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Assay for Bacteria in Porous Media by Diffusion-Weighted NMR
JOURNAL OF MAGNETIC RESONANCE, ARTICLE NO.
Series B 113, 9–15 (1996)
0149
Assay for Bacteria in Porous Media by Diffusion-Weighted NMR K. POTTER,* R. L. KLEINBERG,† F. J. BROCKMAN,‡
AND
E. W. MC FARLAND * ,§
*Department of Chemical Engineering, University of California, Santa Barbara, California 93106-5080; †Schlumberger-Doll Research, Ridgefield, Connecticut; and ‡Environmental Microbiology Group, Pacific Northwest National Laboratory, Richland, Washington Received January 23, 1996; revised July 5, 1996
In this work, an NMR technique capable of detecting bacterial cells and measuring the cell density in suspension and in porous media has been developed. It is based on the pulsed-field-gradient technique and relies on the fact that extracellular water diffuses freely while intracellular water is completely restricted by the relatively impermeable cell wall of the bacterium. At high wave vectors, the signal from extracellular water is completely suppressed while the signal from intracellular water is comparatively unaffected. This technique has been applied to the mapping of bacterial distributions in porous media. This method is presented as a nondestructive, real-time technique for biomass characterization within laboratory column and flow cell experiments, and possibly for monitoring in situ bioremediation. q 1996 Academic Press, Inc.
bution can be performed in the field, using NMR well-logging equipment (2, 3). Bacteria used in the bioremediation process are typically about 1 mm in diameter and the density of cells ranges from 10 6 to 10 9 cells per gram of sediment or soil. Therefore, the NMR signal from the intracellular water protons is likely to be overwhelmed by the signal from surrounding water. Since extracellular water diffuses comparatively freely in contrast to intracellular water, it is possible to use a diffusion filter to eliminate the signal from extracellular water. This is routinely used to acquire spectroscopic information of intracellular components (4). Recently, diffusion-weighted imaging was also used to map the distribution of mammalian cells in a hollow fiber bioreactor; however, NMR measurements of cell density were not reported (5). Sodium NMR has been used to measure the concentration of hybridoma cells within a hollow fiber bioreactor but this technique demands that the sodium concentration of the extracellular fluid is known (6). A reduction in the sodium signal is related to an increase in the cell density. In this work, we rely on the fact that the diffusion of water in small isolated compartments is restricted, and in the presence of a large field gradient there is little signal attenuation. We use this phenomenon to detect mobile protons confined within a bacterial cell. Their small size and the relative impermeability of their cell wall make bacteria ideally suited to this technique. We have used this technique to detect bacteria in suspension and to obtain quantitative measurements of cell density. We also demonstrate the use of this technique in spatially mapping the distribution of bacteria within water-saturated quartz sand packs. Although cell densities employed in these experiments were higher than those found naturally, much lower cell densities can be interrogated with more signal averaging, larger sample sizes, and lower-resolution imaging.
INTRODUCTION
Bioremediation is a technique whereby micro-organisms are used to remove pollutants from the environment. However, few techniques are capable of monitoring microbial populations in situ (1). In this work, we have developed an NMR technique for monitoring the growth and redistribution of bacteria, thereby facilitating the investigation of those factors affecting the bioremediation process. Using this technique, nondestructive fine-scale real-time monitoring of bacterial densities in laboratory columns and intermediate-scale flow cell experiments can be realized. At present, bacterial distributions are determined by destructive sampling of the subsoil or sediment and subsequent extraction of cells or cellular components from a limited number of samples. Furthermore, in laboratory experiments, these analyses can only be performed at the beginning and end of the experiment. The overall result of biomass characterization techniques is a serious loss in spatial and temporal information. The ability to monitor microbial biomass growth and distribution within laboratory columns and flow cells will provide a critical link to scaling microbial processes to the field scale. It is hoped that with the development of this technology in situ measurements of microbial distri-
Basic Concept The pulsed-field-gradient spin-echo (PGSE) technique is routinely used to measure self-diffusion coefficients (7, 8). The technique involves applying a gradient pulse of ampli-
§ To whom correspondence should be addressed.
9
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1064-1866/96 $18.00 Copyright q 1996 by Academic Press, Inc. All rights of reproduction in any form reserved.
