Real-time bacterial microcolony counting using on-chip microscopy

Observing microbial colonies is the standard method for determining the microbe titer and investigating the behaviors of microbes. Here, we report an automated, real-time bacterial microcolony-counting system implemented on a wide field-of-view (FOV), on-chip microscopy platform, termed ePetri. Using sub-pixel sweeping microscopy (SPSM) with a super-resolution algorithm, this system offers the ability to dynamically track individual bacterial microcolonies over a wide FOV of 5.7 mm × 4.3 mm without requiring a moving stage or lens. As a demonstration, we obtained high-resolution time-series images of S. epidermidis at 20-min intervals. We implemented an image-processing algorithm to analyze the spatiotemporal distribution of microcolonies, the development of which could be observed from a single bacterial cell. Test bacterial colonies with a minimum diameter of 20 μm could be enumerated within 6 h. We showed that our approach not only provides results that are comparable to conventional colony-counting assays but also can be used to monitor the dynamics of colony formation and growth. This microcolony-counting system using on-chip microscopy represents a new platform that substantially reduces the detection time for bacterial colony counting. It uses chip-scale image acquisition and is a simple and compact solution for the automation of colony-counting assays and microbe behavior analysis with applications in antibacterial drug discovery

Equine or porcine synovial fluid as a novel ex vivo model for the study of bacterial free-floating biofilms that form in human joint infections

Bacterial invasion of synovial joints, as in infectious or septic arthritis, can be difficult to treat in both veterinary and human clinical practice. Biofilms, in the form of free-floating clumps or aggregates, are involved with the pathogenesis of infectious arthritis and periprosthetic joint infection (PJI). Infection of a joint containing an orthopedic implant can additionally complicate these infections due to the presence of adherent biofilms. Because of these biofilm phenotypes, bacteria within these infected joints show increased antimicrobial tolerance even at high antibiotic concentrations. To date, animal models of PJI or infectious arthritis have been limited to small animals such as rodents or rabbits. Small animal models, however, yield limited quantities of synovial fluid making them impractical for in vitro research. Herein, we describe the use of ex vivo equine and porcine models for the study of synovial fluid induced biofilm aggregate formation and antimicrobial tolerance. We observed Staphylococcus aureus and other bacterial pathogens adapt the same biofilm aggregate phenotype with significant antimicrobial tolerance in both equine and porcine synovial fluid, analogous to human synovial fluid. We also demonstrate that enzymatic dispersal of synovial fluid aggregates restores the activity of antimicrobials. Future studies investigating the interaction of bacterial cell surface proteins with host synovial fluid proteins can be readily carried out in equine or porcine ex vivo models to identify novel drug targets for treatment of prevention of these difficult to treat infectious diseases

Method of detecting and counting bacteria in body fluids

A novel method is reported for determining bacterial levels in urine samples, which method depends on the quantitative determination of bacterial adenosine triphosphate (ATP) in the presence of non-bacterial ATP. After the removal of non-bacterial ATP, the bacterial ATP is released by cell rupture and is measured by an enzymatic bioluminescent assay using an enzyme obtained from the firefly

Automated Counting of Bacterial Colony Forming Units on Agar Plates

Manual counting of bacterial colony forming units (CFUs) on agar plates is laborious and error-prone. We therefore implemented a colony counting system with a novel segmentation algorithm to discriminate bacterial colonies from blood and other agar plates

Reconstructing the Forest of Lineage Trees of Diverse Bacterial Communities Using Bio-inspired Image Analysis

Cell segmentation and tracking allow us to extract a plethora of cell attributes from bacterial time-lapse cell movies, thus promoting computational modeling and simulation of biological processes down to the single-cell level. However, to analyze successfully complex cell movies, imaging multiple interacting bacterial clones as they grow and merge to generate overcrowded bacterial communities with thousands of cells in the field of view, segmentation results should be near perfect to warrant good tracking results. We introduce here a fully automated closed-loop bio-inspired computational strategy that exploits prior knowledge about the expected structure of a colony's lineage tree to locate and correct segmentation errors in analyzed movie frames. We show that this correction strategy is effective, resulting in improved cell tracking and consequently trustworthy deep colony lineage trees. Our image analysis approach has the unique capability to keep tracking cells even after clonal subpopulations merge in the movie. This enables the reconstruction of the complete Forest of Lineage Trees (FLT) representation of evolving multi-clonal bacterial communities. Moreover, the percentage of valid cell trajectories extracted from the image analysis almost doubles after segmentation correction. This plethora of trustworthy data extracted from a complex cell movie analysis enables single-cell analytics as a tool for addressing compelling questions for human health, such as understanding the role of single-cell stochasticity in antibiotics resistance without losing site of the inter-cellular interactions and microenvironment effects that may shape it