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

ABSTRACT: The cell cycle governs the 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 models-of support vector machine and deep neural network architecture-to classify nuclei in

SUBMITTER: Li G 

PROVIDER: S-EPMC11969383 | biostudies-literature | 2025 Jun

REPOSITORIES: biostudies-literature