 were obtained. There was a lag phase for approximately 21000 s, possibly caused by the stress that bacteria might experience after dilution and/or a loading step,19,24 as well as the time required for generating enough end products to produce detectable increasing conductivity. The lag phase was followed by an acceleration phase during which the growth rate increased until a constant growth rate was achieved. The exponential-like-type phase lasted for around 6000 second, during which a strong linear relationship between NACV and incubation time was observed, suggesting ideal characterization of bacterial proliferation,25 which was superior to those by employing wireless remote query resonant-circuit sensors26-28 and contact impedance,24,25 even optical methods.6-8,19 Subsequently, the growth rate began to decline during the deceleration phase, and eventually the growth ceased, coming to a stationary phase due to resource exhaustion and/or waste accumulation.8,19,25 Most of the time, e.g. in microbial genetics, biochemistry, molecular biology, microbial physiology and



fermentation process, the duration of the lag phase, the growth rate and duration of the exponential portion and the maximum culture density were of the greatest concern,7,12,15 and these parameters were all exhibited on the curve. In addition, during the exponential phase, the NACV was actually proportional to the incubation time, which was proportional to number of viable E. coli. It provided a new approach for evaluating the maximum specific growth rate (µm) by employing the slope of the exponential portion because of the degree of high precision allowed by this device (r2=0.999). A negative control was also performed where only the LB medium was monitored with the EBGS. No growth curve was observed over the same period, suggesting that without viable bacteria there was no conductivity change during the incubation. Figure 2. (A) Typical growth curve of E. coli (NACV vs. incubation time, S-shaped one in blue) in LB medium and response of pure LB medium (horizontal line in red) obtained with the EBGS. Initial inoculum of E. coli was 1.54×104 CFU. (B) A batch of eight growth curves (NACV vs. incubation time) of E. coli. From left to right, the initial inoculum of E. coli were 1.54×109, 1.54×108, 1.54×107, 1.54×106, 1.54×105, 1.54×104 and 1.54×103 CFU, respectively. In each bacterial culture tube 1.3 mL culture medium was loaded. Operation parameter: excitation frequency of 2.0 MHz; excitation amplitude of 16 V; recording rate of per 10 s. Referring to Mytilinaios et al,36 we performed studies of monitoring E. coli growth using established optical methodologies. The results are showed in Fig. S-2A. In comparison with a standard spectrophotometer, the C4D monitoring clearly exhibited more accuracy data, and showed improved performance over the whole range of growth. Based on the report by Yang et al,34 we also developed a direct conductivity method for monitoring bacterial growth, which included a normalization step as well for the data analysis. Results showed the magnitude of the normalized conductivity values varied by more than 25 µS/cm over the duration (Fig. S-2B), causing substantial effects on the accurate characterization of bacterial growth. This phenomenon contributed to the direct galvanic contact of working electrodes and the medium.26 By contrast, non-invasive C4D monitoring plus the new algorithm provided more precise data. Quantitative determination of viable E. coli. Fig. 2B shows the plots of NACV of different initial inoculum of E. coli as a function of incubation time obtained with the EBGS. For simplicity, we defined the time needed for the apparent conductivity change of the medium to reach 0.02 V as a “detectable time.” There was a linear relationship between the logarithmic values of initial inoculum of


