Skip to main content
NCBI home page
ACS Measurement Science Au logoLink to ACS Measurement Science Au
. 2025 Aug 26;5(5):677–686. doi: 10.1021/acsmeasuresciau.5c00060

Enumeration of Bacteria in Suspensions Using Time Domain Reflectometry

Huan Hu †, Yili Lu †,*, Robert Horton ‡, Tusheng Ren †,§
PMCID: PMC12532056  PMID: 41113149

Abstract

Microbial detection techniques, such as bacterial counting, are essential in all aspects of environmental monitoring and analysis. However, the standard plate count method for bacterial enumeration with colony-forming units is time-consuming and labor-intensive. In this study, we present a fast and accurate method to count bacteria cells using the technique of time-domain reflectometry (TDR) based on the electrical properties of bacterial cell suspensions. A series of suspensions with various bacterial concentrations were used as the test materials, and the electrical conductivity (σa) was determined using the TDR method. The TDR measured-σa value was converted to the concentration of bacterial suspension using a pre-established standard curve on three types of bacteria, i.e., Bacillus subtilis (B. subtilis), Pseudomonas fluorescens (P. fluorescens), and Escherichia coli (E. coli). The σa values of suspensions increased exponentially with bacteria concentrations, mainly due to the release of Cl– and extracellular polymeric substances from the cells that were electrically conductive. For the three types of bacterial strains, the lower detection limits were 6 log CFU mL–1 for B. subtilis, and 7 log CFU mL–1 for P. fluorescens and E. coli. Independent evaluation showed that values from the TDR based method matched well with those obtained with the traditional plate count method, with RMSEs of 0.260, 0.166, and 0.198 log CFU mL–1 for B. subtilis, P. fluorescens, and E. coli, respectively. The TDR based approach provides a fast and accurate means for detecting bacterial cell numbers in suspensions.

Keywords: bacterial enumeration, electrical conductivity, time-domain reflectometry, bacterial suspension, plate count method

graphic file with name tg5c00060_0008.jpg

graphic file with name tg5c00060_0006.jpg

1. Introduction

The colony-forming unit (CFU) assay has been widely used as a universal standard for counting viable bacterial cells. , The CFU analysis has the advantages of being simple to use and having a large detection range (i.e., 1 to 108 viable cells in a sample). , However, the common CFU assay methods (e.g., the plate count method), can be time-consuming, labor-intensive, and unable to provide rapid information. There is a need to develop simple and rapid techniques for bacterial enumeration.

Impedance theory, which relies on direct or indirect measurements of electrophysiological and impedance properties of biological cells, has been applied for bacterial enumeration or quantification of foodborne pathogenic bacteria with a short detection time and relatively high accuracy. − Four approaches are available to detect bacterial cells from impedance measurements. The first method relies on the fact that impedance is mainly caused by the release of ionic metabolites from living cells, including energy metabolism (catabolism) and possibly ion exchanges through cell membranes (e.g., K+, Na+ ion channels). The number of bacterial cells is expressed indirectly via changes in the electrical impedance of the medium or reactant solution due to bacterial metabolism. − The second method relies on the fact that cell membranes are highly insulating materials, and the cells attached to a certain kind of electrode surface effectively reduce the electrical conducting area and hence increase its interface impedance. Such electrodes are functionalized with target-specific antibodies and immobilized on the sensor surface. Additionally, the cell interior contains many charged molecules with electrical conductivity as high as 1 S m–1, which provides another way to detect biological cells from impedance change caused by cell lysis or intracellular ion release. − Finally, with the assumption that bacterial cells are distributed homogeneously in a liquid matrix, counting bacteria cells can be completed by directly measuring the changes in the electrical impedance spectra of bacterial suspensions as a function of cell concentration.

Quite a few sensors have been developed for bacterial cell enumeration. Sensors are usually equipped with detecting electrodes that immobilize bacterial cells onto their surfaces, thus electrical signals captured by the electrodes are significantly changed due to the insulating property of the cell membrane. For common electrodes, a key challenge associated with antibody immobilization is the low capture efficiency (CE) of the immobilized surface. There are reports that anti-Escherichia coli (E. coli) O157:H7 antibodies immobilized on the surface of an indium tin oxide-coated glass electrode had only 16% CE for E. coli O157:H7, and the CE of anti-Salmonella antibodies functionalized on a roughened glass surface was lower than 1% for Salmonella. , Antigen–antibody reaction sensors directly enumerate bacterial cells by detecting bacterial metabolites. A limitation of these sensors is that they can only detect specific types of bacterial cells with the same metabolite type. ,

An advantage of the above methods is that they are very sensitive in detecting a small number of bacterial cells. − In practice, however, most of the biodetectors are expensive and difficult to reuse. For example, it is difficult to remove biomolecules immobilized on the interdigitated-array-microelectrode based impedance immunosensors, which reduces the lifetime of the sensor chip and the measurement accuracy. ,, For these types of bacterial detectors, the single detection volume varies from less than 1 mL to a few mL, and the detection time ranges from minutes to hours. These may lead to inaccurate results due to the small sample volume and bacterial colonization due to the prolonged detection time. −

The conductive properties of bacterial cells or suspensions have been proven to be effective for the detection or enumeration of bacteria. ,, However, current techniques generally suffer from relatively long detection times, limited sensing volumes, and are restricted to use on specific bacteria types. The time-domain reflectometry (TDR) measures the electrical conductivity (σa) of a medium by detecting the attenuation of electromagnetic signals. When an electromagnetic wave signal propagates along a sensor installed in the medium, its attenuation is in proportion to the electrical conductivity of the medium. Based on its unique detection principle, the measurement time usually takes only about 80 ns. For decades, the TDR technique has been widely applied to measure the electrical properties of soils, liquids, solid materials, building materials, and food, − with the advantages of providing accurate, real-time, and in situ measurements with options for automation and multiplexing adapted to various application scenarios. , It is also reported that the TDR technique causes a minor disturbance to the sample with the fast and accurate measurement with a relatively large sensing volume, and it is nonselective for the detected samples. Bose et al. first used a TDR sensor to determine the dielectric properties of biological materials (i.e., beef muscle and fat). The use of TDR probes for biorelated measurements is scarce, and their application in the field of biological detection remains to be developed.

