Colony-forming unit explained

In microbiology, a colony-forming unit (CFU, cfu or Cfu) is a unit which estimates the number of microbial cells (bacteria, fungi, viruses etc.) in a sample that are viable, able to multiply via binary fission under the controlled conditions. Counting with colony-forming units requires culturing the microbes and counts only viable cells, in contrast with microscopic examination which counts all cells, living or dead. The visual appearance of a colony in a cell culture requires significant growth, and when counting colonies, it is uncertain if the colony arose from a single cell or a group of cells. Expressing results as colony-forming units reflects this uncertainty.

Theory

The purpose of plate counting is to estimate the number of cells present based on their ability to give rise to colonies under specific conditions of temperature, time, and nutrient medium. Theoretically, one viable cell can give rise to a colony through replication. However, solitary cells are the exception in nature, and in most cases the progenitor of a colony is a mass of cells deposited together.^{[1] [2]} In addition, many bacteria grow in chains (e.g. Streptococcus) or clumps (e.g., Staphylococcus). Estimation of microbial numbers by CFU will, in most cases, undercount the number of living cells present in a sample for these reasons. This is because the counting of CFU assumes that every colony is separate and founded by a single viable microbial cell.^[3]

The plate count is linear for E. coli over the range of 30 to 300 CFU on a standard sized Petri dish.^[4] Therefore, to ensure that a sample will yield CFU in this range requires dilution of the sample and plating of several dilutions. Typically, ten-fold dilutions are used, and the dilution series is plated in replicates of 2 or 3 over the chosen range of dilutions. Often 100 μL are plated but also larger amounts up to 1 mL are used. Higher plating volumes increase drying times but often do not result in higher accuracy, since additional dilution steps may be needed.^[5] The CFU/plate is read from a plate in the linear range, and then the CFU/g (or CFU/mL) of the original is deduced mathematically, factoring in the amount plated and its dilution factor.

An advantage to this method is that different microbial species may give rise to colonies that are clearly different from each other, both microscopically and macroscopically. The colony morphology can be of great use in the identification of the microorganism present.^[6]

A prior understanding of the microscopic anatomy of the organism can give a better understanding of how the observed CFU/mL relates to the number of viable cells per milliliter. Alternatively it is possible to decrease the average number of cells per CFU in some cases by vortexing the sample before conducting the dilution. However, many microorganisms are delicate and would suffer a decrease in the proportion of cells that are viable when placed in a vortex.^[7]

Log notation

Concentrations of colony-forming units can be expressed using logarithmic notation, where the value shown is the base 10 logarithm of the concentration.^{[8] [9] [10]} This allows the log reduction of a decontamination process to be computed as a simple subtraction.

Uses

Colony-forming units are used to quantify results in many microbiological plating and counting methods, including:

• The pour plate method wherein the sample is suspended in a Petri dish using molten agar cooled to approximately 40–45 °C (just above the point of solidification to minimize heat-induced cell death). After the nutrient agar solidifies the plate is incubated.^[11]
• The spread plate method wherein the sample (in a small volume) is spread across the surface of a nutrient agar plate and allowed to dry before incubation for counting.
• The membrane filter method wherein the sample is filtered through a membrane filter, then the filter placed on the surface of a nutrient agar plate. During incubation nutrients leach up through the filter to support the growing cells. As the surface area of most filters is less than that of a standard Petri dish, the linear range of the plate count will be less.
• The Miles and Misra methods or drop-plate method wherein a very small aliquot (usually about 10 microliters) of sample from each dilution in series is dropped onto a Petri dish. The drop dish must be read while the colonies are very small to prevent the loss of CFU as they grow together.

However, with the techniques that require the use of an agar plate, no fluid solution can be used because the purity of the specimen cannot be unidentified and it is not possible to count the cells one by one in the liquid.^[12]

Tools for counting colonies

Counting colonies is traditionally performed manually using a pen and a click-counter. This is generally a straightforward task, but can become very laborious and time-consuming when many plates have to be enumerated. Alternatively semi-automatic (software) and automatic (hardware + software) solutions can be used.^{[13] [14] [15]}

Software for counting CFUs

Colonies can be enumerated from pictures of plates using software tools. The experimenters would generally take a picture of each plate they need to count and then analyse all the pictures (this can be done with a simple digital camera or even a webcam). Since it takes less than 10 seconds to take a single picture, as opposed to several minutes to count CFU manually, this approach generally saves a lot of time. In addition, it is more objective and allows extraction of other variables such as the size and colour of the colonies.