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tude G and duration d before and after the 1807 refocusing pulse. The first gradient pulse dephases the signal while the second gradient pulse restores phase coherence after a diffusion time D. For a freely diffusing species, the diffusion coefficient (D) is calculated from the echo amplitude (A) acquired for a series of gradient values by ln
S D A A0
S
Å 0D D 0
D
d ( gGd ) 2 , 3
[1]
where g is the gyromagnetic ratio (for 1H, g Å 26.75 1 10 7 rad/Ts). All echo amplitudes are normalized to that obtained without any gradient pulses (A0 ) to eliminate any correction for other time-dependent processes occurring during the diffusion time. At moderate values of the wave vector ÉkÉ Å gGd, the signal for a freely diffusing species is strongly suppressed. For water confined within a well-connected porous material, the amplitude of the echo shows diffractionlike behavior which can give important structural information (9). In biological systems, the technique gives information about the size of cells and the permeability of biological membranes (10–12). When the diffusion of water is very restricted (for example, in the pores of a porous material characterized by very low hydraulic permeability), the signal level is little attenuated at high wave vectors (13). These results may be expressed in terms of the probability that a molecule will not diffuse further than a distance 1/k during the time D, the probability of ‘‘return to the origin.’’ Similarly, the water signal from within a bacterium will persist even at high wave vectors because of its relatively small size ( Ç1 mm) and the relative impermeability of its cell wall. Surrounding water in unconsolidated soils is much less restricted, so the associated signal will be suppressed with the application of moderate gradient pulses. EXPERIMENTAL METHODS
The bacteria used in these experiments were Pseudomonas cepacia suspended in physiological saline. The bacteria were isolated from a subsurface sand aquifer in South Carolina at a depth of 660 ft below the surface (14). Cells archived at 0807C were grown to a density of approximately 10 8 cells/ml, concentrated by centrifugation, and washed three times to remove trace nutrients. The final cell density, estimated by optical density, was 2 1 10 10 cells/ml. Cells were stored at 47C for up to six months. We did not expect an increase in the cell count owing to the lack of nutrients; however, under starvation conditions, we did expect the bacterial cell size to decrease. This was later confirmed by light microscopy. At the start of these experiments, cells were typically 1.2 1 2.0 mm and after six months the cells were
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0.5 1 1.0 mm. Different cell densities were obtained by volumetric dilution of this suspension with normal saline. For experimental times up to 8 h, there was no appreciable settling. Disruption of the bacterial cell wall was achieved by sonication (Heat Systems Ultrasonics Inc., Farmingdale, NY) for about 3 minutes. Sand packs with a uniform bacterial distribution were made by adding granular quartz to about 0.5 ml of the bacterial suspension in an NMR tube (o.d. 5 mm). The quartz used in these experiments was sea sand, 40–70 mesh with a low iron content (170 ppm). An inhomogeneous bacterial distribution was prepared by removing the top half of the first sample and replacing it with water-saturated quartz. All quartz samples were imaged immediately after they were prepared to minimize changes in the distribution of bacteria. All diffusion experiments were performed on a Chemagnetics CMX spectrometer coupled to a superconducting wide-bore magnet (diameter 89 mm) operating at 11.7 T (498.6 MHz for 1H). Linear magnetic field gradients were supplied by the z coil of a Doty Scientific self-shielded gradient set driven by a Techron amplifier (Series 7700). The z coil was calibrated from water-diffusion measurements and the maximum gradient strength available was 800 G/cm. The RF probe was a saddle-coil design with an internal diameter of 5 mm and about 5 mm in length. The 907 pulse width for this coil was typically 25 ms. All diffusion data were acquired using the PGSE technique with a repeat time of 10 s. The number of scans for each experiment was 128 and the total time to acquire a single diffusion-filtered spectrum was 21 min. Typical values for D and d were 10 and 4 ms, respectively, and all experiments were conducted at room temperature. The imaging experiments were performed on a lower-field-strength magnet (4.2 T or 180.1 MHz for 1 H) with a Chemagnetics CMX spectrometer using the same gradient set, and the number of scans was doubled to improve the signal-to-noise ratio of the experiment. The RF probe used for these experiments was based on the same design as before except it was 10 mm in length. The diffusion coefficient of water in free solution, in a bacterial suspension with a known cell density, and in a sample consisting of bacteria in water-saturated unconsolidated quartz was measured with the PGSE technique. Onedimensional, diffusion-filtered projections of water-saturated quartz samples with and without bacteria were also acquired. The one-dimensional images were obtained by applying a 5 G/cm frequency-encode gradient along the axis of the NMR tube (15). The spatial resolution of each projection was approximately 50 mm. RESULTS AND DISCUSSION
Our main objective was to determine if the intracellular water of bacteria can be selectively detected using a pulsed-
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FIG. 1. (a) Water signal acquired with the PGSE technique without the application of a gradient. The chemical shift of water is referenced to TMS (0 ppm). (b) Spectra acquired with the PGSE technique after the application of a diffusion gradient (320 G/cm). The broken line gives the spectrum acquired with pure water in the probe, showing there is no observable water signal at the water-proton chemical shift. The solid lines show the spectra obtained for bacterial solutions with Ç10 10 and Ç10 9 cells/ml, respectively.
field-gradient technique. In the absence of a gradient, the proton signal for bulk water is identical to that from a bacterial suspension when acquired with the PGSE RF pulse sequence alone. Figure 1a shows a typical spectrum of water for the experimental parameters, D Å 10 ms, d Å 2 ms, and G Å 0 G/cm. The water peak occurs at a chemical shift of 4.8 ppm relative to TMS. On application of a gradient, the spectrum obtained for the bacterial suspension is very different from that of bulk water. Figure 1b shows those spectra acquired with the PGSE technique using the same experimental parameters but with a gradient G Å 320 G/cm. Note that the vertical scale has changed by a factor of 3000. The broken line is the spectrum for pure water. For this sample, there was no observable signal at the water proton chemical shift while the signal at 6.7 ppm was attributed to the background signal of the probe. The solid dark lines show the signal obtained for bacteria-containing solutions with Ç10 10 and Ç10 9 cells/ml, respectively. The solution containing fewer bacteria gave a lower signal which suggests that the signal detected was indeed from the intracellular water of bacteria. To clearly establish that the observed signal was due solely to water restricted to the bacterial cell, we repeated our experiment using a sonicated bacterial suspension with fewer than 1% intact cells. We found there was no residual signal at the water proton chemical shift for high gradient values. This experiment confirms that to obtain such a large proton signal at high wave vectors the bacterial cell wall must remain intact. In addition, it proves that the residual signal at high wave vectors does not come from macromolecules within the cytoplasm or from other slowly diffusing or immobile entities.