In this study, we develop a TDR based approach to determine bacterial cell concentration in suspensions. The TDR technique is used to measure σa values of bacterial suspensions in sterile double-distilled water, and the new approach is validated against the traditional plate count method for three types of bacterial suspensions. Factors that influence the accuracy of bacteria enumeration with the TDR technique as well as its detection limit are explored.

2. Materials and Methods

2.1. Bacterial Strains

We used standard strains of bacteria: Bacillus subtilis (B. subtilis, ATCC6633, Gram-positive), Pseudomonas fluorescens (P. fluorescens, ATCC13525, Gram-negative), and Escherichia coli (E. coli, ATCC25922, Gram-negative), which were obtained from the Shanghai Bioresource Collection Center. The strains were activated in Luria–Bertani (LB) agar plate (tryptone 10 g L–1, yeast extract 5 g L–1, NaCl 10 g L–1, agar 15 g L–1) prior to the experiments (Figure S2a–c), then transferred to LB medium (tryptone 10 g L–1, yeast extract 5 g L–1, NaCl 10 g L–1), and incubated in an oscillating incubator for 36 h (180 rpm, 28 °C). Finally, the cultured samples were centrifuged at 4000 rpm for 10 min to completely remove the LB medium and obtain pure cell samples for later use.

In the subsequent procedures, the bacterial suspensions were prepared using these pure cell samples for establishing standard curves as well as for determining cell concentrations of unknown samples.

2.2. TDR Electrical Conductivity Measurement

The σa value of the suspension was measured using the TDR technique at a constant temperature of 25 °C. The TDR sensor consists of three stainless steel probes with a length of 70 mm, a diameter of 2 mm, and probe-to-probe spacings of 10 mm. The three probes were fixed with epoxy resin at the sensor head (Figure a), and connected to a coaxial cable (with the inner conductor to the center probe and the shield wires to the outer probes).

1.

1

Open in a new tab

A schematic diagram of the measurement in a bacterial suspension using the TDR technique. (a) TDR sensor structure. (b) Time-domain reflectometer device and TDR waveforms of a bacterial suspension. (c) A schematic diagram of the TDR sensor inserted into a bacterial suspension and the electrical field lines around each TDR probe.

Figure b–c shows a schematic diagram of a σa determination using the TDR technique. The TDR sensor is inserted into the bacterial suspension (Figure c). During the measurement, the TDR device (model TDR200 or TDR100, Campbell Scientific, Logan, UT, USA) emits high-frequency electromagnetic wave signals through an electromagnetic generator, that travels along the coaxial cable (Figure b). When the electromagnetic wave signal reaches the probes submerged in the bacterial suspension, part of the signal is reflected back to the TDR device due to changes in the external impedance (Figure b,c). The TDR waveform (i.e., the reflection coefficient vs apparent distance) of the bacterial suspension is recorded for further analysis (Figure b).

The σa values of the bacterial suspensions are calculated according to the following equation,

σa=KpRtotal−RcfT 1

where K p is the cell constant of the TDR probe (∼8.77 m–1), which is determined in a series of KCl solutions; R total is the total resistance of the TDR reflectometer, coaxial cable (R c), and probes, which can be calculated from the amplitude of the TDR waveforms at very long times. Refer to the Supporting Information for the calculation of R total using eq S1. Previous studies showed that R c is a small fraction of R total that can be neglected.

Finally, σa values were adjusted to values at 25 °C with a temperature factor f T,

fT=11+τ(T−25) 2

where τ is the temperature coefficient of the sample at the reference temperature of 25 °C (0.0191 °C1–), T (°C) is the sample temperature at the measurement time.

2.3. Standard Bacterial Concentration vs σa Curves

We established a standard curve between bacteria concentration and σa for each bacteria type, and then used the relationship to estimate bacterial concentration from TDR-measured σa values. To prepare the bacterial suspensions, the LB medium used for bacterial cell growth needed to be completely removed. As we mentioned earlier, pure cell samples were obtained by centrifugation, and then the original bacterial suspension was obtained by resuspending pure bacterial cells in sterile double distilled water (ddH2O) and adjusting its microbial biomass (OD600) to 1.5. The concentration of each bacterial suspension was determined using the plate count method (Figure S 2d). The concentrations of the original bacterial suspensions were determined to be 0.93 × 108 CFU mL–1, 1.83 × 109 CFU mL–1, and 1.43 × 109 CFU mL–1 for B. subtilis, P. fluorescens, and E. coli, respectively.

Nine bacteria solutions with a concentration gradient were prepared by diluting the original bacterial suspension with dilution factors of 1, 1.25, 2, 5, 10, 12.5, 20, 50, and 100. Then the σa values of the series of diluted suspensions were immediately measured using the TDR method, and the corresponding concentration of each suspension was determined using the plate count method. Three replicates were conducted for each independent suspension sample, and the average σa values were used in the calculation. Finally, for each type of bacteria suspension, the standard curve was established by fitting equations to the measured data.

2.4. Validation of the New Method

To validate the accuracy of TDR determined bacterial concentrations from σa measurement, we prepared additional bacterial suspensions with unknown concentrations for B. subtilis, P. fluorescens, and E. coli using sterile ddH2O according to the previous method. The concentrations of bacterial suspensions were determined using TDR measured σa values based on the standard curves. The data from the plate count method were used to evaluate the accuracy of the TDR based approach.

