A computational pipeline to track chromatophores and analyze their dynamics
Cephalopod chromatophores are small dermal neuromuscular organs, each consisting of a pigment-containing cell and 10–20 surrounding radial muscles. Their expansions and contractions, controlled and coordinated by the brain, are used to modify the animal’s appearance during camouflaging and signaling...
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Main Authors: | , , , |
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Format: | Article |
Language: | English |
Published: |
eLife Sciences Publications Ltd
2025-07-01
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Series: | eLife |
Subjects: | |
Online Access: | https://elifesciences.org/articles/106509 |
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Summary: | Cephalopod chromatophores are small dermal neuromuscular organs, each consisting of a pigment-containing cell and 10–20 surrounding radial muscles. Their expansions and contractions, controlled and coordinated by the brain, are used to modify the animal’s appearance during camouflaging and signaling. Building up on tools developed by this lab, we propose a flexible computational pipeline to track and analyze chromatophore dynamics from high-resolution videos of behaving cephalopods. This suite of functions, which we call CHROMAS, segments and classifies individual chromatophores, compensates for animal movements and skin deformations, thus enabling precise and parallel measurements of chromatophore dynamics and long-term tracking over development. A high-resolution tool for the analysis of chromatophore deformations during behavior reveals details of their motor control and thus, their likely innervation. When applied to many chromatophores simultaneously and combined with statistical and clustering tools, this analysis reveals the complex and distributed nature of the chromatophore motor units. We apply CHROMAS to the skins of the bobtail squid Euprymna berryi and the European cuttlefish Sepia officinalis, illustrating its performance with species with widely different chromatophore densities and patterning behaviors. More generally, CHROMAS offers many flexible and easily reconfigured tools to quantify the dynamics of pixelated biological patterns. |
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ISSN: | 2050-084X |