Journal of Microbiological Methods

Examining microbial colonies on agar plates have been at the core of microbiology for many decades. It is usually done manually, and therefore subject to bias besides requiring a considerable amount of time and effort. In order to optimize and standardize the identification of bacterial colonies growing on agar plates, we have developed an open access tool available on GitHub: ColFeatures. The software allows automated identification of bacterial colonies, extracts key morphological data and generate labels that ensure tracking of temporal development. We included machine learning algorithms that provide sorting of environmental isolates by using cluster methodologies. Furthermore, we show how cluster performance is evaluated using index scores (Silhouette, Calinski-Harabasz, Davies-Bouldin) to ensure the outcome of colony classification. As automation becomes more prominent in microbiology, tools as ColFeatures will assist identification of bacterial colonies on agar plates, bypassing human bias and complementing sequencing or mass spectrometry information that often comes attached with a considerable price tag.

This paper reflects on the development and performance of an advanced artificial intelligence (AI) algorithm for the automated processing and classification of Gram stain images obtained from actual microbiology samples used in clinical microbiology. The aim of the project was to effectively categorize non-standardized Gram stain images into the six most common categories: Gram-negative rods, Gram-positive cocci in chains, Gram-positive cocci in clusters, Gram-positive rods, Gram-negative cocci, and yeasts. The development and testing relied on 1,077 Gram stain images of varying sizes, originating from different laboratories and captured using diverse microscopes at different points in time, resulting in differences in image quality, scaling, color balance, and the presence of artifacts. The dataset was split into 80% training and 20% testing subsets, with the split performed in a stratified manner so that each object group was proportionally represented in both the training and testing sets. Preprocessing involved computer vision techniques to improve contrast and color balance, detect contours and object borders, and implement filtering mechanisms to remove unwanted artifacts. Morphological analysis of shapes was then performed to extract parameters characterizing each contour. Next, human-like classification criteria--based on gradient, morphological features (e.g., shape, size) and spatial arrangement that mimic microbiologists visual assessment--were established, achieving around 92% accuracy in image classification without using machine learning (ML) methods. However, any further improvements turned practically impossible, prompting the use of ML methods. Building on pre-obtained features, a random forest ML algorithm was employed to further refine the criteria, with three models trained and tested successively. The first model determined the Gram stain reaction (positive or negative) of each object. The second model classified objects into one of six predefined categories. The third model aggregated individual object classifications to generate an overall classification for each slide, based on the number of objects observed in each category and their occupied area. Overall, the ML solution was significantly more accurate, reaching 99.9% accuracy in classifying the images into one of the aforementioned groups. The algorithms limitations include inability to classify mixed cultures, as it primarily focuses on the dominant category. In cases where positive and negative objects coexist, the algorithm tends to prioritize Gram-positive objects. Additionally, the current morphological assessment is insufficient for yeast classification. Addressing these limitations is a crucial avenue for future research to enhance the algorithms versatility and accuracy.

Measuring the growth rate of filamentous fungi is an essential phenotype assay in fungal biology, enabling the comparison of nutrient-related fitness metrics across various isolates, species and genera. Conventional methods are time consuming and labor intensive, which prohibits the adaptation and implementation of high-throughput phenotyping. Here, we suggest a high-throughput methodological pipeline to study fungal growth on solid media combining the use of 24-well plates, an automated image acquisition system, and human assisted deep learning analysis of acquired images. Training a deep learning model through an iterative process - with continuous feedback and corrective annotations - enabled the development of a satisfying model that automatically segments pixels belonging to either fungus or background within a few hours. We evaluated this deep learning model by applying it to two test sets: First, a set of 336 images was used to validate the results by comparison with manual measurements. We demonstrate that the automated segmentation approach provides robust estimation of fungal growth not significantly different to manually segmented data. Second, a larger test set consisting of 2,016 images was used to illustrate the scalability of the model. After training the model for less than two hours, the deep learning model segmented the entire image data set automatically within minutes. The presented method is easily scalable and adjustable to other fungi and growth morphologies, due to the interactive training. Moreover, by combining 24-well plates and automatic image acquisition, measurements can be sped up as growth is detected across a smaller surface area than a standard six or nine cm diameter petri dish. The proposed methodological pipeline thus offers a new tool for estimating fungal growth rates, which can accelerate measurements, reduce bias, and increase throughput.

