Set of standards and method of production

Abstract

A set of 10 or more standards containing a defined number of particles from 1000 to 1,000,000 wherein the defined number of particles is within a degree of error of 10% or less between each standard of the set.

Claims

1. A process for operating a modified flow cytometer for forming a set of 10 or more standards containing a defined number of a microorganism, the process comprising: counting and capturing a defined number of microorganisms by a modified flow cytometer having a capture tube under control of a separate microcontroller, the capture tube being positioned in and out of a stream containing the microorganism, wherein forward scatter signals and side scatter signals from the cytometer are sent to a logic circuit to generate a coincidence count signal to count the defined number of microorganisms, the defined number of microorganisms is from 2,000 to 1,000,000; sending the coincidence count signal to the microcontroller to control the positioning of the capture tube in the stream to capture the defined number of microorganism in a single positioning of the capture tube in the stream microorganisms, the microcontroller configured to control the positioning by; maintaining the capture tube in the stream when the number of count signals is less than the defined number of microorganisms; and positioning the capture tube out of the stream when the number of count signals is equal to or greater than the defined number of microorganisms; collecting the captured defined number of microorganisms in a single drop dispensed from the capture tube; and repeating the counting and collecting steps to form a set of standards wherein the defined number of microorganisms are within a degree of error of 10% or less between each standard of the set.

2. The process according to claim 1 wherein the microorganism is selected from bacteria, fungi, yeast, protozoa, or mixtures thereof.

3. The process according to claim 1 wherein the microorganism is selected from Legionella, Salmonella, Leptospirosis, Saccharomyces, Clostridium, Vibrio, Pseudomonas, Bacillus, Streptomyces, Staphylococcus, Campylobacter, Aspergillus, Candida, Enterobacter, Enterococcus, Listeria, Lactobacillus, Citrobacter, Proteus, Lactococcus, Klebsiella, Aeromonas, Zygosaccharomyces, Acinetobacter, Serratia, Edwardsiella, Rhodococcus, Yersinia, Methylobacterium, Gardnerella, Mycobacterium, Bordetalla, Haemophilus, Shigella, Kluyvera, Spirochaeta, Rhizobium, Rhizobacter, Brucella, Neisseria, Rickettsia, Chlamidia, or mixtures thereof.

4. The process according to claim 1 wherein the microorganism is selected from Cryptosporidium, Giardia, Cyclospora, Toxoplasma, Eimeria, or mixtures thereof.

5. The process according to claim 1 wherein the defined number of microorganisms is from 2,000 to 500,000.

6. The process according to claim 5 wherein the defined number of microorganisms is from 5,000 to 100,000.

7. The process according to claim 1 wherein the set of standards is in liquid form, frozen form or solid form.

8. The process according to claim 7 wherein the solid form is a bead or ball containing the defined number of microorganisms.

9. The process according to claim 1 wherein the degree of error is between 1% and 10%.

10. The process according to claim 1 wherein the standard is capable of being transferred between containers in a solid form.

11. The process according to claim 10 wherein the standard is capable of releasing the microorganisms in a liquid.

12. A method for operating a modified flow cytometer, the method comprising: moving a capture tube of a flow cytometer into a stream of microorganisms to capture microorganisms from the stream, wherein the flow cytometer has a digital discrimination unit (SDU) that takes forward scatter and side scatter signals to identify and count microorganisms; receiving a forward scatter signal and a side scatter signal of the stream from the flow cytometer; thresholding the forward scatter signal to generate a thresholded forward scatter signal; thresholding the side scatter signal to generate a thresholded side scatter signal; generating a count signal as a function of the thresholded forward scatter signal and the thresholded side scatter signal, the count signal associated with a single microorganism of the stream; sending the count signal to a separate microcontroller to control the positioning of the capture tube in the stream; maintaining the capture tube in the stream when the number of count signals is less than a microorganism sort number, the microorganism sort number being from 2,000 to 1,000,000; and positioning the capture tube out of the stream when the number of count signals is equal to or greater than the microorganism sort number.

13. The method according to claim 12 wherein the microorganism sort number is from 2,000 to 500,000.

14. The method according to claim 13 wherein the microorganism sort number is from 5,000 to 100,000.

15. A method for operating a modified flow cytometer, comprising: moving a capture tube of a flow cytometer into a stream of microorganisms to capture microorganisms from the stream; generating a count signal associated with a single microorganism of the stream; and sending the count signal to a microcontroller to control the positioning of the capture tube in the stream, the microcontroller configured to control the positioning by: maintaining the capture tube in of the stream when the number of count signals is less than a microorganism sort number, the microorganism sort number being from 1,000 to 1,000,000; and positioning the capture tube out of the stream when the number of count signals is equal to or greater than the microorganism sort number.

Description

BRIEF DESCRIPTION OF THE DRAWINGS

(1) FIG. 1 shows a standard curve between four different gene copy numbers of bacteria.

MODE(S) FOR CARRYING OUT THE INVENTION

(2) When particles such as microorganisms display unique photophysical characteristics, they may be sorted from other components in a mixture using flow cytometry or coulter separation. Size, shape and refractive index of the microorganisms are useful parameters to drive the sorting process since a raw suspension of cultured microorganisms can be discriminated without chemical pre-processing (such as fluorescent labelling).

