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Development of an optical method for identification of bacterial contamination in platelet concentrates – A feasibility study
ARTICLE IN PRESS
Medical Laser Application 24 (2009) 182–193 www.elsevier.de/mla
Development of an optical method for identification of bacterial contamination in platelet concentrates – A feasibility study Stefan Ey, Lesley Hirst Laser- und Medizin-Technologie GmbH, Fabeckstr. 60–62, 14195 Berlin, Germany Received 14 July 2008; accepted 6 May 2009
Abstract The number of platelet concentrates that are lost every year for clinical use, because of either the absence of suitable methods or the presence of only non-specific detection ones, is unacceptable both from a medical and an economic point of view. The clinical methods currently in use are not only too slow and inaccurate but are also too costly. The aim of the presented feasibility study was to overcome these disadvantages by developing a quick and inexpensive method to detect contamination in platelet concentrates. A suitable method was realized based on flow-through cytometry and fluorescence marking of bacteria in the form of an optimized test set-up and evaluation procedure. First measurements using the test set-up were very promising; however, further investigations are necessary to optimize the bacterial staining and to improve the signal-to-noise ratio. r 2009 Elsevier GmbH. All rights reserved. Keywords: Platelets; Contamination; Cytometry; Fluorescence; Scattering
Introduction Blood transfusions are now an inherent part of everyday clinical life. In spite of all the associated advantages for the patient, a blood transfusion also bears an infection risk which should not be neglected, namely the risk of hepatitis B, hepatitis C, HIV, fungi or other microorganisms. A further risk of developing a life-threatening sepsis as a result of a bacterial infection should also not be underestimated. The main bacterial contamination occurs when the donor’s skin is punctured, which in spite of extensive disinfection, can never be totally ruled out. USA statistics give a figure of nine million administered platelet concentrates per year for Corresponding author. Tel.: +49 30 844923 58; fax: +49 30 844923 99. E-mail address: [email protected] (S. Ey).
1615-1615/$ - see front matter r 2009 Elsevier GmbH. All rights reserved. doi:10.1016/j.mla.2009.05.004
the years 1990–1998. Of these concentrates, approximately 0.01–0.03% were contaminated with bacteria. During this period, of the 277 registered fatalities which were due to an infection contracted during a transfusion, 45 cases (17%) could be traced back to a bacterial contamination [1]. Another study from the USA cites the risk of a bacterial sepsis caused by platelet concentrates as being 0.002%. The probability of an infection is in fact 10 times higher than with erythrocyte concentrates, and 24 and 28 times more probable than the infection risk for hepatitis C and HIV-infection, respectively [2]. These estimations clearly show that there is an increased need for action to establish detection methods for the bacterial contamination of blood conserves, not only immediately before every transfusion, but also during the production of blood conserves. It would seem to be even more urgent for platelet concentrates as the
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storage conditions for these blood products (22–24 1C during continual shaking) provide particularly favorable conditions for the multiplication of bacteria. In order to reduce bacterial contamination risks, two main strategies are currently being considered:
To reduce the shelf life to 5 days: Even though the platelets remain functional for 10 days, keeping them for so long is questionable from an economic point of view. In 2002 for example, 14% of the platelet concentrates in Germany were thrown away resulting in a loss of 26.5 million EUR to the German economy. To check every platelet concentrate before use: The USA is a forerunner in this field as routine controls have been carried out there since 2004. In Germany it is planned to introduce a graduated scheme that stipulates the systematic control of all conserves, with the option of carrying out an ‘early’ or ‘late’ control procedure.
Therefore, an ideal screening test for bacterial contamination of platelet concentrates should meet two requirements: (1) in order to avoid sampling associated errors, early screening should be made possible 24 h after the blood has been donated and (2) a quick and straightforward method is needed for screening shortly before the transfusion.
Table 1. Test
Manufacturer Measuring method
‘Early’ detection methods Beckmann FACSb Coulter (Germany) ScansystemTM Hemosystem (France) Hemosystem Optimized ScansystemTM (France) ‘Late’ detection methods eBDS Pall (USA) Pall (USA)
ScansystemTM Hemosystem Platelet Kit (France) a
Sensitivity Earliest sample (cfu/ml)a probing after donation
Natural bacterial growth 1–10 Measurement of CO2 1 generation
Incubation or Staining preparation duration time (min)
Immediately 2d
1–5 d
2h
Fluorescence
Z2c
12–24 h
Fluorescence
30–72 h
1h
Fluorescence
Z2c
30 h
Measurement of O2 1–10 30 min consumption 1–10 30 min Measurement of O2 consumption Fluorescence+membrane Z10–100c 2–4 d filter
Colony forming units per millilitre (cfu/ml). FACS ¼ fluorescence activated cell sorting. c Dependent on type of bacteria. b
Currently there are three groups of tests for bacterial contamination: the ‘classical’ culture methods and the so-called ‘early’ and ‘late’ detection methods, all of which have their limitations (Table 1). The ‘classical’ culture methods, which are the ‘gold standard’ are based on the fact that bacteria will multiply during incubation for a specific time and can then be detected. These methods have the advantage of detecting even low levels of contamination; however, they require several days for incubation and therefore cannot be used for screening. The ‘early’ detection methods give results within a few hours but due to their low sensitivity, they require a certain level of bacterial contamination in order to be effective. If the sampling occurs too early in samples with an initial low bacterial count which grow later (424 h), the contamination will not be detected (Fig. 1). For example, Proprionibacterium spp., a known bacterial contaminant of platelet concentrates, has a delayed growth phase [11,12] and therefore runs the risk of giving a false negative result. ‘Late’ methods are either based on the use of membrane filters to concentrate bacteria [10] and takes too long for the test to be carried out immediately before transfusion, or measurement of the carbon dioxide produced and the oxygen used by the bacteria [9,13], which is quicker but has a very low sensitivity.
Comparison of currently used tests for bacterial contamination.
Classical culture methods Cell culture – BacT/Alert Me´rieux (USA)
BDS
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Test time (min)
– –
5
Refs.
[3]
1
[3–5]
[3,6] -
60
18/24 h
–
[3,8]
24/30/48 h
–
[9]
70 min
10
3
[5,7]
[10]
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Fig. 1. The bacterial growth curves for bacteria with different growth rates. The fast growing bacteria (+++) will definitely be found at the point of sampling. The dormant or very slow growing bacteria (+) will be dealt with by the immune system. The main problems occur with the bacteria which grow later (++) as the early detection methods are not sensitive enough at the time of sampling or can only be used when there is enough growth [14].
From this it is clear that a detection method is required which is sensitive enough to detect a low level of contamination. It must also be easy to handle and quick enough to be integrated into clinical everyday routine for use directly before the conserve is transfused (24 h after blood conserve production). This paper describes our efforts to develop such a method. It uses flow-through cytometry in combination with fluorescence staining and scattering of the excitation light to differentiate bacteria in platelet concentrates. The presented investigations are part of a Master thesis [14].
Fig. 2. Schematic diagram of the laboratory set-up.
Materials and method Set-up In contrast to bacteria, platelets do not contain any DNA. This characteristic can be used to differentiate between the two by selectively staining the bacterial nucleus with a DNA-specific fluorescent stain. For optical discrimination a laboratory set-up was developed as is shown schematically in Fig. 2. The cell suspension (platelets, with or without bacterial contamination) to be investigated was passed through a flow-through cuvette (Cat# 131.050, Hellma GmbH & Co. KG, Germany, square crosssection: 250 mm 250 mm) and embedded as a core stream in a guiding stream of saline solution. In order to produce the core stream, two containers were fixed at different levels, one containing the (carrier) outer flowing liquid layer (saline) and the other one the cell suspension (Fig. 3).
Fig. 3. Schematic diagram of the hydrodynamic focusing.
