NFDI4BIOIMAGE
Published February 28, 2026 | Version v1
Technical note Open

Workflow and Example Dataset for Python-Based Stitching of Confocal Zebrafish Images from VAST BioImager-Based Screening

Authors/Creators

  • 1. Center for Regenerative Therapies Dresden (CRTD)
  • 2. ROR icon Technische Universität Dresden

Contributors

Research group:

  • 1. ROR icon Technische Universität Dresden
  • 2. ROR icon NFDI4BIOIMAGE
  • 3. ROR icon University of Edinburgh

Description

A Python-based stitching script for individual tiles obtained with an automated imaging platform that combines the Vertebrate Automated Screening Technology (VAST) BioImager system (Union Biometrica), the Large Particle (LP) Sampler (Union Biometrica), and the Zeiss Cell Observer Spinning Disk Confocal Microscopic System (SDCM) (Early et al., 2018). The Lyons Lab (https://www.lyons-lab.com/) designed the system as a screening tool for automated, high-resolution, in vivo imaging of 2–5-day-old zebrafish larvae. Two datasets are available to test the script, along with the final stitched files for comparison. 

Files

Python-based stitching script for Zebrafish screening.pdf

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Additional details

Software

Repository URL
https://github.com/SDu90/Image-processing
Programming language
Python
Development Status
Active

References

  • Chang, T.-Y., Pardo-Martin, C., Allalou, A., Wählby, C., Yanik, M.F., 2012. Fully automated cellular-resolution vertebrate screening platform with parallel animal processing. Lab Chip 12, 711–716. https://doi.org/10.1039/C1LC20849G
  • Early, J.J., Marshall-Phelps, K.L., Williamson, J.M., Swire, M., Kamadurai, H., Muskavitch, M., Lyons, D.A., 2018. An automated high-resolution in vivo screen in zebrafish to identify chemical regulators of myelination. eLife 7, e35136. https://doi.org/10.7554/eLife.35136
  • Oprişoreanu, A.-M., Smith, H.L., Krix, S., Chaytow, H., Carragher, N.O., Gillingwater, T.H., Becker, C.G., Becker, T., 2021. Automated in vivo drug screen in zebrafish identifies synapse-stabilising drugs with relevance to spinal muscular atrophy. Disease Models & Mechanisms 14, dmm047761. https://doi.org/10.1242/dmm.047761
  • Pardo-Martin, C., Chang, T.-Y., Koo, B.K., Gilleland, C.L., Wasserman, S.C., Yanik, M.F., 2010. High-throughput in vivo vertebrate screening. Nat Methods 7, 634–636. https://doi.org/10.1038/nmeth.1481
  • Sarkans, U., Chiu, W., Collinson, L. et al., 2021. REMBI: Recommended Metadata for Biological Images—enabling reuse of microscopy data in biology. Nat Methods 18, 1418–1422. https://doi.org/10.1038/s41592-021-01166-8