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金盏花:一款基于机器学习的用于斑马鱼姿态跟踪的网络应用程序。

Marigold: a machine learning-based web app for zebrafish pose tracking.

作者信息

Teicher Gregory, Riffe R Madison, Barnaby Wayne, Martin Gabrielle, Clayton Benjamin E, Trapani Josef G, Downes Gerald B

机构信息

Biology Department, University of Massachusetts Amherst, Amherst, MA, USA.

Molecular and Cellular Biology Graduate Program, University of Massachusetts Amherst, Amherst, MA, USA.

出版信息

BMC Bioinformatics. 2025 Jan 28;26(1):30. doi: 10.1186/s12859-025-06042-2.

DOI:10.1186/s12859-025-06042-2
PMID:39875867
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC11773884/
Abstract

BACKGROUND

High-throughput behavioral analysis is important for drug discovery, toxicological studies, and the modeling of neurological disorders such as autism and epilepsy. Zebrafish embryos and larvae are ideal for such applications because they are spawned in large clutches, develop rapidly, feature a relatively simple nervous system, and have orthologs to many human disease genes. However, existing software for video-based behavioral analysis can be incompatible with recordings that contain dynamic backgrounds or foreign objects, lack support for multiwell formats, require expensive hardware, and/or demand considerable programming expertise. Here, we introduce Marigold, a free and open source web app for high-throughput behavioral analysis of embryonic and larval zebrafish.

RESULTS

Marigold features an intuitive graphical user interface, tracks up to 10 user-defined keypoints, supports both single- and multiwell formats, and exports a range of kinematic parameters in addition to publication-quality data visualizations. By leveraging a highly efficient, custom-designed neural network architecture, Marigold achieves reasonable training and inference speeds even on modestly powered computers lacking a discrete graphics processing unit. Notably, as a web app, Marigold does not require any installation and runs within popular web browsers on ChromeOS, Linux, macOS, and Windows. To demonstrate Marigold's utility, we used two sets of biological experiments. First, we examined novel aspects of the touch-evoked escape response in techno trousers (tnt) mutant embryos, which contain a previously described loss-of-function mutation in the gene encoding Eaat2b, a glial glutamate transporter. We identified differences and interactions between touch location (head vs. tail) and genotype. Second, we investigated the effects of feeding on larval visuomotor behavior at 5 and 7 days post-fertilization (dpf). We found differences in the number and vigor of swimming bouts between fed and unfed fish at both time points, as well as interactions between developmental stage and feeding regimen.

CONCLUSIONS

In both biological experiments presented here, the use of Marigold facilitated novel behavioral findings. Marigold's ease of use, robust pose tracking, amenability to diverse experimental paradigms, and flexibility regarding hardware requirements make it a powerful tool for analyzing zebrafish behavior, especially in low-resource settings such as course-based undergraduate research experiences. Marigold is available at: https://downeslab.github.io/marigold/ .

摘要

背景

高通量行为分析对于药物发现、毒理学研究以及自闭症和癫痫等神经疾病的建模至关重要。斑马鱼胚胎和幼体非常适合此类应用,因为它们能大量产卵、发育迅速、神经系统相对简单,且拥有许多人类疾病基因的直系同源基因。然而,现有的基于视频的行为分析软件可能与包含动态背景或异物的记录不兼容,缺乏对多孔板格式的支持,需要昂贵的硬件,和/或需要相当的编程专业知识。在此,我们介绍Marigold,一款用于斑马鱼胚胎和幼体高通量行为分析的免费开源网络应用程序。

结果

Marigold具有直观的图形用户界面,可跟踪多达10个用户定义的关键点,支持单孔和多孔板格式,除了输出高质量的用于发表的数据可视化结果外,还能导出一系列运动学参数。通过利用高效的、定制设计的神经网络架构,即使在没有独立图形处理单元的普通计算机上,Marigold也能实现合理的训练和推理速度。值得注意的是,作为一款网络应用程序,Marigold无需任何安装,可在ChromeOS、Linux、macOS和Windows的流行网络浏览器中运行。为了证明Marigold的实用性,我们进行了两组生物学实验。首先,我们研究了techno trousers(tnt)突变体胚胎中触觉诱发逃避反应的新方面,该突变体在编码胶质谷氨酸转运体Eaat2b的基因中存在先前描述的功能丧失突变。我们确定了触摸位置(头部与尾部)和基因型之间的差异及相互作用。其次,我们研究了受精后5天和7天(dpf)喂食对幼体视觉运动行为的影响。我们发现,在这两个时间点,喂食和未喂食的鱼在游泳次数和活力方面存在差异,以及发育阶段和喂食方案之间的相互作用。

结论

在本文展示的两个生物学实验中,使用Marigold有助于发现新的行为学结果。Marigold易于使用、姿态跟踪稳健、适用于多种实验范式且对硬件要求灵活,使其成为分析斑马鱼行为的强大工具,尤其适用于基于课程的本科研究经历等资源有限的环境。Marigold可通过以下网址获取:https://downeslab.github.io/marigold/ 。

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