TOWARDS A VIRAL AND OPTICAL APPROACH TO SINGLE-NEURON RETROGRADE RABIES TRACING

Understanding how a neuron’s functional responses arise from synaptic inputs requires precise mapping of its presynaptic partners. An effective tool for such targeted mapping is monosynaptic retrograde tracing via G-deleted rabies virus (RVΔG). However, achieving single-neuron initiation typically requires technically demanding in vivo electroporation of helper plasmids.

To obviate the need for single-neuron electroporation, we are optimising a method combining intersectional viral labelling with optical ablation. Using a dual-recombinase strategy in Rbp4-Cre::Ai94 transgenic mice, we restricted G and TVA expression to ultra-sparse starter populations in cortical Layer 5. Low-titre adeno-associated virus (AAV) drove Cre-dependent Flp recombinase, while high-titre, Flp-dependent AAVs encoded N2c glycoprotein and TVA_66T receptor with distinct fluorescent reporters. Supernumerary starter neurons were eliminated with two-photon laser photoablation before EnvA-pseudotyped RVΔG injection initiated retrograde tracing.

This approach consistently produced ultra-sparse starter populations (<15 neurons) within 2-3 weeks. Photo-ablation, adapted for deep cortical layers, refined starter populations towards single neurons. However, target depth remains a significant challenge, requiring increased laser power and exposure to ensure effective ablation. RVΔG produced bright retrograde labelling of hundreds of presynaptic neurons that were functionally imaged in vivo. Whole-brain volumetric imaging via serial two-photon microscopy revealed labelling of both local and long-range input neurons, including from thalamic nuclei and the frontal cortex.

Our method provides robust genetically-targeted ultra-sparse RVΔG tracing without electroporation, showing strong potential for single-neuron initiation. Integration with in vivo functional imaging enables direct investigation of how presynaptic ensemble activity shapes postsynaptic responses, facilitating mechanistic dissection of connectivity architectures underlying neuronal computations.