(3) Conventional flow cytometers collect scattered or emitted light from the object at the focus point of the laser using photomultiplier tubes (PMT). The output from the PMT is amplified and processed using analogue techniques and this signal is then subsequently digitized to assist the discrimination and reporting processes. The host-computer connected to the flow cytometer is used to program the parameters employed by the on-board microprocessor to control the sorting process.

(4) The present inventors have designed a digital discrimination unit (SDU) that takes the forward scatter and side scatter signals from a flow cytometer to identify and count particles. Additionally, the SDU is employed to control a collection capture tube so that a precisely controlled number of particles is isolated from the sample stream and delivered to the capture vessel. In a standard flow cytometer, the piezoelectric-drive that operates the collection tube is driven in a pulsed mode with an upper frequency limit of 300 Hz. Thus, the maximum capture speed for the collection tube is about 300 events per second. The standard unmodified instrument needs to be run at a reduced rate to ensure all events are captured. Control of the SDU overcomes this limitation by holding the capture tube in position to capture a pre-defined number of particles and then release the mechanism when the correct count is achieved. In this manner, the collection rate can be set much higher so that greatly increased numbers of particles can be collected and high production rates are possible.

(5) Apparatus

(6) A Becton Dickinson FACScalibur flow cytometer that uses a catcher tube that is mechanically moved in and out of the sample stream to capture particles one at a time was modified to enable the catcher to sort batches of particles. The length of the capture was modified as described previously (WO 03/036273) to enable the formation of droplets that contained the sorted particles.

(7) The flow cytometer was used according to the manufacturer's instructions but additional equipment was connected to the cytometer to allow high speed analysis of data. The computer and the software that controls the flow cytometer are capable of analysing data at a rate of up to 6000 particles per second. The computer and software was bypassed to allow the analysis of data at very high speeds (up to 100,000 particles per second). Additional equipment was also connected to the flow cytometer to control the movement of the sorting mechanism as described above.

(8) The forward scatter (FSC) and side scatter (SSC) signals were taken directly from the preamplifier boards and connected to two noise and threshold comparators (LN311, National Semi Conductor). The signals were displayed on an oscilloscope in XY mode to generate a scatter plot. Threshold lines from the LN311 were also displayed on the oscilloscope. Two potentiometers and an amplifier were used to adjust the threshold lines on the oscilloscope to the desired position (just below the population of interest).

(9) A standard CMOS logic circuit was used to generate a coincidence count signal when coincidence pulses (FSC and SSC) greater than the thresholds are detected simultaneously. This coincidence count signal was sent to a dedicated microcontroller (Motorola MC68HC11F1) that counted the number of signals. The microcontroller was connected to the sort board on the flow cytometer via the ribbon cable that carries the signals that control the movement of the capture tube. The microcontroller allowed a number to be entered that was the number of particles to be sorted. The microcontroller then moved the capture tube into the particle stream whilst counting the number of signals received from the CMOS logic circuit. Once the number of signals reached the preset number of particles to be sorted then the catcher tube was moved back out of the stream of particles.

(10) Although a modified cytometer was used to produce set of standards in the examples, it will be appreciated that other means could also be used to obtain the sets of standards with degree of error obtained. Now that it is possible to produce sets of accurate standards, more applications will now be available. Examples include but not limited to microbial, pharmaceutical, clinical, veterinary, food technology, beverage, environmental, potable water, calibration standards for instruments such as cytometers, coulter counters, spectrophotometers, spectrofluorometers, mass spectrometers, luminometers, microscopes, chromatographs, DNA amplification instruments such as PCR machines and real time PCR machines, conductimeters, electrophoresis equipments, microarrays, spectroscopes, spectrographs, photometers, fluorimeters, refractometers, polarimeters, gas chromatographs, X-ray spectrometers, X-ray fluorescence analysers, X-ray diffractometers, nuclear magnetic resonance spectrometers, flow spectrometers and spectroradiometers.

(11) Uses

(12) Now that sets of accurate standards can be produced that contain greater than 1000 particles it means that sets of standards at three different orders of magnitude can be produced. For example, a set of standards that contain 10, 100 and 1000 particles can now be produced. Such sets of standards are useful for calibration of an analytical test because the results from analysing the standards can be graphed to generate a calibration curve. The curve can then be used to predict a value between or outside the values of the sets of standards. To generate an accurate calibration curve it is important that three or more separate calibration standards are used that cover the dynamic range of the test. The generation, of calibration curves with these sets of standards are particularly useful for calibrating nucleic acid analysis methods such as real time polymerase chain reaction (PCR).

(13) The sets of standards according to this invention are useful for quality control purposes. This may include spiking samples and testing the recovery efficiency of a test method for quality control purposes. For example, the performance of a microbiology test method used for the analysis of food can be determined by seeding a sample of food with a known number of microorganisms. For large samples where a portion of the sample is to be tested then there is a need for standard that contains a high number of microorganisms. For example, a 25 gram sample of chicken is tested for the presence of Salmonella by placing the sample into 100 ml of buffered peptone. A standard that contains 10,000 viable Salmonella cells is added to the chicken sample and the entire sample is homogenised. A 1 ml aliquot of the sample is then taken and placed onto a Salmonella Petrifilm. The Petrifilm is incubated and the number of colonies counted. In this example, if 50 colonies are detected then the recovery for the method was 50%. Other food samples may be spiked with a standard in a similar manner. The number of microorganisms required in the standard will depend on the sensitivity of the test method. Water samples, clinical samples, environmental samples and pharmaceutical samples may also be spiked in a similar manner.

