High-throughput multi-camera array microscope platform for automated 3D behavioral analysis of swimming zebrafish larvae
Haitao Chen, Kevin Li, Lucas Kreiss, Paul Reamey, Lain X. Pierce, Ralph Zhang, Ricardo Da Luz, Amey Chaware, Kanghyun Kim, Clare B. Cook, Xi Yang, Joshua F. Lerner, Jed Doman, Aurélien Bègue, John Efromson, Mark Harfouche, Gregor Horstmeyer, Matthew N. McCarroll

TL;DR
A new microscope system captures 3D movements of 48 zebrafish larvae at once, revealing behaviors and traits that 2D methods miss.
Contribution
A high-throughput 24-camera array microscope with mirrored well plates enables automated 3D tracking of multiple zebrafish larvae.
Findings
The system captures 3D skeletal tracking and swim bladder dynamics of up to 48 larvae simultaneously.
A machine learning algorithm enables efficient and scalable 3D behavioral analysis at high frame rates.
The approach is compatible with standard lab workflows in pharmacology, toxicology, and neuroscience.
Abstract
Understanding the behavioral and morphological dynamics of moving model organisms like the zebrafish larvae requires accurate, high-throughput 3D analysis. However, traditional single-view 2D video tracking fails to capture the full scope of natural 3D movements and postural dynamics. Here, we present a novel high-throughput 24-camera array microscope with a co-designed “mirrored well plate" that allows for snapshot imaging of up to 48 wells over a 118 mm × 82 mm field of view from two orthogonal directions (i.e., a top-view and side-view). Accurate 3D position estimation and tracking is achieved with an efficient machine learning algorithm that scales well to high-throughput measurements. The proposed approach automates parallelized 3D model organism behavioral analysis, providing 3D skeletal tracking, swim bladder morphological dynamics, and kinematics of up to 48 swimming zebrafish…
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Taxonomy
TopicsZebrafish Biomedical Research Applications · Advanced Fluorescence Microscopy Techniques · Biomimetic flight and propulsion mechanisms
