Journal
BMC BIOLOGY
Volume 18, Issue 1, Pages -Publisher
BMC
DOI: 10.1186/s12915-020-0745-2
Keywords
Neuron identification; Caenorhabditis elegans; Cell-specific promoters; Volumetric images; Large dataset for cell positions; Biological resources; Computational method; Whole-brain activity imaging
Categories
Funding
- CREST program Creation of Fundamental Technologies for Understanding and Control of Biosystem Dynamics of the Japan Science and Technology Agency (JST) [JPMJCR12W1]
- CisHub of the University of Tokyo
- JST PRESTO [JPMJPR1947]
- MEXT/JSPS KAKENHI [JP26830006, JP18K14848]
- Scientific Research on Innovative Areas [JP16H01418, JP18H04728, JP17H05970, 19H04928]
- [JP17H06113]
- [JP20115002]
- [JP25115010]
- [19H04980]
- [JP20115003]
- [JP25115009]
- [JP18H05135]
- Grants-in-Aid for Scientific Research [19H04928] Funding Source: KAKEN
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Background Annotation of cell identity is an essential process in neuroscience that allows comparison of cells, including that of neural activities across different animals. In Caenorhabditis elegans, although unique identities have been assigned to all neurons, the number of annotatable neurons in an intact animal has been limited due to the lack of quantitative information on the location and identity of neurons. Results Here, we present a dataset that facilitates the annotation of neuronal identities, and demonstrate its application in a comprehensive analysis of whole-brain imaging. We systematically identified neurons in the head region of 311 adult worms using 35 cell-specific promoters and created a dataset of the expression patterns and the positions of the neurons. We found large positional variations that illustrated the difficulty of the annotation task. We investigated multiple combinations of cell-specific promoters driving distinct fluorescence and generated optimal strains for the annotation of most head neurons in an animal. We also developed an automatic annotation method with human interaction functionality that facilitates annotations needed for whole-brain imaging. Conclusion Our neuron ID dataset and optimal fluorescent strains enable the annotation of most neurons in the head region of adult C. elegans, both in full-automated fashion and a semi-automated version that includes human interaction functionalities. Our method can potentially be applied to model species used in research other than C. elegans, where the number of available cell-type-specific promoters and their variety will be an important consideration.
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