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Cell cycle gene regulation dynamics revealed by RNA velocity and deep-learning

Andrea Riba, Attila Oravecz, Matej Durik, Sara Jiménez, Violaine Alunni, Marie Cerciat, Matthieu Jung, Céline Keime, William M. Keyes, View ORCID ProfileNacho Molina
doi: https://doi.org/10.1101/2021.03.17.435887
Andrea Riba
1Institut de Génétique et de Biologie Moléculaire et Cellulaire (IGBMC), Institut National de la Santé et de la Recherche Médicale (INSERM) U964, Centre National de Recherche Scientifique (CNRS) UMR 7104, Université de Strasbourg (UdS), 1 Rue Laurent Fries, 67404 Illkirch, France
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  • For correspondence: ribaa@igbmc.fr molinan@igbmc.fr
Attila Oravecz
1Institut de Génétique et de Biologie Moléculaire et Cellulaire (IGBMC), Institut National de la Santé et de la Recherche Médicale (INSERM) U964, Centre National de Recherche Scientifique (CNRS) UMR 7104, Université de Strasbourg (UdS), 1 Rue Laurent Fries, 67404 Illkirch, France
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Matej Durik
1Institut de Génétique et de Biologie Moléculaire et Cellulaire (IGBMC), Institut National de la Santé et de la Recherche Médicale (INSERM) U964, Centre National de Recherche Scientifique (CNRS) UMR 7104, Université de Strasbourg (UdS), 1 Rue Laurent Fries, 67404 Illkirch, France
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Sara Jiménez
1Institut de Génétique et de Biologie Moléculaire et Cellulaire (IGBMC), Institut National de la Santé et de la Recherche Médicale (INSERM) U964, Centre National de Recherche Scientifique (CNRS) UMR 7104, Université de Strasbourg (UdS), 1 Rue Laurent Fries, 67404 Illkirch, France
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Violaine Alunni
1Institut de Génétique et de Biologie Moléculaire et Cellulaire (IGBMC), Institut National de la Santé et de la Recherche Médicale (INSERM) U964, Centre National de Recherche Scientifique (CNRS) UMR 7104, Université de Strasbourg (UdS), 1 Rue Laurent Fries, 67404 Illkirch, France
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Marie Cerciat
1Institut de Génétique et de Biologie Moléculaire et Cellulaire (IGBMC), Institut National de la Santé et de la Recherche Médicale (INSERM) U964, Centre National de Recherche Scientifique (CNRS) UMR 7104, Université de Strasbourg (UdS), 1 Rue Laurent Fries, 67404 Illkirch, France
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Matthieu Jung
1Institut de Génétique et de Biologie Moléculaire et Cellulaire (IGBMC), Institut National de la Santé et de la Recherche Médicale (INSERM) U964, Centre National de Recherche Scientifique (CNRS) UMR 7104, Université de Strasbourg (UdS), 1 Rue Laurent Fries, 67404 Illkirch, France
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Céline Keime
1Institut de Génétique et de Biologie Moléculaire et Cellulaire (IGBMC), Institut National de la Santé et de la Recherche Médicale (INSERM) U964, Centre National de Recherche Scientifique (CNRS) UMR 7104, Université de Strasbourg (UdS), 1 Rue Laurent Fries, 67404 Illkirch, France
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William M. Keyes
1Institut de Génétique et de Biologie Moléculaire et Cellulaire (IGBMC), Institut National de la Santé et de la Recherche Médicale (INSERM) U964, Centre National de Recherche Scientifique (CNRS) UMR 7104, Université de Strasbourg (UdS), 1 Rue Laurent Fries, 67404 Illkirch, France
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Nacho Molina
1Institut de Génétique et de Biologie Moléculaire et Cellulaire (IGBMC), Institut National de la Santé et de la Recherche Médicale (INSERM) U964, Centre National de Recherche Scientifique (CNRS) UMR 7104, Université de Strasbourg (UdS), 1 Rue Laurent Fries, 67404 Illkirch, France
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  • ORCID record for Nacho Molina
  • For correspondence: ribaa@igbmc.fr molinan@igbmc.fr
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Abstract

The cell cycle is a fundamental process of life, however, a quantitative understanding of gene regulation dynamics in the context of the cell cycle is still far from complete. Single-cell RNA-sequencing (scRNA-seq) technology gives access to its dynamics without externally perturbing the cell. Here, we build a high-resolution map of the cell cycle transcriptome based on scRNA-seq and deep-learning. By generating scRNA-seq libraries with high depth, in mouse embryonic stem cells and human fibroblasts, we are able to observe cycling patterns in the unspliced-spliced RNA space for single genes. Since existing methods in scRNA-seq are not efficient to measure cycling gene dynamics, we propose a deep learning approach to fit these cycling patterns sorting single cells across the cell cycle. We characterize the cell cycle in asynchronous pluripotent and differentiated cells identifying major waves of transcription during the G1 phase and systematically study the G1-G0 transition where the cells exit the cycle. Our work presents to the scientific community a broader understanding of RNA velocity and cell cycle maps, that we applied to pluripotency and differentiation. Our approach will facilitate the study of the cell cycle in multiple cellular models and different biological contexts, such as cancer and development.

Competing Interest Statement

The authors have declared no competing interest.

Copyright 
The copyright holder for this preprint is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under a CC-BY-NC-ND 4.0 International license.
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Posted March 19, 2021.
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Cell cycle gene regulation dynamics revealed by RNA velocity and deep-learning
Andrea Riba, Attila Oravecz, Matej Durik, Sara Jiménez, Violaine Alunni, Marie Cerciat, Matthieu Jung, Céline Keime, William M. Keyes, Nacho Molina
bioRxiv 2021.03.17.435887; doi: https://doi.org/10.1101/2021.03.17.435887
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Cell cycle gene regulation dynamics revealed by RNA velocity and deep-learning
Andrea Riba, Attila Oravecz, Matej Durik, Sara Jiménez, Violaine Alunni, Marie Cerciat, Matthieu Jung, Céline Keime, William M. Keyes, Nacho Molina
bioRxiv 2021.03.17.435887; doi: https://doi.org/10.1101/2021.03.17.435887

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