Bacterial characterization is a crucial discipline within microbiology. Given the manual and labor-intensive nature of this task, our aim is to introduce a semi-automatic segmentation method that enhances efficiency while preserving the rich details of bacterial colonies. We propose using the k-means clusterization algorithm to analyze and segment images of bacterial cultures, specifically those of Pseudomonas koreensis and Escherichia coli. Unlike existing methods that focus primarily on colony counting, our approach emphasizes morphological characterization. In some bacterial cultures, colonies are not well-defined, making manual counting or other automated counting methods unfeasible; i.e. the bacterial growth area is not easily identifiable, thus precise growth tracking is not feasible. Our method enables bacterial growth characterization even in these cases. Our computer vision system identifies and quantifies the diverse morphologies within P. koreensis and E. coli cultures, determining their relative occupancy in an image. Our approach provides valuable insights into the composition, growth patterns, and developmental stages of bacterial colonies, designed to assist both novice and expert microbiologists in bacterial analysis.

Bacteria can exhibit phenotypes, which makes them tolerant against antibiotics. However, often only a few cells of a bacterial population show such so-called persister phenotype, which makes it difficult to study this health-threatening phenotype. We recently found that certain abrupt nutrient-shifts generate E. coli populations that consist of almost only antibiotic tolerant persister cells. Such nearly homogeneous persister cell populations enable assessment with population-averaging experimental methods, such as high-throughput methods. In this paper, we provide a detailed protocol of how to generate such large fraction of tolerant cells using the nutrient-switch approach. Furthermore, we describe how to determine the fraction of cells that enter the tolerant state upon a sudden nutrient shift and describe a new way to assess antibiotic tolerance with flow cytometry. We envision that these methods facilitate research into the important and exciting phenotype of bacterial cells.Competing Interest StatementThe authors have declared no competing interest.View Full Text

Cell lysis is essential for extracting intake genetic material, forming the basis for diagnostic tests, and genetic studies. Commonly used lysis methods include thermal lysis, mechanical force, chemicals, biologicals, and sonication. Determining effective lysis methods for specific cell types is crucial for conducting further research. This study evaluates the lysis efficiency of Candida albicans using the Cellometer X2 fluorescent cell viability counter, employing various lysis methods: thermal, chemical, and enzymatic. Our results indicate that high-pH buffers combined with heat treatment enhance lysis efficiency, while SDS alone or with Proteinase K is insufficient for lysis at room temperature. In contrast, Zymolyase effectively lyses C. albicans at room temperature in 150 minutes, making the automation of room temperature lysis and nucleic acid purification feasible for C. albicans. Overall, this study highlights the Cellometer X2s capability for rapid and direct evaluation of cell lysis efficiency.

IntroductionIn an interlaboratory calibration analysis to validate a methodology that will be proposed as a European standard for domestic laundry disinfection, tests were carried out to detect if there are different behaviors in the measurements regarding accuracies and variabilities. Interlaboratory tests using different doses of disinfectant and microorganisms were carried out. ISO 5725-2 and ISO 13528 form the basis of validations of quantitative methods, providing validation specifications for interlaboratory studies. However, a need for a simple graphical method to detect interlaboratory differences in accuracy and variability was observed. ObjectivesThe general goal of this work is to present a new exploratory methodology, graphical and easy to interpret, that can determine the accuracy and variability (precision) of a variable, and compare it to the methodology applied in ISO 5725-2 and ISO 13528. MethodsWe used confidence probability plots of the multivariate Students t-distribution to observe the accuracy and variability of microbiological measures carried out by different laboratories during a ring trial exercise. A function in R was built for this purpose: Miriam.analysis.ellipse(Y, factor_a, eel.plot = " t-Student"). The different observations of accuracy and variability are represented in the ellipses. If any of the points are outside the ellipse with 95% confidence, we can assume a deviation in accuracy and / or variability. ResultsTwo examples are provided with real microbiological data (logarithmic unit reductions (LR) for Pseudomonas aeruginosa, Escherichia coli, Staphilococcus aureus, Enterococcus hirae, Candida albicans and microbial counts in water (WW)). The proposed new method allowed us to detect possible deviations in the WWMEA variable and we believe it has future application for the rapid control of microbiological measures.