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The residual signal at the water-proton chemical shift for increasing gradient values is shown in Fig. 2 for pure water (closed circles) and for a 2 1 10 10 cells/ml bacterial suspension (open triangles) under the same experimental conditions ( D Å 10 ms, d Å 4 ms). For small gradient values, the data for pure water and the bacterial suspension lie along the same line. At higher gradient values, the signal from bulk water drops to the noise floor of the instrument while a measurable signal from the bacterial suspension persists. The slope of the line fitted through the data points for water is 2.2 1 10 05 cm2 /s which compares well with the diffusion coefficient of bulk water. The line through the later points for the bacterial suspension is straight on the semilogarithmic plot. When this line is extrapolated to zero gradient, an intercept of 0.002 is found. We interpret this as the ratio of intracellular to total protons in the sample. Since this data set was acquired at the end of our study, we used the cell dimensions of starved cells (diameter 0.5 mm, length 1.0 mm) to estimate the bacterial cell count which was found to be 1 1 10 10 cells/ml. This compares favorably with our original cell count. Hence, it is possible to obtain a quantitative measure of the cell density, provided the cell volume is accurately known. This is an important consideration when using the same batch of cells over several months. In some instances, a second diffusion coefficient can be extracted from a line fitted through those points acquired at high gradient values. This gives a reasonable measure of intracellular diffusion only if the distance a molecule diffuses during the measurement time is very small compared to the size of the cell (16, 17). The root-mean-square displacement q of water in 10 ms is xrms Ç (6DD ) Ç 10 mm, assuming the diffusion coefficient of bulk water. Thus, we do not
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FIG. 2. Plot of echo attenuation for increasing gradient values for pure water (closed circles) and for a 2 1 10 10 cells/ml bacterial suspension (open triangles). The slope of the line fitted through the water data indicates a diffusion coefficient of 2.2 1 10 05 cm2 /s. The line drawn through the bacterial data for high gradient values is straight on the semilogarithmic plot.
expect to obtain a valid estimate of intracellular diffusion coefficient when D Å 10 ms, even if much larger eukaryotic cells (diameter 10 mm) were the subject of this study. In 10 ms, a water molecule traverses a bacterium about 10 times. Although a diffusion coefficient can be formally computed, it will simply be the ratio of the square of the cell size to the diffusion time to within a numerical factor of order unity. If the bacterial cell wall was extremely permeable, there would be severe signal loss at high gradient values as water molecules escape to an environment in which they can diffuse relatively freely. This is not consistent with our data. An alternative hypothesis is that the lack of signal attenuation may be attributed to very slow diffusion of fluids within the bacteria. Latour et al. measured the water proton diffusion coefficient in the cytoplasm of red blood cells (11). They prepared the blood cells by the removal of extracellular water by centrifugation, cell lysis with hypotonic saline, and then concentration by dialysis, and found that the diffusion coefficient of water in the cytoplasm was about 70% of the bulk water value. In another study, the diffusion coefficient of onion intracellular fluid was measured, and a similar result obtained (17). The loss of signal from the intracellular water with increasing gradient strength was not due to gross motion of the bacteria, as the bacteria used in this study were starved, and therefore incapable of rapid swimming, which would
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have led to some signal attenuation. We also verified that, over an 8-h period, the convection or settling of the bacteria did not affect the measured bacteria signal significantly. We have also excluded the effect of random Brownian motion of the bacteria. According to the classical theory of Brownian motion, the diffusion coefficient of a spherical particle of radius a in a medium of viscosity h is DÅ
k BT , 6pha
[2]
where kB is Boltzmann’s constant and T is the absolute temperature. The diffusion coefficient of a bacterium in water, D Å 4 1 10 09 cm2 /s, is much smaller than the diffusion coefficient indicated by the slope of the line at high wave vectors. The modest signal attenuation can be understood in terms of the ‘‘probability of return to the origin’’ (13). The fraction of molecules that, at time D, have returned to within a distance 1/kmax of their positions at time zero is given by F( D, kmax ) Å
3 A0k 3max
*
kmax
A(k, D )k 2 dk.
[3]
0
As kmax gets larger, the measurement probes a smaller vol-
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FIG. 3. Plot showing the residual signal intensity acquired with a diffusion filter relative to that without a diffusion filter for water (closed circles) and for bacteria in a water-saturated quartz sand pack (open triangles). Also shown are the predicted bacteria concentration and the noise floor of the instrument.
