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The potential of laser scanning cytometry for early warning of algal blooms in desalination plant feedwater
Desalination 277 (2011) 193–200
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Desalination j o u r n a l h o m e p a g e : w w w. e l s ev i e r. c o m / l o c a t e / d e s a l
The potential of laser scanning cytometry for early warning of algal blooms in desalination plant feedwater Derek R. Vardon a, Mark M. Clark b,1, David A. Ladner c,⁎ a University of Illinois at Urbana-Champaign, Department of Civil and Environmental Engineering, 4153 Newmark Civil Engineering Laboratory, 205 North Mathews Avenue, Urbana, Illinois 61801, USA b Department of Civil and Environmental Engineering, Northwestern University, 2145 Sheridan Road-A321, Evanston, IL 60208, USA c Clemson University, Department of Environmental Engineering and Earth Sciences, 163 Rich Lab, 342 Computer Court, Anderson, SC 29625, USA
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Article history: Received 21 December 2010 Received in revised form 6 April 2011 Accepted 8 April 2011 Available online 8 May 2011 Keywords: Algal bloom Algal monitoring Laser scanning cytometry Seawater desalination Solid phase cytometry
a b s t r a c t When algal blooms (such as red tide) occur, early detection of increased algal growth in intake water offers desalination plant operators advanced warning of severe fouling and provides the necessary time for preventative measures. Monitoring basic water quality measurements such as turbidity, silt density index (SDI), dissolved oxygen, and bulk fluorescence can approximate algal activity; however, these measurements lack the resolution needed to accurately track growth throughout all phases of a bloom event. This study investigates the use of a laser scanning cytometer (LSC) that detects algal cells over a wide range of concentrations (50–150,000 cells/ml), and characterizes cell shape, size, and distribution parameters through fluorescence signal imaging. Water samples are filtered to collect algae on a 0.2-μm membrane surface. Sample volume is variable so cells can be concentrated. The membrane surface is scanned with a 635-nm diode laser and the signal is processed to generate a cell count and distribution image. LSC performance was comparable to fluorescence microscopy and flow cytometry over a range of concentrations that may be encountered during a bloom event. The applicability of LSC was demonstrated using laboratory-grown cultures and seawater samples taken during a bloom event off of the coast of Long Beach, CA. © 2011 Elsevier B.V. All rights reserved.
1. Introduction With an increasing global demand for drinking water, the number and capacity of seawater desalination plants has continued to expand [1,2]. Desalination plants produce drinking water using multi-stage flash (MSF) technology or reverse osmosis (RO) membrane systems; the latter having become more popular due to lower energy consumption. Unfortunately, RO membrane systems can be severely compromised when algal blooms occur in coastal waters [3–5]. Several divisions of algae have been identified in marine blooms including, but not limited to blue-greens [6], diatoms [7], and dinoflagellates [8]; the latter being of particular concern due to their prevalence in harmful redtide events [9]. Algal blooms produce high levels of biomass that foul pretreatment systems and dramatically reduce the plant's operating efficiency [10]. Residual algal biopolymers have also been shown to significantly decrease RO membrane flux [11]. Furthermore, harmful toxins produced by certain species can be released when the cells rupture
Abbreviations: (FLM), fluorescence microscopy; (LSC), laser scanning cytometry. ⁎ Corresponding author. Tel.: + 1 864 656 5572. E-mail addresses: [email protected] (D.R. Vardon), [email protected] (M.M. Clark), [email protected] (D.A. Ladner). 1 Tel.: + 1 847 467 4540; fax: + 1 847 491 4011. 0011-9164/$ – see front matter © 2011 Elsevier B.V. All rights reserved. doi:10.1016/j.desal.2011.04.025
[5,12,13] requiring additional water quality treatment and monitoring efforts. These negative effects result in increased chemical consumption, accelerated membrane fouling rates, and in extreme cases, desalination plants must be taken off-line [5,14]. The problem is further compounded by the increased frequency and severity of coastal algal blooms occurring worldwide [15,16]. Characterization and monitoring of algal growth conditions in source water are critical to the design and operation of seawater desalination plants. With accurate monitoring of algal activity during feasibility and pilot studies, effective pretreatment technologies can be selected to ensure optimum operation of the downstream desalination process [17]. Once online, continuous monitoring of feedwater can also provide plant operators advanced notice of approaching blooms and the necessary time to take mitigative actions. Operator responses can include supplementary chemical treatments, the use of additional pretreatment devices, or the implementation of staff maintenance activities to ensure optimum plant performance [5]. These actions can prevent reductions in plant production capacity and water quality output. Algal growth conditions can be approximated using basic water quality measurements such as optical density, silt density index (SDI), dissolved oxygen content, and bulk chlorophyll fluorescence. However, inaccurate estimates of cell concentrations can result from the lack of resolution provided by basic water quality parameters. Optical density
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measurements are highly dependent on the particulate scattering coefficient which is a complex function of cell size, shape and physiology [18]. This parameter can vary among species and is greatly affected by non-algal suspended particles. Silt density index (SDI) characterizes the fouling potential of water on the membrane, but results are dependent on the pH, membrane type, and concentrations of organic and mineral compounds [19]. Dissolved oxygen levels are more indicative of biological activity but can fluctuate greatly depending on the time of day and location in the water column [20]. Lastly, bulk chlorophyll fluorescence provides results linearly correlated to the concentration of algal cells in the sample; however, the fluorescence emission signal from algal cells can vary throughout the life cycle [21] and can be influenced by intracellular carotenoids and phycobilins released during cell rupture and decay [22]. Monitoring algal growth is especially difficult at low cell concentrations during the onset of a bloom event. If flow cytometry is used, large sample volumes are required to provide a statistically significant algal count; this increases labor and processing time [23]. Highly sensitive molecular labeling methods exist to identify targeted species [24–26], but the strategy is cost prohibitive when applied to continuous monitoring applications and valuable information is lost regarding the algal fouling potential when non-targeted species are ignored. Therefore, RO desalination facilities stand to benefit from a rapid, accurate, low-cost detection system to continuously monitor bulk algal growth conditions over the full range of concentrations encountered during a bloom event. Here we explored the potential of a novel laser scanning cytometry (LSC) method to provide algal cell detection and quantification. LSC, also referred to as solid-phase cytometry [27], uses a membrane filtration technique. Algal cells are concentrated on the membrane and scanned with a laser. Fluorescence signals are used to create an image of algal cells on the membrane surface and the cells are identified and counted. Commercial laser scanning cytometers have demonstrated a detection limit of one cell per membrane for counting water-borne pathogens and toxic algae [28,29]. However, the sophisticated equipment and high capital cost of the commercial device makes it unattractive for routine monitoring use [29]. Our investigation used a prototype LSC that was previously developed for microsphere detection in water treatment research applications [30]. This LSC was built with simple, low-cost components more suitable for routine applications. Our device was tested against fluorescence microscopy (FLM) and flow cytometry to measure its performance for the detection and enumeration of algal cells. The applicability of the LSC was then demonstrated using seawater samples taken during a bloom event off of the coast of Long Beach, CA. 2. Material and methods 2.1. Algal cultures Analyses were conducted with algae grown in the laboratory and sampled from natural waters. Optimization studies were conducted with the species Heterocapsa pygmaea purchased from the ProvasoliGuillard National Center for Culture of Marine Phytoplankton (West Boothbay Harbor, Maine). H. pygmaea is a bloom-forming dinoflagellate species similar to those that cause red tide. The initial sample was spiked into several culture tubes containing f/2 media [31] and prepared with 0.45-μm-filtered San Diego seawater collected by project consultants at MWH (Pasadena, California). The species Spirulina platensis was used for image analysis with the LSC. The S. platensis were purchased from AlgaGen LLC (Vero Beach, Florida) and grown in deionized water spiked with BG11 media obtained from Sigma-Aldrich (St. Louis, Missouri). Both cultures were exposed to a constant mercury-fluorescent light source of 19 μmol-photons/m2 s at room temperature (22–24 °C), and the growth progress was monitored weekly with a Spectra Max Gemini fluorescence microplate reader (Molecular Devices, Sunnyvale, California). Cell concentrations were