Photodynamic Antimicrobial Chemotherapy (PACT) relies on the concomitant use of light and a photosensitizer molecule for microbial disinfection. Adding the dimension of light to the system increases the difficulty and variables to consider, yielding time and material-consuming experiments, with their reproducibility depending greatly on the disclosure of the light irradiation conditions. In the present work we analyse the effect of increasing irradiant exposures and rose bengal (RB) concentrations against Candida albicans ATCC 10231, in a checkerboard fashion. For this, we propose the use of the exponential phase coefficients, growth rate and initial number of cells, to assess the efficiency of the treatment. The coefficients are able to accurately describe the decrease of starting number of cells as consequence of the PACT effect, while also showing the metabolic burden of the presence of RB at high concentrations, with further hints that the incorrect combination of RB and radiant exposure decreases the growth rate without affecting the survivability. Our proposed methodology can then be used as a high-throughput screening method for new photosensitizers and new formulations, exploring their needs of radiant exposure for an efficient microbial disinfection.

Objectivesthis study aimed to develop a fully automated, non-destructive and label-free identification method of bacterial colonies, directly on agar plates, using a combination of digital holography and artificial intelligence and to evaluate its performances. Methodhigh-resolution holographic images of individual colonies on translucent brain-heart agar plates were taken every 30 minutes throughout an 18-hour incubation period (530 MPx for the full plate) using a large field 1x magnification system, a partially coherent LED light source and a high-resolution CMOS sensor. A database containing 49 490 digital holograms of individual colonies from 276 clinical strains belonging to ten of the most prevalent pathogenic bacterial species was used to train the convolutional neural network (CNN). Improvement in the accuracy of the prediction from the CNN algorithms was achieved using the information at different phylogenetic levels. Resultsthe performance of the BAIO-DX solution was assessed on 232 strains belonging to the 10 species used to train the algorithms but also on 64 strains from 8 species not included in the training database. For the species included in the training dataset, this new method identified 86.6% of the strains at the species level with a positive-percent agreement of 96.5%. An additional 48% of the strains not identified at the species level could be identified at the genus level thanks to the phylogenetic interpretation of the results. Conclusionsthese first results validate this approach as a candidate to obtain a fully automated non-destructive and label-free solution for bacterial identification in clinical microbiology laboratories. IMPORTANCE STATEMENTIdentification of pathogenic bacteria by culture-based methods are typically performed using MALDI-TOF mass spectrometry or biochemical systems. While automation and interpretive algorithms based on agar plate imaging and artificial intelligence (AI) has reduced manual steps, bacterial identification is still labor-intensive. Here we developed a fully automated, non-destructive and label-free identification method of bacterial colonies at the species level, directly on agar plates, using a combination of digital holography and convolutional neural network algorithms. After training the system with 276 strains belonging to ten of the most frequent pathogenic bacterial species, the BAIO-DX solution was able to identify 86.6% of new strains from these 10 species with a positive-percent agreement of 96.5%. These thorough proof of concept shows that imaging methods coupled to AI algorithms are promising to reach a fully automated identification of a significant proportion of pathogenic bacteria and has potential to enhance diagnostic workflows in clinical microbiology.

Bead beating is widely used for mechanical lysis of Mycobacterium tuberculosis, a bacterium with a highly resistant, lipid-rich cell wall. Despite its status as a de facto gold standard for mycobacterial lysis, there is no standardized protocol for bead beating, resulting in significant variability across studies. We conducted a literature review of 73 studies, identifying 38 with explicit mycobacterial bead beating protocols. Our analysis revealed heterogeneity in bead types, sizes, device models, and operational parameters, with 37% of studies failing to report critical details such as lysis speed. We experimentally assessed the impact of key variables--tube type, bead quantity, and device settings--on lysis efficiency using qPCR of M. tuberculosis DNA. Results showed that even minor changes, such as tube shape or bead volume, can significantly affect DNA yield. These findings underscore the need for standardized bead-beating protocols to improve reproducibility and comparability. Future efforts should prioritize developing consensus methods tailored to sample type and analytical application.

