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WIREs Syst Biol Med
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Trends in high‐throughput and functional neuroimaging in Caenorhabditis elegans

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The nervous system of Caenorhabditis elegans is an important model system for understanding the development and function of larger, more complex nervous systems. It is prized for its ease of handling, rapid life cycle, and stereotyped, well‐cataloged development, with the development of all 302 neurons mapped all the way from zygote to adult. The combination of easy genetic manipulation and optical transparency of the worm allows for the direct imaging of its interior with fluorescent microscopy, without physically compromising the normal physiology of the animal itself. By expressing fluorescent markers, biologists study many developmental and cell biology questions in vivo; by expressing genetically encoded fluorescent calcium indicators within neurons, it is also possible to monitor their dynamic activity, answering questions about the structure and function of neural microcircuitry in the worm. However, to successfully image the worm it is necessary to overcome a number of experimental challenges. It is necessary to hold worms within the field of view, collect images efficiently and rapidly, and robustly analyze the data obtained. In recent years, a trend has developed toward imaging a large number of worms or neurons simultaneously, directly exploiting the unique properties of C. elegans to acquire data on a scale, which is not possible in other organisms. Doing this has required the development of new experimental tools, techniques, and data analytic approaches, all of which come together to open new perspectives on the field of neurobiology in C. elegans, and neuroscience in general. WIREs Syst Biol Med 2017, 9:e1376. doi: 10.1002/wsbm.1376

This article is categorized under:

  • Analytical and Computational Methods > Analytical Methods
  • Analytical and Computational Methods > Computational Methods
  • Models of Systems Properties and Processes > Organismal Models
Examples of microfluidic platforms for neural imaging in C. elegans. (a) The functional part of a microfluidic device for high‐throughput imaging, phenotyping, and sorting of C. elegans. It can apply flow of a cooling fluid in a channel (blue) on top of the worm imaging channel (red) to immobilize worms. Valves can be pneumatically actuated to handle worms (green). Two outlets can be used for worm sorting based on phenotypes; scale bar is 100 µm. (b) The microfluidic arenas that enable young adult worms to perform crawling locomotion through fluid‐filled channels (gray) between cylindrical microposts (white). Arrows indicate flow direction. Also shown is an oblique view of the micropost array before device assembly (bottom left) and a cross‐section schematic, indicating the glass substrate, polydimethylsiloxane (PDMS) top surface and posts, worm (W) and stimulus fluid (Fl); scale bars, 500 µm. (c) A microfluidic platform allows recording functional calcium imaging of AWA neuron in multiple freely moving animals under controlled chemical stimulation; scale bar(top), 1 mm, scale bar(bottom), 50 µm. (d) A microfluidic device for the delivery of chemicals to the worm's nose while performing functional calcium imaging in C. elegans. Chemical stimulations can be precisely controlled by alternating with buffer flows. (e) A microfluidic device for temporal delivery of oxygen. Gas can be delivered through blue channel on top of worm immobilizing channel (red).
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Imaging setups used for Whole Brain Imaging. (a) Light‐field deconvolution microscopy. (b) Two‐photon light sculpting microscopy. (c) A spinning disk confocal microscopy‐based setup for imaging of freely roaming C. elegans. (d) A separate spinning disk confocal microscopy‐based setup for imaging of freely roaming C. elegans. (e) Representative frames from whole brain imaging videos. The top panel shows an example maximum intensity projection of one frame. The bottom shows a single z plane overlaid with segmented neuronal regions.
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Models of Systems Properties and Processes > Organismal Models
Analytical and Computational Methods > Analytical Methods
Analytical and Computational Methods > Computational Methods

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