Published July 3, 2025 | Version v1
Poster Open

Deep inside a gorgonian coral

  • 1. ROR icon European Molecular Biology Laboratory
  • 2. ROR icon Institut de Ciències del Mar

Description

Deep inside a gorgonian coral

Montserrat Coll Lladó1, Jim Swoger1, Teresa Madurell2

1European Molecular Biology Laboratory (EMBL), C/ Dr. Aiguader 88, 08003 Barcelona, Spain.

2Institut de Ciències del Mar (CSIC), Passeig Marítim 37-49, 08003 Barcelona, Spain.

 

We present a 3D imaging approach for the gorgonian coral Leptogorgia sarmentosa, a sessile colonial cnidarian that poses significant challenges due to its structure: 1) soft polyps and 2) pigmented, spiny skeletal elements (sclerites) alongside a mineralized internal axis. Although most sclerites—calcareous structures essential for support, protection, flexibility and species identification—were lost during the initial clearing steps, we found that sample pretreatment had to balance preserving polyp contrast while mitigating the intense autofluorescence of the axial skeleton.

By incorporating a pretreatment to remove calcium carbonate we significantly improved light penetration and visualization of the organism’s inner architecture, reaching the central axis. This revealed the hollow central core subdivided into chambers, forming a cord along the axis, which was previously obscured by autofluorescence. After clearing, a collagen staining protocol (Timin & Milinkovitch) allowed us to recover the “negative” imprint of the lost sclerites, effectively capturing their shape and distribution within the coenenchyme (colonial tissue).

Overall, our method enables high-resolution 3D imaging of a complex organism from its outermost polyps to its internal axis. The approach can be extended to other members of the same taxonomic group, expanding our toolkit for studying similarly challenging marine invertebrates in three dimensions.

 

Reference:

Grigorii Timin & Michel Milinkovitch. High-resolution confocal and light-sheet imaging of collagen 3D network architecture in very large samples. iScience 26, 2023.

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