(14) The sets of standards according to the present invention are particularly useful for microbiological testing in the pharmaceutical industry. Sterility testing and preservative efficacy testing require samples to be spiked with microorganisms. These methods are described in the US Pharmacopeia and the European Pharmacopeia. The methods require that the numbers of microorganisms that are spiked into the sample are within a narrow range. However, to produce a spike within the narrow range is technically challenging and has a high failure rate. Accurate sets of standards that contained between 1000 and 1,000,000 microorganisms overcome these challenges and are particularly well suited to the Pharmacopeia methods.

(15) Standards that contain between 1000 and 1,000,000 particles are useful for quality control purposes in test methods where a large sample is taken and a smaller subsample is analysed. The standards are also useful for quality control purposes in test methods where the test has a low sensitivity and therefore, a large number of particles are required to produce a positive result.

(16) Multiple aliquots that each contain an approximate number of particles can be generated from standards that contain between 1000 and 1,000,000 particles. By rehydrating or diluting the standard into a known volume of liquid, a suspension is produced that has a known number of particles within a specific volume of liquid. A pipette can then be used to take small aliquots of this liquid for use as standards. This means that many small standards can be produced from a single standard. This is a cost effective solution for applications where a large number of standards are needed. The rehydrated or diluted standard may be stored at a low temperature to allow the production of small subsets of standards over a long period of time.

(17) This method of producing multiple subsets of standards from a set of standards is a particularly useful cost effective method for microbiological testing in the pharmaceutical industry. Quality control laboratories in pharmaceutical companies use large numbers of standards that contain between 10 and 100 microorganisms for growth promotion studies in sterility testing. A standard that contains between 1000 and 1,000,000 microorganisms can be used to produce many small aliquots that contain between 10 and 100 microorganisms. This is a cost effective method of producing standards that meet the level of precision required by the Pharmacopeia.

(18) This method of producing multiple subsets of standards from a set of standards is also useful in the water and food microbiology testing industries. Standards in the form of freeze dried pellets that contain a precise number of microorganisms in the range of between 5 and 1000 are currently used by food and water testing laboratories. These standards are used to spike or test a single sample or process. One standard is used to spike one sample. In instances where a large number of these standards are required the cost of using these standards is prohibitively high. By using a standard that contains a high number of microorganisms the laboratories can generate multiple sub-samples that have sufficient precision for many purposes. An example of this is a set of standards in the form of freeze dried pellets that contains 10,000 viable E. coli cells. A water testing laboratory can use one of these standards each day to generate sub-samples that each contains approximately 100 cells. To do this the laboratory would rehydrate the freeze dried pellet in 10 ml of sterile water. This 10 ml sample is then mixed thoroughly by vortexing. Aliquots of 100 μl volume are then removed from the sample and used to spike water samples. Each 100 μl will contain approximately 100 E. coli cells. The variation of the number of cells within each aliquot will depend on the accuracy and precision of the pipette used but will typically be between 70 and 130 cells. This level of variability is often sufficient for spiking water samples with E. coli. The rehydrated standard can be stored in the fridge for several days without any detrimental effect on the E. coli cells. This is a very cost effective method that satisfies the demand for standards in a water microbiology testing laboratory. There are similar requirements for cost effective standards in food and clinical microbiology laboratories.

(19) Standards that contain between 1000 and 1,000,000 particles are also useful for testing the performance of devices that are designed to remove or inactivate particles. An example of this is a filter that is designed to remove particles from water. The sets of standards according to the present invention can be used to challenge the water filter to accurately determine how many particles pass through the filter. Examples of processes that can be tested in this way include filtration, heat and pressure treatments such as autoclaving, ultra-sonics, flocculation, ionization, chromatography, electrophoresis, drying and chemical or enzymatic lysis. For all of these examples a standard that contains a relatively high number of particles is needed to accurately determine the effectiveness of the removal process.

(20) Another example of where a set of standards that contain between 1000 and 1,000,000 particles is useful is the testing or validation of disinfection methods that inactivate cells or microorganisms. The effectiveness of the disinfection method can be tested by spiking a sample with a standard that contains between 1000 and 1,000,000 microorganisms or cells. The sample is then treated with the disinfection method and then tested to determine how many of the microorganisms or cells are still viable. Examples of disinfection methods include irradiation, heat treatment, chlorination and ozonation.

(21) Similarly these sets of standards are useful for testing the effectiveness of anti-microbials such as antibiotics, toxins and preservatives. Also, the standards are useful for determining the effect of drugs on the viability of host cells. For example, the effectiveness of cancer treatments on cancerous and non-cancerous cells can be evaluated by producing sets of standards of cancerous and non-cancerous cells and testing the effect of the treatment on the viability of the cells within the standards.

(22) Sets of standards that contain high numbers of particles are useful for tracking purposes in industrial, environmental and clinical tracking studies. In such studies standards that contain high numbers of particles are required to allow accurate tracking to a low level. For example, to determine the transport of pathogenic bacteria from a sewage outfall into a river and at subsequent sampling points down stream from the outfall, the sewage would be spiked with a standard that contains a traceable particle such as a fluorescent bead or a genetically modified bacteria. The standard would need to contain a high number of the traceable particle otherwise dilution within the river would rapidly dilute the particle to a level that can not be detected.