2.5. Chemical Properties of Bacterial Suspensions

To further explore the electrical conductivity of the bacterial suspension as affected by bacterial activities, we measured the key bacterial metabolites (extracellular polymeric substances, EPS) and the released ions (Na+, K+, and Cl–) that regulate the intracellular osmotic pressure of the suspensions. Previous studies have shown that the key components of EPS are proteins and polysaccharides. , The content of polysaccharides was determined by using the sulfuric acid-phenol method with a glucose standard. Proteins were measured by using the bicinchoninic acid assay method. The bacterial suspensions were filtered through a 0.45-μm membrane, and the concentrations of Na+, K+, and Cl– were determined by using ion chromatography (940 Professional IC Vario, Switzerland). Refer to the Supporting Information for details about the determination of protein, polysaccharide, and ion contents in the bacterial suspensions.

2.6. Statistical Analysis

Differences in protein, polysaccharide, Na+, K+, and Cl– contents at different suspension concentrations were analyzed by using one-way analysis of variance in the SPSS software (version 25.0, IBM Corp., Armonk, NY, USA). The t-test was used to assess the effect of different times on the number of viable bacteria at a significance level of p < 0.05. Pearson correlation analysis was used to examine the relationship between the concentration of bacterial suspensions obtained with the plate count method and TDR technique, as well as the correlation between EPS content and σa. The accuracy of TDR measured bacterial suspension concentrations was evaluated with root-mean-square error (RMSE).

3. Results and Discussion

3.1. TDR Waveforms of Bacterial Suspensions

A TDR waveform shows the relationship between the reflection coefficient and the apparent distance along the waveguide. In general, the incident voltage amplitude (v 0) depends solely on the propagation of electromagnetic signals along the cable. The final voltage of the waveform (v ∞), which represents the returning pulse voltage after multiple reflections, is strongly related to the σa of the tested media. For the suspensions of B. subtilis, P. fluorescens, and E. coli of different concentrations, the TDR waveforms displayed distinct characteristics in response to bacterial type and suspension concentration (Figure a–c). First, larger v ∞ values appeared in suspensions of greater dilution factors, i.e., a lower suspension concentration resulted in a higher v ∞. This trend was most significant for suspensions with the dilution factors from 1 to 5, and the v ∞ values approached 1 when the dilution factor was greater than 10. Second, the dilution effect on TDR waveform (i.e., the magnitude of v ∞ value) was more significant in the suspensions of B. subtilis and P. fluorescens than of E. coli suspensions. However, this phenomenon had little correlation with the concentrations of the original suspensions, i.e., 0.93 × 108, 1.83 × 109, and 1.43 × 109 CFU mL–1 for B. subtilis, P. fluorescens, and E. coli, respectively. Later, we will discuss the potential factors that influence v ∞ values of the TDR waveforms.

2.

2

Open in a new tab

TDR waveforms (a–c) and standard curves (d–f) of Bacillus subtilis (B. subtilis), Pseudomonas fluorescens (P. fluorescens), and Escherichia coli (E. coli) bacterial suspensions at a series of concentrations. Parameters v 0 and v ∞ represent the amplitudes of the incident voltage and the final voltage (after all multiple reflections have ceased), respectively. The legends 1, 1.25, 2, 5, 10, 12.5, 20, 50, and 100 indicate the dilution factors of the suspensions. Parameter σa denotes the TDR measured electrical conductivity. All the values are the means of three replicates.

3.2. Bacterial Concentration versus σa Standard Curve

Figure d–f presents TDR measured apparent σa values as a function of bacterial concentration, which is expressed as the logarithm number of CFU per milliliter. At a specific bacterial concentration, the σa values of the suspensions were in the order of B. subtilis > P. fluorescens > E. coli. For example, at the concentration of 8 log CFU mL–1, the σa value was about 600 μS cm–1 for B. subtilis suspension and about 50 μS cm–1 for both P. fluorescens and E. coli suspensions.

For all three bacteria types, σa generally increased with bacterial number, and the rate of increase could be divided into two stages: a slow increase at lower concentrations and an exponential increase when the concentration exceeded a threshold level, which was about 7 log CFU mL–1 for B. subtilis and 8 log CFU mL–1 for P. fluorescens and E. coli (Figure d–f). Further analysis indicated that the nonlinear relationship between σa and the logarithm of bacterial concentration could be described with the following exponential function,

y=a×ebx+c 3

where for B. subtilis, P. fluorescens, and E. coli, the fitted values of parameter a are 8.64 × 10–7, 1.47 × 10–7, and 4.54 × 10–8, respectively; the fitted values of parameter b are 2.55, 2.30, and 2.39, respectively; and the fitted values of parameter c are 48.3, 46.0 and 43.7, respectively. In eq , parameter c represents the background electrical conductivity, i.e., the σa value of ddH2O where there are hardly any bacteria in the suspension. In this study, the σa values were in the range of 40–50 μS cm–1 at the lowest concentration (with the dilution factor of 100), which was very close to the σa value of ddH2O (about 40 μS cm–1, Figure S3).

Figure d–f also show that at very low bacteria concentrations, the fitted curves are almost parallel to the x-axis, which indicates the lower limits of TDR measurements. For the three types of bacterial strains, the lower detection limits were 6 log CFU mL–1 for B. subtilis, and 7 log CFU mL–1 for both P. fluorescens and E. coli.

3.3. Accuracy of the TDR Based Approach

Figure presents a comparison of TDR estimated bacterial concentrations versus the results of the plate count method obtained on independent bacterial suspensions. For the three bacteria types, the data generally spread around the 1:1 line, with the correlation coefficients greater than 0.959 and RMSEs less than 0.260 log CFU mL–1, demonstrating that the TDR based method provided reliable estimates of the bacterial cell suspension concentrations. The corresponding TDR waveforms for these bacterial suspensions are presented in Figure S4.

3.

3

Open in a new tab

Comparison of bacterial suspension concentrations estimated using TDR method versus that measured with plate count method for suspensions of Bacillus subtilis (B. subtilis), Pseudomonas fluorescens (P. fluorescens), and Escherichia coli (E. coli). Each data point represents the mean of three replicated measurements. RMSE indicates the errors in the TDR based approach, and ** indicates the correlation is significant at the 0.01 level (p < 0.01).

