Biology subjects

Rochon, P.-L.

Publications and source records attributed to Rochon, P.-L..

3 recordsLinked to original sources

bioRxiv2026

TRACR: an anterograde transneuronal tracing system for genetic access across synapses and longitudinal circuit analysis

Following neural signals as they converge onto and diverge from individual neurons is central to understanding circuit function and disease-related dysfunction. Existing anterograde transneuronal tracers are limited by cytotoxicity and incomplete genetic control over connected partners. To address these limitations, we adapted synthetic Notch designs to create TRanssynaptic Anterograde Circuit Readout (TRACR). Binding of the engineered ligand-receptor across synapses induces TRE-driven reporter transcription, enabling characterization of postsynaptic neurons. TRACR provides segregated genetic access to pre- and postsynaptic populations, and can be combined with markers, sensors, or effectors to expand circuit analysis. By applying TRACR at multiple synapses in the mouse visual system, we show that TRACR labels postsynaptic partners of sensory neurons, long-range projections and local inhibitory interneurons. TRACR signaling is reversible and fails to activate when synapses are absent or disrupted. Together, TRACR is an accessible, AAV-deliverable transneuronal reporting tool for longitudinal analysis of circuit assembly, degeneration, and repair. HIGHLIGHTSO_LITRACR adapts the synNotch system to signal across synapses for tracing postsynaptic targets. C_LIO_LITRACR identifies local and long-range postsynaptic targets in the mouse visual system. C_LIO_LITRACR activation requires intact synaptic connectivity rather than proximity. C_LIO_LITRACR signals are reversible, diminishing upon synapse loss and activating following assembly. C_LI

neuroscience↗

bioRxiv2024

Cadherin 4 assembles a family of color-selective retinal circuits that respond to light offset.

Retinal interneurons and projection neurons (retinal ganglion cells, RGCs) connect in specific combinations in a specialized neuropil called the inner plexiform layer (IPL). The IPL is divided into multiple sublaminae, with neurites of each neuronal type confined to one or a few layers. This laminar specificity is a major determinant of circuit specificity and circuit function. Using a combination of approaches we show that RGCs targeting IPL sublamina 1 and 3a express the adhesion molecule cadherin 4 (Cdh4). Using calcium imaging and iterative immunostaining, we classified Cdh4-RGCs into 9 types that each encode unique aspects of dark visual stimuli. Cdh4 loss selectively disrupted the layer- targeting of these RGCs, reduced their synaptic inputs from interneurons, and severely altered their visual responses. Overexpression of Cdh4 in other retinal neurons directed their neurites to s1-3a through homophilic interactions. Taken together, these results demonstrate that Cdh4 is a novel layer targeting system for nearly a third of all RGC.

neuroscience↗

bioRxiv2021

The cell adhesion molecule Sdk1 shapes assembly of a retinal circuit that detects localized edges.

Nearly 50 different mouse retinal ganglion cell (RGC) types sample the visual scene for distinct features. RGC feature selectivity arises from its synapses with a specific subset of amacrine (AC) and bipolar cell (BC) types, but how RGC dendrites arborize and collect input from these specific subsets remains poorly understood. Here we examine the hypothesis that RGCs employ molecular recognition systems to meet this challenge. By combining calcium imaging and type-specific histological stains we define a family of circuits that express the recognition molecule Sidekick 1 (Sdk1) which include a novel RGC type (S1-RGC) that responds to local edges. Genetic and physiological studies revealed that Sdk1 loss selectively disrupts S1-RGC visual responses which result from a loss of excitatory and inhibitory inputs and selective dendritic deficits on this neuron. We conclude that Sdk1 shapes dendrite growth and wiring to help S1-RGCs become feature selective.

neuroscience↗