The cell suspension was injected into the guiding stream whereby the static pressure difference due to the difference in height (dh) between the containers
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determined the relationship between the core volume flow and outer volume flow. Hydrodynamic focusing led to a decrease in the diameters of both the core stream and the guiding stream. It was possible to adjust the diameter of the core stream relative to the guiding stream by varying the difference in height between the two containers. The adjustment of dh ¼ 100 mm resulted in a sample volume flow of 0.23 mL/s and a sample flow diameter of about 20 mm. This allowed the platelets and bacteria (+ approx. 1–2 mm) to flow almost singly in the core stream. The core stream was in the focus of a laser beam (FDSS-532-30, CryLaS GmbH, Germany) at a wavelength corresponding to the excitation wavelength of the used fluorescent stains (532 nm). The whole set-up was integrated into an optical microscope (Axiophot II, Carl Zeiss AG, Germany). A photomultiplier (PMT+RE1, Honold, Germany) was used in place of a microscope camera to register the fluorescence and the scattering signal was simultaneously detected by a photodiode (OSD 35, Centronic Limited, UK) and subsequently amplified (SR570, Stanford Research, USA). A negative aperture, situated in the optical path at the back of the cuvette, was used to suppress the unscattered laser beam. The 40 /NA ¼ 0.6-objective used not only functioned as a collector for the fluorescence but also served as a focusing optic for the laser beam. If one platelet or bacterium flowed through the illuminated core, it produced a signal due to its size in the scattering channel and the fluorescent marking in the fluorescence channel, respectively. Appropriate dilution ensured that the signal producing particles were far enough apart to prevent the results overlapping, something which would have reduced the sensitivity and specificity of the method.
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The particles in the core stream reached a speed of about 1 m/s. In order to differentiate one particle from another, they had to be at least 50 mm apart in the core stream, i.e. passing the laser spot with a time interval of _ of 50 ms. This related to a maximal particle rate (N) 1 _ _ 20,000 s . Using C ¼ N=V resulted in a preset volume flow (V_ ) of 0.23 mL/s of a maximum particle concentration (C) of 87,000 mL1.
Signal processing The signal of the photomultiplier (fluorescence) and the photodiode (scattering) were simultaneously scanned and digitalized (A/D converter card NI 6132, National Instruments Germany GmbH, Germany) whereby the chronological correlation of the coincidence signals was maintained using specially developed software. A separate threshold was defined for each channel. If a signal longer than one sample exceeded the threshold, either a fluorescent peak or a scattering peak was registered. When the maxima of a scattering and a fluorescent peak occurred at the same time (the difference is maximally 10–20 samples/sample rate), then they were combined to form a coincidence peak (Fig. 4). For offset reduction, the base line was determined in the fluorescence and the scatter channel and subtracted from the measured data. Also the measured data were smoothed in each channel in order to reduce the noise. Different histograms and plots were produced from the peaks registered, which contributed to the evaluation of the measuring data.
Fig. 4. Classification of the detected signals into fluorescence, scatter and coincidence peaks.
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do not have any nuclei, the fluorescent marking of the nucleic acids can be considered a further bacteriaspecific criterion of proof. The preparation of the cell suspensions (platelets with or without bacterial contamination) took place in four steps: preliminary dilution, staining, incubation and adjustment dilution followed by measurement. The cell suspensions, both platelets and bacteria, were initially diluted with phosphate buffered saline (PBS) resulting in a suspension 1000 times more concentrated than was required in the final dilution step. This was done to ensure that the dilution steps remained the same for all the samples and that they could be compared. The staining was carried out in the dark at room temperature.
System evaluation Before starting the measurement of the cell suspensions, the linearity of the system was tested with fluorescent microspheres (beads + 2 mm, Cat# F8825, Invitrogen GmbH, Germany) of a known concentration. For this, a highly concentrated beads solution was diluted with Ultrapures water (Merck Deutschland) in a defined dilution series (100, 200, 300, 457 mL1) and measured. Fluorescence and scattering were measured _ which correlated to for 200 s and the particle rate (N), the coincidence peak rate, was determined for every second. The mean value and the standard deviation were calculated from the measured particle rates (n ¼ 200) per preset concentration. The measured concentrations _ V_ ) from the mean could then be calculated (C ¼ N= values and the known volume flow (V_ ¼ 0.23 mL/s). The results were evaluated by an expected/measured comparison.
Results System linearity
Sample preparation
Fig. 5 shows the actual concentrations (y-axis) calculated from the measured particle rates compared to the expected target concentrations (x-axis) from the dilution series (100, 200, 300, 457 mL1). The handling error was calculated to be 57% according to the Gaussian error propagation law. The standard deviations of the measured concentrations are between 9%
For the cell experiments 5-day-old platelet concentrates were used from the current stock of the blood bank of the Charite´ Berlin. The concentration of the platelets was determined directly in the blood bank and was 1–1.5 109 mL1 depending on the bag. The samples were disposed of if they were not measured within 3 days. During this time they were stored at room temperature (22–25 1C) on a laboratory shaker. Platelet concentrates were spiked with Escherichia coli bacteria. In order to differentiate the bacteria from the platelets, two different stains, SytoTM–80 (Cat# S11361, Invitrogen GmbH, Germany) and PopoTM–3 (Cat# P3584, Invitrogen GmbH, Germany), were used as selective markers (Table 2). The choice of these stains followed compliance with three important criteria: (1) a high affinity for intracellular DNA, (2) the spectral properties of the stains; the fluorescence excitation was l ¼ 532 nm and emission could be detected spectrally separated from the excitation light, (3) both stains could be used to mark the DNA of the bacteria in micromolar concentrations which meant that there was a correspondingly small background fluorescence. In the case of SytoTM–80 there was a clear amplification of the fluorescence emission induced after binding on the DNA. As platelets Table 2.
Fig. 5. Proof of linearity of the measuring method. The dilutions are given with their handling errors on the x-axis, and on the y-axis are the measured concentrations with their standard deviations.
Specification of the DNA stains used.
Stain
Absorption (nm)
Emission (nm)
Chemical characteristics
SytoTM – 80 PopoTM – 3
531 534
545 570
Cell permeable cyanine (vital stain) Cell impermeable cyanine dimer
The spectral characteristics are applicable for each of the DNA-bound stains.
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and 23%. Thus, the absolute values are within the margin of error of the dilution procedure.
Fluorescence–scatter diagram of beads In a fluorescence–scatter diagram (Fig. 6A), it is immediately obvious whether or not two populations of different particles can be separated. Ideally all the particles of a population produce the same level of fluorescence and scatter signal, i.e. all the particles appear in the fluorescence–scatter diagram at the same place, as one point. In a three-dimensional fluorescence–scatter diagram this would be represented by a very high peak at one position. As the particles of a population have a Gaussian distribution of their characteristics (in the case of the beads, the size and the fluorescence) this affects the fluorescence–scatter diagram in the form of a two-dimensional distribution around a single point (Fig. 6A); the coordinates of which correspond to the mean values of the Gaussian distribution of the characteristics shown (Fig. 6B: mean values of the scatter voltage 92972 mV; Fig. 6C: mean values of the fluorescence voltage 1.5070.19 V).
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Fig. 6D shows the fluorescence and scatter signal of a single bead. The width and the shape of the peaks are dependent on the beam profile of the laser, as well as the flow speed and the size (length) of the particles. Ideally, as was the case here, the time between two sampled beads should be about 50 ms in order to be able to distinguish the beads from another.
Measurements on bacteria After the measurement method had been established using the beads, E. coli bacteria were stained with SytoTM–80 (10 mM/L, Fig. 7) and PopoTM–3 (50 mM/L, Fig. 8) and measured. In both cases the stain was incubated with the bacteria for 60 min. As the diagrams show, there is a very low signal-to-noise ratio in the scatter channel. This is apparent from the dense clouds of scatter peaks to the left of the fluorescence threshold in the two fluorescence–scatter diagrams (Figs. 7A and 8A). This follows on in the distribution of the coincidence peaks, originating from the bacteria. As the scatter voltage of the bacteria coincidence peaks is similar to the noise from the scatter channel, the bacteria can almost only be identified using the peaks in the fluorescence
Fig. 6. Measurements on beads (+ 2 mm, Cbeads ¼ 100 mL1, V_ ¼ 0.23 mL/s): (A) fluorescence–scatter diagram. The coincidence peaks of the beads (right) can be clearly distinguished from the scatter peaks (left), brought about by dirt particles, (B) histogram of the scatter voltage, (C) histogram of the fluorescence voltage and (D) fluorescence and scatter signal of a single bead.
