Outils et Macro-programmes développés sur la plateforme / Software and macro-programs developped on the facility

Vous trouverez dans cette rubrique des outils mis au points par l'équipe Morpheme et les ingénieurs de la plateforme MICA / In this section you will find tools developed by the Morpheme team and the engineers of MICA

Morpheme softwares

Macros programs ImageJ/FiJi

Wound healing

Adipocytes

 General use of macros in ImageJ / FiJi : download the file .txt or .ijm to your local plugins folder (Fiji.app\Plugins or ImageJ\Plugins) and restart ImageJ. The corresponding macro will appear in the Plugin menu. To use it : you just need to select it in the menu To modify it : open the file with ImageJ.

  • Wound Healing assay surface measurements : 2 Macros

Keywords : biology : Wound healing, confluence, cell migration / image analysis : surface measurement, kinetics, 2D

Input : Phase contrast images

Goal : Measure the migration of the cells through a surface decrease during the closing of the cellular mat

   1) Macro to be dowloaded : Surface_blessure
Author : / IPMC
Date : June 2013
Input format : .TIFF or .STK image stack openned on ImageJ
Test images : Stack wound healing
What does Surface_blessure.ijm do?

In Images

Input

Results

 Blessure

 Analyse_blessure

 Graphe

 

In a few words

A light intensity correction is applied to the image stack, followed by an edge detection (Find edge), then a low pass filtering  to blur the cellular mat. Segmentation of the empty space is done by manual thresholding based on stack histogram. This surface is then measured with the exclusion of small objects : Min area measured at t+1 = 0.01*Min area measured at t. A S=f(t) plot and bi-exponential fit (speed can be calculated ) is given to the user with an image displaying the analsyis done (Blue : surface thresholded / Red : surface measured / Green : original)

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   2) Macro to be dowloaded : Wound_healingv05
Author : / C3M
Date : 2018
Input format : Images from Incucyte system stored in a folder
What does Wound_healingv05 do?

In a few words

An unsharp mask followed by a minimum filtering is applied to the image stack. Segmentation is done by a fixed threshold and a filtering on a size criterion to give the surfaces through measurements of ROIs obtained. This macro analyses automatically all the images stored in a folder.

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  • Adipocyte surface measurements

Keywords : biology : Adipocytes, cell size / image analysis : surface measurement, 2D

Input : A Folder containing RGB images from brightfield microscopy acquisition of adipocyte tissue sections

Goal : Measure the surface of all the adipocytes in the image

Macro to be dowloaded : Adipocytes_analyse
Author : / IPMC
Date : July 2019
Input format : .TIFF or .JPG 2D image
Test images : Adipocytes_tissue_section
What does Adipocytes_analyse.ijm do?

In Images

Input

Results

 Adipo_analysegif_resized

 Resulta_resized

 Distribution_resized


In a few words

The RGB image is splitted, A variance filter is applied on the green image. The cells are segmented by semi-manual percentile thresholding. The areas of all the cells are analysed with the possibility to filter on the size and circularity at the beginning of the analysis. All the TIF or JPEG images of a folder are analyzed. All the results (the image of the "Results", a table with area values, and the distribution) are saved in an automatic sub-folder \Adipocytes_measurements

Used in

Leboucher A, Pisani DF, Martinez-Gili L, Chilloux J, Bermudez-Martin P, Van Dijck A, Ganief T, Macek B, Becker JAJ, Le Merrer J, Kooy RF, Amri EZ, Khandjian EW, Dumas ME, Davidovic L. The translational regulator FMRP controls lipid and glucose metabolism in mice and humans.  Mol Metab. 2019 Mar;21:22-35.


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Morpheme softwares

  • Obj.MPP (Object detection using a Marked Point Process (in Python 3)): Documentation available on a dedicated page.
    Available at: http://gitlab.inria.fr/edebreuv/Obj.MPP
  • SMLM-CEL0 (Single Molecule Localization in Microscopy – Continuous Exact L0): Single molecule localization in microscopy based on a deconvolution algorithm with a L0-regularization term to promote sparsity. The continuous exact L0 (CEL0) functional is minimized using an iteratively reweighted L1 method (IRL1). This software has been tested within the SMLMS 2016 software benchmarking.
    Available at: http://github.com/esoubies/SMLM-CEL0
  • SPADE (Small Particle Detector): Python software for detecting a collection of small particles in an image. It is based on a marked point process modeling where the objects belong to a predefined dictionary of shapes. Originally, it has been developed within the ANR project RNAGRIMP for detecting granules in cell cytoplasms.
    Available at: http://pypi.python.org/pypi/small-particle-detection and http://gitlab.inria.fr/ncedilni/spade.
  • timagetk (Tissue Image Toolkit): Python package dedicated to image processing of multicellular architectures such as plants or animals, and intended for biologists, modelers and computer scientists. Morpheme has contributed to its development by providing a number of image processing tools.
    Available at: http://timagetk.readthedocs.io