3.4. Electrical Properties of Bacterial Cells Suspended in Sterile ddH2O

Our TDR measured σa values showed that bacterial cell suspensions at high concentrations were more electrically conductive than those at low concentrations (Figure d–f). This implies that the bacterial cells contribute to electrical conduction, which subsequently alters the σa values of the suspensions. Two possible mechanisms may be involved in the phenomenon. First, bacterial cell walls contain higher amounts of anionic groups than cationic groups, which results in negative charges on the bacterial cell walls around neutral pH values. The charges are compensated by counterions that permeate the porous cells and to a lesser extent by co-ions that are expelled from the porous cells, thus conferring electrostatic charges around the cells. , Another possible mechanism explaining the σa trend is the release of metabolic substances and ions by bacterial cells. It is commonly known that the EPS, covering microbial cells, is a fundamental microbial component that determines the physicochemical properties of biofilms. Because of its strong redox ability and 3D structure, the EPS takes a key role in the process of electron transfer between microbial cells and extracellular electron acceptors/donors through the membrane-bound proteins or electron shuttles.

We examined the relationship between EPS (mainly protein and polysaccharide) content and σa of bacterial suspensions at various dilutions (Figure a–b). The results showed that the protein and polysaccharide contents of the three bacterial suspensions decreased significantly as the dilution factor increased in the range of 1 to 10. The protein and polysaccharide contents of the original bacterial suspension of B. subtilis were significantly higher than those of P. fluorescens and E. coli. Both protein and polysaccharide contents significantly correlated with the σa values (Figure a,b, p < 0.01). There are reports that the EPS surrounding Bacillus sp. cells are electrochemically active and play an important transient mediating role in microbial extracellular electron transfer. Thus, for each bacteria type, the σa values of bacterial suspensions at various dilution factors were largely affected by the contents of EPS.

4.

4

Open in a new tab

Comparisons of protein and polysaccharide content (a,b) and Cl–, K+, and Na+ contents of bacterial suspensions (c–e) at different dilution factors. The values of 1, 2, and 10 indicate the dilution factors of the suspensions. Error bars are the standard deviations of three repeated measurements. Different lowercase letters indicate significant differences between different dilution factors of the same bacterial suspension. Significance was assessed using the Pearson r correlation. ** indicates the correlation is significant at the 0.01 level (p < 0.01).

When bacterial cells are suspended in sterile ddH2O, ion leakage through the cytoplasmic membrane, negative electrolyte adsorption, cytoplasmic uptake of ions and ion-specific adsorption may occur, which increases the σa of the suspension. In this study, we attempted to identify whether bacterial cells suspended in sterile ddH2O were changed or destroyed. The bacterial cells were resuspended in sterile ddH2O, and samples were collected immediately after 3 days of incubation under sterile conditions at room temperature. Finally, the cell numbers in all samples were determined using the plate method. The results showed that after incubation for 3 days, the numbers of B. subtilis and P. fluorescens decreased slightly with no significant differences (Figure S5), suggesting that suspending bacterial cells in sterile ddH2O did not cause significant damage in the short term. Similar findings were observed by Shynkaryk et al. who reported that after 1-h incubation in DI water at room temperature, the resuspension of Listeria innocua showed an insignificant effect on bacterial count results.

We also investigated the trend of σa of B. subtilis right after resuspended in sterile ddH2O over a time window of 5 days. The results showed that for the higher concentration of B. subtilis (e.g., B. subtilis 5), the σa value increased significantly from 1.15 dS m–1 on the first day to 1.31 dS m–1 on the fifth day (Figure S6). Under such circumstances, the slight σa increase might be related to ions released by bacterial cells to regulate the osmotic pressure when suspended in sterile ddH2O. In this regard, we treated the bacterial suspension with a 0.45 μm filter membrane and determined the Cl–, K+, and Na+ contents in the filtrate (Figure c–e). The results showed that with the increase of the dilution factors from 1 to 10, the Cl– contents in the filtrates of B. subtilis and P. fluorescens, the K+ contents of B. subtilis, and the Na+ contents of E. coli decreased significantly (Figure c–e). It is noteworthy that the K+ and Na+ contents of the bacterial suspensions were much lower than the Cl– content (Figure c–e). We also observed that the original bacterial suspensions of B. subtilis had significantly higher Cl– contents and thus greater σa values as compared with those of P. fluorescens and E. coli (Figures d and c). This implied that Cl– content was likely a dominant factor that is responsible for the σa increase of the bacterial suspensions.

Differences in cell wall structure may also alter the σa value. We observed higher EPS and Cl– contents in B. subtilis suspensions as compared to those of P. fluorescens and E. coli suspensions (Figure ). The Gram-positive bacteria are surrounded by a cell wall consisting of a 30–100 nm thick peptidoglycan layer, while a 2–7 nm thick layer exists for the Gram-negative bacteria. This would lead to considerably more cross-links in the peptidoglycan for the Gram-positive bacteria, and such a porous structure forms numerous pores in the cell wall with a diameter ranging from 50 to 500 Å to allow for proteins with a molecular weight up to 50 kDa to pass through. − Furthermore, this pattern permits high molecular weight polymeric mediators to pass through and enhances the extracellular electron transfer pathway. , This may explain why the B. subtilis suspension had significantly higher EPS content and σa values.

3.5. Influencing Factors and Implications of the TDR Based Method

First, the TDR based method requires the suspended medium to be sterile ddH2O rather than the LB medium. For the preparation of the original bacterial suspensions, we completely removed the LB medium using high-speed centrifugation. This is caused by the fact that the σa value of LB medium is much larger than that of sterile ddH2O (Figure S3), and removing the LB medium and diluting with sterile ddH2O will ensure that the electromagnetic signal (changes in σa caused by bacterial cells) is detectable with a low background σa value. It is worth noting that the raw materials of LB medium can be different, with high and nonunique σa values, which may bring uncertainties to the electromagnetic signals of the TDR method.