EXAMPLES

Example 1

(23) A flow cytometer was modified to enable drops to be produced that contained very reproducible numbers of particles as described above.

(24) Preparation of Escherichia coli

(25) A strain of E. coli (NCTC 9001) was grown at 37° C. for 24 hours in 1.6% (w/v) tryptone and 1% (w/v) yeast extract at pH 7.2. The cells were initially diluted 1 in 1000 in filtered (0.22 μm) phosphate buffered saline (PBS) (Sigma Chemical Company, Sydney, NSW) and analysed immediately. The dilution of the E. coli was adjusted to obtain the desired number of cells per second on the flow cytometer.

(26) Analysis of E. coli

(27) The sample of E. coli was loaded onto the flow cytometer. Sheath fluid consisted of filtered (0.22 μm) PBS (Sigma Chemical Company) at pH 7.4. The voltages to the side scatter and forward scatter detectors were adjusted until the population representing the E. coli were in the middle of a scatter plot.

(28) The data was examined using the oscilloscope and the two threshold lines were positioned below the population of E. coli cells. The counter was set to a value of 100 and the sorting process was carried out.

(29) The concentration of the E. coli was adjusted to produce a data rate of between 500 and 100,000 cells per second and the sorting process was repeated at different rates of cells per second.

(30) Collection of Droplets Containing E. coli

(31) Drops containing the sorted E. coli were collected directly onto nutrient agar plates and spread by rotating the plate. The plates were then incubated at 37° C. for 12 hours and the colonies counted.

(32) Freezing and Freeze-Drying the Droplets

(33) A sample of E. coli was analysed at a rate of 30,000 cells per second and droplets from the cytometer that contained 13,000 E. coli cells were collected into test tubes that contained liquid nitrogen. After collection of the droplets, the tubes were placed in a Telstar Lyobeta freeze dryer and dried overnight at a vacuum of 2×10-1 Torr and a condenser temperature of −50° C.

(34) The next day, the freeze-dried particles were removed from the freeze drier and individually placed into 10 ml of sterile saline solution (0.7%) and mixed thoroughly. A 200 μl aliquot was then plated out onto nutrient agar, spread with a sterile plastic spreader and incubated at 37° C. for 12 hours.

(35) Preparing Drops by Dilution with a Pipette

(36) As a comparison to the flow cytometer prepared drops, drops containing approximately 10,000 CFU were prepared by serial dilution and then forming drops with a pasteur pipette. The drops were dropped into liquid nitrogen and then freeze dried and then analysed as above.

(37) Results from Sorting 100 Cells at Different Data Rates

(38) The number of E. coli recovered from drops produced at various rates of cells per second.

(39) TABLE-US-00001 TABLE 1 Threshold 500 10,000 30,000 50,000 88 88 93 96 88 83 92 98 85 79 100 99 82 75 88 98 87 80 100 Mean 86.0 81.0 93.3 98.2 Std deviation 2.5 4.8 5.0 1.5 Degree of 2.9 5.9 5.4 1.5 error (% CV)

(40) Recoveries were reproducible and degree of error was tight at rates of 50,000 cells per second.

(41) Results from Freeze Drying Drops Containing 13,000 E. coli

(42) Table 2 details the number of CFU detected in the freeze dried drops that contained 13,000 E. coli cells.

(43) TABLE-US-00002 TABLE 2 Sorted by Drops created by a cytometer pipette 13600 7200 14200 14600 15050 8400 13200 13600 13200 13600 14150 13200 13150 8800 13500 9400 14250 10000 13550 12200 Mean 13,785 11,100 Std deviation 612.8 2630.2 Degree of error 4.4 23.7 (% CV)

(44) The number of CFU detected within the cytometer produced drops is extremely reproducible, over 5 times more reproducible than the drops prepared with a pipette.

Example 2

(45) The same modified cytometer as used in example 1 was utilised to produce drops that contained precise numbers of a microorganism. A specific fragment of DNA within the microorganism was then detected by real-time polymerase chain reaction (RT-PCR). A standard curve was produced by using a log scale of precise numbers of cells each containing a single copy of target GFP DNA.

(46) Preparation of E. coli

(47) Escherichia coli BL21 with an inserted green fluorescent protein gene (GFP) was grown for 21 hours at 37° C. It was diluted in filtered (0.22 μm) de-ionised water and analysed on the cytometer as in example 1, with filtered (0.22 μm) de-ionised water as cytometer sheath fluid.

(48) To sort 5 and 50 cfu the cytometer sorting mechanism was used as detailed in WO 2003/036273. To obtain droplets of 500 and 5000 cfu the modified cytometer was used.

(49) Collection and Freeze-Drying of E. coli Droplets

(50) Three droplets were individually collected directly into a 96-well PCR plate for each of 4 different cfu—5, 50, 500 and 5000 cfu. The plate was freeze dried in a Telstar Lyobeta as described in example 1.

(51) PCR Assay Preparation

(52) Immediately after freeze drying the following PCR assay was added to each reaction (r×n) well: Sybr green master mix (Applied Biosystems, Sydney) 10 μl/r×n, forward and reverse primers for GFP insert 300 nM of each in 2 μl/r×n (Sigma Chemical Company, Sydney, NSW), filtered (0.22 μm) reagent water 8 μl/r×n.

