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NeuroPAL: A Neuronal Polychromatic Atlas of Landmarks for Whole-Brain Imaging in C. elegans
Gonzalo E Mena,
Aravinthan D.T. Samuel,
Posted 20 Jun 2019
bioRxiv DOI: 10.1101/676312
Posted 20 Jun 2019
Resolving whole-brain images of neuronal gene expression or neuronal activity patterns to the level of single neuron types with defined identities represents a major challenge. We describe here the development and use of a multicolor Caenorhabditis elegans transgene, called NeuroPAL (a Neuronal Polychromatic Atlas of Landmarks), to resolve unique neural identities in whole-brain images. NeuroPAL worms share a stereotypical multicolor map, permitting complete, unambiguous and automated determination of individual neuron identities in conjunction with GCaMP-based neuronal activity reporters and GFP/YFP/CFP gene-expression reporters. To demonstrate the method and its potential, we use NeuroPAL and GFP-based reporters to map expression for the whole family of metabotropic acetylcholine, glutamate, and GABA neurotransmitter receptors encoded in the C. elegans genome, revealing a vast number of potential molecular connections that go far beyond the anatomically-defined connectome. We then expand the technique to whole-brain activity, employing NeuroPAL and a panneuronal neural-activity sensor (GCaMP6s) for functional analysis. Using this tool we delineate extensive nervous system activity patterns in response to several stimuli with single, identified neuron resolution. We find that attractive odors sensed by the same neuron class exhibit dissimilar activity patterns implying that, despite their shared valence and stimulus modality, these odors drive distinct neural circuitry. Our results also indicate that the connectome is a poor predictor of functional activity, emphasizing the need for comprehensive brain-activity recordings that delineate behavior-specific circuitry. Lastly, we illustrate the NeuroPAL as an unbiased analysis tool for investigating neuronal cell fate in specific mutant backgrounds. With these applications in mind, we establish a high-throughput software pipeline for automated and semi-automated cell identification using NeuroPAL. In conclusion, we demonstrate the power of the NeuroPAL as a tool for decoding whole-brain gene expression and maps of functional activity.
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