periodically verified with a hemacytometer and visible-light microscopy. A batch culture rotation was employed for perpetual maintenance. An unknown species of wild algae was sampled for analysis during a bloom event off of the coast of Long Beach, CA. An abnormal blue-green discoloration of coastal water was observed on July 22nd, 2010 during daily monitoring by personnel at the Long Beach Water Department's desalination pilot facility. The color change was no longer evident the following day, and samples from both periods were shipped immediately for analysis. Visible-light microscopy verified the presence of algal cells, and samples consisted of predominantly blue-green colored microalgae ranging from 2 to 8 μm in diameter. 2.2. Interfering matrix constituents The influence of interfering matrix constituents was tested using decaying cultures of H. pygmaea and model inorganic and organic compounds. Culture growth was extended into the decay phase to elevate levels of bacteria and extracellular algogenic organic matter (AOM). The model inorganic compound was bentonite (Sigma Aldrich, St. Louis, MO). The model organic compound was humic acid (Sigma Aldrich, St. Louis, MO). 2.3. Membrane filtrations Membrane filtration was used to prepare samples for counting with the LSC. Algal cells were filtered onto 25-mm diameter black tracketched Isopore™ polycarbonate membranes with a 0.2-μm pore size (Millipore, Billerica, Massachusetts). The membrane was secured between a glass column and vacuum filtration apparatus. Sample volumes were adjusted in order to deliver an appropriate number of algal cells (~ 10,000 cells/membrane). The 10,000 cells/membrane target resulted in minimal variability during counting. The sample was prepared by pipetting a predetermined volume of algal culture into 0.45-μm pre-filtered natural seawater. The mixture was shaken sufficiently to ensure sample homogeneity and poured into the glass column. Vacuum was applied to draw the sample through the membrane and distribute the cells uniformly on the membrane surface. Wet membranes were placed on a glass slide and allowed to dry for further analysis with the LSC. 2.4. LSC prototype The laboratory-built LSC consisted of low-cost components in a simplified setup compared to commercially-available instruments. Sample membranes were placed on a rotating stage and scanned with a red, variable power, 635-nm circular beam diode laser (Coherent, Santa Clara, California) as shown in Fig. 1. (This laser is similar to those found in super market scanners.) Scans were conducted with light from the laser reflected off of a dichroic mirror (Q660LP, Chroma, Rockingham, Vermont) towards the membrane. Auto-fluorescent light from algae was separated from reflected laser light using a 685-nm longpass emission filter (HQ700/75m, Chroma, Rockingham, Vermont). The signal was then detected with a photomultiplier tube (HC120-01 module with R6357 detection tube, Hamamatsu, Bridgewater, New Jersey) and converted into a voltage reading that ranged from 0 to 4.5 mV. The computer interface and data acquisition were handled with LabVIEW software (National Instruments, Austin, Texas). The LSC detected algae by holding the laser fixed and moving the stage in an Archimedes' spiral path similar to etchings found on a compact disk. The rotary stage speed and linear velocity of the stage were directed by motors (MM-3M-EX and MM-3M-R stages, National Aperture, Salem, New Hampshire) controlled by a motion controller card (7344 motion controller card, National Instruments, Austin, Texas) and powered from an amplifier system (MC-4SA Multi-Axis Servo amplifier, National Aperture, Salem, New Hampshire) connected to a desktop computer. The path area and track spacing were dictated by the
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linear stage speed and inner/outer radius set points. The laser position on the surface was determined from the radial distance from the origin and degree of rotation. The location of the laser beam relative to the stage was converted into Cartesian coordinates for data storage. The laser intensity and focal distance were adjusted to ensure optimum contrast and fluorescence detection. The PMT data varied along the membrane surface and were stored with the coordinates of the laser beam relative to stage position. Low-resolution and high-resolution scans were performed. Lowresolution scans reduced the time for data collection and processing by sampling a smaller portion of the membrane surface, but resulted in higher variability when estimating cell concentrations. For this setting the rotary stage was set to 90°/s and the linear stage was set to 0.025 mm/s. The scan covered 3.0 to 5.5 mm of radial distance and produced a 100-μm track spacing which covered 27% of the active membrane area as shown in Fig. 2. High-resolution scans were performed when more accurate cell counts and imaging were desired. The spatial resolution of the data was increased in high-resolution scans by slowing the linear stage speed to 0.005 mm/s. This decreased the track spacing to 20 μm and allowed more data points to be collected per unit area. The high-resolution scan significantly increased the data collection and processing time to an average of eight minutes compared with less than one minute for low-resolution scans. Cells were identified with the LSC using a threshold fluorescence signal and an automated counting program written in Matlab (MathWorks Inc., Natick, Massachusetts). The magnitude of the signal observed from an algal cell varied with cell age [21] and the location of the laser on top of the cell, but a signal above the threshold value of 1.1 times background indicated the presence of an algal cell as shown in Fig. 3. This setting maximized the number of data points per algal cell and minimized false positives from background noise.
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magnification to ensure that the cells were distributed uniformly during the filtration process. Occasionally membranes were discarded when the filtration operation distributed cells in a non-uniform manner around the outer perimeter of the filter column. Images were taken at 10× magnification at random locations on the membrane and captured using an Axiocam MRm digital camera (Carl Zeiss AG, Maple Grove, Minnesota). Contact with the illumination light was minimized to prevent photo-bleaching. The number of cells on the membrane surface was determined with fluorescence microscopy by averaging the number of cells counted per viewing window. Typically, the filter volume was controlled to produce ~30 cells per window with a 10× objective. At least seven windows were counted per slide to ensure a significant sample population. Prior statistical studies for sampling algae recommended a range of 30–40 cells per window until at least 200 cells were counted to minimize variability [32]. In that analysis a Poisson distribution of counts per window was assumed. 2.5.2. Flow cytometry Flow cytometry provided a distinct sample-handling method that was tested against the solid-phase filtration analysis used with the LSC and FLM. A BD LSR II commercial flow cytometer (BD Biosciences,
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Fig. 1. LSC Prototype. Light from a 635-nm laser was directed toward the membrane held on the rotary and linear stages, with the height adjusted for optimum focus. The signal from algal auto-fluorescence was detected with the PMT. The filter set included a dichroic mirror and emission filter for reflecting laser light and passing fluoresced light.
