Snakemake workflow: CellOrientation

Test of Uniformity for centrosome orientation at early timepoints during Wound Healing Assay

Author

• Raul Gomez Riera

If you use this workflow in a paper, don't forget to give credits to the author by citing the URL of this repo.

Image acquisition settings for data generation

The wound healing assay was combined with immunofluorescence of the centrosomal protein CEP170 and the Golgi marker GM130. Used fluorophores:

• Channel 0 : Centrosome marker CEp170, Alexa647
• Channel 1 : Golgi marker GM130, Alexa488
• Channel 2 : Nucleus, DAPI

3D multiplex images (stack) of cells were acquired with Stellaris Falcon from Leica.

• Objective Immersion="Oil" LensNA="1.3"
• Model="HC PL APO CS2 40x/1.30 OIL"
• NominalMagnification="40"
• Voxel Size: 0.2841x0.2841x0.3462 um

Note: Images series were acquired along one leading edge of the wound and then along the second edge. The leading edge has to be centered and cell migration oriented top to bottom in the image field of view

Installation

You will need a current version of snakemake on Linux OS to run this workflow. To get snakemake please follow the install instructions on their website, but in brief once conda and mamba are installed you can install snakemake with:

mamba create -n snakemake -c conda-forge -c bioconda snakemake

Afterwards you can activate the conda environment and download the repository. And all additional dependencies will be handled by snakemake.

conda activate snakemake
git clone https://github.com/rgomez-AI/CellOrientation.git

Enviroment creation

Create required environments by going to the directory CellOrientation/workflow

where Snakefile is located and execute the following command:

snakemake --cores all --use-conda --conda-create-envs-only Data_Analysis

Workflow Diagram

A list of components used in this workflow workflow/scripts:

Component	Script	Description
lif2tif	lif2tif.sh	Open .lif file series and convert them into .TiF
split_channels	split_proj_headless.ijm	Z project stack (Standard Deviation) and Split channels
CellProfiler	Orientation.cppipe	Detect centrosome and nucleus Provide X, Y coordinates Classify cells based on its location
Data_Analysis	DataAnalysis_headless.R	Measure centrosome orientation Perform statistical test of uniformity

Input

Acquired images (multichannel, Z stack and series) storaged in .lif format

Sample dataset provided by Jennifer Jungfleisch from Fatima Gebauer Lab are images of human melanoma cells: download

Running

To execute change current directory to the directory workflow where Snakefile is located.

snakemake --cores all --use-conda Data_Analysis

Output

As an output there are two files:

• results/INNERCells.pdf which contain the analysis for cells located at the inner region.

• results/OUTTERCells.pdf which contain the analysis for cells located at the edge region.

To inspect the quality of image segmentation and centrosome detection visit

• workflow/CP_OUT

Report generation

For report generation snakemake required pygments module and it can be installed with:

pip install pygments

Afterward you can create a report file with the name report.html as the example bellow:

snakemake Data_Analysis --report report.html