Visualizing Bacterial Colony Morphologies Using Time-Lapse Imaging Chamber MOCHA

Modeling Microbial Community Networks: Methods and Tools for Studying Microbial Interactions

Microbial interactions function as a fundamental unit in complex ecosystems. By characterizing the type of interaction (positive, negative, neutral) occurring in these dynamic systems, one can begin to unravel the role played by the microbial species. Towards this, various methods have been developed to decipher the function of the microbial communities. The current review focuses on the various qualitative and quantitative methods that currently exist to study microbial interactions. Qualitative methods such as co-culturing experiments are visualized using microscopy-based techniques and are combined with data obtained from multi-omics technologies (metagenomics, metabolomics, metatranscriptomics). Quantitative methods include the construction of networks and network inference, computational models, and development of synthetic microbial consortia. These methods provide a valuable clue on various roles played by interacting partners, as well as possible solutions to overcome pathogenic microbes that can cause life-threatening infections in susceptible hosts. Studying the microbial interactions will further our understanding of complex less-studied ecosystems and enable design of effective frameworks for treatment of infectious diseases.

Rapid Bacterial Detection and Gram-Identification Using Bacterially Activated, Macrophage-Membrane-Coated Nanowired-Si Surfaces in a Microfluidic Device

Bacterially induced sepsis requires rapid bacterial detection and identification. Hours count for critically ill septic patients, while current culture-based detection requires at least 10 h up to several days. Here, we apply a microfluidic device equipped with a bacterially activated, macrophage-membrane-coating on nanowired-Si adsorbent surfaces for rapid, bacterial detection and Gram-identification in bacterially contaminated blood. Perfusion of suspensions of Gram-negative or Gram-positive bacteria through a microfluidic device equipped with membrane-coated adsorbent surfaces detected low (<10 CFU/mL) bacterial levels. Subsequent, in situ fluorescence-staining yielded Gram-identification for guiding antibiotic selection. In mixed Escherichia coli and Staphylococcus aureus suspensions, Gram-negative and Gram-positive bacteria were detected in the same ratios as those fixed in suspension. Results were validated with a 100% correct score by blinded evaluation (two observers) of 15 human blood samples, spiked with widely different bacterial strains or combinations of strains, demonstrating the potential of the platform for rapid (1.5 h in total) diagnosis of bacterial sepsis.

Bridging the gap with bacterial art

Living art made with bacteria is gaining global attention, spreading from laboratories into the public domain: from school STEAM (Science, Technology, Engineering, the Arts, and Mathematics) events to art galleries, museums, community labs, and ultimately to the studios of microbial artists. Bacterial art is a synthesis of science and art that can lead to developments in both fields. Through the 'universal language of art', many social and preconceived ideas-including abstract scientific concepts-can be challenged and brought to the public attention in a unique way. By using bacteria to create publicly accessible art pieces, the barriers between humans and microbes can be lessened, and the artificial separation of the fields of science and art may be brought one step closer. Here, we document the history, impact, and current moment in the field of microbiologically inspired art for the benefit of educators, students, and the interested public. We provide a comprehensive historical background and examples of ancient bacterial art from cave paintings to uses in modern synthetic biology, a simple protocol for conducting bacterial art in a safe and responsible manner, a discussion of the artificial separation of science and art, and the future implications of art made from living microbes.