Varshney and Li used a low σa mannitol solution to minimize the effect of the culture medium on the direct detection of bacterial cells. Yang resuspended bacterial cells in the phosphate buffered saline (PBS, ∼58 mS cm–1) and deionized water (DI, ∼3 μS cm–1), respectively. The impedance spectra of Salmonella suspensions in DI water responded quite differently to cell concentrations in the frequency range from 100 Hz to 10 kHz, whereas the impedance spectra of Salmonella suspensions in PBS showed no significant differences at any frequency. This was because the background impedance spectra of PBS were much larger than those of the bacterial cells. In addition, changes in the bacterial cell growth cycle also alter the σa values of the suspension, as this process is accompanied by the release of some intracellular substances or cell death. Therefore, the TDR method should measure the σa values right after the suspension is prepared. In this case, the sterile ddH2O hardly provides available substances for bacterial growth, preventing bacterial multiplication in a relatively short-term (Figure S5), which in turn, largely ensures the stability and accuracy of the results under such experimental procedures.

Second, it is important to mention that the TDR based method has a maximum detection limit. The TDR waveform of the LB medium significantly differed from that of sterile ddH2O (Figure S3) because the LB medium contained many ions and compounds with high σa values, which might exceed the maximum detection limit of the TDR method for measuring σa. In a physical sense, using eq S1 requires that ρ∞ is less than 1 (or R total is greater than 0) to obtain a meaningful σa, with v ∞ varying between 0 and 2v 0 (eq S2). In Figure S3, v ∞ of the LB medium is close to 0 and ρ∞ is close to −1, resulting in a R total value close to 0. In this case, the method of Heimovaara et al. is no longer applicable for the calculation of σa.

It should be noted that the detection limits of the TDR based method are also affected by the sensor dimensions. As mentioned earlier, the TDR sensor type used in this study fails to respond in suspensions of low bacterial concentrations. It is found that the lower detection limit of the TDR sensor was reached when the concentrations of bacterial suspension were less than about 6 log CFU mL–1 (B. subtilis) and 7 log CFU mL–1 (P. fluorescens and E. coli), respectively (Figure d–f). In addition, σa is affected by temperature so that the measurement should be performed at a constant temperature environment. Further studies should focus on the integrative effects of sensor dimension, bacteria type, or diluted medium on bacteria cell count results using the TDR based approach.

Finally, compared with the plate count method, the TDR method for enumeration of bacteria in suspensions offers several advantages (Figure ). First, the TDR method saves time and reduces the costs of measurement. The plate count method consists of several routine procedures such as preparation of LB agar medium, dilution, inoculation and/or determination, and counting of colonies, which require many disposable plastic and reagent consumables (Figure a). On the other hand, the TDR method only involves centrifugation and resuspension of the tested samples, which not only saves measurement time but also reduces the costs (Figure b). Our estimation indicated that for counting 10 bacterial suspension samples, the TDR method can save 95.5% of detection time and as high as 83.1% of the costs as compared with the plate count method (Figure c). Second, regardless of the volume of the sample to be detected, the plate count method only uses 1 mL of the sample for analysis, which is subjected to sampling errors. In contrast, the TDR method gives more representative data because the detection volume can be in the range of 50–100 mL. While the TDR method cannot fully replace the plate count technique, it serves as a valuable supplementary tool for rapid bacterial enumeration when paired with a pre-established standard curve correlating suspension concentration and σa values.

5.

5

Open in a new tab

Comparisons of time and cost (c) required for the counting of 10 bacterial suspension samples by the plate count method (a) and the TDR method (b).

For future applications, the TDR technique demonstrates strong potential for rapid and accurate bacterial enumeration due to its fast, highly automated, in situ, and real-time measurement capabilities. The TDR system is particularly suited for biofilm systems involving a single bacterial species, such as continuous long-term cultivations in bioreactors with stirred suspension batch experiments. − This study offers new insights and strategies for employing TDR-based bacterial cell counting across diverse applications.

4. Conclusions

This study demonstrated for the first time that a fast and relatively accurate method to estimate bacterial cell count using σa values can be determined with a TDR technique. The standard curves, i.e., the σa and bacterial concentration relationships, were described with an exponential function for the three types of bacteria (B. subtilis, P. fluorescens, and E. coli). Compared with the traditional plate count method, the TDR based method provides accurate and rapid CFU counts for suspensions with random bacterial concentrations that were diluted with sterile ddH2O. The σa values of the suspensions were highly correlated with the EPS and ion contents due to the electrically conductive components of Cl–, proteins, and polysaccharides in suspensions. The standard curves, detection limit as well as the σa values also depended on the bacteria type, in which the electrically conduction pathway may be through the extracellular electron transfer. Further work on improving the detection range and limit of the TDR based method is recommended. Overall, due to the accurate, quick, highly automated, and real-time measurements, this study provides new insights and strategies for the subsequent use of TDR techniques in various aspects of bacterial cell counting.

Supplementary Material

tg5c00060_si_001.pdf (603.5KB, pdf)

Acknowledgments

This research was supported by the National Natural Science Foundation of China (42307390), the U.S. National Science Foundation (2037504), and USDA-NIFA Multi-State Project 5188. This work is also supported by the Research group of soil health and black soil protection of China Agricultural University. The authors would like to thank Dr. Zizhong Li and Dr. Xiaorong Zhao from China Agricultural University for laboratory and financial assistance. Special thanks to Dr. Shuang Wang from Ningbo University for the laboratory experiment instructions.

The Supporting Information is available free of charge at https://pubs.acs.org/doi/10.1021/acsmeasuresciau.5c00060.