ume around the origin. As this volume is reduced, we can expect fewer molecules to have returned to it at time D. Therefore, F( D, kmax ) is a decreasing function of kmax , which implies that A(k, D ) is a decreasing function of k. Diffusion filtering was applied to a quartz sand pack containing 2 1 10 10 cells/ml in the pore fluid. Figure 3 shows the residual signal intensity acquired with a diffusion filter relative to that with no diffusion filter for a tube of water (closed circles) and for bacteria in water-saturated quartz sand (open triangles). The solid line shows the diffusion attenuation for bulk water. The residual signal at high gradient values is from the bacteria. The broken line shown is straight on the semilogarithmic plot. The volume fraction of bacteria in the fluid-saturated sand pack can be estimated by the extrapolation of this line to zero. The predicted bacteria signal, calculated from the product of bacterium volume and the number density in the liquid phase, is plotted as a closed square in Fig. 3. For this estimate, the bacteria were assumed to be 1 mm in diameter and roughly spherical; however, the larger intercept value suggests that we have underestimated the bacterial cell volume. This is indeed possible as this experiment was conducted in the early months of this study. The dashed line near the bottom of the plot represents the noise floor of the instrument. The sand used in this investigation was fairly coarse, having grain sizes around 300 mm, and the pores were in the range of 50 mm. Thus, the restriction of extracellular water diffusion due to the presence of solid is insignificant. In very fine silts and consolidated rocks, pore sizes can be 1 mm or smaller. In such circumstances, the ability of the diffusion filter to distinguish extracellular water from intracellular water is reduced. The results demonstrate that at sufficiently high gradient
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values, the signal from the extracellular water is eliminated. While we have used a rather high concentration of bacteria, note that the bacteria signal is a factor of 400 above the noise floor; we discuss below how the signal-to-noise ratio can be enhanced still further. Furthermore, this diffusion filter can be used to spatially resolve bacteria populations in sand packs without interference from surrounding sand grains or water. Using a single frequency-encode gradient in conjunction with a diffusion filter, the bacterial distribution within a water-saturated quartz sand pack can be identified from a one-dimensional signal intensity profile. The diffusion-weighted one-dimensional proton density map of pure water in unconsolidated quartz is shown in Fig. 4a. The gradients were 0 and 75 G/ cm for the respective profiles with D Å 10 ms and d Å 4 ms in both cases. The diffusion filter was chosen so that there was no residual signal from freely diffusing water. The quartz sample extended beyond the sensitive volume of the coil, and thus the profile represents the proton density along the axis of the RF probe. The application of the gradient completely suppresses the signal from bulk water. The efficacy of this technique for mapping the bacterial distribution in water-saturated quartz sand was investigated using one sample in which the bacteria were uniformly distributed and a second in which the bacterial distribution was nonuniform. Without the diffusion filter, the signal from the water protons was uniform in both cases as shown in Figs. 4b and 4c. Upon the application of a diffusion filter (75 G/cm), the bacteria signal could be clearly distinguished with a signal-tonoise ratio Ç4. A uniform signal was obtained in the sample where the bacteria were evenly distributed throughout the quartz sand pack, Fig. 4b, while for the sample with an inhomogeneous bacterial distribution resulting from the removal of bacteria from the top of the sample (shown on the right of the image profile), the signal was not uniform, Fig. 4c. The interface between the water-saturated quartz with and without bacteria was gradually sloping because, when preparing the top layer of quartz, we first added a layer of water to which we added more quartz. We believe that the water might have depleted the density of bacteria at the interface. The signal acquired at 75 G/cm is well above the noise floor of the instrument and can be further improved with more signal averaging, larger pixels, or the use of a larger-diameter sample. The quantity of bacteria in the fluid phase of these samples was estimated from the ratio of the filtered to unfiltered signal ( Ç0.01). Assuming the bacterial cells were 1 mm in diameter, the bacterial cell count was 2 1 10 10 cells/ml which compares favorably with the original cell count. CONCLUSIONS
Bacteria in suspension and in water-saturated quartz sand packs can be detected and assayed by suppressing the NMR signal originating from the extracellular water using a
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FIG. 4. One-dimensional, diffusion-weighted proton density maps of (a) water, (b) uniformly distributed bacteria, and (c) inhomogeneously distributed bacteria, in water-saturated quartz sand with and without diffusion filtering. The diffusion filter ( G Å 75 G/cm) was chosen so that there was no residual signal from the surrounding water protons. The pixel resolution was 50 mm.
pulsed-magnetic-field-gradient technique. The distribution of bacteria in water-saturated quartz sand packs can be profiled by suppressing the water signal with a PGSE filter, then applying a standard one-dimensional imaging sequence. These techniques are potentially useful for laboratory-scale column experiments of bacteria growth and mobility in porous media. They can also be used to quantitatively assay bacteria in water-saturated porous media and sediments and to monitor multiplication and movement of bacteria in those materials in the presence of different nutrient sources (e.g., crude oils, vegetable oils) and nutrient gradients. Using similar techniques, oil-field NMR instruments (2, 3) might be applied to environmental monitoring and biomass detection in subsurface sediments. This technology may also be useful in biotechnology and food preservation.
03 and NSF PYI Award DIR-9057151 to E.W.M. The NMR facility at UCSB was funded, in part, by NSF Grant DMR 92-22527. We thank Dr. Ray Wildung for his help in initiating this research and thank Mr. Michael Mann for his technical assistance. We thank Patricia White for her assistance in preparing this manuscript.
ACKNOWLEDGMENTS
6. A. Mancuso, E. J. Fernandez, H. W. Blanch, and D. S. Clark, Bio/ Technology 8, 1282–1285 (1990).
This research was supported by the Department of Energy, Subsurface Science Program 299395A-F6 (F.J.B., K.P.), and NIH Grant GM48887-
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REFERENCES 1. R. M. Atlas, C&E News, April 3, 32–42 (1995). 2. R. L. Kleinberg, A. Sezginer, D. D. Griffin, and M. Fukuhara, J. Magn. Reson. 97, 466–485 (1992). 3. Z. Taicher, G. Coates, Y. Gitartz, and L. Berman, Magn. Reson. Imaging 12, 285–289 (1994). 4. P. C. M. van Zijl, C. T. W. Moonen, P. Faustino, J. Pekar, O. Kaplan, and J. S. Cohen, Proc. Natl. Acad. Sci. USA 88, 3228–3232 (1991). 5. R. Callies, M. E. Jackson, and K. M. Brindle, Bio/Technology 12, 75–78 (1994).
7. E. O. Stejskal and J. E. Tanner, J. Chem. Phys. 42, 288–292 (1965).
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BACTERIAL ASSAY BY DIFFUSION-WEIGHTED NMR 8. P. Stilbs, Prog. NMR Spectrosc. 19, 1–45 (1987). 9. P. T. Callaghan, A. Coy, D. MacGowan, K. J. Packer, and F. O. Zelaya, Nature (London) 351, 467–444 (1991). 10. J. E. Tanner, Arch. Biochem. Biophys. 224, 416–428 (1983). 11. L. L. Latour, K. Svoboda, P. P. Mitra, and C. H. Sotak, Proc. Natl. Acad. Sci. USA 91, 1229–1233 (1994). 12. D. G. Cory and A. N. Garroway, Magn. Reson. Med. 14, 435–444 (1990).