• OpenCFU is a free and open-source program designed to optimise user friendliness, speed and robustness. It offers a wide range of filters and control as well as a modern user interface. OpenCFU is written in C++ and uses OpenCV for image analysis.^[16]
• NICE is a program written in MATLAB that provides an easy way to count colonies from images.^[17]
• ImageJ and CellProfiler: Some ImageJ macros^[18] and plugins and some CellProfiler pipelines^[19] can be used to count colonies. This often requires the user to change the code in order to achieve an efficient work-flow, but can prove useful and flexible. One main issue is the absence of specific GUI which can make the interaction with the processing algorithms tedious.

In addition to software based on traditional desktop computers, apps for both Android and iOS devices are available for semi-automated and automated colony counting. The integrated camera is used to take pictures of the agar plate and either an internal or an external algorithm is used to process the picture data and to estimate the number of colonies.^{[20] [21] [22]}

Automated systems

Many of the automated systems are used to counteract human error as many of the research techniques done by humans counting individual cells have a high chance of error involved. Due to the fact that researchers regularly manually count the cells with the assistance of a transmitted light, this error prone technique can have a significant effect on the calculated concentration in the main liquid medium when the cells are in low numbers.^[23] Completely automated systems are also available from some biotechnology manufacturers.^{[24] [25]} They are generally expensive and not as flexible as standalone software since the hardware and software are designed to work together for a specific set-up.Alternatively, some automatic systems use the spiral plating paradigm.^[26]

Some of the automated systems such as the systems from MATLAB allow the cells to be counted without having to stain them. This lets the colonies to be reused for other experiments without the risk of killing the microorganisms with stains. However, a disadvantage to these automated systems is that it is extremely difficult to differentiate between the microorganisms with dust or scratches on blood agar plates because both the dust and scratches can create a highly diverse combination of shapes and appearances.^[27]

Alternative units

Instead of colony-forming units, the parameters Most Probable Number (MPN) and Modified Fishman Units (MFU)^[28] can be used. The Most Probable Number method counts viable cells and is useful when enumerating low concentrations of cells or enumerating microbes in products where particulates make plate counting impractical.^[29] Modified Fishman Units take into account bacteria which are viable, but non-culturable.

See also

• Cell counting
• Growth medium
• Miles and Misra method
• Most probable number
• Replica plating
• Viral plaque

Further reading

• Book: Fishman . William H. . Bernfeld . Peter . 1955 . [31] Glucuronidases . Methods in Enzymology . 1 . 262–9 . 978-0-12-181801-2 . 10.1016/0076-6879(55)01035-5 . registration .