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Fig. 7. Measurement of Escherichia coli bacteria stained with SytoTM–80 (CSyto80 ¼ 10 mM/L, tstain ¼ 60 min, T ¼ 25 1C, CbactE500 mL1): (A) fluorescence–scatter diagram, (B) fluorescence and scatter signal of a single bacterium, (C) histogram of the scatter voltage and (D) histogram of the fluorescence voltage.
Fig. 8. Measurement of Escherichia coli bacteria stained with PopoTM–3 (CPopo3 ¼ 50 mM/L; tstain ¼ 60 min; T ¼ 25 1C; CbactE10 mL1): (A) fluorescence–scatter diagram, (B) fluorescence and scatter signal of a single bacterium, (C) histogram of the scatter voltage and (D) histogram of the fluorescence voltage.
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channel (Figs. 7D and 8D). In the scatter histogram (Figs. 7C and 8C), distribution of the scatter and coincidence peaks clearly overlaps, whereas in the fluorescence histogram (Figs. 7D and 8D) the fluorescence and coincidence peaks can be distinctly separated. However, it is of importance to note exactly when the sample was measured, i.e. how much time has passed since the cells were stained with the fluorescent stain.
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Fig. 9 shows the fluorescence intensities for both stains and Fig. 10 shows the relative proportion of the peak modes in the total rate dependent on the measuring time. In the case of the E. coli bacteria stained with SytoTM–80, the fluorescence intensity decreased with the measuring time (Fig. 9A). In contrast, the fluorescence intensity of the PopoTM–3 stained bacteria remained constant, or even increased slightly (Fig. 9B).
Fig. 9. Plot against time of the fluorescence voltage of a stained Escherichia coli sample. The bacteria were stained with (A) SytoTM–80 (CSyto80 ¼ 20 mM/L; tstain ¼ 60 min; T ¼ 25 1C) and with (B) PopoTM–3 (CPopo3 ¼ 50 mM/L; tstain ¼ 60 min; T ¼ 25 1C).
Fig. 10. Measurement of a stained Escherichia coli sample. The bacteria were stained with (A) SytoTM–80 (CSyto–80 ¼ 20 mM/L, tstain ¼ 60 min, T ¼ 25 1C) and with (B) PopoTM–3 (CPopo3 ¼ 50 mM/L, tstain ¼ 60 min, T ¼ 25 1C). With increasing measure time, changes are observed in the relative proportion of the detected peaks (fluorescence, scatter or coincidence peaks) of the total rate (sum of all the peaks/measuring time).
Fig. 11. Measurements on SytoTM–80 stained platelets (CSyto80 ¼ 10 mM/L; tstain ¼ 20 min; T ¼ 25 1C). The figures show the fluorescence–scatter diagrams. The measurements were made 20 min (A) and 90 min (B) after the staining.
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Fig. 12. Measurements with SytoTM–80-stained platelets (CSyto80 ¼ 10 mM/L; tstain ¼ 20, 40, 60 min; T ¼ 25 1C): (A) progression of the relative share of the fluorescent platelets with time and (B) progression of the mean fluorescence voltage for the different incubation times.
(Fig. 11B) after staining with SytoTM–80. After some time, the fluorescence intensity of the platelets decreased. In Fig. 12 it can be seen how the relative part of the platelets, detected as coincidence peaks, and the fluorescence voltage decreases exponentially with time. This behavior is in direct contrast to the stained bacteria (Figs. 9 and 10) and indicates that the platelets are nonspecifically stained.
Fig. 13. Fluorescence–scatter diagram for a platelet sample spiked with Escherichia coli (100:1). The marked thresholds for classification have four to six times the standard deviation of the noise of the scattering or the fluorescence. The platelets marked with a triangle were mistakenly identified as bacteria as they exhibited fluorescence above the threshold level. This led to an increase in the proportion of the false positives and to a decrease in the specificity. In contrast, the fluorescence peaks raised the proportion of the false negatives and consequently led to a reduction in the sensitivity [14].
The relative part of the peak modes shows that the proportion of the fluorescent peaks remains constant for both stains. However, the scatter peaks increase for SytoTM–80 by the same amount as the coincidence peaks decrease (Fig. 10A), in the case of PopoTM–3 they remain almost constant (Fig. 10B).
Measurement of platelets Fig. 11 shows the fluorescence–scatter diagrams of platelets stained in the same way as the E. coli. The measurements were made 20 min (Fig. 11A) or 90 min
Platelets spiked with bacteria If a platelet sample is spiked (100:1) with E. coli, it results in a fluorescence–scatter diagram such as the one shown in Fig. 13. A closer look at the peaks shows that the platelets and the bacteria overlap in the region of noise from the fluorescence and scatter channel.
Discussion Optimization of the set-up The achieved core flow diameter of 20 mm is of the same order as the samples under examination (E. coli: +E2 mm, lengthE5–20 mm, platelets: +E3–4 mm). However, it is our aim to halve the core flow diameter to about 10 mm, which will mean that the intensity difference of the laser light at the center and edge of the core flow will become smaller (Fig. 14) and the intensity distribution in the fluorescence channel histogram will become thinner. An increase in the laser beam radius would lead to the same result but would at the same time lower the utilizable intensity. A beam profile adjustment without a loss in intensity of about 10 mm in the core stream region would be optimal. The hydrodynamic focusing makes it possible to reduce the measuring time per sample volume. For this the core volume flow must be raised whilst keeping the core flow diameter constant. As this is
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Fig. 14. Illustration of the intensity differences of the laser beam in the middle and at the edge of the core flow, for two different laser beam radii (40 and 80 mm) and two different core flow diameters (10 and 25 mm). The larger the laser beam radius and the smaller the core flow diameter, the smaller the intensity difference between the core flow middle and the core flow edge.
not possible by varying the static pressure difference of the liquid column, other possible solutions must be sought.
System linearity The linearity of the method could be confirmed using beads in a concentration series. The measured concentrations deviate from the calculated values of 9% and 23%. They are within the handling error of 57% and are therefore significant. The handling error is probably smaller as it can only be calculated from the assessed partial uncertainties. As has already been shown with the beads, the signal-to-noise ratio in the scatter channel must be increased from 1:3 to 1:5 in order to be able to discriminate large variations in the particle size.
Fluorescence staining The fluorescence staining of the E. coli with SytoTM–80 has shown that the fluorescence intensity decreases with the measuring time. This could be due to the fact that not all the stain molecules intercalate and
therefore diffuse back into the surrounding medium (PBS) due to the concentration difference, or they are bleached. In contrast, the fluorescence intensity remains constant with PopoTM–3 and even increases slightly with increasing measuring time. The range of the bacterial size, which leads to a wide distribution of the fluorescence intensities obtained, cannot be compensated for by either of the stains because the stain concentrations within the bacteria are directly dependent on the size of the bacteria. An increase in the stain concentration or an extension of the incubation time leads to a wider distribution and therefore better differentiation of the smaller bacteria from the noise signal. An increase in the incubation time conflicts with the requirement to develop a quick detection method. Furthermore, higher concentration of stains leads to a higher fluorescent background and therefore to a deterioration in the signal-to-noise ratio. Using the same staining method as for the E. coli, the platelets are stained unspecifically which decreases exponentially with time after further dilution. During the staining procedure the stain diffuses into the platelets until equilibrium has been reached. After dilution the platelets continue to fluoresce but with
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time the concentration of the stain within, or on the surface of the platelets, decreases. The stain molecules diffuse out of the platelets into the surrounding medium until the stain concentration is once more in equilibrium. For this reason it is important to keep both the incubation short and the stain concentration low in order to ensure that the method is quick. On the other hand there must be a certain concentration of the stain within the bacteria to allow the platelets to be differentiated from the bacteria. This can be partly counteracted by lysing the platelets prior to staining or by staining them for as short a time as possible (o2–3 min). Then 40–60 min after dilution of the sample with PBS, the concentration of the stain within the platelets will be so low that it will not be registered by the coincident peaks.