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13. P. P. Mitra, L. L. Latour, R. L. Kleinberg, and C. H. Sotak, J. Magn. Reson. A 114, 47–58 (1995). 14. F. J. Brockman, B. A. Denovan, R. J. Hicks, and J. K. Fredrickson, Appl. Environ. Microbiol. 55, 1029–1032 (1989). 15. P. C. Lauterbur, Nature (London) 242, 190–191 (1973). 16. R. C. Wayne and R. M. Cotts, Phys. Rev. 151, 264–272 (1966); Erratum: Phys. Rev. 159, 486 (1967). 17. L. L. Latour, P. P. Mitra, R. L. Kleinberg, and C. H. Sotak, J. Magn. Reson. A 101, 342–346 (1993).
magbas
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Series B 113, 9–15 (1996)
0149
Assay for Bacteria in Porous Media by Diffusion-Weighted NMR K. POTTER,* R. L. KLEINBERG,† F. J. BROCKMAN,‡
AND
E. W. MC FARLAND * ,§
*Department of Chemical Engineering, University of California, Santa Barbara, California 93106-5080; †Schlumberger-Doll Research, Ridgefield, Connecticut; and ‡Environmental Microbiology Group, Pacific Northwest National Laboratory, Richland, Washington Received January 23, 1996; revised July 5, 1996
In this work, an NMR technique capable of detecting bacterial cells and measuring the cell density in suspension and in porous media has been developed. It is based on the pulsed-field-gradient technique and relies on the fact that extracellular water diffuses freely while intracellular water is completely restricted by the relatively impermeable cell wall of the bacterium. At high wave vectors, the signal from extracellular water is completely suppressed while the signal from intracellular water is comparatively unaffected. This technique has been applied to the mapping of bacterial distributions in porous media. This method is presented as a nondestructive, real-time technique for biomass characterization within laboratory column and flow cell experiments, and possibly for monitoring in situ bioremediation. q 1996 Academic Press, Inc.
bution can be performed in the field, using NMR well-logging equipment (2, 3). Bacteria used in the bioremediation process are typically about 1 mm in diameter and the density of cells ranges from 10 6 to 10 9 cells per gram of sediment or soil. Therefore, the NMR signal from the intracellular water protons is likely to be overwhelmed by the signal from surrounding water. Since extracellular water diffuses comparatively freely in contrast to intracellular water, it is possible to use a diffusion filter to eliminate the signal from extracellular water. This is routinely used to acquire spectroscopic information of intracellular components (4). Recently, diffusion-weighted imaging was also used to map the distribution of mammalian cells in a hollow fiber bioreactor; however, NMR measurements of cell density were not reported (5). Sodium NMR has been used to measure the concentration of hybridoma cells within a hollow fiber bioreactor but this technique demands that the sodium concentration of the extracellular fluid is known (6). A reduction in the sodium signal is related to an increase in the cell density. In this work, we rely on the fact that the diffusion of water in small isolated compartments is restricted, and in the presence of a large field gradient there is little signal attenuation. We use this phenomenon to detect mobile protons confined within a bacterial cell. Their small size and the relative impermeability of their cell wall make bacteria ideally suited to this technique. We have used this technique to detect bacteria in suspension and to obtain quantitative measurements of cell density. We also demonstrate the use of this technique in spatially mapping the distribution of bacteria within water-saturated quartz sand packs. Although cell densities employed in these experiments were higher than those found naturally, much lower cell densities can be interrogated with more signal averaging, larger sample sizes, and lower-resolution imaging.
INTRODUCTION
Bioremediation is a technique whereby micro-organisms are used to remove pollutants from the environment. However, few techniques are capable of monitoring microbial populations in situ (1). In this work, we have developed an NMR technique for monitoring the growth and redistribution of bacteria, thereby facilitating the investigation of those factors affecting the bioremediation process. Using this technique, nondestructive fine-scale real-time monitoring of bacterial densities in laboratory columns and intermediate-scale flow cell experiments can be realized. At present, bacterial distributions are determined by destructive sampling of the subsoil or sediment and subsequent extraction of cells or cellular components from a limited number of samples. Furthermore, in laboratory experiments, these analyses can only be performed at the beginning and end of the experiment. The overall result of biomass characterization techniques is a serious loss in spatial and temporal information. The ability to monitor microbial biomass growth and distribution within laboratory columns and flow cells will provide a critical link to scaling microbial processes to the field scale. It is hoped that with the development of this technology in situ measurements of microbial distri-
Basic Concept The pulsed-field-gradient spin-echo (PGSE) technique is routinely used to measure self-diffusion coefficients (7, 8). The technique involves applying a gradient pulse of ampli-
§ To whom correspondence should be addressed.
9
AID
JMRB 7449
/
6o11$$$$41
09-25-96 21:37:59
1064-1866/96 $18.00 Copyright q 1996 by Academic Press, Inc. All rights of reproduction in any form reserved.
magbas
AP: Mag Res, Series B
10
POTTER ET AL.