In microbiology, the estimation of the growth rate of microorganisms is a critical parameter to describe a new strain or characterize optimal growth conditions. Traditionally, this parameter is estimated by selecting subjectively the exponential phase of the growth, and then determining the slope of this curve section, by linear regression. However, for some experiments, the number of points to describe the growth can be very limited, and consequently such linear model will not fit, or the parameters estimation can much lower and strongly variable. In this paper, we propose a tools to estimate growth parameters using a logistic Verhulst model that take into account the entire growth curve for the estimation of the growth rate. The efficiency of such model is compared to the linear model. Finally, the novelty of our work is to propose a "Shiny-web application", online, without any programming or modelling skills, to allow estimating growth parameters including growth rate, maximum population, and beginning of the exponential phase, as well as an estimation of their variability. The final results can be displayed in the form of a scatter plot representing the model, its efficiency and the estimated parameters are downloadable.

The motility mechanisms of microorganisms are critical virulence factors, enabling their spread and survival during infection. Motility is frequently characterized by qualitative analysis of macroscopic colonies, yet the standard quantification method has mainly been limited to manual measurement. Recent studies have applied deep learning for classification and segmentation of specific microbial species in microscopic images, but less work has focused on macroscopic colony analysis. Here, we advance computational tools for analyzing colonies of Proteus mirabilis, a bacterium that produces a macroscopic bullseye-like pattern via periodic swarming, a process implicated in its virulence. We present a dual-task pipeline for segmenting (1) the macroscopic colony including faint outer swarm rings, and (2) internal ring boundaries, unique features of oscillatory swarming. Our convolutional neural network for patch-based colony segmentation and U-Net with a VGG-11 encoder for ring boundary segmentation achieved test Dice scores of 93.28% and 83.24%, respectively. The predicted masks at times improved on the ground truths from our automated annotation algorithms. We demonstrate how application of our pipeline to a typical swarming assay enables ease of colony analysis and precise measurements of more complex pattern features than those which have been historically quantified.

Loop-mediated isothermal amplification (LAMP) is a widely used rapid and affordable molecular DNA amplification method with minimal resource requirements. However, visual interpretation of results is subjective and prone to errors, leading to potential false-positive and negative results. To address this limitation, a machine-learning approach is proposed for automated LAMP classification based on digital images. The approach utilizes You Only Look Once (YOLOv8), a fast and robust object detection algorithm to locate and classify tubes within LAMP images, enabling automated categorization as positive or negative. The trained model achieved a high overall accuracy of 95.5% in classifying LAMP images into positive or negative. Additionally, the approach had a 98.0% precision and 92.7% recall for positive cases and 93.4% precision and 98.2% recall for negative cases, demonstrating its potential for real-time LAMP diagnosis and enhanced assay performance. This project demonstrated the platforms suitability for real-time testing, offering an easy operation and rapid results.

1.Spotted fever rickettsiosis plagues countries around the world. One of the deadliest of this group, Rickettsia rickettsii, responsible for Rocky Mountain spotted fever, is an emerging tickborne illness in North America. The predominant clinical diagnostic is PCR based but does not work until disease has progressed to a severe phase of infection, at which point the outcome of a full recovery is significantly decreased. An alternative, loop mediated isothermal amplification through one-step strand displacement (LAMP-OSD) assay, was developed to improve diagnostic speed and sensitivity. Synthetic dsDNA genes from the 17 kDa surface antigen precursor (AY281069.1) amplified between fifteen minutes to an hour and were detected to concentrations as low as 102 copies/L. This RMSF LAMP-OSD assay shows promise to deliver results in just a few hours and the detection limit is potentially 100 times more sensitive than qPCR based assays.

