Validation of an automated colony counting system for group A Streptococcus

Increasing segmentation performance with synthetic agar plate images

Background

Agar plate analysis is vital for microbiological testing in industries like food, pharmaceuticals, and biotechnology. Manual inspection is slow, laborious, and error-prone, while existing automated systems struggle with the complexity of real-world agar plates. A shortage of diverse datasets hinders the development and evaluation of robust automated systems.

Methods

In this paper, two new annotated datasets and a novel methodology for synthetic agar plate generation are presented. The datasets comprise 854 images of cultivated agar plates and 1,588 images of empty agar plates, encompassing various agar plate types and microorganisms. These datasets are an extension of the publicly available BRUKERCOLONY dataset, collectively forming one of the largest publicly available annotated datasets for research. The methodology is based on an efficient image generation pipeline that also simulates cultivation-related phenomena such as haemolysis or chromogenic reactions.

Results

The augmentations significantly improved the Dice coefficient of trained U-Net models, increasing it from 0.671 to 0.721. Furthermore, training the U-Net model with a combination of real and 150% synthetic data demonstrated its efficacy, yielding a remarkable Dice coefficient of 0.729, a substantial improvement from the baseline of 0.518. UNet3+ exhibited the highest performance among the U-Net and Attention U-Net architectures, achieving a Dice coefficient of 0.767.

Conclusions

Our experiments showed the methodology's applicability to real-world scenarios, even with highly variable agar plates. Our paper contributes to automating agar plate analysis by presenting a new dataset and effective methodology, potentially enhancing fully automated microbiological testing.

Development of an Opsonophagocytic Killing Assay Using HL-60 Cells for Detection of Functional Antibodies against Streptococcus pyogenes

The clinical development of group A streptococcal (GAS) vaccines will require the implementation of a standardized, high-throughput assay to measure the activity of functional opsonic antibodies in vaccine recipients. In the present study, we adapted and modified the HL-60-based protocol that was developed for the detection of opsonic antibodies against Streptococcus pneumoniae for use with multiple M types of GAS. Modifications of the assay conditions permitted the evaluation of 21 different M types of GAS in the assay. The specificity of the antibody-mediated opsonization was demonstrated by inhibition with homologous, but not heterologous, M proteins. Maximum rates of opsonophagocytic killing (OPK) of 14 different M types promoted by rabbit antiserum against the 30-valent M protein-based vaccine were comparable in whole-blood and HL-60 assays. Data are also presented showing OPK serum titers (opsonic index) of naturally acquired human antibodies present in IVIG [intravenous immune globulin (human)]. Results of the HL-60 assay performed on different days using 21 different M types of GAS and IVIG as the antibody source were significantly concordant. This report indicates that the OPK assay conditions may be optimized for the measurement of opsonic antibodies against a number of epidemiologically important M types of GAS and, once standardized, should facilitate the clinical development of effective vaccines to prevent these infections.IMPORTANCE Measuring functional opsonic antibodies against group A streptococci is an important component of the clinical development path for effective vaccines. Prior studies have used an assay developed over 60 years ago that relied on whole human blood as the source of phagocytes and complement, both of which are critical components of antibody-mediated killing assays. In this study, we adapted an assay that uses the HL-60 human promyelocytic leukemia cell line as phagocytic cells and baby rabbit serum as a source of complement for detection of opsonic antibodies against group A streptococci. On the basis of some of the known biological characteristics of the bacteria, we modified the assay conditions to support the evaluation of 21 epidemiologically important M types and demonstrated the utility and reproducibility of the assay for measurement of functional opsonic antibody levels.

Digital, Rapid, Accurate, and Label-Free Enumeration of Viable Microorganisms Enabled by Custom-Built On-Glass-Slide Culturing Device and Microscopic Scanning

Accurately measuring the number of viable microorganisms plays an essential role in microbiological studies. Since the conventional agar method of enumerating visible colonies is time-consuming and not accurate, efforts have been made towards overcoming these limitations by counting the invisible micro-colonies. However, none of studies on micro-colony counting was able to save significant time or provide accurate results. Herein, we developed an on-glass-slide cell culture device that enables rapid formation of micro-colonies on a 0.38 mm-thick gel film without suffering from nutrient and oxygen deprivation during bacteria culturing. Employing a phase contrast imaging setup, we achieved rapid microscopic scanning of micro-colonies within a large sample area on the thin film without the need of fluorescent staining. Using Escherichia coli (E. coli) as a demonstration, our technique was able to shorten the culturing time to within 5 h and automatically enumerate the micro-colonies from the phase contrast images. Moreover, this method delivered more accurate counts than the conventional visible colony counting methods. Due to these advantages, this imaging-based micro-colony enumeration technique provides a new platform for the quantification of viable microorganisms.

Automated counting of bacterial colonies on agar plates based on images captured at near-infrared light

Counting colonies is usually used in microbiological analysis to assess if samples meet microbiological criteria. Although manual counting remains gold standard, the process is subjective, tedious, and time-consuming. Some developed automatic counting methods could save labors and time, but their results are easily affected by uneven illumination and reflection of visible light. To offer a method which counts colonies automatically and is robust to light, we constructed a convenient and cost-effective system to obtain images of colonies at near-infrared light, and proposed an automatic method to detect and count colonies by processing images. The colonies cultured by using raw cows' milk were used as identification objects. The developed system mainly consisted of a visible/near-infrared camera and a circular near-infrared illuminator. The automatic method proposed to count colonies includes four steps, i.e., eliminating noises outside agar plate, removing plate rim and wall, identifying and separating clustered or overlapped colonies, and counting colonies by using connected region labelling, distance transform, and watershed algorithms, etc. A user-friendly graphic user interface was also developed for the proposed method. The relative error and counting time of the automatic counting method were compared with those of manual counting. The results showed that the relative error of the automatic counting method was -7.4%~ + 8.3%, with average relative error of 0.2%, and the time used for counting colonies on each agar plate was 11-21 s, which was 15-75% of the time used in manual counting, depending on the numbers of colonies on agar plates. The proposed system and automatic counting method demonstrate promising performance in terms of precision, and they are robust and efficient in terms of labor- and time-savings.