A novel time-lapse imaging method for studying developing bacterial biofilms

In nature, bacteria prevailingly reside in the form of biofilms. These elaborately organized surface-bound assemblages of bacterial cells show numerous features of multicellular organization. We recently showed that biofilm growth is a true developmental process, which resembles developmental processes in multicellular eukaryotes. To study the biofilm growth in a fashion of eukaryotic ontogeny, it is essential to define dynamics and critical transitional phases of this process. The first step in this endeavor is to record the gross morphological changes of biofilm ontogeny under standardized conditions. This visual information is instrumental in guiding the sampling strategy for the later omics analyses of biofilm ontogeny. However, none of the currently available visualizations methods is specifically tailored for recording gross morphology across the whole biofilm development. To address this void, here we present an affordable Arduino-based approach for time-lapse visualization of complete biofilm ontogeny using bright field stereomicroscopy with episcopic illumination. The major challenge in recording biofilm development on the air-solid interphase is water condensation, which compromises filming directly through the lid of a Petri dish. To overcome these trade-offs, we developed an Arduino microcontroller setup which synchronizes a robotic arm, responsible for opening and closing the Petri dish lid, with the activity of a stereomicroscope-mounted camera and lighting conditions. We placed this setup into a microbiological incubator that maintains temperature and humidity during the biofilm growth. As a proof-of-principle, we recorded biofilm development of five Bacillus subtilis strains that show different morphological and developmental dynamics.

A Noninvasive Method for Time-Lapse Imaging of Microbial Interactions and Colony Dynamics

Complex interactions between microbial populations can greatly affect the overall properties of a microbial community, sometimes leading to cooperation and mutually beneficial coexistence, or competition and the death or displacement of organisms or subpopulations. Interactions between different biofilm populations are highly relevant in diverse scientific areas, from antimicrobial resistance to microbial ecology. The utilization of modern microscopic techniques has provided a new and interesting insight into how bacteria interact at the cellular level to form and maintain microbial biofilms. However, our ability to follow complex intraspecies and interspecies interactions in vivo at the microscopic level has remained somewhat limited. Here, we detailed BacLive, a novel noninvasive method for tracking bacterial growth and biofilm dynamics using high-resolution fluorescence microscopy and an associated ImageJ processing macro (https://github.com/BacLive) for easier data handling and image analysis. Finally, we provided examples of how BacLive can be used in the analysis of complex bacterial communities.

IMPORTANCE Communication and interactions between single cells are continuously defining the structure and composition of microbial communities temporally and spatially. Methods routinely used to study these communities at the cellular level rely on sample manipulation which makes microscopic time-lapse experiments impossible. BacLive was conceived as a method for the noninvasive study of the formation and development of bacterial communities, such as biofilms, and the formation dynamics of specialized subpopulations in time-lapse experiments at a colony level. In addition, we developed a tool to simplify the processing and analysis of the data generated by this method.

Surveying a Swarm: Experimental Techniques to Establish and Examine Bacterial Collective Motion

The survival and successful spread of many bacterial species hinges on their mode of motility. One of the most distinct of these is swarming, a collective form of motility where a dense consortium of bacteria employ flagella to propel themselves across a solid surface. Surface environments pose unique challenges, derived from higher surface friction/tension and insufficient hydration. Bacteria have adapted by deploying an array of mechanisms to overcome these challenges. Beyond allowing bacteria to colonize new terrain in the absence of bulk liquid, swarming also bestows faster speeds and enhanced antibiotic resistance to the collective. These crucial attributes contribute to the dissemination, and in some cases pathogenicity, of an array of bacteria. This mini-review highlights; 1) aspects of swarming motility that differentiates it from other methods of bacterial locomotion. 2) Facilitatory mechanisms deployed by diverse bacteria to overcome different surface challenges. 3) The (often difficult) approaches required to cultivate genuine swarmers. 4) The methods available to observe and assess the various facets of this collective motion, as well as the features exhibited by the population as a whole.

