
==== Front
bioRxiv
BIORXIV
bioRxiv
2692-8205
Cold Spring Harbor Laboratory

10.1101/2024.08.30.610553
preprint
1
Article
Predicting cell cycle stage from 3D single-cell nuclear-stained images
Li Gang
Nichols Eva K. http://orcid.org/0000-0002-8501-8028

Browning Valentino E. http://orcid.org/0000-0002-2345-2240

Longhi Nicolas J.
Camplisson Conor
Beliveau Brian J. http://orcid.org/0000-0003-1314-3118

Noble William Stafford http://orcid.org/0000-0001-7283-4715

01 9 2024
2024.08.30.610553https://creativecommons.org/licenses/by-nc/4.0/ This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License, which allows reusers to distribute, remix, adapt, and build upon the material in any medium or format for noncommercial purposes only, and only so long as attribution is given to the creator.
http://biorxiv.org/lookup/doi/10.1101/2024.08.30.610553
nihpp-2024.08.30.610553.pdf
Abstract

The cell cycle governs the proliferation, differentiation, and regeneration of all eukaryotic cells. Profiling cell cycle dynamics is therefore central to basic and biomedical research spanning development, health, aging, and disease. However, current approaches to cell cycle profiling involve complex interventions that may confound experimental interpretation. To facilitate more efficient cell cycle annotation of microscopy data, we developed CellCycleNet, a machine learning (ML) workflow designed to simplify cell cycle staging with minimal experimenter intervention and cost. CellCycleNet accurately predicts cell cycle phase using only a fluorescent nuclear stain (DAPI) in fixed interphase cells. Using the Fucci2a cell cycle reporter system as ground truth, we collected two benchmarking image datasets and trained two ML models—a support vector machine (SVM) and a deep neural network—to classify nuclei as being in either the G1 or S/G2 phases of the cell cycle. Our results suggest that CellCycleNet outperforms state-of-the-art SVM models on each dataset individually. When trained on two image datasets simultaneously, CellCycleNet achieves the highest classification accuracy, with an improvement in AUROC of 0.08–0.09. The model also demonstrates excellent generalization across different microscopes, achieving an AUROC of 0.95. Overall, using features derived from 3D images, rather than 2D projections of those same images, significantly improves classification performance. We have released our image data, trained models, and software as a community resource.
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pmc