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2.5. Comparative algal enumeration techniques 2.5.1. Fluorescence microscopy Fluorescence microscopy (FLM) was used as a comparative algal counting technique with membrane filtrations. A Zeiss Axioscope (Carl Zeiss AG, Maple Grove, Minnesota) was used with an X-Cite 120 fluorescent bulb (EXFO, Mississauga, Canada). A Cy3 filter set was employed (Chroma, Rockingham, Vermont) having an excitation filter with 488-nm cutoff (shortpass) and an emission filter with a 675-nm cutoff (longpass). The membrane surface was visually scanned at 5×
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2.6. Performance evaluation of LSC
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San Jose, California) was used to analyze algal samples on two fluorescence channels: a 488-nm argon-ion, air-cooled laser (Coherent, Santa Clara, California) with a 530/30 bandpass filter was used for algal cell excitation, and a 633-nm, red He Ne UniphaseTM Laser (JPS, Milpitas, California) was used for detecting fluorescent calibration beads. The voltage was set at 385 V on the blue 488-nm laser and 242 V on the red 633-nm laser. Both channels provided 4-decade amplification. The fluorescence data were analyzed using FCS Express flow analysis software (De Novo, Los Angeles, California). Samples were prepared with 900 μl of algae added to 100 μl of 10-μm Flow Check™ Fluorosphere calibration beads (Beckman Coulter, Fullerton, California) at a concentration of 1.35× 106 beads/ml. The flow rate of the machine was verified by counting the number of bead events recorded in a given period and calculating the volume processed using the bead concentration. Sample tubes were vortexed prior to analysis to ensure homogeneity. When estimating sample concentrations with the flow cytometer, front and side-scatter properties of the algae allowed them to be easily distinguished from other particulate matter in the sample (Fig. 4a). Compared to the calibration beads, the algal cells showed a wider spread in scatter parameters due to disparities in cell size and shape. Separate data groupings in the bead scatter region corresponded to conjoined beads registering as a single event. The differences in the fluorescence properties of each particle also aided in discriminating algal cells. The fluorescence measurements were compared on both the 488-nm and 633-nm channels. Separation between the algae and background particulate material occurred most dramatically on the 488-nm channel (Fig. 4b) while the difference in excitation wavelength of the algae and calibration beads was best observed at 633 nm (Fig. 4c). Based on the scatter and fluorescence signal parameters, particles were gated and labeled accordingly.
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The ability to detect and enumerate algal cells over a wide range of conditions was tested with the LSC and compared to FLM and flow cytometry. The following aspects of the LSC performance were explored.
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2.6.1. Cell visualization and enumeration The imaging capabilities of the LSC were assessed by comparing results against FLM. Distributions of H. pygmaea and S. platensis were examined by outlining an interrogation region of ~4 mm2 on the membrane and filtering cells at a concentration of ~10,000 cells/ml. A low-resolution scan with the LSC quickly located the coordinates of the interrogation region, and a high-resolution scan provided the visualization data. An image of each cell distribution was generated with the LSC, and FLM images were taken at 100× magnification. The robustness of the LSC's automated counting program was then verified against manual counting of cell distribution images. Six membranes were prepared with low cell densities (~50–100 cells/ scan area) to facilitate manual counting. High-resolution scans with the LSC provided images of the membrane surface and automated count results were compared against manual tallying. A paired t-test was then conducted to determine the parity of each method.
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2.6.2. Linear response range and method variability Once it was determined that the LSC could effectively locate and automatically count cells on the membrane surface, the performance of the device was tested over a range of concentrations that occur during a bloom event. Twenty samples of varying concentrations were prepared and the filter volume was adjusted to maintain ~ 10,000 cells per membrane for analysis. Cells were counted manually with the FLM and high-resolution scans were counted automatically with the LSC. The performance of the LSC and FLM was also tested outside of the optimum value of ~ 10,000 cells per membrane to measure variability. The range of ~400–40,000 cells per membrane was prepared by incrementally decreasing the volume of algae spiked into seawater.
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Side Scatter Fig. 4. Flow cytometry plot of an algal sample. The forward and side-scatter parameters plotted were indicative of particle size and morphology (a). Fluorescence measurements on the 488-nm (b) and 633-nm (c) fluorescence channels were used to further determine sample composition and estimate cell concentrations. Red, blue, and green data points indicate algae, beads, and background particles, respectively.
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The samples were scanned at high resolution on the LSC. Low numbers of cells per membrane were of particular interest due to the LSC's ability to scan larger areas of the membrane surface. Triplicate filtrations were performed at a reduced volume to provide a low number of cells per membrane. The triplicates were counted on the LSC and FLM to determine the variability of each technique. A one-way ANOVA test was then performed on the LSC data to determine whether variability in counting was due to membrane preparation or scan performance. A constant volume of algal culture was sampled and filtered onto three different membranes for analysis. Triplicate scans were then performed on each of the membranes and the scan area was varied by repositioning the membrane on the LSC stage. The counts were then analyzed and an ANOVA test was calculated using Matlab's statistical toolbox (MathWorks Inc., Natick, Massachusetts). 2.6.3. Interference of matrix constituents The influence of dissolved inorganic and organic material in the water matrix was tested using the LSC. Cultures of H. pygmaea were extended into the decay phase and mixed with increasing concentrations of equal parts bentonite and humic acid, ranging from 0 to 25 mg/l each. Cell concentrations were estimated using low-resolution scans on the LSC and scans were conducted in triplicate to determine method variability. Data analysis was conducted using the default signal threshold determined from previous optimization studies. 2.6.4. Filtration versus fluid stream method Cell counts and sample processing times with LSC and FLM were compared against commercial flow cytometry. Algal samples were fixed in a glutaraldehyde PBS solution [31] that was diluted to give triplicates
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of six different concentrations. The samples were then counted in nonconsecutive order on the flow cytometer and re-vortexed between measurements. For the LSC and FLM, sample membrane slides were prepared via filtration to distribute ~10,000 cells per membrane. Membrane slides were repositioned between laser scans and microscope counts to cover different areas of the membrane with each replicate and provide randomness in sampling. 2.6.5. Analysis of an algal bloom in coastal waters As a final evaluation, the applicability of the LSC was demonstrated using seawater samples taken during an algal bloom event off the coast of Long Beach, CA. Analyses were conducted over a range of concentrations (~300,000–1,000,000 cells/ml) by performing dilutions with seawater samples taken during normal (non-bloom) conditions. Filtrations were conducted to deliver ~10,000 cells per membrane for analysis with LSC and FLM. High resolution scans were conducted with the LSC and digital images were counted manually with the FLM as a comparative method. 3. Results and discussion 3.1. Cell visualization and enumeration The LSC produced imaging results similar to FLM and approximate cell size and shape characteristics could be distinguished. The LSC was able to identify the spherical outline of H. pygmaea (Fig. 5a) as well as the oblong narrow spirals of S. platensis (Fig. 5b) when compared to FLM images (Fig. 5c and d respectively). Measuring the auto-fluorescence signal also minimized interference from particulate matter similar to the cell size and shape.
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Fig. 5. LSC images of cell distributions of H. Pygmaea (a) and S. platensis (b). The membrane area was scanned with the LSC and an image was produced by applying a gradient surface plot to the data. Similar images of each distribution were also taken with the FLM as shown in (c) and (d) respectively.