An Inexpensive Imaging Platform to Record and Quantitate Bacterial Swarming

Bacterial swarming refers to a rapid spread, with coordinated motion, of flagellated bacteria on a semi-solid surface (Harshey, 2003). There has been extensive study on this particular mode of motility because of its interesting biological and physical relevance, e.g., enhanced antibiotic resistance (Kearns, 2010) and turbulent collective motion ( Steager et al., 2008 ). Commercial equipment for the live recording of swarm expansion can easily cost tens of thousands of dollars (Morales- Soto et al., 2015 ); yet, often the conditions are not accurately controlled, resulting in poor robustness and a lack of reproducibility. Here, we describe a reliable design and operations protocol to perform reproducible bacterial swarming assays using time-lapse photography. This protocol consists of three main steps: 1) building a "homemade," environment-controlled photographing incubator; 2) performing a bacterial swarming assay; and 3) calculating the swarming rate from serial photos taken over time. An efficient way of calculating the bacterial swarming rate is crucial in performing swarming phenotype-related studies, e.g., screening swarming-deficient isogenic mutant strains. The incubator is economical, easy to operate, and has a wide range of applications. In fact, this system can be applied to many slowly evolving processes, such as biofilm formation and fungal growth, which need to be monitored by camera under a controlled temperature and ambient humidity.

Inexpensive Apparatus for High-Quality Imaging of Microbial Growth on Agar Plates

The ability to capture images of results or processes is an important tool in the biologist’s tool kit. In microbiology, capturing high-quality images of microbial growth on agar plates is difficult due to the reflective surface of the plates and limitations in common photography techniques. Equipment is available to overcome these challenges, but acquisition costs are high. We have developed and tested an inexpensive and efficient apparatus for high-quality imaging of microbial colonies. The imaging box, as we have named the apparatus, is designed to eliminate glare and reduce reflections on the surface of the plate while providing uniform, diffuse light from all sides. The imaging box was used to capture hundreds of images in research and teaching lab settings.

Early detection and classification of live bacteria using time-lapse coherent imaging and deep learning

Early identification of pathogenic bacteria in food, water, and bodily fluids is very important and yet challenging, owing to sample complexities and large sample volumes that need to be rapidly screened. Existing screening methods based on plate counting or molecular analysis present various tradeoffs with regard to the detection time, accuracy/sensitivity, cost, and sample preparation complexity. Here, we present a computational live bacteria detection system that periodically captures coherent microscopy images of bacterial growth inside a 60-mm-diameter agar plate and analyses these time-lapsed holograms using deep neural networks for the rapid detection of bacterial growth and the classification of the corresponding species. The performance of our system was demonstrated by the rapid detection of Escherichia coli and total coliform bacteria (i.e., Klebsiella aerogenes and Klebsiella pneumoniae subsp. pneumoniae) in water samples, shortening the detection time by >12 h compared to the Environmental Protection Agency (EPA)-approved methods. Using the preincubation of samples in growth media, our system achieved a limit of detection (LOD) of ~1 colony forming unit (CFU)/L in ≤9 h of total test time. This platform is highly cost-effective (~$0.6/test) and has high-throughput with a scanning speed of 24 cm2/min over the entire plate surface, making it highly suitable for integration with the existing methods currently used for bacteria detection on agar plates. Powered by deep learning, this automated and cost-effective live bacteria detection platform can be transformative for a wide range of applications in microbiology by significantly reducing the detection time and automating the identification of colonies without labelling or the need for an expert.

Measurement and Modeling of Microbial Growth Using Timelapse Video

The development of timelapse videos for the investigation of growing microbial colonies has gained increasing interest due to its low cost and complexity implementation. In the present study, a simple experimental setup is proposed for periodic snapshot acquisition of a petri dish cultivating a fungus of the genus Candida SPP, thus creating a timelapse video. A computational algorithm, based on image processing techniques is proposed for estimating the microbial population and for extracting the experimental population curves, showing the time evolution of the population of microbes at any region of the dish. Likewise, a novel mathematical population evolution modeling approach is reported, which is based on the logistic function (LF). Parameter estimation of the aforementioned model is described and visually assessed, in comparison with the conventional and widely-used LF method. The effect of the image analysis parameterization is also highlighted. Our experiments take into account different area sizes, i.e., the number of pixels in the neighborhood, to generate population curves and calculate the model parameters. Our results reveal that, as the size of the area increases, the curve becomes smoother, the signal-to-noise-ratio increases and the estimation of model parameters becomes more accurate.