Code accompanying the manuscript "In-vivo quantitative image analysis of age-related morphological changes of C. elegans neurons reveals a correlation between neurite bending and novel neurite outgrowths" by Hess et al. https://doi.org/10.1523/ENEURO.0014-19.2019
You can download the full dataset from Zenodo.
NeuronMorphologyQuantificationData ├── ALM │ ├── images | │ ├── CONDITION_AGE_SERIES_NAME.tif | │ └── ... │ └── trees | ├── CONDITION_AGE_SERIES_NAME.swc | └── ... └── PLM ├── images │ ├── CONDITION_AGE_SERIES_NAME.tif │ └── ... └── trees ├── CONDITION_AGE_SERIES_NAME.swc └── ...
Preprocessed images can be found in the 'images' folder. The 'trees' folders contain neuron tracings from the APP2 algorithm with manual annotations.
- Python 3.6
- The following python packages*
- numpy 1.16.2
- matplotlib 3.0.3
- scipy 1.2.1
- scikit-image 0.15.0
- SimpleITK 1.2.0
- tqdm 4.31.1
*All these packages can be automatically installed with pip by running
python -m pip install -r requirements.txt on the code directory
- Clone the repository and download the full data from Zenodo
- In batchProcessing.py set the parameter root to the directory containing the data
- Run the script batchProcessing.py
Contains individual length measurements of structures (i. e. soma-outgrowths)
Contains mesaruements corresponding to individual neurons (i. e. sharp-bend counts)
Contains a copy of the parameters used
This folder contains .swc files with classified nodes.
This folder contains .swc files with sharp bends and beads visualised.
You can use software like neuTube to visualize the results.
Internal use of the .swc format
The .swc format allows representation tree structures and is the most common output format of neuron tracing algorithms. The following table contains an example of four connected nodes and a corresponding visualization.
We use .swc files in this work as (1) input trees (neuron tracing output with manual
annotations), to (2) visualize the results of our classification
and to (3) visualize the quantification of kinks and bends. Neuronal trees are internally represented as
numpy.ndarrays of shape
Our pipeline expects .swc files with the types of all nodes set to 0. To annotate PVM-connections (PLM-sidebranches that connect to the PVM neuron that cannot be differentiated from neurite-outgrowths) set the types of one or more of their nodes to 1 or 2. If a crossing PVM process is not properly detected as such you can annotate it by setting one or more nodes to 3.
Classified neuron trees (../classifiedtrees) have the following naming convention:
|Soma node||0||Beloning to soma.|
|Mainbranch||1||Belonging to main branch.|
|Neurite outgrowth||2||Belonging to process sprouting from main branch.|
|Soma outgrowth||3||Belonging to process sprouting from soma node.|
|Blob||5||Sharp bend in process that is not an outgrowth event.|
|PVM-crossing||6||Process of PVM neuron that crosses the PLM neuron.|
|PVM-connection||7||Regular connection(s) (1-2) to PVM neurons (no branching event).|
|Unknown||9||None of the above.|
|Silenced outgrowth||10||Outgrowth event in the first or last segment of the mainbranch, silenced as in those regions a lot of tracing errors occur.|
For the visualization of wavyness (../wavytrees/COND_AGE_SERIES_NAME.swc) the radii of all nodes of the mainbranch are set to 0.5. The type of a node corresponts to the mapping of its angle (0-180 degrees) to an integer in the range 1-10 inclusive. The radii of nodes detected as sharp bends are set to 3.
Visualization of beads (../wavytrees/COND_AGE_SERIES_NAME_beads.swc) is done by doubling the radius of the bead-nodes and setting their type to 1.