Literature DB >> 35510843

Using positional information to provide context for biological image analysis with MorphoGraphX 2.0.

Sören Strauss1, Adam Runions1, Brendan Lane1,2, Dennis Eschweiler3, Namrata Bajpai1, Nicola Trozzi1,2, Anne-Lise Routier-Kierzkowska4, Saiko Yoshida1, Sylvia Rodrigues da Silveira4, Athul Vijayan5, Rachele Tofanelli5, Mateusz Majda1,2, Emillie Echevin4, Constance Le Gloanec4, Hana Bertrand-Rakusova4, Milad Adibi1, Kay Schneitz5, George W Bassel6, Daniel Kierzkowski4, Johannes Stegmaier3, Miltos Tsiantis1, Richard S Smith1,2.   

Abstract

Positional information is a central concept in developmental biology. In developing organs, positional information can be idealized as a local coordinate system that arises from morphogen gradients controlled by organizers at key locations. This offers a plausible mechanism for the integration of the molecular networks operating in individual cells into the spatially coordinated multicellular responses necessary for the organization of emergent forms. Understanding how positional cues guide morphogenesis requires the quantification of gene expression and growth dynamics in the context of their underlying coordinate systems. Here, we present recent advances in the MorphoGraphX software (Barbier de Reuille et al., 2015⁠) that implement a generalized framework to annotate developing organs with local coordinate systems. These coordinate systems introduce an organ-centric spatial context to microscopy data, allowing gene expression and growth to be quantified and compared in the context of the positional information thought to control them.
© 2022, Strauss et al.

Entities:  

Keywords:  A. thaliana; convolutional neural networks; developmental biology; morphogenesis; plant biology; positional information; quantification; segmentation

Mesh:

Year:  2022        PMID: 35510843      PMCID: PMC9159754          DOI: 10.7554/eLife.72601

Source DB:  PubMed          Journal:  Elife        ISSN: 2050-084X            Impact factor:   8.713


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