  • Supporting Information S1: Additional details on calculation of R total; Supporting Information S2: Measurement descriptions of proteins, polysaccharides and ions in bacterial suspensions; Supporting Information S3: Supporting data and results on the diagrams for the experiment setup details, the measured TDR waveforms, and the electrical conductivity dynamics of the cells suspended in sterile ddH2O (PDF)

CRediT: Huan Hu conceptualization, data curation, formal analysis, methodology, writing - original draft; Yili Lu conceptualization, funding acquisition, supervision, writing - review & editing; Robert Horton funding acquisition, writing - review & editing; Tusheng Ren funding acquisition, resources, supervision, writing - review & editing.

The authors declare no competing financial interest.

References

  1. Hazan R., Que Y. A., Maura D., Rahme L. G.. A method for high throughput determination of viable bacteria cell counts in 96-well plates. BMC Microbiol. 2012;12:259. doi: 10.1186/1471-2180-12-259. [DOI] [PMC free article] [PubMed] [Google Scholar]
  2. Lázár V., Snitser O., Barkan D., Kishony R.. Antibiotic combinations reduce Staphylococcus aureus clearance. Nature. 2022;610(7932):540–546. doi: 10.1038/s41586-022-05260-5. [DOI] [PMC free article] [PubMed] [Google Scholar]
  3. Kamrad S., Rodriguez-Lopez M., Cotobal C., Correia-Melo C., Ralser M., Bähler J.. Pyphe, a python toolbox for assessing microbial growth and cell viability in high-throughput colony screens. eLife. 2020;9:e55160. doi: 10.7554/eLife.55160. [DOI] [PMC free article] [PubMed] [Google Scholar]
  4. Zheng E. J., Stokes J. M., Collins J. J.. Eradicating bacterial persisters with combinations of strongly and weakly metabolism-dependent antibiotics. Cell Chem. Biol. 2020;27:1544. doi: 10.1016/j.chembiol.2020.08.015. [DOI] [PubMed] [Google Scholar]
  5. Alves J., Sargison F. A., Stawarz H., Fox W. B., Huete S. G., Hassan A., McTeir B., Pickering A. C.. A case report: Insights into reducing plastic waste in a microbiology laboratory. Access Microbiol. 2021;3(3):000173. doi: 10.1099/acmi.0.000173. [DOI] [PMC free article] [PubMed] [Google Scholar]
  6. Radke S. A., Alocilja E. C.. A high density microelectrode array biosensor for detection of E. coli O157: H7. Biosens. Bioelectron. 2005;20:1662–1667. doi: 10.1016/j.bios.2004.07.021. [DOI] [PubMed] [Google Scholar]
  7. Varshney M., Li Y.. Interdigitated array microelectrode based impedance biosensor coupled with magnetic nanoparticle-antibody conjugates for detection of Escherichia coli O157: H7 in food samples. Biosens. Bioelectron. 2007;22:2408–2414. doi: 10.1016/j.bios.2006.08.030. [DOI] [PubMed] [Google Scholar]
  8. Yang L., Bashir R.. Electrical/electrochemical impedance for rapid detection of foodborne pathogenic bacteria. Biotechnol. Adv. 2008;26:135–150. doi: 10.1016/j.biotechadv.2007.10.003. [DOI] [PubMed] [Google Scholar]
  9. Owicki J. C., Parce J. W.. Biosensors based on the energy metabolism of living cells: The physical chemistry and cell biology of extracellular acidification. Biosens. Bioelectron. 1992;7(4):255–272. doi: 10.1016/0956-5663(92)87004-9. [DOI] [PubMed] [Google Scholar]
  10. Silley P., Forsythe S.. Impedance microbiology-A rapid change for microbiologists. J. Appl. Bacteriol. 1996;80:233–243. doi: 10.1111/j.1365-2672.1996.tb03215.x. [DOI] [PubMed] [Google Scholar]
  11. Wawerla M., Stolle A., Schalch B., Eisgruber H.. Impedance microbiology: Applications in food hygiene. J. Food Prot. 1999;62:1488–1496. doi: 10.4315/0362-028X-62.12.1488. [DOI] [PubMed] [Google Scholar]
  12. Tang H., Zhang W., Geng P., Wang Q. J., Jin L. T., Wu Z. R., Lou M.. A new amperometric method for rapid detection of Escherichia coli density using a self-assembled monolayer-based bienzyme biosensor. Anal. Chim. Acta. 2006;562:190–196. doi: 10.1016/j.aca.2006.01.061. [DOI] [Google Scholar]
  13. Pethig R., Markx G. H.. Applications of dielectrophoresis in biotechnology. Trends Biotechnol. 1997;15:426–432. doi: 10.1016/S0167-7799(97)01096-2. [DOI] [PubMed] [Google Scholar]
  14. Suehiro J., Ohtsubo A., Hatano T., Hara M.. Selective detection of bacteria by a dielectrophoretic impedance measurement method using an antibody-immobilized electrode chip. Sens. Actuators, B. 2006;119:319–326. doi: 10.1016/j.snb.2005.12.027. [DOI] [Google Scholar]
  15. Cheng X., Liu Y. S., Irimia D., Demirci U., Yang L., Zamir L., Rodriguez W. R., Toner M., Bashir R.. Cell detection and counting through cell lysate impedance spectroscopy in microfluidic devices. Lab Chip. 2007;7:746–755. doi: 10.1039/B705082H. [DOI] [PMC free article] [PubMed] [Google Scholar]
  16. Shynkaryk M. V., Lebovka N. I., Lanoiselle J. L., Nonus M., Bedel-Clotour C., Vorobiev E.. Electrically-assisted extraction of bio-products using high pressure disruption of yeast cells (Saccharomyces cerevisiae) J. Food Eng. 2009;92:189–195. doi: 10.1016/j.jfoodeng.2008.10.041. [DOI] [Google Scholar]
