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            <funder>This work has been supported by the French government under management of Agence Nationale de la Recherche (ANR) as part of the “Investissements d’avenir” program, reference ANR-19-P3IA-0001 (PRAIRIE 3IA Institute; TW), the France 2030 program PRAIRIE-PSAI with reference number ANR-23-IACL-0008 (TW), and the ANR project TRANSFACT, reference ANR-19-CE12-0007 (EB, FM, TW). Furthermore, we also acknowledge support by France-BioImaging, reference ANR-10-INBS-04 (EB). Furthermore, this work was supported by a government grant managed by the Agence Nationale de la Recherche under the France 2030 program, with the reference numbers ANR-24-EXCI-0001 (EB, FM, TW), ANR-24-EXCI-0002 (EB, FM, TW), ANR-24-EXCI-0003 (EB, FM, TW), ANR-24-EXCI-0004 (EB, FM, TW), ANR-24-EXCI-0005 (EB, FM, TW).</funder>
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              <p>&lt;div&gt;&lt;p&gt;The cell cycle is a series of regulated stages during which a cell grows, replicates its DNA, and divides. It consists of four phases -two growth phases (G1 and G2), a replication phase (S), and a division phase (M) -each characterized by distinct transcriptional programs and impacting most other cellular processes. In imaging assays, the cell cycle phase can be identified using specific cell-cycle markers. However, the use of dedicated cell-cycle markers can be impractical or even prohibitive, as they occupy fluorescent channels that may be needed for other reporters. To address this limitation we propose a method to infer the cell cycle phase from a widely used fluorescent reporter: SiR-DNA, thereby bypassing the need for phase-specific markers while leveraging information already present in common experimental setups.&lt;/p&gt;&lt;p&gt;Our method is based on a Variational Auto-Encoder (VAE), enhanced with two auxiliary tasks: predicting the average intensity of phase-specific markers and enforcing temporal consistency through latent space regularization. The reconstruction task ensures that the latent space captures cell cycle-relevant features, while the temporal constraint promotes biological plausibility. The resulting model, CC-VAE, classifies cell cycle phases with high accuracy from widely used DNA markers and can thus be applied to high-content screening datasets not specifically designed for cell cycle analysis. CC-VAE is freely available, along with a new, publicly released dataset comprising over 600,000 labeled HeLa Kyoto nuclear images to support further development and benchmarking in the community.&lt;/p&gt;&lt;/div&gt;</p>
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