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. 2022 Apr 5;73(7):1926-1933.
doi: 10.1093/jxb/erac005.

The INDEPTH (Impact of Nuclear Domains on Gene Expression and Plant Traits) Academy: a community resource for plant science

Christophe Tatout  1 , Guillaume Mougeot  1   2 , Geraint Parry  3   4 , Célia Baroux  5 , Mónica Pradillo  6 , David Evans  2
Affiliations

The INDEPTH (Impact of Nuclear Domains on Gene Expression and Plant Traits) Academy: a community resource for plant science

Christophe Tatout et al. J Exp Bot. .

Abstract

This Community Resource paper introduces the range of materials developed by the INDEPTH (Impact of Nuclear Domains on Gene Expression and Plant Traits) COST Action made available through the INDEPTH Academy. Recent rapid growth in understanding of the significance of epigenetic controls in plant and crop science has led to a need for shared, high-quality resources, standardization of protocols, and repositories for open access data. The INDEPTH Academy provides a range of masterclass tutorials, standardized protocols, and teaching webinars, together with a rapidly developing repository to support imaging and spatial analysis of the nucleus and deep learning for automated analysis. These resources were developed partly as a response to the COVID-19 pandemic, but also driven by needs and opportunities identified by the INDEPTH community of ~200 researchers in 80 laboratories from 32 countries. This community report outlines the resources produced and how they will be extended beyond the INDEPTH project, but also aims to encourage the wider community to engage with epigenetics and nuclear structure by accessing these resources.

Keywords: COST Action; image repository; plants; protocols; tutorials; webinars.

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Figures

Fig. 1.
Fig. 1.
Knowledge and resources generated by INDEPTH.
Fig. 2.
Fig. 2.
Image segmentation of a 3D nucleus from the IDP4002 dataset through the Mask-RCNN deep-learning model. Example of a raw image of a 3D nucleus from the IDP4002 dataset (left), the corresponding 3D segmentation performed semi-automatically with ilastik (middle), and the automatic segmentation obtained with Mask-RCNN pre-trained on a 2D dataset of nuclei (right). The small extra objects segmented in this last image illustrate the noise sensitivity of Mask-RCNN on our dataset and the need for retraining it.
See this image and copyright information in PMC

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