ePoster

EXPANSION MICROSCOPY ENABLES SYNAPTIC-RESOLUTION IMAGING OF THE ZEBRA FINCH SONG SYSTEM ACROSS DEVELOPMENT

Andreea-Maria Aldea

MRC Laboratory of Molecular Biology

FENS Forum 2026 (2026)
Barcelona, Spain
Board PS06-09PM-299

Presentation

Date TBA

Board: PS06-09PM-299

Poster preview

EXPANSION MICROSCOPY ENABLES SYNAPTIC-RESOLUTION IMAGING OF THE ZEBRA FINCH SONG SYSTEM ACROSS DEVELOPMENT poster preview

Event Information

Poster Board

PS06-09PM-299

Abstract

Zebra finches learn their song during a critical period in which the synaptic connectivity of the song motor pathway is dramatically restructured. The robust nucleus of the arcopallium (RA), a premotor cortex analogue that drives vocal output and a key node in this pathway, receives convergent input from both the motor pathway (HVC) and the anterior forebrain pathway (LMAN). The balance between these inputs shifts as the bird transitions from variable vocal babbling to a stable, crystallised song. Despite decades of work on the physiology and behavioural relevance of this process, we still lack a detailed picture of how synaptic architecture in RA changes at the level of individual synapses across development. Here, we establish an expansion microscopy (ExM) pipeline for synaptic-resolution imaging in songbird brain tissue. We applied ExM to zebra finch brain sections containing RA, and validated tissue preservation and antigen retention against mouse cortical tissue processed in parallel. We confirmed reliable immunolabelling of pre- and postsynaptic markers, as well as connexins, demonstrating that ExM preserves the molecular architecture required to study both chemical and electrical synaptic connectivity in avian tissue. We are now applying this pipeline across developmental timepoints spanning the sensorimotor learning period to characterise how synaptic architecture in RA changes as song crystallises. This work extends previous applications of expansion microscopy in songbird tissue to synaptic-resolution analysis and will provide a foundation for mapping input-specific connectivity changes during vocal learning.

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