Cell discrimination The variation of the bacteria and platelet size, and the associated variation in the concentration of the bound stain lead to a Gaussian distribution of the resulting signal peaks. This leads to an overlapping of the distribution of the noise and the detected signal in their respective measuring channels, resulting in a reduction in the sensitivity and specificity of the procedure. The measurements showed that the set-up is basically suitable for detection of bacteria, such as E. coli, in platelet concentrates. Assuming that all bacteria in Fig. 13 lead to a coincident event, the sensitivity and specificity will be 57% and 99%, respectively. The current sample preparation method takes about 30–70 min, including staining of the bacteria, and the measurement procedure takes about 2 min for a 28 mL sample volume. Semi-automatic evaluation of the sample takes a further 15 min, but this could most certainly be reduced to less than 5 min. If 106–107particles were measured, 75–750 s would be needed for a maximum possible concentration of 85,000 mL1 and a sample volume flow of 0.23 mL/s. In order to reduce the measurement time, the sample volume flow could be increased. This would make a change in the current geometry of the hydrodynamic focusing necessary because the sample flow diameter is proportional to the sample volume flow.
Conclusion There are several established methods that can be used to minimize the contamination risk of blood conserves in clinical practice. However, they all involve time and cost intensive procedures. First trials for the detection of bacterial contamination in platelet concentrates using a simplified cytometric method have been
promising but it will be some time before the method can compete with established methods. We are however, convinced that this will be worth the effort. When compared with FACS, the advantages of our method are not immediately apparent. FACS uses several wavelengths for detection and sorts the sample contents, both features requiring a complex design and handling method. Only one wavelength is used for fluorescence detection which is why there are fewer demands on the stain. The scattering signal is also detected, so, what advantages does our method have to offer? Considering the measuring techniques alone, the commercial FACS method is clearly better. However, we have concentrated our efforts on the calculation of the digitalized signal, not only with respect to the signal level, but also to the time sequence, i.e. signal shape and the statistical assessment. This is still at the trial stage and still requires a satisfactory measuring set-up. In order to attain a higher sensitivity and high signal-to-noise ratio to facilitate the detection of the weak fluorescence and scattering signal, it will be necessary to redesign the setup and optimize the system. However, when this has been achieved, the next step will be to optimize both the staining procedure and the algorithm after which it will be possible to compare the method to an already established system.
Acknowledgment This work was supported by the Federal Ministry of Economics (Grant # IW 061173), the Senate of Berlin (ProFit OptiRem) and the European Union (EFRE).
Zusammenfassung Machbarkeitsstudie zur Entwicklung eines optischen Verfahrens zur Identifizierung kontaminierter Thrombozytenkonzentrate Der Anteil der Thrombozytenkonzentrate, die der klinischen Verwendung ja¨hrlich durch fehlende oder unzula¨ngliche Detektionsverfahren entgehen, ist aus medizinischer und wirtschaftlicher Sicht untragbar. Die derzeit verfu¨gbaren Verfahren sind fu¨r den klinischen Alltag zu langsam oder zu ungenau und zudem sehr kostspielig. Gegenstand der vorgestellten Machbarkeitsstudie ist die Entwicklung eines Verfahrens zur Detektion von kontaminierten Thrombozytenkonzentraten, welches diese Nachteile ausra¨umt. Basierend auf der Durchflusszytometrie und Fluoreszenzmarkierung von Bakterien sowie einer optimierten Messanordnung und Auswertung, sollte ein entsprechendes Detektionsverfahren realisiert werden. Nach der Etablierung der
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Messanordnung wurden erste vielversprechende Messungen durchgefu¨hrt. Es fanden erste Untersuchungen zur Fa¨rbung der Bakterien mit verschiedenen Fluoreszenzfarbstoffen statt. Dabei zeigte sich, dass die gewa¨hlte Messapparatur zuna¨chst verbessert werden muss, um die geringen Streu- und Fluoreszenzintensita¨ten verla¨sslich zu detektieren, bevor mit der Farbstoffauswahl und der Algorithmenoptimierung fortgefahren werden kann.
[7]
[8]
[9] Schlu¨sselwo¨rter: Thrombozyten; Bakterienkontamination; Zytometrie; Fluoreszenz; Streuung [10]
References [1] Arendt A, Carmean J, Koch E, Rolfs R, Mottice S, Strausbaugh L, et al. Fatal bacterial infections associated with platelet transfusions – United States, 2004. MMWR 2005;54(07):168–70. [2] Chang AH, Kirsch CM, Mobashery N, Johnson N, Levitt LJ. Streptococcus bovis septic shock due to contaminated transfused platelets. Am J Hematol 2004;77(3):282–6. [3] Mu¨ller TH, Mohr H, Montag T. Methods for the detection of bacterial contamination in blood products. Clin Chem Lab Med 2008;46(7):933–46. [4] Mohr H, Lambrecht B, Bayer A, Spengler HP, Nicol SB, Montag T, et al. Basics of flow cytometry-based sterility testing of platelet concentrates. Transfusion 2006;46(1): 41–9. [5] Schmidt M, Hourfar MK, Nicol SB, Wahl A, Heck J, Weis C, et al. A comparison of three rapid bacterial detection methods under simulated real-life conditions. Transfusion 2006;46(8):1367–73. [6] Jacobs MR, Bajaksouzian S, Windau A, Palavecino EL, Yomtovian R. Evaluation of the Scansystem method for
[11]
[12]
[13]
[14]
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detection of bacterially contaminated platelets. Transfusion 2005;45(2):265–9. Schmidt M, Weis C, Heck J, Montag T, Nicol SB, Hourfar MK, et al. Optimized Scansystem platelet kit for bacterial detection with enhanced sensitivity: detection within 24 h after spiking. Vox Sang 2005;89(3):135–9. McDonald CP, Pearce S, Wilkins K, Colvin J, Robbins S, Colley L, et al. Pall eBDS: an enhanced bacterial detection system for screening platelet concentrates. Transfus Med 2005;15(4):259–68. McDonald CP, Colvin J, Smith R, Wilkins K, Robbins S, Barbara JA. A novel method for the detection of bacteria in platelet concentrates utilizing oxygen consumption as a marker for bacterial growth. Transfus Med 2004;14(6): 391–8. Ribault S, Harper K, Grave L, Lafontaine C, Nannini P, Raimondo A, et al. Rapid screening method for detection of bacteria in platelet concentrates. J Clin Microbiol 2004;42(5):1903–8. Mohammadi T, Pietersz RN, Vandenbroucke-Grauls CM, Savelkoul PH, Reesink HW. Detection of bacteria in platelet concentrates: comparison of broad-range realtime 16S rDNA polymerase chain reaction and automated culturing. Transfusion 2005;45(5):731–6. de Korte D, Marcelis JH, Verhoeven AJ, Soeterboek AM. Diversion of first blood volume results in a reduction of bacterial contamination for whole-blood collections. Vox Sang 2002;83(1):13–6. Holme S, McAlister MB, Ortolano GA, Chong C, Cortus MA, Jacobs MR, et al. Enhancement of a culture-based bacterial detection system (eBDS) for platelet products based on measurement of oxygen consumption. Transfusion 2005;45(6):984–93. Ey S. Development of a cytometric method for the determination of bacterial contamination in blood products. Master thesis, Technische Fachhochschule Berlin, 2007.