Notes and References

1. Amann . R I . Ludwig . W . Schleifer . K H . 1995 . Phylogenetic identification and in situ detection of individual microbial cells without cultivation . Microbiological Reviews . en . 59 . 1 . 143–169 . 10.1128/mr.59.1.143-169.1995 . 0146-0749 . 239358 . 7535888.
2. Staley . James T. . Konopka . Allan . 1985 . Measurement of In Situ Activities of Nonphotosynthetic Microorganisms in Aquatic and Terrestrial Habitats . Annual Review of Microbiology . en . 39 . 1 . 321–346 . 10.1146/annurev.mi.39.100185.001541 . 3904603 . 0066-4227.
3. Book: Goldman. Emanuel. Green. Lorrence H. Practical Handbook of Microbiology, Second Edition (Google eBook). 24 August 2008. CRC Press, Taylor and Francis Group. USA. 978-0-8493-9365-5. 864. Second. 2014-10-16.
4. Breed . RS . Dotterrer . WD . The Number of Colonies Allowable on Satisfactory Agar Plates . Journal of Bacteriology . 1 . 3 . 321–31 . May 1916 . 10.1128/JB.1.3.321-331.1916 . 16558698 . 378655.
5. Schug. Angela R.. Bartel. Alexander. Meurer. Marita. Scholtzek. Anissa D.. Brombach. Julian. Hensel. Vivian. Fanning. Séamus. Schwarz. Stefan. Feßler. Andrea T.. 1 December 2020 . Comparison of two methods for cell count determination in the course of biocide susceptibility testing. Veterinary Microbiology. en. 251. 108831. 10.1016/j.vetmic.2020.108831. 33202368. 225308316 .
6. Badieyan . Saeedesadat . Dilmaghani-Marand . Arezou . Hajipour . Mohammad Javad . Ameri . Ali . Razzaghi . Mohammad Reza . Rafii-Tabar . Hashem . Mahmoudi . Morteza . Sasanpour . Pezhman . 17 July 2018 . Detection and Discrimination of Bacterial Colonies with Mueller Matrix Imaging . Scientific Reports . en . 8 . 1 . 10815 . 10.1038/s41598-018-29059-5 . 2045-2322 . 6050273 . 30018335.
7. Foladori . Paola . Laura . Bruni . Gianni . Andreottola . Giuliano . Ziglio . 2007 . Effects of sonication on bacteria viability in wastewater treatment plants evaluated by flow cytometry—Fecal indicators, wastewater and activated sludge . Water Research . en . 41 . 1 . 235–243 . 10.1016/j.watres.2006.08.021. 17052743 .
8. Web site: Log10 Colony Forming Units per Gram . Titi Tudorancea Encyclopedia . 25 September 2016.
9. Web site: Viable Cell Counts . Daniel Y. C. . Fung . 2009 . Bioscience International . 25 September 2016.
10. Web site: Principles of microbiological testing: Statistical basis of sampling . Martin . Cole . 1 November 2005 . International Commission on Microbiological Specifications for Foods (ICMSF) . 25 September 2016 . https://web.archive.org/web/20171031120510/http://www.icmsf.org/pdf/Nov05Symp/ColeFinal2.pdf . 31 October 2017 . dead .
11. Web site: USP 61: Microbial Enumeration Tests . 21 May 2024 . United States Pharmacopeia.
12. Web site: Reynolds . Jackie . Serial Dilution Protocols . www.microbelibrary.org . 15 November 2015 . dead . https://web.archive.org/web/20151117014924/http://www.microbelibrary.org/component/resource/laboratory-test/2884-serial-dilution-protocols. 17 November 2015.
13. Brugger . Silvio D. . Baumberger . Christian . Jost . Marcel . Jenni . Werner . Brugger . Urs . Mühlemann . Kathrin . 2012-03-20 . Bereswill . Stefan . Automated Counting of Bacterial Colony Forming Units on Agar Plates . PLOS ONE . en . 7 . 3 . e33695 . 10.1371/journal.pone.0033695 . 1932-6203 . 3308999 . 22448267 . free . 2012PLoSO...733695B .
14. Khan . Arif ul Maula . Torelli . Angelo . Wolf . Ivo . Gretz . Norbert . 8 May 2018 . AutoCellSeg: robust automatic colony forming unit (CFU)/cell analysis using adaptive image segmentation and easy-to-use post-editing techniques . Scientific Reports . en . 8 . 1 . 7302 . 10.1038/s41598-018-24916-9 . 2045-2322 . 5940850 . 29739959.
15. Zhang . Louis . 5 November 2022 . Machine learning for enumeration of cell colony forming units . Visual Computing for Industry, Biomedicine, and Art . 5 . 1 . 26 . 10.1186/s42492-022-00122-3 . 2524-4442 . 9637067 . 36334176 . free .