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E. coli and detectable times over the range of 1.54×103–1.54×109 CFU (Fig. S-3). The correlation coefficient of the regression line (r) was 0.9977, matching those obtained with OD measurements37 and contact impedance or conductivity measurements.24,34,35 Inversely, the detectable time was proportional to the initial inoculum of E. coli. We demonstrated that the viability of unknown samples could be measured with this calibration curve, and that the values were in good agreement with those obtained with plate counting method (Fig. S-4). This result suggested that this strategy could be used to quantify rapidly target bacteria based on the growth kinetics, just like employing optics,14 PCR,9 calorimetry22 and other electrochemistry17,37 methods. Moreover, a good reproducibility of this multichannel measurement was suggested, being evaluated with reproducible tests (Supplementary Information). Study of stress effect of salinity on E. coli growth. We used a salinity gradient to probe E. coli growth. Fig. 3A shows typical growth curves obtained with the EBGS. As concentration of NaCl increased, lower maximum growth rates and maximum outputs were obtained, due to its inhibitory effect on bacterial growth.27 This result was in agreement with those obtained with conventional optical methods.36 Of note, there was an interesting phenomenon for the curves—the salinity had little to no effect on lag phase duration. As well known, the length of the lag phase depended upon the conditions of inoculated medium. Smaller environmental change yielded shorter adaptation phases. This phenomenon suggested that the variation of NaCl in the LB medium was slight environmental change for E. coli over the range of 1.0 - 4.0 g/L. Figure 3. Typical growth curves (NACV vs. incubation time) of E. coli in the presence of different concentrations of NaCl (A), Pb (B) and Cd (C), obtained with the EBGS. In (A), from top to bottom, the NaCl concentrations in the LB medium were 1.0, 2.0 3.0 and 4.0 g/L, respectively. The initial inoculum of E. coli were all 4.94×108 CFU. In (B), the concentrations of Pb ion in the LB medium (from left to right) were 0, 7.7×10−10, 7.7×10−9, 7.7×10−8, 7.7×10−7, 7.7×10−6 and 7.7×10−5 g/mL, respectively. A batch of seven culture tubes, in each one 1.56×107 CFU E. coli was inoculated, was simultaneously monitored. The rest channel of the EBGS was used for monitoring negative control sample, generating a horizontal line (in red). In (C), the concentrations of Cd ion in the LB medium (from left to right) were 0, 7.7×10−10, 7.7×10−9, 7.7×10−8, 7.7×10−7, 7.7×10−6 and 7.7×10−5 g/mL, respectively. Apart from the negative control one, 3.10×109 CFU E. coli were inoculated in each culture tube. Other culture conditions and parameters were the same as in Fig. 2. Investigation of toxic effect of heavy metals on E. coli growth. Better understanding of the



characteristics of bacterial growth under heavy metals stress benefit many tasks.38,39 Fig. 3B shows the growth curves of E. coli in the presence of Pb ion. Over the range of 7.7×10−10–7.7×10−6 g/mL, the duration of all lag phases were proportional to the concentration of Pb ion; and at a concentration of 7.7×10−5 g/mL, Pb2+ inhibited the growth completely. Similarly, the presence of 7.7×10−6 g/mL Cd2+ inhibited the growth completely (Fig. 3C). These results demonstrated that the EBGS could be used to characterize the toxic effect of heavy ions on bacterial growth, similar to OD methods.39 Performance of antimicrobial susceptibility testing. To demonstrate the capacity of the EBGS for antibiotics susceptibility testing, the responses of E. coli growth toward three kinds of antibiotics were measured. Fig. 4A shows growth curves of E. coli in the presence of chloramphenicol. As anticipated, at high concentrations above the minimal inhibitory concentration (e.g. ≥5.0 µg/mL),15 the growth was inhibited completely. Conversely, over a range below the minimal inhibitory concentrations, the growth rate decreased, and the lag phase duration extended, with increasing antibiotic concentrations. After a short lag phase, a positive control culture with no antibiotic in the medium showed a rapid increase in NACV, followed by a slow decrease of growth rate over the remainder of the incubation period. This phenomenon was similar to antibiotic susceptibility testing results reported elsewhere.2,4,5,27 In the case of penicillin G, for which recommended concentrations are 0.125–16.00 µg/mL,4 little inhibition effect on the growth at concentrations below 0.50 µg/mL was displayed (Fig. 4B). For malachite green, a concentration of 0.77 µg/mL was enough to cease the bacterial growth completely (Fig. 4C). Figure 4. Growth curves (NACV vs. incubation time) of E. coli responding to (A) chloramphenicol, (B) penicillin G and (C) malachite green, obtained with the EBGS. For performing the antimicrobial susceptibility testing, 6.21×109, 2.06×109 and 3.37×109 CFU initial inoculum of E. coli were used in the three cases, respectively. Both of the concentrations of chloramphenicol and penicillin were at a tenfold gradient over the range of 0.05 ng/mL to 50.00 µg/mL. The concentrations of malachite green were at a tenfold gradient over the range of 0.08 ng/mL to 76.92 µg/mL. Other culture conditions and parameters were the same as in Fig. 2. Feature of the strategy. Being inspired by existing online methods, particularly optical15,16 and remote electrical approaches,26,27 we constructed this novel strategy for achieving high-throughput and automatic characterization of bacterial growth. The key distinguishing features endued it serious advantages as follows.

