Predicting cell cycle stage from 3D single-cell nuclear-stained images

The cell cycle governs proliferation of all eukaryotic cells. Profiling cell cycle dynamics is therefore central to basic and biomedical research. However, current approaches to cell cycle profiling involve complex interventions that may confound experimental interpretation. We developed CellCycleNet, a machine learning (ML) workflow, to simplify cell cycle staging from fluorescent microscopy data 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 2D and 3D ML modelsof support vector machine (SVM) and deep neural network architectureto classify nuclei in the G1 or S/G2 phases. Our results show that 3D CellCycleNet outperforms SVM models on each dataset. When trained on two image datasets simultaneously, CellCycleNet achieves the highest classification accuracy (AUROC of 0.94-0.95). Overall, we found that using 3D features, rather than 2D features alone, significantly improves classification performance for all model architectures. We released our image data, models, and software as a community resource.

Li G, Nichols EK, Browning VE, Longhi NJ, Sanchez-Forman M, Camplisson CK, Beliveau BJ, Noble WS

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idr0167-li-cellcyclenet/experimentB ()

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Data for this study is available at the BioImage Archive: S-BIAD1752.

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