Medical Laser Application 24 (2009) 182–193 www.elsevier.de/mla
Development of an optical method for identification of bacterial contamination in platelet concentrates – A feasibility study Stefan Ey, Lesley Hirst Laser- und Medizin-Technologie GmbH, Fabeckstr. 60–62, 14195 Berlin, Germany Received 14 July 2008; accepted 6 May 2009
Abstract The number of platelet concentrates that are lost every year for clinical use, because of either the absence of suitable methods or the presence of only non-specific detection ones, is unacceptable both from a medical and an economic point of view. The clinical methods currently in use are not only too slow and inaccurate but are also too costly. The aim of the presented feasibility study was to overcome these disadvantages by developing a quick and inexpensive method to detect contamination in platelet concentrates. A suitable method was realized based on flow-through cytometry and fluorescence marking of bacteria in the form of an optimized test set-up and evaluation procedure. First measurements using the test set-up were very promising; however, further investigations are necessary to optimize the bacterial staining and to improve the signal-to-noise ratio. r 2009 Elsevier GmbH. All rights reserved. Keywords: Platelets; Contamination; Cytometry; Fluorescence; Scattering
Introduction Blood transfusions are now an inherent part of everyday clinical life. In spite of all the associated advantages for the patient, a blood transfusion also bears an infection risk which should not be neglected, namely the risk of hepatitis B, hepatitis C, HIV, fungi or other microorganisms. A further risk of developing a life-threatening sepsis as a result of a bacterial infection should also not be underestimated. The main bacterial contamination occurs when the donor’s skin is punctured, which in spite of extensive disinfection, can never be totally ruled out. USA statistics give a figure of nine million administered platelet concentrates per year for Corresponding author. Tel.: +49 30 844923 58; fax: +49 30 844923 99. E-mail address: [email protected] (S. Ey).
1615-1615/$ - see front matter r 2009 Elsevier GmbH. All rights reserved. doi:10.1016/j.mla.2009.05.004
the years 1990–1998. Of these concentrates, approximately 0.01–0.03% were contaminated with bacteria. During this period, of the 277 registered fatalities which were due to an infection contracted during a transfusion, 45 cases (17%) could be traced back to a bacterial contamination [1]. Another study from the USA cites the risk of a bacterial sepsis caused by platelet concentrates as being 0.002%. The probability of an infection is in fact 10 times higher than with erythrocyte concentrates, and 24 and 28 times more probable than the infection risk for hepatitis C and HIV-infection, respectively [2]. These estimations clearly show that there is an increased need for action to establish detection methods for the bacterial contamination of blood conserves, not only immediately before every transfusion, but also during the production of blood conserves. It would seem to be even more urgent for platelet concentrates as the
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storage conditions for these blood products (22–24 1C during continual shaking) provide particularly favorable conditions for the multiplication of bacteria. In order to reduce bacterial contamination risks, two main strategies are currently being considered:
To reduce the shelf life to 5 days: Even though the platelets remain functional for 10 days, keeping them for so long is questionable from an economic point of view. In 2002 for example, 14% of the platelet concentrates in Germany were thrown away resulting in a loss of 26.5 million EUR to the German economy. To check every platelet concentrate before use: The USA is a forerunner in this field as routine controls have been carried out there since 2004. In Germany it is planned to introduce a graduated scheme that stipulates the systematic control of all conserves, with the option of carrying out an ‘early’ or ‘late’ control procedure.
Therefore, an ideal screening test for bacterial contamination of platelet concentrates should meet two requirements: (1) in order to avoid sampling associated errors, early screening should be made possible 24 h after the blood has been donated and (2) a quick and straightforward method is needed for screening shortly before the transfusion.
Table 1. Test
Manufacturer Measuring method
‘Early’ detection methods Beckmann FACSb Coulter (Germany) ScansystemTM Hemosystem (France) Hemosystem Optimized ScansystemTM (France) ‘Late’ detection methods eBDS Pall (USA) Pall (USA)
ScansystemTM Hemosystem Platelet Kit (France) a
Sensitivity Earliest sample (cfu/ml)a probing after donation
Natural bacterial growth 1–10 Measurement of CO2 1 generation
Incubation or Staining preparation duration time (min)
Immediately 2d
1–5 d
2h
Fluorescence
Z2c
12–24 h
Fluorescence
30–72 h
1h
Fluorescence
Z2c
30 h
Measurement of O2 1–10 30 min consumption 1–10 30 min Measurement of O2 consumption Fluorescence+membrane Z10–100c 2–4 d filter
Colony forming units per millilitre (cfu/ml). FACS ¼ fluorescence activated cell sorting. c Dependent on type of bacteria. b
Currently there are three groups of tests for bacterial contamination: the ‘classical’ culture methods and the so-called ‘early’ and ‘late’ detection methods, all of which have their limitations (Table 1). The ‘classical’ culture methods, which are the ‘gold standard’ are based on the fact that bacteria will multiply during incubation for a specific time and can then be detected. These methods have the advantage of detecting even low levels of contamination; however, they require several days for incubation and therefore cannot be used for screening. The ‘early’ detection methods give results within a few hours but due to their low sensitivity, they require a certain level of bacterial contamination in order to be effective. If the sampling occurs too early in samples with an initial low bacterial count which grow later (424 h), the contamination will not be detected (Fig. 1). For example, Proprionibacterium spp., a known bacterial contaminant of platelet concentrates, has a delayed growth phase [11,12] and therefore runs the risk of giving a false negative result. ‘Late’ methods are either based on the use of membrane filters to concentrate bacteria [10] and takes too long for the test to be carried out immediately before transfusion, or measurement of the carbon dioxide produced and the oxygen used by the bacteria [9,13], which is quicker but has a very low sensitivity.
Comparison of currently used tests for bacterial contamination.
Classical culture methods Cell culture – BacT/Alert Me´rieux (USA)
BDS
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Test time (min)
– –
5
Refs.
[3]
1
[3–5]
[3,6] -
60
18/24 h
–
[3,8]
24/30/48 h
–
[9]
70 min
10
3
[5,7]
[10]
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Fig. 1. The bacterial growth curves for bacteria with different growth rates. The fast growing bacteria (+++) will definitely be found at the point of sampling. The dormant or very slow growing bacteria (+) will be dealt with by the immune system. The main problems occur with the bacteria which grow later (++) as the early detection methods are not sensitive enough at the time of sampling or can only be used when there is enough growth [14].
From this it is clear that a detection method is required which is sensitive enough to detect a low level of contamination. It must also be easy to handle and quick enough to be integrated into clinical everyday routine for use directly before the conserve is transfused (24 h after blood conserve production). This paper describes our efforts to develop such a method. It uses flow-through cytometry in combination with fluorescence staining and scattering of the excitation light to differentiate bacteria in platelet concentrates. The presented investigations are part of a Master thesis [14].
Fig. 2. Schematic diagram of the laboratory set-up.
Materials and method Set-up In contrast to bacteria, platelets do not contain any DNA. This characteristic can be used to differentiate between the two by selectively staining the bacterial nucleus with a DNA-specific fluorescent stain. For optical discrimination a laboratory set-up was developed as is shown schematically in Fig. 2. The cell suspension (platelets, with or without bacterial contamination) to be investigated was passed through a flow-through cuvette (Cat# 131.050, Hellma GmbH & Co. KG, Germany, square crosssection: 250 mm 250 mm) and embedded as a core stream in a guiding stream of saline solution. In order to produce the core stream, two containers were fixed at different levels, one containing the (carrier) outer flowing liquid layer (saline) and the other one the cell suspension (Fig. 3).
Fig. 3. Schematic diagram of the hydrodynamic focusing.