  17. Yang L.. Electrical impedance spectroscopy for detection of bacterial cells in suspensions using interdigitated microelectrodes. Talanta. 2008;74:1621–1629. doi: 10.1016/j.talanta.2007.10.018. [DOI] [PubMed] [Google Scholar]
  18. Ruan C. M., Yang L. J., Li Y. B.. Immunobiosensor chips for detection of Escherichia coli O157: H7 using electrochemical impedance spectroscopy. Anal. Chem. 2002;74:4814–4820. doi: 10.1021/ac025647b. [DOI] [PubMed] [Google Scholar]
  19. Brewster J. D., Gehring A. G., Mazenko R. S., Van Houten L. J., Crawford C. J.. Immunoelectrochemical assays for bacteria: Use of epifluorescence microscopy and rapid-scan electrochemical techniques in development of an assay for Salmonella . Anal. Chem. 1996;68:4153–4159. doi: 10.1021/ac9604503. [DOI] [PubMed] [Google Scholar]
  20. Suehiro J., Noutomi D., Shutou M., Hara M.. Selective detection of specific bacteria using dielectrophoretic impedance measurement method combined with an antigen-antibody reaction. J. Electrost. 2003;58:229–246. doi: 10.1016/S0304-3886(03)00062-7. [DOI] [Google Scholar]
  21. Gómez-Sjöberg R., Morisette D. T., Bashir R.. Impedance microbiology-on-a-chip: Microfluidic bioprocessor for rapid detection of bacterial metabolism. J. Microelectromech. Syst. 2005;14:829–838. doi: 10.1109/JMEMS.2005.845444. [DOI] [Google Scholar]
  22. Yan X. F., Wang M. H., An D.. Progress of interdigitated array microelectrodes based impedance immunosensor. Chin. J. Anal. Chem. 2011;39:1601–1610. doi: 10.1016/S1872-2040(10)60478-1. [DOI] [Google Scholar]
  23. Brooks J. L., Mirhabibollahi B., Kroll R. G.. Experimental enzyme-linked amperometric immunosensors for the detection of salmonellas in foods. J. Appl. Bacteriol. 1992;73:189–196. doi: 10.1111/j.1365-2672.1992.tb02977.x. [DOI] [PubMed] [Google Scholar]
  24. Ruan C., Wang H., Li Y.. A bienzyme electrochemical biosensor coupled with immunomagnetic separation for rapid detection of Escherichia coli O157: H7 in food samples. Trans. ASAE. 2002;45:249. doi: 10.13031/2013.7853. [DOI] [Google Scholar]
  25. Su X. L., Li Y. B.. A self-assembled monolayer-based piezoelectric immunosensor for rapid detection of Escherichia coli O157: H7. Biosens. Bioelectron. 2004;19:563–574. doi: 10.1016/S0956-5663(03)00254-9. [DOI] [PubMed] [Google Scholar]
  26. Van der Wal A., Minor M., Norde W., Zehnder A. J. B., Lyklema J.. Conductivity and dielectric dispersion of gram-positive bacterial cells. J. Colloid Interface Sci. 1997;186:71–79. doi: 10.1006/jcis.1996.4615. [DOI] [PubMed] [Google Scholar]
  27. Wilson W. W., Wade M. M., Holman S. C., Champlin F. R.. Status of methods for assessing bacterial cell surface charge properties based on zeta potential measurements. J. Microbiol Methods. 2001;43:153–164. doi: 10.1016/S0167-7012(00)00224-4. [DOI] [PubMed] [Google Scholar]
  28. Dalton F. N., Herkelrath W. N., Rawlins D. S., Rhoades J. D.. Time-domain reflectometry-simultaneous measurement of soil-water content and electrical-conductivity with a single probe. Science. 1984;224:989–990. doi: 10.1126/science.224.4652.989. [DOI] [PubMed] [Google Scholar]
  29. Ren T., Noborio K., Horton R.. Measuring soil water content, electrical conductivity, and thermal properties with a thermo-time domain reflectometry probe. Soil Sci. Soc. Am. J. 1999;63:450–457. doi: 10.2136/sssaj1999.03615995006300030005x. [DOI] [Google Scholar]
  30. Phillipson M. C., Baker P. H., Davies M., Ye Z., McNaughtan A., Galbraith G. H., McLean R. C.. Moisture measurement in building materials: An overview of current methods and new approaches. Build Serv. Eng. Res. Technol. 2007;28:303–316. doi: 10.1177/0143624407084184. [DOI] [Google Scholar]
  31. Lu Y., Liu X., Zhang M., Heitman J., Horton R., Ren T.. Thermo–time domain reflectometry method: Advances in monitoring in situ soil bulk density. Soil Sci. Soc. Am. J. 2020;84:1354–1360. doi: 10.1002/saj2.20147. [DOI] [Google Scholar]
  32. He H., Aogu K., Li M., Xu J., Sheng W., Jones S. B., Lv J.. Chapter Three -A review of time domain reflectometry (TDR) applications in porous media. Adv. Agron. 2021;168:83–155. doi: 10.1016/bs.agron.2021.02.003. [DOI] [Google Scholar]
  33. Baker J. M., Allmaras R. R.. System for automating and multiplexing soil moisture measurement by time-domain reflectometry. Soil Sci. Soc. Am. J. 1990;54:1–6. doi: 10.2136/sssaj1990.03615995005400010001x. [DOI] [Google Scholar]
  34. Herkelrath W. N., Hamburg S. P., Murphy F.. Automatic, real-time monitoring of soil moisture in a remote field area with time domain reflectometry. Water Resour. Res. 1991;27:857–864. doi: 10.1029/91WR00311. [DOI] [Google Scholar]
  35. Bose T. K., Bottreau A. M., Chahine R.. Development of a dipole probe for the study of dielectric-properties of biological substances in radiofrequency and microwave region with time-domain reflectometry. IEEE Trans. Instrum. Meas. 1986;35:56–60. doi: 10.1109/TIM.1986.6499057. [DOI] [Google Scholar]
