High-throughput multi-camera array microscope platform for automated 3D behavioral analysis of swimming zebrafish larvae

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 larvae at up to several hundred frames per second. The result is an efficient and scalable solution for high-throughput 3D behavioral studies with broad compatibility with standard workflows across laboratories and procedures working with pharmacology, toxicology, and neuroscience.

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Publication Details

Journal
Communications Biology
Published
2026-01-23
DOI
https://doi.org/10.1038/s42003-025-09421-w
Primary Topic
Zebrafish Biomedical Research Applications
Type
article
Field-Weighted Citation Impact
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article

High-throughput multi-camera array microscope platform for automated 3D behavioral analysis of swimming zebrafish larvae

Gregor Horstmeyer, Lucas Kreiß, Jed Doman, Mark Harfouche et al.
Communications Biology
Zebrafish Biomedical Research Applications
article

High-throughput multi-camera array microscope platform for automated 3D behavioral analysis of swimming zebrafish larvae

Gregor Horstmeyer, Lucas Kreiß, Jed Doman, Mark Harfouche, John Efromson, Lain X. Pierce, Paul Reamey, Amey Chaware, Roarke Horstmeyer, Kevin Li, Joshua F. Lerner, Ricardo Da Luz, Ralph Zhang, Xi Yang, Haitao Chen, Aurélien Bègue, Kanghyun Kim, Clare B. Cook, Matthew N. McCarroll
article en

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 larvae at up to several hundred frames per second. The result is an efficient and scalable solution for high-throughput 3D behavioral studies with broad compatibility with standard workflows across laboratories and procedures working with pharmacology, toxicology, and neuroscience.

Communications BiologyVol. 9(1)
Duke University (US), University of California, San Francisco (US)
Openalex Percentile: Top 82%
Zebrafish Biomedical Research Applications
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