D.R. Vardon et al. / Desalination 277 (2011) 193–200 Table 1 Results from the LSC algorithm and manual tallying of the number of cells per membrane for six filtered samples. A paired t-test was performed to confirm the parity of the methods. LSC (cells/memb)
Manual (cells/memb)
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The paired t-test conducted with the LSC's automated program and manual counting also confirmed the parity of each method (p-value of 0.71) as shown in Table 1. Auto-enumeration provided by the LSC significantly reduced the count estimation time (b5 s) and prevented operator bias and fatigue associated with manual counting. 3.2. Linear response range and method variability The LSC showed a linear response with FLM over a wide range of concentrations (~50–150,000 cells/ml) as shown in Fig. 6. Accurate detection at low concentrations with the LSC extended well below the recommended limit of 250 cells/ml for commercial flow cytometry [23]. In addition, the linear response range at high concentration with the LSC surpassed that of satellite imaging which can underestimate concentrations larger than 1000 cells/ml [33]. The linear performance at high and low concentrations suggests that the LSC may be more effective than either flow cytometry or satellite imaging for determining algal concentrations in plant feedwater. Variability increased with the FLM at low numbers of cells per membrane (b5000 cells per membrane) as the number of cells per viewing window became sparse. The LSC, however, was able to count a significantly higher number of sample events by covering a larger area with the automated scanning method. The LSC provided reduced variability (0.25 coefficient of variation) compared to FLM (0.40 coefficient of variation) when triplicates of a dilute sample were counted (~ 200–400 cells per membrane). At high cell densities (N15,000 cells per membrane) counting was problematic with both methods. The reduced spacing between cells inhibited discrimination of single and multiple events with the LSC's automated counting program. Similarly, overcrowding made it difficult to keep track of cells with the FLM. Increased variability resulted between both methods at high cell 106
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Fig. 7. Performance of LSC and FLM with varying cells per membrane. Filter volume was varied to change the number of cells present on the membrane surface. Higher variability resulted above 12,000 cells per membrane for both the LSC and FLM.
densities as indicated by the data spread in Fig. 7. Therefore, subsequent filtrations at reduced sample volumes would be required with the LSC when high cell densities are present. The primary source of LSC variability during cell counts was caused by sample preparation, not by the instrument. An ANOVA test of the LSC counts concluded that multiple scans of the same membrane did not affect the estimate of the sample concentration (p-value of 0.90). In contrast, repeated sampling and filtration of the same algal culture introduced variability that influenced the estimated concentration (p-value of 0.019). Variability in sample preparation was attributed to a non-homogenous cell distribution within the culture container. It was determined that counting multiple sample filtrations would provide a more accurate estimate of the sample concentration than performing multiple scans of the same membrane. 3.3. Influence of interfering matrix constituents Cell counts with LSC remained relatively constant (lowest p-value of 0.35 at 25 mg/l) with increasing bentonite and humic acid concentrations (Fig. 8). The algal cell fluorescence signal was uninhibited across all foulant levels (mean value of ~0.5–0.6 mV). The background signal increased marginally, however the noise remained well below the detection threshold for algal cells.
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with the LSC and FLM allowed for a direct comparison of algal cells on the membrane surface. Cell concentrations ranged from ~300,000 to 1,000,000 cells/ml and a high degree of correlation was observed between the LSC and FLM (Pearson value of 0.958). Variability was attributed to sampling error and reduced cell diameter (4–8 μm) which decreased the number of data points per cell during LSC scans. Flow cytometry was not used with the natural bloom event due to timing and instrument availability. Based on previous results (Section 3.4) we would expect that flow cytometry and LSC would perform similarly. Flow cytometry may have an advantage in aiding with species identification because of its multi-fluorescence channel capabilities, but the LSC would be expected to count low-concentration samples more quickly.
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Flow cytometer counts (cells/ml) Fig. 9. LSC, FLM, and flow cytometry linear correlation for measuring concentrations between ~ 700 and 7,000 cells/ml. Filtration-based analysis methods (LSC and FLM) were compared against fluid stream analysis (flow cytometry).
3.4. Comparison to flow cytometry Cell counts with the LSC also demonstrated a linear correlation with flow cytometry over the range of ~ 700–7000 cells/ml (Fig. 9). However, sample flow rates were significantly different between the membrane filtration method and the flow cytometer. A filtration rate of 12 ml/min was recorded when preparing sample slides for the LSC and FLM, while the flow cytometer flow rate averaged ~35 μl/min. This difference in sample processing time provides the LSC a distinct advantage for monitoring dilute concentrations of algae compared to fluid stream analytical devices. Even if the flow cytometer were operated at a maximum flow rate of 250 μl/min [23], an additional 3 h and 15 min would be required to process 12 ml that can be filtered in one minute with the LSC. 3.5. LSC performance during a natural-bloom event Cell counts with the LSC were comparable to FLM when estimating the concentration of wild algae sampled during a bloom event off the coast of Long Beach, CA (Fig. 10). The similar filtration-based method
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4. Conclusions This study demonstrated that the LSC was capable of rapidly detecting and enumerating algal cell populations over a wide range of concentrations using both laboratory-grown cultures and a sample of a naturally occurring bloom. Spatial fluorescence imaging with the LSC accurately depicted distinctions in cell morphology and allowed for the counting of individual cells, which would not be possible with bulk fluorescence measurements. A combination of organic and inorganic interfering matrix material had a negligible effect on cell count estimations with the LSC. The scalable sample filtration method and rapid processing times make the LSC adept at measuring algal concentrations throughout the bloom lifecycle and would provide operators needed time to take preventive actions to mitigate a bloom's impact on desalination plant production. The benefits of automated counting, low-cost construction, and integration into existing water quality measurements warrant future investigations with the LSC as a monitoring method for tracking bulk algal growth conditions at desalination treatment facilities. Acknowledgements This project was principally funded by the United States Department of the Interior, Bureau of Reclamation through the Desalination and Water Purification Research Program. Additional funding was received from the Department of Civil and Environmental Engineering at the University of Illinois. We would like to thank Manish Kumar and Barbara Pilas for their invaluable input and help with the project. Dian Tanuwidjaja at the Long Beach Water Department (California, USA) is thanked for collecting the naturally occurring algal bloom sample.
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Fig. 10. Response of LSC and FLM when estimating cell concentrations of wild algae sampled during a bloom event off the coast of Long Beach, CA. Cell counts ranged from ~ 300,000 to 1,000,000 cells/ml.
[1] I. Bremere, M. Kennedy, A. Stikker, J. Schippers, How water scarcity will effect the growth in the desalination market in the coming 25 years, Desalination 138 (Sep. 2001) 7–15. [2] K.V. Reddy, N. Ghaffour, Overview of the cost of desalinated water and costing methodologies, Desalination 205 (Feb. 2007) 340–353. [3] P.K. Abdul Azis, I. Al-Tisan, M. Al-Daili, T.N. Green, A.G.I. Dalvi, M.A. Javeed, Effects of environment on source water for desalination plants on the eastern coast of Saudi Arabia, Desalination 132 (Dec. 2000) 29–40. [4] M. Petry, M.A. Sanz, C. Langlais, V. Bonnelye, J.-P. Durand, D. Guevara, W.M. Nardes, C.H. Saemi, The El Coloso (Chile) reverse osmosis plant, Desalination 203 (Feb. 2007) 141–152. [5] D.A. Caron, M. Garneau, E. Seubert, M.D.A. Howard, L. Darjany, A. Schnetzer, I. Cetinic, G. Filteau, P. Lauri, B. Jones, Harmful algae and their potential impacts on desalination operations off southern California, Water Research 44 (2010) 385–416. [6] K.G. Sellner, Physiology, ecology, and toxic properties of marine cyanobacteria blooms, Limnology and Oceanography 42 (Jul. 1997) 1089–1104. [7] C.A. Scholin, F. Gulland, G.J. Doucette, S. Benson, M. Busman, F.P. Chavez, J. Cordaro, R. DeLong, A. De Vogelaere, J. Harvey, M. Haulena, K. Lefebvre, T. Lipscomb, S. Loscutoff, L.J. Lowenstine, R. Marin III, P.E. Miller, W.A. McLellan, P.D.R. Moeller, C.L. Powell, T. Rowles, P. Silvagni, M. Silver, T. Spraker, V. Trainer, F.M. Van Dolah, Mortality of sea lions along the central California coast linked to a toxic diatom bloom, Nature 403 (Jan. 2000) 80–84. [8] C.A. Heil, P.M. Glibert, C. Fan, Prorocentrum minimum (Pavillard) Schiller: A review of a harmful algal bloom species of growing worldwide importance, Harmful Algae 4 (Mar. 2005) 449–470.