tude G and duration d before and after the 1807 refocusing pulse. The first gradient pulse dephases the signal while the second gradient pulse restores phase coherence after a diffusion time D. For a freely diffusing species, the diffusion coefficient (D) is calculated from the echo amplitude (A) acquired for a series of gradient values by ln
S D A A0
S
Å 0D D 0
D
d ( gGd ) 2 , 3
[1]
where g is the gyromagnetic ratio (for 1H, g Å 26.75 1 10 7 rad/Ts). All echo amplitudes are normalized to that obtained without any gradient pulses (A0 ) to eliminate any correction for other time-dependent processes occurring during the diffusion time. At moderate values of the wave vector ÉkÉ Å gGd, the signal for a freely diffusing species is strongly suppressed. For water confined within a well-connected porous material, the amplitude of the echo shows diffractionlike behavior which can give important structural information (9). In biological systems, the technique gives information about the size of cells and the permeability of biological membranes (10–12). When the diffusion of water is very restricted (for example, in the pores of a porous material characterized by very low hydraulic permeability), the signal level is little attenuated at high wave vectors (13). These results may be expressed in terms of the probability that a molecule will not diffuse further than a distance 1/k during the time D, the probability of ‘‘return to the origin.’’ Similarly, the water signal from within a bacterium will persist even at high wave vectors because of its relatively small size ( Ç1 mm) and the relative impermeability of its cell wall. Surrounding water in unconsolidated soils is much less restricted, so the associated signal will be suppressed with the application of moderate gradient pulses. EXPERIMENTAL METHODS
The bacteria used in these experiments were Pseudomonas cepacia suspended in physiological saline. The bacteria were isolated from a subsurface sand aquifer in South Carolina at a depth of 660 ft below the surface (14). Cells archived at 0807C were grown to a density of approximately 10 8 cells/ml, concentrated by centrifugation, and washed three times to remove trace nutrients. The final cell density, estimated by optical density, was 2 1 10 10 cells/ml. Cells were stored at 47C for up to six months. We did not expect an increase in the cell count owing to the lack of nutrients; however, under starvation conditions, we did expect the bacterial cell size to decrease. This was later confirmed by light microscopy. At the start of these experiments, cells were typically 1.2 1 2.0 mm and after six months the cells were
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0.5 1 1.0 mm. Different cell densities were obtained by volumetric dilution of this suspension with normal saline. For experimental times up to 8 h, there was no appreciable settling. Disruption of the bacterial cell wall was achieved by sonication (Heat Systems Ultrasonics Inc., Farmingdale, NY) for about 3 minutes. Sand packs with a uniform bacterial distribution were made by adding granular quartz to about 0.5 ml of the bacterial suspension in an NMR tube (o.d. 5 mm). The quartz used in these experiments was sea sand, 40–70 mesh with a low iron content (170 ppm). An inhomogeneous bacterial distribution was prepared by removing the top half of the first sample and replacing it with water-saturated quartz. All quartz samples were imaged immediately after they were prepared to minimize changes in the distribution of bacteria. All diffusion experiments were performed on a Chemagnetics CMX spectrometer coupled to a superconducting wide-bore magnet (diameter 89 mm) operating at 11.7 T (498.6 MHz for 1H). Linear magnetic field gradients were supplied by the z coil of a Doty Scientific self-shielded gradient set driven by a Techron amplifier (Series 7700). The z coil was calibrated from water-diffusion measurements and the maximum gradient strength available was 800 G/cm. The RF probe was a saddle-coil design with an internal diameter of 5 mm and about 5 mm in length. The 907 pulse width for this coil was typically 25 ms. All diffusion data were acquired using the PGSE technique with a repeat time of 10 s. The number of scans for each experiment was 128 and the total time to acquire a single diffusion-filtered spectrum was 21 min. Typical values for D and d were 10 and 4 ms, respectively, and all experiments were conducted at room temperature. The imaging experiments were performed on a lower-field-strength magnet (4.2 T or 180.1 MHz for 1 H) with a Chemagnetics CMX spectrometer using the same gradient set, and the number of scans was doubled to improve the signal-to-noise ratio of the experiment. The RF probe used for these experiments was based on the same design as before except it was 10 mm in length. The diffusion coefficient of water in free solution, in a bacterial suspension with a known cell density, and in a sample consisting of bacteria in water-saturated unconsolidated quartz was measured with the PGSE technique. Onedimensional, diffusion-filtered projections of water-saturated quartz samples with and without bacteria were also acquired. The one-dimensional images were obtained by applying a 5 G/cm frequency-encode gradient along the axis of the NMR tube (15). The spatial resolution of each projection was approximately 50 mm. RESULTS AND DISCUSSION
Our main objective was to determine if the intracellular water of bacteria can be selectively detected using a pulsed-
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FIG. 1. (a) Water signal acquired with the PGSE technique without the application of a gradient. The chemical shift of water is referenced to TMS (0 ppm). (b) Spectra acquired with the PGSE technique after the application of a diffusion gradient (320 G/cm). The broken line gives the spectrum acquired with pure water in the probe, showing there is no observable water signal at the water-proton chemical shift. The solid lines show the spectra obtained for bacterial solutions with Ç10 10 and Ç10 9 cells/ml, respectively.
field-gradient technique. In the absence of a gradient, the proton signal for bulk water is identical to that from a bacterial suspension when acquired with the PGSE RF pulse sequence alone. Figure 1a shows a typical spectrum of water for the experimental parameters, D Å 10 ms, d Å 2 ms, and G Å 0 G/cm. The water peak occurs at a chemical shift of 4.8 ppm relative to TMS. On application of a gradient, the spectrum obtained for the bacterial suspension is very different from that of bulk water. Figure 1b shows those spectra acquired with the PGSE technique using the same experimental parameters but with a gradient G Å 320 G/cm. Note that the vertical scale has changed by a factor of 3000. The broken line is the spectrum for pure water. For this sample, there was no observable signal at the water proton chemical shift while the signal at 6.7 ppm was attributed to the background signal of the probe. The solid dark lines show the signal obtained for bacteria-containing solutions with Ç10 10 and Ç10 9 cells/ml, respectively. The solution containing fewer bacteria gave a lower signal which suggests that the signal detected was indeed from the intracellular water of bacteria. To clearly establish that the observed signal was due solely to water restricted to the bacterial cell, we repeated our experiment using a sonicated bacterial suspension with fewer than 1% intact cells. We found there was no residual signal at the water proton chemical shift for high gradient values. This experiment confirms that to obtain such a large proton signal at high wave vectors the bacterial cell wall must remain intact. In addition, it proves that the residual signal at high wave vectors does not come from macromolecules within the cytoplasm or from other slowly diffusing or immobile entities.
