Imaging neuropeptide release at synapses with a genetically engineered reporter
- California Institute of Technology, United States
- University of Southern California, United States
- Oak Crest Institute of Science, United States
Abstract
Research on neuropeptide function has advanced rapidly, yet there is still no spatio-temporally resolved method to measure the release of neuropeptides in vivo. Here we introduce Neuropeptide Release Reporters (NPRRs): novel genetically-encoded sensors with high temporal resolution and genetic specificity. Using the Drosophila larval neuromuscular junction (NMJ) as a model, we provide evidence that NPRRs recapitulate the trafficking and packaging of native neuropeptides, and report stimulation-evoked neuropeptide release events as real-time changes in fluorescence intensity, with sub-second temporal resolution.
Data availability
Source data of EM for Figure 1 and 3. Codes used for Figure 2 and 3.
Article and author information
Author details
Funding
National Institutes of Health (R21EY026432)
- David J Anderson
National Institutes of Health (R01DA031389)
- David J Anderson
National Institutes of Health (NS091546)
- Dion K Dickman
The funders had no role in study design, data collection and interpretation, or the decision to submit the work for publication.
Reviewing Editor
- Leslie C Griffith, Brandeis University, United States
Version history
- Received: February 27, 2019
- Accepted: June 25, 2019
- Accepted Manuscript published: June 26, 2019 (version 1)
- Version of Record published: July 4, 2019 (version 2)
- Version of Record updated: December 11, 2019 (version 3)
Copyright
© 2019, Ding et al.
This article is distributed under the terms of the Creative Commons Attribution License permitting unrestricted use and redistribution provided that the original author and source are credited.
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Imaging neuropeptide release at synapses with a genetically engineered reporter
eLife 8:e46421.
https://doi.org/10.7554/eLife.46421
Further reading
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- Neuroscience
The central tendency bias, or contraction bias, is a phenomenon where the judgment of the magnitude of items held in working memory appears to be biased toward the average of past observations. It is assumed to be an optimal strategy by the brain and commonly thought of as an expression of the brain’s ability to learn the statistical structure of sensory input. On the other hand, recency biases such as serial dependence are also commonly observed and are thought to reflect the content of working memory. Recent results from an auditory delayed comparison task in rats suggest that both biases may be more related than previously thought: when the posterior parietal cortex (PPC) was silenced, both short-term and contraction biases were reduced. By proposing a model of the circuit that may be involved in generating the behavior, we show that a volatile working memory content susceptible to shifting to the past sensory experience – producing short-term sensory history biases – naturally leads to contraction bias. The errors, occurring at the level of individual trials, are sampled from the full distribution of the stimuli and are not due to a gradual shift of the memory toward the sensory distribution’s mean. Our results are consistent with a broad set of behavioral findings and provide predictions of performance across different stimulus distributions and timings, delay intervals, as well as neuronal dynamics in putative working memory areas. Finally, we validate our model by performing a set of human psychophysics experiments of an auditory parametric working memory task.