16. Geissmann . Quentin . OpenCFU, a new free and open-source software to count cell colonies and other circular objects . PLOS ONE . 8 . 2 . e54072 . 2013 . 23457446 . 3574151 . 10.1371/journal.pone.0054072. 1210.5502 . 2013PLoSO...854072G . free .
17. Clarke . Matthew L. . Burton . Robert L. . Hill . A. Nayo . Litorja . Maritoni . Nahm . Moon H. . Hwang . Jeeseong . Low-cost, high-throughput, automated counting of bacterial colonies . Cytometry Part A . August 2010 . 77 . 8 . 790–797 . 10.1002/cyto.a.20864 . 20140968 . 2909336.
18. Cai . Zhongli . Chattopadhyay . Niladri . Liu . Wenchao Jessica . Chan . Conrad . Pignol . Jean-Philippe . Reilly . Raymond M. . Optimized digital counting colonies of clonogenic assays using ImageJ software and customized macros: Comparison with manual counting . International Journal of Radiation Biology . November 2011 . 87 . 11 . 1135–1146 . 10.3109/09553002.2011.622033 . 21913819 . 25417288.
19. Bray . Mark-Anthony . Vokes . Martha S. . Carpenter . Anne E. . Using CellProfiler for Automatic Identification and Measurement of Biological Objects in Images . Current Protocols in Molecular Biology . January 2015 . 109 . 1 . 14.17.1–14.17.13 . 10.1002/0471142727.mb1417s109. 25559103 . 4302752 .
20. Web site: Arduengo . Michele . Now Available for Purchase: Promega Colony Counter App . Promega Connections . 29 March 2013.
21. Moucka . Michael . Muigg . Veronika . Schlotterbeck . Ann-Kathrin . Stöger . Laurent . Gensch . Alexander . Heller . Stefanie . Egli . Adrian . Performance of four bacterial cell counting apps for smartphones . Journal of Microbiological Methods . August 2022 . 199 . 106508 . 10.1016/j.mimet.2022.106508. free . 35691441 .
22. Austerjost. Jonas. Marquard. Daniel. Raddatz. Lukas. Geier. Dominik. Becker. Thomas. Scheper. Thomas. Lindner. Patrick. Beutel. Sascha. August 2017. A smart device application for the automated determination of E. coli colonies on agar plates. Engineering in Life Sciences. en. 17. 8. 959–966. 10.1002/elsc.201700056. 32624845. 6999497. 1618-0240. free.
23. Jarvis . Basil . Errors associated with colony count procedures . Statistical Aspects of the Microbiological Examination of Foods . 2016 . 119–140 . 10.1016/b978-0-12-803973-1.00007-3 . 978-0-12-803973-1 . Elsevier.
24. Heuser . Elisa . Becker . Karsten . Idelevich . Evgeny A. . Evaluation of an Automated System for the Counting of Microbial Colonies . Microbiology Spectrum . 17 August 2023 . 11 . 4 . e00673-23 . 10.1128/spectrum.00673-23. 37395656 . 10433998 .
25. Web site: Fully Automatic Colony Counter by AAA Lab Equipment Video . LabTube . 2018-09-28 . August 7, 2015.
26. Gilchrist . J. E. . Campbell . J. E. . Donnelly . C. B. . Peeler . J. T. . Delaney . J. M. . 1973 . Spiral Plate Method for Bacterial Determination . Applied Microbiology . en . 25 . 2 . 244–252 . 10.1128/am.25.2.244-252.1973 . 0003-6919 . 380780 . 4632851.
27. Automated Counting of Bacterial Colony Forming Units on Agar Plates . PLOS ONE . 20 March 2012 . 1932-6203 . 3308999 . 22448267 . 7 . 3 . e33695 . 10.1371/journal.pone.0033695 . Silvio D. . Brugger . Christian . Baumberger . Marcel . Jost . Werner . Jenni . Urs . Brugger . Kathrin . Mühlemann . 2012PLoSO...733695B . free.
28. Dehority . B A . Tirabasso . P A . Grifo . A P . 1989 . Most-probable-number procedures for enumerating ruminal bacteria, including the simultaneous estimation of total and cellulolytic numbers in one medium . Applied and Environmental Microbiology . en . 55 . 11 . 2789–2792 . 10.1128/aem.55.11.2789-2792.1989 . 0099-2240 . 203169 . 2624460.
29. Web site: Blodgett . Robert . Bacterial Analytical Manual: Most Probable Number from Serial Dilutions . October 2010 . United States Food and Drug Administration.