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The residual signal at the water-proton chemical shift for increasing gradient values is shown in Fig. 2 for pure water (closed circles) and for a 2 1 10 10 cells/ml bacterial suspension (open triangles) under the same experimental conditions ( D Å 10 ms, d Å 4 ms). For small gradient values, the data for pure water and the bacterial suspension lie along the same line. At higher gradient values, the signal from bulk water drops to the noise floor of the instrument while a measurable signal from the bacterial suspension persists. The slope of the line fitted through the data points for water is 2.2 1 10 05 cm2 /s which compares well with the diffusion coefficient of bulk water. The line through the later points for the bacterial suspension is straight on the semilogarithmic plot. When this line is extrapolated to zero gradient, an intercept of 0.002 is found. We interpret this as the ratio of intracellular to total protons in the sample. Since this data set was acquired at the end of our study, we used the cell dimensions of starved cells (diameter 0.5 mm, length 1.0 mm) to estimate the bacterial cell count which was found to be 1 1 10 10 cells/ml. This compares favorably with our original cell count. Hence, it is possible to obtain a quantitative measure of the cell density, provided the cell volume is accurately known. This is an important consideration when using the same batch of cells over several months. In some instances, a second diffusion coefficient can be extracted from a line fitted through those points acquired at high gradient values. This gives a reasonable measure of intracellular diffusion only if the distance a molecule diffuses during the measurement time is very small compared to the size of the cell (16, 17). The root-mean-square displacement q of water in 10 ms is xrms Ç (6DD ) Ç 10 mm, assuming the diffusion coefficient of bulk water. Thus, we do not
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FIG. 2. Plot of echo attenuation for increasing gradient values for pure water (closed circles) and for a 2 1 10 10 cells/ml bacterial suspension (open triangles). The slope of the line fitted through the water data indicates a diffusion coefficient of 2.2 1 10 05 cm2 /s. The line drawn through the bacterial data for high gradient values is straight on the semilogarithmic plot.
expect to obtain a valid estimate of intracellular diffusion coefficient when D Å 10 ms, even if much larger eukaryotic cells (diameter 10 mm) were the subject of this study. In 10 ms, a water molecule traverses a bacterium about 10 times. Although a diffusion coefficient can be formally computed, it will simply be the ratio of the square of the cell size to the diffusion time to within a numerical factor of order unity. If the bacterial cell wall was extremely permeable, there would be severe signal loss at high gradient values as water molecules escape to an environment in which they can diffuse relatively freely. This is not consistent with our data. An alternative hypothesis is that the lack of signal attenuation may be attributed to very slow diffusion of fluids within the bacteria. Latour et al. measured the water proton diffusion coefficient in the cytoplasm of red blood cells (11). They prepared the blood cells by the removal of extracellular water by centrifugation, cell lysis with hypotonic saline, and then concentration by dialysis, and found that the diffusion coefficient of water in the cytoplasm was about 70% of the bulk water value. In another study, the diffusion coefficient of onion intracellular fluid was measured, and a similar result obtained (17). The loss of signal from the intracellular water with increasing gradient strength was not due to gross motion of the bacteria, as the bacteria used in this study were starved, and therefore incapable of rapid swimming, which would
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have led to some signal attenuation. We also verified that, over an 8-h period, the convection or settling of the bacteria did not affect the measured bacteria signal significantly. We have also excluded the effect of random Brownian motion of the bacteria. According to the classical theory of Brownian motion, the diffusion coefficient of a spherical particle of radius a in a medium of viscosity h is DÅ
k BT , 6pha
[2]
where kB is Boltzmann’s constant and T is the absolute temperature. The diffusion coefficient of a bacterium in water, D Å 4 1 10 09 cm2 /s, is much smaller than the diffusion coefficient indicated by the slope of the line at high wave vectors. The modest signal attenuation can be understood in terms of the ‘‘probability of return to the origin’’ (13). The fraction of molecules that, at time D, have returned to within a distance 1/kmax of their positions at time zero is given by F( D, kmax ) Å
3 A0k 3max
*
kmax
A(k, D )k 2 dk.
[3]
0
As kmax gets larger, the measurement probes a smaller vol-
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FIG. 3. Plot showing the residual signal intensity acquired with a diffusion filter relative to that without a diffusion filter for water (closed circles) and for bacteria in a water-saturated quartz sand pack (open triangles). Also shown are the predicted bacteria concentration and the noise floor of the instrument.
