NanoNET Toolbox

Generation and Analysis of NET Filament Line Profiles

This repository provides tools for studying neutrophil extracellular traps (NETs) from light microscopy data.
NanoNET consists of the following workflows:

• NanoNET Detection – Automated segmentation and extraction of NET filament profiles from light microscopy data (e.g., SIM, STED, SMLM).
• NanoNET Analysis – Statistical correlation and periodicity analysis of filament profiles.

Detailed information on sample preparation, imaging, and NanoNET can be found in the following paper:
Winkler et al. - Nanoscale Mapping Reveals Periodic Organization of Neutrophil Extracellular Trap Proteins, Nano Letters (2026).
DOI: https://doi.org/10.1021/acs.nanolett.5c05175

---

📦 Workflows

1. NET Detection

Purpose: Extract NET filament line profiles from 2D/3D microscopy stacks. Line profiles for all channels (three-channel images expected) are measured along a continuous DNA backbone channel.

Workflow steps:

1. Preprocessing

   • (3D only) Create maximum-intensity projections from z-ranges containing NET signal.
   • (Optional) Cropping: remove non-NET regions or artifacts (e.g., cell remnants).

2. Automated segmentation (NanoNET Detection)
   Set parameters:

   • Data folder: select the folder containing TIFF files (2D images or 3D projections).
   • File extension: choose the image file type (e.g. .tif).
   • Minimal profile length (µm): profiles shorter than this threshold are ignored.
   • Channel selection: define the DNA backbone channel (requires continuous DNA labeling).
   • Correct local contrast: compensates for shading/illumination inhomogeneity.
   • Line width (px): width over which intensities are integrated for line profiles.

Outputs

• Skeletonized NET filaments
• Per-channel line intensity profiles (CSV)
• Parameter/configuration files for reproducibility

---

2. NET Analysis

Purpose: Perform correlation analysis of NET filament line profiles to identify structural periodicities and colocalization patterns.

Workflow steps:

1. Input: line intensity profiles from NET Detection (CSV files).

2. Automated analysis
   Set parameters:

   • Fragments: enable Break into fragments to split profiles into segments of defined length (e.g., 1500 nm).
   • Pixel size (nm/px): set the image sampling.
   • Shift intervals: maximum lags for auto-/cross-correlation (steps are pixels).
   • Plot only first peak: if enabled, only the first peak in the correlogram is considered.
   • Minimal profile length: excludes shrt line profiles.
   • Resolution: expected resolution of the microscopy technique. This is required for the next point.
   • Suppress sub-reslution peaks: affects only auto-correlation traces and is required for analyszing periodicities.

Outputs

• Correlation tables (auto- & cross-correlation values)
• Averaged correlation profiles
• Extracted periodicities (histograms)

---

⚙️ Requirements

• NET Detection

  • Fiji/ImageJ with NanoNET plugin — https://imagej.net/software/fiji/

• NET Analysis

  • Python 3.9.13
  • Jupyter Notebook

📥 Python package installation

Install the Python dependencies with pip (recommended in a virtual environment):

# Upgrade pip
pip install --upgrade pip

# Core scientific stack
pip install pandas numpy scipy matplotlib

# Image IO and processing
pip install scikit-image tifffile imageio

# Jupyter (for running the notebooks/GUI)
pip install notebook ipykernel

# Optional: plotting helpers
pip install seaborn

---

📖 References

1. NanoNET (study)
   Gimber N., et al. (2025). Nanoscale Mapping Reveals Periodic Organization of Neutrophil Extracellular Trap Proteins. bioRxiv. https://doi.org/10.1101/2025.07.28.665103

2. Image processing methods
   Otsu, N. (1979). A threshold selection method from gray-level histograms. IEEE Transactions on Systems, Man, and Cybernetics, 9(1), 62–66. https://doi.org/10.1109/TSMC.1979.4310076
   Buades, A., Coll, B., & Morel, J.-M. (2005). A non-local algorithm for image denoising. CVPR 2005, 2, 60–65. https://doi.org/10.1109/CVPR.2005.38

3. ImageJ / Fiji
   Schindelin, J., et al. (2012). Fiji: an open-source platform for biological-image analysis. Nature Methods, 9(7), 676–682. https://doi.org/10.1038/nmeth.2019
   Schneider, C. A., Rasband, W. S., & Eliceiri, K. W. (2012). NIH Image to ImageJ: 25 years of image analysis. Nature Methods, 9(7), 671–675. https://doi.org/10.1038/nmeth.2089

4. Python ecosystem
   Virtanen, P., et al. (2020). SciPy 1.0: fundamental algorithms for scientific computing in Python. Nature Methods, 17, 261–272. https://doi.org/10.1038/s41592-019-0686-2
   Harris, C. R., et al. (2020). Array programming with NumPy. Nature, 585, 357–362. https://doi.org/10.1038/s41586-020-2649-2
   Hunter, J. D. (2007). Matplotlib: A 2D graphics environment. Computing in Science & Engineering, 9(3), 90–95. https://doi.org/10.1109/MCSE.2007.55
   McKinney, W. (2010). Data Structures for Statistical Computing in Python. Proc. of the 9th Python in Science Conf. (SciPy 2010), 51–56. https://doi.org/10.25080/Majora-92bf1922-00a
   van der Walt, S., et al. (2014). scikit-image: image processing in Python. PeerJ, 2, e453. https://doi.org/10.7717/peerj.453
   Gohlke, C. (tifffile). tifffile: Read and write TIFF files. https://pypi.org/project/tifffile/