How microorganisms respond to and interact with their environment can vary significantly from individual to individual, which can have important microbiological and ecological implications. However, most microscopy techniques can only observe motile microorganisms for short times because of their limited fields of view. Using Lagrangian tracking, a single microorganism can be followed in 3D, potentially indefinitely, allowing to decipher individual phenotypical traits. Current Lagrangian tracking methods use the fluorescence signal emitted by the microorganism as feedback to keep it in focus. However, over long times, epifluorescent imaging can induce photobleaching and photodamage, and importantly, not all microorganisms can easily be made fluorescent. Additionally, traditional algorithms used in feedback loops to determine microorganism position are prone to errors, especially in optically complex media. Here, we present a faster, more reliable, and versatile Lagrangian tracking method that uses deep learning to determine the 3D position of the microorganism. This new method demonstrates enhanced accuracy and speed in tracking fluorescent bacteria with fluorescence microscopy als
Metagenomics has lowered the barrier to microbial discovery--enabling the identification of novel microbes without isolation--but cultures remain imperative for the deep study of microbes. Cultivation and isolation of non-model microbes remains a major challenge, despite advances in high-throughput culturomic methods. The quantity of simultaneous experimental variables is constrained by time and resources, but the list can be reduced using computational biology. Given an annotated genome, metabolic modelling can be used to predict source nutrients required for the growth of a microbe, which acts as an initial screen to inform culture and isolation experiments. This chapter provides an overview of metabolic networks and modelling and how they can be used to predict the nutrient requirements of a microorganism, followed by a sample protocol using a toy metabolic network, which is then expanded to a genome-scale metabolic network application. These methods can be applied to any metabolic network of interest--which in turn can be created from any genome of interest--and are a starting point for experimental validation of source nutrients required for microorganisms that remain uncultiv
Active movement is essential for the survival of microorganisms like bacteria, algae and unicellular parasites. In three dimensions, both swimming and gliding microorganisms often exhibit helical trajectories. One such case are malaria parasites gliding through 3D hydrogels, for which we find that the internal correlation time for the stochastic process generating propulsion is similar to the time taken for one helical turn. Motivated by this experimental finding, here we theoretically analyze the case of finite internal correlation time for microorganisms with helical trajectories as chiral active particles with an Ornstein-Uhlenbeck process for torque. We present an analytical solution which is in very good agreement with computer simulations. We then show that for this type of internal noise, chirality and rotation increase the persistence of motion and results in helical trajectories that have a larger long-time mean squared displacement than straight trajectories at the same propulsion speed. Finally we provide experimental evidence for this prediction for the case of the malaria parasites.
Microorganisms live in inherently dynamic environments where fluctuations in biotic and abiotic factors shape their behaviour, physiology, and fitness. The concept of ecological memory: the lasting imprint of prior environmental cues, suggests that past exposures can exert prolonged effects on microbial growth, resilience, and phenotypic expressions. For motile microbes in aquatic ecosystems, environmental variability is mediated by fluid motion, which may engender a form of hydrodynamic memory, whereby prior exposure to specific spatio-temporal cues influence future growth and migratory behaviour. Yet, the emergence of such flow-induced memory, or its long-term consequences for trait evolution and population dynamics, remain unexplored. We integrate millifluidic flow control, high-resolution cell tracking, and tunable hydrodynamic cues to quantify growth and migration of Heterosigma akashiwo, a model microbe, across growth stages. Using two complementary perturbation scenarios: standard (flow after static conditions) and reverse (flow before static growth), we test how the temporal structure of forcing shapes multigenerational responses. This combinatorial design disentangles expo