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Desalination j o u r n a l h o m e p a g e : w w w. e l s ev i e r. c o m / l o c a t e / d e s a l
The potential of laser scanning cytometry for early warning of algal blooms in desalination plant feedwater Derek R. Vardon a, Mark M. Clark b,1, David A. Ladner c,⁎ a University of Illinois at Urbana-Champaign, Department of Civil and Environmental Engineering, 4153 Newmark Civil Engineering Laboratory, 205 North Mathews Avenue, Urbana, Illinois 61801, USA b Department of Civil and Environmental Engineering, Northwestern University, 2145 Sheridan Road-A321, Evanston, IL 60208, USA c Clemson University, Department of Environmental Engineering and Earth Sciences, 163 Rich Lab, 342 Computer Court, Anderson, SC 29625, USA
a r t i c l e
i n f o
Article history: Received 21 December 2010 Received in revised form 6 April 2011 Accepted 8 April 2011 Available online 8 May 2011 Keywords: Algal bloom Algal monitoring Laser scanning cytometry Seawater desalination Solid phase cytometry
a b s t r a c t When algal blooms (such as red tide) occur, early detection of increased algal growth in intake water offers desalination plant operators advanced warning of severe fouling and provides the necessary time for preventative measures. Monitoring basic water quality measurements such as turbidity, silt density index (SDI), dissolved oxygen, and bulk fluorescence can approximate algal activity; however, these measurements lack the resolution needed to accurately track growth throughout all phases of a bloom event. This study investigates the use of a laser scanning cytometer (LSC) that detects algal cells over a wide range of concentrations (50–150,000 cells/ml), and characterizes cell shape, size, and distribution parameters through fluorescence signal imaging. Water samples are filtered to collect algae on a 0.2-μm membrane surface. Sample volume is variable so cells can be concentrated. The membrane surface is scanned with a 635-nm diode laser and the signal is processed to generate a cell count and distribution image. LSC performance was comparable to fluorescence microscopy and flow cytometry over a range of concentrations that may be encountered during a bloom event. The applicability of LSC was demonstrated using laboratory-grown cultures and seawater samples taken during a bloom event off of the coast of Long Beach, CA. © 2011 Elsevier B.V. All rights reserved.
1. Introduction With an increasing global demand for drinking water, the number and capacity of seawater desalination plants has continued to expand [1,2]. Desalination plants produce drinking water using multi-stage flash (MSF) technology or reverse osmosis (RO) membrane systems; the latter having become more popular due to lower energy consumption. Unfortunately, RO membrane systems can be severely compromised when algal blooms occur in coastal waters [3–5]. Several divisions of algae have been identified in marine blooms including, but not limited to blue-greens [6], diatoms [7], and dinoflagellates [8]; the latter being of particular concern due to their prevalence in harmful redtide events [9]. Algal blooms produce high levels of biomass that foul pretreatment systems and dramatically reduce the plant's operating efficiency [10]. Residual algal biopolymers have also been shown to significantly decrease RO membrane flux [11]. Furthermore, harmful toxins produced by certain species can be released when the cells rupture
Abbreviations: (FLM), fluorescence microscopy; (LSC), laser scanning cytometry. ⁎ Corresponding author. Tel.: + 1 864 656 5572. E-mail addresses: [email protected] (D.R. Vardon), [email protected] (M.M. Clark), [email protected] (D.A. Ladner). 1 Tel.: + 1 847 467 4540; fax: + 1 847 491 4011. 0011-9164/$ – see front matter © 2011 Elsevier B.V. All rights reserved. doi:10.1016/j.desal.2011.04.025
[5,12,13] requiring additional water quality treatment and monitoring efforts. These negative effects result in increased chemical consumption, accelerated membrane fouling rates, and in extreme cases, desalination plants must be taken off-line [5,14]. The problem is further compounded by the increased frequency and severity of coastal algal blooms occurring worldwide [15,16]. Characterization and monitoring of algal growth conditions in source water are critical to the design and operation of seawater desalination plants. With accurate monitoring of algal activity during feasibility and pilot studies, effective pretreatment technologies can be selected to ensure optimum operation of the downstream desalination process [17]. Once online, continuous monitoring of feedwater can also provide plant operators advanced notice of approaching blooms and the necessary time to take mitigative actions. Operator responses can include supplementary chemical treatments, the use of additional pretreatment devices, or the implementation of staff maintenance activities to ensure optimum plant performance [5]. These actions can prevent reductions in plant production capacity and water quality output. Algal growth conditions can be approximated using basic water quality measurements such as optical density, silt density index (SDI), dissolved oxygen content, and bulk chlorophyll fluorescence. However, inaccurate estimates of cell concentrations can result from the lack of resolution provided by basic water quality parameters. Optical density
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measurements are highly dependent on the particulate scattering coefficient which is a complex function of cell size, shape and physiology [18]. This parameter can vary among species and is greatly affected by non-algal suspended particles. Silt density index (SDI) characterizes the fouling potential of water on the membrane, but results are dependent on the pH, membrane type, and concentrations of organic and mineral compounds [19]. Dissolved oxygen levels are more indicative of biological activity but can fluctuate greatly depending on the time of day and location in the water column [20]. Lastly, bulk chlorophyll fluorescence provides results linearly correlated to the concentration of algal cells in the sample; however, the fluorescence emission signal from algal cells can vary throughout the life cycle [21] and can be influenced by intracellular carotenoids and phycobilins released during cell rupture and decay [22]. Monitoring algal growth is especially difficult at low cell concentrations during the onset of a bloom event. If flow cytometry is used, large sample volumes are required to provide a statistically significant algal count; this increases labor and processing time [23]. Highly sensitive molecular labeling methods exist to identify targeted species [24–26], but the strategy is cost prohibitive when applied to continuous monitoring applications and valuable information is lost regarding the algal fouling potential when non-targeted species are ignored. Therefore, RO desalination facilities stand to benefit from a rapid, accurate, low-cost detection system to continuously monitor bulk algal growth conditions over the full range of concentrations encountered during a bloom event. Here we explored the potential of a novel laser scanning cytometry (LSC) method to provide algal cell detection and quantification. LSC, also referred to as solid-phase cytometry [27], uses a membrane filtration technique. Algal cells are concentrated on the membrane and scanned with a laser. Fluorescence signals are used to create an image of algal cells on the membrane surface and the cells are identified and counted. Commercial laser scanning cytometers have demonstrated a detection limit of one cell per membrane for counting water-borne pathogens