Back

A Toolkit for In Vivo Mapping and Modulating Neurotransmission at Single-Cell Resolution

Cuentas-Condori, A.; Chanaba-Lopez, P.; Thomas, M.; Feng, L.; Wolfe, A.; Agoba, P.; Schwartz, M. L.; Brown, M.; Ebert, M.; Jorgensen, E.; Bargmann, C. I.; Colon-Ramos, D. A.

2025-08-18 neuroscience
10.1101/2025.08.18.670838 bioRxiv
Show abstract

Understanding the organization and regulation of neurotransmission at the level of individual neurons and synapses requires tools that can track and manipulate transmitter-specific vesicles in vivo. Here, we present SynaptoTagMe, a suite of genetic tools in Caenorhabditis elegans to fluorescently label and conditionally ablate the vesicular transporters for glutamate, GABA, acetylcholine, and monoamines. Using a structure-guided approach informed by protein topology and evolutionary conservation, we engineered endogenously tagged versions for each transporter that maintain their physiological function while allowing for cell-specific, bright, and stable visualization. We also developed conditional knockout strains that enable targeted disruption of neurotransmitter synthesis or packaging in single neurons. We applied this toolkit to map co-expression of vesicular transporters across the C. elegans nervous system, revealing that over 10% of neurons exhibit co-transmission. Using the ADF sensory neuron as a case study, we demonstrate that serotonin and acetylcholine are trafficked in partially distinct vesicle pools. Our approach provides a powerful platform for mapping, monitoring, and manipulating neurotransmitter identity and use in vivo. The molecular strategies described here are likely applicable across species, offering a generalizable approach to dissect synaptic communication in vivo.

Published in eLife (predicted rank #1) · training set

Matching journals

The top 2 journals account for 50% of the predicted probability mass.

50% of probability mass above

"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.