Type of bacterial counting
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Types of Bacterial Counting Methods
Bacterial counting is a fundamental task in microbiology, essential for various applications such as clinical diagnostics, food safety, and water quality assessment. Several methods have been developed to count bacteria, each with its own advantages and limitations. This article provides an overview of the most common bacterial counting techniques, including traditional methods, advanced spectrometric approaches, and automated systems.
Traditional Bacterial Counting Methods
Viable Plate Count Method
The viable plate count method is one of the most widely used techniques for determining bacterial numbers. This method involves diluting a bacterial sample and spreading it on an agar plate. After incubation, the number of colony-forming units (CFUs) is counted. This method is indirect and only counts live bacteria, as each viable cell forms a distinct colony3. The accuracy of this method can be improved by plating duplicates or triplicates of each dilution.
Spectrophotometric Analysis
Spectrophotometric analysis, also known as turbidimetric analysis, measures the turbidity of a bacterial culture to estimate cell density. This method is faster than the plate count method and measures both live and dead bacteria. The turbidity is measured by the amount of light transmitted through the culture, which decreases as the bacterial population increases3. However, this method is less sensitive and requires bacterial suspensions of 10^7 cells or greater.
Advanced Spectrometric Approaches
Lanthanide-Encoding Inductively Coupled Plasma Mass Spectrometry (ICP-MS)
A novel approach for counting and recognizing single bacterial cells involves lanthanide-encoding ICP mass spectrometry. This method uses noncanonical alkyne-D-alanine (aDA) metabolically incorporated into the bacterial cell wall, followed by europium-tagging. The europium-tagged cells are then detected using single particle ICP-MS, providing a highly sensitive and selective method for bacterial counting. This technique also allows for the simultaneous recognition of different bacterial strains through lanthanide-coded polyclonal antibodies1.
Resazurin-Amplified Picoarray Detection (RAPiD)
RAPiD is a microfluidic device-based method that uses resazurin, a fluorescent dye, to detect bacterial growth in picoliter-sized isolation chambers. This method allows for the rapid and precise counting of viable bacteria by detecting fluorescence from microcolonies proliferated from single bacteria. RAPiD can quantify viable cells in samples within approximately three hours, making it a promising tool for various microbiological applications4.
Automated Bacterial Counting Systems
Convolutional Neural Networks (CNN) in Digital Microbiology Imaging
Automated systems using convolutional neural networks (CNN) have been developed to count bacterial colonies on culture plates. These systems can process digital images of plates, identify colonies, and count them with high accuracy. CNN-based methods outperform traditional techniques and are particularly useful in full laboratory automation systems2. Additionally, CNNs can classify bacterial sub-populations, providing a high-throughput and autonomous approach to bacterial counting10.
Automated Colony Counters
Several automated colony counting systems have been developed to reduce the labor and error associated with manual counting. These systems use image processing algorithms to detect and count colonies on agar plates. For example, a system using a novel segmentation algorithm and a Bayes classifier has shown excellent performance in counting colonies of various bacterial species5. Another system, NICE (NIST's Integrated Colony Enumerator), uses a digital camera or scanner to count colonies rapidly and accurately, making it suitable for high-throughput applications7.
Gold Nanoparticles Conjugated with Aptamers
A rapid bacterial counting method using gold nanoparticles conjugated with DNA aptamers has been developed for water quality assessment. This method involves staining bacterial cells captured on a filter membrane and measuring the staining intensity with a handheld detector. The technique offers a quick turnover time of 20 minutes and a detection limit as low as 100 CFU/mL, making it suitable for on-site applications8.
Conclusion
Bacterial counting methods range from traditional techniques like viable plate counts and spectrophotometric analysis to advanced spectrometric approaches and automated systems. Each method has its own strengths and is suitable for different applications. Advances in technology, such as the use of CNNs and microfluidic devices, are making bacterial counting faster, more accurate, and less labor-intensive. These innovations are crucial for improving diagnostics, ensuring food safety, and monitoring water quality.
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Most relevant research papers on this topic
Counting and Recognizing Single Bacterial Cells by a Lanthanide-Encoding Inductively Coupled Plasma Mass Spectrometric Approach.
A lanthanide-encoding inductively coupled plasma mass spectrometric approach allows for the first time counting and recognizing single bacterial cells, improving diagnosis and understanding of cell-to-cell variability.
Bacterial colony counting with Convolutional Neural Networks in Digital Microbiology Imaging
Convolutional Neural Networks deep learning approach outperforms handcrafted features and conventional reference techniques in bacterial colony counting in Digital Microbiology Imaging, making it a preferred solution for clinical microbiology laboratories.
Highly CitedSimple and Precise Counting of Viable Bacteria by Resazurin-Amplified Picoarray Detection.
RAPiD is a fast, accurate method for counting viable bacteria in microbiological applications, enabling food quality monitoring and clinical diagnosis in less than 3 hours.
Automated Counting of Bacterial Colony Forming Units on Agar Plates
The automated colony counting system with a novel segmentation algorithm accurately counts bacterial colonies on agar plates, improving accuracy and performance in bacterial colony counting.
Highly CitedAn automated bacterial colony counting and classification system
Our fully automatic bacterial colony counter can count and classify colonies using digital camera images, offering improved precision and recall while reducing labor and time in biomedical assays.
Low‐cost, high‐throughput, automated counting of bacterial colonies
NICE, a low-cost, high-throughput, automated counting system for bacterial colonies, accurately correlates with manual counting results and is free from NIST.
Highly CitedA rapid total bacterial count method using gold nanoparticles conjugated with an aptamer for water quality assessment.
The AB2-AuNP method, using gold nanoparticles conjugated with an aptamer, provides a rapid, on-site method for detecting bacteria in water samples, with sensitivity as low as 100 CFU mL-1.
Bacteria counting method based on polyaniline/bacteria thin film.
The proposed method, based on polyaniline/bacteria thin film, is a simple and rapid method for bacterial counting, with acceptable stability, reproducibility, and switchable ability.
Automated classification of bacterial cell sub-populations with convolutional neural networks
Convolutional neural networks (cCNN) can accurately classify and count bacterial cell sub-populations, providing a 10.2 times more consistent and 3.8 times faster alternative to manual microscopy.
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