(53) The PCR plate was cycled forty times on a 95° C. for 15 seconds, 60° C. for 1 minute cycle using a Realplex S4 (Eppendorf, Germany).

(54) Results from Sorting 5, 50, 500 and 5000 cfu of E. coli for PCR Analysis

(55) The three replicates of each of the four cfu were plotted on a log scale of starting cfu number (which equals the starting number of copies of GFP DNA). The number of thermal cycles (Ct) required for the Realplex instrument to detect Sybr green fluorescence, above a background baseline threshold of fluorescence were plotted on the Y axis. A trendline is shown between the four different copy numbers is shown in FIG. 1.

(56) TABLE-US-00003 TABLE 3 Fluorescence detection at cycle Mean Ct Standard Copies of DNA number (Ct) (n = 3) deviation (n = 3) 5 33.9 33.6 1.5 5 35 5 32 50 30 29.9 0.5 50 29.4 50 30.3 500 26.5 26.4 0.4 500 26 500 26.7 5000 23 23.2 0.2 5000 23.4 5000 23.2

(57) The variation between replicates of a precise number of E. coli sorted is very precise, enabling an accurate standard curve to be generated with accurate absolute known amounts of starting copy number. The variation increased with lower copy numbers to the limit of detection of the PCR methodology.

(58) A standard method for determining the number of starting copies of DNA within a standard curve preparation rely on a qualitative spectrophotometer optical density analysis of a concentrated stock of DNA and then accurate diluting of that stock. The actual number of DNA copies is therefore always approximate with a possible wide degree of variation between replicate samples.

Example 3

(59) To demonstrate the typical accuracy of a prior art method to obtain a set of standards, a comparative example was carried out. A dilution of microbial cells was prepared and freeze dried to provide freeze dried particles containing 1,000,000 live cells.

(60) Preparation of Cells

(61) Enterococcus faecalis NCTC 775 was grown at 37° C. for 24 hours in 1.6% (w/v) tryptone and 1% (w/v) yeast extract at pH 7.2. The culture contained approximately 2.5×10.sup.9 cells/ml.

(62) Dilution of Cells

(63) Several different dilutions were prepared to test for final cell numbers after freeze drying. These dilutions were prepared twice on consecutive days.

(64) Dilution 1: 50% neat culture mixed with 50% PBS

(65) Dilution 2: 5% neat culture mixed with 95% PBS

(66) Dilution 3: 0.5% neat culture mixed with 99.5% PBS

(67) Preparation of Freeze Dried Droplets

(68) Each culture was mixed and then a glass Pasteur pipette was filled and used to produce single droplets of culture fluid. The droplets were allowed to fall into test tubes containing liquid nitrogen to freeze. The tubes were then transferred to the Telstar Lyobeta and freeze dried as detailed in example 1.

(69) The next day, the freeze-dried particles were removed from the freeze dryer and individually placed into 50 ml of sterile saline solution (0.7%) and mixed thoroughly.

(70) To get a count on an agar plate that was less than 300 cfu and therefore countable on an agar plate, the freeze dried droplets were diluted as follows:

(71) Dilution 1: 10 μl from 50 ml dilution was added to 1 ml saline, mixed, 20 μl added to each plate (dilution factor×250,000)

(72) Dilution 2: 5 μl from 50 ml dilution was plated on each plate (dilution factor×10,000)

(73) Dilution 3: 50 μl from 50 ml dilution was plated on each plate (dilution factor×1000)

(74) All dilutions were plated out onto horse blood agar, spread with a sterile plastic spreader and incubated at 37° C. for 15 hours.

(75) Results

(76) The results of the numbers of bacteria are summarized in Table 4.

(77) TABLE-US-00004 TABLE 4 Dilution 1 Dilution 2 Dilution 3 n = 15 n = 15 n = 15 Mean (cfu) 111.9  243 279.1 SD 21.6 67.6 63.7 Degree of error 19.3 27.8 22.8 (% CV) cfu per freeze  2.7 × 10.sup.7 2.4 × 10.sup.6 2.8 × 10.sup.5 dried particle Repeat Experiment Mean (cfu) >300 *  56 61 SD Unknown 27.7 19.4 Degree of error Unknown 49.5 31.8 (% CV) cfu per freeze >7.5 × 10.sup.7 5.6 × 10.sup.5 6.1 × 10.sup.4 dried particle * Greater than 300 cfu per plate are too many colonies to count, therefore accurate plate counts were not obtained.

(78) The closest result to 1,000,000 cells per freeze dried particle was obtained using dilution 2 in the first experiment. However, when the experiment was repeated, either the initial cell number in the neat culture or the dilution series differed to give considerably different colony counts in the repeat dilutions. The same number of cells within a freeze dried particle is not possible when dilutions of cells are prepared.

Example 4

(79) In order to further demonstrate the efficiency of the present invention over prior art means of preparing samples of 2000 to 1,000,000 microorganisms, a comparative example is provided below.

