Cecelia: a multifunctional image analysis toolbox for decoding spatial cellular interactions and behaviour

Dominik Schienstock, Jyh Liang Hor, Sapna Devi and Scott N. Mueller ()
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Dominik Schienstock: The University of Melbourne, The Peter Doherty Institute for Infection and Immunity
Jyh Liang Hor: The University of Melbourne, The Peter Doherty Institute for Infection and Immunity
Sapna Devi: The University of Melbourne, The Peter Doherty Institute for Infection and Immunity
Scott N. Mueller: The University of Melbourne, The Peter Doherty Institute for Infection and Immunity

Nature Communications, 2025, vol. 16, issue 1, 1-14

Abstract: Abstract With the ever-increasing complexity of microscopy modalities, it is imperative to have computational workflows that enable researchers to process and perform in-depth quantitative analysis of the resulting images. However, workflows that allow flexible, interactive and intuitive analysis from raw images to analysed data are lacking for many experimental use-cases. Notably, integrated software solutions for analysis of complex 3D and live cell images are sorely needed. To address this, we present Cecelia, a toolbox that integrates various open-source packages into a coherent data management suite to make quantitative multidimensional image analysis accessible for non-specialists. We describe the application of Cecelia to several immunologically relevant scenarios and the development of an unbiased approach to distinguish dynamic cell behaviours from live imaging data. Cecelia is available as a software package with a Shiny app interface ( https://github.com/schienstockd/cecelia ). We envision that this framework and its approaches will be of broad use for biological researchers.

Date: 2025
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DOI: 10.1038/s41467-025-57193-y

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