Methods, plots and numerical results for Safieddine et al. (2020)

This repository gathers the code used for the following paper:

Title: A choreography of centrosomal mRNAs reveals a conserved localization mechanism involving active polysome transport

Authors: Adham Safieddine^1,2,*, Emeline Coleno^1,2, Soha Salloum^1,2,3,+, Arthur Imbert^4,5,6,+, Abdel-Meneem Traboulsi^1,2, Oh Sung Kwon⁷, Frederic Lionneton⁸, Virginie Georget⁸, Marie-Cécile Robert^1,2, Thierry Gostan¹, Charles Lecellier^1,2, Racha Chouaib^1,2,5, Xavier Pichon^1,2, Hervé Le Hir³ , Kazem Zibara⁵, Florian Müller^9,10, Thomas Walter^4,5,6, Marion Peter^1,2, Edouard Bertrand^1,2,11,*

¹Institut de Génétique Moléculaire de Montpellier, University of Montpellier, CNRS, Montpellier, France ²Equipe labélisée Ligue Nationale Contre le Cancer, University of Montpellier, CNRS, Montpellier, France ³ER045, PRASE, DSST, Faculty of Sciences-I, Lebanese University, Beirut, Lebanon ⁴MINES ParisTech, PSL-Research University, CBIO-Centre for Computational Biology, 77300 Fontainebleau, France ⁵Institut Curie, 75248 Paris Cedex, France ⁶INSERM, U900, 75248 Paris Cedex, France ⁷Institut de biologie de l'Ecole normale supérieure (IBENS), Ecole normale supérieure, CNRS, INSERM, PSL Research University, 46 rue d'Ulm, 75005, Paris, France ⁸BioCampus Montpellier, CNRS UMS3426, 141, rue de la Cardonille, 34094 Montpellier Cedex 5, France ⁹Unité Imagerie et Modélisation, Institut Pasteur and CNRS UMR 3691, 28 rue du Docteur Roux, 75015 Paris; France ¹⁰C3BI, USR 3756 IP CNRS – Paris, France ¹¹Institut de Génétique Humaine, University of Montpellier, CNRS, Montpellier, France

⁺Equal contributions ^*To whom correspondence should be addressed.

Prerequisites

The analysis pipeline consists of three different resources that are best run in dedicated virtual environments:

• BigFISH, a python library to process smFISH images. It allows to detect mRNAs and centrosomes in 2D, format segmentation masks and compute spatial features at the cell-level.
  • Run the command pip install big-fish==0.4.0 in an empty virtual environment to reproduce our python environment and run BigFISH methods.
• Cellpose, a Deep Learning based approach for nuclei and cells segmentation.
  • Run the command pip install -r requirements_cellpose.txt in an empty virtual environment to reproduce our python environment and run Cellpose model.
• A more general environment with classic scientific libraries to perform statistical tests and plot results. We use it as a kernel for the final Ipython notebook results.ipynb.
  • Run the command pip install -r requirements_notebook.txt in an empty virtual environment to reproduce our python environment and run the results.ipynb notebook.

Dataset

The analysis is performed on 2D images obtained with a Maximum Intensity Projection (MIP). In a high-throughput screening framework, the pipeline can scale to several plates and hundreds of wells. All images have four channels with dedicated purposes:

• smFISH - mRNAs detection
• Dapi - nuclei segmentation
• GFP - centrosomes detection and cells segmentation
• CellMask - cells segmentation

If you have any question relative to the image acquisition, please contact Edouard Bertrand or Adham Safieddine.

Pipeline

Below, we provide a quick overview of the different steps in the analysis, and in which environment they are performed. A more detailed description of each step is provided in the following sections:

1. Preprocessing (BigFISH)
2. Spot detection (BigFISH)
3. Segmentation (Cellpose)
4. Postprocessing (BigFISH)
5. Features (BigFISH)
6. Plot (notebook)

If you have any question relative to the image analysis, please contact Arthur Imbert or Adham Safieddine (or open an issue).

1. Preprocessing

Images are first renamed according to their plate, well, gene, treatment and field of view:

• bigfish_scripts/rename_images.py

To fit with the segmentation template of Cellpose, images are organized in different folders, one folder per well:

• bigfish_scripts/preprocess_nuc.py
• bigfish_scripts/preprocess_cell.py

A last script allows us to visualize all our MIPs:

• bigfish_scripts/plot_mip.py

Dapi	smFISH	GFP	CellMask

2. Spot detection

A first script detect mRNAs spots, decompose clustered regions and detect foci from smFISH channel:

• bigfish_scripts/detect_rna.py

A second script detect centrosomes in the GFP channel:

• bigfish_scripts/detect_centrosome.py

3. Segmentation

We use the Cellpose model to segment nuclei and cells. An API is available online or the github repository can be clone locally.

4. Postprocessing

Integer masks obtained from the segmentation are cleaned and we ensure every segmented cell match a nucleus:

• bigfish_scripts/postprocess_segmentation.py

We assign detected mRNAs and centrosomes to an unique pair of cell and nucleus:

• bigfish_scripts/extract_cell.py
• bigfish_scripts/merge_df_extraction.py

Specific plots can be saved for every segmented cell:

• bigfish_scripts/plot_extraction.py

5. Features

Based on the detection and segmentation results, spatial features around the centrosomes can be computed:

• bigfish_scripts/compute_features.py
• bigfish_scripts/merge_df_features.py

6. Plot

The .csv file provided is a cleaned version of the merged dataframe we obtain at the end of the pipeline. It allows us to directly run the notebook and build the synthetic plot:

• notebook/results.ipynb

Results

Licensing

• BigFISH and the rest of the code provided in this repository (notebook) are BSD-licenced (3 clause):

Copyright © 2020, Arthur Imbert
All rights reserved.

Redistribution and use in source and binary forms, with or without modification, are permitted provided that the following conditions are met:

• Redistributions of source code must retain the above copyright notice, this list of conditions and the following disclaimer.
• Redistributions in binary form must reproduce the above copyright notice, this list of conditions and the following disclaimer in the documentation and/or other materials provided with the distribution.
• Neither the name of the copyright holder nor the names of its contributors may be used to endorse or promote products derived from this software without specific prior written permission.

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