(80) Prior to the present invention, if samples of say 100,000 microorganisms were required, then serial dilutions were prepared from a concentrated sample of the microorganism. The number of microorganisms in the sample was measured (by any suitable means). If the sample had less that the desired number, then the sample was supplemented with the required number of microorganisms. Similarly, if the sample had more that the desired number, then an aliquot was removed from the sample to reduce the number of microorganisms. The process was repeated until the rough number was obtained. If a set of standards was required, then the process was carried out on multiple samples. As can be seen, this process is time consuming and did not result in a set of standards having a degree of error of 10% or less.

(81) Using such clumsy methods would result in degrees of error of greater than about 20%. Prior to the present invention, it was not possible to achieve a set of samples wherein the defined number of particles is within a degree of error of 10% or less between each standard.

(82) The present invention is suitable for preparing standards for the following applications: quality control of microbiological reagents, media and processes; laboratory proficiency schemes; quality control of test kits; challenge studies; disinfectant efficacy studies; testing of anti-microbial treatments; quality control of pharmaceutical testing; quality control of food testing; quality control of clinical testing; quality control of water testing and quality control of beverage testing.

(83) Table 5 contains a list of bacteria that are suitable as standards according to the present invention. It will be appreciated, however, that this list is not exhaustive.

(84) TABLE-US-00005 TABLE 5 Acinetobacter baumannii ATCC19606 Aeromonas hydrophila ATCC7966 Aspergillus niger ATCC16404 Bacillus cereus NCTC 10320 Bacillus cereus NCTC 7464 Bacillus subtilis ATCC6633 Bacteroides fragilis ATCC285 Campylobacter jejuni NCTC 11322 Candida albicans ATCC2091 Candida albicans ATCC10231 Citrobacter freundii ATCC8090 Clostridium sporogenes ATCC19404 Clostridium perfringens ATCC3124 Enterobacter aerogenes ATCC13048 Enterobacter cloacae ATCC23355 Enterococcus faecalis ATCC19433 Enterococcus faecalis ATCC29212 Enterococcus faecalis ATCC33186 Enterococcus hirae ATCC10541 Escherichia coli ATCC10536 Escherichia coli ATCC922 Escherichia coli ATCC35218 Escherichia coli ATCC8739 Escherichia coli ATCC11229 Pseudomonas fluorescens Escherichia coli NCTC 9001 Escherichia coli 0157 NCTC 12900 Haemophilus influenzae NCTC 11931 Haemophilus influenzae ATCC35056 Haemophilus influenzae ATCC49247 Klebsiella aerogenes NCTC 9528 Klebsiella pneumoniae ATCC13883 Lactobacillus brevis ATCC8287 Listeria monocytogenes ATCC7644 Listeria monocytogenes NCTC7973 Listeria monocytogenes NCTC11994 Mycobacterium smegmatis CIP 7326 Mycobacterium terrae ATCC15755 Neisseria gonorrhoeae NCTC 8375 Neisseria gonorrhoeae ATCC49226 Neisseria lactamica ATCC23970 Neisseria meningitidis ATCC3090 Neisseria sicca ATCC9913 Proteus mirabilis ATCC14153 Proteus vulgaris ATCC13315 Pseudomonas aeruginosa ATCC27853 Pseudomonas aeruginosa CIP A22 Pseudomonas aeruginosa ATCC9027 Pseudomonas aeruginosa ATCC15442 Rhodococcus equi NCTC 1621 Salmonella poona NCTC 4840 Salmonella niarembe NCTC 8279 Salmonella salford IMVS 1710 Salmonella typhimurium ATCC14028 Serratia marcescens ATCC8100 Shigella flexneri ATCC12022 Shigella sonnei ATCC931 Staphylococcus aureus ATCC6538P Staphylococcus aureus ATCC9144 Staphylococcus aureus ATCC923 Salmonella abaetetuba Staphylococcus aureus ATCC29213 Staphylococcus aureus NCTC 10442 Staphylococcus aureus ATCC6538 Staphylococcus aureus NCTC 6571 Staphylococcus epidermidis ATCC 12228 Streptococcus agalactiae ATCC13813 Streptococcus pneumoniae ATCC6303 Streptococcus pneumoniae ATCC49619 Streptococcus pyogenes ATCC19615 Yersinia enterocolitica ATCC9610 Zygosaccharomyces bailii NCYC 417

Example 6

(85) Sets of 2000 samples containing 10,000 CFU were prepared for Aspergillus niger, Enterobacter aerogenes, Pseudomonas fluorescens, Escherichia coli and Enterobacter aerogenes. The processes described in Example 1 were used to prepare sets of samples by either the present invention or by creating drops with a pipette (prior art).

(86) Samples from each set were analysed by plating onto appropriate agar plates to determine the number of CFU within each sample. The degree of error of each sample set are presented in the Table 6 below.

(87) TABLE-US-00006 TABLE 6 Batch Degree of error Number Batch Size Organism (% CV) Prepared using prior art methods 522 Aspergillus niger 20.8 524 Aspergillus niger 27.4 531 Enterobacter aerogenes 25.0 535 Pseudomonas fluorescens 28.3 537 Aspergillus niger 28.5 Prepared using present invention 538 Escherichia coli 6.3 541 Enterobacter aerogenes 4.2 542 Aspergillus niger 7.8 543 Enterobacter aerogenes 8.4

(88) The degree of error of the sets of samples prepared according to the present invention ranged from 4.2% to 8.4%. In comparison, the degree of error for the samples prepared by the most accurate prior art method known to the inventors was greater than 20%. Furthermore, other methods would have a degree of error of greater than 30%.

