A deep learning approach for time-consistent cell cycle phase prediction from microscopy data.

ABSTRACT: 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 pres

SUBMITTER: Bonte T 

PROVIDER: S-EPMC12711074 | biostudies-literature | 2025 Dec

REPOSITORIES: biostudies-literature