Microorganism enumeration is an essential task in many applications, such as assessing contamination levels or ensuring health standards when evaluating surface cleanliness. However, it's traditionally performed by human-supervised methods that often require manual counting, making it tedious and time-consuming. Previous research suggests automating this task using computer vision and machine learning methods, primarily through instance segmentation or density estimation techniques. This study conducts a comparative analysis of vision transformers (ViTs) for weakly-supervised counting in microorganism enumeration, contrasting them with traditional architectures such as ResNet and investigating ViT-based models such as TransCrowd. We trained different versions of ViTs as the architectural backbone for feature extraction using four microbiology datasets to determine potential new approaches for total microorganism enumeration in images. Results indicate that while ResNets perform better overall, ViTs performance demonstrates competent results across all datasets, opening up promising lines of research in microorganism enumeration. This comparative study contributes to the field of mi
When Carl Sagan and Ed Salpeter envisioned potential Sinkers, Floaters, and Hunters living in Jupiter's clouds in 1976 (C. Sagan & E. E. Salpeter 1976), the nature of life in Earth's atmosphere remained widely unknown. Decades later, research has revealed a remarkable variety of microorganisms in our atmosphere. However, the spectral features of airborne microbes as biomarkers for detecting atmospheric life remained a mystery. Here, we present the first reflectance spectra of biopigments of atmospheric microorganisms based on laboratory cultivars of seven microbial strains isolated from Earth's atmosphere. We show their distinct UV-resistant biosignatures and their impacts on models of diverse planetary scenarios, using Habitable Worlds Observatory (HWO) parameters. The reflectance of these biopigments from aerial bacteria creates the means to detect them on other Earth-like planets. It provides a paradigm shift that moves the search for life beyond the surface of a planet to ecosystems in atmospheres and clouds.
Dispersal is essential to the plethora of motile microorganisms living in porous environments, yet how it relates to movement patterns and pore space structure remains largely unknown. Here we investigate numerically the long-time dispersal of a run-and-tumble microorganism that remains trapped at solid surfaces and escapes from them by tumbling. We find that dispersal and mean run time are connected by a universal relation, that applies for a variety of porous microstructures and swimming strategies. We explain how this generic dependence originates in the invariance of the mean free path with respect to the movement pattern, and we discuss the optimal strategy that maximizes dispersal. Finally, we extend our approach to microorganisms moving along the surface. Our results provide a general framework to quantify dispersal that works across the vast diversity of movement patterns and porous media.
A promising approach to transport cargo at the microscale lies within the use of self-propelled microorganisms, whose motion entrains that of passive particles. However, most applications remain limited to just a few passive particles of similar size as the microorganisms, since the transport mechanism relies on the interaction between individual swimmers and single particles. Here, we demonstrate how to control the collective transport of hundreds of large passive particles with phototactic microalga. Using directional light stimuli in suspensions of Chlamydomonas reinhardtii, we trigger bioconvection rolls capable of macroscale transport. Passive particles an order of magnitude larger than the microalgae are either attracted or repelled by the rolls depending on their density. Using experiments and simulation, we rationalize these bioconvective flows and describe how to harness them for cargo transport, with future applications in targeted drug delivery and decontamination.
In this paper we study the dynamics of stochastic microorganism flocculation models. Given the strong influence of environmental and seasonal fluctuations that are present in these models, we propose a stochastic model that includes multiple layers of stochasticity, from small Brownian fluctuations, to possibly large changes due to environmental `shifts'. We are able to give a full classification of the asymptotic behavior of these models. New techniques had to be developed to prove the persistence and extinction of the process as the system is not in Kolmogorov form and, as a result, the analysis is significantly more involved.