(89) Using the present invention, a number of different sets of samples having numbers of 10,000 microorganisms have been produced. The degree of error between the samples in each set have ranged from 4.0% to 8.5%.

(90) The present invention has also reduced production times. Typically, a set of 4000 standards for a bacterium can be made in 2 hours. Prior to the present invention, such production runs were not possible and could take a day or more.

(91) TABLE-US-00007 TABLE 7 Results of providing 10,000 organisms in a standard by the manual manipulation Batch No Batch Size Organism FD Mean FD SD FD % CV Pass/Fail 202 180 Escherichia coli 9266.7 1507.0 16.3 Pass 235 299 Salmonella abaetetuba 11729.0 1932.7 16.5 Pass 246 360 Escherichia coli 10311.1 2007.1 19.5 Pass 247 360 Escherichia coli 9294.4 1871.0 20.1 Pass 248 720 Enterobacter aerogenes 10760.0 2090.6 19.4 Pass 249 360 Escherichia coli 12325.0 2368.5 19.2 Fail 250 330 Escherichia coli 10344.4 1608.2 15.5 Pass 253 360 Escherichia coli 8427.8 1801.7 21.4 Fail 254 660 Enterobacter aerogenes 11260.0 2135.5 19.0 Pass 258 540 Escherichia coli 10792.0 2033.6 18.8 Pass 260 20 Staphylococcus aureus 13200.0 2022.0 15.3 Fail 263 760 Escherichia coli 6616.3 1696.5 25.6 Fail 264 0 Escherichia coli 0.0 0.0 Fail 267 1215 Escherichia coli 8040.0 2308.5 28.7 Fail 271 1240 Escherichia coli 9955.1 1947.8 19.6 Pass 277 480 Enterobacter aerogenes 11310.6 2311.3 20.4 Pass 288 540 Staphylococcus aureus 18904.0 4039.6 21.4 Fail 292 2160 Enterobacter aerogenes 9180.0 3100.3 33.8 Fail 294 1717 Enterobacter aerogenes 9884.0 2708.1 27.4 Fail 295 356 Staphylococcus aureus 6405.6 1811.4 28.3 Fail 296 355 Staphylococcus aureus 12916.7 3472.8 26.9 Fail 297 541 Enterobacter aerogenes 12064.0 3160.1 26.2 Fail 298 360 Enterobacter aerogenes 8369.4 1442.6 17.2 Fail 299 360 Enterobacter aerogenes 15855.6 2738.3 17.3 Fail 300 359 Staphylococcus aureus 10572.2 2430.8 23.0 Pass 301 360 Enterobacter aerogenes 15600.0 2259.7 14.5 Fail 303 2120 Staphylococcus aureus 10908.0 2063.6 18.9 Pass 304 2159 Escherichia coli 11906.0 2199.7 18.5 Pass 305 671 Enterobacter aerogenes 10188.0 1986.2 19.5 Pass 309 2149 Enterobacter aerogenes 9162.1 1810.2 19.8 Pass 310 2095 Escherichia coli 7783.0 2462.5 31.6 Fail 312 2002 Escherichia coli 10583.7 1729.1 16.3 Pass 329 2160 Staphylococcus aureus 0.0 0.0 Fail 340 1440 Staphylococcus aureus 11475.0 1700.3 14.8 Pass 354 640 Enterobacter aerogenes 8044.0 1830.9 22.8 Fail 370 1080 Candida albicans 6204.0 2276.9 36.7 Fail 381 1160 Candida albicans 7240.0 1437.7 19.9 Fail 384 1238 Aspergillus niger 10697.5 1431.8 13.4 Pass 386 1550 Escherichia coli 10176.0 1784.1 17.5 Pass 391 2000 Staphylococcus aureus 11408.2 1320.0 11.6 Pass 392 1700 Candida albicans 12913.2 1166.4 9.0 Out of Spec Pass 395 1650 Candida albicans 6555.0 2016.1 30.8 Fail 399 891 Staphylococcus aureus 8929.0 651.1 7.3 Pass 400 1980 Candida albicans 58.8 51.0 86.8 Fail 412 1936 Candida albicans 5801.0 1372.5 23.7 Fail 414 1774 Escherichia coli 10723.0 1388.1 12.9 Pass 415 1076 Enterobacter aerogenes 5227.0 926.8 17.7 Fail 416 1487 Escherichia coli 8793.5 2025.3 23.0 Fail 427 1954 Escherichia coli 9328.7 1927.9 20.7 Fail 430 2103 Escherichia coli 9548.0 973.8 10.2 Pass 432 1965 Staphylococcus aureus 8446.0 1001.0 11.9 Pass 434 2091 Staphylococcus aureus 8737.5 1175.9 13.5 Passed and Extended 417 1850 Enterobacter aerogenes 6294.0 2280.1 36.2 Fail 438 939 Enterobacter aerogenes 9162.1 1810.2 19.8 Extended Stability 446 1900 Candida albicans 8918.0 795.2 8.9 Pass 451 1717 Bacillus cereus 9716.0 708.0 7.3 Passed and Extended 509 1877 Enterobacter aerogenes 7578.0 915.7 12.1 Fail 514 1909 Enterobacter aerogenes 9215.3 2234.6 24.2 Fail 520 1643 Enterobacter aerogenes 7886.3 775.5 9.8 Fail 531 Enterobacter aerogenes 4858.0 1214.5 25.0 Fail mean % 20.7 CV