The cell suspension was injected into the guiding stream whereby the static pressure difference due to the difference in height (dh) between the containers
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determined the relationship between the core volume flow and outer volume flow. Hydrodynamic focusing led to a decrease in the diameters of both the core stream and the guiding stream. It was possible to adjust the diameter of the core stream relative to the guiding stream by varying the difference in height between the two containers. The adjustment of dh ¼ 100 mm resulted in a sample volume flow of 0.23 mL/s and a sample flow diameter of about 20 mm. This allowed the platelets and bacteria (+ approx. 1–2 mm) to flow almost singly in the core stream. The core stream was in the focus of a laser beam (FDSS-532-30, CryLaS GmbH, Germany) at a wavelength corresponding to the excitation wavelength of the used fluorescent stains (532 nm). The whole set-up was integrated into an optical microscope (Axiophot II, Carl Zeiss AG, Germany). A photomultiplier (PMT+RE1, Honold, Germany) was used in place of a microscope camera to register the fluorescence and the scattering signal was simultaneously detected by a photodiode (OSD 35, Centronic Limited, UK) and subsequently amplified (SR570, Stanford Research, USA). A negative aperture, situated in the optical path at the back of the cuvette, was used to suppress the unscattered laser beam. The 40 /NA ¼ 0.6-objective used not only functioned as a collector for the fluorescence but also served as a focusing optic for the laser beam. If one platelet or bacterium flowed through the illuminated core, it produced a signal due to its size in the scattering channel and the fluorescent marking in the fluorescence channel, respectively. Appropriate dilution ensured that the signal producing particles were far enough apart to prevent the results overlapping, something which would have reduced the sensitivity and specificity of the method.
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The particles in the core stream reached a speed of about 1 m/s. In order to differentiate one particle from another, they had to be at least 50 mm apart in the core stream, i.e. passing the laser spot with a time interval of _ of 50 ms. This related to a maximal particle rate (N) 1 _ _ 20,000 s . Using C ¼ N=V resulted in a preset volume flow (V_ ) of 0.23 mL/s of a maximum particle concentration (C) of 87,000 mL1.
Signal processing The signal of the photomultiplier (fluorescence) and the photodiode (scattering) were simultaneously scanned and digitalized (A/D converter card NI 6132, National Instruments Germany GmbH, Germany) whereby the chronological correlation of the coincidence signals was maintained using specially developed software. A separate threshold was defined for each channel. If a signal longer than one sample exceeded the threshold, either a fluorescent peak or a scattering peak was registered. When the maxima of a scattering and a fluorescent peak occurred at the same time (the difference is maximally 10–20 samples/sample rate), then they were combined to form a coincidence peak (Fig. 4). For offset reduction, the base line was determined in the fluorescence and the scatter channel and subtracted from the measured data. Also the measured data were smoothed in each channel in order to reduce the noise. Different histograms and plots were produced from the peaks registered, which contributed to the evaluation of the measuring data.
Fig. 4. Classification of the detected signals into fluorescence, scatter and coincidence peaks.
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do not have any nuclei, the fluorescent marking of the nucleic acids can be considered a further bacteriaspecific criterion of proof. The preparation of the cell suspensions (platelets with or without bacterial contamination) took place in four steps: preliminary dilution, staining, incubation and adjustment dilution followed by measurement. The cell suspensions, both platelets and bacteria, were initially diluted with phosphate buffered saline (PBS) resulting in a suspension 1000 times more concentrated than was required in the final dilution step. This was done to ensure that the dilution steps remained the same for all the samples and that they could be compared. The staining was carried out in the dark at room temperature.
System evaluation Before starting the measurement of the cell suspensions, the linearity of the system was tested with fluorescent microspheres (beads + 2 mm, Cat# F8825, Invitrogen GmbH, Germany) of a known concentration. For this, a highly concentrated beads solution was diluted with Ultrapures water (Merck Deutschland) in a defined dilution series (100, 200, 300, 457 mL1) and measured. Fluorescence and scattering were measured _ which correlated to for 200 s and the particle rate (N), the coincidence peak rate, was determined for every second. The mean value and the standard deviation were calculated from the measured particle rates (n ¼ 200) per preset concentration. The measured concentrations _ V_ ) from the mean could then be calculated (C ¼ N= values and the known volume flow (V_ ¼ 0.23 mL/s). The results were evaluated by an expected/measured comparison.
Results System linearity
Sample preparation
Fig. 5 shows the actual concentrations (y-axis) calculated from the measured particle rates compared to the expected target concentrations (x-axis) from the dilution series (100, 200, 300, 457 mL1). The handling error was calculated to be 57% according to the Gaussian error propagation law. The standard deviations of the measured concentrations are between 9%
For the cell experiments 5-day-old platelet concentrates were used from the current stock of the blood bank of the Charite´ Berlin. The concentration of the platelets was determined directly in the blood bank and was 1–1.5 109 mL1 depending on the bag. The samples were disposed of if they were not measured within 3 days. During this time they were stored at room temperature (22–25 1C) on a laboratory shaker. Platelet concentrates were spiked with Escherichia coli bacteria. In order to differentiate the bacteria from the platelets, two different stains, SytoTM–80 (Cat# S11361, Invitrogen GmbH, Germany) and PopoTM–3 (Cat# P3584, Invitrogen GmbH, Germany), were used as selective markers (Table 2). The choice of these stains followed compliance with three important criteria: (1) a high affinity for intracellular DNA, (2) the spectral properties of the stains; the fluorescence excitation was l ¼ 532 nm and emission could be detected spectrally separated from the excitation light, (3) both stains could be used to mark the DNA of the bacteria in micromolar concentrations which meant that there was a correspondingly small background fluorescence. In the case of SytoTM–80 there was a clear amplification of the fluorescence emission induced after binding on the DNA. As platelets Table 2.
Fig. 5. Proof of linearity of the measuring method. The dilutions are given with their handling errors on the x-axis, and on the y-axis are the measured concentrations with their standard deviations.
Specification of the DNA stains used.
Stain
Absorption (nm)
Emission (nm)
Chemical characteristics
SytoTM – 80 PopoTM – 3
531 534
545 570
Cell permeable cyanine (vital stain) Cell impermeable cyanine dimer
The spectral characteristics are applicable for each of the DNA-bound stains.
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and 23%. Thus, the absolute values are within the margin of error of the dilution procedure.
Fluorescence–scatter diagram of beads In a fluorescence–scatter diagram (Fig. 6A), it is immediately obvious whether or not two populations of different particles can be separated. Ideally all the particles of a population produce the same level of fluorescence and scatter signal, i.e. all the particles appear in the fluorescence–scatter diagram at the same place, as one point. In a three-dimensional fluorescence–scatter diagram this would be represented by a very high peak at one position. As the particles of a population have a Gaussian distribution of their characteristics (in the case of the beads, the size and the fluorescence) this affects the fluorescence–scatter diagram in the form of a two-dimensional distribution around a single point (Fig. 6A); the coordinates of which correspond to the mean values of the Gaussian distribution of the characteristics shown (Fig. 6B: mean values of the scatter voltage 92972 mV; Fig. 6C: mean values of the fluorescence voltage 1.5070.19 V).
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Fig. 6D shows the fluorescence and scatter signal of a single bead. The width and the shape of the peaks are dependent on the beam profile of the laser, as well as the flow speed and the size (length) of the particles. Ideally, as was the case here, the time between two sampled beads should be about 50 ms in order to be able to distinguish the beads from another.
Measurements on bacteria After the measurement method had been established using the beads, E. coli bacteria were stained with SytoTM–80 (10 mM/L, Fig. 7) and PopoTM–3 (50 mM/L, Fig. 8) and measured. In both cases the stain was incubated with the bacteria for 60 min. As the diagrams show, there is a very low signal-to-noise ratio in the scatter channel. This is apparent from the dense clouds of scatter peaks to the left of the fluorescence threshold in the two fluorescence–scatter diagrams (Figs. 7A and 8A). This follows on in the distribution of the coincidence peaks, originating from the bacteria. As the scatter voltage of the bacteria coincidence peaks is similar to the noise from the scatter channel, the bacteria can almost only be identified using the peaks in the fluorescence
Fig. 6. Measurements on beads (+ 2 mm, Cbeads ¼ 100 mL1, V_ ¼ 0.23 mL/s): (A) fluorescence–scatter diagram. The coincidence peaks of the beads (right) can be clearly distinguished from the scatter peaks (left), brought about by dirt particles, (B) histogram of the scatter voltage, (C) histogram of the fluorescence voltage and (D) fluorescence and scatter signal of a single bead.