Microorganisms such as bacteria and fungi play essential roles in many application fields, like biotechnique, medical technique and industrial domain. Microorganism counting techniques are crucial in microorganism analysis, helping biologists and related researchers quantitatively analyze the microorganisms and calculate their characteristics, such as biomass concentration and biological activity. However, traditional microorganism manual counting methods, such as plate counting method, hemocytometry and turbidimetry, are time-consuming, subjective and need complex operations, which are difficult to be applied in large-scale applications. In order to improve this situation, image analysis is applied for microorganism counting since the 1980s, which consists of digital image processing, image segmentation, image classification and suchlike. Image analysis-based microorganism counting methods are efficient comparing with traditional plate counting methods. In this article, we have studied the development of microorganism counting methods using digital image analysis. Firstly, the microorganisms are grouped as bacteria and other microorganisms. Then, the related articles are summarize
Microorganisms can create engineered materials with exquisite structures and living functionalities. Although synthetic biology tools to genetically manipulate microorganisms continue to expand, the bottom-up rational design of engineered living materials still relies on prior knowledge of genotype-phenotype links for the function of interest. Here, we utilize a high-throughput directed evolution platform to enhance the fitness of whole microorganisms under selection pressure and identify novel genetic pathways to program the functionalities of engineered living materials. Using Komagataeibacter sucrofermentans as a model cellulose-producing microorganism, we show that our droplet-based microfluidic platform enables the directed evolution of these bacteria towards a small number of cellulose overproducers from an initial pool of 40'000 random mutants. Sequencing of the evolved strains reveals an unexpected link between the cellulose-forming ability of the bacteria and a gene encoding a protease complex responsible for protein turnover in the cell. The ability to enhance the fitness of microorganisms towards specific phenotypes and to discover new genotype-phenotype links makes this
Scaling laws illuminate Nature's fundamental biological principles and guide bioinspired materials and structural designs. In simple cases they are based on the fundamental principle that all laws of nature remain unchanged (i.e., invariant) under a change of units. A more general framework is a change of variables for the governing laws that takes all equations, boundary, and interaction conditions into themselves. We consider an accepted macroscale system of partial differential equations including coupled fluid dynamics, nonlinear elasticity, and rigid body mechanics for a complex organism. We show that there is a set of scaling laws where length, time, density, elastic modulus, viscosity, and gravitational constant undergo nontrivial scaling (Table 1). We compare these results to extensive data sets mined from the literature on beating frequency of flying, swimming, and running animals, speed of bacteria, insects, fish, mammals and reptiles, leg stiffness of mammals, and modulus of elasticity of plants. The uniform agreement of the scaling laws with the dynamics of fauna, flora, and microorganisms supports the dominating role of coupled nonlinear elasticity and fluid dynamics i
The motion of microorganisms in their natural habitat is strongly influenced by their propulsion mechanisms, geometrical constraints, and random fluctuations. Here, we study numerically the first-passage-time (FPT) statistics of microswimmers, modeled as force-dipoles, to reach a no-slip wall. Our results demonstrate that hindered diffusion near the wall can increase the median FPT by orders of magnitude compared to "dry" agents, while the intricate interplay of active motion and hydrodynamic attraction speeds up the arrival at large Péclet numbers (measuring the importance of self-propulsion relative to diffusion). Strikingly, it leads to a non-monotonic behavior as a function of the dipole strength, where pushers reach the wall significantly faster than pullers. The latter become slower at an intermediate dipole strength and are more sensitive to their initial orientation, displaying a highly anisotropic behavior.
To provide insight into the basic properties of emerging structures when bacteria or other microorganisms conquer surfaces, it is crucial to analyze their growth behavior during the formation of thin films. In this regard, many theoretical studies focus on the behavior of elongating straight objects. They repel each other through volume exclusion and divide into two halves when reaching a certain threshold length. However, in reality, hardly any object of a certain elongation is perfectly straight. Therefore, we here study the consequences of the curvature of individuals on the growth of colonies and thin active films. This individual curvature, so far hardly considered, turns out to qualitatively affect the overall growth behavior of the colony. Particularly, strings of stacked curved cells emerge that show branched structures, while the size of orientationally ordered domains in the colony is significantly decreased. Furthermore, we identify emergent spatio-orientational coupling that is not observed in colonies of straight cells. Our results are important for a fundamental understanding of the interaction and spreading of microorganisms on surfaces, with implications for medical
With the acceleration of urbanization and living standards, microorganisms play increasingly important roles in industrial production, bio-technique, and food safety testing. Microorganism biovolume measurements are one of the essential parts of microbial analysis. However, traditional manual measurement methods are time-consuming and challenging to measure the characteristics precisely. With the development of digital image processing techniques, the characteristics of the microbial population can be detected and quantified. The changing trend can be adjusted in time and provided a basis for the improvement. The applications of the microorganism biovolume measurement method have developed since the 1980s. More than 62 articles are reviewed in this study, and the articles are grouped by digital image segmentation methods with periods. This study has high research significance and application value, which can be referred to microbial researchers to have a comprehensive understanding of microorganism biovolume measurements using digital image analysis methods and potential applications.