ume around the origin. As this volume is reduced, we can expect fewer molecules to have returned to it at time D. Therefore, F( D, kmax ) is a decreasing function of kmax , which implies that A(k, D ) is a decreasing function of k. Diffusion filtering was applied to a quartz sand pack containing 2 1 10 10 cells/ml in the pore fluid. Figure 3 shows the residual signal intensity acquired with a diffusion filter relative to that with no diffusion filter for a tube of water (closed circles) and for bacteria in water-saturated quartz sand (open triangles). The solid line shows the diffusion attenuation for bulk water. The residual signal at high gradient values is from the bacteria. The broken line shown is straight on the semilogarithmic plot. The volume fraction of bacteria in the fluid-saturated sand pack can be estimated by the extrapolation of this line to zero. The predicted bacteria signal, calculated from the product of bacterium volume and the number density in the liquid phase, is plotted as a closed square in Fig. 3. For this estimate, the bacteria were assumed to be 1 mm in diameter and roughly spherical; however, the larger intercept value suggests that we have underestimated the bacterial cell volume. This is indeed possible as this experiment was conducted in the early months of this study. The dashed line near the bottom of the plot represents the noise floor of the instrument. The sand used in this investigation was fairly coarse, having grain sizes around 300 mm, and the pores were in the range of 50 mm. Thus, the restriction of extracellular water diffusion due to the presence of solid is insignificant. In very fine silts and consolidated rocks, pore sizes can be 1 mm or smaller. In such circumstances, the ability of the diffusion filter to distinguish extracellular water from intracellular water is reduced. The results demonstrate that at sufficiently high gradient
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values, the signal from the extracellular water is eliminated. While we have used a rather high concentration of bacteria, note that the bacteria signal is a factor of 400 above the noise floor; we discuss below how the signal-to-noise ratio can be enhanced still further. Furthermore, this diffusion filter can be used to spatially resolve bacteria populations in sand packs without interference from surrounding sand grains or water. Using a single frequency-encode gradient in conjunction with a diffusion filter, the bacterial distribution within a water-saturated quartz sand pack can be identified from a one-dimensional signal intensity profile. The diffusion-weighted one-dimensional proton density map of pure water in unconsolidated quartz is shown in Fig. 4a. The gradients were 0 and 75 G/ cm for the respective profiles with D Å 10 ms and d Å 4 ms in both cases. The diffusion filter was chosen so that there was no residual signal from freely diffusing water. The quartz sample extended beyond the sensitive volume of the coil, and thus the profile represents the proton density along the axis of the RF probe. The application of the gradient completely suppresses the signal from bulk water. The efficacy of this technique for mapping the bacterial distribution in water-saturated quartz sand was investigated using one sample in which the bacteria were uniformly distributed and a second in which the bacterial distribution was nonuniform. Without the diffusion filter, the signal from the water protons was uniform in both cases as shown in Figs. 4b and 4c. Upon the application of a diffusion filter (75 G/cm), the bacteria signal could be clearly distinguished with a signal-tonoise ratio Ç4. A uniform signal was obtained in the sample where the bacteria were evenly distributed throughout the quartz sand pack, Fig. 4b, while for the sample with an inhomogeneous bacterial distribution resulting from the removal of bacteria from the top of the sample (shown on the right of the image profile), the signal was not uniform, Fig. 4c. The interface between the water-saturated quartz with and without bacteria was gradually sloping because, when preparing the top layer of quartz, we first added a layer of water to which we added more quartz. We believe that the water might have depleted the density of bacteria at the interface. The signal acquired at 75 G/cm is well above the noise floor of the instrument and can be further improved with more signal averaging, larger pixels, or the use of a larger-diameter sample. The quantity of bacteria in the fluid phase of these samples was estimated from the ratio of the filtered to unfiltered signal ( Ç0.01). Assuming the bacterial cells were 1 mm in diameter, the bacterial cell count was 2 1 10 10 cells/ml which compares favorably with the original cell count. CONCLUSIONS
Bacteria in suspension and in water-saturated quartz sand packs can be detected and assayed by suppressing the NMR signal originating from the extracellular water using a
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FIG. 4. One-dimensional, diffusion-weighted proton density maps of (a) water, (b) uniformly distributed bacteria, and (c) inhomogeneously distributed bacteria, in water-saturated quartz sand with and without diffusion filtering. The diffusion filter ( G Å 75 G/cm) was chosen so that there was no residual signal from the surrounding water protons. The pixel resolution was 50 mm.
pulsed-magnetic-field-gradient technique. The distribution of bacteria in water-saturated quartz sand packs can be profiled by suppressing the water signal with a PGSE filter, then applying a standard one-dimensional imaging sequence. These techniques are potentially useful for laboratory-scale column experiments of bacteria growth and mobility in porous media. They can also be used to quantitatively assay bacteria in water-saturated porous media and sediments and to monitor multiplication and movement of bacteria in those materials in the presence of different nutrient sources (e.g., crude oils, vegetable oils) and nutrient gradients. Using similar techniques, oil-field NMR instruments (2, 3) might be applied to environmental monitoring and biomass detection in subsurface sediments. This technology may also be useful in biotechnology and food preservation.
03 and NSF PYI Award DIR-9057151 to E.W.M. The NMR facility at UCSB was funded, in part, by NSF Grant DMR 92-22527. We thank Dr. Ray Wildung for his help in initiating this research and thank Mr. Michael Mann for his technical assistance. We thank Patricia White for her assistance in preparing this manuscript.
ACKNOWLEDGMENTS
6. A. Mancuso, E. J. Fernandez, H. W. Blanch, and D. S. Clark, Bio/ Technology 8, 1282–1285 (1990).
This research was supported by the Department of Energy, Subsurface Science Program 299395A-F6 (F.J.B., K.P.), and NIH Grant GM48887-
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13. P. P. Mitra, L. L. Latour, R. L. Kleinberg, and C. H. Sotak, J. Magn. Reson. A 114, 47–58 (1995). 14. F. J. Brockman, B. A. Denovan, R. J. Hicks, and J. K. Fredrickson, Appl. Environ. Microbiol. 55, 1029–1032 (1989). 15. P. C. Lauterbur, Nature (London) 242, 190–191 (1973). 16. R. C. Wayne and R. M. Cotts, Phys. Rev. 151, 264–272 (1966); Erratum: Phys. Rev. 159, 486 (1967). 17. L. L. Latour, P. P. Mitra, R. L. Kleinberg, and C. H. Sotak, J. Magn. Reson. A 101, 342–346 (1993).
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