SAM Spaghetti

SAM Sequence Primordia Alignment, GrowtH Estimation, Tracking & Temporal Indexation

Author

Guillaume Cerutti

Contributors

Christophe Godin, Jonathan Legrand, Carlos Galvan-Ampudia, Teva Vernoux

Teams

RDP Team Signal, Inria project team Mosaic

Institutes

Inria, INRA, CNRS

Language

Python

Supported OS

Linux, MacOS

Licence

Cecill-C

Description

This package provides scripts to reproduce the analysis pipelines described in the article Temporal integration of auxin information for the regulation of patterning and used to reconstruct population averages of Shoot Apical Meristems (SAM) of Arabidopsis thaliana with quantitative gene expression and hormonal signal 2D maps. It essentially gives access to two major quantitative image analysis and geometrical interpretation pipelines:

Image quantification & alignment	Starting from microscopy acquisitions (CZI) of SAMs expressing an Auxin sensor (DII) and a CLV3 fluorescent reporter, this pipeline quantifies image intensity at cell level and performs an alignment of time lapse sequences into a common SAM reference frame.
PIN image polarity analysis	Using microscopy acquisitions of SAMs expressing a fluorescent auxin carrier (PIN) and a cell wall staining, this pipeline estimates polarities at cell level. It can also use the result from the previous pipeline to superimpose aligned auxin and PIN information.

Requirements

• timagetk

• cellcomplex

• tissue_nukem_3d

• tissue_paredes

Installation Procedure

• Installation
  • Conda environment
    • Creating a new environment (recommended)
    • In an existing Python 3.7 environment
    • About Conda
  • Developer procedure
    • Download the sources of sam_spaghetti
    • Create a new conda environment with all dependencies
    • Install the sam_spaghetti library

Command Line Scripts

• File architecture requirements for executing the scripts
  • Script directory arguments
  • Configuration .csv files
• SAM Sequence Detect, Quantify & Align
  • Arguments
• SAM Sequence Segment & Quantify Polarities

Notebook Examples

• SAM Sequence Nuclei Detection, Quantification and Alignment
  • Library imports
  • Parameters
  • Microscopy image loading
  • Multichannel image sequence
  • Nuclei detection and image signal quantification
  • L1 Nuclei quantified signals
  • 2D Maps of epidermal Auxin level
  • Sequence rigid image registration
  • Sequence alignment : CZ centering + P0 identification
  • Aligned signal maps
  • Detection of organ primordia
• Replaying the notebook examples on your own installation
  • Prerequisites : installation and data
  • Run Jupyter and open a notebook example
  • Run the notebook

Contents

• Index

• Module Index

• Search Page