and toxic algae [28,29]. However, the sophisticated equipment and high capital cost of the commercial device makes it unattractive for routine monitoring use [29]. Our investigation used a prototype LSC that was previously developed for microsphere detection in water treatment research applications [30]. This LSC was built with simple, low-cost components more suitable for routine applications. Our device was tested against fluorescence microscopy (FLM) and flow cytometry to measure its performance for the detection and enumeration of algal cells. The applicability of the LSC was then demonstrated using seawater samples taken during a bloom event off of the coast of Long Beach, CA. 2. Material and methods 2.1. Algal cultures Analyses were conducted with algae grown in the laboratory and sampled from natural waters. Optimization studies were conducted with the species Heterocapsa pygmaea purchased from the ProvasoliGuillard National Center for Culture of Marine Phytoplankton (West Boothbay Harbor, Maine). H. pygmaea is a bloom-forming dinoflagellate species similar to those that cause red tide. The initial sample was spiked into several culture tubes containing f/2 media [31] and prepared with 0.45-μm-filtered San Diego seawater collected by project consultants at MWH (Pasadena, California). The species Spirulina platensis was used for image analysis with the LSC. The S. platensis were purchased from AlgaGen LLC (Vero Beach, Florida) and grown in deionized water spiked with BG11 media obtained from Sigma-Aldrich (St. Louis, Missouri). Both cultures were exposed to a constant mercury-fluorescent light source of 19 μmol-photons/m2 s at room temperature (22–24 °C), and the growth progress was monitored weekly with a Spectra Max Gemini fluorescence microplate reader (Molecular Devices, Sunnyvale, California). Cell concentrations were
periodically verified with a hemacytometer and visible-light microscopy. A batch culture rotation was employed for perpetual maintenance. An unknown species of wild algae was sampled for analysis during a bloom event off of the coast of Long Beach, CA. An abnormal blue-green discoloration of coastal water was observed on July 22nd, 2010 during daily monitoring by personnel at the Long Beach Water Department's desalination pilot facility. The color change was no longer evident the following day, and samples from both periods were shipped immediately for analysis. Visible-light microscopy verified the presence of algal cells, and samples consisted of predominantly blue-green colored microalgae ranging from 2 to 8 μm in diameter. 2.2. Interfering matrix constituents The influence of interfering matrix constituents was tested using decaying cultures of H. pygmaea and model inorganic and organic compounds. Culture growth was extended into the decay phase to elevate levels of bacteria and extracellular algogenic organic matter (AOM). The model inorganic compound was bentonite (Sigma Aldrich, St. Louis, MO). The model organic compound was humic acid (Sigma Aldrich, St. Louis, MO). 2.3. Membrane filtrations Membrane filtration was used to prepare samples for counting with the LSC. Algal cells were filtered onto 25-mm diameter black tracketched Isopore™ polycarbonate membranes with a 0.2-μm pore size (Millipore, Billerica, Massachusetts). The membrane was secured between a glass column and vacuum filtration apparatus. Sample volumes were adjusted in order to deliver an appropriate number of algal cells (~ 10,000 cells/membrane). The 10,000 cells/membrane target resulted in minimal variability during counting. The sample was prepared by pipetting a predetermined volume of algal culture into 0.45-μm pre-filtered natural seawater. The mixture was shaken sufficiently to ensure sample homogeneity and poured into the glass column. Vacuum was applied to draw the sample through the membrane and distribute the cells uniformly on the membrane surface. Wet membranes were placed on a glass slide and allowed to dry for further analysis with the LSC. 2.4. LSC prototype The laboratory-built LSC consisted of low-cost components in a simplified setup compared to commercially-available instruments. Sample membranes were placed on a rotating stage and scanned with a red, variable power, 635-nm circular beam diode laser (Coherent, Santa Clara, California) as shown in Fig. 1. (This laser is similar to those found in super market scanners.) Scans were conducted with light from the laser reflected off of a dichroic mirror (Q660LP, Chroma, Rockingham, Vermont) towards the membrane. Auto-fluorescent light from algae was separated from reflected laser light using a 685-nm longpass emission filter (HQ700/75m, Chroma, Rockingham, Vermont). The signal was then detected with a photomultiplier tube (HC120-01 module with R6357 detection tube, Hamamatsu, Bridgewater, New Jersey) and converted into a voltage reading that ranged from 0 to 4.5 mV. The computer interface and data acquisition were handled with LabVIEW software (National Instruments, Austin, Texas). The LSC detected algae by holding the laser fixed and moving the stage in an Archimedes' spiral path similar to etchings found on a compact disk. The rotary stage speed and linear velocity of the stage were directed by motors (MM-3M-EX and MM-3M-R stages, National Aperture, Salem, New Hampshire) controlled by a motion controller card (7344 motion controller card, National Instruments, Austin, Texas) and powered from an amplifier system (MC-4SA Multi-Axis Servo amplifier, National Aperture, Salem, New Hampshire) connected to a desktop computer. The path area and track spacing were dictated by the
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linear stage speed and inner/outer radius set points. The laser position on the surface was determined from the radial distance from the origin and degree of rotation. The location of the laser beam relative to the stage was converted into Cartesian coordinates for data storage. The laser intensity and focal distance were adjusted to ensure optimum contrast and fluorescence detection. The PMT data varied along the membrane surface and were stored with the coordinates of the laser beam relative to stage position. Low-resolution and high-resolution scans were performed. Lowresolution scans reduced the time for data collection and processing by sampling a smaller portion of the membrane surface, but resulted in higher variability when estimating cell concentrations. For this setting the rotary stage was set to 90°/s and the linear stage was set to 0.025 mm/s. The scan covered 3.0 to 5.5 mm of radial distance and produced a 100-μm track spacing which covered 27% of the active membrane area as shown in Fig. 2. High-resolution scans were performed when more accurate cell counts and imaging were desired. The spatial resolution of the data was increased in high-resolution scans by slowing the linear stage speed to 0.005 mm/s. This decreased the track spacing to 20 μm and allowed more data points to be collected per unit area. The high-resolution scan significantly increased the data collection and processing time to an average of eight minutes compared with less than one minute for low-resolution scans. Cells were identified with the LSC using a threshold fluorescence signal and an automated counting program written in Matlab (MathWorks Inc., Natick, Massachusetts). The magnitude of the signal observed from an algal cell varied with cell age [21] and the location of the laser on top of the cell, but a signal above the threshold value of 1.1 times background indicated the presence of an algal cell as shown in Fig. 3. This setting maximized the number of data points per algal cell and minimized false positives from background noise.