Microorganisms play a vital role in human life. Therefore, microorganism detection is of great significance to human beings. However, the traditional manual microscopic detection methods have the disadvantages of long detection cycle, low detection accuracy in large orders, and great difficulty in detecting uncommon microorganisms. Therefore, it is meaningful to apply computer image analysis technology to the field of microorganism detection. Computer image analysis can realize high-precision and high-efficiency detection of microorganisms. In this review, first,we analyse the existing microorganism detection methods in chronological order, from traditional image processing and traditional machine learning to deep learning methods. Then, we analyze and summarize these existing methods and introduce some potential methods, including visual transformers. In the end, the future development direction and challenges of microorganism detection are discussed. In general, we have summarized 142 related technical papers from 1985 to the present. This review will help researchers have a more comprehensive understanding of the development process, research status, and future trends in the fie
Microorganisms are widely distributed in the human daily living environment. They play an essential role in environmental pollution control, disease prevention and treatment, and food and drug production. The analysis of microorganisms is essential for making full use of different microorganisms. The conventional analysis methods are laborious and time-consuming. Therefore, the automatic image analysis based on artificial neural networks is introduced to optimize it. However, the automatic microorganism image analysis faces many challenges, such as the requirement of a robust algorithm caused by various application occasions, insignificant features and easy under-segmentation caused by the image characteristic, and various analysis tasks. Therefore, we conduct this review to comprehensively discuss the characteristics of microorganism image analysis based on artificial neural networks. In this review, the background and motivation are introduced first. Then, the development of artificial neural networks and representative networks are presented. After that, the papers related to microorganism image analysis based on classical and deep neural networks are reviewed from the perspecti
In this investigation, we explore the thermal effects on a suspension containing isotropic scattering phototactic microorganisms. The setup involves illuminating the suspension with collimated irradiation from the top, coupled with heating or cooling applied from the bottom. The governing equations encompass the Navier-Stokes equations with the Boussinesq approximation, the diffusion equation for motile microorganisms, and the energy equation for temperature. Using linear perturbation theory, we conduct a comprehensive analysis of the suspension's linear stability. The findings of this investigation reveal that the suspension experiences increased stability as a consequence of scattering.
Many medical or pharmaceutical processes have strict guidelines regarding continuous hygiene monitoring. This often involves the labor-intensive task of manually counting microorganisms in Petri dishes by trained personnel. Automation attempts often struggle due to major challenges: significant scaling differences, low separation, low contrast, etc. To address these challenges, we introduce AttnPAFPN, a high-resolution detection pipeline that leverages a novel transformer variation, the efficient-global self-attention mechanism. Our streamlined approach can be easily integrated in almost any multi-scale object detection pipeline. In a comprehensive evaluation on the publicly available AGAR dataset, we demonstrate the superior accuracy of our network over the current state-of-the-art. In order to demonstrate the task-independent performance of our approach, we perform further experiments on COCO and LIVECell datasets.
This article investigates the linear stability of thermal-bioconvection within a suspension containing phototactic microorganisms heated from below. In suspension, the upper surface is taken as stress-free, while the lower surface is taken as rigid. The resulting eigenvalue problem, including the bioconvection Rayleigh and thermal Rayleigh numbers, is resolved numerically. Changes in the critical total intensity and Lewis number do not impact the critical threshold of the thermal Rayleigh number; however, they notably influence the critical bioconvection Rayleigh number. The critical total intensity and Lewis number destabilize the suspension. It is observed that heating from below enhances the instability of the layer. At higher temperatures, Rayleigh-B$\acute{e}$nard convection dominates bioconvection, resulting in a single convection cell.