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Optical Coherence Tomography: Imaging Mouse Retinal Ganglion Cells In Vivo
Published on: September 22, 2017
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All-optical inter-layers functional connectivity investigation in the mouse retina.
Giulia Lia Beatrice Spampinato1, Emiliano Ronzitti1, Valeria Zampini1
1Sorbonne Université, INSERM, CNRS, Institut de la Vision, 75012 Paris, France.
Cell Reports Methods
|September 1, 2022
Summary
We created a new microscope for studying neural circuits. This tool maps how specific cells in the mouse retina connect and communicate, advancing our understanding of neural computations.
Area of Science:
- Neuroscience
- Optical Imaging
- Circuitry Analysis
Background:
- Understanding neural circuits requires tools to probe cell interactions.
- Interrogating multi-layered neural systems presents significant technical challenges.
Purpose of the Study:
- To develop a multi-unit microscope for all-optical interrogation of inter-layer neural circuits.
- To map functional connectivity between specific cell types in the mouse retina.
Main Methods:
- Developed a novel multi-unit microscope enabling simultaneous two-photon (2P) functional imaging and 2P multiplexed holographic optogenetics.
- Applied the system to the mouse retina, activating single or groups of rod bipolar cells (RBCs).
- Recorded evoked responses in ganglion cells (GCs) with single-cell resolution and cell-type specificity.
Main Results:
- Successfully mapped functional connectivity between RBCs and GCs in the mouse retina.
- Derived cellular receptive fields using a logistic model to quantify RBC influence on GC types.
- Demonstrated cell-type-specific and single-cell resolution mapping of neural information transfer.
Conclusions:
- The developed microscope precisely interrogates multi-layered circuits by simultaneously imaging and controlling neuronal activity at distinct axial planes.
- This technology provides a powerful approach to dissect complex neural circuits and understand neural computations.
- Enables detailed investigation of information transfer within neural networks.

