ARCOS

ARCOS - Automated Recognition of Correlated Structures

About

ARCOS is a computational method to detect and quantify collective, spatio-temporally correlated phenomena. The algorithm identifies and tracks spatial clusters in time-lapse images. Although designed to analyze signalling phenomena in biological cells or cell collectives, it is applicable to other systems even outside of the realm of cell biology.

Code

We provide open-source implementations of ARCOS for:

• Python

• R

Both code bases can handle segmented data and raster images, although the Python version contains additional optimizations and features to handle large datasets.

GUI

Additionally, two dedicated interactive plugins for napari image viewer are available:

• arcos-gui − to handle data from image segmentation

• arcosPx-napari − to handle raster images

The napari plugins enables anyone without extensive programming knowledge to explore parameters through an intuitive GUI on a platform that emerges as a de-facto standard for viewing multidimensional images.

Demos

• Demo of the arcos-gui napari plugin (YouTube)

• Demo of the entire workflow from raw images to detection of collective events (YouTube)

Publications

The original ARCOS algorithm is now published in the Journal of Cell Biology (JCB): https://doi.org/10.1083/jcb.202207048

For a complete tutorial on how to use ARCOS from raw images to full analysis refer to our publication in Methods in Microscopy (MiM): https://doi.org/10.1515/mim-2024-0003

and the corresponding GitHub repository containing a detailed notebook and installation instructions.

If you use this method in your research, please cite our papers:

@article{10.1083/jcb.202207048,
    author = {Gagliardi, Paolo Armando and Grädel, Benjamin and Jacques, Marc-Antoine and Hinderling, Lucien and Ender, Pascal and Cohen, Andrew R. and Kastberger, Gerald and Pertz, Olivier and Dobrzyński, Maciej},
    title = "{Automatic detection of spatio-temporal signaling patterns in cell collectives}",
    journal = {Journal of Cell Biology},
    volume = {222},
    number = {10},
    pages = {e202207048},
    year = {2023},
    month = {07},
    issn = {0021-9525},
    doi = {10.1083/jcb.202207048},
    url = {https://doi.org/10.1083/jcb.202207048},
}

and if you used the tutorial please also cite:

@article{arcos-tutorial-2024,
    author = {Dobrzyński, Maciej and Grädel, Benjamin and Gagliardi, Paolo Armando and Pertz, Olivier},
    title = {Quantification of collective signalling in time-lapse microscopy images},
    journal = {Methods in Microscopy},
    volume = {1},
    number = {1},
    pages = {19--30},
    year = {2024},
    month = {06},
    issn = {2942-3899},
    doi = {doi:10.1515/mim-2024-0003},
    url = {https://doi.org/10.1515/mim-2024-0003},
}

How to use ARCOS

Detecting Collective signalling events in epithelial Cells

Analysing Collective Phenomena in Honeybees

How ARCOS works

Learn the fundamentals of ARCOS to get a deeper understanding of our main features:

Event Detection and Tracking

Data Requirements

Preprocessing