Simplicity. In common online monitoring with optical patterns, pretreatments of samples and


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mediums are often needed to match the requirement of a certain degree of transparency.23 By contrast, these are not necessary with the EBGS because that turbidity and color interference are not of concern. Therefore, relevant auxiliary devices and operation steps are avoided, simplifying the analysis substantially. Moreover, this implies special meaning for investigations pertaining to the effect of nanoparticles on bacterial growth because that the use of optics-based assays is limited by potential interference.14 In existing contact impedance/capacitance systems,23-25,40 or even remote electrical approaches,26-28 there are working electrodes or sensors immersed in culture medium. In theory the renewal step is indispensable for obtaining repeatable and accurate data. With our strategy, this step is eliminated due to the completely non-invasive manner of monitoring.

High-resolution. C4D measurement shows outstanding properties with respect to extremely fast

response. This capacity allows a much high resolution when the user describes growth parameters as a function of time. To the best of our knowledge, the recording rate of current online monitoring methods are on the order of minute timescale.2,4,17

Accuracy and reproducibility. In conventional bacterial growth assays with optical approach, there

is a systematic variation. Even with optimum conditions, growth rate values show ±2.5% variation using 6-12 replicate wells.1 Meanwhile, variable reproducibility is also one of the main challenges of impedance biosensors for characterizing bacterial growth.23,26 The EBGS shows good accuracy and reproducibility both for measuring growth rate and for quantitative determination. Neither growth rate nor initial inoculum displayed systematic differences between the values obtained by three independent operators (RSD≤0.22%), suggesting the reproducibility equaled the advanced PCR method.9

Miniaturization and affordability. Substituting optics, PCR, calorimetry and gas chromatography

with an electrical system is advantageous in regards to affordability due to its avoidance of signal-transforming components.31 In case of large-scale production, the cost of each device is around 400 $. Moreover, any chemical (e.g. pH-sensitive fluorescent nanoparticles2), biotic (e.g. antibody19 and aptamer40) or physical (e.g. magnetic bead21) indicators/auxiliary materials are not needed, effectively reducing the cost of the trial further.

Versatility. Here it has been approved that quantitative determination of target bacteria, studying

the effect of environmental factors on bacterial metabolism, characterization of toxic function of chemical metals toward bacteria and antimicrobial susceptibility testing can all be performed. In principle, some other tasks, which commonly rely on sophisticated instrumentations and intensive labor,



such as microbial genetics, biochemistry, molecular biology, and microbial physiology,7,25 as well optimization and development of medium formula,6 pharmaceutical testing and development of new antimicrobial agents,13 clinical test19 and control of bioprocesses, can also be performed due to these features mentioned above. Though bacteria clustered together and sank down during their growth, it affected slightly the monitoring (Supplementary Information). At present, the EBGS could not be used to view the response of individual cell. However, for many tasks (such as antimicrobial susceptibility testing and development of new antimicrobial agents), this is not a shortcoming because the characterization of whole bacterial population was more meaningful.21 To improve the strategy for more widespread applications, and to match the requirements for point-of-care use, the construction of commercial prototype ventilation and lighting systems should be considered for meeting the requirement of special cases (e.g., oxygen supply and/or stirring).

CONCLUSIONS We built a novel strategy for online monitoring bacterial growth automatically with the developed EBGS, of which an eight-channel C4D was used to record the change of conductivity of the medium. Characterization of E. coli growth, quantitative determination of viable E. coli, investigation of toxic/stress effects from chemicals and antimicrobial susceptibility testing all could be realized. In comparison with conventional online monitoring methods, this strategy has the advantages of simplicity, accuracy, reproducibility, affordability, versatility and easy miniaturization. Overall, the main finding of this work was the feasibility of rendering substantial bacterial growth-based tasks simple and cost-effective, including activities in specialized laboratories or, potentially, at point-of-care.

ASSOCIATED CONTENT Supporting Information The Supporting Information is available free of charge on the ACS Publications website at DOI:

ORCID Keming Qu: 0000-0002-8237-3737 Chuan Zhao: 0000-0001-7007-5946

Notes


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The authors declare no competing financial and non-financial interests.

ACKNOWLEDGMENTS This work was supported by Central Public Interest Scientific Institution Basal Research Fund, CAFS (2016RC-BR02), Key R&D Program of Shandong Province (2016GSF120008), Special Scientific Research Funds for Central Non-profit Institutes and Yellow Sea Fisheries Research Institute, Chinese Academy of Fishery Sciences (20603022016003).

