This repository contains the code specifically targeting segmentation and localization of ferritin-labeled proteins in TEM images, for the paper:
Genetically Encoded FerriTag as a Specific Label for Cryo-Electron Tomography
Chang Wang, Amin Khosrozadeh, Ioan Iacovache, Benoît Zuber
Cryo-electron tomography (cryoET) combined with subtomogram averaging has revolutionized structural biology by enabling near-atomic resolution imaging of cellular proteins. However, accurately localizing proteins within live cells remains challenging. To address this, we developed a genetically encoded FerriTag labeling strategy using rapamycin-induced oligomerization (FKBP-FRB system) combined with ferritin as a contrast marker for precise protein localization. Our method accurately identifies target proteins in cryoET images, bridging the gap between molecular localization and structural analysis.
Here, we present a Fiji macro for ferritin segmentation and Python for subsequent quantitative analysis:
- Gaussian filtering and intensity normalization using CLIJ2
- Intensity transformations and intermode threshold binarization
- Morphological refinement (erosion/dilation)
- Localization analysis
- Fiji/ImageJ with CLIJ2 (GPU-accelerated plugin)
- Python 3.8+
- scikit-image
- NumPy
If you use this code or approach in your research, please cite our paper:
Wang, C., Khosrozadeh, A., Iacovache, I. & Zuber, B. (2024). Genetically Encoded FerriTag as a Specific Label for Cryo-Electron Tomography. bioRxiv, https://doi.org/10.1101/2024.09.10.612178
- Fiji/ImageJ: Schindelin et al. (2012). Fiji: an open-source platform for biological-image analysis. Nature Methods, 9(7), 676–682.
- CLIJ: Haase et al. (2020). CLIJ: GPU-accelerated image processing for everyone. Nat Methods, 17, 5–6.
- scikit-image: van der Walt et al. (2014). PeerJ, 2:e453.
For detailed method implementations and inquiries, please refer to our publication or contact the corresponding author.