A variety of costly research-grade imaging devices are available for the detection of spectroscopic features. Here we present an affordable, open-source and versatile device, suitable for a range of applications. We provide the files to print the imaging chamber with commonly available 3D printers and instructions to assemble it with easily available hardware. The imager is suitable for rapid sample screening in research, as well as for educational purposes. We provide details and results for an already proven set-up which suits the needs of a research group and students interested in UV-induced near-infrared fluorescence detection of microbial colonies grown on Petri dishes. The fluorescence signal confirms the presence of bacteriochlorophyll a in aerobic anoxygenic phototrophic bacteria (AAPB). The imager allows for the rapid detection and subsequent isolation of AAPB colonies on Petri dishes with diverse environmental samples. To this date, 15 devices have been build and more than 7000 Petri dishes have been analyzed for AAPB, leading to over 1000 new AAPB isolates. Parts can be modified depending on needs and budget. The latest version with automated switches and double band pass filters costs around 350{euro} in materials and resolves bacterial colonies with diameters of 0.5 mm and larger. The low cost and modular build allow for the integration in high school classes to educate students on light properties, fluorescence and microbiology. Computer-aided design of 3D-printed parts and programming of the employed Raspberry Pi computer could be incorporated in computer sciences classes. Students have been also inspired to do agar art with microbes. The device is currently used in seven different high schools in Finland. Additionally, a science education network of Finnish universities has incorporated it in its program for high school students. Video guides have been produced to facilitate easy operation and accessibility of the device. O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=62 SRC="FIGDIR/small/543100v2_ufig1.gif" ALT="Figure 1"> View larger version (29K): org.highwire.dtl.DTLVardef@6dfcb7org.highwire.dtl.DTLVardef@ea7fbcorg.highwire.dtl.DTLVardef@1681e78org.highwire.dtl.DTLVardef@a88d51_HPS_FORMAT_FIGEXP M_FIG C_FIG

IntroductionThe high number of uncontrollable variables in microbiological systems increases experimental complexity and reduces accuracy, potentially leading to data misinterpretation or uncorrectable errors. During an interlaboratory calibration analysis it was observed that the microbial logarithmic reduction (LR) caused by disinfectants depends not only on the type of disinfectant but also on the initial microbial load in the fabric carriers, which can produce a misinterpretation of the results. Fabric carriers are commonly used in standard tests such as EN16616 and ASTM2274. ObjectiveA method based on statistical calibration is proposed using a regression line between N0 (initial microbial load in the carrier) and LR to eliminate the influence of one on the other. ResultsAn example with Candida albicans is presented. Once the method was applied, the influence of N0 on LR was eliminated and the new LR values can be used for factorial experiments, for example, to check the efficacy of disinfectants or detergents without depending on the microbial load placed in the carrier.

The quantification of Bradyrhizobium diazoefficiens in inoculants traditionally relies on culture-based assays, which are labor-intensive and time-consuming. To address this limitation, we validated a PMA-qPCR assay as a rapid and reliable alternative for estimating viable Bradyrhizobium counts. The assay demonstrated strong performance, achieving approximately 95% efficiency, a standard deviation of 0.3 log CFU/ml, and an intra-assay reproducibility with a coefficient of variation less than 10%. Key experiments optimized PMA concentration to ensure selective exclusion of non-viable cells without compromising viable cell quantification. Discrimination threshold assessment confirmed the assays ability to differentiate quarter-strength dilutions. Final validation against plate counting revealed an 82% correlation, significantly reducing processing time from 120 hours to just 5 hours. This is the first study to apply PMA-qPCR specifically for Bradyrhizobium diazoefficiens quantification in inoculants. The results highlight its potential as a high-throughput tool for microbial viability assessment, offering improvements in efficiency and precision for batch-to-batch quality control in industrial applications.

