https://github.com/bencardoen/subprecisioncontactdetection.jl

A Julia package to detect contacts below the precision of a superresolution microscope between 2 subcellular organelles in 2-channel superresolution (STED) microscopy.
https://github.com/bencardoen/subprecisioncontactdetection.jl

contact endoplasmic-reticulum interaction mam merc mitochondria sted-microscopy sub-precision superresolution

Last synced: 2 months ago
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A Julia package to detect contacts below the precision of a superresolution microscope between 2 subcellular organelles in 2-channel superresolution (STED) microscopy.

• Host: GitHub
• URL: https://github.com/bencardoen/subprecisioncontactdetection.jl
• Owner: bencardoen
• License: agpl-3.0
• Created: 2022-08-15T21:57:05.000Z (almost 3 years ago)
• Default Branch: main
• Last Pushed: 2024-11-25T21:22:31.000Z (7 months ago)
• Last Synced: 2025-04-12T16:47:52.990Z (3 months ago)
• Topics: contact, endoplasmic-reticulum, interaction, mam, merc, mitochondria, sted-microscopy, sub-precision, superresolution
• Language: Julia
• Homepage:
• Size: 3.14 MB
• Stars: 3
• Watchers: 1
• Forks: 0
• Open Issues: 3
• Metadata Files:
  • Readme: README.md
  • License: LICENSE
  • Citation: CITATION.cff

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README

        
# SubPrecisionContactDetection.jl

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Detects sub-precision contacts between subcellular organelles in 2 and 3D STED (precision ~ 50-150nm)

superresolution microscopy, for example endoplasmum reticulum and mitochondria (contacts ~ 20-100nm).

Where a pixel precise segmentation is not feasible due to the precision of the microscope, and colocalization does not describe the interface in a meaningful way, SubPrecisionContactDetection can reconstruct the plausible interface between the organelles.

An example rendering of the postprocessed contact zones (white) between endoplasmum reticulum (green) and mitochondria (red) is shown here [(source)](https://www.biorxiv.org/content/10.1101/2022.06.23.497346v1.full.pdf):

![](example.png)

### Features

- Fast: using multiple threads, and Julia's fast LLVM JIT code

- Reproducible: tests ensure backwards compatibility

- Configurable: Can process deconvolved or raw images, with optional extra denoising

- Rich: provides interpretable features for each detected contact

- Confidence map for each voxel (each voxel has a p-value)

### Cite

If you find this project useful, please cite

```bibtex

@article{cardoen2023membrane,

  title={Membrane contact site detection (MCS-DETECT) reveals dual control of rough mitochondria--ER contacts},

  author={Cardoen, Ben and Vandevoorde, Kurt R and Gao, Guang and Ortiz-Silva, Milene and Alan, Parsa and Liu, William and Tiliakou, Ellie and Vogl, A Wayne and Hamarneh, Ghassan and Nabi, Ivan R},

  journal={Journal of Cell Biology},

  volume={223},

  number={1},

  pages={e202206109},

  year={2023},

  publisher={Rockefeller University Press}

}

```