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magnification to ensure that the cells were distributed uniformly during the filtration process. Occasionally membranes were discarded when the filtration operation distributed cells in a non-uniform manner around the outer perimeter of the filter column. Images were taken at 10× magnification at random locations on the membrane and captured using an Axiocam MRm digital camera (Carl Zeiss AG, Maple Grove, Minnesota). Contact with the illumination light was minimized to prevent photo-bleaching. The number of cells on the membrane surface was determined with fluorescence microscopy by averaging the number of cells counted per viewing window. Typically, the filter volume was controlled to produce ~30 cells per window with a 10× objective. At least seven windows were counted per slide to ensure a significant sample population. Prior statistical studies for sampling algae recommended a range of 30–40 cells per window until at least 200 cells were counted to minimize variability [32]. In that analysis a Poisson distribution of counts per window was assumed. 2.5.2. Flow cytometry Flow cytometry provided a distinct sample-handling method that was tested against the solid-phase filtration analysis used with the LSC and FLM. A BD LSR II commercial flow cytometer (BD Biosciences,
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2.5. Comparative algal enumeration techniques 2.5.1. Fluorescence microscopy Fluorescence microscopy (FLM) was used as a comparative algal counting technique with membrane filtrations. A Zeiss Axioscope (Carl Zeiss AG, Maple Grove, Minnesota) was used with an X-Cite 120 fluorescent bulb (EXFO, Mississauga, Canada). A Cy3 filter set was employed (Chroma, Rockingham, Vermont) having an excitation filter with 488-nm cutoff (shortpass) and an emission filter with a 675-nm cutoff (longpass). The membrane surface was visually scanned at 5×
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2.6. Performance evaluation of LSC
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San Jose, California) was used to analyze algal samples on two fluorescence channels: a 488-nm argon-ion, air-cooled laser (Coherent, Santa Clara, California) with a 530/30 bandpass filter was used for algal cell excitation, and a 633-nm, red He Ne UniphaseTM Laser (JPS, Milpitas, California) was used for detecting fluorescent calibration beads. The voltage was set at 385 V on the blue 488-nm laser and 242 V on the red 633-nm laser. Both channels provided 4-decade amplification. The fluorescence data were analyzed using FCS Express flow analysis software (De Novo, Los Angeles, California). Samples were prepared with 900 μl of algae added to 100 μl of 10-μm Flow Check™ Fluorosphere calibration beads (Beckman Coulter, Fullerton, California) at a concentration of 1.35× 106 beads/ml. The flow rate of the machine was verified by counting the number of bead events recorded in a given period and calculating the volume processed using the bead concentration. Sample tubes were vortexed prior to analysis to ensure homogeneity. When estimating sample concentrations with the flow cytometer, front and side-scatter properties of the algae allowed them to be easily distinguished from other particulate matter in the sample (Fig. 4a). Compared to the calibration beads, the algal cells showed a wider spread in scatter parameters due to disparities in cell size and shape. Separate data groupings in the bead scatter region corresponded to conjoined beads registering as a single event. The differences in the fluorescence properties of each particle also aided in discriminating algal cells. The fluorescence measurements were compared on both the 488-nm and 633-nm channels. Separation between the algae and background particulate material occurred most dramatically on the 488-nm channel (Fig. 4b) while the difference in excitation wavelength of the algae and calibration beads was best observed at 633 nm (Fig. 4c). Based on the scatter and fluorescence signal parameters, particles were gated and labeled accordingly.
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2.6.1. Cell visualization and enumeration The imaging capabilities of the LSC were assessed by comparing results against FLM. Distributions of H. pygmaea and S. platensis were examined by outlining an interrogation region of ~4 mm2 on the membrane and filtering cells at a concentration of ~10,000 cells/ml. A low-resolution scan with the LSC quickly located the coordinates of the interrogation region, and a high-resolution scan provided the visualization data. An image of each cell distribution was generated with the LSC, and FLM images were taken at 100× magnification. The robustness of the LSC's automated counting program was then verified against manual counting of cell distribution images. Six membranes were prepared with low cell densities (~50–100 cells/ scan area) to facilitate manual counting. High-resolution scans with the LSC provided images of the membrane surface and automated count results were compared against manual tallying. A paired t-test was then conducted to determine the parity of each method.
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2.6.2. Linear response range and method variability Once it was determined that the LSC could effectively locate and automatically count cells on the membrane surface, the performance of the device was tested over a range of concentrations that occur during a bloom event. Twenty samples of varying concentrations were prepared and the filter volume was adjusted to maintain ~ 10,000 cells per membrane for analysis. Cells were counted manually with the FLM and high-resolution scans were counted automatically with the LSC. The performance of the LSC and FLM was also tested outside of the optimum value of ~ 10,000 cells per membrane to measure variability. The range of ~400–40,000 cells per membrane was prepared by incrementally decreasing the volume of algae spiked into seawater.
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Side Scatter Fig. 4. Flow cytometry plot of an algal sample. The forward and side-scatter parameters plotted were indicative of particle size and morphology (a). Fluorescence measurements on the 488-nm (b) and 633-nm (c) fluorescence channels were used to further determine sample composition and estimate cell concentrations. Red, blue, and green data points indicate algae, beads, and background particles, respectively.
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The samples were scanned at high resolution on the LSC. Low numbers of cells per membrane were of particular interest due to the LSC's ability to scan larger areas of the membrane surface. Triplicate filtrations were performed at a reduced volume to provide a low number of cells per membrane. The triplicates were counted on the LSC and FLM to determine the variability of each technique. A one-way ANOVA test was then performed on the LSC data to determine whether variability in counting was due to membrane preparation or scan performance. A constant volume of algal culture was sampled and filtered onto three different membranes for analysis. Triplicate scans were then performed on each of the membranes and the scan area was varied by repositioning the membrane on the LSC stage. The counts were then analyzed and an ANOVA test was calculated using Matlab's statistical toolbox (MathWorks Inc., Natick, Massachusetts). 2.6.3. Interference of matrix constituents The influence of dissolved inorganic and organic material in the water matrix was tested using the LSC. Cultures of H. pygmaea were extended into the decay phase and mixed with increasing concentrations of equal parts bentonite and humic acid, ranging from 0 to 25 mg/l each. Cell concentrations were estimated using low-resolution scans on the LSC and scans were conducted in triplicate to determine method variability. Data analysis was conducted using the default signal threshold determined from previous optimization studies. 2.6.4. Filtration versus fluid stream method Cell counts and sample processing times with LSC and FLM were compared against commercial flow cytometry. Algal samples were fixed in a glutaraldehyde PBS solution [31] that was diluted to give triplicates
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of six different concentrations. The samples were then counted in nonconsecutive order on the flow cytometer and re-vortexed between measurements. For the LSC and FLM, sample membrane slides were prepared via filtration to distribute ~10,000 cells per membrane. Membrane slides were repositioned between laser scans and microscope counts to cover different areas of the membrane with each replicate and provide randomness in sampling. 2.6.5. Analysis of an algal bloom in coastal waters As a final evaluation, the applicability of the LSC was demonstrated using seawater samples taken during an algal bloom event off the coast of Long Beach, CA. Analyses were conducted over a range of concentrations (~300,000–1,000,000 cells/ml) by performing dilutions with seawater samples taken during normal (non-bloom) conditions. Filtrations were conducted to deliver ~10,000 cells per membrane for analysis with LSC and FLM. High resolution scans were conducted with the LSC and digital images were counted manually with the FLM as a comparative method. 3. Results and discussion 3.1. Cell visualization and enumeration The LSC produced imaging results similar to FLM and approximate cell size and shape characteristics could be distinguished. The LSC was able to identify the spherical outline of H. pygmaea (Fig. 5a) as well as the oblong narrow spirals of S. platensis (Fig. 5b) when compared to FLM images (Fig. 5c and d respectively). Measuring the auto-fluorescence signal also minimized interference from particulate matter similar to the cell size and shape.
