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Robotic sorting of zebrafish embryos

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Abstract

Transcriptomics and metabolomics, two biological research fields that need large numbers of zebrafish embryos, require the removal of unfertilised or nonviable zebrafish embryos. Biologists routinely conduct the tedious, error-prone, and time-consuming manual sorting of embryos. We suggest a novel approach that combines deep learning and microfluidics for automated sorting to overcome this difficulty. To determine the developmental stage and viability of zebrafish eggs, we trained an optimized YOLOv5 model with 95.8% accuracy and a processing speed of 10.6 ms per frame, classifying them as dead, unfertilised, or alive. The eggs are contained in traps on a microfluidic chip using micro-pumps. After that, the deep learning system can identify and automatically sort the eggs according to their viability by positioning this chip on an XYZ motorized stage. The sorting experiment was conducted in two modes: without feedback and with feedback while using the dead egg position. The first one had a sorting success rate of 90% as opposed to 97.9% for the feedback mode with 3 seconds required for each dead egg. This automated approach provides a precise and efficient way to handle a large number of zebrafish embryos while also greatly reducing the workload associated with manual sorting. The success rates attained demonstrate the usefulness and effectiveness of our suggested methodology, opening new avenues for biological research involving accurate embryo selection.

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Data Availability

The datasets supporting in this study are available from the corresponding author upon reasonable request.

Code availability

The codes used for zebrafish embryo detection and sorting in this study are publicly available at the GitHub repository: https://github.com/AliouneDiouf/zebappdetect.git

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Acknowledgements

The authors thank Edouard Manzoni and Marco Amaral, technicians for the Animal Facility and Engineering Aquatic Models Platform of Sorbonne University for providing us fresh zebrafish eggs.

Funding

This study was funded by the Université franco-italienne (UFI) / Universitá Italo Francese (UIF).

Author information

Author notes

  1. Ferhat Sadak, Edison Gerena, Abdelkrim Mannioui, Daniela Zizioli, Irene Fassi, Mokrane Boudaoud, Giovanni Legnani and Sinan Haliyo contributed equally to this work.

Authors and Affiliations

  1. Sorbonne Université, Institut des Systèmes Intelligents et de Robotique (ISIR), Paris, France

    Alioune Diouf, Ferhat Sadak, Edison Gerena, Mokrane Boudaoud & Sinan Haliyo

  2. Department of Industrial Engineering, Universitá degli Studi di Brescia, Brescia, Italy

    Alioune Diouf & Giovanni Legnani

  3. Department of Mechanical Engineering, Bartin University, Bartin, Türkiye

    Ferhat Sadak

  4. Institute of Intelligent Industrial Technologies and Systems for Advanced Manufacturing National Research Council of Italy, Milan, Italy

    Irene Fassi

  5. Sorbonne Université, Institut de Biologie Paris-Seine (IBPS), Paris, France

    Abdelkrim Mannioui

  6. Department of Molecular and Translational Medicine, Universitá degli Studi di Brescia, Brescia, Italy

    Daniela Zizioli

Authors
  1. Alioune Diouf
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  2. Ferhat Sadak
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  3. Edison Gerena
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  4. Abdelkrim Mannioui
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  5. Daniela Zizioli
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  6. Irene Fassi
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  7. Mokrane Boudaoud
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  8. Giovanni Legnani
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  9. Sinan Haliyo
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Contributions

All authors have made substantial contributions regarding the conception and design of the study. Acquisition of images and analysis (AD, SH), labelling images (AD,AM,DZ), development of the deep learning model (AD, FS), Robotic phase sorting (AD, EG, MB), mechanical design and manufacturing of the microfluidic chip (AD, IF, GL). (AD,FS) wrote the main manuscript text and final approval of the version submitted (all authors).

Corresponding author

Correspondence to Alioune Diouf.

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Cite this article

Diouf, A., Sadak, F., Gerena, E. et al. Robotic sorting of zebrafish embryos. J Micro-Bio Robot 20, 3 (2024). https://doi.org/10.1007/s12213-024-00167-y

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