Morphological switching in response to environmental stimuli is a well-known phenomenon in fungi, leading to diverse morphotypes. Microscopic observation remains a widely used approach to study these phenotypes. However, variation in sample preparation and operators skill can limit the scale of sample processing or introduce bias. Although several image-based cell detection tools have been developed, most are tailored to specific applications or limited to a particular taxon. To address the need for a tool applicable to the polymorphic, yeast-like fungus Aureobasidium pullulans, and with potential applicability to other taxa, we developed TU_MyCo-Vision, an Ultralytics YOLO (You Only Look Once) based object detection tool for identifying 13 fungal morphotypes in bright-field microscopic images. The tool integrates a YOLOv11m-based object detector trained on a custom dataset of 1,504 annotated images and a standalone graphical user interface that enables downstream data analysis and visualization of results. The best-performing model (Zulu_s3) achieved a mean precision of 73.4%, a recall of 66.5%, a mean average precision at 50% IoU (mAP@50) of 73.5%, and a mean average precision at varying IoU thresholds between 50 to 90% IoU (mAP@50-95) of 54.5% across all 13 classes. The single-group analysis pipeline was validated on a 90-image test set, generating six quantitative summaries, including absolute counts, relative and mean relative abundance plots, stacked bar plots, and clustered heatmaps. Multi-group evaluation on previously unseen datasets comprising Candida albicans, Komagataella phaffii, and Aspergillus niger spores demonstrated the tools potential applicability to other genera. TU_MyCo-Vision is distributed as a fully packaged, cross-platform executable, eliminating the need for environment setup or manual installation of dependencies. Built entirely on open-source frameworks, it provides a foundational and potentially extensible solution for automated fungal morphology detection and analysis. Author SummaryWe developed TU_MyCo-Vision to address challenges in fungal microscopic imaging. Fungi, such as Aureobasidium pullulans, display a remarkable ability to switch cell shapes (up to thirteen in this species alone) depending on their environment. While microscopy remains a popular method for observing these changes, manual analysis is limited by individual expertise and the number of images that can be processed, often making results subjective and difficult to scale. To overcome these challenges, we built an Ultralytics YOLOv11-based cell detector that can automatically detect and categorize thirteen fungal cell shapes from brightfield microscopic images. We designed TU_MyCo-Vision to be accessible, with a simple graphical user interface, integrated data analysis suite, and distribution as a standalone application for both Windows and macOS, so it can be used even by those with limited computational skills. Our tool demonstrated strong performance, achieving over 73% precision. Importantly, it also worked well on images from other fungal species, showing potential to be further developed as a general fungal cell morphology tool. We hope TU_MyCo-Vision will contribute to making standardized, high-throughput phenotyping of fungi accessible to a broader community.

Total and viable microbial cell counts are increasingly important for applications including live biotherapeutic products, food safety, and probiotics. In microbiology, cells are quantified using methods such as colony forming unit (CFU), flow cytometry, and polymerase chain reaction (PCR), but different methods measure different aspects of the cells (measurands), and results may not be directly comparable across methods. In the absence of a ground-truth reference material for cell count, one cannot quantify the accuracy of any cell counting method, which limits method performance assessments and comparisons. Herein, a modified analysis of cell counting methods based on the ISO 20391-2:2019 standard was developed and demonstrated for microbial cell samples diluted over a log-scale range of concentrations. Escherichia coli samples ranging in concentration from approximately 5 x 105 cells/mL to 2 x 107 cells/mL were quantified using CFU, Coulter principle, fluorescence flow cytometry, and impedance flow cytometry. Quality metrics modified from the ISO standard were calculated for each method and shown to be repeatable across replicate experiments. The quality metrics illustrate large differences in proportionality and variability across methods, with total cell counts in good agreement and viable cell count having more variability. As the ISO standard is meant to guide fit-for-purpose method selection, interpretation of the results and quality metrics can drive method choice and optimization. The framework introduced here will help researchers select fit-for-purpose counting methods for quantification of microbial total and viable cells across a range of applications.

Tuberculosis poses a global health challenge, demanding improved diagnostics and therapies. Distinguishing between Mycobacterium tuberculosis (M. tb) and Mycobacterium bovis (M. bovis) infections holds critical "One Health" significance due to zoonotic nature of these infections and inherent resistance of M. bovis to pyrazinamide, a key part of Directly Observed Treatment, Short-course (DOTS) regimen. Furthermore, most of the currently used molecular detection methods fail to distinguish between the two species. To address this, our study presents an innovative molecular-biosensing strategy. We developed a label-free citrate-stabilized silver nanoparticle aggregation assay, offering sensitive, cost-effective, and swift detection. For molecular detection, genomic markers unique to M. tb and M. bovis were targeted using species-specific primers. In addition to amplifying species-specific regions, these primers also aid detection of characteristic deletions in each of the mycobacterial species. Post polymerase chain reaction (PCR), we compared two highly sensitive visual detection methods with respect to the traditional agarose gel electrophoresis. The paramagnetic bead-based bridging flocculation assay, successfully discriminates M. tb from M. bovis with a sensitivity of ~40 bacilli. The second strategy exploits citrate-stabilized silver nanoparticle which aggregates in the absence of amplified dsDNA on addition of sodium chloride (NaCl). This technique enables precise, sensitive and differential detection of as few as ~4 bacilli. Our study hence advances tuberculosis detection, overcoming challenges of M. tb and M. bovis differentiation offering a quicker alternative to time-consuming methods.