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Fig. 7. Measurement of Escherichia coli bacteria stained with SytoTM–80 (CSyto80 ¼ 10 mM/L, tstain ¼ 60 min, T ¼ 25 1C, CbactE500 mL1): (A) fluorescence–scatter diagram, (B) fluorescence and scatter signal of a single bacterium, (C) histogram of the scatter voltage and (D) histogram of the fluorescence voltage.
Fig. 8. Measurement of Escherichia coli bacteria stained with PopoTM–3 (CPopo3 ¼ 50 mM/L; tstain ¼ 60 min; T ¼ 25 1C; CbactE10 mL1): (A) fluorescence–scatter diagram, (B) fluorescence and scatter signal of a single bacterium, (C) histogram of the scatter voltage and (D) histogram of the fluorescence voltage.
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channel (Figs. 7D and 8D). In the scatter histogram (Figs. 7C and 8C), distribution of the scatter and coincidence peaks clearly overlaps, whereas in the fluorescence histogram (Figs. 7D and 8D) the fluorescence and coincidence peaks can be distinctly separated. However, it is of importance to note exactly when the sample was measured, i.e. how much time has passed since the cells were stained with the fluorescent stain.
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Fig. 9 shows the fluorescence intensities for both stains and Fig. 10 shows the relative proportion of the peak modes in the total rate dependent on the measuring time. In the case of the E. coli bacteria stained with SytoTM–80, the fluorescence intensity decreased with the measuring time (Fig. 9A). In contrast, the fluorescence intensity of the PopoTM–3 stained bacteria remained constant, or even increased slightly (Fig. 9B).
Fig. 9. Plot against time of the fluorescence voltage of a stained Escherichia coli sample. The bacteria were stained with (A) SytoTM–80 (CSyto80 ¼ 20 mM/L; tstain ¼ 60 min; T ¼ 25 1C) and with (B) PopoTM–3 (CPopo3 ¼ 50 mM/L; tstain ¼ 60 min; T ¼ 25 1C).
Fig. 10. Measurement of a stained Escherichia coli sample. The bacteria were stained with (A) SytoTM–80 (CSyto–80 ¼ 20 mM/L, tstain ¼ 60 min, T ¼ 25 1C) and with (B) PopoTM–3 (CPopo3 ¼ 50 mM/L, tstain ¼ 60 min, T ¼ 25 1C). With increasing measure time, changes are observed in the relative proportion of the detected peaks (fluorescence, scatter or coincidence peaks) of the total rate (sum of all the peaks/measuring time).
Fig. 11. Measurements on SytoTM–80 stained platelets (CSyto80 ¼ 10 mM/L; tstain ¼ 20 min; T ¼ 25 1C). The figures show the fluorescence–scatter diagrams. The measurements were made 20 min (A) and 90 min (B) after the staining.
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Fig. 12. Measurements with SytoTM–80-stained platelets (CSyto80 ¼ 10 mM/L; tstain ¼ 20, 40, 60 min; T ¼ 25 1C): (A) progression of the relative share of the fluorescent platelets with time and (B) progression of the mean fluorescence voltage for the different incubation times.
(Fig. 11B) after staining with SytoTM–80. After some time, the fluorescence intensity of the platelets decreased. In Fig. 12 it can be seen how the relative part of the platelets, detected as coincidence peaks, and the fluorescence voltage decreases exponentially with time. This behavior is in direct contrast to the stained bacteria (Figs. 9 and 10) and indicates that the platelets are nonspecifically stained.
Fig. 13. Fluorescence–scatter diagram for a platelet sample spiked with Escherichia coli (100:1). The marked thresholds for classification have four to six times the standard deviation of the noise of the scattering or the fluorescence. The platelets marked with a triangle were mistakenly identified as bacteria as they exhibited fluorescence above the threshold level. This led to an increase in the proportion of the false positives and to a decrease in the specificity. In contrast, the fluorescence peaks raised the proportion of the false negatives and consequently led to a reduction in the sensitivity [14].
The relative part of the peak modes shows that the proportion of the fluorescent peaks remains constant for both stains. However, the scatter peaks increase for SytoTM–80 by the same amount as the coincidence peaks decrease (Fig. 10A), in the case of PopoTM–3 they remain almost constant (Fig. 10B).
Measurement of platelets Fig. 11 shows the fluorescence–scatter diagrams of platelets stained in the same way as the E. coli. The measurements were made 20 min (Fig. 11A) or 90 min
Platelets spiked with bacteria If a platelet sample is spiked (100:1) with E. coli, it results in a fluorescence–scatter diagram such as the one shown in Fig. 13. A closer look at the peaks shows that the platelets and the bacteria overlap in the region of noise from the fluorescence and scatter channel.
Discussion Optimization of the set-up The achieved core flow diameter of 20 mm is of the same order as the samples under examination (E. coli: +E2 mm, lengthE5–20 mm, platelets: +E3–4 mm). However, it is our aim to halve the core flow diameter to about 10 mm, which will mean that the intensity difference of the laser light at the center and edge of the core flow will become smaller (Fig. 14) and the intensity distribution in the fluorescence channel histogram will become thinner. An increase in the laser beam radius would lead to the same result but would at the same time lower the utilizable intensity. A beam profile adjustment without a loss in intensity of about 10 mm in the core stream region would be optimal. The hydrodynamic focusing makes it possible to reduce the measuring time per sample volume. For this the core volume flow must be raised whilst keeping the core flow diameter constant. As this is
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Fig. 14. Illustration of the intensity differences of the laser beam in the middle and at the edge of the core flow, for two different laser beam radii (40 and 80 mm) and two different core flow diameters (10 and 25 mm). The larger the laser beam radius and the smaller the core flow diameter, the smaller the intensity difference between the core flow middle and the core flow edge.
not possible by varying the static pressure difference of the liquid column, other possible solutions must be sought.
System linearity The linearity of the method could be confirmed using beads in a concentration series. The measured concentrations deviate from the calculated values of 9% and 23%. They are within the handling error of 57% and are therefore significant. The handling error is probably smaller as it can only be calculated from the assessed partial uncertainties. As has already been shown with the beads, the signal-to-noise ratio in the scatter channel must be increased from 1:3 to 1:5 in order to be able to discriminate large variations in the particle size.
Fluorescence staining The fluorescence staining of the E. coli with SytoTM–80 has shown that the fluorescence intensity decreases with the measuring time. This could be due to the fact that not all the stain molecules intercalate and
therefore diffuse back into the surrounding medium (PBS) due to the concentration difference, or they are bleached. In contrast, the fluorescence intensity remains constant with PopoTM–3 and even increases slightly with increasing measuring time. The range of the bacterial size, which leads to a wide distribution of the fluorescence intensities obtained, cannot be compensated for by either of the stains because the stain concentrations within the bacteria are directly dependent on the size of the bacteria. An increase in the stain concentration or an extension of the incubation time leads to a wider distribution and therefore better differentiation of the smaller bacteria from the noise signal. An increase in the incubation time conflicts with the requirement to develop a quick detection method. Furthermore, higher concentration of stains leads to a higher fluorescent background and therefore to a deterioration in the signal-to-noise ratio. Using the same staining method as for the E. coli, the platelets are stained unspecifically which decreases exponentially with time after further dilution. During the staining procedure the stain diffuses into the platelets until equilibrium has been reached. After dilution the platelets continue to fluoresce but with
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time the concentration of the stain within, or on the surface of the platelets, decreases. The stain molecules diffuse out of the platelets into the surrounding medium until the stain concentration is once more in equilibrium. For this reason it is important to keep both the incubation short and the stain concentration low in order to ensure that the method is quick. On the other hand there must be a certain concentration of the stain within the bacteria to allow the platelets to be differentiated from the bacteria. This can be partly counteracted by lysing the platelets prior to staining or by staining them for as short a time as possible (o2–3 min). Then 40–60 min after dilution of the sample with PBS, the concentration of the stain within the platelets will be so low that it will not be registered by the coincident peaks.