(92) TABLE-US-00008 TABLE 8 Results of providing 10,000 organisms in a standard by the present invention Batch Batch Size Organism FD Mean FD SD FD % CV Pass/Fail 538 Escherichia coli 9220.8 583.2 6.3 Pass 541 Enterobacter aerogenes 9087.0 383.8 4.2 Pass 542 Aspergillus niger 10918.0 854.0 7.8 Pass 543 Enterobacter aerogenes 8034.0 670.9 8.4 Passed and Released 544 1297 Escherichia coli 10461.0 773.5 7.4 Passed and Extended 551 1928 Aspergillus niger 11940.0 675.0 5.7 Passed and Released 569 2764 Aspergillus niger 18090.0 1344.0 7.4 Fail 575 820 Enterobacter aerogenes 9992.0 568.8 5.7 Passed and Extended 580 1020 Clostridium perfringens 8140.0 415.4 5.1 Passed and Released 584 1431 Enterobacter aerogenes 9068.0 718.7 7.9 Passed and Released 595 2220 Candida albicans 10714.0 384.4 3.6 Passed and Extended 615 3150 Escherichia coli 10441.0 580.7 5.6 Passed and Extended 619 2160 Lactobacillus fermentum 7845.0 565.6 7.2 Fail 646 1820 Lactobacillus fermentum 10880.0 658.7 6.1 Passed and Released 669 2200 Clostridium perfringens 6226.0 395.0 6.3 Fail 672 1020 Staphylococcus aureus 10986.0 604.7 5.5 Passed and Released 707 3470 Enterobacter aerogenes 10428.0 397.5 3.8 Passed and Released 710 1180 Clostridium perfringens 10988.0 599.9 5.5 Passed and Released mean 6.1 % CV

(93) TABLE-US-00009 TABLE 9 Results of providing 5,000 organisms in a standard by the present invention Batch No Batch Size Organism FD Mean FD SD FD % CV Pass/Fail 549 Escherichia coli 411.0 31.7 7.7 Fail 554 Escherichia coli 560.3 35.7 6.4 Passed and Released 563 Staphylococcus aureus 452.3 42.4 9.4 Passed and Released 564 Candida albicans 582.1 52.8 9.1 Passed and Released 600 Aspergillus niger 542.7 46.9 8.6 Passed and Released 601 Candida albicans 560.3 23.2 4.1 Fail 602 Pseudomonas aeruginosa 426.1 71.1 16.7 Fail 613 Escherichia coli 590.1 17.5 3.0 Passed and Extended 616 Candida albicans 580.4 14.0 2.4 Passed and Released 617 Staphylococcus aureus 583.1 39.5 6.8 Passed and Released 618 Pseudomonas aeruginosa 577.7 32.8 5.7 Fail 621 Bacillus subtilis 521.2 15.8 3.0 Passed and Extended 622 Enterococcus faecalis 582.8 32.7 5.6 Passed and Extended 625 Escherichia coli 557.3 17.4 3.1 Passed and Extended 632 Bacillus subtilis 395.8 13.8 3.5 Fail 633 Pseudomonas aeruginosa 553.5 51.3 9.3 Passed and Released 636 Streptococcus pyogenes 551.8 33.2 6.0 Passed and Extended 642 Bacillus subtilis 406.6 36.4 9.0 Fail 647 Bacillus subtilis 578.5 40.5 7.0 Passed and Extended 649 Clostridium sporogenes 705.3 50.2 7.1 Fail 650 Clostridium sporogenes 516.1 47.3 9.2 Fail 652 Clostridium sporogenes 501.2 42.7 8.5 Fail 656 Clostridium sporogenes 545.2 36.7 6.7 Passed and Extended 657 Clostridium sporogenes 585.0 39.4 6.7 Passed and Released 663 Clostridium sporogenes 403.0 47.4 11.8 Fail 665 Clostridium sporogenes 562.8 21.9 3.9 Passed and Released 666 Salmonella abony 517.7 26.5 5.1 Passed and Extended 674 Pseudomonas aeruginosa 505.5 28.2 5.6 Passed and Released 694 Aspergillus niger 460.2 58.5 12.7 Fail 695 Aspergillus niger 371.1 42.8 11.5 Fail 697 Aspergillus niger 585.0 28.0 4.8 Passed and Released 704 Aspergillus niger 540.8 41.1 7.6 Passed and Released 713 Clostridium sporogenes 527.8 19.6 3.7 Passed and Released 715 Pseudomonas aeruginosa 587.7 21.6 3.7 Passed and Released 725 Enterococcus faecalis 583.4 22.6 3.9 Pass 729 Aspergillus niger 529.3 29.5 5.6 Passed and Released 738 Salmonella abony 532.0 41.3 7.8 Passed and Released mean 6.8 % CV

(94) It will be appreciated by persons skilled in the art that numerous variations and/or modifications may be made to the invention as shown in the specific embodiments without departing from the spirit or scope of the invention as broadly described. The present embodiments are, therefore, to be considered in all respects as illustrative and not restrictive.