Multiplex Neural Circuit Tracing With G-Deleted Rabies Viral Vectors

Area of Science:

• Neuroscience
• Molecular Biology
• Genetics

Background:

• Neural circuits form complex networks essential for brain function.
• Understanding circuit interactions is key to deciphering brain mechanisms.
• Current rabies virus (RV) tracing methods have limitations in mapping multiple, interacting circuits simultaneously.

Purpose of the Study:

• To develop a multiplex RV tracing system enabling simultaneous mapping of distinct neuronal populations and their interactions.
• To overcome the limitations of existing EnvA/TVA-mediated RV tracing for complex circuit analysis.
• To provide a tool for differential labeling of circuits to study parallel and integrated information processing in the brain.

Main Methods:

• Optimized distinct viral envelopes (oEnvX) and their corresponding receptors (oTVX) for multiplexing RVΔG tracing.
• Developed chimeric envelopes (oEnvB, oEnvE) and optimized receptors (oTVB-L, oTVB-H, oTVE-H) based on ASLV systems.
• Demonstrated RVΔG infection independence between oEnvA/oTVA, oEnvB/oTVB, and oEnvE/oTVE systems.
• Performed in vivo proof-of-concept tracing from distinct layer 5 neuron populations and identified common inputs to V1 neurons.

Main Results:

• Successfully multiplexed RVΔG circuit tracing using orthogonal Env/TVA systems.
• Demonstrated the ability to trace connections from two distinct classes of layer 5 neurons simultaneously.
• Identified common inputs from the lateral geniculate nucleus to different neuronal populations in V1.
• Showcased the differential labeling capacity of the new systems for studying circuit interactions.

Conclusions:

• The developed multiplex RV tracing systems (oEnvA/oTVA, oEnvB/oTVB, oEnvE/oTVE) enable simultaneous mapping of multiple neural circuits.
• This approach overcomes previous limitations, allowing for detailed analysis of parallel and integrated information processing in the brain.
• The multiplexing capability provides a powerful new tool for dissecting the complexity of neural network organization and function.