High-throughput 3D tracking of bacteria on a standard phase contrast microscope

Taute, Katja M.; Gude, Sebastian; Tans, Sander J.; Shimizu, Thomas S.

doi:10.1038/ncomms9776

Cited by 175 publications

(199 citation statements)

References 43 publications

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“…All crossing events were inspected manually to prevent this. To identify runs and tumbles, we utilized a method based on reference (Taute et al, 2015) which was modified from the approach used by Berg and Brown (1972). Briefly, for each cell the segmentation routine results in a matrix of spatial locations

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Section: Materials and Methodsmentioning

confidence: 99%

Environment determines evolutionary trajectory in a constrained phenotypic space

Fraebel

Mickalide

Schnitkey

et al. 2017

eLife

113

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Constraints on phenotypic variation limit the capacity of organisms to adapt to the multiple selection pressures encountered in natural environments. To better understand evolutionary dynamics in this context, we select Escherichia coli for faster migration through a porous environment, a process which depends on both motility and growth. We find that a tradeoff between swimming speed and growth rate constrains the evolution of faster migration. Evolving faster migration in rich medium results in slow growth and fast swimming, while evolution in minimal medium results in fast growth and slow swimming. In each condition parallel genomic evolution drives adaptation through different mutations. We show that the trade-off is mediated by antagonistic pleiotropy through mutations that affect negative regulation. A model of the evolutionary process shows that the genetic capacity of an organism to vary traits can qualitatively depend on its environment, which in turn alters its evolutionary trajectory.

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.…”

Section: Materials and Methodsmentioning

confidence: 99%

Environment determines evolutionary trajectory in a constrained phenotypic space

Fraebel

Mickalide

Schnitkey

et al. 2017

eLife

113

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“…As a consequence of the steady improvement of the spatiotemporal resolution of microscopic techniques, advances in automated particle tracking and motion analysis Arhel et al, 2006;Jaqaman et al, 2008;Chenouard et al, 2014;Smith et al, 2015;Genovesio et al, 2006), and the availability of software tools Schindelin et al, 2012;de Chaumont et al, 2012;Eliceiri et al, 2012;Perry et al, 2012;Swedlow and Eliceiri, 2009;Tinevez et al, 2016;Jaqaman et al, 2008;Kalaidzidis, 2009;Incardona and Sbalzarini, 2014), SPT holds the promise of becoming amenable to multidimensional high-throughput data collection schemas (Damm and Pelkmans, 2006;Snijder et al, 2012;Rämö et al, 2014;Taute et al, 2015).…”

Section: Description Of the Problemmentioning

confidence: 99%

OMEGA: a software tool for the management, analysis, and dissemination of intracellular trafficking data that incorporates motion type classification and quality control

Rigano

Galli

Clark

et al. 2018

Preprint

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MOTIVATIONParticle tracking coupled with time-lapse microscopy is critical for understanding the dynamics of intracellular processes of clinical importance. Spurred on by advances in the spatiotemporal resolution of microscopy and automated computational methods, this field is increasingly amenable to multi-dimensional high-throughput data collection schemes (Snijder et al., 2012). Typically, complex particle tracking datasets generated by individual laboratories are produced with incompatible methodologies that preclude comparison to each other. There is therefore an unmet need for data management systems that facilitate data standardization, meta-analysis, and structured data dissemination. The integration of analysis, visualization, and quality control capabilities into such systems would eliminate the need for manual transfer of data to diverse downstream analysis tools. At the same time, it would lay the foundation for shared trajectory data, particle tracking, and motion analysis standards.RESULTSHere, we present Open Microscopy Environment inteGrated Analysis (OMEGA), a cross-platform data management, analysis, and visualization system, for particle tracking data, with particular emphasis on results from viral and vesicular trafficking experiments. OMEGA provides intuitive graphical interfaces to implement integrated particle tracking and motion analysis workflows while providing easy to use facilities to automatically keep track of error propagation, harvest data provenance and ensure the persistence of analysis results and metadata. Specifically, OMEGA: 1) imports image data and metadata from data management tools such as the Open Microscopy Environment Remote Objects (OMERO; Allan et al., 2012); 2) tracks intracellular particles movement; 3) facilitates parameter optimization and trajectory results inspection and validation; 4) performs downstream trajectory analysis and motion type classification; 5) estimates the uncertainty propagating through the motion analysis pipeline; and, 6) facilitates storage and dissemination of analysis results, and analysis definition metadata, on the basis of our newly proposed FAIRsharing.org complainant Minimum Information About Particle Tracking Experiments (MIAPTE; Rigano and Strambio-De-Castillia, 2016; 2017) guidelines in combination with the OME-XML data model (Goldberg et al., 2005). In so doing, OMEGA maintains a persistent link between raw image data, intermediate analysis steps, the overall analysis output, and all necessary metadata to repeat the analysis process and reproduce its results.Availability and implementationOMEGA is a cross-platform, open-source software developed in Java. Source code and cross-platform binaries are freely available on GitHub at https://github.com/OmegaProject/Omega (doi: 10.5281/zenodo.2535523), under the GNU General Public License v.3.Contactcaterina.strambio@umassmed.edu and alex.rigano@umassmed.eduSupplementary informationSupplementary Material is available at BioRxiv.org

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“…Moreover, recent work has shown that it is possible to use comparisons of observed diffraction patterns with reference libraries to perform high-throughput tracking of swimming bacteria in 3D without specialized instrumentation. 34 Multiimaging modalities have proven valuable not only in the medical field, e.g. , PET-CT, but also in cellular microscopy.…”

mentioning

confidence: 99%

High-Speed “4D” Computational Microscopy of Bacterial Surface Motility

Anda

Lee

et al. 2017

ACS Nano

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Bacteria exhibit surface motility modes that play pivotal roles in early-stage biofilm community development, such as type IV pili-driven “twitching” motility and flagellum-driven “spinning” and “swarming” motility. Appendage-driven motility is controlled by molecular motors, and analysis of surface motility behavior is complicated by its inherently 3D nature, the speed of which is too fast for confocal microscopy to capture. Here, we combine electromagnetic field computation and statistical image analysis to generate 3D movies close to a surface at 5 ms time resolution using conventional inverted microscopes. We treat each bacterial cell as a spherocylindrical lens and use finite element modeling to solve Maxwell’s equations and compute the diffracted light intensities associated with different angular orientations of the bacterium relative to the surface. By performing cross-correlation calculations between measured 2D microscopy images and a library of computed light intensities, we demonstrate that near-surface 3D movies of Pseudomonas aeruginosa translational and rotational motion are possible at high temporal resolution. Comparison between computational reconstructions and detailed hydrodynamic calculations reveals that P. aeruginosa act like low Reynolds number spinning tops with unstable orbits, driven by a flagellum motor with a torque output of ~2 pN μm. Interestingly, our analysis reveals that P. aeruginosa can undergo complex flagellum-driven dynamical behavior, including precession, nutation, and an unexpected taxonomy of surface motility mechanisms, including upright-spinning bacteria that diffuse laterally across the surface, and horizontal bacteria that follow helicoidal trajectories and exhibit superdiffusive movements parallel to the surface.

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High-throughput 3D tracking of bacteria on a standard phase contrast microscope

Cited by 175 publications

References 43 publications

Environment determines evolutionary trajectory in a constrained phenotypic space

Environment determines evolutionary trajectory in a constrained phenotypic space

OMEGA: a software tool for the management, analysis, and dissemination of intracellular trafficking data that incorporates motion type classification and quality control

High-Speed “4D” Computational Microscopy of Bacterial Surface Motility

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