DeepCristae, a CNN for the restoration of mitochondria cristae in live microscopy images

Mitochondria play an essential role in the life cycle of eukaryotic cells. However, we still don't know how their ultrastructure, like the cristae of the inner membrane, dynamically evolves to regulate these fundamental functions, in response to external conditions or during interaction with other cell components. Although high-resolution fluorescent microscopy coupled with recently developed innovative probes can reveal this structural organization, their long-term, fast and live 3D imaging remains challenging. To address this problem, we have developed a CNN, called DeepCristae, to restore mitochondria cristae in low spatial resolution microscopy images. Our network is trained from 2D STED images using a novel loss specifically designed for cristae restoration. To efficiently increase the size of the training set, we also developed a random image patch sampling centered on mitochondrial areas. To evaluate DeepCristae, quantitative assessments are carried out using metrics we derived by focusing on the mitochondria and cristae pixels rather than on the whole image as usual. Depending on the conditions of use indicated, DeepCristae works well on broad microscopy modalities (Stimulated Emission Depletion (STED), Live-SR, AiryScan and LLSM). It is ultimately applied in the context of mitochondrial network dynamics during interaction with endo/lysosome membranes.

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Publication Details

Journal
Communications Biology
Published
2025-02-26
DOI
https://doi.org/10.1038/s42003-025-07684-x
Citations
1
Primary Topic
Cell Image Analysis Techniques
Type
article
Field-Weighted Citation Impact
1.63

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article

DeepCristae, a CNN for the restoration of mitochondria cristae in live microscopy images

Anaïs Badoual, Tianyan Liu, Charles Kervrann, Zhixing Chen et al.
1 citations
Communications Biology
Cell Image Analysis Techniques
1.63
article

DeepCristae, a CNN for the restoration of mitochondria cristae in live microscopy images

Anaïs Badoual, Tianyan Liu, Charles Kervrann, Zhixing Chen, Julien Dumont, Ludovic Leconte, Cesar Augusto Valades‐Cruz, Jean Salamero, Salomé Papereux
article en
1 citations

Abstract

Mitochondria play an essential role in the life cycle of eukaryotic cells. However, we still don't know how their ultrastructure, like the cristae of the inner membrane, dynamically evolves to regulate these fundamental functions, in response to external conditions or during interaction with other cell components. Although high-resolution fluorescent microscopy coupled with recently developed innovative probes can reveal this structural organization, their long-term, fast and live 3D imaging remains challenging. To address this problem, we have developed a CNN, called DeepCristae, to restore mitochondria cristae in low spatial resolution microscopy images. Our network is trained from 2D STED images using a novel loss specifically designed for cristae restoration. To efficiently increase the size of the training set, we also developed a random image patch sampling centered on mitochondrial areas. To evaluate DeepCristae, quantitative assessments are carried out using metrics we derived by focusing on the mitochondria and cristae pixels rather than on the whole image as usual. Depending on the conditions of use indicated, DeepCristae works well on broad microscopy modalities (Stimulated Emission Depletion (STED), Live-SR, AiryScan and LLSM). It is ultimately applied in the context of mitochondrial network dynamics during interaction with endo/lysosome membranes.

Communications BiologyVol. 8(1)
Centre National de la Recherche Scientifique (FR), Institut national de recherche en sciences et technologies du numérique (FR), Inserm (FR), Collège de France (FR), Peking University (CN), Université Paris Sciences et Lettres (FR), Centre Interdisciplinaire de Recherche en Biologie (FR), Centre Inria de l'Université de Rennes (FR), Biologie cellulaire et Cancer (FR), Université de Rennes (FR), Institut Curie (FR)
Agence Nationale de la Recherche
Responsible consumption and production
Openalex Percentile: Top 16%
Cell Image Analysis Techniques
1.63
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