REFERENCES (1) Hall, B.G.; Acar, H.; Nandipati, A.; Barlow, M. Mol. Biol. Evol. 2013, 31, 232-238. (2) Si, Y.; Grazon, C.; Clavier, G.; Rieger, J.; Audibert, J.-F.; Sclavi, B.; Méallet-Renault, R. Biosens. Bioelectron. 2016, 75, 320-327. (3) Godin, M.; Delgado, F. F.; Son, S.; Grover, W. H.; Bryan, A. K.; Tzur, A.; Jorgensen, P.; Payer, K.; Grossman, A. D.;

Kirschner, M. W.; Manalis S. R. Nat. Methods 2010, 7, 387-390.

(4) Theophel, K.; Schacht, V. J.; Schlüter, M.; Schnell, S.; Stingu, C.-S.; Schaumann, R.; Bunge, M. Front. Microbiol. 2014, 5, 544. (5) Leonard, H.; Halachmi, S.; Ben-Dov, N.; Nativ, O.; Segal, E. ACS Nano 2017, 11, 6167-6177. (6) Cuevas, D. A.; Edwards, R. A. Bioinformatics 2017, 33, 1905-1906. (7) Koch, A. L. Chapter 9: Growth measurement, category: Microbial genetics and molecular biology. 2007, pp172-197. (8) Balagaddé, F. K.; You, L.; Hansen, C. L.; Arnold, F. H.; Quake, S. R. Science 2005, 309, 137-140. (9) Zhang, L.; Narita, Y.; Gao, L.; Ali, M.; Oshiki, M.; Okabe, S. Water Res. 2017, 116, 296-303. (10) Syal, K.; Mo, M.; Yu, H.; Iriya, R.; Jing, W.; Guodong, S.; Wang, S.; Grys, T. E.; Haydel, S. E.; Tao, N. Theranostics 2017, 7, 1795-1805. (11) Cha, X.; Dong, C.; Deng, Y. Anal. Chem. 2008, 80, 7820-7825. (12) Lin, H.-L.; Lin, C.-C.; Lin, Y.-J.; Lin, H.-C.; Shih, C.-M.; Chen, C.-R.; Huang, R.-N.; Kuo, T.-C. Appl. Environ. Microb. 2010, 76, 1683-1685. (13) Toprak, E.; Veres, A.; Michel, J.-B.; Chait, R.; Hartl, D. L.; Kishony, R. Nat. Genet. 2012, 44, 101-105. (14) Qiu, T. A.; Nguyen, T. H. T.; Hudson-Smith, N. V.; Clement, P. L.; Forester, D.-C.; Frew, H.; Hang, M. N.; Murphy, C. J.; Hamers, R. J.; Feng, Z. V.; Haynes, C. L. Anal. Chem. 2017, 89, 2057-2064. (15) Deris, J. B.; Kim, M.; Zhang, Z.; Okano, H.; Hermsen, R.; Groisman, A.; Hwa, T.



