实验动物与比较医学

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斑马鱼品系及斑马鱼房的自动化管理

卜纪雯1()(), 华叶2, 金仕容2, 任宁欣1, 李福宁1, 杜久林1   

  1. 1.中国科学院脑科学与智能技术卓越创新中心, 感觉整合与行为研究组, 上海 200031
    2.中国科学院脑科学与智能技术卓越创新中心, 全脑介观神经联接图谱研究平台(斑马鱼), 上海 200031
  • 出版日期:2026-03-04
  • 通讯作者: 卜纪雯(1982—),女,硕士,高级实验师,研究方向:分子发育生物学,E-mail:jwbu@ion.ac.cn。ORCID:0009-0006-0933-7314
  • 作者简介:华叶(1983—),女,硕士,助理研究员,研究方向:斑马鱼早胚发育与精子发生,E-mail:ye.hua@ion.ac.cn;
    金仕容(1994—),女,硕士,实验师,研究方向:水产养殖。E-mail:18817772747@163.com
    第一联系人:卜纪雯负责项目管理,把握研究方向,方法设计,参与管理规则的制定和修正,负责写作,修订和绘图;华叶负责方法设计,负责斑马鱼品系的管理,管理规则的制定和修正,相应管理模块的设计;论文讨论,修订;金仕容负责方法设计,负责斑马鱼的养殖、斑马鱼房硬件设施的管理,管理规则的制定和修正,相应管理模块的设计;论文讨论;任宁欣负责方法设计,负责斑马鱼的养殖、斑马鱼房硬件设施的管理,相应管理模块的设计和修正;论文讨论,修订,参与部分绘图;李福宁参与论文作图,和部分内容讨论;杜久林负责研究工作的监督指导,参与研究内容的讨论,提供研究经费和相关资源。
    卜纪雯(1982—),女,硕士,高级实验师,研究方向:分子发育生物学,E-mail:jwbu@ion.ac.cn
  • 基金资助:
    国家自然科学基金创新研究群体项目“全脑尺度感觉-运动转导的神经机制”(32321003)

Automated Management of Zebrafish Strains and Its Facilities

BU Jiwen1()(), HUA Ye2, JIN Shirong2, REN Ningxin1, LI Funing1, DU Jiulin1   

  1. 1.Lab of Sensory Integration & Behavior, Center for Excellence in Brain Science and Intelligence Technology, Chinese Academy of Sciences, Shanghai, 200031, China
    2.Center for Excellence in Brain Science and Intelligence Technology, Chinese Academy of Sciences, Research Platform of Whole Brain Mesoscopic Neural Junction Map (Zebrafish), Shanghai, 200031, China
  • Published:2026-03-04
  • Contact: BU Jiwen (ORCID: 0009-0006-0933-7314), E-mail:jwbu@ion.ac.cn

摘要:

目的 本研究旨在提升斑马鱼品系及鱼房管理效率,变革依赖人力的传统管理范式。推动管理模式从传统人工操作向标准化、数字化、自动化与精细化的全面转型,从而显著提升管理质量与工作效率。 方法 中国科学院脑科学与智能技术卓越创新中心感觉整合与行为研究组自主开发了“斑马鱼房及品系自动化管理系统V1.0”(软著登记号:2021SR0236837),完整记录了斑马鱼房全部运行数据及斑马鱼品系完整数据。基于以上数据的深度挖掘并结合管理经验,将管理规定转化为可执行的系统指令集,构建了“监测-分析-预警-任务派发-执行-记录”的全流程自动化管理闭环。 结果 本管理系统已稳定运行五年,成功应用于三个斑马鱼实验室,累计为78名科研人员提供支持,高效管理了1 108个质粒和1 123个斑马鱼品系,其中291个品系已纳入斑马鱼精子库管理。采用李克特五点量表对28名核心用户进行使用调研,四个大类共计14个评估维度平均分(M=4.46~4.89)均远高于中点值(3分),表明用户对系统的各个维度均给予了高度评价。同时,绝大多数维度标准差处于较低水平(SD≤ 0.44),反映出用户评价具有良好的一致性。 结论 本研究融合信息技术、大数据分析和现代管理理念,实现了斑马鱼房管理的“数据采集-分析执行-系统优化”的良性循环,显著提升了斑马鱼房的管理效率与精细化水平,推动了管理模式向自动化方向发展。

关键词: 斑马鱼, 信息化管理系统, 大数据分析, 自动化管理

Abstract:

Objective This study focuses on enhancing zebrafish line and facility management by shifting from a manual, labor-intensive paradigm toward a standardized, digitalized, automated, and refined model. The goal is to achieve a marked improvement in both management quality and operational efficiency. Methods The laboratory independently developed the "Zebrafish Facility and Strain Automation Management System V1.0 (Software Copyright Registration No.2021SR0236837), which comprehensively records all operational data of the zebrafish facility and complete strain data. By deeply analyzing these datasets and integrating practical management expertise, we have translated management protocols into executable system command sets, establishing a fully automated, closed-loop management framework: "Monitoring → Analysis → Early Warning → Task Assignment → Execution → Recording. Results Since its launch in April 2020, the system has been operating stably for five years and has been successfully applied in three zebrafish laboratories, providing support to 78 researchers. It has efficiently managed 1,108 plasmids, 1,123 zebrafish strains, and approximately 5,400 tanks. Among these, 291 strains have been incorporated into the zebrafish sperm bank. A Likert five-point scale survey was conducted among 28 core users, covering 14 evaluation dimensions across four major categories. The results showed high average scores (M = 4.46–4.89), significantly exceeding the midpoint value of 3. This indicates that users provided highly positive evaluations for all aspects of the system. Additionally, user feedback demonstrated high consistency, with most dimensions exhibiting low standard deviations (SD ≤ 0.44). Conclusion This study integrates information technology, big data analytics, and modern management concepts to achieve a virtuous cycle of "data collection – analysis and execution – system optimization" in zebrafish strain and facility management. It significantly enhances the operational efficiency and fine?grained management level of zebrafish strain and facilities and drives the shift of management models toward automation.

Key words: Zebrafish, Information Management System, Automated Management, Big Data Analysis

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