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Fig. 5. LSC images of cell distributions of H. Pygmaea (a) and S. platensis (b). The membrane area was scanned with the LSC and an image was produced by applying a gradient surface plot to the data. Similar images of each distribution were also taken with the FLM as shown in (c) and (d) respectively.
D.R. Vardon et al. / Desalination 277 (2011) 193–200 Table 1 Results from the LSC algorithm and manual tallying of the number of cells per membrane for six filtered samples. A paired t-test was performed to confirm the parity of the methods. LSC (cells/memb)
Manual (cells/memb)
101 59 63 101 54 63 P-value of paired t-test: 0.71
98 61 62 103 56 63
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LSC counts (cells/membrane)
198
The paired t-test conducted with the LSC's automated program and manual counting also confirmed the parity of each method (p-value of 0.71) as shown in Table 1. Auto-enumeration provided by the LSC significantly reduced the count estimation time (b5 s) and prevented operator bias and fatigue associated with manual counting. 3.2. Linear response range and method variability The LSC showed a linear response with FLM over a wide range of concentrations (~50–150,000 cells/ml) as shown in Fig. 6. Accurate detection at low concentrations with the LSC extended well below the recommended limit of 250 cells/ml for commercial flow cytometry [23]. In addition, the linear response range at high concentration with the LSC surpassed that of satellite imaging which can underestimate concentrations larger than 1000 cells/ml [33]. The linear performance at high and low concentrations suggests that the LSC may be more effective than either flow cytometry or satellite imaging for determining algal concentrations in plant feedwater. Variability increased with the FLM at low numbers of cells per membrane (b5000 cells per membrane) as the number of cells per viewing window became sparse. The LSC, however, was able to count a significantly higher number of sample events by covering a larger area with the automated scanning method. The LSC provided reduced variability (0.25 coefficient of variation) compared to FLM (0.40 coefficient of variation) when triplicates of a dilute sample were counted (~ 200–400 cells per membrane). At high cell densities (N15,000 cells per membrane) counting was problematic with both methods. The reduced spacing between cells inhibited discrimination of single and multiple events with the LSC's automated counting program. Similarly, overcrowding made it difficult to keep track of cells with the FLM. Increased variability resulted between both methods at high cell 106
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FLM counts (cells/membrane)
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Fig. 7. Performance of LSC and FLM with varying cells per membrane. Filter volume was varied to change the number of cells present on the membrane surface. Higher variability resulted above 12,000 cells per membrane for both the LSC and FLM.
densities as indicated by the data spread in Fig. 7. Therefore, subsequent filtrations at reduced sample volumes would be required with the LSC when high cell densities are present. The primary source of LSC variability during cell counts was caused by sample preparation, not by the instrument. An ANOVA test of the LSC counts concluded that multiple scans of the same membrane did not affect the estimate of the sample concentration (p-value of 0.90). In contrast, repeated sampling and filtration of the same algal culture introduced variability that influenced the estimated concentration (p-value of 0.019). Variability in sample preparation was attributed to a non-homogenous cell distribution within the culture container. It was determined that counting multiple sample filtrations would provide a more accurate estimate of the sample concentration than performing multiple scans of the same membrane. 3.3. Influence of interfering matrix constituents Cell counts with LSC remained relatively constant (lowest p-value of 0.35 at 25 mg/l) with increasing bentonite and humic acid concentrations (Fig. 8). The algal cell fluorescence signal was uninhibited across all foulant levels (mean value of ~0.5–0.6 mV). The background signal increased marginally, however the noise remained well below the detection threshold for algal cells.
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FLM counts (cells/ml) Fig. 6. Linear response range of LSC and FLM for estimating sample concentrations plotted with respect to unity on a logarithmic scale. A linear correlation was seen from ~ 50 to 150,000 cells/ml.
0
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Bentonite & Humic Acid Concentration (mg/l) Fig. 8. Influence of membrane foulant (equal parts bentonite and humic acid) on LSC cell counts. Error bars represent standard error.
D.R. Vardon et al. / Desalination 277 (2011) 193–200
FLM & LSC counts (cells/ml)
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with the LSC and FLM allowed for a direct comparison of algal cells on the membrane surface. Cell concentrations ranged from ~300,000 to 1,000,000 cells/ml and a high degree of correlation was observed between the LSC and FLM (Pearson value of 0.958). Variability was attributed to sampling error and reduced cell diameter (4–8 μm) which decreased the number of data points per cell during LSC scans. Flow cytometry was not used with the natural bloom event due to timing and instrument availability. Based on previous results (Section 3.4) we would expect that flow cytometry and LSC would perform similarly. Flow cytometry may have an advantage in aiding with species identification because of its multi-fluorescence channel capabilities, but the LSC would be expected to count low-concentration samples more quickly.
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Flow cytometer counts (cells/ml) Fig. 9. LSC, FLM, and flow cytometry linear correlation for measuring concentrations between ~ 700 and 7,000 cells/ml. Filtration-based analysis methods (LSC and FLM) were compared against fluid stream analysis (flow cytometry).
3.4. Comparison to flow cytometry Cell counts with the LSC also demonstrated a linear correlation with flow cytometry over the range of ~ 700–7000 cells/ml (Fig. 9). However, sample flow rates were significantly different between the membrane filtration method and the flow cytometer. A filtration rate of 12 ml/min was recorded when preparing sample slides for the LSC and FLM, while the flow cytometer flow rate averaged ~35 μl/min. This difference in sample processing time provides the LSC a distinct advantage for monitoring dilute concentrations of algae compared to fluid stream analytical devices. Even if the flow cytometer were operated at a maximum flow rate of 250 μl/min [23], an additional 3 h and 15 min would be required to process 12 ml that can be filtered in one minute with the LSC. 3.5. LSC performance during a natural-bloom event Cell counts with the LSC were comparable to FLM when estimating the concentration of wild algae sampled during a bloom event off the coast of Long Beach, CA (Fig. 10). The similar filtration-based method
12
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4. Conclusions This study demonstrated that the LSC was capable of rapidly detecting and enumerating algal cell populations over a wide range of concentrations using both laboratory-grown cultures and a sample of a naturally occurring bloom. Spatial fluorescence imaging with the LSC accurately depicted distinctions in cell morphology and allowed for the counting of individual cells, which would not be possible with bulk fluorescence measurements. A combination of organic and inorganic interfering matrix material had a negligible effect on cell count estimations with the LSC. The scalable sample filtration method and rapid processing times make the LSC adept at measuring algal concentrations throughout the bloom lifecycle and would provide operators needed time to take preventive actions to mitigate a bloom's impact on desalination plant production. The benefits of automated counting, low-cost construction, and integration into existing water quality measurements warrant future investigations with the LSC as a monitoring method for tracking bulk algal growth conditions at desalination treatment facilities. Acknowledgements This project was principally funded by the United States Department of the Interior, Bureau of Reclamation through the Desalination and Water Purification Research Program. Additional funding was received from the Department of Civil and Environmental Engineering at the University of Illinois. We would like to thank Manish Kumar and Barbara Pilas for their invaluable input and help with the project. Dian Tanuwidjaja at the Long Beach Water Department (California, USA) is thanked for collecting the naturally occurring algal bloom sample.
LSC counts (cells/ml)
References
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Fig. 10. Response of LSC and FLM when estimating cell concentrations of wild algae sampled during a bloom event off the coast of Long Beach, CA. Cell counts ranged from ~ 300,000 to 1,000,000 cells/ml.
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