Science 2013, 342 (6162), 1237435. (16) Page, L.S.; Raoult, D.; Rolain, J.-M. J. M. Int. J. Antimicrob. Agents 2015, 45, 61-65. (17) Groisman, A.; Lobo, C.; Cho, H.; Campbell, J. K.; Dufour, Y. S.; Stevens, A. M.; Levchenko, A. Nat. methods 2005, 2, 685-689. (18) Kuru, E.; Tekkam, S.; Hall, E.; Brun, Y.V.; Nieuwenhze, M.S.V. Nat. Protoc. 2015, 10, 33-52. (19) Borisenkoa, V.; Goh, M.C. Anal. Methods 2017, 9, 2392-2396. (20) Horst, A.M.; Vukanti, R.; Priester, J.H.; Holden, P.A. Small 2013, 9, 1753-1764. (21) Kinnunen, P.; Sinn, I.; McNaughton, B. H.; Newton, D. W.; Burns, M. A.; Kopelman, R. Biosens. Bioelectron. 2011, 26, 2751-2755. (22) Bonkat, G.; Braissant, O.; Widmer, A. F.; Frei, R.; Rieken, M.; Wyler, S.; Gasser, T. C.; Wirz, D.; Daniels, A. U.; Bachmann, A. BJU Int. 2012, 110, 892-897. (23) Ahmed, A.; Rushworth, J.V.; Hirst, N.A.; Millner, P.A. Clin. Microbiol. Rev. 2014, 27, 631-646. (24) Settu, K.; Chen, C.-J.; Liu, J.-T.; Chen, C.-L.; Tsai, J.-Z. Biosens. Bioelectron. 2015, 66, 244-250. (25) Ghafar-Zadeh, E.; Sawan, M.; Chodavarapu, V.P.; Hosseini-Nia, T. IEEE T. Biomed. Circ. S. 2010, 4, 232-238. (26) Ong, K.G.; Wang, J.; Singh, R.S.; Bachas, L.G.; Grimes, C.A. Biosens. Bioelectron. 2001, 16, 305-312. (27) Huang, S.; Pang, P.; Xiao, X.; He, L.; Cai, Q.; Grimes, C. A. Sensor. Actuat. B-Chem. 2008, 131, 489-495. (28) Huang, S.; Wang, Y.; Ge, S.; Cai, Q.; Grimes, C.A. Sensor. Actuat. B-Chem. 2010, 150, 412-416. (29) Kuban, P.; Hauser, P.C. Electrophoresis 2011, 32, 30-42. (30) Kuban, P.; Hauser, P.C. Electrophoresis 2004, 25, 3387-3397. (31) Zhang, X.; Li, Q.; Jin, X.; Jiang, C.; Lu, Y.; Tavallaie, R.; Gooding, J.J. Sci. Re-UK 2015, 5, 12539. (32) Zhang, X.; Huang, M.; Yang, Q.; Ding, D.; Zhao, J.; Yang, W.; Qu, K. Chinese Chem. Lett. 2017, 28, 1406-1412. (33) Zhang, X.; Huang, M.; Zhao, J.; Liu, J.; Yang, W.; Qu, K. Measurement 2018, 116, 458-463. (34) Yang, L.; Banada, P.P.; Liu, Y.-S.; Bhunia, A.K.; Bashir, R. Biotechnol. Bioeng. 2005, 92, 685-694. (35) Varshney, M.; Li, Y. Talanta 2008, 74, 518-525. (36) Mytilinaios, I.; Bernigaud, I.; Belot, V.; Lambert, R.J.W. J. Appl. Microbiol. 2015, 118, 161-174.


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(37) Russell, S.M. J. Food Protect. 2000, 63, 1179-1183. (38) Hassen, A.; Saidi, N.; Cherif, M.; Boudabous, A. Bioresource Technol. 1998, 64, 7-15. (39) Trchounian, K.; Poladyan, A.; Trchounian, A. Appl. Energ. 2016, 177, 335-340. (40) Jo, N.; Kim, B.; Lee, S.-M.; Oh, J.; Park, I. H.; Lim, K. J.; Shin, J.-S.; Yoo, K.-H. Biosens. Bioelectron. 2018, 102, 164-170.



A

1

D 6

B

2 3

5

4 C

Figure 1.

A

B

0.10

0.08

NACV (V)

0.06 0.04 y = 1E-05x - 0.2999 R² = 0.999

0.02

0.06 0.04 0.02

0.00 (0.02) 15000

0.12 0.10

0.08

NACV (V)

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60

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0.00 (0.02) 25000

35000

45000

0

10000

20000

30000

Incubation time (s)

Incubation time (s)

Figure 2.


40000

Page 15 of 17

0.14

A

NACV (V)

0.12 0.10 0.08 0.06 0.04 0.02 0.00 5000

8000

11000

14000

17000

(0.02) 8000

12000

16000

20000

24000

5000

8000

11000

0.10

B

NACV (V)

0.08 0.06 0.04 0.02 0.00

0.10

C

0.08

NACV (V)

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60


0.06 0.04 0.02 0.00 (0.02) 2000

14000

Incubation time (s)

Figure 3.



A

0.10

NACV (V)

0.08 0.06 0.04 0.02 0.00 (0.02) 1000 B

4000

7000

10000 13000

5000

8000

11000 14000

5000

8000

11000

0.10

NACV (V)

0.08 0.06 0.04 0.02 0.00 (0.02) 2000 C

0.10 0.08

NACV (V)

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60

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0.06 0.04 0.02 0.00 (0.02) 2000

14000

Incubation time (s)

Figure 4.


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For TOC only


Online Monitoring of Bacterial Growth with an Electrical Sensor

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