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Mesmerize is a dynamically adaptable user-friendly analysis platform for 2D and 3D calcium imaging data

Author

Listed:
  • Kushal Kolar

    (University of Bergen)

  • Daniel Dondorp

    (University of Bergen)

  • Jordi Cornelis Zwiggelaar

    (University of Bergen)

  • Jørgen Høyer

    (University of Bergen)

  • Marios Chatzigeorgiou

    (University of Bergen)

Abstract

Calcium imaging is an increasingly valuable technique for understanding neural circuits, neuroethology, and cellular mechanisms. The analysis of calcium imaging data presents challenges in image processing, data organization, analysis, and accessibility. Tools have been created to address these problems independently, however a comprehensive user-friendly package does not exist. Here we present Mesmerize, an efficient, expandable and user-friendly analysis platform, which uses a Findable, Accessible, Interoperable and Reproducible (FAIR) system to encapsulate the entire analysis process, from raw data to interactive visualizations for publication. Mesmerize provides a user-friendly graphical interface to state-of-the-art analysis methods for signal extraction & downstream analysis. We demonstrate the broad scientific scope of Mesmerize’s applications by analyzing neuronal datasets from mouse and a volumetric zebrafish dataset. We also applied contemporary time-series analysis techniques to analyze a novel dataset comprising neuronal, epidermal, and migratory mesenchymal cells of the protochordate Ciona intestinalis.

Suggested Citation

  • Kushal Kolar & Daniel Dondorp & Jordi Cornelis Zwiggelaar & Jørgen Høyer & Marios Chatzigeorgiou, 2021. "Mesmerize is a dynamically adaptable user-friendly analysis platform for 2D and 3D calcium imaging data," Nature Communications, Nature, vol. 12(1), pages 1-15, December.
    • Handle: RePEc:nat:natcom:v:12:y:2021:i:1:d:10.1038_s41467-021-26550-y
    DOI: 10.1038/s41467-021-26550-y
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    References listed on IDEAS

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    1. Julia Cox & Lucas Pinto & Yang Dan, 2016. "Calcium imaging of sleep–wake related neuronal activity in the dorsal pons," Nature Communications, Nature, vol. 7(1), pages 1-7, April.
    2. Shelley Stall & Lynn Yarmey & Joel Cutcher-Gershenfeld & Brooks Hanson & Kerstin Lehnert & Brian Nosek & Mark Parsons & Erin Robinson & Lesley Wyborn, 2019. "Make scientific data FAIR," Nature, Nature, vol. 570(7759), pages 27-29, June.
    3. Takeo Horie & Ryoko Shinki & Yosuke Ogura & Takehiro G. Kusakabe & Nori Satoh & Yasunori Sasakura, 2011. "Ependymal cells of chordate larvae are stem-like cells that form the adult nervous system," Nature, Nature, vol. 469(7331), pages 525-528, January.
    4. Linfeng Yang & Rajarshi P Ghosh & J Matthew Franklin & Simon Chen & Chenyu You & Raja R Narayan & Marc L Melcher & Jan T Liphardt, 2020. "NuSeT: A deep learning tool for reliably separating and analyzing crowded cells," PLOS Computational Biology, Public Library of Science, vol. 16(9), pages 1-20, September.
    5. Jeffrey M. Perkel, 2018. "Data visualization tools drive interactivity and reproducibility in online publishing," Nature, Nature, vol. 554(7690), pages 133-134, February.
    6. Charles R. Harris & K. Jarrod Millman & Stéfan J. Walt & Ralf Gommers & Pauli Virtanen & David Cournapeau & Eric Wieser & Julian Taylor & Sebastian Berg & Nathaniel J. Smith & Robert Kern & Matti Picu, 2020. "Array programming with NumPy," Nature, Nature, vol. 585(7825), pages 357-362, September.
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