Cell discrimination The variation of the bacteria and platelet size, and the associated variation in the concentration of the bound stain lead to a Gaussian distribution of the resulting signal peaks. This leads to an overlapping of the distribution of the noise and the detected signal in their respective measuring channels, resulting in a reduction in the sensitivity and specificity of the procedure. The measurements showed that the set-up is basically suitable for detection of bacteria, such as E. coli, in platelet concentrates. Assuming that all bacteria in Fig. 13 lead to a coincident event, the sensitivity and specificity will be 57% and 99%, respectively. The current sample preparation method takes about 30–70 min, including staining of the bacteria, and the measurement procedure takes about 2 min for a 28 mL sample volume. Semi-automatic evaluation of the sample takes a further 15 min, but this could most certainly be reduced to less than 5 min. If 106–107particles were measured, 75–750 s would be needed for a maximum possible concentration of 85,000 mL1 and a sample volume flow of 0.23 mL/s. In order to reduce the measurement time, the sample volume flow could be increased. This would make a change in the current geometry of the hydrodynamic focusing necessary because the sample flow diameter is proportional to the sample volume flow.
Conclusion There are several established methods that can be used to minimize the contamination risk of blood conserves in clinical practice. However, they all involve time and cost intensive procedures. First trials for the detection of bacterial contamination in platelet concentrates using a simplified cytometric method have been
promising but it will be some time before the method can compete with established methods. We are however, convinced that this will be worth the effort. When compared with FACS, the advantages of our method are not immediately apparent. FACS uses several wavelengths for detection and sorts the sample contents, both features requiring a complex design and handling method. Only one wavelength is used for fluorescence detection which is why there are fewer demands on the stain. The scattering signal is also detected, so, what advantages does our method have to offer? Considering the measuring techniques alone, the commercial FACS method is clearly better. However, we have concentrated our efforts on the calculation of the digitalized signal, not only with respect to the signal level, but also to the time sequence, i.e. signal shape and the statistical assessment. This is still at the trial stage and still requires a satisfactory measuring set-up. In order to attain a higher sensitivity and high signal-to-noise ratio to facilitate the detection of the weak fluorescence and scattering signal, it will be necessary to redesign the setup and optimize the system. However, when this has been achieved, the next step will be to optimize both the staining procedure and the algorithm after which it will be possible to compare the method to an already established system.
Acknowledgment This work was supported by the Federal Ministry of Economics (Grant # IW 061173), the Senate of Berlin (ProFit OptiRem) and the European Union (EFRE).
Zusammenfassung Machbarkeitsstudie zur Entwicklung eines optischen Verfahrens zur Identifizierung kontaminierter Thrombozytenkonzentrate Der Anteil der Thrombozytenkonzentrate, die der klinischen Verwendung ja¨hrlich durch fehlende oder unzula¨ngliche Detektionsverfahren entgehen, ist aus medizinischer und wirtschaftlicher Sicht untragbar. Die derzeit verfu¨gbaren Verfahren sind fu¨r den klinischen Alltag zu langsam oder zu ungenau und zudem sehr kostspielig. Gegenstand der vorgestellten Machbarkeitsstudie ist die Entwicklung eines Verfahrens zur Detektion von kontaminierten Thrombozytenkonzentraten, welches diese Nachteile ausra¨umt. Basierend auf der Durchflusszytometrie und Fluoreszenzmarkierung von Bakterien sowie einer optimierten Messanordnung und Auswertung, sollte ein entsprechendes Detektionsverfahren realisiert werden. Nach der Etablierung der
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Messanordnung wurden erste vielversprechende Messungen durchgefu¨hrt. Es fanden erste Untersuchungen zur Fa¨rbung der Bakterien mit verschiedenen Fluoreszenzfarbstoffen statt. Dabei zeigte sich, dass die gewa¨hlte Messapparatur zuna¨chst verbessert werden muss, um die geringen Streu- und Fluoreszenzintensita¨ten verla¨sslich zu detektieren, bevor mit der Farbstoffauswahl und der Algorithmenoptimierung fortgefahren werden kann.
[7]
[8]
[9] Schlu¨sselwo¨rter: Thrombozyten; Bakterienkontamination; Zytometrie; Fluoreszenz; Streuung [10]
References [1] Arendt A, Carmean J, Koch E, Rolfs R, Mottice S, Strausbaugh L, et al. Fatal bacterial infections associated with platelet transfusions – United States, 2004. MMWR 2005;54(07):168–70. [2] Chang AH, Kirsch CM, Mobashery N, Johnson N, Levitt LJ. Streptococcus bovis septic shock due to contaminated transfused platelets. Am J Hematol 2004;77(3):282–6. [3] Mu¨ller TH, Mohr H, Montag T. Methods for the detection of bacterial contamination in blood products. Clin Chem Lab Med 2008;46(7):933–46. [4] Mohr H, Lambrecht B, Bayer A, Spengler HP, Nicol SB, Montag T, et al. Basics of flow cytometry-based sterility testing of platelet concentrates. Transfusion 2006;46(1): 41–9. [5] Schmidt M, Hourfar MK, Nicol SB, Wahl A, Heck J, Weis C, et al. A comparison of three rapid bacterial detection methods under simulated real-life conditions. Transfusion 2006;46(8):1367–73. [6] Jacobs MR, Bajaksouzian S, Windau A, Palavecino EL, Yomtovian R. Evaluation of the Scansystem method for
[11]
[12]
[13]
[14]
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detection of bacterially contaminated platelets. Transfusion 2005;45(2):265–9. Schmidt M, Weis C, Heck J, Montag T, Nicol SB, Hourfar MK, et al. Optimized Scansystem platelet kit for bacterial detection with enhanced sensitivity: detection within 24 h after spiking. Vox Sang 2005;89(3):135–9. McDonald CP, Pearce S, Wilkins K, Colvin J, Robbins S, Colley L, et al. Pall eBDS: an enhanced bacterial detection system for screening platelet concentrates. Transfus Med 2005;15(4):259–68. McDonald CP, Colvin J, Smith R, Wilkins K, Robbins S, Barbara JA. A novel method for the detection of bacteria in platelet concentrates utilizing oxygen consumption as a marker for bacterial growth. Transfus Med 2004;14(6): 391–8. Ribault S, Harper K, Grave L, Lafontaine C, Nannini P, Raimondo A, et al. Rapid screening method for detection of bacteria in platelet concentrates. J Clin Microbiol 2004;42(5):1903–8. Mohammadi T, Pietersz RN, Vandenbroucke-Grauls CM, Savelkoul PH, Reesink HW. Detection of bacteria in platelet concentrates: comparison of broad-range realtime 16S rDNA polymerase chain reaction and automated culturing. Transfusion 2005;45(5):731–6. de Korte D, Marcelis JH, Verhoeven AJ, Soeterboek AM. Diversion of first blood volume results in a reduction of bacterial contamination for whole-blood collections. Vox Sang 2002;83(1):13–6. Holme S, McAlister MB, Ortolano GA, Chong C, Cortus MA, Jacobs MR, et al. Enhancement of a culture-based bacterial detection system (eBDS) for platelet products based on measurement of oxygen consumption. Transfusion 2005;45(6):984–93. Ey S. Development of a cytometric method for the determination of bacterial contamination in blood products. Master thesis, Technische Fachhochschule Berlin, 2007.