  36. Heimovaara T. J., Focke A. G., Bouten W., Verstraten J. M.. Assessing temporal variations in soil-water composition with time-domain reflectometry. Soil Sci. Soc. Am. J. 1995;59:689–698. doi: 10.2136/sssaj1995.03615995005900030009x. [DOI] [Google Scholar]
  37. Huisman J. A., Lin C. P., Weihermüller L., Vereecken H.. Accuracy of bulk electrical conductivity measurements with time domain reflectometry. Vadose Zone J. 2008;7:426–433. doi: 10.2136/vzj2007.0139. [DOI] [Google Scholar]
  38. Sheng G. P., Yu H. Q., Li X. Y.. Extracellular polymeric substances (EPS) of microbial aggregates in biological wastewater treatment systems: A review. Biotechnol. Adv. 2010;28:882–894. doi: 10.1016/j.biotechadv.2010.08.001. [DOI] [PubMed] [Google Scholar]
  39. Lin D., Story S. D., Walker S. L., Huang Q., Cai P.. Influence of extracellular polymeric substances on the aggregation kinetics of TiO2 nanoparticles. Water Res. 2016;104:381–388. doi: 10.1016/j.watres.2016.08.044. [DOI] [PubMed] [Google Scholar]
  40. Dubois M., Gilles K. A., Hamilton J. K., Rebers P. A., Smith F.. Colorimetric method for determination of sugars and related substances. Anal. Chem. 1956;28:350–356. doi: 10.1021/ac60111a017. [DOI] [Google Scholar]
  41. Jones S. B., Wraith J. M., Or D.. Time domain reflectometry measurement principles and applications. Hydrol Process. 2002;16:141–153. doi: 10.1002/hyp.513. [DOI] [Google Scholar]
  42. Flemming H. C., Wingender J., Szewzyk U., Steinberg P., Rice S. A., Kjelleberg S.. Biofilms: An emergent form of bacterial life. Nat. Rev. Microbiol. 2016;14:563–575. doi: 10.1038/nrmicro.2016.94. [DOI] [PubMed] [Google Scholar]
  43. Xiao Y., Zhang E., Zhang J., Dai Y., Yang Z., Christensen H. E. M., Ulstrup J., Zhao F.. Extracellular polymeric substances are transient media for microbial extracellular electron transfer. Sci. Adv. 2017;3:e1700623. doi: 10.1126/sciadv.1700623. [DOI] [PMC free article] [PubMed] [Google Scholar]
  44. Shynkaryk M. V., Pyatkovskyy T., Yousef A. E., Sastry S. K.. In-situ monitoring of inactivation of Listeria innocua under high hydrostatic pressure using electrical conductivity measurement. J. Food Eng. 2020;285:110087. doi: 10.1016/j.jfoodeng.2020.110087. [DOI] [Google Scholar]
  45. Vollmer W., Blanot D., de Pedro M. A.. Peptidoglycan structure and architecture. FEMS Microbiol. Rev. 2008;32:149–167. doi: 10.1111/j.1574-6976.2007.00094.x. [DOI] [PubMed] [Google Scholar]
  46. Demchick P., Koch A. L.. The permeability of the wall fabric of Escherichia coli and Bacillus subtilis . J. Bacteriol. 1996;178:768–773. doi: 10.1128/jb.178.3.768-773.1996. [DOI] [PMC free article] [PubMed] [Google Scholar]
  47. Touhami A., Jericho M. H., Beveridge T. J.. Atomic Force Microscopy of Cell Growth and Division in Staphylococcus aureus. J. Bacteriol. 2004;186:3286–3295. doi: 10.1128/JB.186.11.3286-3295.2004. [DOI] [PMC free article] [PubMed] [Google Scholar]
  48. Vollmer W.. Structural variation in the glycan strands of bacterial peptidoglycan. FEMS Microbiol. Rev. 2008;32:287–306. doi: 10.1111/j.1574-6976.2007.00088.x. [DOI] [PubMed] [Google Scholar]
  49. Pankratova G., Hederstedt L., Gorton L.. Extracellular electron transfer features of Gram-positive bacteria. Anal. Chim. Acta. 2019;1076:32–47. doi: 10.1016/j.aca.2019.05.007. [DOI] [PubMed] [Google Scholar]
  50. Pankratova G., Szypulska E., Pankratov D., Leech D., Gorton L.. Electron transfer between the Gram-positive Enterococcus faecalis bacterium and electrode surface through osmium redox polymers. ChemElectrochem. 2019;6:110–113. doi: 10.1002/celc.201800683. [DOI] [Google Scholar]
  51. Jiang Y., Liu Y., Zhang X., Gao H., Mou L., Wu M., Zhang W., Xin F., Jiang M.. Biofilm application in the microbial biochemicals production process. Biotechnol. Adv. 2021;48:107724. doi: 10.1016/j.biotechadv.2021.107724. [DOI] [PubMed] [Google Scholar]
  52. Cuny L., Pfaff D., Luther J., Ranzinger F., Ödman P., Gescher J., Guthausen G., Horn H., Hille-Reichel A.. Evaluation of productive biofilms for continuous lactic acid production. Biotechnol. Bioeng. 2019;116:2687–2697. doi: 10.1002/bit.27080. [DOI] [PubMed] [Google Scholar]
  53. Wang Y. J., Liao Q., Wang Y. Z., Zhu X., Li J.. Effects of flow rate and substrate concentration on the formation and H2 production of photosynthetic bacterial biofilms. Bioresour. Technol. 2011;102:6902–6908. doi: 10.1016/j.biortech.2011.04.026. [DOI] [PubMed] [Google Scholar]
  54. Seručnik M., Dogsa I., Zadravec L. J., Mandic-Mulec I., Žnidaršič-Plazl P.. Development of a microbioreactor for Bacillus subtilis biofilm cultivation. Micromachines. 2024;15:1037. doi: 10.3390/mi15081037. [DOI] [PMC free article] [PubMed] [Google Scholar]

Associated Data

This section collects any data citations, data availability statements, or supplementary materials included in this article.

Supplementary Materials

tg5c00060_si_001.pdf (603.5KB, pdf)

Articles from ACS Measurement Science Au are provided here courtesy of American Chemical Society

RESOURCES